Population-Based Study Designs in Molecular Epidemiology Montserrat García-Closas, Roel Vermeulen, David Cox, Qing Lan, Neil E

Total Page:16

File Type:pdf, Size:1020Kb

Population-Based Study Designs in Molecular Epidemiology Montserrat García-Closas, Roel Vermeulen, David Cox, Qing Lan, Neil E UNIT 4. INTEGRATION OF BIOMARKERS INTO EPIDEMIOLOGY STUDY DESIGNS CHAPTER 14. Population-based study designs in molecular epidemiology Montserrat García-Closas, Roel Vermeulen, David Cox, Qing Lan, Neil E. Caporaso, and Nathaniel Rothman Summary Introduction This chapter will discuss design There is a wealth of existing and by these technologies can still be considerations for epidemiological emerging opportunities to apply a classified into the classic biomarker studies that use biomarkers in the vast array of new biomarker discovery categories defined more than 20 framework of etiologic investigations. technologies, such as genome- years ago (Figure 14.1), which The main focus will be on describing wide scans of common genetic include biomarkers of exposure, the incorporation of biomarkers variants, mRNA and microRNA intermediate endpoints (e.g. into the main epidemiologic study expression arrays, proteomics, biomarkers of early biologic effect), designs, including cross-sectional metabolomics and adductomics, disease and susceptibility (10–17). UNIT 4 or short-term longitudinal designs to further our understanding of Biomarkers in epidemiological to characterize biomarkers, and the etiology of a broad range of studies can also be used to evaluate prospective cohort and case– diseases (1–9). These approaches behavioural characteristics that 14 CHAPTER control studies to evaluate are allowing investigators to explore affect the likelihood of exposure, biomarker-disease associations. biologic responses to exogenous such as tobacco smoking, as well as The advantages and limitations of and endogenous exposures, clinical behaviour and progression each design will be presented, and evaluate potential modification of disease. The use of biomarkers the impact of study design on the of those responses by variants associated with exposure, feasibility of different approaches in essentially the entire genome, disease development, and clinical to exposure assessment and and define disease processes progression within the same overall biospecimen collection and at the chromosomal, DNA, RNA design is of increasing interest and processing will be discussed. and protein levels. At the same has recently been termed ‘integrative time, most biomarkers analysed epidemiology’ (18,19). Unit 4 • Chapter 14. Population-based study designs in molecular epidemiology 241 Figure 14.1. A continuum of biomarker categories reflecting the carcinogenic process resulting from xenobiotic exposures Figure compiled from (10) and (21, copyright © 2008, Informa Healthcare. Reproduced with permission of Informa Healthcare). Regardless of the appellation The focus of this chapter is with respect to exposure variables used to describe the use of on design considerations for and intermediate endpoints, and, biomarkers in epidemiologic epidemiological studies that use in some instances, disease at a research, be it “molecular biomarkers, primarily in the context specific point in time. Short-term epidemiology,” “integrative of etiologic research, including longitudinal biomarker studies epidemiology,” or the more limited cross-sectional or short-term are studies in which subjects are “genetic epidemiology,” the longitudinal designs to characterize prospectively followed for a short successful application of new and biomarkers, and prospective cohort period of time (usually a few weeks established biomarker technologies and case–control studies to evaluate to up to a year). Investigations still depends on integrating them into biomarker–disease associations. A are usually performed on healthy the appropriate study design with description of the general principles subjects exposed to particular careful attention to the time-tested of study design (29–31) is outside exogenous or endogenous agents principles of the epidemiologic the scope of this chapter. Instead, where the biomarker is treated as method (16,20–24). Basic principles the focus is on describing the the outcome variable. These studies in vetting new biomarkers and incorporation of biomarkers into the generally focus on exposure and technologies in pilot or transitional main epidemiologic study designs, intermediate endpoint biomarkers, studies apply now more than ever pointing out the advantages and and sometimes evaluate genetic and (25–28). Understanding how to limitations of each, and showing other modifiers of the exposure– collect, process and store biologic how study design affects the endpoint relationship. samples (see Chapter 3), and the feasibility of different approaches factors that influence biomarker to both exposure assessment Questions addressed by levels, with particular attention and biospecimen collection and cross-sectional and short-term to within- and between-person processing. longitudinal studies variation for non-fixed biomarkers (see Chapter 9), are key concerns. Study designs in molecular Cross-sectional and/or short-term Testing for and optimizing laboratory epidemiology longitudinal studies are often used accuracy and precision are also as follows: critical to the successful use of Cross-sectional and short- 1) To answer questions about biomarkers in epidemiology studies term longitudinal studies with whether or not a given population (see Chapter 8). Finally, selecting biomarker endpoints has been exposed to a particular the most appropriate, effective, and compound, the level of exposure, logistically feasible study design to In epidemiological terms, a cross- the range of the exposure, and the use a given biomarker technology sectional study refers to a study external and internal determinants that answers a particular research design in which all of the information of the exposure. For instance, question remains of paramount refers to the same point in time. recent studies on haemoglobin importance. As such, these studies provide a adducts of acrylamide have shown ‘snapshot’ of the population status that exposure to this toxic chemical 242 is widespread in the general recent introduction into commerce, patterns. Sometimes a biomarker of population, due to dietary and the time between first exposure and exposure can be used only in cross- lifestyle habits (32,33). the occurrence of any chronic health sectional studies to determine if a 2) To evaluate intermediate effect is most likely too short. In this population is exposed to an agent of biologic effects from a wide range particular example, the assessment concern, or used as an independent of exposures in the diet and of preclinical indicators of disease marker of exposure in studies environment, as well as from lifestyle (e.g. markers of pulmonary evaluating intermediate biomarker factors (e.g. obesity and reproductive inflammation) in asymptomatic endpoints. status). This design can be used individuals would be of importance The applicability of exposure to provide mechanistic insight into to identify potential adverse health biomarkers in cross-sectional well established exposure–disease effects at an early stage. studies depends on certain intrinsic relationships, and to supplement 4) To study changes in exposure features related to the marker suggestive but inconclusive evidence and/or intermediate endpoints to itself (e.g. half-life, variability, and on the possible adverse health determine the effectiveness of specificity of the marker) and the effects of an exposure. For instance, intervention studies. For instance, exposure pattern (see Chapter 9). studies have used haematological the effect of exercise and weight The first requirements for successful endpoints to investigate the effects loss interventions on serum application of an exposure marker of benzene on the blood forming levels of four biomarkers related are that the assay is reliable and system at low levels of exposure to knee osteoarthritis (cartilage accurate (see Chapter 8), the marker (34,35). These studies have found oligometric matrix protein (COMP), is detectable in human populations, decreased levels in peripheral blood hyaluronan, antigenic keratin and important effect modifiers (e.g. cell counts at exposures below 1 sulfate, and transforming growth nutrition and demographic variables) ppm, indicating that at low levels of factor-β-1 (TGF-β1)), and clinical and kinetics are known (20). Second, exposure, perturbations in the blood outcome measures (e.g. medial joint the timing of sample collection in forming system can be detected. space, pain) were examined (37). combination with the biological half- These results hinted at the possibility Intervention programmes indeed life of a biomarker of exposure is key, of increased risk of leukaemia at resulted in changes in COMP (which as this determines the exposure time low levels of benzene exposure, was associated with decreased knee window that a marker of exposure given the putative link between pain) and TGF-β1. reflects. The time of collection may benzene poisoning (a severe form of be critical if, as is often the case in haematotoxicity) and increased risk Cross-sectional and short- cross-sectional studies, only one for leukaemia. term longitudinal studies using sample per subject can be obtained 3) To evaluate whether or not exposure markers on a given occasion, and if the there are early biologic perturbations exposure is of brief duration, highly caused by new exposures, or recent Biomarkers of exposure measure variable in time, or has a distinct changes in lifestyle factors that have the level of an external agent, exposure pattern (e.g. diurnal not been present long enough to have
Recommended publications
  • The Very Process of Taking a Pill May Create Expectations in a Patient Which May Affect Reactions to the Pill
    Ch 1 Pause for Thought Questions p. 36 1- Why is it important to use placebos and a double-blind approach in some studies? The very process of taking a pill may create expectations in a patient which may affect reactions to the pill. To help eliminate the role of patient expectation during testing, a placebo (or fake medicine) is used on subjects in a control group. A researcher’s expectations may also affect results. Therefore, double-blind studies are often used. In such studies, neither the researcher nor the participants know which group is which. 2- Assume that researchers find that people’s memories are sharpest right after they’ve eaten lunch. What hidden variables may have affected these results? Other variables that may play a role (hidden variables): Time of Day Type of food eaten Meaning of “lunch” - May mean “time away from work” - Subjective State of feeling free (not food) helps memory. Socializing at lunch may make a person more alert and then able to take-in more info and retain it longer. 3- How might you use the scientific method to study factors that affect obedience? Devise a simple study, and identify the following: hypothesis, subjects, independent variable, dependent variable, experimental group, and control group. Hypothesis: If employee fears/believes they will be punished if they don’t follow directives, then they will be obedient to directives of bosses/people in “higher” positions than themselves in the workplace. Subjects: Employees Independent Variable: Directive given with harsh consequences for not following through. Dependent Variable: Obedience (Level of) Experimental Group: Group with “harsh” bosses (very by the book; give extreme consequences) present during their work day.
    [Show full text]
  • Genomic Epidemiology and Recent Update on Nucleic Acid–Based Diagnostics for COVID-19
    Current Tropical Medicine Reports https://doi.org/10.1007/s40475-020-00212-3 COVID-19 IN THE TROPICS: IMPACT AND SOLUTIONS (AJ RODRIGUEZ-MORALES, SECTION EDITOR)) Genomic Epidemiology and Recent Update on Nucleic Acid–Based Diagnostics for COVID-19 Ali A. Rabaan1 & Shamsah H. Al-Ahmed2 & Ranjit Sah3 & Jaffar A. Al-Tawfiq4,5,6 & Shafiul Haque7 & Harapan Harapan8,9,10 & Kovy Arteaga-Livias11,12 & D. Katterine Bonilla Aldana13,14 & Pawan Kumar 15 & Kuldeep Dhama16 & Alfonso J. Rodriguez-Morales12,13,14,17 Accepted: 10 September 2020 # Springer Nature Switzerland AG 2020 Abstract Purpose of the Review The SARS-CoV-2 genome has been sequenced and the data is made available in the public domain. Molecular epidemiological investigators have utilized this information to elucidate the origin, mode of transmission, and contact tracing of SARS-CoV-2. The present review aims to highlight the recent advancements in the molecular epidemiological studies along with updating recent advancements in the molecular (nucleic acid based) diagnostics for COVID-19, the disease caused by SARS-CoV-2. Recent Findings Epidemiological studies with the integration of molecular genetics principles and tools are now mainly focused on the elucidation of molecular pathology of COVID-19. Molecular epidemiological studies have discovered the mutability of SARS-CoV-2 which is of utmost importance for the development of therapeutics and vaccines for COVID-19. The whole world is now participating in the race for development of better and rapid diagnostics and therapeutics for COVID-19. Several molecular diagnostic techniques have been developed for accurate and precise diagnosis of COVID-19. Summary Novel genomic techniques have helped in the understanding of the disease pathology, origin, and spread of COVID- 19.
    [Show full text]
  • Design of Prospective Studies
    Design of Prospective Studies Kyoungmi Kim, Ph.D. June 14, 2017 This seminar is jointly supported by the following NIH-funded centers: Seminar Objectives . Discuss about design of prospective longitudinal studies . Understand options for overcoming shortcomings of prospective studies . Learn to determine how many subjects to recruit for a follow-up study What is a prospective study? Study subjects of disease‐free at enrollment are followed for a period of time and periodically checked for progress to see who/when gets the outcome in question ‐thus be able to establish a temporal relationship between exposure & outcome. What is a prospective study? During the follow‐up, data is collected on the factors of interest, including: •When the subject develops the condition •When they drop out of the study or become “lost” •When they exposure status changes •When they die Example: Framingham Study . An original cohort of 5,209 subjects from Framingham, MA between the ages of 30 and 62 years of age was recruited and followed up for 20 years. A number of hypotheses were generated and described by Dawber et al. in 1980 listing various presupposed risk factors such as increasing age, increased weight, tobacco smoking, elevated blood pressure and cholesterol and decreased physical activity. It is largely quoted as a successful longitudinal study owing to the fact that a large proportion of the exposures chosen for analysis were indeed found to correlate closely with the development of cardiovascular disease. Example: Framingham Study . Biases exist: – It was a study carried out in a single population in a single town, brining into question the generalizability and applicability of this data to different groups.
    [Show full text]
  • Role and Limitations of Epidemiology in Establishing a Causal Association Eduardo L
    Seminars in Cancer Biology 14 (2004) 413–426 Role and limitations of epidemiology in establishing a causal association Eduardo L. Franco a,∗, Pelayo Correa b, Regina M. Santella c, Xifeng Wu d, Steven N. Goodman e, Gloria M. Petersen f a Departments of Epidemiology and Oncology, McGill University, 546 Pine Avenue West, Montreal, QC, Canada H2W1S6 b Department of Pathology, Louisiana State University Health Sciences Center, New Orleans, LA, USA c Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA d Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA e Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA f Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA Abstract Cancer risk assessment is one of the most visible and controversial endeavors of epidemiology. Epidemiologic approaches are among the most influential of all disciplines that inform policy decisions to reduce cancer risk. The adoption of epidemiologic reasoning to define causal criteria beyond the realm of mechanistic concepts of cause-effect relationships in disease etiology has placed greater reliance on controlled observations of cancer risk as a function of putative exposures in populations. The advent of molecular epidemiology further expanded the field to allow more accurate exposure assessment, improved understanding of intermediate endpoints, and enhanced risk prediction by incorporating the knowledge on genetic susceptibility. We examine herein the role and limitations of epidemiology as a discipline concerned with the identification of carcinogens in the physical, chemical, and biological environment. We reviewed two examples of the application of epidemiologic approaches to aid in the discovery of the causative factors of two very important malignant diseases worldwide, stomach and cervical cancers.
    [Show full text]
  • Study Designs in Biomedical Research
    STUDY DESIGNS IN BIOMEDICAL RESEARCH INTRODUCTION TO CLINICAL TRIALS SOME TERMINOLOGIES Research Designs: Methods for data collection Clinical Studies: Class of all scientific approaches to evaluate Disease Prevention, Diagnostics, and Treatments. Clinical Trials: Subset of clinical studies that evaluates Investigational Drugs; they are in prospective/longitudinal form (the basic nature of trials is prospective). TYPICAL CLINICAL TRIAL Study Initiation Study Termination No subjects enrolled after π1 0 π1 π2 Enrollment Period, e.g. Follow-up Period, e.g. three (3) years two (2) years OPERATION: Patients come sequentially; each is enrolled & randomized to receive one of two or several treatments, and followed for varying amount of time- between π1 & π2 In clinical trials, investigators apply an “intervention” and observe the effect on outcomes. The major advantage is the ability to demonstrate causality; in particular: (1) random assigning subjects to intervention helps to reduce or eliminate the influence of confounders, and (2) blinding its administration helps to reduce or eliminate the effect of biases from ascertainment of the outcome. Clinical Trials form a subset of cohort studies but not all cohort studies are clinical trials because not every research question is amenable to the clinical trial design. For example: (1) By ethical reasons, we cannot assign subjects to smoking in a trial in order to learn about its harmful effects, or (2) It is not feasible to study whether drug treatment of high LDL-cholesterol in children will prevent heart attacks many decades later. In addition, clinical trials are generally expensive, time consuming, address narrow clinical questions, and sometimes expose participants to potential harm.
    [Show full text]
  • Why Do We Need Longitudinal Survey Data?
    HEATHER JOSHI University College London, UK Why do we need longitudinal survey data? Knowing people’s history helps in understanding their present state and where they are heading Keywords: life chances, inequality, mobility, gender, cohort, panel studies ELEVATOR PITCH Work experiences of men and women explain little of the Information from longitudinal surveys transforms gap in their hourly pay in the UK Cohorts snapshots of a given moment into something with a Born 1946 time dimension. It illuminates patterns of events within Age 26 31 an individual’s life and records mobility and immobility 43 Born 1958 between older and younger generations. It can track Age 23 33 the different pathways of men and women and people 42 of diverse socio-economic background through the life Born 1970 Gap explained by differences Age 26 in work experience & education course. It can join up data on aspects of a person’s life, 30 Unexplained gap 34 health, education, family, and employment and show how 0510 15 20 25 30 35 40 45 these domains affect one another. It is ideal for bridging Men’s pay minus women’s pay as % of men’s pay the different silos of policies that affect people’s lives. Source: [1]; Table 7.12. KEY FINDINGS Pros Cons Longitudinal surveys form a record of Longitudinal survey data are expensive to collect continuities and transitions of a life as they and challenging to analyze. happen, information not reliably gained through Data must be collected over a long timeframe to recollection or cross-section surveys. show relevant, long-term results.
    [Show full text]
  • Molecular Epidemiology and Whole-Genome Analysis of Bovine Foamy Virus in Japan
    viruses Article Molecular Epidemiology and Whole-Genome Analysis of Bovine Foamy Virus in Japan Hirohisa Mekata 1,* , Tomohiro Okagawa 2, Satoru Konnai 2,3 and Takayuki Miyazawa 4 1 Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan 2 Department of Advanced Pharmaceutics, Faculty of Veterinary Medicine, Hokkaido University, Sapporo 060-0818, Japan; [email protected] (T.O.); [email protected] (S.K.) 3 Department of Disease Control, Faculty of Veterinary Medicine, Hokkaido University, Sapporo 060-0818, Japan 4 Laboratory of Virus-Host Coevolution, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan; [email protected] * Correspondence: [email protected]; Tel./Fax: +81-985-58-7881 Abstract: Bovine foamy virus (BFV) is a member of the foamy virus family in cattle. Information on the epidemiology, transmission routes, and whole-genome sequences of BFV is still limited. To understand the characteristics of BFV, this study included a molecular survey in Japan and the determination of the whole-genome sequences of 30 BFV isolates. A total of 30 (3.4%, 30/884) cattle were infected with BFV according to PCR analysis. Cattle less than 48 months old were scarcely infected with this virus, and older animals had a significantly higher rate of infection. To reveal the possibility of vertical transmission, we additionally surveyed 77 pairs of dams and 3-month-old calves in a farm already confirmed to have BFV. We confirmed that one of the calves born from a dam with BFV was infected.
    [Show full text]
  • Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings
    Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Nishihara, Reiko, Tyler J. VanderWeele, Kenji Shibuya, Murray A. Mittleman, Molin Wang, Alison E. Field, Edward Giovannucci, Paul Lochhead, and Shuji Ogino. 2015. “Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings.” European Journal of Epidemiology 30 (10): 1129–35. https://doi.org/10.1007/ s10654-015-0088-4. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41392032 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP HHS Public Access Author manuscript Author Manuscript Author ManuscriptEur J Epidemiol Author Manuscript. Author Author Manuscript manuscript; available in PMC 2016 October 07. Published in final edited form as: Eur J Epidemiol. 2015 October ; 30(10): 1129–1135. doi:10.1007/s10654-015-0088-4. Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings Reiko Nishiharaa,b,c, Tyler J. VanderWeeled,e, Kenji Shibuyac, Murray A. Mittlemand,f, Molin Wangd,e,g, Alison E. Fieldd,g,h,i, Edward Giovannuccia,d,g, Paul Lochheadi,j, and Shuji Oginob,d,k aDepartment of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, Massachusetts 02115 USA bDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Boston, Massachusetts 02215 USA cDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan dDepartment of Epidemiology, Harvard T.H.
    [Show full text]
  • Molecular Epidemiology and Precision Medicine
    Pharmacogenetics: Past, Present, and Future Brooke L. Fridley, PhD Associate Professor of Biostatistics University of Kansas Medical Center Site Director, K-INBRE Bioinformatics Core Director, Biostatistics and Informatics Shared Resource, University of Kansas Cancer Center What is Pharmacogenomics? Pharmacogenomics & Precision Medicine Aims to deliver the correct drug or treatment: • At the right time • To the right patient • At the right dose Can only be achieved when we have accurate clinical tests (BIOMARKER) and companion drugs at our disposal What is a Biomarker? • Biomarker: “A characteristic that is objectively measured and evaluated as an indicator of – normal biological processes, – pathogenic processes, or – pharmacologic responses to a therapeutic intervention.” Biomarkers Definitions Working Group. Biomarkers and surrogate end points: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89–95 Candidate Gene Era 1950 – 1990 – Structure of 1980 – 1985 – HGP DNA TPMT PCR begins 1997 – 1977 – 1983 – First 1987 – NHGRI Sanger human P450 formed Sequencing disease supergene gene family mapped Candidate • Genotyping Variant & • RT-PCR Gene • Re-sequencing genes (exons) with Sanger Studies Sequencing PK and PD and * Alleles (haplotypes) PGx Effect = Gene*Drug Interaction Drug 1 Drug 2 Genome-wide Array Era 2003 – FDA 2002 – issues draft 1998 -Illumina HapMap guidelines for founded begins PGx 2001 – 1st 2003 – 2004 – 1st NGS version of Completion of platform human HGP genome completed Genome- • SNP arrays (~mid 2000s) wide • mRNA arrays (~mid 1990s) Association Studies • Methylation arrays (~2008-2010) Study of Genetic Association with Drug Response Phenotypes Cases: Non-responders Cases: Responders Genetic association studies look at the frequency of genetic changes to try to determine whether specific changes are associated with a phenotype.
    [Show full text]
  • Recommendations for Naming Study Design and Levels of Evidence
    Recommendations for Naming Study Design and Levels of Evidence for Structured Abstracts in the Journal of Orthopedic & Sports Physical Therapy (JOSPT) Last revised 07-03-2008 The JOSPT as a journal that primarily focuses on clinically based research, attempts to assist readers in understanding the quality of research evidence in its published manuscripts by using a structured abstract that includes information on the “study design” and the classification of the “level of evidence.” While no single piece of information or classification can signify the quality and clinical relevance of published studies, a consistent approach to communicating this information is a step towards assisting our readers in evaluating new knowledge. Outline of the structured abstract for research reports published in JOSPT Study design Objectives Background Methods and Measures Results Conclusions Level of Evidence Suggestions for Naming Study Designs We recommend that authors attempt to use the following terminology when naming their research designs. Use of consistent terminology will make it easier for readers to identify the nature of the study and will allow us to track the type of studies published more easily. We recognize that this list is not all-inclusive and that more appropriate descriptors might be suitable for some studies. In those cases, investigators are encouraged to pick the most appropriate descriptors for their study. These suggestions are provided as a means of encouraging consistency where it would be both useful and informative and their use is expected where they do apply. Quantitative Clinical Study Categories include (Therapy, Prevention, Etiology, Harm, Prognosis, Diagnosis, Differential Diagnosis, Symptom Prevalence, Economic Analysis, and Decision Analysis).
    [Show full text]
  • Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data
    Twin Research and Human Genetics (2020), 23, 145–155 doi:10.1017/thg.2020.53 Article Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data René Pool1,2* , Fiona A. Hagenbeek1,2* , Anne M. Hendriks1,2 , Jenny van Dongen1,2 , Gonneke Willemsen1 , Eco de Geus1,2 BBMRI Metabolomics Consortium3, Ko Willems van Dijk4,5,6 , Aswin Verhoeven7 , H. Eka Suchiman8 , Marian Beekman8 , P. Eline Slagboom8 , Amy C. Harms9,10 , Thomas Hankemeier9,10 and Dorret I. Boomsma1,2 1Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands, 2Amsterdam Public Health Research Institute, Amsterdam, the Netherlands, 3Members of the BBMRI Metabolomics Consortium are listed after the abstract, 4Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands, 5Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands, 6Department of Internal Medicine Division Endocrinology, Leiden University Medical Center, Leiden, the Netherlands, 7Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands, 8Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands, 9Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands and 10The Netherlands Metabolomics Centre, Leiden, the Netherlands Abstract Metabolites are small molecules involved in cellular metabolism where
    [Show full text]
  • Choosing the Right Study Design
    Choosing the right study design Caroline Sabin Professor of Medical Statistics and Epidemiology Institute for Global Health Conflicts of interest I have received funding for the membership of Data Safety and Monitoring Boards, Advisory Boards and for the preparation of educational materials from: • Gilead Sciences • ViiV Healthcare • Janssen‐Cilag Main types of study design BEST QUALITY Randomised controlled trial (RCT) EVIDENCE Cohort study Case‐control study Cross‐sectional study Case series/case note review ‘Expert’ opinion WORST QUALITY EVIDENCE Experimental vs. Observational Experimental study Investigator intervenes in the care of the patient in a pre‐planned, experimental way and records the outcome Observational study Investigator does not intervene in the care of a patient in any way, other than what is routine clinical care; investigator simply records what happens Cross‐sectional vs. Longitudinal Cross‐sectional study Patients are studied at a single time‐point only (e.g. patients are surveyed on a single day, patients are interviewed at the start of therapy) Longitudinal study Patients are followed over a period of time (days, months, years…) Assessing causality (Bradford Hill criteria) • Cause should precede effect • Association should be plausible (i.e. biologically sensible) • Results from different studies should be consistent • Association should be strong • Should be a dose‐response relationship between the cause and effect •Removal of cause should reduce risk of the effect Incidence vs. prevalence Incidence: proportion
    [Show full text]