Genomic and environmental risk factors for cardiometabolic diseases in Africa: methods used for Phase 1 of the AWI-Gen population cross-sectional study

ABSTRACT There is an alarming tide of cardiovascular and metabolic disease (CMD) sweeping across Africa. This may be a result of an increasingly urbanized lifestyle characterized by the growing consumption of processed and calorie-dense food, combined with physical inactivity and more sedentary behaviour. While the link between lifestyle and public health has been extensively studied in Caucasian and African American populations, few studies have been conducted in Africa. This paper describes the detailed methods for Phase 1 of the AWI-Gen study that were used to capture phenotype data and assess the associated risk factors and end points for CMD in persons over the age of 40 years in sub-Saharan Africa (SSA). We developed a population-based cross-sectional study of disease burden and phenotype in Africans, across six centres in SSA. These centres are in West Africa (Nanoro, Burkina Faso, and Navrongo, Ghana), in East Africa (Nairobi, Kenya) and in South Africa (Agincourt, Dikgale and Soweto). A total of 10,702 individuals between the ages of 40 and 60 years were recruited into the study across the six centres, plus an additional 1021 participants over the age of 60 years from the Agincourt centre. We collected socio-demographic, anthropometric, medical history, diet, physical activity, fat distribution and alcohol/tobacco consumption data from participants. Blood samples were collected for disease-related biomarker assays, and genomic DNA extraction for genome-wide association studies. Urine samples were collected to assess kidney function. The study provides base-line data for the development of a series of cohorts with a second wave of data collection in Phase 2 of the study. These data will provide valuable insights into the genetic and environmental influences on CMD on the African continent.

[1]  S. Tollman,et al.  Sociodemographic, socioeconomic, clinical and behavioural predictors of body mass index vary by sex in rural South African adults-findings from the AWI-Gen study , 2018, Global Health Action.

[2]  M. Alberts,et al.  Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa , 2018, Global health action.

[3]  M. Ramsay,et al.  Gender differences in sociodemographic and behavioural factors associated with BMI in an adult population in rural Burkina Faso – an AWI-Gen sub-study , 2018, Global health action.

[4]  M. Ramsay,et al.  Demographic, socio-economic and behavioural correlates of BMI in middle-aged black men and women from urban Johannesburg, South Africa , 2018, Global health action.

[5]  M. Ramsay,et al.  Socio-demographic and behavioural determinants of body mass index among an adult population in rural Northern Ghana: the AWI-Gen study , 2018, Global health action.

[6]  G. Asiki,et al.  Sociodemographic and behavioural factors associated with body mass index among men and women in Nairobi slums: AWI-Gen Project , 2018, Global health action.

[7]  D. Canning,et al.  Cohort Profile: Health and Ageing in Africa: a Longitudinal Study of an INDEPTH Community in South Africa (HAALSI). , 2018, International journal of epidemiology.

[8]  A. Zhernakova,et al.  The importance of cohort studies in the post-GWAS era , 2018, Nature Genetics.

[9]  N. Sewankambo,et al.  The path to longer and healthier lives for all Africans by 2030: the Lancet Commission on the future of health in sub-Saharan Africa , 2017, The Lancet.

[10]  C. Rotimi,et al.  Diabetes in sub-Saharan Africa: from clinical care to health policy. , 2017, The lancet. Diabetes & endocrinology.

[11]  Gretchen A. Stevens,et al.  Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies , 2017, International journal of epidemiology.

[12]  S. Tollman,et al.  Regional and Sex Differences in the Prevalence and Awareness of Hypertension: An H3Africa AWI-Gen Study Across 6 Sites in Sub-Saharan Africa. , 2017, Global heart.

[13]  M. Ramsay,et al.  Establishing an academic biobank in a resource-challenged environment , 2017, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.

[14]  Hynek Pikhart,et al.  Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants , 2017, The Lancet.

[15]  P. Byass,et al.  Understanding and acting on the developmental origins of health and disease in Africa would improve health across generations , 2017, Global health action.

[16]  Eld,et al.  H3Africa AWI-Gen Collaborative Centre: a resource to study the interplay between genomic and environmental risk factors for cardiometabolic diseases in four sub-Saharan African countries , 2016, Global health, epidemiology and genomics.

[17]  Ashutosh Kumar Singh,et al.  Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, The Lancet.

[18]  P. Tindana,et al.  Broad Consent for Genomic Research and Biobanking: Perspectives from Low- and Middle-Income Countries. , 2016, Annual review of genomics and human genetics.

[19]  P. Byass,et al.  Diverse Empirical Evidence on Epidemiological Transition in Low- and Middle-Income Countries: Population-Based Findings from INDEPTH Network Data , 2016, PloS one.

[20]  M. Ramsay,et al.  African partnerships through the H3Africa Consortium bring a genomic dimension to longitudinal population studies on the continent. , 2016, International journal of epidemiology.

[21]  Tran Quoc Bao,et al.  Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants , 2016, The Lancet.

[22]  D. Stein,et al.  Obtaining informed consent for genomics research in Africa: analysis of H3Africa consent documents , 2015, Journal of Medical Ethics.

[23]  P. Byass,et al.  Health & Demographic Surveillance System Profile: The Dikgale Health and Demographic Surveillance System. , 2015, International journal of epidemiology.

[24]  Blessing Mberu,et al.  Health & Demographic Surveillance System Profile: The Nairobi Urban Health and Demographic Surveillance System (NUHDSS). , 2015, International journal of epidemiology.

[25]  J. Menken,et al.  Cardiometabolic disease risk and HIV status in rural South Africa: establishing a baseline , 2015, BMC Public Health.

[26]  C. Rotimi,et al.  The H3Africa policy framework: negotiating fairness in genomics , 2015, Trends in genetics : TIG.

[27]  Scott Hazelhurst,et al.  The Process of Installing REDCap, a Web Based Database Supporting Biomedical Research , 2014, Applied Clinical Informatics.

[28]  S. Norris,et al.  Staging reproductive aging using Stages of Reproductive Aging Workshop + 10 in black urban African women in the Study of Women Entering and in Endocrine Transition , 2014, Menopause.

[29]  M. Ramsay,et al.  Ethical issues in genomic research on the African continent: experiences and challenges to ethics review committees , 2014, Human Genomics.

[30]  Faisal M. Fadlelmola,et al.  Enabling Genomic Revolution in Africa , 2019, The Genetics of African Populations in Health and Disease.

[31]  M. McCarthy,et al.  Research Capacity: Enabling African Scientists to Engage Fully in the Genomic Revolution , 2014 .

[32]  Margaret Thorogood,et al.  Self-reported health and health care use in an ageing population in the Agincourt sub-district of rural South Africa , 2013, Global health action.

[33]  M. Bergmann,et al.  Why do participants enroll in population biobank studies? A systematic literature review , 2013, Expert review of molecular diagnostics.

[34]  M. Taskinen,et al.  South African Dyslipidaemia Guideline Consensus Statement , 2013 .

[35]  V. Basevi Standards of Medical Care in Diabetes—2013 , 2012, Diabetes Care.

[36]  E. Vicaut,et al.  Mannheim Carotid Intima-Media Thickness and Plaque Consensus (2004–2006–2011) , 2012, Cerebrovascular Diseases.

[37]  L. Hou,et al.  The Interaction between Pesticide Use and Genetic Variants Involved in Lipid Metabolism on Prostate Cancer Risk , 2012, Journal of cancer epidemiology.

[38]  J. de Vries,et al.  "It's for a good cause, isn't it?" - Exploring views of South African TB research participants on sample storage and re-use , 2012, BMC medical ethics.

[39]  D. Kwiatkowski,et al.  Seeking consent to genetic and genomic research in a rural Ghanaian setting: A qualitative study of the MalariaGEN experience , 2012, BMC medical ethics.

[40]  Peter Byass,et al.  The INDEPTH Network: filling vital gaps in global epidemiology , 2012, International journal of epidemiology.

[41]  P. Sluss,et al.  Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging. , 2012, The Journal of clinical endocrinology and metabolism.

[42]  P. Sluss,et al.  Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging , 2012, Menopause.

[43]  M. Taskinen,et al.  South African Dyslipidaemia Guideline Consensus Statement: , 2012, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.

[44]  V. Basevi,et al.  Standards of Medical Care in Diabetes—2012 , 2011, Diabetes Care.

[45]  Z. Mchiza,et al.  Diet and mortality rates in Sub-Saharan Africa: Stages in the nutrition transition , 2011, BMC public health.

[46]  D. Dunger,et al.  Validation of Ultrasound Estimates of Visceral Fat in Black South African Adolescents , 2011, Obesity.

[47]  Gretchen A. Stevens,et al.  National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5·4 million participants , 2011, The Lancet.

[48]  A. Alkerwi,et al.  Prevalence of the metabolic syndrome in Luxembourg according to the Joint Interim Statement definition estimated from the ORISCAV-LUX study , 2011, BMC public health.

[49]  F. Bull,et al.  Global physical activity questionnaire (GPAQ): nine country reliability and validity study. , 2009, Journal of physical activity & health.

[50]  N. D. de Vries,et al.  Stigma of People with HIV/AIDS in Sub-Saharan Africa: A Literature Review , 2009, Journal of tropical medicine.

[51]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[52]  B. Coll,et al.  Carotid intima-media thickness measurements: Techniques and clinical relevance , 2008, Current atherosclerosis reports.

[53]  P. Amuna,et al.  Epidemiological and nutrition transition in developing countries: impact on human health and development , 2008, Proceedings of the Nutrition Society.

[54]  D. Yach,et al.  Cohort Profile: Mandela's children: the 1990 Birth to Twenty study in South Africa. , 2007, International journal of epidemiology.

[55]  E. Vicaut,et al.  Mannheim Carotid Intima-Media Thickness Consensus (2004–2006) , 2006, Cerebrovascular Diseases.

[56]  Lilani Kumaranayake,et al.  Constructing socio-economic status indices: how to use principal components analysis. , 2006, Health policy and planning.

[57]  G. Curhan,et al.  Screening, monitoring, and treatment of albuminuria: Public health perspectives. , 2006, Journal of the American Society of Nephrology : JASN.

[58]  Talita Adão Perini,et al.  Cálculo do erro técnico de medição em antropometria Cálculo del error técnico en la medición de antropometria Technical error of measurement in anthropometry , 2005 .

[59]  Daniel W. Jones,et al.  The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. , 2003, JAMA.

[60]  R. Collins,et al.  Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies , 2002, The Lancet.

[61]  O Wink,et al.  Validity and reproducibility of ultrasonography for the measurement of intra-abdominal adipose tissue , 2001, International Journal of Obesity.

[62]  A. Astrup,et al.  Obesity : Preventing and managing the global epidemic , 2000 .

[63]  A. Spurdle,et al.  XX true hermaphroditism in southern African blacks: exclusion of SRY sequences and uniparental disomy of the X chromosome. , 1995, American journal of medical genetics.

[64]  Y. Nakahori,et al.  Sex identification by polymerase chain reaction using X-Y homologous primer. , 1991, American journal of medical genetics.

[65]  Shirley A. Miller,et al.  A simple salting out procedure for extracting DNA from human nucleated cells. , 1988, Nucleic acids research.

[66]  J. Ewing,et al.  Detecting alcoholism. The CAGE questionnaire. , 1984, JAMA.

[67]  R. Levy,et al.  Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. , 1972, Clinical chemistry.

[68]  A. Omran The epidemiologic transition. A theory of the epidemiology of population change. , 1971, The Milbank Memorial Fund quarterly.

[69]  Dan J Stein,et al.  H3Africa: current perspectives , 2018 .

[70]  The Lancet Global Health Collaborating to ease Africa's data drought. , 2017, The Lancet. Global health.

[71]  Steven N Goodman,et al.  The research-treatment distinction: a problematic approach for determining which activities should have ethical oversight. , 2013, The Hastings Center report.

[72]  S. Clark,et al.  Profile: Agincourt Health and Socio-demographic Surveillance System , 2012, International journal of epidemiology.

[73]  H. Tinto,et al.  HEALTH AND DEMOGRAPHIC SURVEILLANCE SYSTEM PROFILE Profile : Nanoro Health and Demographic Surveillance System , 2012 .

[74]  D. Azongo,et al.  Profile of the Navrongo Health and Demographic Surveillance System. , 2012, International journal of epidemiology.

[75]  N. McGrath,et al.  HEALTH AND DEMOGRAPHIC SURVEILLANCE SYSTEM PROFILE Profile: The Karonga Health and Demographic Surveillance System , 2012 .

[76]  S. Virani Non-HDL cholesterol as a metric of good quality of care: opportunities and challenges. , 2011, Texas Heart Institute journal.

[77]  A. Omran The epidemiologic transition: a theory of the epidemiology of population change. 1971. , 2005, The Milbank quarterly.

[78]  E Guillibert,et al.  [Detecting alcoholism]. , 1984, Soins; la revue de reference infirmiere.