Effects of Heavy Alcohol Intake on Lipoproteins, Adiponectin and Cardiovascular Risk

D 1266 OULU 2014 D 1266

UNIVERSITY OF OULU P.O.BR[ 00 FI-90014 UNIVERSITY OF OULU FINLAND ACTA UNIVERSITATIS OULUENSIS ACTA UNIVERSITATIS OULUENSIS ACTA

SERIES EDITORS DMEDICA Sanna Kuusisto

ASCIENTIAE RERUM NATURALIUM Sanna Kuusisto Professor Esa Hohtola EFFECTS OF HEAVY ALCOHOL BHUMANIORA INTAKE ON LIPOPROTEINS, University Lecturer Santeri Palviainen CTECHNICA ADIPONECTIN AND Postdoctoral research fellow Sanna Taskila CARDIOVASCULAR RISK DMEDICA Professor Olli Vuolteenaho ESCIENTIAE RERUM SOCIALIUM University Lecturer Veli-Matti Ulvinen FSCRIPTA ACADEMICA Director Sinikka Eskelinen GOECONOMICA Professor Jari Juga

EDITOR IN CHIEF Professor Olli Vuolteenaho PUBLICATIONS EDITOR

Publications Editor Kirsti Nurkkala UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF MEDICINE, ISBN 978-952-62-0625-7 (Paperback) INSTITUTE OF CLINICAL MEDICINE, ISBN 978-952-62-0626-4 (PDF) DEPARTMENT OF INTERNAL MEDICINE; ISSN 0355-3221 (Print) BIOCENTER OULU; ISSN 1796-2234 (Online) MEDICAL RECEARCH CENTER; OULU UNIVERSITY HOSPITAL

ACTA UNIVERSITATIS OULUENSIS D Medica 1266

SANNA KUUSISTO

EFFECTS OF HEAVY ALCOHOL INTAKE ON LIPOPROTEINS, ADIPONECTIN AND CARDIOVASCULAR RISK

Academic Dissertation to be presented with the assent of the Doctoral Training Committee of Health and Biosciences of the University of Oulu for public defence in Auditorium 8 of Oulu University Hospital, on 5 December 2014, at 12 noon

Supervised by Professor Markku Savolainen Docent Minna Hannuksela Doctor Tuire Salonurmi

Reviewed by Professor Onni Niemelä Docent Vesa Olkkonen

Opponent Docent Katariina Öörni

ISBN 978-952-62-0625-7 (Paperback) ISBN 978-952-62-0626-4 (PDF)

ISSN 0355-3221 (Printed) ISSN 1796-2234 (Online)

Cover Design Raimo Ahonen

JUVENES PRINT TAMPERE 2014 Kuusisto, Sanna, Effects of heavy alcohol intake on lipoproteins, adiponectin and cardiovascular risk. University of Oulu Graduate School; University of Oulu, Faculty of Medicine, Institute of Clinical Medicine, Department of Internal Medicine; Biocenter Oulu; Medical Recearch Center; Oulu University Hospital Acta Univ. Oul. D 1266, 2014 University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract The effect of alcohol intake on the pathophysiology of atherosclerotic cardiovascular disease is controversial, especially with respect to heavy alcohol intake. The pathobiology behind atherosclerosis is a complex and multiparametric phenomenon, therefore a self-organizing map (SOM), an unsupervised learning based artificial neural network technique, was applied in the present work. This study was carried out to investigate the effect of heavy alcohol intake on the pathophysiology of atherosclerosis, including several lipoproteins and adiponectin, an adipocyte- derived cytokine that may ameliorate atherosclerosis. Firstly, the effect of heavy alcohol intake on the capacity of HDL and its subclasses (HDL2 and HDL3) to mediate cholesterol efflux from macrophages was studied. Secondly, data of ultracentrifugally isolated lipoproteins were fed into SOM analysis to investigate whether this method can find diverse lipoprotein phenotypes from the heterogeneous lipoprotein data. Thirdly, the aforementioned method was applied to multivariate data of alcohol drinkers to study whether distinct metabolic profiles are associated to heavy alcohol consumption. The results revealed that HDL2, not HDL3, of heavy alcohol drinkers had an enhanced capacity to remove cholesterol from macrophages when compared with control persons. SOM analysis enhanced the ultracentrifugally based lipoprotein data and depicted several novel lipoprotein phenotypes. In addition, lipoprotein-based SOM analysis found two distinct metabolic profiles in heavy alcohol drinkers: an anti-atherogenic and a metabolic syndrome-like profile with opposite metabolic features, such as characteristics of lipoproteins, plasma concentration of adiponectin and prevalence of metabolic syndrome. These profiles also tended to differ in their CV risk. In conclusion, the enhanced cholesterol efflux capacity of HDL2 in heavy drinkers is an anti- atherogenic change linked to alcohol drinking. However, clinically it may be important to be aware that although heavy alcohol drinkers have a low LDL-C level, they differ in their other lipoprotein measures, forming distinct phenotypes with potentially different CV risks. Finally, SOM analysis of ultracentrifugally based lipoprotein data generates in silico classification of lipoprotein particles and thereby offers a new tool for lipoprotein research.

Keywords: adiponectin, alcohol drinking, cardiovascular risk, cholesterol efflux, high- density lipoprotein, lipoproteins, self organizing map

Kuusisto, Sanna, Runsaan alkoholinkäytön vaikutukset lipoproteiineihin, adiponektiiniin ja sydän- ja verisuontautiriskiin. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta, Kliinisen lääketieteen laitos, Sisätaudit; Biocenter Oulu; Medical Recearch Center; Oulun yliopistollinen sairaala Acta Univ. Oul. D 1266, 2014 Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä Alkoholinkäytön vaikutus ateroskleroottisen sydän- ja verisuonitaudin patofysiologiaan on kiis- tanalainen, etenkin runsaan alkoholinkäytön kohdalla. Koska patobiologia ateroskleroosin taus- talla on monimutkainen ilmiö, tässä työssä sovellettiin menetelmänä itseorganisoituvaa karttaa, joka on ohjaamattomaan oppimiseen perustuva neuroverkkomalli. Tutkimuksen tavoitteena oli selvittää runsaan alkoholinkäytön vaikutusta ateroskleroosin patofysiologisiin merkkiaineisiin, mukaan lukien useita lipoproteiineja sekä adiponektiini, rasva- soluperäinen sytokiini, joka voi lievittää ateroskleroosia. Ensimmäisessä osatyössä tutkittiin runsaan alkoholinkäytön vaikutusta HDL:n ja sen alafraktioiden (HDL2 ja HDL3) kykyyn poistaa kolesterolia makrofageista. Toisessa osatyössä ultrasentrifuugaukseen perustuva lipoproteiiniai- neisto syötettiin itseorganisoituvaan karttaan. Työssä selvitettiin löytäisikö menetelmä erilaisia lipoproteiinifenotyyppejä heterogeenisestä aineistosta. Kolmannessa osatyössä em. menetelmää sovellettiin monimuuttuja-aineistoon, joka koostui runsaasti alkoholia käyttävistä ja verrokeista. Tutkittiin, liittyykö runsaaseen alkoholinkäyttöön erilaisia metabolisia profiileja. Tulokset osoit- tivat, että suurkuluttajien HDL2-hiukkasen kolesterolinpoistokyky makrofageista oli suurempi kuin verrokeilla. Itseorganisoituvaan karttaan perustuva lipoproteiinien luokittelumenetelmä löy- si useita uusia lipoproteiinifenotyyppejä. Lisäksi, em. menetelmä löysi suurkuluttajilta kaksi eri- laista metabolista profiilia: anti-aterogeeninen ja metabolisen syndrooman kaltainen. Näillä oli vastakkaiset metaboliset piirteet, kuten lipoproteiinien ominaisuudet, adiponektiinin pitoisuus plasmassa ja metabolisen syndrooman esiintyvyys. Profiileihin liittyi mahdollisesti myös erilainen sydän- ja verisuonitautiriski. Tutkimus osoittaa, että alkoholin suurkuluttajilla havaittu parempi HDL2:n kyky poistaa kolesterolia soluista on anti-aterogeeninen muutos, joka liittyy alkoholin käyttöön. Kliinisesti voi olla merkittävää, että vaikka alkoholin suurkuluttajilla oli pieni LDL-C pitoisuus, he jakaan- tuivat muiden lipoproteiiniperäisten muuttujien perusteella kahteen eri fenotyyppiryhmään, joi- hin liittyi erilainen sydäntautiriski. Lisäksi itseorganisoituva kartta loi ultrasentrifugoinnilla eris- tetyille lipoproteiineille in silico -luokittelun, joten se tarjoaa uuden työkalun lipoproteiinitutki- mukseen.

Asiasanat: adiponektiini, alkoholin käyttö, HDL, itseorganisoituva kartta, kolesterolin ulosvirtaus, lipoproteiinit, sydän- ja verisuonitautiriski

Acknowledgements

The present work was carried out in the Department of Internal Medicine, Biocenter Oulu, University of Oulu and Medical Research Center, Oulu University Hospital. I wish to express my thanks to all the people who have participated in constructing these excellent research facilities I was able to enjoy during this work. I am grateful to my supervisors, Prof. Markku Savolainen, Doc. Minna Hannuksela and Ph.D Tuire Salonurmi, for their great expertise and kind guidance. Markku trusted me and gave me endless encouragement throughout the time I spent in his group. His outstanding knowledge of science was very important to me. Minna guided me into the lipoprotein field and supported me in many ways during my growth as a researcher. She always had time for my questions. Tuire gave me much appreciated support and encouragement during the last stages of this work. Her valuable comments for this thesis were very helpful to me. I acknowledge the official referees, Prof. Onni Niemelä and Doc. Vesa Olkkonen for their excellent comments to improve this thesis. I thank M.Sc Anna Vuolteenaho for the careful revision of the language of this thesis. I am deeply indebted to all my co-authors. Especially, I express my thanks to Prof. Mika Ala-Korpela and Doc. Matti Jauhiainen, whose deep knowledge of science generated interesting discussions that were very important to me. Your contribution for this work was invaluable. My special thanks go to M.Sc (Tech.) Tomi Peltola, who kindly did the self-organizing map analyzes for variables Framingham risk and Finrisk published only in this thesis. My warm gratitude goes to Sari Pyrhönen, Saara Korhonen and Marja-Leena Kytökangas for their skillful technical assistance, as well as their kind advice with various practical matters in laboratory experiments. All my colleagues at the research lab of the Internal Medicine and Medical Research Center are greatly acknowledged for all the shared moments and discussions about science and everyday life. I express my special thanks to Ph.D Antti Nissinen for skillful technical assistance with computers. My warmest thanks go to Ph.D Eija Nissilä for being my friend and colleague and thereby sharing good moments in the lab and everyday life. The help of the secretaries, Marita Ahola, Terttu Niemelä and Anne Salovaara, is also acknowledged, as is the valuable help of the staff of the Alcoholism Treatment Unit of Oulu. My heartfelt thanks go to all my friends for supporting me and sharing the good and bad things in my life. I am indebted to Raija Sääskilahti, my biology teacher in high school, for her way of teaching and inspiring her students. I am sure that is what

7 stimulated me to take up my studies in biochemistry. My deepest thanks go to my deceased father Veli and my mother Siiri, who provided an encouraging atmosphere for me as a child to grow up in. Your interest toward my scientific work was valuable to me. I thank my husband, Erkki, for helping me with numerous technical problems with the computer and for tolerating me during the time I wrote this thesis. This work was financially supported by the Sigrid Juselius Foundation, the Maud Kuistila Memorial Foundation, the Finnish Cardiovascular Research Foundation, the Finnish Cultural Foundation, the Jenny and Antti Wihuri Foundation and the Orion-Farmos Research Foundation.

Kuopio, 25th of September Sanna Kuusisto

8 Abbreviations

ABCA1 ATP-binding cassette transporter sub-family A, member 1 ABCG1 ATP-binding cassette transporter sub-family G, member 1 AcLDL acetylated LDL AdipoR1 adiponectin receptor 1 AdipoR2 adiponectin receptor 2 ALT alanine aminotransferase ANCOVA analysis of covariance apo apolipoprotein AST aspartate aminotransferase ATP adenosine triphosphate BAC blood alcohol concentration BMI body mass index C cholesterol CAD coronary artery disease CDT carbohydrate-deficient transferrin CE cholesterol esters CETP cholesterol ester transfer protein CHD coronary heart disease CRP C-reactive protein CV Cardiovascular CVD cardiovascular disease DBP diastolic blood pressure DM diabetes mellitus EL endothelial lipase FC free cholesterol fG fasting glucose FRS Framingham score GF graduated frequency GGE gradient gel electrophoresis GGT γ-glutamyltransferase HAECs human aortic endothelial cells HbA1c glycosylated hemoglobin HDL high-density lipoprotein

HDL2 high-density lipoprotein class 2

HDL3 high-density lipoprotein class 3

9 HL hepatic lipase HMDMs human monocyte-derived macrophages HMW high molecular weight HOMA-IR homeostatic model assessment score for insulin resistance ICAM-1 intercellular adhesion molecule 1 IDF international diabetes federation IDL intermediate-density lipoprotein IFG impaired fasting glucose IGT impaired glucose tolerance IL interleukin kDa kilodalton KO knock out LCAT lecitin-cholesterol acyltransferase LDL low-density lipoprotein LPL lipoprotein lipase LWM low molecular weight MCP-1 monocyte chemoattractant protein-1 MCV mean corpuscular volume of erythrocytes MetS metabolic syndrome MMW middle molecular weight NCEP national cholesterol education program NMR nuclear magnetic resonance PL phospholipids PLTP phospholipid transfer protein QF quantity-frequency measure SBP systolic blood pressure SOM self-organizing map SR-BI scavenger receptor class B type I TG triglycerides TLFB timeline followback TNF-α tumor necrosis factor alpha UCF ultracentrifugation VCAM-1 vascular cell adhesion molecule 1 VLDL very-low-density lipoprotein WC waist circumference WHO World Health Organization WHR waist-to-hip ratio

10 List of original articles

This thesis is based on the following publications, which are referred throughout the text by their Roman numerals:

I Mäkelä SM, Jauhiainen M, Ala-Korpela M, Metso J, Lehto T, Savolainen MJ,

Hannuksela ML (2008) HDL2 of heavy alcohol drinkers enhances cholesterol efflux from RAW-macrophages via phospholipid-rich HDL2b particles. Alcohol Clin Exp Res 32(6):991-1000. II Kumpula LS*, Mäkelä SM*, Mäkinen V-P, Karjalainen A, Liinamaa JM, Kaski K, Savolainen MJ, Hannuksela ML and Ala-Korpela M (2010) Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps. J Lipid Res 51(2):431-9. III Kuusisto** SM, Peltola T, Kumpula LS, Mäkinen V-P, Salonurmi T, Hedberg P, Savolainen MJ, Hannuksela ML and Ala-Korpela M (2012) The interplay between lipoprotein phenotypes and alcohol consumption. Ann Med 44(5):513-22.

* equal contribution ** Kuusisto née Mäkelä

12 Content

Abstract Tiivistelmä Acknowledgements 7 Abbreviations 9 List of original articles 11 Content 13 1 Introduction 17 2 Review of the literature 19 2.1 Defining heavy drinking ...... 19 2.1.1 Definition of heavy drinking in Finland ...... 19 2.1.2 Measures of alcohol consumption ...... 20 2.1.3 Biomarkers of heavy alcohol intake ...... 22 2.2 Effect of alcohol on cardiovascular disease (CVD) ...... 25 2.2.1 Association of alcohol consumption and CVD ...... 25 2.2.2 Effect of alcohol on metabolic syndrome ...... 28 2.3 Lipoproteins, their atherogenicity and effect of alcohol on them ...... 30 2.3.1 Chylomicrons ...... 31 2.3.2 VLDL, IDL and LDL ...... 32 2.3.3 HDL ...... 33 2.3.4 Effect of alcohol on lipoproteins ...... 39 2.4 Adiponectin and its aterogenicity ...... 42 2.4.1 Adiponectin ...... 42 2.4.2 Adiponectin has anti- and proatherogenic properties ...... 43 2.5 Effect of alcohol on adiponectin ...... 45 2.5.1 Effect of alcohol on blood concentration of adiponectin in humans ...... 45 2.5.2 Effect of alcohol on blood concentration of adiponectin in rodents ...... 46 2.6 Self-organizing map (SOM) analysis as a tool to classify multiple relations in data set ...... 46 3 Purpose of the study 49 4 Subjects, materials and methods 51 4.1 Subjects ...... 51 4.1.1 Heavy alcohol drinkers and controls (I,III) ...... 51

13 4.1.2 Subjects used for establishing lipoprotein SOM analysis (II) ...... 52 4.2 Materials ...... 52 4.2.1 Isolated lipoproteins from human subjects (II) ...... 52 4.3 Methods ...... 53 4.3.1 Measurement of biochemical variables in heavy alcohol drinkers and controls (I,III) ...... 53 4.3.2 Assessment of Metabolic syndrome, CV-risk and HOMA- IR ...... 54 4.3.3 Analysis of lipoprotein particles (I,II,III) ...... 55 4.3.4 Statistical methods ...... 56 5 Results 59 5.1 Clinical characteristics of the study subjects ...... 59 5.1.1 Alcohol biomarkers, bilirubin, albumin and creatinine ...... 59 5.1.2 Concentrations of lipids and lipoproteins ...... 60 5.2 Characterization of lipoprotein particles (I,II,III) ...... 62 5.2.1 Establishing lipoprotein profiles based SOM analysis (II) ...... 62 5.2.2 Characteristics of HDL particles in heavy alcohol drinkers and controls (I,III) ...... 64 5.2.3 Characteristics of VLDL, IDL and LDL in heavy alcohol drinkers and controls (III) ...... 68 5.3 Metabolic characteristics of the alcohol drinkers (III) ...... 72 5.3.1 CETP, metabolic syndrome, waist circumference, blood pressure and adiponectin (III) ...... 72 5.3.2 Glucose, HbA1C and HOMA-IR (III) ...... 73 5.3.3 Cardiovascular risk (III) ...... 73 5.4 Summary of metabolic characteristics of alcohol drinkers from the SOM analysis (III) ...... 74 6 Discussion 75 6.1 Retrospective diary and alcohol biomarkers as a measure of alcohol consumption ...... 75 6.2 HDL of heavy alcohol drinkers (I,II,III) ...... 76 6.2.1 Role of CETP in the HDL subgroups of heavy alcohol drinkers ...... 77

6.2.2 Effect of phospholipid-rich HDL2 of the heavy alcohol drinkers in cholesterol efflux (I) ...... 77 6.3 VLDL, IDL and LDL of heavy alcohol drinkers ...... 79

14 6.4 Heavy alcohol intake relates to high plasma adiponectin level (III) 80 6.5 Association of alcohol intake with lipoprotein phenotypes in relation to cardiovascular risk (III) ...... 81 6.5.1 Concerns of CV risk evaluation by CV risk tools in this study ...... 84 6.6 SOM analysis as a tool to classify lipoproteins ...... 85 6.7 Study limitations ...... 86 6.7.1 Confounders ...... 86 6.7.2 Liver function ...... 88 6.7.3 Sample size ...... 88 6.8 Future aspects ...... 89 7 Conclusions 91 References 93 Original articles 131

16 1 Introduction

Substantial epidemiological data demonstrates a J- or U-shaped relationship between alcohol consumption and cardiovascular disease (Corrao et al. 2000, Costanzo et al. 2011). However, the effect of alcohol consumption on atherosclerosis, a common manifestation of cardiovascular disease, is controversial (Bogousslavsky et al. 1990, Demirovic et al. 1993, Juonala et al. 2009, Kauhanen et al. 1999, Kiechl et al. 1994, Kiechl et al. 1998, Lee et al. 2009, Mukamal et al. 2003, Pletcher et al. 2005, Schminke et al. 2005, Zureik et al. 2004). Atherosclerosis is a multi-factorial disease, and its pathophysiology is complex (Libby & Theroux 2005). Behind the outcomes of atherosclerotic cardiovascular disease there may lie metabolic syndrome in which lipoprotein abnormalities, insulin resistance and abdominal obesity are clustered together (Grundy 2008). Lipoproteins play a central role in the pathophysiology of atherosclerosis. Thus, half of the cardiovascular benefits of alcohol intake have been explained by the alcohol-caused increase in serum high-density lipoprotein cholesterol (HDL-C) concentration and 18% by the reduction of the low-density lipoprotein cholesterol (LDL-C) concentration (Langer et al. 1992). In addition to lipoproteins, several inflammatory modulators, classical and novel cytokines, are involved in the pathophysiology of atherosclerosis (Tedgui & Mallat 2006). For example, adiponectin, derived from adipose tissue, can ameliorate atherosclerosis (Okamoto et al. 2002, Yamauchi et al. 2003b). The lipoprotein metabolism constitutes a complex crosstalk between lipoproteins, lipid-modifying enzymes and lipid transfer and exchange proteins. Therefore, measuring only the plasma concentrations of lipoproteins cannot explain the overall lipid-related cardiovascular status of an individual: recent studies point out that the functionality of lipoproteins overcomes the lipoprotein plasma concentration measures (Favari et al. 2013, Khera et al. 2011). One of the measures of HDL functionality is the capacity of HDL to promote cholesterol efflux, which is a key anti-atherogenic process and the first step in the reverse cholesterol transport pathway. The effect of alcohol consumption on cholesterol efflux is largely unknown, especially with respect to heavy alcohol intake. In previous studies, the effects of alcohol on the pathophysiology of atherosclerosis, including lipoprotein metabolism, have mainly been investigated in case-control research settings and by using linear correlation analysis. However, the relationships in biological processes are often multiparametric and nonlinear, and a lot of information may thus go unnoticed due to limited view on the data by linear tools.

17 Self-organizing map (SOM) based methodology is a pattern recognition technique, which organizes multiple input variables by an unsupervised algorithm according to similarity criteria (Kohonen 2013). Therefore, SOM provides an ultimate tool to study complex and non-linear associations related to the pathophysiology of atherosclerosis in heavy alcohol drinkers. This thesis was carried out to clarify the effect of heavy alcohol intake on the pathophysiology of atherosclerosis and also to investigate the potential of purely data- driven SOM methodology in lipoprotein research. Firstly, the effect of heavy alcohol consumption on cholesterol efflux, a metric of HDL function, was investigated. Secondly, SOM-based methodology was applied to ultracentrifugation-based lipoprotein data to investigate the potential of SOM to enhance the data and to find the multi-dimensional interrelationships between lipoprotein particles. Finally, SOM was applied to characterize complex interrelationships between various lipoprotein particles and their connection with other metabolic parameters, such as metabolic syndrome (MetS) and plasma adiponectin level, in relation to alcohol consumption.

18 2 Review of the literature

2.1 Defining heavy drinking

According to the Finnish Current Care Guidelines 2011, the excess use of alcohol can be divided into three categories. 1) The risky use of alcohol means drinking over the limits for risky drinking (see Table 1), but remarkable harmful effects of alcohol or dependence do not exist. 2) The harmful use of alcohol, defined by physical or mental harm, but dependence does not exist. 3) Alcohol dependence (alcoholism), defined by compulsive drinking, withdrawal symptoms and the increase of tolerance of alcohol and the continuation of drinking despite harm. This thesis will not take a stand on the harmful effects of alcohol or alcoholism as a disease. However, the measurement of alcohol consumption in surveys is problematic, since it is mainly based on the individual’s own description (Sobell & Sobell 2003) and none of the laboratory markers of alcohol consumption are accurate enough to determine the degree of alcohol use (Hannuksela et al. 2007).

2.1.1 Definition of heavy drinking in Finland

Based on epidemiological data, the Finnish Current Care committee has come up with a definition of the limits for risky drinking (see Table 1 for details). Epidemiological data demonstrate that an alcohol intake of up to 2-4 drinks per day for men and 1-2 drinks per day for women reduces total mortality, after which mortality increases (a meta-analysis of 34 prospective studies) (Costanzo et al. 2011). On the other hand, a meta-analysis of 123 high-quality studies shows that the risk for several diseases is already increased by two drinks (25 g alcohol) per day (Corrao et al. 1999). A drink in Finland is 33 cl of regular beer, 12 cl of mild wine or 4 cl of spirits ≈ 12 grams of ethanol (Seppä et al. 2010). However, a standard drink in the USA contains 14 g of alcohol (Sobell & Sobell 2003), in Great Britain 8 g of alcohol, and in Japan 19.75 g of alcohol (Dufour 1999). Thus, there is large variation in the definition of a drink between countries.

19 Table 1. Definition of heavy drinking in Finland

Alcohol intake Dosesa for Men Dosesa for Women Daily (regularly) >4 >2 One occasion >7 >5 Weekly >24 >16 aOne dose is equal to 12 g of ethanol. Data from (Treatment of alcohol abuse: Current Care Guidelines 2011).

However, alcohol use below the aforementioned levels cannot be considered safe, since alcohol has many harmful effects on health, such as injuries, depression, insomnia, neurological effects and gastric illnesses, and long-term use of alcohol increases the risk of liver cirrhosis, pancreatitis and different cancers (Seppä et al. 2010).

2.1.2 Measures of alcohol consumption

Generally, for the purpose of surveys, alcohol consumption can be assessed by summary or daily drinking methods (Sobell & Sobell 2003). For health care purposes, the Alcohol Use Disorders Identification Test (Saunders et al. 1993) or Sort Alcohol Dependence Data questionnaire (Davidson & Raistrick 1986, Raistrick et al. 1983) may be used to assess alcohol problems. These questionnaires define hazardous and harmful alcohol consumption in addition to dependence.

Examples of Summary methods

Summary methods give an average or typical alcohol drinking pattern. In frequency measures, the typical drinking frequency within a given timeframe (e.g. past year) is measured based on drinking categories, such as “never”, “less than once a month”, “1 to 3 times per month”, “1 to 2 times per week”, “3 to 5 times per week”, “daily”, or “almost daily” (reviewed in Rehm 1998). A more accurate measure is a quantity- frequency (QF) measure (see example in Table 2). QF methods cover the drinking frequency and average quantity of alcohol consumed per occasion and therefore provide the researchers an opportunity to calculate the total amount of alcohol consumed (Dufour 1999, Rehm 1998). The simplest QF method gives an average quantity of drinks per average drinking day. (Sobell & Sobell 2003). Graduated frequency (GF) measures (see example in Table 2) define the largest amount of alcohol consumed at any one drinking occasions during the past year, and on the

20 contrary, the number of occasions when progressively lower amounts of alcohol are consumed (Dufour 1999, Rehm 1998). Life-time drinking measures capture the overall frequency and quantity of drinking over long periods of time (Skinner & Sheu 1982, Sobell & Sobell 2003).

Table 2. Examples of quantity-frequency (QF) and graduated frequency (GF) questions

QF questions GF question Options 1) In the last 12 months how often 1) Please record how often in a) every day did you have an alcoholic drink? the last 12 months you have b) 5–6 days per week had each of the following c) 3–4 days per week number of standard drinks in a d) 1–2 days per week day: 20 or more, 11–19, 7–10, e) 2–3 days a month five to six, three to four and one f) about one day a to two standard drinks a day. month g) less often h) no longer drink

2) On a day that you have an a) 13 or more drinks alcoholic drink, how many standard b) 11–12 drinks drinks do you usually have? c) 7 to 10 drinks d) 5 to 6 drinks e) 3 to 4 drinks f) 1 to 2 drinks

Questions are from (Stockwell et al. 2004)

Examples of daily drinking methods

Daily drinking methods give an estimate of drinking on each day in a given timeframe. The Alcohol Timeline Followback (TLFB) method is a calendar-based form with memory hints, where people fill in retrospective information about their drinking for each day, including abstinent days. (Sobell & Sobell 2003). In short-term recall methods, like weekly drinking recall record, each drink consumed over the short term (e.g. a week) is recorded (Rehm 1998). In dietary methods, the study subjects may report any drink immediately after drinking during a given timeframe (Rehm 1998).

21 Comparison of alcohol consumption measures

Validity in an alcohol survey is mostly defined based on comparison of alcohol consumption in the survey to alcohol sales for the same region (Rehm 1998). The QF method covers about 40 to 65% of estimated alcohol consumption based on alcohol sales data (Hoyer et al. 1995, Single & Wortley 1994, Stockwell et al. 2004). However, compared with alcohol sales, the GF method yields a better coverage (about 60 to 70%) (Stockwell et al. 2004). Generally, daily drinking measures provide higher estimates of drinking than QF measures (Lemmens et al. 1992, O'Hare 1991, Redman et al. 1987). The disadvantages of QF measures are that they cannot determine drinking patterns and they underestimate heavy drinking occasions (Greenfield & Kerr 2008). Moreover, including questions about binge drinking in QF questionnaires increases the prevalence of heavy drinkers in the survey (Stahre et al. 2006). Thus, heavy drinkers are detected better by diary than by QF methods (Flegal 1990, O'Hare 1991, Redman et al. 1987). The GF method seems to give the best estimates of heavy alcohol drinking when compared with QF or weekly drinking recall method (Rehm 1998). The World Health Organization (WHO) recommends the use of GF questionnaires over the past year or at least simple QF questions with additional questions about occasions of heavy alcohol use in international alcohol surveys (WHO 2000). Drinking assessment methods asking about drinking during a short reference period cause more hazardous variability to the results than summary methods (e.g. GF) and may result in findings suggesting lower drinking or even non-drinkers when the drinking occasions occurred prior to the investigation (Greenfield & Kerr 2008, Redman et al. 1987). For example, a one-week diary method detected 14% of drinkers as non-drinkers as compared with the QF method (Redman et al. 1987), whereas a 28-day drinking diary is well consistent with its 12-month GF-based counterparts (Greenfield et al. 2009). The problem with methods with a long reference period is forgetting (Greenfield & Kerr 2008), since TLFB (a retrospective calendar based diary method with memory hints) for 30 to 366 days, seems to underestimate alcohol consumption as compared with real-time reported alcohol consumption (Carney et al. 1998, Searles et al. 2002).

2.1.3 Biomarkers of heavy alcohol intake

GGT (γ-glutamyltransferase), AST (aspartate aminotransferase), ALT (alanine aminotransferase) mean corpuscular volume of erythrocytes (MCV) and CDT

22 (carbohydrate-deficient transferrin) can be defined as traditional or conventional biomarkers of heavy alcohol drinking (Niemelä 2007, Sobell & Sobell 2003). These markers are introduced in Table 3. Due to quite low specificity and sensitivity, none of the biomarkers of alcohol abuse can be used alone as diagnostic tools (Table 3). In addition to conventional markers, a large number of new alcohol markers have been used to detect heavy alcohol consumption, such as fatty acid ethyl esters, ethyl glucuronide, ethyl sulfate, phosphatidyl ethanol, platelet monoamine oxidase type B activity and hydroxytryptophol (Hannuksela et al. 2007, Litten et al. 2010, Niemelä 2007). However, these new markers have not yet been applied to clinical care settings.

Ethanol concentration

Ethanol can be detected directly from body fluids and vapors, such as from breath (Swift 2003), blood and urine (Jones 2006, Swift 2003). The average elimination rate of ethanol from the body is 0.10–0.15 grams of ethanol per kg of body weight per hour (Jones 2010). This means that a person weighing 70 kg will eliminate one bottle of wine (≈ 70 g of ethanol) at least in 10 h. Therefore, ethanol itself is not measurable after ≈ 10–12 h of ingestion of alcohol (Helander et al. 1999). However, recent ethanol measurement is useful for example to check drunk drivers (Tsai et al. 2010) and in monitoring alcohol withdrawal therapy (Hannuksela et al. 2007).

Gammaglutamyltransferase

GGT, a cell membrane protein, operates in glutathione metabolism. This enzyme catalyzes the transfer of glutamyl residue from glutathione to an acceptor amino acid (reviewed in Whitfield 2001). Measurement of GGT activity is generally used as a liver function test and also as a marker of alcohol abuse (Alatalo et al. 2009, reviewed inWhitfield 2001). Alcohol may increase GGT in serum e.g. by affecting its synthesis or release due to hepatocyte damage (reviewed in Hannuksela et al. 2007). The specificity and sensitivity of GGT are better than those of AST and ALT (Table 3). Low-to-moderate alcohol intake (< 40 g per day) for several weeks may increase the level of GGT (Alatalo et al. 2009, Hietala et al. 2005). Drinking intensity (drinks per drinking day) affects GGT level more than drinking frequency (number of drinking days) (Anton et al. 1998). Obesity increses the impact of alcohol consumption on GGT. Increased GGT is also linked to diabetes, metabolic syndrome and cardiovascular diseases (reviewed in Niemelä & Alatalo 2010).

23 Aspartate aminotransferase and alanine aminotransferase

Aminotransferases operate in amino acid degradation, which takes place mainly in the liver (reviewed in Brosnan & Brosnan 2009). Aminotransferases catalyze the transfer of an α-amino group from amino acids to alpha-keto acid (α-ketoglutarate in the case of aspartate and alanine) to form glutamate, which is further deaminated to form ammonium and converted into urea (Brosnan & Brosnan 2009, Stryer 1995). The specificity and sensitivity of AST and ALT are low (Table 3). Since alcohol consumption may increase AST more than ALT, an AST to ALT ratio over 2 may suggest liver disease to have an alcoholic etiology (Cohen & Kaplan 1979, Hannuksela et al. 2007). Even low-to-moderate alcohol intake (< 40 g per day) for several weeks may increase the levels of AST and ALT (Alatalo et al. 2009). Notably, obesity increases the effect of alcohol on ALT (Alatalo et al. 2009).

Mean corpuscular volume of erythrocytes

Excess alcohol consumption causes several hematological abnormalities, increased mean cell volume of erythrocytes being the most common one (Latvala et al. 2004). Heavy alcohol drinking increases MCV (reviewed in Litten et al. 2010). In addition, low-to-moderate drinking (< 40 g/day) may alter the red blood cell volume (Koivisto et al. 2006). As the half-life of red blood cells is approximately 13 to 27 weeks (Litten et al. 2010), the increased MCV is measurable longer than other traditional markers (Table 3). However, the sensitivity and specificity of this method are quite low (Table 3). Furthermore, MCV is also increased in several other conditions, such as deficiencies of vitamin B12 and folic acid, liver diseases, hematological diseases, hypothyroidism and reticulocytosis (Litten et al. 2010, Niemelä 2007).

Carbohydrate-deficient transferrin

Human transferrin transports iron in the blood. Transferrin is synthesized in the liver and glycosylated in the Golgi complex. Desialylation occurs on hepatic plasma membranes (reviewed in Sillanaukee et al. 2001). The major glycosylated form of transferrin contains four sialic acid residues, but also minor isoforms with varying numbers of sialic acids are identified in humans. CDT refers to transferrin glycoforms of asialo-, monosialo- and disialotransferrin (reviewed in Bortolotti et al. 2006, Hannuksela et al. 2007). The effect of alcohol on sialylation / desialylation pathways may be the reason for the formation of CDT (Sillanaukee

24 et al. 2001). Alcohol intake above 60–80 g per day increases CDT values (Bortolotti et al. 2006, Hannuksela et al. 2007, Sillanaukee et al. 2001). The sensitivity of CDT varies, but the specificity of this measure is high (Table 3).

Combined GGT-CDT

The combination of GGT and CDT by the formula [0.8 x ln(GGT) + 1.3 x ln(CDT)] forms a marker that can discriminate social drinkers (alcohol consumption < 20 g/day) from alcohol abusers (alcohol consumption > 60 g/day) better than traditional markers alone (GGT, CDT, AST, ALT or MCV) (Hietala et al. 2006, Sillanaukee et al. 2001). However, social drinkers (alcohol consumption ≤ 40 g/day) cannot be distinguished from abstainers (Hietala et al. 2006). The sensitivity and specificity of the combined GGT-CDT were 75%–90% and 93%–98%, respectively (Hietala et al. 2006, Sillanaukee et al. 2001).

Table 3. Traditional biomarkers of heavy drinking

Marker Detection timea.b,c Sensitivitya Specificitya Type of drinking characterizeda,c GGT 2–3 weeks 34–85% 11–95% Chronic heavy drinking AST 2–3 weeks 15–69% low Chronic heavy drinking ALT 2–3 weeks 18–58% low Chronic heavy drinking MCV 2–4 months 34–89% 26–95% Chronic heavy drinking CDT 2–3 weeks 39–94% 82–100% Moderate to high drinking GGT-CDT 2–3 weeks 75–90% 93–98% Moderate to high drinking Data from reviews of a (Hannuksela et al. 2007), b (Niemelä 2007) and c (Litten et al. 2010).

2.2 Effect of alcohol on cardiovascular disease (CVD)

Alcohol consumption may be protective against coronary heart disease (CHD) (Corrao et al. 2000), although consumption of > 2 drinks per day increases the risk for several other diseases, as well as total mortality (Corrao et al. 1999, Di Castelnuovo et al. 2006).

2.2.1 Association of alcohol consumption and CVD

The association of alcohol consumption with CVD shows a J- or U-shaped curve (Corrao et al. 2000, Costanzo et al. 2011, Foerster et al. 2009, Marmot & Brunner 1991). A meta-analysis of 51 studies showed a J-shaped association between the risk

25 of CHD and alcohol consumption, suggesting a protective effect for up to 72 g/day, the lowest risk being at 20 g/day (Corrao et al. 2000). Light to moderate alcohol intake (2.5-14.9 g/day) was associated with 14–25% reduction in several CVD outcomes compared with abstainers in a meta-analysis of 84 studies, but the risk of CHD mortality (31 studies) decreased at all levels of alcohol intake investigated (>2.5 g/day to >60 g/day) (Ronksley et al. 2011). Interestingly, a J-shaped curve between vascular events and alcohol consumption (a meta-analysis of 16 studies) has been defined for wine and beer drinkers, but not for drinkers of spirits (Costanzo et al. 2011). However, Fillmore et al. demonstrated that if meta-analysis is performed for studies that do not include former drinkers in the abstainers category, then a J-shape curve does not exist, but the abstainers and moderate drinkers are at equal risk for CHD mortality (Fillmore et al. 2007). On the other hand, according to the National Health Survey study of the U.S population including 247,207 participants, former drinkers do not contaminate the abstainers’ category, since the risk for CVD mortality is similar among lifelong abstainers and former drinkers (Mukamal et al. 2010).

Association of alcohol intake and atherosclerosis

Several large-scale studies of alcohol consumption and atherosclerosis are introduced in Table 4. The effect of alcohol on atherosclerosis, a common manifestation of cardiovascular disease is inconsistent (Table 4). The level of alcohol intake (grams of alcohol per day) is quite low in most of the studies (Table 4). There are only three studies (Table 4), where alcohol consumption categories over 80 g per day have been investigated (Kiechl et al. 1994, Kiechl et al. 1998, Schminke et al. 2005). In these studies the association of alcohol intake with atherosclerosis was either U- or J- shaped, with the lowest atherosclerosis at alcohol consumption of 50 g/day to 80 g/day, indicating that a protective effect of alcohol on atherosclerosis may exist even at quite high amounts of alcohol intake. In addition to the amount of alcohol consumed, the drinking pattern may affect the development of atherosclerosis, since atherosclerosis progression is faster among binge drinkers than those with a distributed drinking pattern (Kauhanen et al. 1999) and amongst binge drinkers, those drinking ≥ 6 portions per sitting have faster progression of atherosclerosis than those drinking < 6 portions (Rantakömi et al. 2009). Interestingly, apoE knockout mice on an atherogenic diet who were given the same total amount of alcohol either daily and moderately (blood alcohol concentration (BAC) 0.07%) or on weekends at heavy doses (BAC 0.23%) show that

26 atherosclerosis was increased in “binge drinking” mice and decreased in “moderately drinking” mice as compared with mice fed a diet without alcohol (Liu et al. 2011).

Table 4. Association of alcohol intake with ultrasonography or computed tomography scanning analysed atherosclerosis (The studies > 200 subjects are considered).

Author Study Number of Alcohol Conclusion of the results type subjects consumption Bogousslavsky et Cross- 261 0 to > 40 g/day Inverse linear association between alcohol al. 1990 sectional intake and severity of internal carotid artery stenosis. Demirovic et al. Cross- 10 578 ≥10 to >40 g/day No association between alcohol intake 1993 sectional and carotid IMT. Kiechl et al. 1994 Cross- 460 0 to ≥100 g/day U-shaped association between alcohol sectional intake and carotid atherosclerosis with nadir at 50g/day and risk increases at consumption over ≈ 99g/day. Kiechl et al. 1998 5 year follow up 780 0 g/day to ≥100 J-shaped association between alcohol study g/day intake and progression of carotid atherosclerosis. Nadir at 1-50 g/day. Risk increases at consumption over ≈ 75 g/day. Kauhanen et al. 4 year follow up 1022 ≤4 to >22 g/daya No difference in baseline atherosclerosis 1999 study between alcohol drinking groups. Mukamal et al. Cross- 4247 0 to 24+ g/day J-shaped association between carotid 2003 sectional atherosclerosis and alcohol consumption. Zureik et al. 2004 Cross- 6631 0 to >36 g/day No marked relationships of alcohol sectional consumption with atherosclerosis. Pletcher et al. Cross- 3037. 0 to ≥ 27 g/day The prevalence of coronary calcification 2005 sectional associates directly with alcohol consumption. Schminke et al. Cross- 2420. 0 to >80 g/day in J-shaped association between carotid IMT 2005 sectional men and 0 to >20 and alcohol consumption. Nadir at 61 to g/day in women. 80 g/day in men. No association in women. Juonala et al. Cross- 2074 0 to ≥ 56 g per day Alcohol consumption directly associates 2009 sectional with carotid IMT. Lee et al. 2009 Cross- 4302 0 to > 40 g/day. Alcohol consumption correlates negatively sectional with carotid IMT, but positively with carotid plaques in men. No correlation in women. Xie et al. 2012 Cross- 13,037 0 to ≥ 50 g/day. J-shaped curve between alcohol sectional consumption and carotid IMT. The carotid atherosclerosis was analyzed by ultrasonography in all studies except (Pletcher et al. 2005), where computed tomography scanning was used for analysis. Alcohol consumption is expressed as grams per day (g/day). aAlcohol consumption is converted to g/day by assuming that 1 dose is equal to 13 grams of alcohol.

27 2.2.2 Effect of alcohol on metabolic syndrome

Definition of metabolic syndrome (MetS)

MetS is defined by a cluster of interrelated risk factors for CVD and diabetes (Alberti et al. 2009). MetS increases the risk for CVD events and death approximately two- fold (Gami et al. 2007) and the risk for diabetes by 5-fold (Grundy 2008). Several organizations have drawn up criteria for the definition of MetS (Alberti & Zimmet 1998, Alberti et al. 2005, Balkau & Charles 1999, Grundy et al. 2005, NCEP Expert Panel 2001). However, all criteria include a combination of metabolic risk factors, such as abdominal obesity, elevated blood pressure, dysglycemia and dyslipidemia (as an example see Table 5). The most frequently used criteria in the literature are the NCEP, IDF and WHO criteria (Grundy 2008). Therefore, these are introduced in Table 5.

28 Table 5. Definition of metabolic syndrome by the WHO, NCEP and IDF criteria.

WHO 1998; (Alberti & Updated NCEP ATP III 2004: IDF 2005: (Alberti et al. Zimmet 1998) (Grundy et al. 2005) 2005) Definition criteria IFG, IGT or DM and/or At least 3 of the following: Central obesity and any insulin resistance + at least two of the following: two of the following: Glucose IFGa, IGTa or DMa fG ≥ 5.6 mmol/L fG ≥ 5.6 mmol/L or previously diagnosed T2DM Insulin resistance Measured by - - hyperinsulinemic euglycemic clampb Blood pressure ≥ 160 / 90 mmHg ≥ 130 / 85 mmHg SBP ≥ 130, DBP ≥ 85 mmHg or treatment Lipids TG TG ≥ 1.7 mmol/L TG ≥ 1.70 mmol/L TG > 1.7 mmol/L or treatment

HDL HDL < 0.9 mmol/L in men HDL < 1.04 mmol/L in men HDL-C < 1.03 mmol/L and < 1.0 mmol/L in and < 1.30 mmol/L in women in men, < 1.29 mmol/L women in women or treatment Central obesity WHR > 0.9 in men and > WC > 102 cm in men and ethnicity specific WC 0.85 in women and/or > 88 cm in women values BMI > 30 kg/m2 Other microalbuminuriaa none none

DM, diabetes mellitus; fG, fasting glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; WHR, Waist-to-Hip Ratio. a (Alberti et al. 2005) bGlucose uptake by the lowest quartile of the background population.

Prevalence of metabolic syndrome

The prevalence of MetS in adult population is between 20% to 30% in most countries in the world (Grundy 2008). Therefore it should be considered in CVD surveys. The prevalence of MetS in Finns aged 24-75 was 36% according to the national FINRISK 2007 study (Sundvall et al. 2011). The prevalence in elderly Finns was 25 to 35% in men and 21–48% in women depending on the definition (Saukkonen et al. 2012), but among young Finns the prevalence of MetS was only 19% (Laitinen et al. 2012). The

29 prevalence of MetS in the U.S. population aged  20 years studied in the National Health and Nutrition Examination Survey 2003–2006 was 34.3% (Ford et al. 2010).

Effect of alcohol consumption on metabolic syndrome

In addition to alcohol intake, the prevalence of metabolic syndrome is affected by certain lifestyle factors, such as physical activity, diet and smoking (Zhu et al. 2004). However, after controlling for several lifestyle factors, including e.g. diet or energy intake, the relation between alcohol consumption and MetS seems not to be clear. In some studies, alcohol consumption is associated with a higher (Chen et al. 2012, Kim et al. 2012) or lower prevalence of MetS (Djousse et al. 2004), whereas others report no association (Buja et al. 2010, Wannamethee et al. 2006). The selection of reference group may cause some confusion concerning the relation between alcohol consumption and MetS, since ex-drinkers are reported to have a higher prevalence of MetS than lifetime abstainers (Park 2012). The amount of alcohol consumption that may increase the risk for MetS is not clear. A study of 1,529 U.S. adults, participants in the National Health and Nutrition Examination Survey, showed that an increased risk of metabolic syndrome was associated with daily consumption of alcohol that exceeded one drink per day for women and two drinks per day for men and binge drinking once per week or more (Fan et al. 2008). In line with this study, Barrio-Lopez et al. (Barrio-Lopez et al. 2013) show that the intake of seven or more drinks per week increases the risk for developing of MetS markedly. However, Lee et al. indicate that seven drinks at a typical occasion increases the risk of MetS in men, but only 3 or more drinks are needed in women (Lee 2012). On the other hand, a meta-analysis of 7 cross-sectional studies indicates that alcohol consumption of less than 20 g/day in women and less than 40 g/day in men reduced the prevalence of MetS (Alkerwi et al. 2009).

2.3 Lipoproteins, their atherogenicity and effect of alcohol on them

The lipoproteins are spherical particles that transport lipids, mainly TG and cholesterol, in the plasma (reviewed in Hegele 2009). The lipoprotein core consists mainly of triglycerides and cholesteryl esters and the surface layer of phospholipids, unesterified cholesterol and different apolipoproteins (Hegele 2009, Hevonoja et al. 2000). However, a decrease in lipoprotein particle radius may increase the percentages of TG and CE molecules on the particle surface, suggesting that HDL

30 particles have the highest percentages of CE and TG on lipoprotein surface (Kumpula et al. 2008). The lipoproteins can be distinguished and classified based on density (Havel et al. 1955, Krauss & Burke 1982), size (Blanche et al. 1981, Krauss & Burke 1982) and surface charge (Asztalos et al. 2005, Kunitake et al. 1985). The five main classes of lipoproteins classified by ultracentrifugal densities are shown in Table 6 (reviewed in Hannuksela et al. 2002, Hegele 2009).

Table 6. Composition of triglycerides, cholesterol and apolipoproteins in lipoprotein classes distinguished by density.

Lipoprotein Density Diameter C TG Major apolipoproteins class (g/mL) (nm) (mass%) (mass%) Chylomicrons < 0.960 80–1200 3–7 80–95 apoB-48 VLDL 0.960–1.006 30–80 20–30 45–65 apoB-100, apoE, apoC IDLa 1.006–1.019 25–35 17–45 15–40 apoB-100, apoE LDL 1.019–1.063 20–25 51–58 4–8 apoB-100 HDL 1.063–1.210 5–12 18–25 2–7 apoA-I, ApoA-II, ApoE, apoC Data are from (Hannuksela et al. 2002, Hegele 2009). a IDL chol and TG values based on (Sattar et al. 1997, Tilly-Kiesi et al. 1995, Tilly-Kiesi et al. 1996). VLDL, very-low-density lipoprotein; IDL, intermediate- density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein; C, cholesterol; TG, triglycerides; apo, apolipoprotein.

2.3.1 Chylomicrons

The triglyceride-rich chylomicrons are the largest lipoprotein particles in the circulation (Table 6). Dietary fat, which contains mainly triglycerides but also phospholipids, fatty acids, cholesterol and fat-soluble vitamins, is absorbed in the intestine and incorporated into chylomicrons in enterocytes. Chylomicron formation takes place in the Golgi, where microsomal triglyceride transfer protein mediates the synthesis by assembling TGs and structural protein apoB-48 into chylomicrons that receive cholesterol, phospholipids and apolipoproteins and enter the circulation via the lymphatic system (reviewed in Ros 2000). In addition to apoB-48, mature chylomicrons in the circulation contain also other apolipoproteins, such as apoA-I, apoA-II, apoA-IV, apoE and apoC (Mahley et al. 1984, Ros 2000). ApoC-II activates lipoprotein lipase (LPL) that is located on the luminal surface of the capillary endothelium (Mead et al. 2002, reviewed in Wang & Eckel 2009). LPL hydrolyzes TGs in chylomicrons and produces fatty acids and monoacylglycerols for utilization by tissues (Mead et al. 2002, Wang & Eckel 2009).

31 The surface remnants of chylomicrons are transferred to HDL particles (Redgrave & Small 1979, Wang & Eckel 2009) and the remaining chylomicron remnants are taken up by the liver via LDL receptor and LDL receptor-related protein (reviewed in Havel & Hamilton 2004)

Chylomicrons are atherogenic particles

Postprandial lipoproteins have atherogenic properties and they are linked to CAD progression (reviewed in Hyson et al. 2003). Although CM remnants are rapidly removed from the plasma of normolipidemic subjects (Berr & Kern 1984, Cortner et al. 1987), delayed clearance of post prandial CM remnants is observed in normolipidemic CAD patients (Groot et al. 1991). In addition, increased apoB-48 (Masuda et al. 2012, Mori et al. 2013) and an increased ratio of apoB-48 to apoB-100 raises the prevalence for CAD (Simons et al. 1987) indicating the atherogenicity of an increased level of chylomicrons as compared to other apoB-containing lipoproteins (VLDL, IDL, LDL). CM remnants may also promote atherosclerosis progression in the arterial wall, since cell culture studies have shown that CM remnants promote foam cell formation from human monocyte-derived macrophages (HMDMs) (Batt et al. 2004, Yu & Mamo 2000), J774 macrophages (Napolitano et al. 2001) and THP-1 macrophages (Batt et al. 2004).

2.3.2 VLDL, IDL and LDL

The structural protein in TG-rich VLDL particles is apoB-100 (Table 6). VLDL synthesis takes place in the endoplasmic reticulum of hepatocytes, where microsomal triglyceride transfer protein mediates the assembly of lipids to apoB cotranslationally and more TG is added to the VLDL particles in the late phases of the secretory pathway (reviewed in Shelness & Sellers 2001). In the plasma, lipoprotein lipase (LPL) hydrolyses TGs in VLDL particles and as VLDL becomes smaller, the surface remnants are transported to HDL particles and in this process VLDL particles are converted to IDL particles (reviewed inVergès 2005). VLDL remnants are taken up by liver receptors similar to chylomicron remnants (Havel & Hamilton 2004). Hydrolysis of IDL particles by hepatic lipase generates LDL particles (reviewed in Berneis & Krauss 2002, Vergès 2005), which are the major cholesterol-containing lipoprotein particles in the circulation (Vergès 2005). Each LDL particle contains a single molecule of apoB-100 (Table 6) (Vergès 2005). LDL particles can either be produced through the VLDL-IDL-LDL cascade or directly from the liver (Berneis &

32 Krauss 2002). LDL is removed from the circulation via LDL receptor (apoB/E receptor) (Brown & Goldstein 1979, Vergès 2005). The liver is able to secrete different kinds of apoB-containing lipoprotein particles depending on the availability of TG: at low TG levels, the apoB is secreted as IDL or LDL particles, whereas at normal or high TG levels, VLDL is released (Berneis & Krauss 2002).

Atherogenicity of VLDL, IDL and LDL

In addition to high LDL-C, a rise in VLDL and IDL increases the risk for heart diseases (reviewed in Varbo et al. 2014, Würtz et al. 2011). Several epidemiological studies have pointed out that non-HDL-C (defined as cholesterol in VLDL, IDL and LDL collectively) is a better predictor of cardiovascular events than LDL-C level alone (Boekholdt et al. 2012, Pischon et al. 2005a, Ridker et al. 2005). Furthermore, increased remnant cholesterol concentration (defined as cholesterol in VLDL, and IDL in fasting state and as these two plus cholesterol in chylomicron remnants in nonfasting state) is a risk factor for heart disease (Varbo et al. 2013). As for the lipoprotein subclasses, substantial evidence indicates that a rise in small LDL particles, as well as IDL particles is atherogenic (Hoogeveen et al. 2014, Ito et al. 2013, Tsai et al. 2014, Williams et al. 2014). However, the atherogenicity of VLDL subclasses has been less studied and seems not to be clear, since according to some studies both large (Colhoun et al. 2002) and small VLDL (Williams et al. 2014, Würtz et al. 2011) are associated with atherosclerosis. Although the hallmark of the pathophysiology of atherosclerosis is the accumulation of LDL-derived cholesterol in arterial walls (Glass & Witztum 2001, reviewed in Maiolino et al. 2013), it should be emphasized that in addition to LDL (Henriksen et al. 1981), VLDL and IDL can induce atherosclerotic changes in the arterial wall by promoting foam cell formation (Hakamata et al. 1998, Lu et al. 2009).

2.3.3 HDL

HDL metabolism and the reverse cholesterol transport pathway was one of the main topics in this thesis. Therefore this lipoprotein class will be introduced in more detail in the following paragraphs.

33 HDLs are heterogeneous particles

HDLs are the densest and smallest lipoproteins in the circulation (Table 6). ApoA-I is the major apolipoprotein in HDL particles, comprising 70% of the HDL apolipoprotein content (reviewed in Santos-Gallego et al. 2008). However, HDL is the carrier of a large number of different proteins in the circulation, since 56 proteins are found to be associated with HDL particles, including for example apolipoproteins, such as apoA, C, D, E, F, J, L and M, lipid transfer proteins, such as PLTP and CETP and acute-phase proteins, such as C-reactive protein and serum amyloid A (Levels et al. 2011, Rezaee et al. 2006). HDL, with a diameter of 5-12 nm (Table 6), consists of a heterogeneous population of particles that can be separated into further subfractions according to several physical and biochemical properties (Fig. 1). Two major subclasses of HDL separated by density are HDL2 (1.063 g/mL–1.125 g/mL) and HDL3 (1.125 g/mL– 1.21 g/mL). Isolation of HDL fractions by density gradient ultracentrifugation also allows for the analysis of the composition of HDL fractions, such as different fatty acids (Boshtam et al. 2013), phospholipids (PL), cholesterol (C), triglycerides (TG)) and total protein content (Liinamaa et al. 1997a), as was done in this thesis. HDLs can also be divided into subpopulations by apolipoprotein content, such as A-I, A-I/A-II HDL (Cheung & Albers 1982, Duverger et al. 1994), apoE-containing HDL (Kothapalli et al. 2012) and apoM-containing HDL (Christoffersen et al. 2006). HDL subclasses can be separated by size using native gradient gel electrophoresis (GGE) (Blanche et al. 1981, Vekic et al. 2013), size exclusion chromatography (Okazaki et al. 2005) or NMR techniques that also allow the measurement of HDL particle numbers in the plasma (Jeyarajah et al. 2006, reviewed in Otvos et al. 2002). For example, the native GGE used in this thesis, separates HDL into 5 different subclasses (HDL2b, HDL2a, HDL3a, HDL3b, HDL3c) (Blanche et al. 1981). Agarose gel electrophoresis separates HDLs according to surface charge into alpha, pre-alpha, pre- beta or gamma migrating particles. The majority of HDL particles in the circulation are alpha-migrating spherical particles. Pre-beta-migrating particles represent 5–15% of plasma apoA-I concentration and are either discoidal particles with apoA-I, PL and FC or lipid poor apoA-I (reviewed in Barter et al. 2003, Santos-Gallego et al. 2008). Combining the size and charge analysis by 2-dimensional gel electrophoresis, the alpha and pre-beta migrating HDL particles can be further separated into tighter subfractions (Asztalos et al. 2000, Asztalos et al. 2004, Asztalos et al. 2005).

34 Surface charge / Shape Plasma HDL

pre β‐ α‐HDL γ‐HDL HDL Density

HDL2 HDL3 Size

HDL2b HDL2a HDL3a HDL3b HDL3c Apolipoprotein content

AI HDL AI / AII HDL

Fig. 1. Plasma HDLs are heterogeneous particles in terms of size, shape, surface charge, hydrated density, apolipoprotein content and lipid composition.

Biogenesis of HDL

HDL particle assembly is mediated by ATP-binding cassette transporter A1 (ABCA1) through cholesterol efflux to apoA-I (Timmins et al. 2005, Vedhachalam et al. 2007). ABCA1 is expressed in several tissues and cell types (Wellington et al. 2002), but only liver (Hamilton et al. 1976), intestine (Green et al. 1978, Yamaguchi et al. 2014) and adipose tissue (Chung et al. 2011) have a significant contribution into plasma HDL-C. ABCA1 in the liver seems to have a major role in HDL biogenesis, since HDL-C concentrations were 80% lower in liver-specific ABCA1 KO mice compared with wild type littermates (Timmins et al. 2005). On the other hand, the intestinal ABCA1 seems to contribute ≈ 30% (Brunham et al. 2006) and adipocyte ABCA1 ≈ 15% (Chung et al. 2011) of the plasma HDL-C concentration. The importance of ABCA1 in HDL biogenesis has been clearly shown in Tangier disease patients who have a mutation in ABCA1 and extremely low HDL-C levels (Bodzioch et al. 1999, Brooks-Wilson et al. 1999, Rust et al. 1999). In addition, ABCA1 KO mice have very low concentrations of HDL (Francone et al. 2003, McNeish et al. 2000).

35 Discoidal pre-β-HDL particles can be generated by intracellular or extracellular lipidation of A-I or as a side product of PLTP, CETP and HL mediated remodeling of spherical HDL particles (reviewed in Rye & Barter 2014). Discoidal pre-β- HDL particles may also be formed when lipid-free apoA-I associates with surface remnants (PL and FC) that are generated by the hydrolysis of TG-rich lipoproteins by LPL (Rye & Barter 2014). Discoidal pre-β-HDL particles are further converted to spherical mature HDL particles (Clay et al. 2000, Koukos et al. 2007) through the esterification of free cholesterol by LCAT in a reaction that was firstly proposed by Glomset (Glomset 1968).

Cholesterol efflux

Cholesterol efflux to lipid-poor apolipoproteins, such as apoA-I is mediated by ABCA1 (Brooks-Wilson et al. 1999, Nagata et al. 2013, Rust et al. 1999), whereas ATP-binding cassette transporter G1 (ABCG1) mediates cholesterol efflux to spherical HDL particles (Gelissen et al. 2006, Vaughan & Oram 2005, Wang et al. 2004). In addition, ABCA1 and ABCG1 synergize in the efflux process, since pre-β-HDL particles generated by ABCA1 are good substrates for ABCG1 (Gelissen et al. 2006, Lorenzi et al. 2008, Vaughan & Oram 2006). ABCA1 binds directly to apoA-I (Shao et al. 2010, Vaughan et al. 2009), whereas ABCG1 does not bind lipoproteins (Wang et al. 2004). ABCG1 is most likely an intracellular sterol transporter that facilitates the availability of lipids at cell surface, where they can be picked up by HDL (Tarling & Edwards 2011). Interestingly, KO mice studies indicate that ABCG1 does not affect plasma lipid levels (Kennedy et al. 2005, Tarling et al. 2010), unlike ABCA1 (Brunham et al. 2006, Chung et al. 2011, Timmins et al. 2005). Scavenger receptor class B type I (SR-BI) mediates cholesterol efflux to various HDL subpopulations (Asztalos et al. 2005) and is mainly dependent on acceptor particle PL content (Yancey et al. 2000, Yancey et al. 2004). ABCA1 and ABCG1 are clear efflux-transporters requiring ATP for the process (reviewed in Oram & Vaughan 2006) but SR-B1 mediates the bi-directional flux of cholesterol and selective uptake of CE, PL and TG in peripheral cells, as reviewed by (Santos-Gallego et al. 2008, Valacchi et al. 2011, Yancey et al. 2003). However, SR-B1 is mainly expressed in the liver and steroidogenic tissues (Valacchi et al. 2011). The relative contribution of different efflux pathways to cholesterol removal has been investigated in macrophages loaded with AcLDL (FC labeled) obtained

36 from SR-B1, ABCA1 and ABCG1 KO mice. According to these in vitro studies, ABCA1 accounts for 35%–37%, ABCG1 for 21%–24%, SR-B1 for 9% and aqueous diffusion for 30–35% of total cholesterol efflux (Adorni et al. 2007). However, in cholesterol unloaded cells, the efflux is mainly mediated through aqueous diffusion (70% to 90%), whereas in cholesterol loaded cells, the ABCA1 seems to be the major pathway for removal of cholesterol to serum (50%) (Adorni et al. 2007). Taken together, ABCA1- and ABCG1-mediated cholesterol efflux from macrophage foam cells accounts for 70% of total cholesterol efflux (Yvan- Charvet et al. 2010).

Remodeling of HDL particles in the circulation

HDL particles are continuously remodeled in the circulation by several factors. Different lipases, such as hepatic lipase (HL) and endothelial lipase (EL) hydrolyze TG and PL molecules in HDL particles (Barter et al. 2003, Santos-

Gallego et al. 2008). PLTP can convert HDL either to larger HDL2-like particles, simultaneously releasing lipid-poor pre-β-HDL, or to small HDL particles (Chirackal Manavalan et al. 2014, Huuskonen et al. 2000, Jauhiainen et al. 1993). While CETP has also been shown to be able to generate pre-β-HDL particles (Francone et al. 1996, Kunitake et al. 1992), the major role remains with PLTP (Lie et al. 2001). During lipolysis PLTP transfers PL remnants from VLDL to HDL particles (Jiang et al. 1999, Qin et al. 2000) and increases the size of HDL (Patsch et al. 1978, Taskinen et al. 1982a). Since HDL particles are enriched in CE and apoB-containing lipoproteins are TG-rich, CETP promotes the net transfer of HDL cholesteryl esters (CE) to apoB-containing lipoproteins in exchange for TG (Koivuniemi et al. 2012, Liinamaa et al. 1997a, Qiu et al. 2007). TG-enriched HDL particles are good substrates for lipases that hydrolyze TGs in the HDL particles thus generating small dense HDL particles (Barter & Kastelein 2006, Rye & Barter 2014). Hypertriglyceridemic state promotes this remodeling process (Castle et al. 1998). In accordance with CETP function, large HDL particles (Gomaraschi et al. 2014, Matsuura et al. 2006) enriched with CE and depleted of TG are found in CETP-deficient subjects (Matsuura et al. 2006).

Delivery of HDL cholesterol into liver

HDL cholesteryl esters can either be transferred to apoB-containing lipoproteins through the action of CETP (reviewed inBarter & Kastelein 2006, Hamilton &

37 Deckelbaum 2007, Koivuniemi et al. 2012) and removed via LDL-receptors (Brown & Goldstein 1979, Vergès 2005) or they can be removed by the liver via SR-B1-mediated selective uptake of HDL-CE, as reviewed by (Chapman 2006, Connelly & Williams 2004). HDL can also be endocytosed to the liver as a holoparticle (uptake of HDL protein and lipid moieties) by the β-chain of ATP synthase (Fabre et al. 2010, Martinez et al. 2003).

HDL are anti-atherogenic particles

Infusion of HDL mimetic particles or apoA-I reduces the progression of atherosclerosis (Borthwick et al. 2012, reviewed in Kingwell & Chapman 2013, Tardy et al. 2014). HDL prevents atherosclerosis through several mechanisms. HDL particles are able to remove excess cholesterol from the cells (Gelissen et al. 2006, reviewed in Westerterp et al. 2014), but HDL has also other anti-atherogenic functions, such as anti-inflammatory (reviewed in Barter et al. 2004), anti-oxidative (Barter et al. 2004), vasorelaxant (Rämet et al. 2003), anti-thrombotic actions (Mineo et al. 2006), and anti-apoptotic (Sugano et al. 2000), and quite recently, anti-obesity functions have been ascribed to HDL (Ruan et al. 2011, reviewed in Wang & Peng 2012). Prospective epidemiological studies show that low HDL-C is associated with a high incidence of CVD (Assmann et al. 1996, Gordon et al. 1977). Epidemiological data suggest that every 0.03 mM increment in HDL-C reduces CHD risk by 2 to 3% (Gordon et al. 1989), but the HDL subpopulation responsible for the decreased risk is not clear. Detailed HDL subpopulation analysis has shown that CHD patients have lower concentrations of large-sized HDL particles (Asztalos et al. 2000, Kuller et al. 2002, Lamon-Fava et al. 2008, Tian et al. 2014b) and higher levels of small HDL (Asztalos et al. 2000, Tian et al. 2014b) and lipid poor pre-β HDL particles (Lamon- Fava et al. 2008, Miida et al. 1996, Tian et al. 2014b). Interestingly, the increase in large HDL particle level is more strongly associated with decreased CHD prevalence than HDL-C concentration (Asztalos et al. 2004) and low HDL2-C concentration has been postulated as an emerging lipid risk factor for CVD (Sailam et al. 2008). However, other studies indicate that the protective effects may be mediated through both HDL2 (large) and HDL3 (small) subclasses (Gaziano et al. 1993, Stampfer et al. 1991). Despite a clear anti-atherogenic role of HDL particles, therapies that increase HDL-C do not decrease cardiovascular outcomes (reviewed in Hafiane et al. 2014) and therefore HDL research has turned to evaluating the function and quality of HDL

38 instead of HDL-C quantity, as reviewed by (Camont et al. 2011, Rosenson et al. 2013).

2.3.4 Effect of alcohol on lipoproteins

Both moderate and heavy alcohol consumption affects the plasma concentration of lipoprotein lipids (Table 7). The changes presented in Table 7 are introduced in more details in the following two chapters.

Table 7. The effect of alcohol consumption on plasma concentration of cholesterol, triglycerides and phospholipids in VLDL, IDL, LDL and HDL

Lipoprotein class Effect of moderate alcohol intake Effect of heavy alcohol intake VLDL VLDL-TG  or , VLDL-TG  or  VLDL-C  or  VLDL-C  IDL IDL-C ? IDL-C  LDL LDL-C  or  LDL-C  or  LDL-PL ,  or  LDL-TG ,  or  Total HDL HDL-C , HDL-PL , HDL-TG  HDL-C , HDL-PL , HDL-TG 

HDL2 / HDL3 HDL3-C  or HDL2-C 

HDL2-C  and HDL3-C  or HDL2-C  and HDL3-C 

HDL2-PL 

HDL2-TG  or  VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein;

HDL, high-density lipoprotein; HDL2, high-density lipoprotein class 2; HDL3, high-density lipoprotein class 3 TG, triglycerides; C, cholesterol and PL, phospholipids. Concentration is increased , decreased  or unchanged .

Effect of alcohol on chylomicrons, VLDL, IDL and LDL

Acute alcohol ingestion with a meal increases the concentration of postprandial triglycerides due to decreased breakdown of chylomicrons and VLDL remnants, but after overnight fasting the levels are back to normal (reviewed in Van de Wiel 2012). In the fasting state, alcohol consumption may increase the concentration of plasma VLDL TGs (Contaldo et al. 1989, Savolainen et al. 1986, Taskinen et al. 1985), since alcohol administration increases the synthesis of TGs and production of VLDL in the liver (Sane et al. 1984, Savolainen et al. 1986). Chronic alcohol consumption may lead to severe hypertriglyceridemia (Bessembinders et al. 2011). After alcohol administration, the increase in TG concentration and VLDL production rate seem to

39 be more pronounced in obese individuals than in lean subjects (Crouse & Grundy 1984). In moderate drinkers, the concentration of VLDL TGs is either increased (Contaldo et al. 1989) or unchanged (Hansen et al. 2005, Välimäki et al. 1988). In heavy alcohol drinkers, the VLDL TGs may be increased (Kervinen et al. 1991, Liinamaa et al. 1998), but often remain unchanged (Liinamaa et al. 1997b, Taskinen et al. 1982b, Välimäki et al. 1986). This may be caused by an increased VLDL turnover rate (Sane et al. 1984), which may be due to increased LPL activity in heavy alcohol drinkers (Taskinen et al. 1982b, Taskinen et al. 1985). The cholesterol concentration of VLDL in heavy alcohol drinkers is often unchanged (Kervinen et al. 1991, Liinamaa et al. 1997b, Liinamaa et al. 1998, Välimäki et al. 1986). In moderate drinkers, VLDL cholesterol seems to be unchanged (Välimäki et al. 1988) or increased (Contaldo et al. 1989). The effect of alcohol on IDL has not been studied much. Heavy alcohol consumption may decrease the IDL concentration, since IDL total mass and cholesterol concentration tend to be reduced in heavy alcohol drinkers (Kervinen et al. 1991, Liinamaa et al. 1997b). Acute ingestion of a moderate dose of alcohol (40 g) seems not to affect IDL total mass (Goldberg et al. 1984). The effect of moderate alcohol consumption on LDL is inconsistent (Table 7). In some studies, moderate alcohol consumption does not affect the LDL-C level (Contaldo et al. 1989, Crouse & Grundy 1984, Välimäki et al. 1988) or LDL composition (Välimäki et al. 1988). In other studies, LDL-C is decreased (Tolstrup et al. 2009, Wakabayashi 2013). The concentration of LDL-C and total mass of LDL is often reduced in heavy drinkers (Kervinen et al. 1991, Liinamaa et al. 1997b, Taskinen et al. 1982b), but LDL-C may also be unchanged after heavy alcohol intake (Taskinen et al. 1985, Välimäki et al. 1988). The effect of heavy alcohol intake on the concentration of PL and TG in LDL particles is controversial (Kervinen et al. 1991, Liinamaa et al. 1997b, Taskinen et al. 1985, Välimäki et al. 1988) (Table 7).

Effect of alcohol on HDL

An increasing number of studies show that alcohol consumption is associated with an elevated plasma concentration of HDL-C (Clevidence et al. 1995, Galan et al. 2014, Hartung et al. 1990, Muth et al. 2010, Parlesak et al. 2014, Waśkiewicz & Sygnowska 2013). Numerous controlled studies of alcohol intake for several weeks have shown that alcohol consumption increases HDL-C (Kralova Lesna et al. 2010,

40 Sierksma et al. 2004a, van der Gaag et al. 2001). Alcohol consumption of 30 g per day would be expected to increase HDL-C concentration about 0.1 mmol/L (3.99 mg/dL) and to reduce CHD risk by 24.7% (Rimm et al. 1999). The effect of alcohol intake on HDL is summarized in Table 7. In addition to HDL-C, moderate alcohol consumption also increases the other lipid moieties of HDL, such as HDL-PL and HDL-TG (Parlesak et al. 2014, van der Gaag et al. 2001, van Tol et al. 1998). Moderate alcohol consumption seems to mainly affect the HDL3 subfraction, since HDL3-cholesterol (Nishiwaki et al. 1994,

Sillanaukee et al. 2000) concentration and HDL3 total mass (Haskell et al. 1984) were increased after moderate alcohol intake without affecting HDL2 (Haskell et al. 1984, Nishiwaki et al. 1994). However, moderate alcohol intake may also increase cholesterol concentration in both subclasses of HDL (Clevidence et al. 1995, Hartung et al. 1990, Parlesak et al. 2014). Moreover, a recent study reported that the number of large HDL particles is higher in regular alcohol drinkers than in non-drinkers (Muth et al. 2010). Similarly to moderate alcohol consumption, also heavy alcohol intake increases HDL-C concentration (Liinamaa et al. 1997a, Taskinen et al. 1982b, Välimäki et al. 1986, Waśkiewicz & Sygnowska 2013). Additionally, HDL-PL (Liinamaa et al. 1997a, Taskinen et al. 1985) and HDL-CE (Liinamaa et al. 1997a) concentrations are increased in heavy drinkers. However, HDL-TG concentration often remains unchanged in heavy alcohol drinkers (Liinamaa et al. 1997a, Taskinen et al. 1982b, Taskinen et al. 1985, Välimäki et al. 1986), but it may also be reduced (Välimäki et al. 1986), in heavy alcohol drinkers. In contrast to moderate drinking, heavy alcohol consumption was reported to increase mainly the HDL2 subfraction without affecting

HDL3 (Taskinen et al. 1985, Välimäki et al. 1986, Välimäki et al. 1993) or to increase the cholesterol in both subclasses (Dai et al. 1985, Sillanaukee et al. 1993). The effect of alcohol on HDL subfractions may be dose-dependent, since 30 g of alcohol per day was shown to increase only HDL3 (Välimäki et al. 1988), while both HDL2 and HDL3 were reported to be increased by an alcohol consumption of 60 g per day (Välimäki et al. 1988, Välimäki et al. 1991). In addition to increased cholesterol in HDL2, heavy alcohol intake also increases the PLs in HDL2 (Taskinen et al. 1985, Välimäki et al.

1986), while data on the TG content of HDL2 are controversial (Liinamaa et al. 1997a, Taskinen et al. 1985, Välimäki et al. 1986)(Table 7).

41 2.4 Adiponectin and its aterogenicity

2.4.1 Adiponectin

Four research groups found adiponectin (Hu et al. 1996, Maeda et al. 1996, Nakano et al. 1996, Scherer et al. 1995), an adipocyte-derived cytokine, in the middle of the 1990s. At that time different nomenclatures existed. Scherer et al. named the protein as Acrp30 (adipocyte complement-related protein of 30 kDa) (Scherer et al. 1995) and Nakano et al. named it GBP28 (Gelatin-binding protein of 28kDa) (Nakano et al. 1996). The gene transcript was called apM1 (Adipose Most abundant Gene transcript) by (Maeda et al. 1996) and adipoQ by (Hu et al. 1996). The adipocyte-specific molecule was characterized as a 247-244 amino acid protein (Hu et al. 1996, Maeda et al. 1996, Nakano et al. 1996, Scherer et al. 1995) with a molecular weight of 28 to 30 kDa (Nakano et al. 1996, Scherer et al. 1995). Two adiponectin receptors (AdipoR1 and AdipoR2) have been identified (Bjursell et al. 2007). Initially, abundant expression of AdipoR1 was detected in skeletal muscle and AdipoR2 in the liver (Yamauchi et al. 2003a). However, to date, both receptors have been shown to be expressed in a variety of organs and cell types, e.g. in the brain (Bjursell et al. 2007), hypothalamus (Guillod-Maximin et al. 2009), pituitary (Rodriguez-Pacheco et al. 2007), testis (Caminos et al. 2008), placenta (Caminos et al. 2005, Chen et al. 2006), pancreatic β-cells (Kharroubi et al. 2003), endothelial cells (Motoshima et al. 2004), and peripheral blood mononuclear cells (Pang & Narendran 2008). Adiponectin receptors reveal different functional role, since AdipoR1-deficient mice are obese and glucose intolerant (Bjursell et al. 2007), in contrast to AdipoR2-deficient mice, which exhibit a lean phenotype and show improved glucose tolerance (Bjursell et al. 2007, Liu et al. 2007). However, AdipoR2 KO mice may have enhanced susceptibility to type 2 diabetes (Liu et al. 2007). On the other hand, overexpression of AdipoR1 and AdipoR2 in leptin receptor-deficient mice (Yamauchi et al. 2007), as well as oral administration of an AdipoR1&2 agonist (Okada-Iwabu et al. 2013) ameliorate insulin resistance and diabetes. The blood concentration of adiponectin is very high, ranging from 1.9 to 17 mg/mL in healthy individuals (Arita et al. 1999). Adiponectin circulates in the blood as multimeric complexes. Low molecular weight (LMW), middle molecular weight (MMW) and high molecular weight (HMW) isoforms have been found in humans (Hada et al. 2007, Waki et al. 2003) (Miyazaki et al. 2014), the molecular weights ranging from 65 kda to over 400 kda (reviewed in Magkos & Sidossis 2007). In healthy humans, the concentration of HMW adiponectin is approximately 50% of the

42 total adiponectin level, whereas the levels of MMW and LMW adiponectin account for 25% of total adiponectin concentration (Ebinuma et al. 2006). Typically adiponectin concentrations are higher in women than in men (Arita et al. 1999, Nishizawa et al. 2002, Shand et al. 2003) and seem to increase with age (Adamczak et al. 2005, Daimon et al. 2003). Seasonal variation in an individual’s adiponectin concentration is rather low (Lee et al. 2007). Low plasma adiponectin concentrations are related to coronary artery disease (CAD) (Cesari et al. 2006, Saely et al. 2007), as well as other metabolic diseases, such as obesity (Arita et al. 1999, Weyer et al. 2001), type 2 diabetes (Hotta et al. 2000, Weyer et al. 2001), and metabolic syndrome (Ryo et al. 2004, Saely et al. 2007). The protective effect of adiponectin on metabolic diseases is likely mediated through the HMW isoform (Miyazaki et al. 2014, Pajvani et al. 2004, Tsushima et al. 2008). Therefore, HMW adiponectin has been suggested as a marker of care monitoring in subjects with metabolic disorders (Hirose et al. 2010).

2.4.2 Adiponectin has anti- and proatherogenic properties

Antiatherogenic effects of adiponectin – evidence from animal studies

Adiponectin KO mice exhibit increased thickening of arterial neointima (Kubota et al. 2002, Matsuda et al. 2002) and increased smooth muscle cell proliferation after arterial injury (Matsuda et al. 2002). Adenoviral (Okamoto et al. 2002) or transgenic (Yamauchi et al. 2003b) administration of adiponectin ameliorate atherosclerosis in apoE-deficient mice. In addition, the administration of adiponectin in apoE-deficient mice suppresses the mRNA (Okamoto et al. 2002) and protein levels (Yamauchi et al. 2003b) of class A scavenger receptor in the vascular tissues, as well as mRNA levels of VCAM-1 (Okamoto et al. 2002) and protein levels of TNF-α (Yamauchi et al. 2003b). Elevated TNF-α levels are suppressed by adenovirus-mediated supplementation of adiponectin also in adiponectin KO mice (Maeda et al. 2002). In addition, increased serum levels of TNF-α, monocyte chemoattractant protein-1 (MCP-1), interleukins (IL) (IL-12p70 and IL-6) together with increased adhesion molecule (VCAM-1 and ICAM-1) expressions in aortic sections in adiponectin deficient mice with experimental sepsis are suppressed by adiponectin treatment (Teoh et al. 2008).

43 Antiatherogenic effects of adiponectin – evidence from in vitro studies

Cell culture studies have shown that adiponectin may directly affect the pathophysiological processes behind the progression of atherosclerosis. Adiponectin can affect the vascular cells by stimulating nitric oxide production from endothelial cells (Chen et al. 2003, Hattori et al. 2008) through endothelial nitric oxide synthase activation (Hattori et al. 2008) and suppress the migration of endothelial (Mahadev et al. 2008) and smooth muscle cells (Matsuda et al. 2002). Adiponectin may also affect the proliferation of smooth muscle cells (Matsuda et al. 2002). In addition, adiponectin has been shown to inhibit TNF-α-induced adhesion of monocytic THP-1 cells to human aortic endothelial cells (HAECs) (Ouchi et al. 1999) and the expression of cell adhesion molecules VCAM-1, ICAM-1 (Kawanami et al. 2004, Ouchi et al. 1999) and E-selectin (Ouchi et al. 1999) on HAECs. Moreover, adiponectin may suppress the inflammatory response, since it decreases C-reactive protein (CRP) secretion from HAECs (Devaraj et al. 2008), as well as TNF-α secretion from human macrophages (Yokota et al. 2000). Adiponectin can prevent atherosclerosis by inhibiting the transformation of macrophages to foam cells, since it suppresses the AcLDL-mediated lipid accumulation in HMDM (Ouchi et al. 2001). Furthermore, THP-1 macrophages transduced with the adiponectin cDNA exhibit decreased lipid accumulation and oxLDL uptake (Tian et al. 2009). Adiponectin may affect reverse cholesterol transport, because apoA-I-mediated cholesterol efflux and ABCA1 expression are decreased in macrophages of adiponectin KO mice, and ABCA1 expression is upregulated after adiponectin treatment of HMDMs (Tsubakio-Yamamoto et al. 2008). In addition, THP-1 macrophages transduced with adiponectin exhibit increased HDL-mediated cholesterol efflux (Tian et al. 2009). Adiponectin increases ABCA1 protein level and apoA-I secretion from HepG2 cells (Matsuura et al. 2007), indicating that it may affect HDL assembly in the liver. On the other hand, adipocytes have binding sites for HDL (Fong et al. 1985) and HDL can induce adiponectin expression in these cells (Van Linthout et al. 2010). In agreement with the studies described above, adiponectin shows a strong positive correlation with HDL-C in humans (Cote et al. 2005, Kangas-Kontio et al. 2010, Kazumi et al. 2004, Yamamoto et al. 2002, Zietz et al. 2003).

44 Proatherogenic effects of adiponectin

Nevertheless, adiponectin also has proinflammatory and proatherogenic properties, since globular adiponectin can induce the expressions of MCP-1 and cell adhesion molecules, such as VCAM-1 and E-selectin (Kase et al. 2007, Tomizawa et al. 2009), as well as interleukins IL-6 and IL-8 (Tomizawa et al. 2009) in human umbilical vein endothelial cells. HMW adiponectin can induce the secretion of IL-6, MCP-1 and IL- 8 from human monocytes (Abke et al. 2006). However, adiponectin isoforms may affect cytokine release differentially, since the LMW adiponectin induces the secretion of IL-6, while the HMW isoform reduces the IL-6 release from human monocytic cells (Neumeier et al. 2006).

2.5 Effect of alcohol on adiponectin

2.5.1 Effect of alcohol on blood concentration of adiponectin in humans

Several controlled trials have shown that moderate alcohol consumption, ranging from ≈ 32 to 40 grams of alcohol per day for 17 days to 6 weeks increases blood adiponectin concentration (Beulens et al. 2007, Beulens et al. 2008, Beulens et al. 2006, Joosten et al. 2008, Sierksma et al. 2004b). In addition, alcohol consumption and adiponectin level are positively associated in various cross-sectional studies (Englund Ogge et al. 2006, Pischon et al. 2005b, Shai et al. 2004). However, some recent cross-sectional studies have not found any relation between moderate alcohol consumption and serum adiponectin level (Jung et al. 2013, Makita et al. 2013). Interestingly, alcohol dehydrogenase genotype may affect the association between alcohol consumption and adiponectin (Maeda et al. 2014). Alcohol consumption seems to increase the HMW isoform of adiponectin (Beulens et al. 2007, Joosten et al. 2008). Alcohol consumption increases the adiponectin concentration independent of the type of alcoholic beverage, since blood adiponectin concentrations have been shown to be increased after consumption of red wine (Beulens et al. 2006), white wine (Joosten et al. 2008), beer (Beulens et al. 2008) or whisky (Beulens et al. 2007, Sierksma et al. 2004b). However, according to one study, the type of alcoholic beverage might have a different effect on adiponectin concentration in men and women, since red wine consumption increased the adiponectin concentration in women, but not in men, whereas beer and ethanol solution intake increased the adiponectin level in men, but not in women, in a three-week intervention study of

45 healthy individuals (Imhof et al. 2009). One drink (15 g) just before meal seems not to affect the postprandial adiponectin concentration (Greenfield et al. 2005). The effect of heavy alcohol intake on adiponectin levels is not clear. Alcohol withdrawal decreases serum adiponectin in chronic alcohol drinkers (Buechler et al. 2009), as well as in alcohol-dependent patients (Hillemacher et al. 2009). Interestingly, the serum of heavy drinkers may enhance the secretion of adiponectin, since alcohol per se was not able to increase the release of adiponectin from adipocytes but the ability of serum from chronic alcohol drinkers to stimulate adiponectin secretion decreased during alcohol withdrawal (Buechler et al. 2009). On the contrary, recent cross-sectional studies show that adiponectin levels are lower in heavy drinkers than in non-drinkers (Jung et al. 2013, Kawamoto et al. 2010).

2.5.2 Effect of alcohol on blood concentration of adiponectin in rodents

Chronic alcohol feeding of rats decreases blood adiponectin concentration (Chen et al. 2009, Song et al. 2008, Thakur et al. 2006), as well as adiponectin mRNA level in adipose tissue (Kang et al. 2007, Song et al. 2008). Serum levels of adiponectin in rats were also decreased by long-term low ethanol (0.1%) feeding (Tian et al. 2014a). However, decreased adiponectin levels are normalized by taurine (Chen et al. 2009) or betaine (Song et al. 2008) supplementation, suggesting that the alcohol-induced reduction in adiponectin concentration is mediated through decreased homocysteine levels (Song et al. 2008) and reduced antioxidant activity (Chen et al. 2009).

2.6 Self-organizing map (SOM) analysis as a tool to classify multiple relations in data set

SOM analysis is a neural network technique, which organizes the input variables by an unsupervised algorithm according to similarity criteria (Fig. 2). In SOM analysis, multiple non-linear statistical relationships in a data set are transformed into a visually more approachable layout, e.g. a two-dimensional regular grid of nodes, called a map. The SOM algorithm generates the feature vectors so that they optimally describe the original data set. (Kohonen 1998, Kohonen 2013, Suna et al. 2006). The SOM technique is widely used in diverse fields, e.g. in industry, finance, natural sciences, and linguistics (Kohonen 2013). In biomedical sciences, SOM is

46 applied to classify multiple relations in complex data sets, e.g. in the analysis of gene- expression data (Nikkilä et al. 2002), characterization of insulin resistance syndrome (Valkonen et al. 2002), nuclear magnetic resonance (NMR) based multi-metabolite phenotyping of Alzheimer disease (Tukiainen et al. 2008), NMR metabolomics-based phenotyping of diabetic nephropathy (Mäkinen et al. 2013) and NMR-based cardiovascular risk assessment (Würtz et al. 2012). In this thesis, SOM was applied for the first time to data on ultracentrifugally isolated lipoproteins (Studies II and III).

47 A) INPUT DATA as vectors B) SELF‐ORGANIZING PROCESS C) VISUALIZATION (variables connected (network of model individuals (network is visualized with individuals) by similarity in feature vectors) by means of two‐ dimensional map) N i = i i i d (d HDL-C, d LDL-C, d VLDL-TG…) w E

S k HDL‐C j

LDL‐C

VLDL‐TG

j,k = j,k j,k j,k X (X HDL-C, X LDL-C, X VLDL-TG…)

Fig. 2. Basics of the self-organizing map analysis. A) The input variables (here: HDL-C, LDL-C, VLDL-TG etc.) are fed into SOM analysis as an input vector for each individual or item (i), an individual is thus described (di) by input variables. In this thesis, input vectors were formed from plasma samples of study subjects connected with ≤ 40 lipoprotein related input variables (Studies II and III; see Subjects, materials and methods for details). B) Based on the input vectors, the averaging process of the SOM algorithm generates so-called feature vectors (a.k.a. “model individuals”), each having a definite location in the variable space anchored in similarity criteria. A model individual represents a set of similar input individuals with respect to their input variables, thus model individuals are not exactly the same as input individuals. C) The relationships between the model individuals are transformed into a two-dimensional map, in which each cell (node) represents one model individual having a definite location (j,k) in the map. More similar model individuals are placed close to each other, whereas less similar ones are situated farther away, as in the network of model individuals in space (see B). A map is visualized for each input variables by color coding: bluish colors refer to low and reddish colors to high values as compared with the average value of the variable (white). For illustrative purposes, in the figure, the head colors of the input individuals (see A) and the model individuals (see B) refer to similar color-coding. For interpretation of the results, compass directions can be used to locate clusters of variables in the map (Mäkinen et al. 2008, Tukiainen et al. 2008). Here, individuals located in the south and south-east corner of the SOM (illustrated by the black circle) have high HDL-C, but low LDL-C and VLDL-TG values.

48 3 Purpose of the study

This study was carried out to clarify the effect of heavy alcohol consumption on the pathophysiology of atherosclerosis and to investigate the potential of purely data- driven SOM methodology in lipoprotein research. The study was focused on lipoproteins, reverse cholesterol transport and atherosclerosis-associated metabolic profiles. The specific aims were:

1. To study how heavy alcohol intake affects the HDL2- and HDL3-mediated cholesterol efflux and HDL subpopulation distribution. 2. To study whether the ultracentrifugally based lipoprotein data can be enhanced by SOM analysis and whether the SOM method can find diverse lipoprotein phenotypes in a heterogeneous lipoprotein data set with a wide range in lipoprotein values. 3. To study whether SOM analysis of lipoprotein data can depict distinct metabolic profiles related to heavy alcohol consumption.

50 4 Subjects, materials and methods

4.1 Subjects

4.1.1 Heavy alcohol drinkers and controls (I,III)

All study subjects were men. Heavy alcohol drinkers were recruited from the subjects admitted as inpatients for withdrawal therapy at the Alcoholism Treatment Unit of Oulu. This unit helps people to terminate their alcohol or drug dependence and also provides rehabilitation service. The controls were recruited based on the population register of the Oulu area with the inclusion criteria of 20–65 years, or among the employees of the Stora Enso Company and other employees working in the province of Oulu. The Ethics Committee of the Northern Ostrobothnia Hospital District, Oulu, Finland, approved the study in accordance with the Declaration of Helsinki. Alcohol intake of the study subjects during the previous 14 days was documented by an interview and expressed as grams of pure alcohol per day. According to the interview, almost all heavy alcohol drinkers had a long history (several years) of heavy alcohol intake. The subjects were also interviewed about their medical history, such as cardiovascular disease and hypertension. The basic characteristics of the study subjects are presented in Table 8.

Table 8. Basic characteristics of study subjects in Studies I and III.

Variable Heavy drinkers Controls Study I Study III Study I Study III Subjects (no.) 6 80 6 83 Age (years) 42 (28–51) 49 (46–51)a,b 47 (37–54) 51 (49–53) BMI (kg/m2) 22.1 (19.9–25.6) 23.7 (22.2–27.2)*** 23.6 (22.9–29.4) 25.9 (24.4–27.9) Alcohol intake (g/day) 230 (125–345)** 156 (101–204)*** 10 (0–19) 9 (1–17) Smokers (%) 6 (100%)c 62 (78%)d *** 2 (33%) 18 (22%) CVD (%) 2 (40%)c 11 (14%)d 1 (17%)c 8 (10%) Hypertension (%) 1 (20%)e,c 17 (21%)d 1 (17%)e,c 19 (23%) Data are expressed as median and (interquartile range) or as amean and (95% confidence interval for mean). BMI, Body mass index; CVD, cardiovascular disease; BP, blood pressure. CVD and hypertension cases are based on self-report (interview). Smokers, CVD and hypertension cases are presented as numbers and (percentages). The statistically significant difference in variables between heavy alcohol drinkers and controls within studies is indicated as *p < 0.05, **p ≤ 0.01 and ***p ≤ 0.001, Mann-Whitney U-test and bStudent’s t-test, cFisher’s exact test and dPearson chi-square-test. enumber (n) of study subjects is 5, fn = 47, gn = 49.

51 Age did not differ significantly between the study subjects (Table 8; Studies I, III). The body mass index was lower in heavy alcohol drinkers than in the controls in Study III, but did not differ in Study I. The heavy drinkers were mainly smokers (100% in Study I and 78% in Study III (Table 8), whereas 33% (Study I) and 22% (Study III) of the controls smoked. There were no differences in CVD and hypertension incidence between the study subjects (Table 8).

4.1.2 Subjects used for establishing lipoprotein SOM analysis (II)

Previously analyzed lipoprotein lipid values were available for 233 subjects (53% females; 47% males), consisting of heavy alcohol drinkers (40%), hysterectomized postmenopausal women on estrogen replacement therapy (41%) and apparently healthy control individuals (19%) (Table 9). Details are given in the original publications (Karjalainen et al. 2000, Liinamaa et al. 1997b). These subjects were used, since a large variety of lipoprotein data (see Table 10) was preferred in SOM analysis to investigate the ability of SOM to depict distinct lipoprotein phenotypes.

Table 9. Description of subjects used for lipoprotein phenotyping (Study II)

Variable Heavy alcohol Hysterectomized women Healthy controls drinkers on estrogen therapy Number of subjects (% of total) 122 (40%) 123 (41%) 57 (19%) Age 41 (39–43) 54 (53–56)a 46 (42–53)a BMI 25 (24–26) 26 (26–27) 26 (25–27) Men / women 105 / 17 0 / 123 45 / 12 Data are expressed as mean and (95% confidence interval of mean) or a median and (interquartile range). BMI, Body mass index.

4.2 Materials

4.2.1 Isolated lipoproteins from human subjects (II)

Lipoprotein data included 302 distinct plasma samples (from 233 subjects, see Table 9) with plasma concentration of lipoprotein lipids (mmol/L) for TG, C, FC, CE and PL and total protein (mg/dL). Lipoprotein plasma concentrations of VLDL-TG, IDL-

C, LDL-C, HDL2-C and HDL3-C in the data set are described in Table 10. The overall lipoprotein data are described in the original publication II.

52 Table 10. Average plasma triglyceride or cholesterol concentrations in lipoprotein fractions isolated by ultracentrifugation (Study II).

Lipoprotein lipid Plasma concentration (mmol/l) VLDL-TG 1.01 ± 1.58 IDL-C 0.25 ± 0.23 LDL-C 3.39 ± 1.20

HDL2-C 1.25 ± 0.60

HDL3-C 0.75 ± 0.26 Data are expressed as mean and SD. VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL2, high-density lipoprotein class two; HDL3, high-density lipoprotein class three; TG, triglycerides and C, cholesterol.

4.3 Methods

4.3.1 Measurement of biochemical variables in heavy alcohol drinkers and controls (I,III)

Blood concentration of variables related to alcohol intake, liver and kidney function, glucose, insulin and glycosylated hemoglobin (HbA1c) were measured in the clinical laboratory of Oulu University Hospital by standard clinical chemical methods. GGT- CDT was calculated based on laboratory values of GGT and CDT by the formula [0.8 x ln(GGT) + 1.3 x ln(CDT)] (Hietala et al. 2006). Elevated plasma values for alcohol biomarkers (ALT, AST, CDT, GGT and GGT-CDT) were determined according to the reference limits given by the clinical laboratory of Oulu University Hospital. The methods used for the analysis of the lipid status, adiponectin and CETP activity in the heavy alcohol drinkers and controls are briefly illustrated in Table 11.

53 Table 11. Laboratory methods used for analysis of lipid status, CETP and adiponectin.

Variable Description of method Study Total C Commercial enzymatic colorimetric method. I,III VLDL-TG Enzymatic colorimetric method from ultracentrifugally isolated VLDL fraction. I,III IDL-C Enzymatic colorimetric method from ultracentrifugally isolated IDL fraction. III LDL-C Calculated as the cholestrol in ultracentrifugally isolated VLDL-free fraction minus I,III HDL-C (I) or measured by enzymatic colorimetric method from ultracentrifugally isolated LDL fraction (III). HDL-C Enzymatic colorimetric method. Measured from plasma after removal of apoB- I,III containing lipoproteins (I) or from ultracentrifugally isolated HDL-fraction (III). CETP activity Based on the measurement of the blood CETP-mediated transfer of radioactively I,III labeled cholesterol esters from donor LDL to acceptor HDL. Measured either from plasma or serum after removal of apoB-containing lipoproteins Adiponectin Commercial enzyme-linked immunosorbent assay III VLDL, very-low-density lipoproteins; IDL, intermediate-density lipoproteins; LDL, low-density lipoproteins; HDL, high-density lipoproteins; CETP, cholesterol ester transfer protein; C, cholesterol and TG, triglycerides. The loss of lipoproteins in ultracentrifugation method was corrected as described previously (Niemi et al. 2009) in Study III, but not in Study I, therefore the plasma concentrations of HDL-C and LDL- C in study I are generally lower than in Study III (see Table 13).

4.3.2 Assessment of Metabolic syndrome, CV-risk and HOMA-IR

The prevalence of MetS in the study subjects was calculated according to NCEP ATP III criteria (see Table 5), which define MetS based on waist circumference, total plasma triglycerides, plasma HDL-C, blood pressure and fasting glucose. The Homeostatic Model Assessment score for insulin resistance, HOMA-IR (Matthews et al. 1985), was defined as fasting insulin (mU/mL) x fasting glucose (mmol/L) / 22.5. CHD-risk was assessed by the Framingham risk score (Wilson et al. 1998) and Finrisk methods (Vartiainen et al. 2007). The Framingham risk score for men was calculated as cholesterol points (with years, total cholesterol, HDL-C, blood pressure, diabetes and smoking status as components) (Wilson et al. 1998). Finrisk for men (with years, total cholesterol, HDL-C, systolic blood pressure, diabetes, smoking status, parent’s infarction as components) was calculated as previously described (Vartiainen et al. 2007), but the risk component “parent’s infarction” was supposed to be “no infarction in parents” for all subjects, since this information was lacking in the data.

54 4.3.3 Analysis of lipoprotein particles (I,II,III)

The original articles describe the lipoprotein isolation and composition measurements in detail and therefore only brief descriptions are given here.

Isolation and measurement of chemical composition of lipoproteins (I,II,III)

Lipoproteins were isolated from EDTA or citrate plasma by sequential ultracentrifugation (Havel et al. 1955) using density ranges of < 1.006 g/mL for VLDL, 1.006-1.019 g/mL for IDL, 1.019-1.063 g/mL for LDL and 1.063-1.210 g/mL for HDL (Study II). In addition to total HDL, HDL2 (d = 1.063 to 1.210 g/mL) and

HDL3 (d = 1.125 to 1.210 g/mL) fractions were isolated in Studies I and II. The concentrations of C, FC, PL and TG in the isolated lipoprotein fractions were determined by enzymatic colorimetric methods and the concentrations of cholesteryl esters (CE) were calculated by subtracting total cholesterol from FC. The concentration of total protein was measured by the method of Lowry (Lowry et al. 1951). Lipid concentrations in isolated lipoprotein fractions were expressed as mmol/L in plasma (see Table 13). The chemical composition of lipoprotein particles was expressed as mass percentage (Table 14 and Table 16) or as mmol lipids/g proteins (in SOM analysis) (Fig. 4 and Fig. 5).

Calculations of lipoprotein diameters (II,III)

For diameter estimations, the lipoproteins were assumed as spherical particles and the total volumes of all lipoprotein particles were calculated by summing the volumes of lipid and protein molecules (Kumpula et al. 2008).

Analysis of HDL subclasses by gel electrophoresis (I)

The analysis of HDL subclasses is described in detail in the original article I. Briefly, ultracentrifugally isolated HDL and standard proteins (diameter in nm) were electrophoresed on 4 to 30% native polyacrylamide gels and stained with Coomassie brilliant blue. Thereafter, the gels were scanned and the intensity of the stained HDL bands was analyzed as a function of electrophoretic mobility (cm) (Blanche et al. 1981). HDL subclass distribution was determined from these intensity curves using Gaussian model ﬁtting (Verdery et al. 1989). HDL subclass quantities were expressed as percentages of total HDL (the area of each subclass of the total HDL area; the total

55 HDL area being the sum of all the subclass areas). Five HDL subclasses were distinguished with the following HDL diameter ranges: HDL2b (13.56 to 11.67 nm),

HDL2a (12.20 to 10.40 nm), HDL3a (11.24 to 9.55 nm), HDL3b (10.21 to 8.86 nm), and HDL3c (9.30 to 8.31 nm).

HDL-mediated cholesterol efflux (I)

To measure the cholesterol acceptance capacity of HDL particles isolated from heavy alcohol drinkers and controls, RAW 264.7 macrophages (cell line from an Abelson murine leukemia virus-induced tumor) were labeled overnight with 3H-cholesterol. Prior to the efﬂux experiments, the cells were kept in serum-free medium for 1 hour to balance cellular cholesterol pools and washed with PBS. HDL samples (total HDL,

HDL2, and HDL3) were incubated with the labeled cells for 4 hours. Thereafter, medium was collected and ﬁltered to remove any contaminating cells and the cells were lysed in 1 M NaOH overnight. Aliquots of the medium and lysed cells were counted for radioactivity, and cholesterol efﬂux was expressed as percentage of radioactive cholesterol released in the medium of the total radioactive cholesterol present in the well. Detailed description of the experiment is found in the original publication I.

4.3.4 Statistical methods

Basic statistical analysis (I,III)

The IBM SPSS statistical software (versions 19-22) (SPSS, Inc., Chicago, IL, USA) was used for statistical analyses. The normal distribution of the data was defined by Kolmogorov-Smirnov test or by looking at the histograms of variables. Statistical significance of variables between heavy alcohol drinkers and controls was assessed by Student’s t-test (for normally distributed data) or by Mann-Whitney U test (non- normally distributed data). The categorical variables were analyzed by Pearson chi- square test or Fisher’s exact test, when appropriate. Data were logarithmically transformed to obtain a normal distribution, when appropriate. Analysis of covariance (ANCOVA) with adjustment for logarithmically transformed BMI was used to analyze statistical significance in variables between heavy alcohol drinkers and controls, when appropriate (Table 13). Data of lipoprotein chemical composition (Table 14 and Table 16) were mainly non-normally distributed; therefore the Mann-

56 Whitney U test was used throughout this data. The correlations between HDL- mediated cholesterol efflux and HDL lipids were analyzed by Spearman correlation coefﬁcients. Data are expressed as mean with 95% confidence interval (CI) or as median with interquartile range (IR).

Self-organizing map analysis (II and III)

In this thesis, the SOM analysis was implemented on multivariate lipoprotein lipid data (Studies II and III). Details of the SOM analysis are presented in original publications II and III and therefore only a brief description is given here. The basics of lipoprotein-based SOM analysis are presented in Fig. 2. The lipoprotein data were fed into SOM analysis in two different forms, concentration variables and compositional variables. The concentration variables were the lipoprotein (VLDL, IDL, LDL, and either HDL (Study III) or HDL2 and HDL3 (Study II)) plasma concentration values for TG, PL, FC, and CE. Lipoprotein compositional variables were generated by scaling the concentration variables with the total protein amount in each lipoprotein class (e.g. VLDL-TG/protein (mmol/g)). Thus, a total of 40 lipids-related input variables were used in Study II and 32 in Study III. These variables were placed into SOM as an input vector for each plasma sample obtained from study subjects, and the SOM algorithm then transformed these vectors into a two-dimensional map with feature vectors or so-called model individuals (see Fig. 2). In Study III, the lipoprotein-based SOM analysis was extended by visualization of variables that were not used in the map organization (e.g. adiponectin, metabolic syndrome etc.). First, a lipoprotein-based SOM map (see the paragraph above) was generated and then the best matching unit locations of each individual were used to tie these variables in the map. After that, the organization of these variables was visualized by a smoothed component plane. The component planes in Study III are grouped in the different figures (Fig. 4,Fig. 5 and Fig. 6), but they can be directly compared within the figures, since they visualize the same self-organized map analysis.

58 5 Results

5.1 Clinical characteristics of the study subjects

The clinical characteristics of heavy alcohol drinkers and controls are presented in Table 13. The SOM component planes of selected clinical characteristics are shown in the original publication III, Fig. 3 and therefore they are not presented in this thesis.

5.1.1 Alcohol biomarkers, bilirubin, albumin and creatinine

In addition to alcohol consumption data obtained by interview, alcohol biomarkers (CDT, ALT, AST, GGT) were analyzed (Table 12 and Table 13). To analyze the liver and kidney status of the study subjects, the liver enzyme activities (the majority of them are also alcohol biomarkers), creatinine and albumin were measured (Table 12 and Table 13). The heavy drinkers had higher levels of several markers of alcohol consumption, such as CDT and liver enzyme activities GGT, ALT, AST and alkaline phosphatase (ALP)) when compared with controls (Study III;Table 13). Creatinine was lower and bilirubin slightly higher in heavy alcohol drinkers than in the controls in Study III. Liver enzyme activities, bilirubin and creatinine concentrations did nor differ between the study groups in Study I. Albumin did not differ between the heavy alcohol drinkers and controls (Studies I, III). As could be expected, the majority of heavy alcohol drinkers had elevated CDT and GGT-CDT, compared to only a few of the controls in Study III (Table 12). Only half of the heavy drinkers had elevated liver enzyme activities (ALT, AST and GGT) in Study I and less than half in Study III (Table 12). Elevated liver enzyme activities were defined as moderately elevated if values were  3 x upper reference limit (Dyslipideamia: Current Care Guidelines 2013), and the rest had substantially elevated values. In those heavy drinkers who had elevated liver enzyme activities, ALT, AST and GT values were moderately elevated in 96%, 72% and 80% of the subjects, respectively, while the rest had substantially elevated values (Study III). In Study I, AST, ALT and GT values were only moderately elevated, except in one subject. Only few (Study III) or none (study I) of the controls had elevated liver enzyme activities (Table 12). Of those controls with elevated liver enzymes, all had only moderately elevated values.

59 Table 12. Elevated alcohol biomarkers in study subjects

Variable Heavy drinkers Controls Study I Study III Study I Study III

Subjects (no.) 6 80 6 83 ALT (%) 3 (50 %) 24 (30 %) 0 (0 %) 4 (5 %) AST (%) 3 (50 %) 39 (49 %) 0 (0 %) 5 (6 %) GGT (%) 3 (50 %) 39 (49 %) 0 (0 %) 4 (5 %) a b CDT% (%) - 33 (77 %) - 3 (6 %) a b GGT-CDT (%) - 38 (88 %) - 6 (12 %)

Elevated values are expressed as numbers and (percentages). Elevated plasma values for alcohol biomarkers (ALT, AST, GT, CDT, GGT-CDT) were determined according to the reference limits of biomarkers given by the clinical laboratory of Oulu University Hospital. ALT, alanine aminotransferase; AST, aspartate amininotransferase; CDT, cardohydrate-deficient transferrin; GT, γ-glutamyltransferase. aNumber of subjects (n) is 43, bn = 49.

5.1.2 Concentrations of lipids and lipoproteins

Total cholesterol did not differ between the study subjects (Studies I, III;Table 13), but HDL-C concentration was higher in heavy alcohol drinkers than in the controls in both studies (Table 13). VLDL-TG and LDL-C did not differ between the study subjects in Study I, but in Study III the VLDL-TG concentration was higher and LDL-C concentration lower in heavy alcohol drinkers than in the controls. The IDL-C concentration (measured only in Study III) was lower in heavy alcohol drinkers than in the controls.

60 Table 13. Clinical and biochemical characteristics of study subjects.

Variable Heavy drinkers Controls Study I Study III Study I Study III Subjects (no.) 6 80 6 83 WC (cm) - 97 (93–101)a,b,g - 97 (94–100)h SBP (mmHg) - 144 (138–151)a,b,g*** - 131 (126–135)h DBP (mmHg) - 93 (90–96)a,b,g*** - 79 (76–82)h Adiponectin (µg/mL) - 14.0 (12.7–15.3)a,c*** - 7.7 (7.1–8.4) C (mmol/L) 4.95 (4.38–5.45) 5.02 (4.82–5.22)a,b,c 4.80 (4.28–5.60) 5.06 (4.82–5.30) VLDL-TG (mmol/L) 0.33 (0.22–0.74) 0.92 (0.63–1.50)c*** 0.58 (0.30–0.89) 0.73 (0.45–1.01) IDL-C (mmol/L) - 0.08 (0.06–0.15)a,b,c*** - 0.15 (0.09–0.24) LDL-C (mmol/L) 2.41 (2.02–3.03) 2.78 (2.59–2.97)a,b,c*** 3.23 (2.33–3.74) 3.78 (3.54–4.01) HDL-C (mmol/L) 1.54 (1.30–1.96)** 2.60 (2.36–2.84)a,b,c,i*** 1.03 (0.91–1.10) 1.74 (1.63–1.85)j CETP act.(nmol/mL/h) 23 (16–30)* 84 (79–89)a,b** 31 (28–36) 94 (90–99) Glucose (mmol/L) - 5.5 (5.3–6.3)g - 5.7 (5.3–6.1)h Insulin (mU/mL) - 8.5 (5.9–13.5) - 8.3 (5.5–12.9) HbA1c (%) - 5.5 (5.2–5.7)f - 5.5 (5.3–5.7)h CDT (%) - 3.9 (2.5–5.7)l*** - 1.7 (1.6–2.0)h GGT (U/L) 154 (29–248) 86 (43–206)*** 26 (13–42) 29 (22–45) AST (U/L) 57 (19–216) 48 (34–91)*** 19 (16–23) 26 (22–32) ALT (U/L) 63 (19–164) 49 (26–95)*** 29 (16–34) 30 (22–42) ALP (U/L) 68 (63–95) 83 (77–89)a,b*** 71 (54–81) 62 (58–65) Bilirubin (μmol/L) 17 (8–27) 16 (10–22)* 13 (11–23) 12 (10–17) Albumin (g/L) 44 (41–45) 42 (41–43)a,b,k 44 (42–45) 43 (43–44)j Creatinine (μmol/L) 58 (52–65)* 66 (55–75)*** 77 (66–86) 76 (64–84) HOMA-IR - 2.2 (1.4–3.4)f - 2.0 (1.3–3.5)h MetS (%) - 30d,g - 18h Framingham risk - 8.0 (5.0–13.0)f*** - 5.0h (3.5.8.0) Finrisk (coronary) - 1.5 (0.7–2.9)g - 1.9h (1.0–3.7) Data are expressed as median and (interquartile range) or as amean and (95% confidence interval for mean). WC, Waist Circumference; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; C, Cholesterol, VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein, CETP act., plasma cholestryl ester transfer protein activity; HbA1c, glycosylated hemoglobin; CDT %, carbohydrate-deficient transferrin from total transferrin; GGT, γ- glutamyltransferase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase, HOMA-IR, Homeostatic Model Assessment score for insulin resistance; MetS, Metabolic syndrome. The statistically significant difference in variables between heavy alcohol drinkers and controls within studies are indicated as *p < 0.05, **p ≤ 0.01 and ***p ≤ 0.001, Mann-Whitney U-test and bStudent’s t-test, cANCOVA with adjustment to BMI and dPearson chi-square-test. gNumber of subjects (n) is 47, hn = 49, in = 76, jn = 83, kn = 78, en = 45, fn=46, ln=43.

61 5.2 Characterization of lipoprotein particles (I,II,III)

To find out how heavy alcohol intake affects the lipoprotein particles, the chemical composition of ultracentrifugally (UCF) isolated lipoprotein fractions (VLDL, IDL, LDL and HDL) was measured (Table 14 and Table 16). HDL particles were also analyzed at subpopulation level and cholesterol efflux to HDL particles was measured (see original publication I for details). UCF-based lipoprotein data were analyzed by SOM, firstly, to determine the ability of SOM to enhance the data and to find lipoprotein phenotypes beyond the ultracentrifugally isolated lipoprotein particle types (see original publication II for details), and secondly, to study if SOM analysis of lipoprotein data can find metabolic profiles related to heavy alcohol consumption (Fig. 4, Fig. 5, Fig. 6 and Fig. 7).

5.2.1 Establishing lipoprotein profiles based SOM analysis (II)

One of the aims was to find out whether experimental lipoprotein data could be extended by SOM analysis. The results from this SOM analysis are presented in detail in the original publication II and therefore they are only briefly introduced in this synopsis. The different groups of subjects that were used as a source of lipoproteins (see Table 9 for details) in the SOM analysis are shown in Fig. 3.

Distribution of the subjects in SOM map

The comparison of the individuals between the different study groups may not have clinical relevance as such, but it is interesting to see how these groups dissimilar in terms of lipoprotein values are situated in the map. Heavy alcohol drinkers, estrogen- treated women and healthy controls formed distinct groupings in the SOM, thus they have a tendency to differ in their lipoprotein profile (Fig. 3), as could be expected. Heavy alcohol drinkers occupy mostly the western side of the SOM, while hysterectomized estrogen-treated postmenopausal women and healthy controls are located mostly in the east. However, the separation is unambiguous for none of the groups, and, for example, in the northeast corner of the SOM the distribution is 20% of heavy alcohol drinkers, 23% of estrogen-treated women and 56% of apparently healthy controls. The highest BMI is located in the northeast and northwest corner of the SOM; a high BMI does thus not characterize any of the groups (see also Table 9). However, heavy alcohol drinkers separate into two groups by BMI (Fig. 3), a high BMI characterizes heavy alcohol drinkers in the northwest corner of the SOM, while

62 the heavy drinkers in the southwest have a low BMI. The heavy alcohol drinkers are mostly men (Table 9,Fig. 3) and the youngest group among the subjects (Table 9, Fig. 3).

Fig. 3. Distribution of the subjects in the SOM analysis based on the combined concentration and compositional lipoprotein variables (see Materials and Methods section for details). The groups (heavy alcohol drinkers, estrogen-treated women and healthy controls) are described as percentages of the total study population. In all the component planes the values are color-coded to visualize whether they are above (reddish), at (white) or below (bluish) the median of the variable. The numbers on selected units give the local mean value for that particular region. BMI, body mass index.

‘In silico’ lipoprotein phenotypes generated by SOM analysis

SOM analysis of lipoprotein data (the combination of compositional and concentration data of lipoproteins, see materials and methods for details) revealed various interrelationships beyond ultracentrifugally isolated lipoprotein fractions. The ultracentrifugally separated lipoprotein classes (VLDL, IDL, LDL, HDL2 and HDL3) extended in silico to five different lipoprotein phenotypes (A to E), each with distinct compositional characteristics and a distinct plasma concentration profile (see Fig. 4 in the original publication II). Interestingly, different lipoprotein phenotypes are connected with a similar plasma concentration profile (Fig. 4 in the original publication II). High plasma

63 concentration of LDL is associated with different LDL particle compositions in phenotypes C, D and E. Even though the plasma concentrations of HDL2 are quite similar in phenotypes B and E, the compositions of the HDL2 particles are opposite between the phenotypes. Large TG-rich and CE-poor HDL2 particles characterize phenotype B, while HDL2 particles are small, CE-rich and TG-poor in phenotype E. Actually, all lipoprotein particles in phenotype B are enriched in TG, except IDL, which is relatively TG-poor. These results indicate that UCF-based lipoprotein data can be extended in silico by SOM analysis. In fact, SOM analysis revealed in silico phenotypes of lipoproteins beyond the ultracentrifugally separeted lipoprotein classes.

Distribution of the heavy alcohol drinkers in lipoprotein phenotypes

Comparison of the individuals within the same study group, such as heavy alcohol drinkers, could be considered. The component planes shown in Fig. 3 in this thesis can directly be connected and compared with Fig. 4 in the original publication II. There is a preponderance of heavy alcohol drinkers in lipoprotein phenotypes A and B (see Fig. 3 and the original publication II; Fig. 4). Heavy alcohol drinkers in phenotype A are characterized by a high plasma concentration of HDL2 and a low plasma concentration of VLDL together with quite TG-poor HDL2 particles and low BMI. On the contrary, the heavy drinkers in phenotype B have high plasma concentration of VLDL together with increased concentration of TG-enriched, enlarged VLDL particles and high BMI. Actually, all lipoprotein particles (except IDL) are TG-enriched in phenotype B. Phenotypes A and B are most prevalent among the heavy alcohol drinkers but are also seen in a small number of controls (western side of the SOM, see Fig. 3 and the original publication II; Fig 4). Thus, there are lipoprotein phenotypes that are more prevalent than others in heavy alcohol drinkers in the current study population.

5.2.2 Characteristics of HDL particles in heavy alcohol drinkers and controls (I,III)

Chemical composition of HDL particles (I,III)

The chemical composition of ultracentrifugally isolated HDL in heavy alcohol drinkers and controls is presented in Table 14.

64 Table 14. Chemical composition of HDL particles among heavy alcohol drinkers and controls

Group Study I Study III

Heavy alcohol drinkers Number of study subjects 6 76 HDL-TG% 2.3 (1.8–3.6)* 3.2 (2.2–4.9) HDL-FC% 4.3 (3.4–4.5) 2.9 (2.1–3.6)** HDL-CE% 22.3 (21.1–24.1) 23.3 (21.0–25.6)*** HDL-PL% 30.2 (28.6–31.6)** 27.3 (26.0–29.5)*** HDL-protein% 40.8 (39.3–41.6)* 41.7 (38.4–45.8)*** Controls Number of study subjects 6 82 HDL-TG% 4.6 (4.1–5.3) 3.7 (2.8–4.4) HDL-FC% 2.9 (2.7–3.4) 2.5 (2.3–2.9)

HDL-CE% 21.4 (20.1–24.0) 21.7 (20.3–23.1) HDL-PL% 26.6 (25.1–27.6) 24.1 (22.8–25.0) HDL-protein% 44.3 (43.0–45.1) 47.3 (45.9–49.6) Chemical composition is presented as mass percentage of each lipid or protein from the total mass, which is the sum of the masses of TG, FC, CE, PL and protein in the particular lipoprotein fraction. Data are expressed as median and (interquartile range). Statistically significant differences in variables between heavy alcohol drinkers and controls are indicated as *p < 0.05, **p ≤ 0.01 and ***p ≤ 0.001, Mann-Whitney U-test. HDL, high-density lipoproteins; TG, triglycerides; FC, free cholesterol; CE, cholesterol esters and PL, phospholipids.

The total HDL particles isolated from heavy alcohol drinkers had a higher mass percentage of phospholipids (PL) (Studies I, III), free cholesterol (FC) (Study III) and cholesterol esters (CE) (Study III). The HDL protein content was lower in heavy alcohol drinkers than in the controls (Studies I, III). The percentage of TG was lower in heavy alcohol drinkers than in the controls in Study I, but did not differ in Study III.

‘In silico’ HDL phenotypes in heavy alcohol drinkers and controls (III)

The SOM analysis of lipoprotein concentrational and compositional variables revealed distinct groupings of study subjects, indicating that alcohol consumption is linked to distinct lipoprotein characteristics. Thus, heavy alcohol drinkers mainly locate in the eastern part of the SOM while controls occupy the western region (Fig.

65 4). The SOM component planes for HDL characteristics are presented in Fig. 4, for apoB-containing lipoprotein characteristics in Fig. 5, and for metabolic characteristics in Fig. 6. These three figures are directly comparable, since they all visualize the same SOM map.

Heavy drinkers Alcohol consumption HDL-C HDL diameter

% p=2.0x10-8 g/day p=1.7x10-7 mmol/l q=4.7x10-7 nm q=0.0036

HDL-TG* HDL-PL* HDL-FC* HDL-CE*

mmol/g q=1.6x10-5 mmol/g q=7.1x10-6 mmol/g q=7.2x10-6 mmol/g q=7.2x10-6

Fig. 4. Self-organizing map component planes for HDL characteristics and alcohol consumption. HDL plasma concentration is expressed as HDL-C. HDL compositional measures (marked with an asterisk) are expressed as mmols of lipids (TG, PL, FC and CE) per grams of total protein in HDL fraction (see materials and methods for details). Colorings from reddish through white to bluish denote whether the value of the variable is above (red) or below (blue) the average level of the variable (white). The numbers in the hexagonal units of the SOM planes denote the mean value of the variable in this particular unit. Individuals are in the same place in each of the SOM planes within the study, therefore the planes in Fig. 4, Fig. 5 and Fig. 6 can be directly compared. HDL, high-density lipoprotein; TG, triglycerides; PL, phospholipids; FC, free cholesterol and CE, cholesterol esters.

Heavy alcohol drinkers are characterized by large HDL particles (Fig. 4). The HDL-C concentration is higher in the heavy alcohol drinkers than in the controls (Table 13). Furthermore, SOM analysis shows that heavy drinkers differentiate in silico into two groups by HDL-C concentration. Subjects with the highest HDL-C concentrations have HDL particles that are rich in PL, FC and CE and quite poor in TG. On the other hand, heavy drinkers with a low plasma concentration of HDL have TG-enriched HDL particles that are otherwise relatively lipid-poor (Fig. 4).

66 Controls have one type of HDL particles that are relatively small and lipid-poor (western region of the SOM, Fig. 4).

Subpopulations of HDL (I)

Since there is a large variety of HDL particles, HDL subpopulation analysis was performed in heavy alcohol drinkers and controls (see original publication I, Fig. 3 for details). The heavy alcohol drinkers tended to have two-fold higher proportion of large HDL2b subclass than the controls (15.6% vs. 7.9%, p = 0.055). On the other hand, the proportion of HDL3b was 14% (p < 0.05) lower in heavy alcohol drinkers than controls. In all subjects, alcohol consumption correlated positively with an increase in the proportion of HDL2b subclass (rs = 0.694, p < 0.05, n = 12).

Cholesterol efflux to the HDL (I)

One of the aims was to study the functionality of HDL in cholesterol efflux, the first important step in the reverse cholesterol transport pathway. The efflux, adjusted to

HDL protein, was analyzed for total HDL and its main subclasses (HDL2 and HDL3).

Cholesterol efﬂux to total HDL and HDL2 was higher in the heavy drinkers than in the controls (13% and 22%, respectively), while there were no differences in cholesterol efﬂux to HDL3 between the study groups (see Fig. 2 in the original publication I for details).

Alcohol consumption correlated strongly with HDL2-mediated efflux (rs = 0.718, p ≤ 0.01), but not with total HDL- or HDL3-mediated efflux (Table 15). In addition, plasma HDL-C cholesterol correlated with total HDL- and HDL2-mediated efflux, but not with HDL3-mediated efflux. Several other cardiovascular risk factors did not correlate with HDL-mediated cholesterol efflux (Table 15).

67 Table 15. Correlation between HDL-mediated cholesterol efflux, alcohol consumption and cardiovascular risk factors.

Variable total HDL efflux HDL2 efflux HDL3 efflux (n=12) (n=12) (n=12)

Alcohol consumption (g/day) NS rs = 0.718** NS Age (years) NS NS NS BMI (m2/kg) NS NS NS Cholesterol (mmol/L) NS NS NS

HDL-C (mmol/L) rs = 0.741** rs = 0.741** NS LDL-C (mmol/L) NS NS NS TG (mmol/L) NS NS NS Statistically significant Spearman correlation coefficient are indicated as, *P < 0.05 and **P ≤ 0.01. NS, non significant correlation when P > 0.05. Cholesterol efflux is expressed as percentage of radioactive cholesterol released in medium from total radioactive cholesterol of cells at baseline (see methods for details).

To clarify the effects of different lipid components of HDL particles in the efflux process, correlation analysis was performed between HDL lipids and HDL- mediated cholesterol efflux within each HDL particle type (total HDL, HDL2 and

HDL3). Interestingly, within HDL particles, the cholesterol efflux capacity of total HDL and HDL subfractions correlated strongly with HDL phospholipids (see Table 4 in the original publication I for details). The Spearman correlation coefﬁcients between the cholesterol efﬂux to HDL and HDL phospholipids were rs = 0.713 (p ≤ 0.01) for total HDL, rs =0.748 (p ≤ 0.01) for HDL2, and rs = 0.608

(p < 0.05) for HDL3. Notably, HDL cholesterol correlated with HDL-mediated cholesterol efflux only in HDL2 fraction (rs = 0.63, p ≤ 0.01), but not in total HDL or HDL3 fractions (see Table 4 in the original publication I for details).

5.2.3 Characteristics of VLDL, IDL and LDL in heavy alcohol drinkers and controls (III)

Chemical composition of VLDL, IDL and LDL (III)

The mass percentage composition of lipids and proteins in VLDL, IDL and LDL particles among heavy alcohol drinkers and controls are illustrated in Table 16.

68 Table 16. Chemical composition of VLDL, IDL and LDL particles among heavy alcohol drinkers and controls

Lipid Lipoprotein Heavy alcohol drinkers (n = 80 ) Controls (n = 83) TG (%) VLDL 57.5 (53.0–61.1)*** 52.0 (49.2–54.2) IDL 35.0 (29.5–42.2)*** 26.4 (22.9–29.9)a LDL 7.8 (6.4–9.9)*** 5.8 (4.9–7.4)

FC (%) VLDL 3.9 (3.4–4.6)*** 4.4 (4.1–5.0) IDL 6.7 (4.7–10.1)*** 7.6 (6.7–8.3)a LDL 8.6 (7.0–9.4)*** 9.3 (8.4–10.1)

CE (%) VLDL 7.9 (6.4–10.2)*** 9.9 (7.8–11.8) IDL 19.5 (14.7–24.7)*** 28.2 (21.9–32.5)a LDL 40.0 (36.7–42.1) *** 41.6 (39.2–44.6)

PL (%) VLDL 14.6 (13.8–15.9)*** 15.7 (15.0–16.6) IDL 20.7 (18.3–22.2)* 21.3 (20.0–23.0)a LDL 23.2 (22.3–24.0)** 22.7 (22.3–23.1)

Protein (%) VLDL 15.1 (12.2–18.9)*** 17.7 (15.0–20.7) IDL 16.6 (11.6–19.7) 15.4 (13.5–18.8)a LDL 20.2 (18.8–21.3) 19.4 (18.1–21.0) Data are expressed as median and (interquartile range). Statistically significant differences in variables between heavy alcohol drinkers and controls are indicated as *p < 0.05, **p ≤ 0.01 and ***p ≤ 0.001, Mann-Whitney U-test. VLDL, very-low-density lipoproteins; IDL, intermediate-density lipoproteins; LDL, low-density lipoproteins; TG, triglycerides; FC, free cholesterol; CE, cholesterol esters and PL, phospholipids. an = 82.

The percentage of TG was higher in VLDL, IDL and LDL particles in heavy alcohol drinkers than in the controls, while the percentages of FC and CE were lower. The PL content was lower in VLDL and IDL particles of heavy alcohol drinkers than in the controls, but in the case of LDL particles the situation was the opposite. The protein content was lower in heavy drinkers than in the controls in VLDL particles, but did not differ between the study subjects in IDL and LDL particles.

69 ‘In silico’ phenotypes of VLDL, IDL and LDL in heavy alcohol drinkers and controls (III)

The SOM component planes for characteristics of apoB-containing lipoproteins (VLDL, IDL and LDL) are presented in Fig. 5. The component planes for plasma total cholesterol and total triglycerides concentrations are also shown in Fig. 5, since the LDL particle is the most cholesterol containing and VLDL particle the most triglycerides containing lipoprotein in plasma (Table 6). Total cholesterol concentration did not differ between heavy drinkers and controls (Fig. 5, Table 13), but the highest total cholesterol in controls (the northwest corner of the SOM) is mainly accounted for by the high LDL-C level, whereas that in heavy drinkers is explained by high HDL-C levels (southeast corner of the SOM, Fig. 4 and Fig. 5). Heavy alcohol drinkers differ from controls in terms of plasma LDL-C level. High LDL-C characterizes controls, whereas LDL-C is low in heavy alcohol drinkers (Fig. 5, Table 13). The LDL particle diameter is similar between the groups (Fig. 5), but the composition is different: the LDL particles in controls are TG-poor, while those in heavy drinkers are either TG-rich (northeast corner of the SOM) or relatively TG-poor (southeast corner of the SOM Fig. 5). However, heavy alcohol drinkers are divided into two subgroups by IDL-C and VLDL-TG (and total TG) concentrations in SOM analysis, although the crude classification into heavy drinkers and controls (Table 13) shows low IDL-C and high VLDL-TG in heavy alcohol drinkers as compared with controls. A subgroup of heavy drinkers (the northeast corner of the SOM) has high concentrations of VLDL-TG and IDL-C together with relatively TG-enriched VLDL and LDL particles. In contrast, the other subgroup of heavy drinkers at the southeast corner of the SOM has low VLDL- TG and IDL-C, and the VLDL and LDL particles are relatively TG-poor.

mmol/g q mmol/g = 6.0x10 nm q = q 1.6x10 nm mmol/g q mmol/g = 1.0x10 q mmol/g = 7.9x10 mmol/g q= 3.5x10 LL imtr IDL Diameter VLDL Diameter p = 2.0x10 % mmol/l q = 1.2x10 = q mmol/l LLF* ILF* LDL-FC* IDL-FC* VLDL-FC* LLT* D-G LDL-TG* IDL-TG* VLDL-TG* LDL-C IDL-C VLDL-TG ev rnes TtlT Total C Total TG Heavydrinkers LLC* ILC* LDL-CE* IDL-CE* VLDL-CE* LLP* ILP* LDL-PL* IDL-PL* VLDL-PL* -8 -7 -5 -7 -7 -6 -7 mmol/g q mmol/g = 1.3x10 mmol/g q = 0.0050 mmol/g mmol/g q =0.022 mmol/g q = 0.0050 mmol/g q= 1.1x10 nm q =2.8x10 q nm q mmol/l = 6.9x10 mmol/g q mmol/g = 1.1x10 mmol/l = mmol/l q 4.2x10 LDL Diameter LDLDiameter -7 -6 -6 -5 -5 -6 mmol/g q = 0.20 q = 0.20 mmol/g nm q = 0.0057 q = 0.0057 nm q = 0.0076 mmol/l mmol/g mmol/g q =0.026 mmol/g mmol/g 4.9x10q = mmol/l q =2.9x10 -7 -5

Fig. 5. Self-organizing map component planes for apoB-c ontaining lipoprotein characteristics and plasma C and TG concentrations. Concentrations of total C and TG in plasma and in lipoprotein (VLDL, IDL and LDL) fractions are shown in the tw o top lines. The compositional measures (marked with an asterisk) are expressed as mmols of lipids (TG, PL, FC and CE) per grams of total protein in each lipoprotein fracti on (see materials and methods for details). Individuals are in the same place in ea ch of the SOM planes within the study, and therefor e the planes in Fig. 4, Fig. 5 and Fig. 6 can be directly compared. VLDL, very-low - density lipoproteins; IDL, intermediate-density lipoproteins; LDL, low-density lipoproteins; C, cholesterol, TG, triglycerides; PL, phospholipids; FC, free cholesterol; CE, cholesterol esters

5.3 Metabolic characteristics of the alcohol drinkers (III)

To study metabolic characteristics related to in silico lipoprotein phenotypes and heavy alcohol consumption, the SOM was organized according to the lipoprotein concentration and composition measures and metabolic variables (see Table 13) were visualized based on this map (Fig. 6) (see methods for details).

Alcohol consumption Adiponectin CETP activity

g/day p=1.7x10-7 g/ml p=6.1x10-7 nmol/ml/h p=0.021

BMI Waist circumference MetS

kg/m2 p=0.001 cm p=0.001 % p=0.006

HOMA-IR Framingham risk Finrisk

Score p=0.162 Score p=0.006 Score p=0.119

Fig. 6. Self-organizing map (SOM) component planes for alcohol consumption, plasma adiponectin concentration, cholesterol ester transfer protein activity, variables related to metabolic syndrome, insulin resistance and cardiovascular risk. CETP, cholesterol ester transfer protein; BMI, body mass index, MetS, metabolic syndrome, HOMA-IR, homeostasis model assessment score for insulin resistance. The SOM component planes in Fig. 4, Fig. 5 and Fig. 6 can be directly compared and interpreted together, since they all visualize the same SOM analysis.

5.3.1 CETP, metabolic syndrome, waist circumference, blood pressure and adiponectin (III)

Heavy drinkers had lower activity of CETP than the controls (Fig. 6, Table 13). Interestingly, the heavy drinker group with the highest prevalence of MetS (Fig. 6 (at

the northeast corner of the SOM) has also low HDL-C (Fig. 4) and high levels of VLDL-C and IDL-C (Fig. 5). On the contrary, the heavy drinkers (at the southeast corner of the SOM) with a low prevalence of MetS (Fig. 6) have high HDL-C (Fig. 4) and low VLDL-C and IDL-C (Fig. 5). Waist circumference was the highest in the heavy drinkers with MetS (Fig. 6), as could be expected, since it is one of the components of MetS. However, those with the highest waist circumference did not have the highest BMI (Fig. 6). High blood pressure also characterizes heavy alcohol drinkers (Table 13). Generally, heavy alcohol drinkers had a higher plasma adiponectin concentration than the controls (Table 13, Fig. 6, the southeast corner of the SOM). However, according to the SOM analysis, some heavy alcohol drinkers had a relatively low plasma adiponectin concentration (Fig. 6, the northeast corner of the SOM). Interestingly, the heavy drinkers with a low plasma adiponectin concentration had the highest prevalence of MetS (Fig. 6).

5.3.2 Glucose, HbA1C and HOMA-IR (III)

Glucose, HbA1C and HOMA-IR did not differ between the study subjects (Table 13), and did not show statistically significant groupings in the lipoprotein-based SOM analysis (data not shown for HbA1c and HOMA-IR, see (Fig. 6) for glucose).

5.3.3 Cardiovascular risk (III)

Framingham risk (calculated as total cholesterol points, see methods for details) was higher in heavy alcohol drinkers than in the controls (Table 13 and Fig. 6). Finrisk (cardiovascular) did not differ between the subjects (Table 13). However, in lipoprotein-based SOM analysis, heavy alcohol drinkers in the southeast corner of the SOM tended to have a lower cardiovascular risk assessed by the Finrisk tool than the heavy alcohol drinkers in the northeast corner of the SOM (Fig. 6), although the differences did not reach statistical significance. Framingham risk as LDL points and Finrisk for stroke and total cardiovascular risk did not show statistically significant groupings in the SOM analysis (data not shown).

5.4 Summary of metabolic characteristics of alcohol drinkers from the SOM analysis (III)

The main findings from the SOM analysis of lipoprotein concentration and composition measures of heavy alcohol drinkers and controls (Fig. 4, Fig. 5 and Fig. 6) can be described by four different lipoprotein phenotypes (A to D, Fig. 7). To summarize the main results from Study III, the associations of heavy alcohol intake and metabolic variables with these four lipoprotein phenotypes are shown in Fig. 7.

Alc cons. g/day MetS % Framingham risk

Adiponectin ug/ml Lipoprotein phenotypes Finrisk A C

B D

ABCD

VLDL-TG  IDL-C     A = High LDL-C phenotype LDL-C     B = Low TG phenotype C = MetS-like phenotype HDL-C   D = Anti-atherogenic VLDL particle size phenotype IDL particle size  LDL particle size  HDL particle size  

Fig. 7. Association of alcohol intake, MetS, CV-risk scores and adiponectin with four lipoprotein phenotypes (A to D, see grey SOM plane above). The summary of the metabolic characteristics of the heavy alcohol drinkers and controls, derived from the SOM analysis of the lipoprotein data per se (for details and statistics see, Fig. 4, Fig. 5 and Fig. 6). Two downward (one downward, one upward, two upward) arrows indicate that the mean value for the group of individuals lies within the lowest (second lowest, second highest, highest) 20% of the values of all the individuals. Mean values that lie within 20% of values around the median are indicated with horizontal arrows.

6 Discussion

6.1 Retrospective diary and alcohol biomarkers as a measure of alcohol consumption

WHO recommends the use of GF measures over the past year for monitoring alcohol consumption in surveys (WHO 2000), since drinking assessment methods asking about drinking during a short reference period cause more hazardous variability than the summary methods (e.g. GF) (Greenfield & Kerr 2008, Redman et al. 1987), and QF methods are not able to determine drinking patterns and they underestimate heavy drinking occasions (Greenfield & Kerr 2008). In this thesis a retrospective 14-day diary was used (see section Subjects on page 51 for details), since knowledge of alcohol intake during the previous days was desired because lipoprotein levels change toward the control’s levels quite rapidly after cessation of alcohol drinking (Kervinen et al. 1991, Taskinen et al. 1982b). Despite the WHO recommendations, the use of a retrospective diary over a long period seems to give good knowledge about overall alcohol intake, since a 28-day drinking diary is well consistent with the 12-month GF- based equivalents (Greenfield et al. 2009). In addition to the alcohol consumption data obtained by a retrospective diary, alcohol biomarkers were analyzed. Among the alcohol biomarkers used in this study (Table 12), liver enzyme activities were shown to be quite poor, while the CDT and GGT-CDT turned out to be the best biomarkers (Table 3). In accordance, liver enzymes were poor detectors of heavy drinkers, whereas the majority of heavy drinkers were detected by CDT and GGT-CDT, indicating that they had consumed at least 60 grams of alcohol per day for weeks, as reviewed by (Bortolotti et al. 2006, Hannuksela et al. 2007, Sillanaukee et al. 2001). This is in line with the alcohol consumption data obtained by the retrospective diary (see Table 8). Sensitivity of GGT-CDT is higher than of CDT alone (Table 3), meaning that GGT-CDT can detect real heavy drinkers as heavy drinkers better than CDT alone. In line with this, GGT-CDT detected 88% of heavy drinkers while CDT detected 77% of heavy drinkers as heavy drinkers in this study (Table 12). However, the sensitivity and specificity of the best biomarker, GGT-CDT, is not 100% (Table 3), indicating that GGT-CDT cannot detect all heavy drinkers as heavy drinkers and it can also detect low drinkers as heavy drinkers, as may have happened in this study in the case of some heavy drinkers and controls (Table 12).

New, more specific and sensitive alcohol biomarkers are therefore needed for clinical care settings (Niemelä 2007).

6.2 HDL of heavy alcohol drinkers (I,II,III)

Alcohol consumption causes several changes in the lipoprotein metabolism (reviewed in Brinton 2012, Hannuksela et al. 2004). However, the effect of alcohol consumption on lipoprotein concentration and composition is not clear (see Table 7 for details). Several changes were also observed in this study, and they will be discussed in the following chapters. Alcohol modulates HDL metabolism, leading to a rise in HDL-C (Clevidence et al. 1995, Hartung et al. 1990, Muth et al. 2010, Taskinen et al. 1982b) and changes in the composition of HDL particles and in the activity of lipid transfer and exchange proteins, such as PLTP, CETP and LCAT (Hannuksela et al. 2004). Notably, with an alcohol consumption of 30 g per day one would expect an increase in HDL-C concentration of about 0.1mmol/L (Brien et al. 2011, Rimm et al. 1999). High plasma concentration of HDL-C has been found in moderate (Beulens et al. 2004, Brien et al. 2011, van der Gaag et al. 2001) as well as in heavy alcohol drinkers (Liinamaa et al. 1997a, Savolainen et al. 1990, Sillanaukee et al. 1993)(see Table 7 for details) and was detected also in this study (Table 14). The HDL particles of heavy drinkers were rich in phospholipids and poor in protein (Table 14), indicating HDL subpopulation changes towards larger HDL particles. Actually, the heavy alcohol drinkers tended to have two-fold higher proportion of large HDL2b subclass than the controls, which is in line with previous studies, demonstrating that alcohol intake is associated with large- sized HDL particles (Mukamal et al. 2007, Muth et al. 2010, Schafer et al. 2007). However, in some earlier studies alcohol consumption seemed to affect the smaller

HDL3 subclass with no effect on large HDL2 (Haskell et al. 1984, Sillanaukee et al. 1993). The discrepancy in the alcohol effect on HDL subclasses could be due to differing effects of alcohol on PLTP activity between studies, since PLTP modifies HDL and affects HDL size distribution (Rye & Barter 2014). Unfortunately, PLTP activity was not determined in the aforementioned studies. PLTP activity is usually increased in heavy alcohol drinkers, but remains unchanged in moderate drinkers (Hannuksela et al. 2004). However, the effect of alcohol on the PLTP remodeling process seems not to have been studied.

6.2.1 Role of CETP in the HDL subgroups of heavy alcohol drinkers

In this study, the self-organizing map analysis of lipoprotein data revealed that high and low concentrations of HDL-C are connected with heavy alcohol intake (Fig. 4), although heavy alcohol consumption is typically linked to increased HDL-C (Liinamaa et al. 1997a, Taskinen et al. 1982b, Välimäki et al. 1986), as was also found in this study in the case-control research frame (see discussion above). Moreover, heavy alcohol intake with high plasma concentration of HDL-C relates to large CE-rich and relatively TG-poor HDL particles and, on the contrary, low concentration of HDL-C is connected with TG-enriched and relatively CE-poor HDL particles (Fig. 4). CETP might have a role in the formation of these compositionally different HDL particles, since CETP promotes an equimolar heteroexchange of neutral lipids between lipoproteins, resulting in net transfer of CE from HDL to apoB-containing lipoprotein particles and TG in the opposite direction (Koivuniemi et al. 2012, Qiu et al. 2007). In this study, low plasma CETP activity was associated with high alcohol intake (Fig. 6 and Table 13), as previously described (Hannuksela et al. 1992, Savolainen et al. 1990, Välimäki et al. 1993). In previous studies, low CETP activity was connected with a high concentration of HDL-C and CE-rich and TG-poor HDL particles (Chantepie et al. 2012, Matsuura et al. 2006), which is in accordance with the findings in this study (Fig. 4). By contrast, heavy drinkers with low HDL-C concentration had HDL particles that were TG-enriched and relatively CE-poor (the northeast region of the SOM, Fig. 4 and Fig. 7). A possible explanation for the compositionally different HDL particles in heavy alcohol drinkers is that VLDL-TG levels differ between the subjects with high and low HDL-C concentrations and therefore CETP promotes the TG-enrichment of HDL particles in subjects with low HDL-C and high plasma concentration of VLDL-TG (Castle et al. 1998, reviewed in Verges 2009) (Fig. 7). Besides, in line with findings in this study, the TG-enriched lipoproteins are rapidly cleared from the circulation, thus contributing to the low HDL-C level (Verges 2009).

6.2.2 Effect of phospholipid-rich HDL2 of the heavy alcohol drinkers in cholesterol efflux (I)

The effect of alcohol on the cholesterol efflux capacity of HDL and its subfractions is poorly understood. Previous studies have mainly focused on the effect of alcohol on cholesterol efflux potential of plasma and serum, which has

been either increased (Beulens et al. 2004, Senault et al. 2000, Sierksma et al. 2004a, van der Gaag et al. 2001) or unchanged (Kralova Lesna et al. 2010, Perret et al. 2002) after moderate alcohol intake (≈ 24 to 47 g per day). None of the studies have investigated the cholesterol efflux capacity of HDL in moderate drinkers. In heavy alcohol drinkers (alcohol consumption > 80 g per day), only two previous studies have investigated the cholesterol efflux to HDL (Marmillot et al. 2007, Rao et al. 2000), showing decreased efflux capacity of HDL in alcoholics which may be explained by a decreased sphingomyelin content of HDL (Marmillot et al. 2007). Opposite results were found in this study, as the cholesterol efflux to total

HDL was 13% and to HDL2 22% higher in heavy alcohol drinkers than in the controls. The increased efflux was due to the HDL2 subfraction, since no difference in cholesterol efflux to HDL3 between the study subjects was detected. In addition, alcohol consumption correlated strongly with HDL2-mediated efflux, but not with the

HDL3-mediated efflux (Table 15). The methodological differences may partly explain the different results between our study and previous studies, where HDL was isolated by heparin-MnCl2 precipitation (apoB-depleted serum) and AcLDL (CE labelled) loaded macrophages were used (Marmillot et al. 2007, Rao et al. 2000). In this study, HDL was isolated by sequential ultracentrifugation and macrophages labeled with free cholesterol were used. ApoB-depleted serum contains cholesterol acceptors for the ABCA1-dependent efflux pathway (Weibel et al. 2014), such as apoA-I and pre-β HDL (Brooks-Wilson et al. 1999, Westerterp et al. 2014). However these acceptors are removed from the ultracentrifugally isolated HDL fraction (used in this study)(Havel et al. 1955). Therefore, the cholesterol acceptors are different between this study and previous studies, although both used the HDL fraction. In addition, different cholesterol loading procedures may activate different efflux pathways, since cholesterol segregates into different cellular pools (Chen et al. 2001, Wang et al. 2007). LDL- loaded macrophages show higher cholesterol efflux to HDL than acetylated LDL (AcLDL) loaded macrophages and on the other hand, apoA-I-mediated efflux is greater from AcLDL-loaded than LDL-loaded macrophages (Wang et al. 2007). Therefore, the AcLDL loading used in previous studies may not promote optimal cholesterol efflux into HDL. Changes in cholesterol efflux capacity of plasma after alcohol intake (36 grams per day for 4 weeks) have been shown to correlate with changes in plasma HDL-C and apoA-I level (Kralova Lesna et al. 2010). In this study, plasma HDL-C correlated positively with the ability of HDL2 particles, but not of HDL3 particles, to receive cholesterol, (Table 15). This finding is logical, since the concentration of HDL2

accounts for the majority of total HDL cholesterol in plasma especially in heavy alcohol drinkers (Liinamaa et al. 1997b). However, within HDL particles, the capacity of HDL to accept cholesterol associated more strongly with an increase in HDL phospholipids than HDL cholesterol (see Table 4 in the original publication I for details). In the study of Kralova et al. 2010 the HDL phospholipids were not measured. The finding in this study is consistent with studies showing that the phospholipid concentration of HDL particles is the major determinant of the cholesterol acceptance capacity of serum (Fournier et al. 1996, Fournier et al. 1997). Several other studies also show the importance of phospholipids in cholesterol efflux (Fournier et al. 1996, Jian et al. 1997, Tchoua et al. 2010). The mechanism for increased efflux upon phospholipid enrichment of HDL particles remains unsolved, but it may be mediated through SR-BI (Jian et al. 1998, Yancey et al. 2000). It could be speculated that the discrepancy observed in the cholesterol acceptance capacity of HDL of heavy alcohol drinkers in this study and previous studies (Marmillot et al. 2007, Rao et al. 2000) might be explained by different HDL subfractions with distinct amounts of phospholipid present in HDL particles. Unfortunately, HDL phospholipids were not measured in the previous studies (Marmillot et al. 2007, Rao et al. 2000). Furthermore, the HDL phospholipids seem to be better predictors of coronary heart disease than HDL-C (Piperi et al. 2004). Interestingly, recent studies demonstrate that the cholesterol acceptor capacity of serum is a stronger predictor of arterial status (Doonan et al. 2014, Favari et al. 2013, Khera et al. 2011) than HDL-C level, indicating that the functionality of HDL particles is a more important factor than high HDL-C level in preventing CHD.

6.3 VLDL, IDL and LDL of heavy alcohol drinkers

The reported effect of alcohol intake on plasma VLDL-TG are inconsistent (Table 7). Plasma VLDL-TG concentration is either unchanged (Liinamaa et al. 1997b, Taskinen et al. 1982b, Välimäki et al. 1986) or increased (Kervinen et al. 1991, Liinamaa et al. 1998) in heavy alcohol drinkers. In line with earlier findings, the plasma VLDL-TG concentration (Table 13) as well as the mass percentage of TGs of total VLDL mass (Table 16) was higher in heavy alcohol drinkers than in the controls (Study II), or the VLDL-TG concentration was unchanged between study subjects in Study I. However, SOM analysis of the lipoprotein data (Study II) defined two different subgroups of plasma VLDL-TG concentrations (as well as TG concentrations) in heavy alcohol drinkers: one with a high VLDL-TG concentration (Fig. 5, northeast

corner of the SOM), and the other with a low VLDL-TG concentration. The liver may produce large TG-enriched VLDL particles at high TG availability and on the other hand, smaller VLDL particles may be produced at low TG levels (Berneis & Krauss 2002). In consensus with this, VLDL particles in the high VLDL-TG subgroup were large and enriched in TGs, while they were relatively TG-poor and quite small in the low VLDL-TG subgroup (Fig. 5). Similarly to previous studies, the IDL-C (Kervinen et al. 1991, Liinamaa et al. 1997b) and LDL-C (Kervinen et al. 1991, Liinamaa et al. 1997b, Taskinen et al. 1982b) concentrations were lower in heavy alcohol drinkers than in the controls (Table 13). However, in a case-control research setting, the SOM analysis of lipoprotein data illustrated that heavy alcohol intake is associated with either high or low concentrations of IDL-C (Fig. 5) and despite similar LDL-C level, the LDL particle composition differs between the heavy alcohol drinkers in the northeast and southeast areas of the SOM (Fig. 5). LDL particles are either enriched in TGs or relatively TG-poor (Fig. 5). In line with this study, earlier studies show discrepancies in TG concentrations of LDL particles isolated from heavy drinkers (Kervinen et al. 1991, Liinamaa et al. 1997b, Välimäki et al. 1988). In addition, heavy alcohol intake is associated with LDL and IDL particles that are either FC-poor or -rich (Fig. 5), similar to HDL particles in the same region (Fig. 4). Notably, a low level of FC may increase the oxidative vulnerability of these lipoproteins (Tribble et al. 1992). This study among others prove that the LDL particle phenotype may vary despite a similar LDL-C concentration (Kang et al. 2002, Katzel et al. 1994) (Fig. 5). Inconsistency in earlier studies analyzing the effect of heavy alcohol consumption on HDL, VLDL, IDL and LDL may partly be explained by the distinct lipoprotein subgroups of heavy alcohol drinkers found in this study. However, no conclusion on the causality between heavy alcohol consumption and formation of different lipoprotein subgroups can be drawn from the present data.

6.4 Heavy alcohol intake relates to high plasma adiponectin level (III)

Accumulating evidence suggests that moderate alcohol consumption increases plasma adiponectin levels (Beulens et al. 2007, Beulens et al. 2008, Beulens et al. 2006, Brien et al. 2011, Joosten et al. 2008, Sierksma et al. 2004b). In chronic alcohol drinkers, alcohol withdrawal decreases serum adiponectin concentration, suggesting that adiponectin levels may also be increased in heavy alcohol drinkers (Buechler et al. 2009, Hillemacher et al. 2009). Interestingly, ethanol

may have a direct effect on adiponectin release, since in vitro incubation of human adipose tissue with ethanol increases adiponectin secretion (Wandler et al. 2008). In the present study, heavy alcohol drinkers had higher plasma adiponectin concentrations than the low-to-moderate drinkers (Table 13), and the subjects with the highest alcohol consumption had the highest adiponectin concentrations (Fig. 6, the southeast corner of the SOM). However, the SOM analysis depicted also heavy alcohol drinkers with relatively low plasma adiponectin concentrations (Fig. 6, northeast region of the SOM). Heavy drinkers with high adiponectin levels had high plasma concentrations of HDL-C and low concentrations of apoB-containing lipoproteins in addition to a low prevalence of metabolic syndrome (see Fig. 4, Fig. 5, Fig. 6 and Fig. 7, southeast region of the SOM), whereas the opposite was observed in heavy drinkers with lower adiponectin concentrations (see Fig. 4, Fig. 5, Fig. 6 and Fig. 7, northeast region of the SOM). These results are in agreement with previous studies in non-alcoholic subjects, since adiponectin is positively correlated with HDL-C concentration (Cote et al. 2005, Kangas-Kontio et al. 2010, Kazumi et al. 2004, Yamamoto et al. 2002, Zietz et al. 2003). In addition, several studies show that adiponectin can directly affect HDL metabolism (Matsuura et al. 2007, Tian et al. 2009, Tsubakio-Yamamoto et al. 2008). On the other hand, HDL can affect adiponectin levels: expression of human apoA-I in mice increased plasma concentration of adiponectin and adiponectin gene expression was amplified by incubation of HDL with adipocytes (Van Linthout et al. 2010). In line with the present study, low levels of adiponectin are associated with metabolic syndrome (Ryo et al. 2004, Saely et al. 2007) and its components, such as low TG, low HDL and high waist circumference (Gable et al. 2006) (northeast region of the SOM, Fig. 4, Fig. 5, Fig. 6 and Fig. 7). Interestingly, subjects with genetic hypoadiponectnemia display a metabolic syndrome-like phenotype (Kondo et al. 2002, Ohashi et al. 2004).

6.5 Association of alcohol intake with lipoprotein phenotypes in relation to cardiovascular risk (III)

Four lipoprotein phenotypes arose from the SOM analysis of lipoprotein data per se (Fig. 7). Heavy alcohol intake associates with two different phenotypes, namely a MetS-like phenotype and an anti-atherogenic phenotype (Fig. 7). On the other hand, low-to-moderate drinkers (controls) are characterized by a high LDL-C phenotype and a low TG phenotype.

Although heavy alcohol consumption is associated to CVD (Corrao et al. 2000), interview-based CVD and hypertension prevalency is similar in heavy alcohol drinkers and controls in this study (Table 8), but the risk for cardiovascular disease assessed as Framingham score (FRS) appears higher in heavy alcohol drinkers than in the controls (Table 13, Fig. 6). However, the blood pressure is higher in heavy alcohol drinkers than in the controls (Table 13), as could be expected (Millwood et al. 2013, Waśkiewicz & Sygnowska 2013). On the contrary, the Finrisk tool tends to determine two subgroups of CV risk in heavy alcohol drinkers: a higher Finrisk value is associated with the MetS-like phenotype and a lower with the anti-atherogenic phenotype (Fig. 7). Even though low-to-moderate drinkers have quite low CV-risk (Fig. 6 and Fig. 7), they have high LDL-C concentration (Table 13 and Fig. 5), which is one of the traditional risk markers of cardiovascular disease (NCEP Expert Panel 2001, Perk et al. 2012). However, low-to-moderate drinkers differ clearly into two distinct phenotypes with other lipoprotein measures, such as VLDL-TG and IDL-C levels (Fig. 7). In addition to LDL-C, also VLDL and IDL increase the risk for CVD (Boekholdt et al. 2012, Varbo et al. 2014, Würtz et al. 2011). Therefore, the high LDL-C phenotype with high VLDL-TG and IDL-C levels denotes a more unfavorable phenotype than low TG phenotype with lower levels of these lipoproteins (Fig. 7). The benefits of alcohol consumption in cardiovascular diseases are generally explained by the HDL-C-raising effect (Gaziano et al. 1993, Langer et al. 1992, Mukamal et al. 2005), except in one recent study showing that HDL-C does not affect the relationship between alcohol and CHD (Magnus et al. 2011). In the present work, heavy alcohol drinkers with the anti-atherogenic phenotype had high HDL-C among other lipoprotein characteristics (e.g. PL-enriched large HDL particles (Asztalos et al. 2004, Piperi et al. 2004) together with low LDL (reviewed in Besseling et al. 2013) and low plasma concentration of TG (reviewed in Cullen 2000) (Fig. 4, Fig. 5 and Fig. 7) that are linked to a low risk for atherosclerosis (reviewed in Barter 2011, Gordon et al. 1989, Würtz et al. 2011). In addition, the prevalence of MetS was low in this phenotype (Fig. 7). The adiponectin concentration was also highest in this phenotype (Fig. 6, Fig. 7), in line with the evidence that adiponectin has multiple anti- atherosclerotic functions (Hattori et al. 2008, Kubota et al. 2002, Mahadev et al. 2008, Matsuda et al. 2002, Okamoto et al. 2002, Yamauchi et al. 2003b). On the contrary, a low concentration of adiponectin may be an independent risk factor for CHD (Frystyk et al. 2007, Persson et al. 2010) and it is also connected to other related metabolic diseases associated with CVD, such as obesity (Arita et al. 1999, Weyer et al. 2001) and type 2 diabetes (Hotta et al. 2000, Weyer et al. 2001). A recent large- scale (45,891 individuals) study showed that adiponectin-decreasing alleles are

associated with an increased risk for metabolic disorders (Dastani et al. 2012). However, a meta-analysis of 17 prospective epidemiological studies indicates that the association of adiponectin to CHD is not exclusive (Hao et al. 2013). The protective effect of adiponectin against CHD may be mediated through HDL metabolism, since accumulating evidence suggests a crosstalk between adiponectin and HDL (Matsuura et al. 2007, Tian et al. 2009, Tsubakio-Yamamoto et al. 2008, Van Linthout et al. 2010). The association between HDL-C and adiponectin was obvious in the present study as well (Fig. 4 and Fig. 6). In line with the metabolic findings described above, the CV risk (determined by the Finrisk method) tended to be lower in the anti- atherogenic- than in the MetS-like phenotype (Fig. 7). The MetS-like phenotype shows lipoprotein characteristics of the metabolic syndrome e.g. high plasma TG (Shojaee-Moradie et al. 2013) and IDL-C level (Chan et al. 2003) and low plasma level of HDL-C) (Fig. 7) (reviewed in Nesto 2005). Notably, the prevalence of metabolic syndrome is also highest in this phenotype (Fig. 7). The compositional features of lipoprotein particles in the MetS-like phenotype, such as the TG-enrichment (Fig. 4, Fig. 5 and Fig. 7), are typical of MetS (de Souza et al. 2008, Hansel et al. 2004, reviewed in Kontush & Chapman 2006). Lipoprotein particles of this kind were also observed in heavy drinkers with phenotype B (see original publication II, Fig. 4 for details). TG-enriched lipoproteins possess atherogenic features, since they may alter the cholesterol delivery and removal pathways (Rashid et al. 2002). In addition, TG-enriched dysfunctional HDL shows attenuated antioxidative (Hansel et al. 2004, Kontush & Chapman 2006) and anti- apoptotic activity (de Souza et al. 2008, Kontush & Chapman 2006), as well as cholesterol efflux capacity (Kontush & Chapman 2006). In previous studies, heavy alcohol consumption was connected to MetS (Jarvis et al. 2007, Zhu et al. 2004), but the positive association of alcohol consumption with MetS is not exclusive (see chapter 2.2.2 Effect of alcohol on metabolic syndrome). On the other hand, the association between alcohol consumption and CVD shows either a J- or U-shaped relation (Corrao et al. 2000, Costanzo et al. 2011, Foerster et al. 2009, Marmot & Brunner 1991), and the relation between alcohol consumption and atherosclerosis is not clear (see Table 4). It is therefore possible that the different subgroups of heavy alcohol drinkers described here might have influenced the connection of alcohol intake with metabolic syndrome and CVD in previous studies, resulting in inconclusive associations.

6.5.1 Concerns of CV risk evaluation by CV risk tools in this study

CV risk was quite low in all phenotypes of the present study (Fig. 7), since a high risk in the Framingham method has been defined as ≥ 20% (Conroy et al. 2003, Pyorälä et al. 1994). A 20% risk in the Framingham method corresponds to a 10 % risk in the Finrisk method (Vartiainen et al. 2010). However, there are no exact levels of absolute risk defined that prompt further intervention (Conroy et al. 2003). Therefore, the question arises of whether the metabolic syndrome might increase the CV risk beyond traditional risk scores? The CV risk caused by MetS may not be entirely accounted for by traditional risk scores, since MetS seems to increase the risk of major coronary events irrespective of FRS (Girman et al. 2004) and subjects with a low risk assessed by the SCORE tool (> 5%) and MetS are at higher risk for fatal CVD than those without MetS (DECODE Study Group 2007). In addition, Mets is associated to outcomes of CHD even after controlling for several traditional risk factors (Gami et al. 2007, Ninomiya et al. 2004). Nevertheless, other studies have not found any increase of CV-risk beyond FRS (McNeill et al. 2005, Wannamethee et al. 2005). Different CV risk analysis tools do not give a similar absolute risk, since CVD end-points and predictive factors are defined differently between the methods (Berger et al. 2010, Conroy et al. 2003, Vartiainen et al. 2007, Wilson et al. 1998). The sensitivity and specificity of detecting true CVD by analysis tools vary a lot between the CVD risk score, Framingham and SCORE functions (Ketola et al. 2010). In this study, CV risk analyzed by the Framingham method did not differ between the two phenotypes of heavy alcohol drinkers, but the Finrisk (coronary risk) tended to vary between the phenotypes (Fig. 7). A special feature of the population in this study (Study III) is a wide range of HDL-C values (Table 13, Fig. 4), since alcohol intake raises HDL-C (Muth et al. 2010). The Finrisk (Vartiainen et al. 2007) is the only of the analysis methods used that counts HDL-C values as a continuous variable (Framingham and Finrisk)(Conroy et al. 2003, Vartiainen et al. 2007, Wilson et al. 1998). Therefore, it is not a surprise that the Finrisk method tends to count varying risk for the two metabolic phenotypes associated with heavy alcohol intake (Fig. 7). Although epidemiological studies indicate that every 0.03 mmol/L increment in HDL- C decreases CHD risk by 2 to 3% (Gordon et al. 1989), the basic SCORE risk analysis method does not count HDL-C at all (Conroy et al. 2003) and the Framingham risk gives similar HDL-C-based points for all subjects with HDL-C values over 1.56 mmol/L (Wilson et al. 1998). A majority (72%) of the subjects in this study (Study III) had HDL-C values over 1.56 mmol/L (Table 13). Thus,

although the metabolic phenotypes differ in HDL-C (Fig. 4), they get similar HDL-C based points in the Framingham score. However, when describing the CV risk of individuals in the present study it should be considered that although increased plasma cholesterol and LDL cholesterol are among the main risk factors for CVD, hypertriglyceridemia, which is not included in the CV-risk functions, has been defined as an independent CVD risk factor (NCEP Expert Panel 2001, Perk et al. 2012). In addition, metabolic syndrome has been recognized as a secondary target of risk-lowering (NCEP Expert Panel 2001).

6.6 SOM analysis as a tool to classify lipoproteins

Already in the 1960s Fredrickson and his co-workers classified lipoproteins based on paper chromatography and characterized lipoprotein patterns in different dyslipidemias (Levy & Fredrickson 1968). However, due to the complexity of lipoproteins, there are no single proper measures for lipoproteins. Rather, there are many techniques that classify lipoproteins, such as those that separate them based on density (Havel et al. 1955, Krauss & Burke 1982), size (Blanche et al. 1981, Jeyarajah et al. 2006, Krauss & Burke 1982, Okazaki et al. 2005), surface charge (Asztalos et al. 2005, Kunitake et al. 1985) and apolipoprotein content (Cheung & Albers 1982, Christoffersen et al. 2006, Yamauchi et al. 1999). Sequential ultracentrifugation, used in this study, is a widely used method for the isolation of lipoprotein particles based on density, permitting further analyses of their molecular composition (Kervinen et al. 1991, Liinamaa et al. 1997b, Välimäki et al. 1988). The molecular composition of lipoprotein particles plays an important role in several metabolic processes (de Souza et al. 2008, Hansel et al. 2004, Kontush & Chapman 2006, Rashid et al. 2002, Tribble et al. 1992) that may underlie the pathophysiology of metabolic disorders. In this study, UCF-based lipoprotein data were computationally analyzed by SOM (Studies II and III). SOM analysis enhanced the lipoprotein data and per se generated in silico lipoprotein subclasses or so-called lipoprotein phenotypes (see the original publication II for details). Although the SOM method is widely used for several other purposes (Hyvönen et al. 2001, Mäkinen et al. 2008, Nikkilä et al. 2002, Suna et al. 2006, Valkonen et al. 2002), it has not been applied to lipoprotein data before Study II. In Study III, the SOM-based lipoprotein analysis method was applied to biomedical data. The advantage of SOM in complex biomedical research is that it enables the discovery of multiple associations, which may also be non-linear; for example, SOM analysis of lipoprotein data

revealed that high and low concentrations of HDL-C together with two different phenotypes of HDL are associated with heavy alcohol intake (Fig. 4). Consequently, it would generally be beneficial to computationally enhance the UCF-based lipoprotein data as illustrated in this work (Studies II and III). In particular, a deeper insight into the compositional variations in lipoproteins appears a fundamental issue (Hevonoja et al. 2000, Kumpula et al. 2008, Vuorela et al. 2010).

6.7 Study limitations

6.7.1 Confounders

In addition to alcohol intake, several other factors affect the lipoprotein concentration (Table 17). Furthermore, alcohol may affect lipoproteins differently for example depending on diet (Rumpler et al. 1999), physical activity (Hartung et al. 1993), gender (Weidner et al. 1991, Whitfield et al. 2013) and genetic background (Fumeron et al. 1995, Liinamaa et al. 1997b). Thus, the effect of alcohol consumption on lipoproteins is challenging to study due to the complex interactions between the multiple factors involved.

Table 17. Factors affecting lipoprotein concentration

Factor Reference

Age Reviewed in Kolovou et al. 2011 and Walter 2009 Alcohol Reviewed in Hannuksela et al. 2004

Diet Reviewed in Schwingshackl & Hoffmann 2013 Exercise Ballantyne et al. 1982, Hartung et al. 1986 Gender Lin et al. 2013, Su et al. 2012

Genetic factors Corella et al. 2011, Freeman et al. 1990, Hagberg et al. 2000, Smalinskiene et al. 2013 Smoking Porkka & Ehnholm 1996, Whitehead et al. 1996

In this thesis, all the study subjects were men of similar age (Table 8). However, BMI was lower in heavy alcohol drinkers than in the controls in Study III, but did not differ in Study I (Table 8). Therefore, BMI was adjusted statistically for Study III, when appropriate (Table 13). The prevalence of smoking was different between heavy drinkers and controls (Table 8). This might affect the results as

smoking is known to decrease HDL levels (Brischetto et al. 1983, Stubbe et al. 1982) and the plasma cholesterol efflux capacity in smokers is lower than in non- smokers (Kralova Lesna et al. 2012). Cigarette smoke may decrease the cholesterol efflux capacity of HDL by increasing lipid peroxidation in the HDL particles (Ueyama et al. 1998). In Studies I and III, in spite of a higher smoking prevalence, the heavy drinkers had higher HDL-C levels and higher cholesterol efflux capacity of HDL than the control subjects. Actually, the higher prevalence of smoking among heavy alcohol drinkers may dilute the increasing effect of alcohol consumption on HDL-C and cholesterol efflux capacity of HDL. The difference in genetic background between heavy alcohol drinkers and controls (Study II) and, on the other hand, between the two observed metabolic phenotypes in heavy alcohol drinkers (Study III) may have influenced the results. At least polymorphisms of the CETP gene (TaqI B) and apoE phenotypes should be considered, since they affect the lipoprotein levels (Bennet et al. 2007, Thompson et al. 2008). The aforementioned CETP gene polymorphism is a restriction fragment length polymorphism indicated by digestion of a PCR- amplified CETP gene fragment by Tag I restriction enzyme (Drayna & Lawn 1987). Generally, in non-drinkers, the CETP gene (TaqI B) B2 carriers have higher HDL-C levels than B1 carriers (Fumeron et al. 1995, Gudnason et al. 1999, Hannuksela et al. 1994, Zhou et al. 2008). The CETP TaqI B polymorphism affects the level of HDL-C only moderately, since meta-analysis of 73 studies including 68,134 subjects indicates that each allele inherited for the TaqI B variant increases HDL-C from 3 to 5% (Thompson et al. 2008). The TaqI B polymorphism does not affect the VLDL-C and LDL-C levels (Hannuksela et al. 1994, Thompson et al. 2008). However, alcohol consumption may modulate the effect of the B2 allele on HDL-C, since the effect of B2 on plasma HDL-C was absent in alcohol drinkers of less than 25 g/day, but increased commensurably with a higher alcohol consumption: HDL-C of homozygotes for B2 allele was 30% higher than homozygotes for the B1 allele in alcohol consumers of more than 75 g/day (Fumeron et al. 1995). Thus, the CETP TaqI B polymorphism could have partially affected the difference in HDL-C levels between heavy drinkers and controls (Table 13) and between the two metabolic phenotypes of heavy drinkers (Fig. 7). This will be investigated in future studies. Six apoE genotypes associate almost linearly with the LDL-C level and coronary risk (meta-analysis of 82 studies for lipid levels and 121 studies for coronary outcomes), in the following order: ε2/ε2 < ε2/ε3 < ε2/ε4 < ε3/ε3 < ε3/ε4 < ε4/ε4 (Bennet et al. 2007). In ε2/ε2 carriers, the LDL-C values are about 30%

lower than in ε4/ε4 ones and coronary risk for ε2/ε2 carriers is 20% lower than for those with ε3/ε3 (Bennet et al. 2007). The effect of apoE alleles on HDL-C is weak, but inverse to LDL-C (Bennet et al. 2007). Since the effect of apoE polymorphism on plasma HDL-C concentration is minor (Bennet et al. 2007), in this thesis, only the effect on LDL-C is discussed. The difference in apoE phenotypes between heavy alcohol drinkers and controls could have partially influenced the difference between LDL-C concentrations in these groups (Table 13). However, LDL-C seems to be lower in heavy drinkers than in the controls even if alteration in apoE phenotype is taken into account (Liinamaa et al. 1997b).

6.7.2 Liver function

Since the liver is the central organ in lipoprotein metabolism (reviewed inBerneis & Krauss 2002, Sahebkar et al. 2014), it is important to exclude the possibility that the results have been influenced by liver malfunction. Although the heavy alcohol drinkers had a long history of heavy alcohol use, they did not show any laboratory signs of liver malfunction. The majority of heavy alcohol drinkers had either normal (Table 12) or only moderately elevated liver enzyme activities, indicating that they did not have liver-associated malfunction. In addition, their albumin levels did not differ from those of the controls (Table 13) and their HDL-C concentration were not decreased, suggesting that their liver was able to secrete and synthesize proteins normally. In addition, to avoid the influence of liver disease in the present study, the subjects with previous pancreatitis or hepatitis, or an AST to ALT ratio > 2 were excluded from Study III, and in Study I none of the study subjects had an AST to ALT ratio > 2. Therefore, it is not likely that liver malfunction has biased the study.

6.7.3 Sample size

The sample size in Study I was small (see Table 8). Nevertheless, the efflux capacity of HDL isolated from heavy alcohol drinkers was higher than for controls also in another sample set including 10 heavy alcohol drinkers and 12 controls (see Study I). However, previous studies dealing with the effect of heavy alcohol intake on HDL-mediated cholesterol efflux had even smaller sample sizes (less than 10 subjects per study group) (Marmillot et al. 2007, Rao et al. 2000). In addition, the sample size in the SOM analysis of lipoprotein data and metabolic variables was quite small in Study III. However, the overall sample size in the SOM analysis with lipoprotein measurements was 163. Therefore there are

≈ 40 individuals in each lipoprotein phenotype. More studies with larger sample sizes are definitely needed to verify the effect of heavy alcohol intake on cholesterol efflux as well as the connections of heavy alcohol intake with the different metabolic profiles.

6.8 Future aspects

This work shows that SOM analysis can enhance the UCF-based lipoprotein data. The present study also indicates that different metabolic profiles and higher cholesterol efflux capacity of HDL are related to heavy alcohol consumption, but the causality between heavy alcohol intake and the observed changes was not proven. In the future, to study causality, the effect of cessation of alcohol consumption on cholesterol efflux and lipoprotein phenotypes should be investigated, since diet-controlled cross-over studies cannot be performed to assess the effect of heavy alcohol intake. SOM analysis of UCF-based lipoprotein data generated in silico lipoprotein phenotypes depending on the heterogeneity of input data profiles (Studies II and III): Five different lipoprotein phenotypes arose from data per se in Study II and four in Study III. In addition, two lipoprotein phenotypes together with different metabolic profiles associated with heavy alcohol consumption in Study III. Deeper characterization of the lipoprotein phenotypes will be of future interest. Lipoprotein metabolism could be described as a crosstalk between lipoprotein particles enzymes and lipid transfer proteins (Hannuksela et al. 2002, Hegele 2009), in which various lipid molecules are transferred or exchanged between lipoprotein particles and tissues. Therefore it will be interesting to assess the association of e.g. PLTP, CETP and LCAT activities with these in silico depicted lipoprotein phenotypes. Genetic background, e.g. polymorphisms in CETP and apoE genes, affects the lipoprotein levels (Bennet et al. 2007, Thompson et al. 2008). Thus the association of such polymorphisms with lipoprotein phenotypes analyzed by SOM will be investigated. The functionality of HDL particles seems to be a better predictor of arterial status than plasma HDL-C level (Favari et al. 2013, Khera et al. 2011). Therefore, the capacity of HDL particles to mediate cholesterol efflux in different lipoprotein phenotypes will be investigated. Compositionally different lipoprotein particles were associated with a similar plasma concentration profile of lipoprotein lipids (Studies II and III). Therefore, in the future, it would be interesting to answer the question: Does lipoprotein compostion (e.g. lipoprotein particle PL content) predict metabolic disturbances

(e.g. CVD, T2DM) better than plasma lipid values? However, a large follow-up study with stratified disturbance (e.g. CVD) is needed to assess this question.

7 Conclusions

1. Heavy alcohol drinkers had higher cholesterol efflux capacity of HDL2 than the controls. This was associated with an increase in HDL2 phospholipids and with a concomitant increase in large HDL2b particles. The cholesterol efflux to HDL3 did not differ between the study groups. 2. UCF-based lipoprotein data can be enhanced by SOM analysis. SOM analysis enabled a combination of plasma lipoprotein concentrations and corresponding compositional features of the lipoprotein particles resulting in novel lipoprotein phenotypes. 3. Lipoprotein-based grouping in SOM analysis revealed complex associations between lipoproteins, adiponectin, MetS and alcohol intake and depicted distinct metabolic profiles related to alcohol consumption. Two distinctive metabolic profiles were associated with heavy alcohol intake: Firstly, an anti-atherogenic profile (large PL-enriched and TG-poor HDL particles, high HDL-C, low VLDL-TG and IDL together with high adiponectin concentration and low prevalence of MetS) and secondly, a MetS-like profile with the opposite metabolic characteristics (TG- enriched lipoprotein particles, low HDL-C, and high VLDL-TG with relatively low adiponectin and high prevalence of MetS). Notably, these metabolic profiles arose from the SOM analysis of the lipoprotein data per se, indicating the potential of data- driven grouping of the lipoprotein data in identifying complex metabolic phenomena. 4. In general, this study shows clearly that compositionally different lipoprotein particles can be connected with similar plasma concentration profile. In clinical setting, the cardiovascular status estimation of an individual is typically based on the plasma lipid and lipoprotein concentrations (e.g. total cholesterol, LDL-C, HDL-C and triglycerides). In addition, the CV-risk tools (Framingham, Score, Finrisk), helping physicians to make a decision on cardiovascular status, are mainly based on plasma values of lipoproteins. Thus, clinically, it is important to consider that potential lipid-related risk for metabolic disturbances may vary considerably between two individuals with a similar plasma concentration of e.g. LDL-C or HDL-C due to variation in the composition of lipoprotein particles. In the future, a deeper knowledge of lipoproteins, such as lipoprotein composition, may also help physicians to identify heavy alcohol drinkers who may have a higher CV-risk, even though their LDL-C level is low.

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Original articles

I Mäkelä SM, Jauhiainen M, Ala-Korpela M, Metso J, Lehto T, Savolainen MJ,

Hannuksela ML (2008) HDL2 of heavy alcohol drinkers enhances cholesterol efflux from RAW-macrophages via phospholipid-rich HDL2b particles. Alcohol Clin Exp Res 32(6):991–1000. II Kumpula LS, Mäkelä SM, Mäkinen V-P, Karjalainen A, Liinamaa JM, Kaski K, Savolainen MJ, Hannuksela ML ja Ala-Korpela M (2010) Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps. J Lipid Res 51(2):431–9. III Kuusisto SM, Peltola T, Kumpula LS, Mäkinen V-P, Salonurmi T, Hedberg P, Savolainen MJ, Hannuksela ML and Ala-Korpela M (2012) The interplay between lipoprotein phenotypes and alcohol consumption. Ann Med 44(5):513–22. Reprinted with permission from John Wiley and Sons (I), American Society of Biochemistry and Molecular Biology (II) and Informa Healthcare (III).

Original publications are not included in the electronic version of the dissertation.

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132 ACTA UNIVERSITATIS OULUENSIS SERIES D MEDICA

1252. Jämsä, Ulla (2014) Kuntoutuksen muutosagentit : tutkimus työelämälähtöisestä oppimisesta ylemmässä ammattikorkeakoulutuksessa 1253. Kaikkonen, Leena (2014) p38 mitogen-activated protein kinase and transcription factor GATA-4 in the regulation of cardiomyocyte function 1254. Finnilä, Mikko A. J. (2014) Bone toxicity of persistent organic pollutants 1255. Starck, Tuomo (2014) Dimensionality, noise separation and full frequency band perspectives of ICA in resting state fMRI : investigations into ICA in resting state fMRI 1256. Karhu, Jaana (2014) Severe community- acquired pneumonia – studies on imaging, etiology, treatment, and outcome among intensive care patients 1257. Lahti, Anniina (2014) Epidemiological study on trends and characteristics of suicide among children and adolescents in Finland 1258. Nyyssönen, Virva (2014) Transvaginal mesh-augmented procedures in gynecology : outcomes after female urinary incontinence and pelvic organ prolapse surgery 1259. Kummu, Outi (2014) Humoral immune response to carbamyl-epitopes in atherosclerosis 1260. Jokinen, Elina (2014) Targeted therapy sensitivity and resistance in solid malignancies 1261. Amegah, Adeladza Kofi (2014) Household fuel and garbage combustion, street vending activities and adverse pregnancy outcomes : Evidence from Urban Ghana 1262. Roisko, Riikka (2014) Parental Communication Deviance as a risk factor for thought disorders and schizophrenia spectrum disorders in offspring : The Finnish Adoptive Family Study 1263. Åström, Pirjo (2014) Regulatory mechanisms mediating matrix metalloproteinase- 8 effects in oral tissue repair and tongue cancer 1264. Haikola, Britta (2014) Oral health among Finns aged 60 years and older : edentulousness, fixed prostheses, dental infections detected from radiographs and their associating factors 1265. Manninen, Anna-Leena (2014) Clinical applications of radiophotoluminescence (RPL) dosimetry in evaluation of patient radiation exposure in radiology : Determination of absorbed and effective dose

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