Proinsulin and Insulin Resistance

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.". *!/,)!"."! .-!,*! .-. 00 Dissertation for the Degree of Doctor of Philosophy (Faculty of Medicine) in Geriatrics presented at Uppsala University in 2002

Abstract Zethelius, B. 2002. Proinsulin and Insulin Sensitivity as Predictors of Type 2 Diabetes Mellitus and Coronary Heart Disease – Clinical Epidemiological Studies with up to 27 years of follow-up. Acta Universitatis Upsaliensis. Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1217. 67 pp. Uppsala. ISBN 91-554-5493-3.

Defects in insulin secretion and insulin action are the major abnormalities in the development of Type 2 diabetes. Hyperinsulinemia is a risk marker for Type 2 diabetes and according to some, but not in all studies also for coronary heart disease (CHD). Conventional insulin assays measure immunoreactive insulin including proinsulin-like molecules. Proinsulin and insulin measured by specific methods, insulin sensitivity measured by the euglycemic insulin clamp and early insulin response after a glucose challenge give more detailed information and may be better estimates of true risk for Type 2 diabetes and CHD. This study examined relationships between proinsulin, insulin, insulin secretion and insulin sensitivity for the development of Type 2 diabetes and CHD. The investigation of the prognostic significance of proinsulin and insulin for the development of Type 2 diabetes and CHD was performed in prospective studies of 50-year and 70-year-old men in a population-based cohort. The results indicated, that increased proinsulin concentrations, was a marker of increased risk for Type 2 diabetes independent of measurements of insulin secretion and insulin sensitivity whereas insulin was not. Proinsulin was shown to be a predictor for CHD mortality and morbidity, respectively, independent of conventional risk factors, whereas insulin was not. Insulin sensitivity measured by the gold standard euglycemic insulin clamp at age 70 was a predictor of CHD morbidity, independently of established risk factors. In summary, these data provide evidence that an increased concentration of proinsulin and not an elevated plasma insulin level per se, that constitutes the association with Type 2 diabetes and CHD and that insulin resistance per se, is associated with CHD risk. Key words: diabetes, coronary heart disease, insulin, proinsulin, prognosis, mortality. Björn Zethelius, Department of Public Health and Caring Sciences, Section of Geriatrics, Box 609, SE-751 25, Uppsala, Sweden © Björn Zethelius 2002 ISSN 0282-7476 ISBN 91-554-5493-3 Printed in Sweden by Uppsala University, Tryck & Medier, Uppsala 2002

”När man ställer upp hypoteser får man inte vara småaktig” August Strindberg

PAPERS

This thesis is based on the following papers, which are referred to in the text by their Roman numerals: I. Zethelius B, Byberg L, Hales C N, Lithell H, Berne C. Proinsulin and Acute Insulin Response Independently Predict Type 2 Diabetes Mellitus in Men. Report from a 27 Years of Follow-Up Study. Accepted for publication in Diabetologia. * II. Zethelius B, Hales C N, Lithell H, Berne C. Insulin Resistance, Impaired Early Insulin Response and Insulin Propetides as Predictors of the Development of Type 2 Diabetes. A Population Based, 7-year Follow-Up Study in Elderly Men. Submitted for publication. III. Zethelius B, Byberg L, Hales C N, Lithell H, Berne C. Proinsulin is an Independent Predictor of Coronary Heart Disease. Report From a 27-Year Follow-Up Study. Circulation 2002; 105: 2153-8. † IV. Zethelius B, Hales C N, Lithell H, Berne C. Insulin Resistance as a Predictor of Coronary Heart Disease in a Population-Based, Follow-Up Study in Elderly Men Using the Euglycemic Insulin Clamp. Submitted for publication.

Opponent Professor Björn Fagerberg Department of Internal Medicine Sahlgrenska University Hospital Göteborg University, Sweden

Supervisors Professor Christian Berne Department of Medical Sciences Uppsala University, Sweden

Professor Hans Lithell Geriatrics Department of Public Health and Caring Sciences Uppsala University, Sweden

4 CONTENTS

ABBREVIATIONS 6 INTRODUCTION 7 Type 2 diabetes diagnostic criteria 8 Prevalence and incidence of Type 2 diabetes 8 Coronary heart disease (CHD) diagnostic criteria 9 Prevalence and incidence of CHD 10 Comorbidity of Type 2 diabetes and CHD 10 Proinsulin and insulin 11 Insulin secretion 13 Insulin receptor signalling 14 Insulin action – insulin resistance 15 Insulin resistance and atherosclerosis 15 Methods for measuring insulin and proinsulin 15 Risk factors for Type 2 diabetes and CHD 16 AIMS OF THE STUDY 19 METHODS 20 Subjects 20 Study design and populations 21 Investigations 21 Outcome definitions 24 Statistical analyses 25 Approval of Ethics Committee 26 DISCUSSION OF METHODS 27 RESULTS 31 DISCUSSION 42 Proinsulin as a predictor for Type 2 diabetes 42 Proinsulin and impaired beta cell function 43 Proinsulin and insulin resistance 43 Proinsulin an integrated marker for beta cell function and insulin sensitivity 44 Precision of risk marker measurements 44 Proinsulin as a predictor of CHD 45 Comorbidity between Type 2 diabetes and CHD 46 Are proinsulin or insulin causal factors for CHD? - Quality of evidence 46 Criteria of causality of epidemiological associations 47 Clinical relevance – screening? 49 Limitations 50 CONCLUSIONS 52 SVENSK SAMMANFATTNING (Swedish summary) 53 ACKNOWLEDGEMENTS 54 REFERENCES 56

5 ABBREVIATIONS

AIR Acute insulin response BMI Body mass index BP Blood pressure CDR Cause of death registry CHD Coronary heart disease CI Confidence interval CVD Cardiovascular disease DBP Diastolic blood pressure ECG Electrocardiogram EIR Early insulin response HDL High-density lipoprotein HR Hazard ratio ICD International classification of disease IMT Intimal media thickness IPR In-patient registry IVGTT Intravenous glucose tolerance test IRI Immunoreactive Insulin LDL Low-density lipoprotein MI Myocardial infarction OHAs Oral hypoglycaemic agents OGTT Oral glucose tolerance test OR Odds ratio PAI-1 Plasminogen activator inhibitor-1 PLMs Proinsulin like molecules PYAR Person years at risk SCR Swedish cancer registry SD Standard deviation SBP Systolic blood pressure UAER Urine albumin excretion rate ULSAM Uppsala longitudinal study of adult men

6 INTRODUCTION

Coronary heart disease is the major cause of mortality and morbidity in the industrialised world [1]. The underlying grounds, atherosclerosis, is a process starting early in life, slowly and silently progressing for decades [2]. The process of athereogenesis was previously considered by many to consist mainly of lipid accumulation within the artery wall. Other processes, as inflammation, are also involved [2]. The risk of coronary heart disease (CHD) or stroke is also up to four times higher in patients with diabetes in comparison to non-diabetic subjects [3]. We are facing a dramatic, worldwide increase in the incidence of Type 2 diabetes [4]. Cost for healthcare is high and increasing [5]. The complications associated with Type 2 diabetes account for the majority of these expenditures and the cardiovascular complications, makes the greater contribution to the cost of diabetes care [6]. Type 2 diabetes, hypertension and dyslipidemia are associated with insulin resistance and an increased risk of CHD [7]. The gold standard method for determining insulin resistance is the euglycemic insulin clamp [8], which is a time and labour consuming investigational procedure. Therefore the extent to which insulin resistance contributes to the risk of subsequent CHD has mainly been studied by using the plasma immunoreactive insulin (IRI) concentration as a surrogate marker of insulin resistance. However, insulin resistance measured by the minimal model technique has been shown to be associated with atherosclerosis [9]. The plasma IRI concentration only moderately correlates to insulin sensitivity measured using the euglycemic insulin clamp [10] and, the role of IRI as a risk factor for CHD has been questioned [11, 12]. Even if IRI is accepted as a marker for CVD it is weak compared to established risk markers as smoking, hypertension and cholesterol [13, 14]. The importance of IRI may as the conventional risk factors, be changed to be weaker at higher age [15] and attenuated by length of follow-up time [16, 17]. Impaired insulin secretion and impaired insulin action, are the major defects in the development of Type 2 diabetes [18, 19]. A low early insulin response (EIR) to a glucose load, [19-23], fasting hyperinsulinemia [21, 24, 25] and insulin resistance, assessed with clamp technique [23], predicted Type 2 diabetes in prospective studies. Fasting concentrations of proinsulin-like molecules (PLMs), i.e. the sum of intact and 32-33 split proinsulin predicted the onset of Type 2 diabetes [26-29] in middle-aged subjects with a follow-up time of up to 4 years. PLMs were predictors of MI [30] and first ever stroke [31], using the nested case control design, in two studies of shorter length of follow-up.

Type 2 diabetes mellitus, diagnostic criteria Type 2 diabetes is a disease characterized by and diagnosed on the basis of an elevated blood glucose concentration. Definitions and criteria have varied over time. The first more generally accepted diagnostic criteria were the US National Diabetes Data Group (NDDG) diagnostic criteria from 1979 [32]. The diagnose of diabetes was established if fasting blood glucose was above or equal to 6.7 mmol/l on more than one occasion or if both the 2-hour sample and some other sample taken after the 75-gram glucose dose administration but before the 2 hour sample should be more than or equal to 10.0 mmol/l. In 1980 the World Health Organization (WHO) adopted the NDDG criteria [33]. In epidemiological studies the oral glucose tolerance test was however preferred to the fasting glucose. In 1985 a modification of the WHO criteria concerned the 2- hour criterion, which implied that it could be used alone without taking into consideration any of the other samples [34]. Thus a simplification, but still the 2-hour criterion was preferred for epidemiological studies. To further simplify the diagnosing of diabetes, new criteria highlighting the use of fasting glucose concentrations, were proposed by the American Diabetes Association (ADA), in 1997 [35]. The WHO adopted the fasting criterion (fasting blood glucose above or equal to 6.1 mmol/l) from the ADA in 1999 [36]. Thus, the fasting and the 2-hour criteria are now both generally accepted for use in epidemiological studies. However, the fasting and the post-load criteria are not inter-changeable, as they do not fully match [37] and there has been a debate of which one to use in epidemiological studies. Type 1 diabetes, is an autoimmune disease where the destruction of beta cells imply an insulin treatment dependency for survival. Type 1diabetes will not be discussed any further in this thesis.

Prevalence and incidence of Type 2 diabetes The prevalence of Type 2 diabetes varies with geographic location, ethnicity and diet [38]. The prevalence in Sweden was 2.6% in 1972, 4.3% in 1987 [39], and in Finland 3.3% in 1987 [40]. Diabetes is according to these studies, much more frequent (16%) in subjects at higher age, above the age of 65 years [39], congruent with results from Finland [40] and Framingham, USA [41]. The Swedish study showed an incidence rate of 2.99 new cases per 1000 subjects per year between 1972 and 1976 and 4.08 new cases per 1000 subjects per year between 1983 and 1987 [39]. The authors concluded that both the prevalence and the incidence rate for Type 2 diabetes were increasing during this time period. A recent study from Sweden showed the prevalence of Type 2 diabetes not to have increased between 1986 and 1999, despite a marked increase in BMI [42]. In contrast, between 1991 and 1995, the prevalence of

8 Type 2 diabetes increased by 6% per year while the incidence rates did not in another setting [43]. It is estimated that the world will expect an increase in the number of diabetics from 135 million in 1995 to 300 million in 2025 [44]. Type 2 diabetes, is increasing due to increasing prevalence of obesity and a more sedentary lifestyle. With counselling to reduce body weight and energy intake [45, 46] and with increased physical activity [45-47], Type 2 diabetes may be prevented in prediabetic states, e.g. patients with IGT.

Coronary heart disease, diagnostic criteria CHD includes a group of diagnoses, with different clinical characteristics but all sharing a common feature, namely the atherosclerosis affecting coronary arteries [2] clinically presenting as myocardial infarction (acute and silent), angina pectoris, acute instable coronary syndrome, atherosclerosis diagnosed by coronary angiography or by findings at autopsy. Acute MI is diagnosed by the presence of two symptoms or signs out of the classical triad, namely ischaemic chest pain, elevations of serum markers or ECG evidence of MI. Criteria have changed over time, as more specific serum markers have become available during the last three decades. The serum markers AST (SGOT) and ALT (SPGT) were utilized for many years but have fallen out because of their time course of elevation is intermediate of CK-MB and LDH. The CK-MB and later Troponin-I and Troponin-T have been introduced as serum markers due to higher specificity. ECG evidence of silent MI defined as a new major Q-wave or LBBB, loss of R-wave progression or T- wave inversion have been stable criteria, during the time course of this study. Angina pectoris is defined as ischaemic chest pain and a positive exercise-test. These diagnostic criteria have been fairly stable over time for the chronic stable angina pectoris. However the unstable acute coronary syndrome is caused by plaque disruption leading to the formation of a thrombus. The clinical presentation of symptoms is correlated to the duration and extent of the myocardial ischemia. The percutaneous coronary artery angiography (PTCA) technique has been continuously improved over time and is not only used for diagnostic purposes for coronary atherosclerosis but also for percutaneous transluminal coronary intervention (e.g. angioplasty, stenting, atherectomy, laser ablation). Coronary artery by-pass graft (CABG) surgery should be mentioned here as it has been more frequently used over the last three decades. In registers, intervention with PTCA or CABG will be registered as angina pectoris with a supplementary code. Finally, patho-anatomical diagnosis at an autopsy belongs to this group of diagnostic criteria. In epidemiological studies on CHD a commonly used methods is scrutiny of hospital discharge notes and death certificates. CHD

9 morbidity, defined by combining data from the cause of death registry and the in-patient registry, is an efficient, validated alternative method to revised hospital discharge notes and death certificates [48].

Prevalence and incidence of coronary heart disease Public registry data from the National Bureau of Statistics in Sweden in 1998 shows 4.9 cardiovascular deaths per 1000 subjects per year between ages 50 to 79 years in Uppsala. The corresponding rate between ages 70 and 79 years was 13.5/1000. Rates in Uppsala are in general some 5-10% lower than national rates for Sweden. Further, there was a decline in rates between the years 1997 and 2000 of about 5-10%. Hospitalisation for CVD during the corresponding time period was 34/1000 between ages 50 and 79 years and 72.0/1000 between ages 70 and 79 years in Uppsala. These figures do however also cover hospitalisation at multiple occasions per subject. Thus the hospitalisation figures are higher than “true” incidence rates. As comparison, data obtained using hospital discharge notes and death certificates in selected US-communities show a slightly increasing incidence rate of hospitalisation for myocardial infarction between 1987 and 1995. However there was a significant annual decrease in mortality from CHD, particularly among younger people during the same time period [49]. The prevalence in the US of anatomic coronary disease at autopsy decreased between 1979 and 1994, particularly among younger people, supporting the notion that the burden of CHD has shifted toward the elderly [50]. The decline in CHD mortality in the US, which has been attributed to reduction in cardiovascular risk factors, began earlier and was steeper in women relative to men, but the decline in women has slowed down [51]. The US national conference on cardiovascular disease has stated that CHD rate was still declining during the 1990s but at a slower rate than in the 1980s [52]. Opposing the positive effects of better hospital care is the negative trend in risk factors in the US. There has been little progress in smoking cessation, physical inactivity and hypertension control and further there was an increasing occurrence of obesity and Type 2 diabetes, however with major differences among subpopulations [52]. The negative trend in risk factors, are consistent with a slowing decline in mortality [52].

Comorbidity of type 2 diabetes and coronary heart disease Type 2 diabetes and CHD share several important characteristics [53]. Both conditions become more prevalent with age, both are associated with obesity, an adverse serum lipid profile and a sedentary lifestyle. Both conditions are insulin resistant states associated with atherosclerosis [9]. Insulin resistance is also the common denominator in the cluster of abnormalities, which together comprise the insulin resistance syndrome, namely Type 2 diabetes, impaired glucose

10 tolerance, hypertension, dyslipidemia, and central obesity [12]. Having the insulin resistance syndrome, imply a higher risk for CHD [54]. Diabetes has been considered one of the cornerstones in the definition of the insulin resistance syndrome proposed by both WHO [35], the European group for the study of insulin resistance (EGIR) [55] and the recently published Adult Treatment Panel definition [56]. The latter is a more clinically oriented definition of the syndrome. There is a two-fold increased risk for all-cause mortality and CHD mortality in diabetic patients compared to the general population [57]. The CHD mortality is equal in patients with Type 2 diabetes without a previous MI as it is in non- diabetic subjects with a prior MI [3]. Thus, in epidemiological studies on the association between insulin resistance and subsequent CHD, the results may be confounded by comorbidity of diabetes [58].

Proinsulin and insulin Proinsulin, the precursor of insulin was first discovered in 1967 [59]. Preproinsulin, the precursor of proinsulin is synthesised on the ribosomes in the endoplasmatic reticule. Preproinsulin is then converted to proinsulin by removal of a signal peptide when it is translocated to the Golgi apparatus. Proinsulin is then processed to insulin and C-peptide (Figure 1) by proconvertases and packed into vacuoles forming the secretory granulae [60-62]. The conversion processes are all energy dependent [63]. It is assumed that the packaging of proinsulin and the proinsulin conversion enzymes into the nascent secretory granules is dependent on an active sorting process [64], but studies have so far not been able to demonstrate these processes in detail [64, 65]. As these processes are not perfect, partially converted split products will occur of which 32-33 split proinsulin is the predominant variant in plasma [66]. Hyperproinsulinemia can be a sign of a primary reduction in insulin secretion capacity [67]. An increased proportion of proinsulin in secretory granules at the time of exocytosis may reflect a slower than normal rate of conversion from proinsulin to insulin [68]. Proinsulin and C-peptide have generally been considered as peptides co-secreted with insulin, having no physiological function themselves. Insulin has been considered the “real” hormone. C-peptide however, may have a role in glucose control in Type 1 diabetes [69]. Proinsulin may play pathophysiological and predictive roles in prediabetic states. Proinsulin and insulin secretion both decrease when blood glucose decreases and both decrease during induced hyperinsulinemia at euglycemic insulin clamp [70]. During hyperproinsulinemic euglycemic clamp, supraphysiological concentrations of proinsulin are needed to suppress insulin secretion [71-73]. A specific exclusive receptor for proinsulin has been demonstrated in intestinal crypt cells where proinsulin works as a growth-stimulating factor [74] and proinsulin and insulin compete for binding to similar receptors [75-77].

Intact proinsulin

32-33 split proinsulin 65-66 split proinsulin

Des 31-32 proinsulin 64-65 split proinsulin

Connecting peptide

Specific insulin

Figure 1. The conversion of intact proinsulin to insulin and connecting peptide.

When binding to the insulin receptor, proinsulin induces cellular glucose uptake and has blood glucose-lowering effects, like insulin. The blood glucose lowering effect of proinsulin has a prolonged action compared to insulin [78]. Proinsulin possesses 1-10% of the insulin binding affinity to the insulin receptor and equimolar binding of insulin and proinsulin result in a comparable effect on glucose transport [75-77]. The glucose-lowering effects of proinsulin, has been tested in several clinical trials in diabetic patients [79]. The clearance of proinsulin and insulin takes place in the kidney [80] and therefore proinsulin and insulin concentrations are increased in chronic renal failure [81]. The proinsulin clearance process is unsaturable and the elimination curve after infusion shows a biphasic shape with a half-life of 25 minutes in the first phase, and a half-life of 1.5 to 2 hours in the second phase [82, 83]. This is in contrast to the shape of the insulin elimination curve, which shows a half-life of 6 minutes [84]. Hyperproinsulinemia may occur as an autosomal dominantly inherited trait [85]. The defect has been identified as a point mutation causing incomplete conversion of proinsulin to insulin at amino acid position 65 on the A-chain [86]. Another rare inherited variant is a point mutation at position 10 in the B chain [87]. This mutation interferes with the packing process of proinsulin [88]. Affected persons have a high fraction of proinsulin of 60-80% of the total immunoreactive insulin, compared to 5-10% in normal individuals, but display a normal glucose metabolism also indicating a probable biological effect of proinsulin in these disorders.

12 Using biosynthetic human proinsulin, in studies on subjects with normal glucose metabolism, proinsulin has been shown to have a greater effect in the liver compared to skeletal muscle. Proinsulin has 12% of the potency of insulin in suppressing hepatic glucose production if equimolar and 8% of the potency of insulin in inducing peripheral glucose uptake [73]. Further, the reducing effect on hepatic glucose output of proinsulin is lasting longer compared to insulin [83]. These findings are of interest in the treatment of patients with Type 2 diabetes, who have clinically significant excessive hepatic glucose output. Proinsulin shows comparable pharmacokinetics and pharmacodynamics to NPH-insulin, has less day-to-day variation in absorption than NPH-insulin and conversion to insulin does not occur after injections [89]. Several clinical trials using proinsulin therapeutically have been launched but were all suspended due to a higher incidence of CHD in the proinsulin treatment arms [79]. Proinsulin has been suspected to have a causal effect, causative of the observed side effects, but this hypothesis was never tested in these clinical trials [79]. Proinsulin is associated with atherogenic risk factors in non-diabetic subjects [90], in patients with Type 2 diabetes [91] and in subjects with MI early in life [92]. Insulin resistance and serum proinsulin was associated with small LDL particle size and proinsulin was more directly related to LDL particle size than insulin was in healthy men [93]. In the same study population, proinsulin but not specific insulin, was associated with carotid intimal-media thickness (IMT) independent of conventional risk factors for atherosclerosis [94]. In vitro, proinsulin increases the plasminogen activator inhibitor-1 (PAI-1) production [95] and in vivo in humans, plasma proinsulin is directly associated with PAI-1 concentrations [96]. PAI-1, being a major inhibitor of fibrinolysis has the capacity of constituting an important factor in the pathogenesis of atherothrombosis preceding the onset of CHD by several years [97, 98]. The described results above are indications supportive of the hypothesis of an association between proinsulin and subsequent CHD, the hypothesis tested and proven in this thesis.

Insulin secretion Insulin release, i.e. regulated exocytosis is a series of different membrane-traffic steps. Several specific proteins effectuate the fusion-reaction between the insulin containing vesicle and the cell membrane. These proteins are the so called: SNARES, Soluble N-ethylmaleimide-sensitive factor attachment protein receptors [99]. Glucose stimulates insulin release by the pancreatic beta cell through ATP- sensitive K+ channels dependent and independent pathways, which results in a biphasic insulin release in response to a square glucose load [100]. The first phase insulin release is triggered by glucose via ATP-sensitive K+ channel-

13 dependent ionic events, elevation of sub-membrane free Ca2+ concentration. In the second phase insulin release, the mechanism above is augmented by ATP sensitive K+ channel-independent, non-ionic signals only vaguely known [100]. The initiation of its development needs another signal of which the mechanism is unknown [101, 102]. However the magnitude of the second phase is determined by the elevation of Ca2+ [101, 102]. A specific alteration of first phase insulin release after a glucose challenge is an early and progressive defect of crucial physiological role in postprandial glucose homeostasis. The effect of first-phase insulin release is primarily achieved in the liver, allowing a prompt inhibition of postprandial hepatic glucose production [103]. Defects in the second-phase insulin release, have not been widely considered as early phenomena in the development of Type 2 diabetes. They may be more important in genetically predisposed subjects than previously recognized [104]. The dynamic regulation of insulin secretion is very complex. There is a time- dependent inhibition of insulin release after short glucose stimulation, which makes the beta cells refractorius [105]. Further, there is a time-dependent potentiation to longer glucose stimulation leading to an amplification of insulin secretion to subsequent glucose stimuli [105]. Long-term hyperglycemia under experimental conditions induces a long lasting depletion of crucial components of the insulin exocytic process distal to closure of ATP-sensitive K+ channels [106]. In mechanistic studies of beta cell processes, the species differences have to be considered. Human islets differ from the rodent islets usually used in vitro experiments in the susceptibility to several noxious substances, e.g. human beta cells are resistant to the rodent beta cell toxin alloxan [107]. Insulin release is pulsatile in vivo [108], in periods of 4-13 minutes, which provides a better signal to target tissues, i.e. pulsatile insulin is more effective at reducing glucose concentrations than the same amount of insulin given at a constant rate. This effect may be explained by the dose-response curve of the insulin receptor and of prevention of receptor desensitisation [109]. Loss of regular pulsatile insulin release is an early symptom of Type 2 diabetes [110]. The quantification of insulin secretion will be accurately assessed only when measured in relation to insulin sensitivity. Shifts in peripheral insulin sensitivity are accompanied by compensatory alterations in beta-cell sensitivity to glucose in a hyperbolic sensitivity-secretion relationship [111].

Insulin receptor signalling Binding of insulin to its receptor is the first step in the cellular action of insulin. This is followed by, an intrinsic tyrosine kinase activation associated with the beta subunit of the receptor. The post receptor signalling involves a cascade of proteins. Briefly, it works as follows: upon activation of the beta subunit

14 tyrosine kinase, several proteins bind to the subunit. Among these proteins, the insulin receptor substrate 1 and phosfatidylinositol 3- kinase (PI3-K) are of main importance, inducing translocation of glucose transporters, GLUT-4, from an intracellular pool to the cell surface [112]. GLUT-4 transport glucose into the cell as a response to insulin but recently the presence of a second signalling pathway has been discovered [113]. In this pathway, the insulin receptor signalling emanates from lipid rafts in the plasma membrane in a PI3-K independent way.

Insulin action – insulin resistance Skeletal muscle is the major peripheral tissue for insulin stimulated glucose disposal [114], and is measured by the euglycemic insulin clamp [8]. The phosphorylation of glucose to glucose-6-phosphate is the first irreversible step in glucose uptake in skeletal muscle. This reaction is catalysed by hexokinase [115]. Hexokinase activity defects have been suggested to be a hereditary abnormality in insulin resistance. Hepatic insulin resistance contributes to hyperglycemia in Type 2 diabetes. This is reflected by an increased hepatic glucose output at a defined insulin concentration [116]. In obesity an increased uptake of glucose into the adipose tissue occurs as compared to lean subjects [117].

Insulin resistance and atherosclerosis Insulin resistance may increase the risk for CHD indirectly through the effects on cardiovascular risk factors or it could directly promote atherosclerosis [118]. It has been shown, using the frequently sampled IVGTT for measuring insulin sensitivity and ultrasonography of the carotid artery measuring IMT, that a high degree of insulin sensitivity is associated with less atherosclerosis [9]. Using the euglycemic insulin clamp and ultrasonography of the carotid and femoral arteries, it has been demonstrated a significant inverse association between insulin sensitivity and carotid IMT but not to femoral IMT [119]. Femoral IMT is associated with blood pressure and other components of the insulin resistance syndrome but not with insulin and proinsulin [120].

Methods for measuring insulin and proinsulin Conventional radioimmunoassays for insulin, measure immunoreactive insulin (IRI) thus, cross-react to different extent with proinsulin like molecules. High performance liquid chromatography (HPLC) methods are good for quantification and identification of the different components of proinsulin like molecules in plasma are of high precision however it is most labor intensive, (i.e. expensive) and need larger amounts of plasma [121].

15 Proinsulin consists of insulin and C-peptide. Insulin antibodies do not bind to C- peptide and C-peptide antibodies will not bind to insulin. The combination of both types of antibodies results in a method for measuring proinsulin like molecules [122, 123]. Later, the site-specific two-site immunometric assay technique has been developed for intact proinsulin and 32-33 split proinsulin [124]. The method has been validated by comparison with HPLC [125].

Risk factors for Type 2 diabetes and coronary heart disease Sex and age. Type 2 diabetes is slightly more prevalent in men than in women in Sweden [39] as opposed to Finland where it is more prevalent in women [40]. Type 2 diabetes, is more frequent at higher age [39-41]. Aging is associated with deterioration of beta-cell function and deteriorating proinsulin to insulin conversion [126]. There is an age-related increase in total body fat and in visceral adiposity until age 65 years related to development of Type 2 diabetes [41] and aging is also associated with lowering of insulin sensitivity. Age is a risk factor for CHD but there is a gender-difference as CHD occurs later in life in women compared to men but the gender-difference in age for CHD is smaller in diabetic patients [127]. Smoking. It is a weak predictor for later Type 2 diabetes in some studies [128, 129]. Heavy smoking induces insulin resistance [130]. Smoking is a major conventional risk factor for CHD [131]. Hypertension – blood pressure. Hypertension, elevated blood pressure and use of antihypertensive drugs are predictors of Type 2 diabetes [21, 53, 132]. Type 2 Diabetes and hypertension are interrelated and both conditions are insulin resistant states [133]. Antihypertensive drugs as beta-blockers and diuretics have been shown to induce insulin resistance [134-138]. Hypertension is a major conventional risk factor for CHD [131]. Successful treatment with antihypertensive drugs reduces the incidence of CHD [139] and estimated optimal treatment needs combination-therapy to achieve treatment goals [140]. Recent data shows an anti-atherosclerotic effect of beta-blockers [141]. However, there has been little progress in hypertension control during the 1990s [52]. Lipids. High total serum cholesterol does not precede Type 2 diabetes but high concentrations of small LDL-cholesterol [142] and triglycerides do [132]. The development of Type 2 diabetes was reduced, by lowering the triglyceride concentration in a clinical trial, using pravastatin [143]. High total cholesterol is a major risk factor for CHD [131]. LDL cholesterol is associated with CHD risk [131], and lowering total and LDL cholesterol by use of statins [144, 145] reduces CHD mortality and morbidity. The size of LDL particles is of importance as small LDL is associated with occurrence of atherosclerosis in the

16 carotid artery [146]. The triglyceride concentration is not an independent predictor of CHD death after adjustment for serum cholesterol in the general population [147] but in patients with impaired glucose tolerance and Type 2 diabetes [148]. Low HDL and high VLDL cholesterol are predictors of CHD in patients with Type 2 diabetes [149]. Patients with Type 2 diabetes, benefit from cholesterol lowering therapy equal to non-diabetic subjects [150-152]. Using statin therapy for lowering of cholesterol concentrations also induces a reduction in the inflammatory biomarker C-reactive protein, in a LDL cholesterol independent manner, which has implications for the development of Type 2 diabetes and CHD [153]. Obesity. Centrally distributed adiposity and obesity are associated with a development of Type 2 diabetes [154, 155]. In certain ethnic groups there is an increasing occurrence of obesity and Type 2 diabetes [156] but also in the general population of the US [52]. However, life style changes inducing weight loss is efficient in reducing the incidence of Type 2 diabetes [47]. General obesity has not been proven as an independent risk factor for CHD. In contrast, measurements of central obesity are strongly associated with CHD [157]. Studies on weight reduction and CHD outcomes are lacking. Physical activity. Physical inactivity is associated with increased risk for Type 2 diabetes [129]. A change of life style including increased physical activity is efficient in reducing the incidence of Type 2 diabetes [47]. However there is little progress in the general population in physical inactivity and an increasing occurrence of obesity and Type 2 diabetes has been observed [52]. Physical inactivity is also associated with increased risk for CHD [158]. Insulin resistance and cardiovascular risk factors are improved with increased leisure time exercise [159]. Microalbuminuria. It is a condition with increased urine excretion albumin rate within the range 30-300 mg/24 hours. It predicts diabetic nephropathy and mortality in Type 2 diabetes [160]. Non-diabetic subjects with a combination of hyperinsulinemia and microalbuminuria have a highly increased risk for CHD [161]. In Type2 diabetes microalbuminuria is a powerful indicator of cardiovascular risk [162]. Antihypertensive treatment reduces albuminuria, delays the progression of diabetic nephropathy, postpones renal failure and improves survival [163]. PAI-1and blood coagulation factors. PAI-1 is associated with the insulin resistance syndrome [53, 164]. It is also a predictor of Type 2 diabetes [165]. PAI-1, being a major inhibitor of fibrinolysis has the capacity of constituting an important factor in the pathogenesis of atherothrombosis preceding the onset of CHD by several years at the population level [97, 98]. Insulin resistance is associated with increased plasma concentrations of the anticoagulatory proteins C and S, which may counteract the concomitantly occurring hypofibrinolysis [166].

17 Inflammation and immunological factors. Chronic inflammation has emerged as a “new” risk factor for the development of Type 2 diabetes. Elevated serum levels of acute-phase proteins, indicating a chronic sub clinical inflammation, have been associated with the insulin resistance syndrome [167]. C-reactive protein is a predictor for Type 2 diabetes [165]. Acute-phase proteins may be of importance for atherosclerosis and CHD [2]. Factors that moderate the changes in atherosclerotic plaque, thrombosis and fibrinolysis, i.e. those with short incubation periods as the inflammation markers, is less estimated than long incubation period conventional risk factors for the importance of CHD [168]. Recent reports show associations between immune responses by circulating antibodies against oxidized LDL-cholesterol and atherosclerosis [169] and also to the insulin resistance syndrome [170]. Free fatty acids. Elevated free fatty acids induce gluconeogenesis in non- diabetic subjects and in patients with Type 2 diabetes [171]. Elevated plasma free fatty acids aggravate the insulin resistant state both in skeletal muscle and in the liver. They have direct vascular effects like promoting endothelial dysfunction [172, 173] and elevating blood pressure [174]. Family history - Genetic factors. A family history of diabetes is positively associated with later development of Type 2 diabetes [132, 154, 175]. A review of genetic studies indicates that inherited insulin resistance is related to mutations in many genes of the insulin-signaling network [112]. A family history of CHD is a stronger predictor for CHD than a family history of Type 2 diabetes [176, 177]. The genetic basis for CHD is heterogeneous [178], as that of Type 2 diabetes [112]. The insulin resistance syndrome. Almost a century ago, one of the first observations on a relationship between diabetes, hypertension and obesity were published [179]. Today these diseases are considered as constituting parts of the insulin resistance syndrome. Gerald Reaven at Stanford University, USA, highlighted the role of insulin resistance in human disease in the Banting lecture in 1988 [180]. Later, the insulin resistance syndrome has been shown to predict CHD in middle-aged men with impaired glucose tolerance [181], in middle-aged healthy men [182] and in elderly men with Type 2 diabetes [54]. A factor analysis of central components of the insulin resistance syndrome produced a single insulin resistance factor that predicted the risk of CHD [182]. Clearly the lack of one widely accepted definition [36, 55, 56] of the insulin resistance syndrome may lead to difficulties in comparing results between studies. The difficulty in defining this syndrome is in part due to the incongruence between a classification based on disease manifestations and phenotypes and the etiologic conceptualisation that cuts across the current disease classifications [183].

18 AIMS OF THE STUDY

In this thesis fasting concentrations of intact and 33-33 split proinsulin, specific insulin, IRI, acute or early insulin response after a glucose load, and insulin sensitivity index at euglycemic insulin clamp are investigated in four prospective studies carried out in a population-based cohort of Uppsala men.

Specific aims were to investigate intact and 32-33 split proinsulin, specific and immunoreactive insulin as predictors: for the development of Type 2 diabetes and to establish the role of AIR in these associations, in middle-aged men (Paper I). for the development of Type 2 diabetes and to establish the role of EIR and the degree of insulin sensitivity in these associations, in elderly men (Paper II). for death from CHD, and further if the predictor by outcome associations affected all cause mortality, in middle-aged men. A secondary aim was to perform a similar analysis for CHD morbidity (Paper III). for CHD, and if such an association was influenced by conventional risk factors, in elderly men. A further aim was to investigate if proinsulin remained a risk marker for CHD independent of degree of insulin sensitivity (Paper IV).

19 METHODS

Subjects This thesis is based on the ULSAM cohort: The Uppsala Longitudinal Study of Adult Men (Figure 2). All men born between 1920 and 1924 and residents in the municipality of Uppsala were invited to participate in a health survey (U-50) carried out between April 1970 and October 1973, at age 50 that aimed at identifying risk factors for cardiovascular disease (CVD) [184]. In all, 2,322 out of 2,841 invited men participated in the survey (82%). The men were invited for re-examinations at age 60 (U-60), carried out between January 1980 and March 1985 (1860, out of 2130 eligible subjects participated) [185], at age 70 (U-70), carried out between August 1991 and May 1995 (1221 out of 1681 eligible men participated) [186] and at age 77 (U-77), carried out between October 1997 and June 2001 (840 out of 1398 eligible men participated). The examinations at U-50 and U-70 were more extensive, the U- 60 and U-77 were more limited.

2841 men in Uppsala born from 1920 to 1924

U-50 2322 participants 519 non-participants

95 deaths, 97 not eligible

U-60 1860 participants 270 non-participants

422 deaths, 219 not eligible

U-70 1221 participants 460 non-participants

748 deaths, 176 not eligible

U-77 839 participants 559 non-participants

813 deaths, Dec 1998

Figure 2. Overview of investigations in the ULSAM cohort. Investigations were performed at ages 50, 60, 70 and 77 years. A comprehensive description of the study is available at http:// www.pubcare.uu.se/ULSAM

Study design and populations The four papers in this thesis are reporting the results from prospective studies performed within the ULSAM cohort. Plasma samples have been stored frozen since U-50 and U-70, allowing us to study proinsulin, using highly specific assays, as a possible predictor at U-50 for the development of Type 2 diabetes and CHD using the follow-up data over 27 years and proinsulin as a possible predictor at U-70 for the development of Type 2 diabetes and CHD using the follow-up data over 7 years. Paper I. The study population is based on the U-50 (baseline) comprising men free from diabetes at U-50 and with information on proinsulin like molecules (PLMs) at U-50 and with follow-up data. This made it possible to investigate baseline data in relation to the development of Type 2 diabetes, diagnosed at U- 60, U-70 and U-77, in all 777, 466 and 348 men respectively. Paper II. The study population is based on the U-70 (baseline) comprising men free from diabetes and with information on PLMs at U-70 and with follow-up data. This made it possible to investigate baseline data in relation to the development of Type 2 diabetes, diagnosed at U-77, in all 667 men. Paper III. The study population is based on the U-50 (baseline) comprising men free from cardiovascular disease and with information on PLMs at U-50 and with follow-up data on cardiovascular diseases according to official registry data available, i.e. until 31 December 1996. The present study was based on subjects with complete data from the baseline investigation, in all 874 men. Paper IV. The study population is based on the U-70 (baseline) comprising of men free from cardiovascular disease and with information on PLMs at U-70 and with follow-up data on CHD according to official registry data available, i.e. until 31 December 1998, in all 986 men, however main analyses were performed in men also free from diabetes at baseline, in all 887 men.

Investigations Blood samples for fasting concentrations at all the investigations described below, were drawn after an overnight fast.

Investigations at age 50 (U-50)

Proinsulin. The concentrations of intact proinsulin and 32-33 split proinsulin were determined using the two-site immunometric assay technique [124], in plasma samples (n= 1306) that had been stored frozen (-70º C) since baseline. Specific insulin. The concentrations of specific insulin were also determined in these samples using the Access Immunoassay System (Sanofi Pasteur

21 Diagnostics, Marmes-la-Coquette, France), which uses a one step chemiluminescent immunoenzymatic assay. Proinsulin and specific insulin analyses were carried out between 1995 and 1998 blinded for outcome, at the Department of Clinical Biochemistry, Addenbrooke’s hospital, Cambridge, UK. Immunoreactive insulin. IRI concentrations were determined with the Phadebas® Insulin Test (Pharmacia AB, Uppsala, Sweden) [184]. Blood glucose. Fasting whole blood glucose concentration was analysed using spectrophotometry and the glucose oxidase method at the department of Clinical chemistry, University hospital Uppsala [184]. Intravenous glucose tolerance test. An IVGTT, using a glucose dose of 0.5 g/kg body weight, was performed in 1792 men at baseline [184]. The serum IRI concentrations during the IVGTT were measured in duplicate blood samples drawn before and at 4 and 6 minutes after the start of the glucose injection. The acute insulin response (AIR) was expressed as the mean value of the serum insulin concentrations determined at 4 and 6 minutes. Blood pressure. BP was measured once on the right arm to the nearest 5 mm Hg in the supine position after 10 minutes rest. Systolic and diastolic blood pressure was defined as Korotkoff Phases I and V, respectively. Serum Lipids. Measurements of serum cholesterol and triglycerides concentrations were performed on a Technicon Auto Analyzer type II. HDL cholesterol was assayed. LDL cholesterol was calculated using Friedewald's formula: LDL=serum cholesterol-HDL-(0.42 x serum triglycerides). The values presented from U-50 are adjusted for enabling comparison with the Monarch method used at U-70. [184, 185]. Anthropometry. Height (without shoes) was measured to the nearest whole cm and weight (in undershorts) to the nearest whole kg. Body mass index (BMI) was calculated as weight (in kg) divided by height (in meters) squared (kg/m2) [184]. Smoking status. Smoking status was established during the baseline interview with one physician, Dr Hans Hedstrand [184]. Questionnaire. Information concerning previous disease, family history and current pharmacological treatment was collected using a medical questionnaire[184], which was used also at U-60, U-70 and U-77.

Investigations at age 60 (U-60)

Blood glucose. Fasting whole blood glucose concentration was analysed in all participants, at U-60 using a glucose oxidase method (GOD-PERID, Boehringer Mannheim GFR) [185].

22 Investigations at age 70 (U-70)

Proinsulin and specific insulin. Concentrations of intact and 32-33 split proinsulin and specific insulin were analysed in plasma samples that had been stored frozen (-70º C) since U-70 using the same methods as described under U- 50 above, carried out blinded for outcome, at the Department of Clinical Biochemistry, Addenbrooke’s Hospital, Cambridge, UK, in 1998. Immunoreactive insulin. Serum IRI concentrations were determined, with the enzymatic immunological assay Enzymmun® (Boehringer Mannheim, Mannheim, Germany) performed in an ES300 automatic analyser (Boehringer Mannheim) [187]. Plasma glucose. Concentrations of plasma glucose were analysed by the glucose dehydrogenase method (Gluc-DH, Merck, Darmstadt, Germany). Oral glucose tolerance test. A 75-gram OGTT was performed and blood samples for plasma glucose and IRI were drawn immediately before, and 30 and 120 min after ingestion of glucose. The early insulin response (EIR) to the oral glucose load was defined as the ratio of the 30 min increment in insulin concentration to the 30 min increment in glucose concentration following oral glucose loading [188]. Euglycemic insulin clamp. A Euglycemic insulin clamp was performed to estimate in vivo sensitivity to insulin with the clamp-technique described by DeFronzo [8], however with insulin (Actrapid Human, Novo) infused at a constant rate of 56 (instead of 40) mU/ /min per body surface area (m2) calculated to achieve nearly complete suppression of hepatic glucose output [189]. The target plasma glucose concentration was 5.1 mmol/l. The glucose disposal (M) was calculated as the amount of glucose (mg) taken up per kilogram of body weight (kg) per minute (min) during the last 60 minutes of the clamp study and is given in mg x kg-1 x min-1. Insulin sensitivity index (M/I) was calculated as glucose disposal rate divided by the mean plasma insulin concentration (mU/l) times 100 during the last 60 minutes of the two-hour clamp and is given in mg x kg-1 x min-1/(100mU/ml). M/I thus represents the amount of glucose metabolised per unit of plasma insulin. The CV was 13.9%. The OGTT and the clamp procedure were separated in time from each other by one week Blood pressure. BP was measured as at the U-50, values was recorded twice and to the nearest even figure and the means of the two values are given for each blood pressure. Hypertension. Hypertension was defined as SBP ≥160 mmHg or DBP ≥95 mmHg at a single office visit, or use of antihypertensive drugs.

23 Serum Lipids. Cholesterol and triglyceride concentrations were analysed using IL Test Cholesterol Trinders's Method and IL Test Enzymatic-colorimetric Method in a Monarch apparatus (Instrumentation Laboratories, Lexington, USA). Anthropometry. Height, body weight and BMI were determined as at U-50. The waist and hip circumferences were measured (cm) in the supine position and the waist/hip ratio was calculated. Smoking status. Smoking status was taken from the questionnaire.

Investigations at age 77 (U-77)

Plasma glucose. Concentrations of plasma glucose were analysed at U-70 by the glucose dehydrogenase method (Gluc-DH, Merck, Darmstadt, Germany).

OUTCOME DEFINITIONS

Type 2 DM, follow-up and outcome Paper I. The outcome variables, described below, were defined using the follow-up data from the second, third and fourth investigations (U-60, U-70 and U-77). Type 2 diabetes was defined according to the World Health Organisation (WHO) criteria from 1999 [36] (using fasting concentrations) and/or use of OHAs. At the first two investigations (U-50 and U-60), fasting blood glucose concentrations were analysed and Type 2 diabetes was thus defined as a fasting blood glucose concentration ≥6.1 mmol/l. At the third and fourth investigations (U-70 and U-77), fasting plasma glucose were analysed and Type 2 diabetes was defined as a fasting plasma glucose concentration ≥7.0 mmol/l. The cumulative incidence of Type 2 diabetes was calculated, from age 50 to age 60 as Type 2 diabetes identified at U-60, from age 50 to age 70 as Type 2 diabetes identified at U-60 or U-70 and from age 50 to age 77 as Type 2 diabetes identified at U-60, U-70 or U-77. Paper II. Type 2 diabetes, at follow-up at U-77, was defined as a fasting plasma glucose ≥ 7.0 mmol/l [36] or the use of oral hypoglycaemic agents.

CHD/CVD outcomes, Official registry data Information concerning mortality and morbidity from incident disease was collected from the official Swedish registries: Cause of Death (CDR), Swedish Cancer (SCR) and In-Patient (IPR) Registries held by The Centre for Epidemiology, National Board of Health and Welfare in Sweden.

24 Paper III. A follow-up study from U-50 where outcome and survival-time variables were defined using the registry data (censor date December 31, 1996). Mortality was defined as death, recorded in the CDR, and morbidity as first time hospitalized, as recorded in the IPR or recording in the CDR or first registration in the SCR, due to the following causes: 1) MI (ICD-9 code: 410), 2) CHD (ICD-9 codes: 410–414), 3) CVD (ICD-9 codes: 390–459), 4) all causes (any ICD-9 code), 5) malignant cancers (ICD-9 codes: 140–208 or 230–239) and 6) all causes excluding CVD. Paper IV. A Follow up study from U-70 where the outcome CHD, was defined using the registry data as death, as recorded in the Cause of Death Registry (CDR), or first time hospitalised for CHD (ICD-9 codes: 410–414, ICD-10 codes I20–I25), as recorded in the In-Patient Registry (IPR), (censor date December 31, 1998).

Statistical analyses The statistical analyses were performed using the statistical software package STATA 5.0 for PC (STATA Corporation, College Station, Texas, USA) (paper III) and STATA 6.0 for PC (paper I, II and IV). All tests were two-tailed and a p-value <0.05 was considered statistically significant. Distribution. The distribution of a continuous variable was tested using Shapiro Wilk’s test. Skewed variables were log transformed to reach normal distribution (w>0.95 assessed using Shapiro Wilk’s test). Normally distributed variables were used in all statistical analyses. Proinsulin, 32-33 split proinsulin, insulin and and glucose concentrations were log transformed to achieve normal distribution. Cross sectional associations. Associations between baseline variables were analysed using Pearson product moment correlations. Group comparisons. The Chi-squared test was used for category variables and Student’s T-test for continuous variables. These tests were mainly used for the evaluation of a potential selection bias for proinsulin determinations at the U-50. Potential differences were investigated in baseline characteristics other than PLMs, between those with and those without their PLMs concentrations determined within the group that developed CHD and within the group that remained free from CHD during follow-up. A similar analysis was performed within the group that developed Type 2 diabetes and within the group that remained free from Type 2 diabetes, during follow-up. Standardization. Continuous variables were standardised to one SD to determine the magnitude of the relationship to, and the statistical significance of the predictors of each of the defined outcomes in the regression analyses.

25 Logistic regression analysis. Logistic regression was used to assess associations between baseline variables and the dichotomous variable Type 2 DM (Papers I and II). Results are presented as odds ratios (ORs) and their 95% confidence intervals (CIs). All analyses were adjusted for age at baseline and length of follow up between investigations. In the multivariate models, analysing fasting intact and 32-33 split proinsulin, specific insulin and IRI (measured at U-50) one by one, adjustments were made for the possible confounding effects of AIR, BMI and fasting glucose as predictors for Type 2 diabetes by multiple logistic regression (Paper I). Further adjustment for insulin was made for the associations between propeptides and Type 2 diabetes. In a similar approach, using multivariate models investigating fasting intact and 32-33 split proinsulin, specific insulin and IRI (measured at U-70) one by one, adjustments were made for EIR, M/I and BMI or WHR, as an indicator of obesity (Paper II). To study possible multiplicative effects of significant predictors, interaction terms between significant predictors of Type 2 diabetes were tested within the multivariate models. Cox’s proportional hazards regression analysis. It was used to assess associations between baseline variables and the dichotomous cardiovascular outcomes (Papers III and IV). Results are presented as hazard ratios (HRs) and their 95%-CI. The magnitude and the statistical significance of the relationships between the predictors were determined for each of the defined outcome variables. In the multivariate models, analysing fasting intact and 32-33 split proinsulin, specific insulin and IRI (measured at U-50) one by one, (Paper III), adjustments were made for the known major risk factors: SBP, LDL/HDL cholesterol ratio and smoking and for the possible confounding effects of BMI, fasting concentrations of blood glucose and serum triglycerides. In a similar approach, analysing M/I, fasting intact and 32-33 split proinsulin, specific insulin and IRI (measured at U-70) one by one (Paper IV), adjustments were made for the following conventional risk factors: Serum cholesterol, smoking, hypertension and BMI. Main analyses were performed excluding subjects with Type 2 diabetes at baseline. In addition separate analyses including subjects with Type 2 diabetes were also performed. Interaction terms between significant predictors of the specified outcomes were tested within the multivariate models. Kaplan-Meier plots were performed to confirm proportionality of hazards.

Approval of Ethics Committee The study was approved of the Ethics Committee, of the Faculty of Medicine at Uppsala University. Informed consent was obtained from all subjects.

26 DISSCUSION OF METHODS

Selection bias. Proinsulin and specific insulin concentrations were analysed in a subset of 1345 subjects (All available, after a freezer failure causing the random loss of samples from the U-50). Full data was possible to obtain in 1306 subjects. We made a comparison, between subjects with proinsulin determinations and those lacking proinsulin determinations. Analyses were performed within the group who subsequently developed Type 2 diabetes or CHD and within the group that remained free from Type 2 diabetes or CHD, according to the other variables studied (IRI, BMI, BP, glucose, triglycerides, cholesterol, smoking status heredity for Type 2 diabetes and CHD). No statistically significant differences were found and we therefore concluded the absence of a selection bias due to the loss of samples. Thus, the subset with determinations of proinsulin at U-50, were considered to be representative of the whole cohort.

Long-term storage effects on proinsulin and specific insulin determinations. As the U-50 samples assayed for PLMs and true insulin were 25 years or more, old we wanted to investigate if there was any indication of whether the absolute values for PLMs or true insulin obtained were influenced by the duration of storage. Frozen storage may affect samples (e.g. degradation, i.e. lowering of concentrations; freeze-drying, i.e. increasing concentrations). All plasma left from the U-50 was used for PLMs and specific insulin determinations and the IRI method used at the U-50 was no longer available. Therefore, we could not reanalyse IRI aiming at studying long-term storage effects on IRI. Instead we performed a comparison between the sum of the three specific peptide measurements from the frozen samples and IRI determinations at baseline using a Bland-Altman plot [190]. Such a plot compares the difference and the mean-value of the compared measurements for each individual. The plot (Figure 3) indicated that the sum of the absolute values of PLMs and specific insulin was not affected in a non-linear way in relation to baseline IRI determinations nor was any range specific trend found for the mean difference to rise or fall with increasing concentrations, r=0.019, p=0.66. If long time storage had influenced the absolute values at all it would have affected all samples in the same manner independently of if the subjects subsequently developed CHD or Type 2 diabetes or not. Further, if absolute values were affected assuming a linear relationship between storage time and effect on absolute values, it would not have affected the variability of the measurements in such a way that it would have diluted any of the observed associations in the statistical analyses.

27 150 • • • • • 100 • • • •• • • • • • • • ••• •••• • •• • • •• ••••••••• • •••• • • • • ••••••••••••••••••••• • • •••••••• • •• 50 •••••••••••••••••••••••••••••••••••••• •• •• •• • • • •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• • •• • • • •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• • •• •• • ••••••••••••·•••••••••••••••••••••••••••••••••••••••••••••••• ••••• • •• • ••• • ••••••••••••••••••••••••••••••••••••••••••••• •••••••••••••• •• •• • •• ••••••••••••••••••••••••••••••••••••••••••••••••••••••• •• • • 0 ••••••••••••••••••••••••••••••••••••••• • • ••• •• •• • • • ••••••••••••••• • •• ••••• • • • • • • • ••• •• •• • • • • • • -50 • • • Difference -100 • -150 • • 0 50 100 150 200 Mean

Figure 3. Bland-Altman-plot. The mean of the sum of specific insulin, intact and 32-33 split proinsulin determinations and baseline IRI determinations at U- 50 plotted versus the difference of the sum of specific determinations and IRI.

However, assuming that for some reason, there was a non-linear relationship between storage time and effect on absolute values, the variability would increase for PLMs and specific insulin determinations and this assumed lower precision in PLMs and specific insulin determinations would dilute the effect- response relationship in the statistical analyses in these cases. We would then have underestimated and not overestimated the observed relationships in papers I and III. Also the sum of the U-70 measurements of PLMs and specific insulin, analysed in plasma stored frozen for about 7 years were compared to IRI determined at the U-70. A Bland-Altman plot [190] did not disclose any non- linear relationships nor any trend for the mean difference to rise or fall with increasing concentrations, paper II, r= -0.038, p=0.28.

Acute and early insulin response. The intravenously administered glucose load at an IVGTT induces a momentaneous increase of insulin release that declines within about 6 to 8 minutes, i.e. the “acute” insulin response. The ingested glucose load at an OGTT however has to be absorbed via the gastrointestinal tract. This slower process will induce a prolonged first phase of the insulin secretion with its peak at about 30 minutes, i.e. the “early” insulin response. Using the IVGTT, the interpretation of the results may differ from those based upon the OGTT as the IVGTT expresses the first phase insulin secretion

28 independent of gut factors e.g. Glucagon Like Peptide-1, Gastric Inhibitory Peptide or gastric emptying. Both AIR and EIR values are, however, point estimates and represent overall integrated measurements of first phase insulin response to a glucose load. Insulin sensitivity. The euglycemic insulin clamp technique is widely regarded as the “gold standard” method to measure insulin sensitivity. The constant insulin infusion is balanced by a variable glucose infusion aiming at keeping or “clamping” the plasma glucose concentration at 5 mmol/l. Using the 56 mU/min per body surface area (m2) dose of the insulin infusion, implies a nearly complete suppression of hepatic glucose output [189]. The amount of exogenous glucose infused to maintain euglycaemia is an estimate of insulin sensitivity, mainly in skeletal muscle. The fasting concentration of insulin is frequently used as a surrogate marker for insulin sensitivity, however the fasting concentration reflect hepatic glucose output during the overnight fasting state, i.e. insulin sensitivity in the liver Mortality and morbidity analyses. The official registries, CDR and IPR are accurate and have a nationwide coverage. No subject was lost to follow-up due to missing registry data. A quality control of the CDR by the Swedish centres of the World Health Organization MONitoring trends and determinants of cardiovascular disease (MONICA) study showed good agreement for registration of MI [191]. CHD morbidity, defined by combining data from the CDR and the IPR, is an efficient, validated alternative to revised hospital discharge notes and death certificates [48]. Epidemiological and statistical methods. The aim of a clinical prospective observational study is to explore physiological phenomena and their relation to disease processes. The prospective character of the studies in this thesis makes it possible to demonstrate if a measured characteristic at baseline is a predictor of future disease or not. If such an association is demonstrated it may however be confounded by other factors. If these factors are known and also are measured, they can be adjusted for. In regression analyses, the change in the dependent factor Y as an effect of a one-unit change in the independent factor X is analysed. In multiple regression analyses further factors X2, X3…Xn can be taken into account. These factors, i.e. confounding factors, can affect the analysis irrespective of if they are believed to take part in the disease process or not. Two types of regression models have been used and will be discussed below. Logistic regression. This type of analysis was used for the outcome variable Type 2 diabetes. As recordings of diabetes on death certificates is not representative of the actual population who die with the disease [192], the CDR is not accurate for the diabetes diagnoses. In addition, most subjects diagnosed with Type 2 diabetes are cared for in outpatient clinics, which implies that they

29 are not recorded in the IPR. Furthermore, the disease onset of Type 2 diabetes is slow and probably up to one third of the subjects with the disease within the population do not yet know their disease status [39]. Therefore, when studying Type 2 diabetes as the outcome, one has to rely on repeated investigations in a sample from the general population [39]. As we do not know an exact date when disease onset occurred, we have to assume that onset date is equal to the date of the reinvestigation. For this reason, we cannot use a time-dependent analysis as the Cox’s proportional hazard regression. Further, when using repeated investigations there will be subjects who are lost to follow-up, generating “missing data” and the number of lost to follow-up will increase with the length of the study. Regression-analyses require “full data”, i.e. missing data is not allowed in the regression calculations. The subjects lost to follow-up however, are probable more prone to disease than the participants and therefore an underestimation of associations observed in the analyses may occur. Cox’s proportional hazards. This type of regression analysis was used for the cardiovascular outcomes. The analysis is a survival analysis also taking time from baseline to disease onset into account, i.e. an ideal analysis to use on registry data. As registry data are considered to be of high quality and was obtained for all subjects in the ULSAM cohort the degree of underestimation may be considered low. However as the follow-up gets longer and subjects reach higher ages a diluting effect on the precision of diagnoses may occur caused both by the difficulties to demarcate a correct diagnosis from the ageing process itself, at higher ages and that the autopsy frequency in Sweden has decreased over time.

30 RESULTS

Paper I The cumulative incidence of Type 2 diabetes, during the course of the study was 5.8% (106/1834) from age 50 to 60; 19.9% (252/1263) from age 50 to 70 and 35.2% (340/967) from age 50 to 77. Corresponding numbers for the subset with proinsulin determinations were 6.1% (48/777), 18.9% (88/466) and 33.0% (115/348). Fasting intact and 32-33 split proinsulin, specific and immunoreactive insulin, as well as AIR, BMI and fasting blood glucose concentrations were all significantly associated with development of Type 2 diabetes over the 27-year follow-up period. Intact proinsulin and 32-33 split proinsulin showed stronger associations with the development of Type 2 diabetes than did specific insulin and IRI. A high AIR was associated with a smaller risk for development of Type 2 diabetes (Figure 4).

) )

% % (

( 40

e e c

c 30

n n

e e

d d i

i 20

c c

n n I I 10

0 Intact 32-33 split Specific AIR proinsulin proinsulin insulin

Figure 4. Cumulative incidence of Type 2 diabetes over 27 years of follow-up, by tertiles of intact and 32-33 split proinsulin and specific insulin, acute insulin response (AIR) at an IVGTT. Filled columns represent the highest tertiles and unfilled columns represent the lowest tertiles.

When adjusted for confounding effects of BMI and fasting glucose concentrations, only intact proinsulin and 32-33 split proinsulin showed a congruent pattern, as they were associated with the development of Type 2 diabetes whereas specific insulin did not. Further adjustment for AIR did not alter these results (Figure 5, table 1)

31 Proinsulin

32-33 split proinsulin

Insulin

0.7 1.0 2.0 3.0

Figure 5. ORs with 95% CIs for Type 2 diabetes over 27 years of follow-up for a one SD increase in intact and 32-33 split proinsulin and specific insulin.

Table 1. Arithmetic means ± SD, and ORs with 95% CIs for the development of Type 2 diabetes over 27 years of follow-up Mean±SD ORs (95%-CI) Model a) 32-33 split proinsulin (pmol/l) 6.9±6.6 1.70 (1.20-2.39) AIR (pmol/l) 416±265 0.50 (0.37-0.69) BMI (kg/m2) 24.9±3.1 2.04 (1.43-2.93) Fasting blood glucose (mmol/l) 4.9±0.5 1.48 (1.09-2.00)

Model b) adjusted as model a) Proinsulin (pmol/l) 2.9±2.6 1.57 (1.16-2.14) AIR, acute insulin response at an IVGTT; BMI, body mass index

A high incidence of 77% for Type 2 diabetes was revealed in the group of subjects defined by the high tertile of proinsulin and the low tertile of AIR, illustrating a multiplicative effect of a concurrent high proinsulin concentration and a low AIR (figure 6). The relative risk for Type 2 diabetes in the group defined by the high tertile of proinsulin and the low tertile of AIR in comparison with the contrast group defined by the low tertile of proinsulin and the high tertile of AIR was 9.23 (95%-CI, 6.49-11.97).

AIR Proinsulin

Figure 6. Cumulative incidence of Type 2 diabetes over 27 years of follow-up, by tertiles of proinsulin concentrations and tertiles of AIR. For tertile limits, see paper I.

Concluding remarks, Paper I We conclude that proinsulin was a strong and a highly statistically significant long-term predictor of the development of Type 2 diabetes in middle-aged men, whereas insulin was not. Further, proinsulin was predicting Type 2 diabetes particularly strong in subjects with a low acute insulin response.

33 Paper II

The incidence of Type 2 diabetes was 13.1% (87/667) from age 70 to 77. Fasting intact and 32-33 split proinsulin, specific insulin and IRI, were significantly associated with development of Type 2 diabetes. Insulin sensitivity index (M/I), but not the crude EIR unadjusted for insulin sensitivity, was associated with conversion to Type 2 diabetes, whereas EIR was associated with Type 2 diabetes, when adjusted for M/I (Figure 7).

)

% 15

(

e 10

n I 5

0 Intact 32-33 split Specific EIR M/I proinsulin proinsulin insulin

Figure 7. Incidence of Type 2 diabetes over 7 years of follow-up from age 70, by tertiles of EIR, M/I and fasting intact and 32-33 split proinsulin and specific insulin concentrations. Filled columns represent the highest tertiles and unfilled columns represent the lowest tertiles.

M/I and EIR were independent predictors of the development of Type 2 diabetes (Table 2a). When 32-33 split proinsulin (Table 2b) or intact proinsulin (Table 2c), one at the time, were added to the model including M/I and EIR, both were significantly associated with development of Type 2 diabetes, whereas specific insulin was not, p=0.409 (Figure 8).

34 Proinsulin

32-33 split proinsulin

Insulin

0.7 1.0 2.0

Figure 8. ORs with 95% CIs for Type 2 diabetes over a 7-year follow-up for a one SD increase in intact and 32-33 split proinsulin and specific insulin at U-70.

Table 2. Arithmetic means ± SD, and ORs with 95% CIs for the development of Type 2 diabetes over 7 years of follow-up Mean±SD Odds Ratio (95% CI) p Model a) EIR (pmol / mmol) 28.6±20.5 0.73 (0.60-0.90) 0.003 M/I (mg x min-1x kg-1/ (100mU/l)) 5.4±2.4 0.55 (0.43-0.70) 0.001 Model b) 32-33 Split proinsulin (pmol/l) 9.3±9.2 1.49 (1.18-1.88) 0.001 EIR 0.71 (0.58-0.87) 0.001 M/I 0.68 (0.53-0.88) 0.004 Model c) Intact proinsulin (pmol/l) 7.0±5.2 1.30 (1.04-1.63) 0.023 EIR 0.72 (0.59-0.89) 0.002 M/I 0.65 (0.50-0.83) 0.001 EIR, early insuline response at an OGTT; M/I, insulin sensitivity index at euglycemic insulin clamp.

35 There was a multiplicative effect of a concurrent high 32-33 split proinsulin concentration and a low M/I, respectively a low EIR, as a high incidence of Type 2 diabetes was revealed in both cases (Figure 9 a-b).

A B Incidence (%)

32-33 Split EIR 32-33 Split M/I proinsulin proinsulin

Figure 9 a-b. Incidence of Type 2 diabetes for the 7-year follow-up by tertiles of 32-33 split proinsulin concentrations and tertiles of EIR (a) and tertiles of M/I (b). T denotes tertiles. For tertile limits, see paper II.

Concluding remarks, Paper II We conclude that proinsulin is a strong and a highly statistically significant predictor of the development of Type 2 diabetes in elderly men, independent of insulin resistance and early insulin response whereas specific insulin is not.

36 Paper III

During the 26.7 years of follow-up, thirty-three percent of the men in the highest tertile of proinsulin died from CHD during the follow-up (Figure 10).

100 95 90 T 1 T 2 85 80 75 Survival (%) 70 T 3 65 60 0 5 10 15 20 25 30 Time (years)

Figure 10. Kaplan-Meier survival curves for death from CHD, by tertiles of proinsulin concentration at baseline. For tertile limits, see paper II.

Intact proinsulin showed the strongest relationship to death from CHD, and specific insulin showed the weakest. The association between intact proinsulin and death from CHD was only slightly reduced when adjusted for: Age, BMI, SBP, smoking status, fasting blood glucose concentration, serum triglycerides and LDL/HDL cholesterol ratio (Table 3), whereas in contrast the corresponding associations for 32-33 split proinsulin and specific insulin with death from CHD turned non-significant (Figure 11). After adjustment for confounders, the relationship between intact proinsulin and death from MI was the strongest, whereas in descending order, relationships were weaker between intact proinsulin and death from CHD, CVD or all cause mortality. This suggests that the association between intact proinsulin and all cause mortality is mainly dependent on the relationship between intact proinsulin and death from MI and CHD. Conventional risk factors, smoking, blood pressure and the LDL/HDL cholesterol ratio were significant predictors of death from MI, CHD and CVD in all analyses. Results for CHD-morbidity were similar to the mortality analyses described above.

37 Proinsulin

32-33 split proinsulin

Insulin

0.7 1.0 2.0

Figure 11. HRs with 95% CIs for death from CHD for 27 years of follow-up, for a one SD increase in intact and 32-33 split proinsulin and specific insulin.

Table 3. Arithmetic means ± SD, and HRs with 95% CIs for fatal CHD (n=107) over the 26.7-year follow-up period Mean ± SD HR (95% CI) (N=874) Intact proinsulin (pmol/l) 3.0±3.2 1.47 (1.18-1.82) Systolic blood pressure (mm Hg) 132±17 1.38 (1.14-1.66) LDL/HDL cholesterol Ratio 4.3±1.9 1.31 (1.12-1.53) Cigarette smoking (%) 53 1.57 (1.03-2.38) LDL, Light-density lipoprotein; HDL, High-density lipoprotein

Concluding remarks, Paper III We conclude that proinsulin is a strong and highly significant predictor of fatal CHD independent of the conventional risk factors smoking, elevated blood pressure and serum cholesterol concentrations. The increased risk for a fatal CHD associated with proinsulin seems to be restricted to the third of the population with the highest proinsulin concentrations.

38 Paper IV

Type 2 diabetes compared with normal glucose tolerance (NGT), showed a relative risk of 1.95 for CHD (p=0.001). There was no significant difference in the CHD incidence between subjects with IGT or NGT (p=0.36), nor was fasting glucose associated with a subsequent CHD in the non-diabetic population. Main analyses were performed in the non-diabetic population.

M/I, intact and 32-33 split proinsulin, specific insulin, IRI, serum cholesterol and BMI were all associated with a subsequent CHD event in the univariate analyses. The continuous variables SBP and DBP were not significantly associated to CHD, whereas the diagnosis of hypertension was. EIR was not a predictor of CHD.

The risk of CHD was higher for men in the lowest tertile compared to men in the highest tertile of M/I (p<0.001) and that the risk of CHD was higher for men in the highest tertile compared to men in the lowest tertile of proinsulin concentrations (p<0.001, Figure 12).

A B

100 100

90 T 3 90 T 1 T 2 T 2 80 80 T 3 70 T 1 70 from(%) CHD Percentage free 60 60 0246 8 02 4 6 8 Time (years) Time (years)

Figure 12. Kaplan-Meier survival curves showing subsequent CHD events during 7.5 years of follow-up, (A) by tertiles of insulin sensitivity (M/I) and (B) by tertiles of plasma proinsulin at baseline. For tertile limits, see paper III.

The association between M/I and CHD adjusted for serum cholesterol and smoking, showed that a 1 SD increase in M/I decreased the risk for a CHD event by 31%. The association remained significant also after further adjustment for hypertension (Table 4). When proinsulin was added to the multivariate models described in table 4a, it predicted CHD independent of cholesterol, smoking and hypertension. The association between M/I and CHD became non-significant when proinsulin was

39 present in the models (Table 4b). Further adjustment for BMI did not alter the results. In contrast to the relationship between intact proinsulin and CHD, corresponding analyses of the relationships between 32-33 split proinsulin, specific insulin, IRI and CHD were non-significant (Figure 13), when adjusted as above.

Proinsulin

32-33 split proinsulin

Insulin

0.7 1.0 2.0

Figure 13. HRs with 95% CIs for subsequent CHD for 7 years of follow-up, for a one SD increase in intact and 32-33 split proinsulin and specific insulin.

Table 4. Multivariate HRs for CHD (CHD) over the 7.5-year follow up, with insulin sensitivity (M/I) included in the model (n=852). Mean, SD HRs (95% CI) p-value Model a) M/I (mg x min-1x kg-1/ (100mU/l)) 5.4±2.5 0.81 (0.67-0.98) 0.034 Total cholesterol (mmol/l) 5.8±1.0 1.23 (1.04-1.46) 0.016 Smoking (%, n) 20.9 (173) 1.41 (0.95-2.10) 0.091 Hypertension (%, n) 41.3 (352) 2.06 (1.43-2.97) 0.001 Model b) adjusted as Model a Intact proinsulin (pmol/l) 7.1±5.4 1.29 (1.04-1.60) 0.021 M/I 0.92 (0.74-1.14) 0.451 M/I, insulin sensitivity at euglycemic insulin clamp

A high incidence of CHD was revealed in the highest tertile of plasma proinsulin concentration in all three tertiles of M/I, indicating an association between proinsulin and CHD, independent of insulin resistance (Figure 14).

A B Incidence (%)

M/IProinsulin M/I Proinsulin

Figure 14. Incidence of CHD over 7.5 years of follow-up, by tertiles of proinsulin concentrations and tertiles of M/I. For tertile limits, see paper IV. The cross sectional association with M/I was stronger for proinsulin (r= -0.51, p<0.001) and for insulin (r= -0.62, p<0.001) compared to the association between EIR and proinsulin (r=0.21, p<0.001) and between EIR and insulin (r=0.36, p<0.001).

Concluding remarks, Paper IV We conclude that insulin sensitivity determined by the euglycemic insulin clamp in elderly men is a predictor of CHD independent of conventional major cardiovascular risk factors. Further, in elderly men, proinsulin is a predictor of CHD independent of conventional major cardiovascular risk factors and of insulin sensitivity.

41 DISCUSSION

In these four prospective studies, perfomed in a population based cohort of Uppsala men, proinsulin and specific insulin were investigated in a comparative manner as predictors of Type 2 diabetes and of CHD, also taking conventional risk factors into account. The studies investigated proinsulin and insulin, as predictors, both in middle aged and in elderly men.

Proinsulin as a predictor for Type 2 diabetes Both fasting 32-33 split proinsulin and intact proinsulin predicted development of Type 2 diabetes, over a 10-year, 20-year and a 27-year follow-up in middle- aged men (paper I) and over a 7-year follow-up in elderly men (paper II), confirming results from some earlier studies with follow-up times less than 7 years [26-28, 193-195]. A concurrent high proinsulin and low AIR were found to interact multiplicatively as predictors, for the development of Type 2 diabetes. Both abnormalities present generated a very high risk of 77% risk of developing Type 2 diabetes (Paper I). A high proinsulin and a low AIR mirror different aspects of beta-cell dysfunction, given the reservation that the association between proinsulin and insulin resistance was not taken into account. Both 32-33 split proinsulin and intact proinsulin, significantly predicted development of Type 2 diabetes also after adjustments for early insulin response (EIR) to oral glucose and insulin sensitivity index (M/I), determined by euglycemic insulin clamp (paper II). This is the first study to investigate propeptide concentrations as predictors for Type 2 diabetes after adjustment for insulin sensitivity determined by the euglycemic insulin clamp. The incidence of Type 2 diabetes is higher in the elderly. The importance of propeptides may differ compared to middle age. We have established that also in elderly men, proinsulin predicted development of Type 2 diabetes, in agreement with previous studies in middle-aged non-diabetic subjects [26-29, 194, 195] and one earlier study in elderly subjects with a shorter follow-up [193]. Specific insulin was not a significant long-term predictor, in middle-aged men, in the multivariate analyses (paper I) nor in elderly after adjustment for insulin sensitivity (paper II). Insulin sensitivity index itself, was a significant predictor for Type 2 diabetes, concluding that insulin resistance and not compensatory hyperinsulinemia is a provocateur of diabetes development. The associations between 32-33 split proinsulin, intact proinsulin and specific insulin, in descending order by their odds ratios, and the development of Type 2 diabetes, are consistent with results from earlier studies [193-195]. However, the

42 differences in the ORs for intact and 32-33 split proinsulin were modest and the dividing line lies between the propeptides and specific insulin as it is not the plasma insulin concentrations per se, but an increase in concentrations of its precursors that constitutes the association with Type 2 diabetes. Thus, immunoreactive insulin assays, cross-reacting with proinsulin, overestimate the strength of the association between insulin, and development of Type 2 diabetes. Some selection by death has occurred over this long follow-up (paper I) and may have weakened the relationship between proinsulin and development of Type 2 diabetes. Proinsulin [196], like Type 2 diabetes [197], is a predictor of cardiovascular death whereby a number of subjects in the higher range of plasma proinsulin will probably have been lost to follow up. Subjects with the insulin resistance syndrome [12] from the original cohort examined in 1970-73 are likely to have escaped follow up at age 70 due to cardiovascular death. Thus, the observed association between insulin resistance, proinsulin and Type 2 diabetes (paper II) is probably underestimated in a selection of survivors, examined at age 70.

Proinsulin and impaired beta cell function In cross sectional studies proinsulin, or the proinsulin to insulin ratio has been considered as a marker for impaired beta cell function [198, 199]. Both proinsulin and AIR (paper I) or EIR (paper II) were independent predictors of type 2 diabetes in the multivariate analyses. Hyperproinsulinemia can be a sign of a pancreatic beta-cell function defect also in elderly, that is augmented by an increased demand placed on the beta cell by hyperglycemia [200] or an insulin resistant state resulting in secretion of immature proinsulin-rich granules in response to an increased demand for insulin [123]. Increased secretion of proinsulin was found to be associated with beta-cell dysfunction [91, 201-204]. Low insulin secretion was associated with disproportionately high proinsulin concentrations [205]. The proinsulin to insulin ratio was not a significant predictor for Type 2 diabetes in our study, consistent with most previous reports [27, 28, 194, 195]. To adjust the associations between proinsulin and development of Type 2 diabetes for insulin resistance, using a direct measurement (paper II) is a better way to control for insulin resistance than using the proinsulin to insulin ratio. Further, 32-33 split proinsulin discriminated best between insulin resistance and insulin secretion [188] in non-diabetic subjects.

Proinsulin and insulin resistance Elevated plasma proinsulin, predicts onset of Type 2 diabetes by up to 27 years, independent of BMI (paper I). The waist to hip ratio may be used as an index of central obesity but such data were not collected at baseline (paper I). Adjustment

43 for BMI or WHR, did not significantly affect any of the observed associations in the multivariate models (paper II), between proinsulin and development of Type 2 diabetes, which is supported by the finding that hyperproinsulinemia is not explained by obesity-associated insulin resistance in subjects with Type 2 diabetes [206]. The relationship between specific insulin and development of Type 2 diabetes was borderline-significant when adjusted for BMI, which is consistent with previous reports [27, 28]. Adjustment for IGT (paper III) had no major impact on the observed associations between the propeptides and progression to Type 2 diabetes. However, adjustment for BMI and IGT may provide “overadjusted” estimates of relative risk, as both are associated with insulin resistance. Proinsulin concentrations are already elevated in the IGT state [207] and that proinsulin was a predictor of Type 2 diabetes independent of IGT, indicate that hyperproinsulinemia foregoes the prediabetic state of IGT. Both proinsulin and insulin sensitivity index were independent predictors of Type 2 diabetes (paper III). Insulin resistance contributes to a functional stress on the beta-cell function. Interpretation of beta-cell function in the context of the degree of insulin sensitivity is critical to defining the importance of defects in beta-cell secretory function to progressive impairments of glucose tolerance [208]. Thus, one cannot entirely separate a pancreatic beta-cell defect from the degree of insulin sensitivity when using proinsulin, as a predictor of worsened glucose tolerance, without comparing it to direct measurements of these two characteristics.

Proinsulin as an integrated marker for beta cell function and insulin sensitivity Early signs of failing beta cell compensation and insulin resistance, independently, precede the onset of Type 2 diabetes in the elderly (paper II) as at younger age [23]. Proinsulin predicted Type 2 diabetes independent of these two characteristics indicating that it may mirror some other beta-cell defect predisposing to Type 2 diabetes not expressed by the EIR, nor influenced by insulin resistance. In terms of being a predictor used alone, for the development of Type 2 diabetes, fasting proinsulin could be considered as a measurement of insulin secretory dysfunction influenced by both the degree of insulin sensitivity and of a pancreatic beta-cell defect.

Precision of risk marker measurements Intact proinsulin was a stronger predictor for Type 2 diabetes and for CHD than specific insulin was. The half-life of proinsulin is considerably longer than that of insulin [83, 84, 124]. Point estimates of insulin concentrations therefore may be more affected by the oscillations in insulin secretion [108]. Proinsulin is subject to smaller fluctuations leading to lower intraindividual variation in

44 proinsulin than in insulin measurements. Therefore the lower precision of a point estimate of the continuously varying plasma concentrations of insulin compared to proinsulin may favour proinsulin in comparison with specific insulin in a regression analysis. Proinsulin may thus have a better predictive capacity than insulin, assuming that both have the same effect on the outcome. Proinsulin may therefore turn out be more appropriate than specific insulin for estimation of future cardiovascular risk and risk for Type 2 diabetes.

Proinsulin as a predictor of coronary heart disease

In middle-aged men (paper III), proinsulin independently predicted death from MI, CHD or CVD over a period of up to 26.7 years, also after adjustments for smoking, blood pressure, LDL/HDL cholesterol ratio, BMI, fasting blood glucose and triglycerides. The association between proinsulin and the cardiovascular outcomes also influenced the all cause mortality. Furthermore, proinsulin showed an association with morbidity caused by the cardiovascular outcomes, whereas the associations between both specific insulin and immunoreactive insulin and these outcomes were not independent of the confounders mentioned above. In elderly men (paper IV), a low insulin sensitivity index [8] predicted subsequent CHD over a 7-year follow-up period. It suggests that insulin resistance makes a major contribution to mortality and morbidity also in the elderly, independent of serum cholesterol, smoking and hypertension. In contrast the findings from the same type of analysis of specific or immunoreactive insulin, commonly used as surrogate markers for insulin sensitivity, were non- significant. Like insulin-mediated glucose uptake, serum proinsulin was independent of the conventional risk factors above and also independent of insulin resistance in predicting CHD (paper IV). At age 70, subjects with the insulin resistance syndrome [12] when examined in 1970-73 are likely to have escaped follow-up due to cardiovascular death. Thus, the observed associations between insulin resistance, proinsulin and CHD are probably underestimated in a selection of survivors in comparison with middle- aged subjects (paper IV). The observation that a decreased insulin-mediated glucose disposal, in contrast to specific insulin concentrations, predicted subsequent CHD, indicate that the link between atherosclerosis and insulin resistance is more associated with impaired insulin action than with the compensatory increase of plasma insulin, which has been suggested to be atherogenic [209]. The weak association between plasma insulin and CHD is supported by evidence from 25 prospective epidemiological studies [13] and from the UKPDS [210] and the association between immunoreactive insulin and CHD was modified by insulin assay used [14].

45 Why is proinsulin a better risk marker for coronary heart disease than insulin is? In analogy with the discussion above, on the higher stability of a point estimate that is favoured in regression analyses, proinsulin may contribute to the better predictive capacity for CHD, in comparison with specific insulin. Proinsulin may therefore turn out be more appropriate than specific insulin for estimation of future cardiovascular risk.

Comorbidity between type 2 diabetes and coronary heart disease The association between immunoreactive plasma insulin and CHD may have been confounded by comorbidity in earlier cohort studies [58]. In order to circumvent such a possibility (paper III and IV), subjects with baseline ECG suggestive of CHD, prevalent cardiovascular disease up to one year from baseline were excluded from our analyses. Since subjects with Type 2 diabetes, who could be expected to have elevated plasma concentrations of proinsulin [203] were included (paper III), the multivariate models were adjusted for fasting glucose concentrations. As proinsulin predicts the development of Type 2 diabetes we performed a separate analysis of those subjects who remained normoglycemic during the course of the study (prevalent and incident diabetes excluded, paper III), in which the relationships between proinsulin and the defined outcomes remained significant. Type 2 diabetes was associated with CHD, whereas IGT was not (paper IV), consistent with previous findings in elderly subjects in Finland [15]. Subjects with diabetes share the combination of an increased risk for CHD [197] and elevated plasma proinsulin [203]. By exclusion of prevalent diabetes at baseline in our main analysis, we avoided confounding by comorbidity of the relationship between proinsulin and CHD. The present demonstration that elevated serum proinsulin is a predictor for CHD also in the elderly non-diabetic population, extends our previous report in middle-aged men, paper I [196] and one report on proinsulin as a predictor of CHD in middle-aged subjects in Britain [211].

Are proinsulin or insulin causal factors for CHD? - Quality of the Evidence Insulin sensitivity measurements have been considered impractical in large-scale cohort studies. Insulin, most often measured as immunoreactive insulin has served as a surrogate measurement of insulin sensitivity. This is less satisfactory as there is a substantially weaker correlation between insulin sensitivity determined by euglycemic insulin clamp and fasting insulin in subjects with higher risk for CHD, i.e. subjects with IGT and diabetes, as compared to normoglycemic subjects [10].

46 Criteria for causality of epidemiological associations

Consistency. The studies published on insulin as a predictor for CHD are not consistent, and univariate associations attenuate in multivariate analyses [13]. The role of IRI as a risk factor for CHD has been controversial [11, 12, 212, 213]. The observation that a decreased insulin-mediated glucose disposal (paper IV), in contrast to specific insulin concentrations, predicted subsequent CHD indicate that the link between atherosclerosis and insulin resistance is more associated with impaired insulin action than with the compensatory increase of plasma insulin. Proinsulin constitutes a larger proportion of the IRI measured after a glucose load compared to the fasting state [124]. The IRI concentrations after an oral carbohydrate intake may therefore represent the contribution of proinsulin to cardiovascular risk better than fasting IRI. In good agreement with this suggestion and our findings (paper I), the area under the curve, as opposed to the fasting concentrations of IRI, was significantly related to CHD [17] and all- cause mortality [214] in a 22-year follow-up of Finnish men. The two published cohort studies comparing proinsulin and insulin, as a predictors for CHD [196, 211] and results in paper IV, are congruent. Proinsulin was a predictor for CHD, also after adjustment for conventional risk factors whereas specific insulin was not. A lag-phase of five years between the elevated plasma proinsulin and the increased risk of CHD was observed in paper III on middle aged subjects, which may explain why two short-term case-control studies failed to reveal the association [30, 215]. Strength of associations. There was an average relative risk of 1.18 (95% CI, 1.08-1.29) for IRI as a predictor for CHD, in a meta-analysis of 12 published studies [14]. A relative risk between 1.0 and 2.0 may be considered modest to moderately strong however the implication regarding the absolute number of diseased, is dependent on the prevalence of the disease. The hazard of 1.47 (95% CI, 1.18-1.82), adjusted for conventional risk factors, paper III [196] and the odds of 1.54 (95% CI, 1.07-2.20), adjusted for conventional risk factors, in a British study [211] and the hazard of 1.29 (95% CI, 1.04-1.60), adjusted for conventional risk factors and insulin sensitivity index (paper IV) for the association between proinsulin and CHD showed the risk to be moderate. However, the risk estimates for proinsulin were of about the same magnitude as for smoking, cholesterol or hypertension in the multivariate models. Dose response relationship. One would expect that the higher the concentration of insulin or proinsulin or degree of insulin resistance is, the greater the risk for CHD. Most studies of insulin as a predictor for CHD, reporting an existing association, did show this except one, which reported a U-shaped relationship [216]. The studies of proinsulin as a predictor for CHD [196, 211] (paper IV)

47 did not show U-shaped associations, neither did insulin sensitivity index as a predictor for CHD (paper IV). Temporality. The referred studies are prospective to their nature, i.e. the increased insulin or proinsulin concentrations and the insulin sensitivity index were determined before the onset of CHD. The three cohort studies on proinsulin as a predictor for CHD comply with this criterion. Also the first study on direct measurements of insulin resistance and incidence of CHD, comply with this criterion (paper IV). Specificity. Insulin does not seem to be a highly specific predictor for CHD [13, 210]. Proinsulin is a specific predictor for CHD, independent of conventional risk factors in three cohort studies [196, 211] (paper IV). The only study on insulin sensitivity measured by the euglycemic insulin clamp showed the insulin sensitivity index to be a specific predictor for CHD, independent of conventional risk factors. Biological plausibility. Insulin resistance and atherosclerosis are associated [9]. The defective insulin action, assessed by the euglycemic insulin clamp, is associated, more strongly than plasma insulin, with the well-established cluster of abnormalities in the insulin resistance syndrome [12]. Also associated with insulin resistance are elevated plasma free fatty acids, which aggravate the insulin resistant state and have direct vascular effects like promoting endothelial dysfunction [172, 173] and elevation of blood pressure [174]. The link between atherosclerosis and insulin resistance was more clearly associated with impaired insulin action than with the compensatory increase of plasma insulin (paper IV). Alternative abnormalities, in promoting an increased risk of CHD are the minor elevations in plasma glucose [217] that followed impaired insulin action, underlying the hypersecretion of insulin and proinsulin. Proinsulin concentrations were associated with coronary artery atherosclerosis, in angiography studies in non-diabetic subjects [218] and with increased intimal media thickness in the common carotid artery [219]. In clinical trials with human proinsulin a several-fold increase in cardiovascular events in comparison with human insulin occurred [79], which suggested that thromboembolic mechanisms had been activated [79]. Proinsulin stimulates production of PAI-1 [95, 96], and may be a putative link between atherothrombosis and elevated proinsulin concentrations preceding CHD [97, 98]. The association between proinsulin and atherosclerosis in the common carotid artery also became attenuated by adjustment for PAI-1 [219]. Plasma proinsulin has stronger associations than insulin with hypertension, dyslipidemia and impaired glucose tolerance [207]. Fasting proinsulin was more strongly correlated to measurements of insulin resistance than to measurements of EIR, similar to the pattern of fasting specific insulin [188, 199]. Thus, rather

48 than being directly associated with its cause, proinsulin may be a marker of an underlying metabolic disturbance predisposing to atherosclerosis [215]. The indirect effects of insulin resistance on CHD, mediated via the risk factors associated with the insulin resistance syndrome are considered the strongest pieces of evidence of the association between insulin resistance and CHD. Proinsulin concentrations increase with insulin resistance in non-diabetic subjects. Some links between possible atherogenic mechanisms and proinsulin have been described, but data so far available is insufficient to provide a causal link between elevated plasma proinsulin and CHD. The proinsulin and insulin concentrations are measured in the fasting state and can be envisaged to reflect hepatic insulin resistance to a greater extent than insulin-mediated glucose uptake during a euglycemic insulin clamp. The latter mainly measures skeletal muscle glucose uptake at an insulin level, at which hepatic glucose production is suppressed in the majority of subjects. Proinsulin, as a marker of insulin resistance and insulin sensitivity index may therefore partly assess different aspects of insulin resistance, which may explain that proinsulin turned out to be independent of insulin sensitivity index as a predictor of CHD. Reversibility. It would be considered the strongest evidence if reversed insulin resistance or hyperproinsulinemia would be associated with a decreased risk for CHD. No such trials have been feasible, nor have any results been published. The reversing of insulin resistance or hyperproinsulinemia with thiazolidine- diones in Type 2 diabetes, thereby investigating the effect on cardiovascular disease outcomes is now ongoing for both pioglitazone and rosiglitazone. The results on cardiovascular outcomes will be available in about six to eight years from now. In these trials it will be possible to investigate the effect of treatment on insulin and proinsulin concentrations in relation to hard outcomes. The only existing evidence from experimental studies on humans available today, are not studies on reversing insulin resistance or proinsulin concentrations but on the contrary increasing the latter. Several clinical trials using proinsulin therapeutically for Type 2 diabetes were launched but they all were suspended due to side effects consisting of a several-fold higher incidence of CHD in the proinsulin treatment groups [79]. Even with these data in mind we have used the term predictor for proinsulin, equivalent to risk marker, instead of risk factor when describing the observed associations in this study in the same manner as others have [30, 31, 196, 211], since an intervention trial that proves a causative role is lacking.

49 Clinical relevance – screening? Screening is presumptive identification of unrecognized disease by the application of tests or other procedures. It is performed without a patient request for specific complaints, and therefore is connected with the need for special ethical considerations. It might imply that individuals that consider themselves as being healthy are identified having a disorder that has not yet been clinically apparent. Screening in larger groups should therefore be used only if it significantly contributes to the health of individuals and it should not be used to identify conditions that are not treatable at present. From a clinical perspective there is little evidence for measuring specific insulin concentrations to help identify subjects at high risk for CHD however, by measuring proinsulin in subjects with other aspects of the insulin resistance syndrome, i.e. hypertension, IGT, dyslipidemia and central obesity extra prognostic information will be obtained in identifying subjects that are at higher risk for CHD. If we, for some reason, know the proinsulin concentration to be high, i.e. a high risk for Type 2 diabetes and CHD, in a subject with IGT, the data available today [45-47] would suggest that the patient should be encouraged to participate in a lifestyle intervention program aiming at preventing a progressive deterioration of the glucose tolerance. However, the lifestyle intervention programs [45-47] did not measure proinsulin. The methods used for proinsulin measurements in this thesis have been validated with HPLC [125]. However, the different proinsulin methods available worldwide need to be standardized in the same manner as insulin methods are, using quality control standards, in accredited laboratories. However, if proinsulin is to be used as a screening instrument we need to have a therapeutic arsenal available, i.e. agents or therapies to use in lowering proinsulin concentrations that are proven to be beneficial in terms of lowering hard outcomes in a clinical trial. We presently lack such data.

Limitations Diagnostic criteria. The diagnostic criteria for Type 2 diabetes have varied during the period corresponding to the follow-up time of the studies reported in this thesis. We have adapted to the fasting criterion, for the diagnosis of diabetes adopted by both ADA and WHO since it allowed us to use data from all investigations performed in the cohort, minimizing selection bias due to the choice of methods at different times. The current diagnostic criteria for diabetes, based on the 2-hour glucose concentration after an OGTT or on the fasting glucose concentration do not fully match [37]. Subjects fulfilling the 2-hour criterion may be classified as non-diabetic based on the fasting glucose concentrations. As subjects were identified by fasting concentrations and the use of OHAs, this may have attenuated the observed associations between insulin

50 propeptides and Type 2 diabetes rather than overestimated them. The results for prediction of Type 2 diabetes were essentially identical when diabetes was diagnosed at age 70 based on the 2-hour glucose concentration at the OGTT and when it was diagnosed on the fasting glucose concentration. Studies on men only, results applicable on women? The studies on proinsulin and risk for Type 2 diabetes in this thesis contain men only. However, the previous studies in middle-aged subjects with shorter follow-up periods have shown essentially the same findings for both men and women. In elderly non- diabetic men and women, proinsulin was more strongly consistently associated with Type 2 DM [193] than was insulin. If proinsulin as a predictor of CHD risk should be applied as a risk factor in women cannot be answered by paper II and IV or the British study [211] as these three studies have been performed in men only. In a cross sectional study of elderly non-diabetic subjects both men and women, proinsulin was more strongly and consistently associated with CHD than specific insulin [220]. Obviously studies on insulin resistance and proinsulin concentrations in the future should cover both sexes. Lack of markers for inflammation and immune responses. Markers for inflammation has been shown to be associated with Type 2 diabetes [165] and CHD [2, 168]. There are also reports on associations between immune responses like circulating antibodies against oxidized LDL-cholesterol, atherosclerosis [169] and the insulin resistance syndrome [170]. Analyses of frozen samples, adding such data to the to the ULSAM data base, would make it possible to perform comparative studies evaluating the contribution of such markers for the prediction of Type 2 diabetes and CHD in relation to conventional risk factors, insulin resistance and proinsulin.

51 CONCLUSIONS

In agreement with the hypothesis, proinsulin predicted Type 2 diabetes and CHD in both middle-aged and elderly men, both in the short and the long perspective, whereas insulin did not.

The prospective association between proinsulin and Type 2 diabetes was independent of first phase insulin response and degree of insulin sensitivity.

Proinsulin predicted Type 2 diabetes particularly strong in subjects with a low first phase insulin response.

Proinsulin is a strong and statistically highly significant predictor of fatal CHD independent of the conventional risk factors.

The association between proinsulin and all cause mortality is mainly dependent on the relationship between proinsulin and death from MI and CHD.

Insulin sensitivity was demonstrated to be a predictor of CHD independent of conventional risk factors in elderly men.

Proinsulin was a predictor of CHD independent of both the conventional risk factors and of insulin sensitivity.

52 SVENSK SAMMANFATTNING (Swedish summary)

Defekter i insulinsekretionen och insulinkänsligheten i målorganen för insulin är de huvudsakliga orsakerna till utvecklandet av typ-2 diabetes. Förhöjd insulinhalt i blodet indikerar nedsatt känslighet för insulin och är en riskmarkör för typ-2 diabetes men också för hjärt-kärlsjukdom enligt vissa studier. Konventionella insulinmätmetoder är ospecifika och mäter immunoreaktivt insulin som förutom insulin också innehåller ett förstadium till insulin, proinsulin. Proinsulin och specifikt insulin, insulinkänslighet bestämd med hyperglykemisk insulin clamp teknik och tidigt insulinsvar vid sockerbelastning ger en bättre och mer detaljerad och precis information. Dessa variabler kan därför utgöra en bättre prediktor för typ-2 diabetes och hjärt-kärlsjukdom. I denna avhandling har proinsulin, specifikt insulin, tidigt insulinsvar vid sockerbelastning och insulinkänslighet jämförts med varandra som prediktorer för utvecklandet av typ-2 diabetes och hjärt-kärlsjukdom. Två studier har omfattat risken för diabetes och två studier risken för hjärt-kärlsjukdom. Män i medelåldern respektive i högre ålder har studerats. Studierna har utförts i en populations-baserad grupp, en kohort som har sitt ursprung i en stor hälsoundersökning som genomfördes i Uppsala 1970 till 1973. Resultaten visar att proinsulin, i motsats till specifikt insulin, är en riskmarkör för utveckling av typ 2 diabetes, oberoende av insulinkänslighet och tidigt insulinsvar vid sockerbelastning. Vidare har visats att proinsulin i motsats till specifikt insulin är en riskmarkör för död respektive sjuklighet i hjärt- kärlsjukdom, oberoende av de kända riskfaktorerna rökning, högt blodtryck och förhöjt kolesterol. Slutligen har visats att insulinkänsligheten i sig är en riskmarkör för hjärt-kärlsjuklighet oberoende av ovanstående riskfaktorer förutom proinsulin. Sammanfattningsvis visar resultaten i avhandlingen att det snarare är proinsulinhalten och inte insulinnivån i blodet som står för de observerade sambanden med typ 2 diabetes och hjärt-kärlsjukdom. Nedsatt känslighet för insulin i sig är förenat med ökad risk för hjärt-kärlsjukdom.

53 ACKNOWLEDGEMENTS

I wish to express my sincere gratitude and appreciation to everyone who has contributed to this thesis. My special thanks go to: Christian Berne, my principal supervisor for introducing me to medical research, for inspiring and constant enthusiasm, patience, faith and support and as a senior colleague during my internship-years at the section for Endocrinology and Diabetology at the University hospital in Uppsala and appreciated friendship. Hans Lithell, my supervisor and head of the department, for unfailing and generous support, for providing a generous research environment and sharing extensive knowledge in the area. For always being able to see possibilities, never failing enthusiasm and appreciated friendship. Nicholas Hales, co-author, for good co-operational work, for generously providing laboratory services, for good advice and contribution to my work. Here I would also thank the technical staff at the Department of Clinical Biochemistry, Addenbrooke's Hospital, Cambridge, UK. Liisa Byberg, co-author, for contributions on previous papers and reports to come, hard work catalogue the ULSAM cohort, for fruitful discussions and appreciated friendship. Lars Berglund, for excellent statistical advice regarding methods in general and multivariate methods in particular, for being a good friend that showed me that you need a PC to make really big mistakes, i.e. the meaning of learning by doing. Rawya Mohsen, for excellent help as a data manager for ULSAM but also for help with my ongoing collaboration with colleagues in Denmark, England and the US. Arvo Hänni, appreciated friend, room mate at the department, fellow-colleague, enthusiastic and productive collaborator in several challenging clinical trials. Always prepared for a good laugh or profound discussions concerning any matter. Maybe that’s why our common room is in a desperate state of overload? Bengt Vessby, head of the section for Clinical Nutrition Research, for sharing his great enthusiasm regarding lipids, and very good clinical advice. Margareta Öhrvall, head at the outpatient clinic for metabolic diseases at Samariterhemmet, for support and appreciated friendship, being a superb boss. Siv Tengblad, Barbro Simu and Eva Seijby, for great laboratory skills and never failing enthusiasm for analyses, new test kits and shipments of frozen samples and for contribution to a good atmosphere at the department. Merike Boberg, for common sense, for kind support and for good advice concerning therapeutically matters in elderly. Friends and colleagues at the Section for Geriatrics for pleasant collaboration and enthusiasm and especially: Kristina Björklund, Johan Ärnlöv and Kristina Dunder for enjoyable discussions on epidemiological methods at the sometimes to long coffee-brakes, Johan Sundström and Ulf Risérus, for showing what a quick at work attitude can do for research speed, Anna Christina Åberg sharing admiration for the Lord of the Rings, Lena Killander for enthusiastic interest in collaborative ULSAM research, Anu Hedman, Meta Falkeborn, Per-Erik Andersson, Richard Reneland, Martin Wohlin and Bernice Wiberg for highlighting of other aspects of the insulin resistance syndrome, Lennart Hansson for his kind and generous attitude, Janne Hall for excellent skills during the clamp studies, Kristin Franzon for patient work at the clinic, Klara Halvarsson-Edlund for providing a new

54 dimension of obesity treatment, Karin Modin for excellent secretarial assistance, who always solve problems before they arise and Lars Lannfelt, taken up his duties as new head of the department of Geriatrics, for bringing new dimensions to the department and to the ULSAM. Friends and colleagues at the section for Clinical Nutrition Research: Agneta Andersson for the real spirit showing that there is always more to give in achieving goals, Johanna Helmersson and Samar Basu for sharing their knowledge in inflammation research, Anette Järvi, Annika Smedman, Eva Södergren, Cecilia Nälsén, Achraf Daryani, Marie Lemcke and Anders Sjödin for a kind and generous attitude, Brita Karlström and her staff of enthusiastic dieticians for keeping up the pace. Birgitta Larsson and the entire staff at the out patient clinic for metabolic diseases at Samariterhemmet for their competence in conducting clinical trials and the day to day handling of all the patients. Special thanks for your patient work on the screening program on 1792 subjects performed during 5 weeks! Gunbritt Ångman, Eva Broman and Agneta Nilsson at the former XENDOS out patient clinic for their competence in conducting clinical trials. Johan Asplund and Peter Hallgren, former senior colleagues at Falu county hospital, who by a phone-call enrolled me to become a diabetologist, tutored me during clinical training as an intern and thus saved me from spending my night-time hours in an operating theatre, sewing cross-stitches under a light bulb. Lars Wibell, senior colleague, giving me great support in the clinical work during my internship-years at the section for Endocrinology and Diabetology, Uppsala University hospital, for great companionship at congress travels. All the patient Uppsala men, for their participation and contribution to the ULSAM study, the fundamental basis of this thesis, and Hans Hedstrand, for initially investigating them. Magnus Levin, old friend from the platoon-officer school at the Royal Uplandic Regiment, who by his own living example shows its device to be true: “Nemo est impossibilis”. All friends and prior co-workers. Per and Annika, – elderly brother, elderly sister, and their families, Mum and Dad for kind support and just being what they are. The persons acknowledged last usually are those who one would like to acknowledge first and most of all: I thank my precious family, Birgitta my partner of life and best friend for love and support and our beloved children Johanna and Petter, who give meaning to my life.

Uppsala, November 2002

Björn Zethelius

The work on this thesis was kindly supported by research grants from The Medical Faculty of Uppsala University, the Swedish Medical Research Council (MFR5446), Medical research Council UK, The Foundation for Geriatric Research, The Swedish National Association against Heart and Lung Disease, the Swedish Diabetes Association Research Fund, the Uppsala Geriatric Fund, the Swedish Council for Planning and Co-ordination of Research, Ernfors Fund for Diabetes Research and the Thureus Foundation for Geriatric Research and the “Solstickan” foundation.

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