Familial aggregation of cardiovascular diseases in African-American pedigrees

What the Jackson Heart Study Has Taught Us About Diabetes and Cardiovascular Disease in the African American Community: a 20-year Appreciation

PURPOSE OF REVIEW

The burden of cardiometabolic diseases such as cardiovascular disease (CVD) and type 2 diabetes (T2D) is pronounced among African Americans. Research has shown that behavioral, social, metabolic, psychosocial, and genetic risk factors of CVD and T2D are closely interwoven. Approximately 20 years ago, the Jackson Heart Study (JHS) was established to investigate this constellation of risk factors.

RECENT FINDINGS

Findings from neighborhood studies emphasize the importance of social cohesion and physical environment in the context CVD and T2D risk. Socioeconomic status factors such as income and education were significant predictors for CVD and T2D. Behavioral studies indicate that modifiable risk factors such as smoking, physical inactivity, lack of sleep, and poor nutrition are associated with CVD risk and all-cause mortality. Mental health also was found to be associated with CVD and T2D. Genetic influences are associated with disease etiology. This review summarizes the joint contributions of CVD and cardiometabolic risk factors in an African American population.

Genetics and ESKD Disparities in African Americans

African Americans have a 2- to 4-fold greater incidence of end-stage kidney disease (ESKD) than whites, which has long raised the possibility of a genetic cause for this disparity. Recent advances in genetic studies have shown a causal association of polymorphisms at the apolipoprotein L1 gene (APOL1) with the markedly increased risk for the nondiabetic component of the overall disparity in ESKD in African Americans. Although APOL1-associated kidney disease is thought to account for a substantial proportion of ESKD in African Americans, not all the increased risk for ESKD is accounted for, and a complete cataloging of disparities in genetic causes of ESKD eludes our current understanding of genetic-associated kidney disease. Genetic testing aids the screening, diagnosis, prognosis, and treatment of diseases with a genetic basis. Widespread use of genetic testing in clinical practice is limited by the small number of actionable genetic variants, limited health literacy of providers and patients, and underlying complex ethical, legal, and social issues. This perspective reviews racial and ethnic differences associated with genetic diseases and the development of ESKD in African Americans and discusses potential uncertainties associated with our current understanding of penetrance of genetically linked kidney disease and population-attributable risk percent.

The relationship of family history and risk of type 2 diabetes differs by ancestry

AIM

Type 2 diabetes (T2DM) in a first-degree relative is a risk factor for incident diabetes. Americans of African ancestry (AA) have higher rates of T2DM than Americans of European ancestry (EA). Thus, we aimed to determine whether the presence, number and kinship of affected relatives are associated with race-specific T2DM incidence in a prospective study of participants from the Genetic Study of Atherosclerosis Risk (GeneSTAR), who underwent baseline screening including a detailed family history.

METHODS

Nondiabetic healthy siblings (n=1405) of patients with early-onset coronary artery disease (18-59 years) were enrolled (861 EA and 544 AA) and followed for incident T2DM (mean 14±6 years).

RESULTS

Baseline age was 46.2±7.3 years and 56% were female. T2DM occurred in 12.3% of EA and 19.1% of AA. Among EA, 32.6% had ≥1 affected first-degree relatives versus 53.1% in AA, P<0.0001. In fully adjusted Cox proportional hazard analyses, any family history was related to incident T2DM in EA (HR=2.53, 95% CI: 1.58-4.06) but not in AA (HR=1.01, 0.67-1.53). The number of affected relatives conferred incremental risk of T2DM in EA with HR=1.82 (1.08-3.06), 4.83 (2.15-10.85) and 8.46 (3.09-23.91) for 1, 2, and ≥3 affected, respectively. In AA only ≥3 affected increased risk (HR=2.45, 1.44-4.19). Specific kinship patterns were associated with incident T2DM in EA but not in AA.

CONCLUSIONS

The presence of any first-degree relative with T2DM does not discriminate risk in AA given the high race-specific prevalence of diabetes. Accounting for the number of affected relatives may more appropriately estimate risk for incident diabetes in both races.

Reversing the tide - diagnosis and prevention of T2DM in populations of African descent

Populations of African descent are at the forefront of the worldwide epidemic of type 2 diabetes mellitus (T2DM). The burden of T2DM is amplified by diagnosis after preventable complications of the disease have occurred. Earlier detection would result in a reduction in undiagnosed T2DM, more accurate statistics, more informed resource allocation and better health. An underappreciated factor contributing to undiagnosed T2DM in populations of African descent is that screening tests for hyperglycaemia, specifically, fasting plasma glucose and HbA_1c, perform sub-optimally in these populations. To offset this problem, combining tests or adding glycated albumin (a nonfasting marker of glycaemia), might be the way forward. However, differences in diet, exercise, BMI, environment, gene-environment interactions and the prevalence of sickle cell trait mean that neither diagnostic tests nor interventions will be uniformly effective in individuals of African, Caribbean or African-American descent. Among these three populations of African descent, intensive lifestyle interventions have been reported in only the African-American population, in which they have been found to provide effective primary prevention of T2DM but not secondary prevention. Owing to a lack of health literacy and poor glycaemic control in Africa and the Caribbean, customized lifestyle interventions might achieve both secondary and primary prevention. Overall, diagnosis and prevention of T2DM requires innovative strategies that are sensitive to the diversity that exists within populations of African descent.

Genetics of Type 2 Diabetes in African Americans

Type 2 diabetes (T2D) is a global health problem showing substantial ethnic disparity in disease prevalence. African Americans have one of the highest prevalence of T2D in the USA but little is known about their genetic risks. This review summarizes the findings of genetic regions and loci associated with T2D and related glycemic traits using linkage, admixture, and association approaches in populations of African ancestry. In particular, findings from genome-wide association and exome chip studies suggest the presence of both ancestry-specific and shared loci for T2D and glycemic traits. Among the European-identified loci that are transferable to individuals of African ancestry, allelic heterogeneity as well as differential linkage disequilibrium and risk allele frequencies pose challenges and opportunities for fine mapping and identification of causal variant(s) by trans-ancestry meta-analysis. More genetic research is needed in African ancestry populations including the next-generation sequencing to improve the understanding of genetic architecture of T2D.

Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94) Collapse

Key Words Collapse

MESH Headings

• Black or African American/genetics
• Diabetes Mellitus, Type 2/genetics
• Diabetes Mellitus, Type 2/pathology
• Genome-Wide Association Study
• HLA-B27 Antigen/genetics
• HMGA2 Protein/genetics
• Humans
• KCNQ1 Potassium Channel/genetics
• Mutant Chimeric Proteins/genetics
• Polymorphism, Single Nucleotide
• Transcription Factor 7-Like 2 Protein/genetics

Collapse

Grants

• R01 DK078616 NIDDK NIH HHS
• HHSN268201100012C NHLBI NIH HHS
• UL1RR025005 NCRR NIH HHS
• N02-HL-6-4278 NHLBI NIH HHS
• R01 HL071025 NHLBI NIH HHS
• R01-HL-088215 NHLBI NIH HHS
• UL1 RR033176 NCRR NIH HHS
• U01 HG007417 NHGRI NIH HHS
• U01-HG-004610 NHGRI NIH HHS
• U01DK057303 NIDDK NIH HHS
• R01 HL103612 NHLBI NIH HHS
• HHSN268201100009I NHLBI NIH HHS
• UL1 TR000445 NCATS NIH HHS
• U01 DK057304 NIDDK NIH HHS
• 32105-6 PHS HHS
• N01-HC-45205 NHLBI NIH HHS
• UL1RR033176 NCRR NIH HHS
• UL1TR000124 NCATS NIH HHS
• Z01-AG000513 NIA NIH HHS
• Z01 AG000513 Intramural NIH HHS
• M01-RR-01346 NCRR NIH HHS
• N01HC45204 NHLBI NIH HHS
• R01 HL061210 NHLBI NIH HHS
• N01-HC-05187 NHLBI NIH HHS
• R01-HL-071252 NHLBI NIH HHS
• HL59684 NHLBI NIH HHS
• R01 HL56266 NHLBI NIH HHS
• R01 HL120393 NHLBI NIH HHS
• R01 HL087698 NHLBI NIH HHS
• R01 HL046380 NHLBI NIH HHS
• S06GM008016-380111 NIGMS NIH HHS
• R01 HL071251 NHLBI NIH HHS
• U01DK57304 NIDDK NIH HHS
• R01 DK070941 NIDDK NIH HHS
• R01-HL-071258 NHLBI NIH HHS
• UL1 TR000150 NCATS NIH HHS
• UL1RR025741 NCRR NIH HHS
• G12 MD007597 NIMHD NIH HHS
• HHSC268200782096C PHS HHS
• R01HL59367 NHLBI NIH HHS
• HHSN268201100010C NHLBI NIH HHS
• U01 DK057298 NIDDK NIH HHS
• R01 HL071259 NHLBI NIH HHS
• UL1 RR025005 NCRR NIH HHS
• UL1-RR-025005 NCRR NIH HHS
• HL071025-01A1 NHLBI NIH HHS
• HL47902 NHLBI NIH HHS
• HHSN268201100008C NHLBI NIH HHS
• U01 HL080295 NHLBI NIH HHS
• N01 AG062101 NIA NIH HHS
• S06GM008016-320107 NIGMS NIH HHS
• HL060894 NHLBI NIH HHS
• N01-HC-48047 NHLBI NIH HHS
• R01-DK-8925601 NIDDK NIH HHS
• U01 DK057295 NIDDK NIH HHS
• HHSN268201100005G NHLBI NIH HHS
• R01 HL071252 NHLBI NIH HHS
• N01HC48049 NHLBI NIH HHS
• Z01 HG200362 Intramural NIH HHS
• HL085251 NHLBI NIH HHS
• Z01HG200362 NHGRI NIH HHS
• M01RR00080 NCRR NIH HHS
• NHGRI T32-HG002536 PHS HHS
• HHSN268201100008I NHLBI NIH HHS
• N01HC45205 NHLBI NIH HHS
• HL047890 NHLBI NIH HHS
• R01 HL087660 NHLBI NIH HHS
• R01CA92447 NCI NIH HHS
• U01 DK057300 NIDDK NIH HHS
• R01 HL059367 NHLBI NIH HHS
• HHSN268201100007C NHLBI NIH HHS
• HL62985 NHLBI NIH HHS
• HL105756 NHLBI NIH HHS
• P30 CA68485 NCI NIH HHS
• HHSN268200800007C NHLBI NIH HHS
• R01-HL-071250 NHLBI NIH HHS
• MC_UU_12015/1 Medical Research Council
• HS08365 AHRQ HHS
• NIH HL 46380 NHLBI NIH HHS
• N01HC95169 NHLBI NIH HHS
• N01AG62101 NIA NIH HHS
• HL061210 NHLBI NIH HHS
• R01 HL085251 NHLBI NIH HHS
• 32118 PHS HHS
• UL1-RR-024156 NCRR NIH HHS
• U01 HL120393 NHLBI NIH HHS
• R01 CA092447 NCI NIH HHS
• 42107-26 PHS HHS
• 32115 PHS HHS
• HHSN268201100011I NHLBI NIH HHS
• P30 DK079637 NIDDK NIH HHS
• HHSN268201100011C NHLBI NIH HHS
• R01 HL086694 NHLBI NIH HHS
• U01 HG004603 NHGRI NIH HHS
• UL1 RR024156 NCRR NIH HHS
• HL047887 NHLBI NIH HHS
• DP3 DK094311 NIDDK NIH HHS
• N01-HC-45204 NHLBI NIH HHS
• R01 HL071250 NHLBI NIH HHS
• T32 HG002536 NHGRI NIH HHS
• U01 DK057303 NIDDK NIH HHS
• N01-HC-95159 NHLBI NIH HHS
• HHSN268201300048C NHLBI NIH HHS
• 1R01AG032098-01A1 NIA NIH HHS
• UL1 RR025741 NCRR NIH HHS
• R01 HL087652 NHLBI NIH HHS
• N01-HC-95095 NHLBI NIH HHS
• M01-RR000052 NCRR NIH HHS
• HL087652 NHLBI NIH HHS
• U01HG004609 NHGRI NIH HHS
• UL1 TR000124 NCATS NIH HHS
• N01 AG062106 NIA NIH HHS
• HL58625-01A1 NHLBI NIH HHS
• HHSN268200625226C PHS HHS
• U01 HG004402 NHGRI NIH HHS
• R01-HL-071251 NHLBI NIH HHS
• N01HC55222 NHLBI NIH HHS
• R01 DK075681 NIDDK NIH HHS
• HL120393 NHLBI NIH HHS
• N01HC85086 NHLBI NIH HHS
• N02HL64278 NHLBI NIH HHS
• M01-RR-000080 NCRR NIH HHS
• R01 HL060944 NHLBI NIH HHS
• 32100-2 PHS HHS
• 24152 PHS HHS
• 32119 PHS HHS
• U01HG004402 NHGRI NIH HHS
• U01 DK057249 NIDDK NIH HHS
• R01 AG032098 NIA NIH HHS
• N01-HC-48050 NHLBI NIH HHS
• R01 HL105756 NHLBI NIH HHS
• U01 HG004609 NHGRI NIH HHS
• M01-RR-00425 NCRR NIH HHS
• U01 HG004599 NHGRI NIH HHS
• U01 HL047902 NHLBI NIH HHS
• HHSN268201100006C NHLBI NIH HHS
• HHSN268201300049C NHLBI NIH HHS
• N01-HC-45134 NHLBI NIH HHS
• R01 DK066358 NIDDK NIH HHS
• 42129-32 PHS HHS
• M01-RR-00827-29 NCRR NIH HHS
• P30 DK063491 NIDDK NIH HHS
• R01 HL071051 NHLBI NIH HHS
• K99 DK081350 NIDDK NIH HHS
• RC1 HL100185 NHLBI NIH HHS
• P20 MD006899 NIMHD NIH HHS
• R01-HL-071259 NHLBI NIH HHS
• R01 HL062985 NHLBI NIH HHS
• N01HC95095 NHLBI NIH HHS
• R01HL087641 NHLBI NIH HHS
• HHSN268201200036C NHLBI NIH HHS
• R01 DK084350 NIDDK NIH HHS
• N01-HC-95169 NHLBI NIH HHS
• HL087660 NHLBI NIH HHS
• HHSN268201100005I NHLBI NIH HHS
• RC1 HL100245 NHLBI NIH HHS
• N01 AG062103 NIA NIH HHS
• U01 NS041588 NINDS NIH HHS
• U01DK057298 NIDDK NIH HHS
• R01-HL-071205 NHLBI NIH HHS
• HL047889 NHLBI NIH HHS
• M01 RR000052 NCRR NIH HHS
• N01-HC-48049 NHLBI NIH HHS
• HL060944 NHLBI NIH HHS
• N01HC95159 NHLBI NIH HHS
• U01DK057300 NIDDK NIH HHS
• NR0224103 NINR NIH HHS
• N01 HC045134 NHLBI NIH HHS
• HHSN268201300047C NHLBI NIH HHS
• R01-HL-087700 NHLBI NIH HHS
• HHSN268201300050C NHLBI NIH HHS
• 32108-9 PHS HHS
• N01HC65226 NHLBI NIH HHS
• R01-DK-075681 NIDDK NIH HHS
• U01 HL072518 NHLBI NIH HHS
• U01 DK078616 NIDDK NIH HHS
• 098395 Wellcome Trust
• HL087698 NHLBI NIH HHS
• R01 HL080295 NHLBI NIH HHS
• U01 DK057329 NIDDK NIH HHS
• MC_PC_U127592696 Medical Research Council
• P30 CA068485 NCI NIH HHS
• HHSN268200782096C NHLBI NIH HHS
• R01 HL088215 NHLBI NIH HHS
• M01 RR010284 NCRR NIH HHS
• M01 RR000425 NCRR NIH HHS
• M01-RR-07122 NCRR NIH HHS
• M01 RR007122 NCRR NIH HHS
• U01DK57292 NIDDK NIH HHS
• R01 HL059684 NHLBI NIH HHS
• HL100245 NHLBI NIH HHS
• M01 RR07122 NCRR NIH HHS
• N01HC48050 NHLBI NIH HHS
• N01HC85082 NHLBI NIH HHS
• N01HC05187 NHLBI NIH HHS
• U01 HG006378 NHGRI NIH HHS
• N01HC48047 NHLBI NIH HHS
• N01HC85083 NHLBI NIH HHS
• HHSN268201100005C NHLBI NIH HHS
• R01 HL071205 NHLBI NIH HHS
• DK063491 NIDDK NIH HHS
• HHSN268201100009C NHLBI NIH HHS
• U01DK57329 NIDDK NIH HHS
• R01 DK053591 NIDDK NIH HHS
• M01 RR000997 NCRR NIH HHS
• UL1 TR001120 NCATS NIH HHS
• HHSN268201100007I NHLBI NIH HHS
• Wellcome Trust
• HHSN268201300046C NHLBI NIH HHS
• N01-HC-95100 NHLBI NIH HHS
• N01AG62103 NIA NIH HHS
• U01 HG004610 NHGRI NIH HHS
• U01DK070657 NIDDK NIH HHS
• R01-HL-071051 NHLBI NIH HHS
• HL080295 NHLBI NIH HHS
• R01 HS008365 AHRQ HHS
• N01AG62106 NIA NIH HHS
• U01-HG-004608 NHGRI NIH HHS
• N01-WH-22110 WHI NIH HHS
• 32122 PHS HHS
• U01DK057249 NIDDK NIH HHS
• U01 DK057292 NIDDK NIH HHS
• N01CO12400 NCI NIH HHS
• N01HC85079 NHLBI NIH HHS
• N02-HL-64278 NHLBI NIH HHS
• R01 HL060894 NHLBI NIH HHS
• R01 AG023629 NIA NIH HHS
• HL100185 NHLBI NIH HHS
• R01 HL087641 NHLBI NIH HHS
• 2M01RR010284 NCRR NIH HHS
• 44221 PHS HHS
• HL103612 NHLBI NIH HHS
• M01 RR000827 NCRR NIH HHS
• M01 RR000080 NCRR NIH HHS
• R01 HL087700 NHLBI NIH HHS
• N01HC85080 NHLBI NIH HHS
• 32111-13 PHS HHS
• AG023629 NIA NIH HHS
• R01 HL071258 NHLBI NIH HHS
• U01 HG004608 NHGRI NIH HHS
• R56 AG023629 NIA NIH HHS
• M01-RR-00997 NCRR NIH HHS
• N01WH22110 WHI NIH HHS
• N01-CO-12400 NCI NIH HHS
• U01 DK070657 NIDDK NIH HHS
• U01DK57295 NIDDK NIH HHS
• N01HC48048 NHLBI NIH HHS
• M01 RR001346 NCRR NIH HHS
• N01-HC-65226 NHLBI NIH HHS
• U01HL72518 NHLBI NIH HHS
• U01-HG-04599 NHGRI NIH HHS
• N01HC85081 NHLBI NIH HHS
• R01HL086694 NHLBI NIH HHS

Collapse

Affiliation(s)

Maggie C. Y. Ng Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Daniel Shriner Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Brian H. Chen Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Center for Metabolic Disease Prevention, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
Jiang Li Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wei-Min Chen Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Xiuqing Guo Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Jiankang Liu Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Suzette J. Bielinski Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
Lisa R. Yanek The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Michael A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Mary E. Comeau Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Laura J. Rasmussen-Torvik Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Richard A. Jensen Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America
Daniel S. Evans San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Yan V. Sun Department of Epidemiology and Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America
Ping An Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Sanjay R. Patel Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
Yingchang Lu The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Jirong Long Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Loren L. Armstrong Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Lynne Wagenknecht Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Lingyao Yang Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Beverly M. Snively Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Nicholette D. Palmer Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Poorva Mudgal Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Carl D. Langefeld Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Keith L. Keene Department of Biology, Center for Health Disparities, East Carolina University, Greenville, North Carolina, United States of America
Barry I. Freedman Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Josyf C. Mychaleckyj Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Uma Nayak Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Leslie J. Raffel Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Mark O. Goodarzi Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Y-D Ida Chen Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Herman A. Taylor Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America Jackson State University, Tougaloo College, Jackson, Mississippi, United States of America
Adolfo Correa Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Mario Sims Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
David Couper Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
James S. Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
Eric Boerwinkle Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Adebowale Adeyemo Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Ayo Doumatey Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Guanjie Chen Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Rasika A. Mathias The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Division of Allergy and Clinical Immunology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Dhananjay Vaidya The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
Andrew B. Singleton Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Alan B. Zonderman Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
Robert P. Igo Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
John R. Sedor Department of Medicine, Case Western Reserve University, MetroHealth System campus, Cleveland, Ohio, United States of America Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, United States of America
the FIND Consortium
Edmond K. Kabagambe Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
David S. Siscovick Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
Barbara McKnight Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Kenneth Rice Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wen-Chi Hsueh Department of Medicine, University of California, San Francisco, California, United States of America
Wei Zhao Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Lawrence F. Bielak Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Aldi Kraja Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Michael A. Province Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Erwin P. Bottinger The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Omri Gottesman The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Qiuyin Cai Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Wei Zheng Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
William J. Blot Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee; International Epidemiology Institute, Rockville, Maryland, United States of America
William L. Lowe Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Jennifer A. Pacheco Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Dana C. Crawford Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
the eMERGE Consortium
the DIAGRAM Consortium
Elin Grundberg Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
the MuTHER Consortium
Stephen S. Rich Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
M. Geoffrey Hayes Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Xiao-Ou Shu Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Ruth J. F. Loos The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Ingrid B. Borecki Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Patricia A. Peyser Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Steven R. Cummings San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Bruce M. Psaty Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America Department of Health Services, University of Washington, Seattle, Washington, United States of America
Myriam Fornage Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Sudha K. Iyengar Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
Michele K. Evans Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore Maryland, United States of America
Diane M. Becker The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
W. H. Linda Kao Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
James G. Wilson Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Michèle M. Sale Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
Simin Liu Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Department of Epidemiology, University of California Los Angeles, Los Angeles, California, United States of America Departments of Epidemiology and Medicine, Brown University, Providence, Rhode Island, United States of America
Charles N. Rotimi Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Donald W. Bowden Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
for the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium

Collapse

Genetic epidemiology of type 2 diabetes and cardiovascular diseases in Africa

The burdens of type 2 diabetes (T2D) and cardiovascular diseases (CVD) are increasing in Africa. T2D and CVD are the result of the complex interaction between inherited characteristics, lifestyle, and environmental factors. The epidemic of obesity is largely behind the exploding global incidence of T2D. However, not all obese individuals develop diabetes and positive family history is a powerful risk factor for diabetes and CVD. Recent implementations of high throughput genotyping and sequencing approaches have advanced our understanding of the genetic basis of diabetes and CVD by identifying several genomic loci that were not previously linked to the pathobiology of these diseases. However, African populations have not been adequately represented in these global genomic efforts. Here, we summarize the state of knowledge of the genetic epidemiology of T2D and CVD in Africa and highlight new genomic initiatives that promise to inform disease etiology, public health and clinical medicine in Africa.

Transferability and fine mapping of type 2 diabetes loci in African Americans: the Candidate Gene Association Resource Plus Study

Type 2 diabetes (T2D) disproportionally affects African Americans (AfA) but, to date, genetic variants identified from genome-wide association studies (GWAS) are primarily from European and Asian populations. We examined the single nucleotide polymorphism (SNP) and locus transferability of 40 reported T2D loci in six AfA GWAS consisting of 2,806 T2D case subjects with or without end-stage renal disease and 4,265 control subjects from the Candidate Gene Association Resource Plus Study. Our results revealed that seven index SNPs at the TCF7L2, KLF14, KCNQ1, ADCY5, CDKAL1, JAZF1, and GCKR loci were significantly associated with T2D (P < 0.05). The strongest association was observed at TCF7L2 rs7903146 (odds ratio [OR] 1.30; P = 6.86 × 10⁻⁸). Locus-wide analysis demonstrated significant associations (P(emp) < 0.05) at regional best SNPs in the TCF7L2, KLF14, and HMGA2 loci as well as suggestive signals in KCNQ1 after correction for the effective number of SNPs at each locus. Of these loci, the regional best SNPs were in differential linkage disequilibrium (LD) with the index and adjacent SNPs. Our findings suggest that some loci discovered in prior reports affect T2D susceptibility in AfA with similar effect sizes. The reduced and differential LD pattern in AfA compared with European and Asian populations may facilitate fine mapping of causal variants at loci shared across populations.

Abnormal glucose metabolism in Hispanic parents of children with acanthosis nigricans

Objective. Assess the prevalence of abnormal glucose metabolism among Hispanic parents of children with acanthosis nigricans (AN). Methods. Hispanic families (n = 258) were evaluated for metabolic and anthropometric parameters including fasting glucose levels and AN status. Results. Mothers with AN+ children had IFG (17.3%) and 4% had glucose levels ≥126 mg/dL (P = 0.028) compared to 7.1% and 1.8% of mothers with AN− children, respectively. Mothers of AN+ children also had greater odds of having impaired fasting glucose levels (OR: 3.917, 95% CI: 1.475–10.404; P < 0.004) but this was not the case for fathers (OR: 1.125, 95% CI: 0.489–2.586; P = 0.781). Mothers of AN+ children were also more likely to be AN+ (OR: 5.76, 95% CI: 2.98–11.13, P < 0.001). Screening discovered glucose levels >126 mg/dL in 9% of fathers with AN+ children. Conclusions. Hispanic mothers of AN+ children are at higher risk of carbohydrate metabolism abnormalities. AN in children can be a marker for prevention and delay programs aimed at identifying adults at risk for diabetes.

Exploration of the utility of ancestry informative markers for genetic association studies of African Americans with type 2 diabetes and end stage renal disease

Admixture and population stratification are major concerns in genetic association studies. We wished to evaluate the impact of admixture using empirically derived data from genetic association studies of African Americans (AA) with type 2 diabetes (T2DM) and end-stage renal disease (ESRD). Seventy ancestry informative markers (AIMs) were genotyped in 577 AA with T2DM-ESRD, 596 AA controls, 44 Yoruba Nigerian (YRI) and 39 European American (EA) controls. Genotypic data and association results for eight T2DM candidate gene studies in our AA population were included. Ancestral estimates were calculated using FRAPPE, ADMIXMAP and STRUCTURE for all AA samples, using varying numbers of AIMs (25, 50, and 70). Ancestry estimates varied significantly across all three programs with the highest estimates obtained using STRUCTURE, followed by ADMIXMAP; while FRAPPE estimates were the lowest. FRAPPE estimates were similar using varying numbers of AIMs, while STRUCTURE estimates using 25 AIMs differed from estimates using 50 and 70 AIMs. Female T2DM-ESRD cases showed higher mean African proportions as compared to female controls, male cases, and male controls. Age showed a weak but significant correlation with individual ancestral estimates in AA cases (r2 = 0.101; P = 0.019) and in the combined set (r2 = 0.131; P = 3.57 x 10(-5)). The absolute difference between frequencies in parental populations, absolute delta, was correlated with admixture impact for dominant, additive, and recessive genotypic models of association. This study presents exploratory analyses of the impact of admixture on studies of AA with T2DM-ESRD and supports the use of ancestral proportions as a means of reducing confounding effects due to admixture.

Association of the mu-opioid receptor gene with type 2 diabetes mellitus in an African American population

African Americans (AA) are at increased risk for developing type 2 diabetes mellitus (T2DM) relative to European Americans. We previously detected linkage of T2DM to 6q24-q27 (LOD 2.26) at 163.5 cM, closest to marker D6S1035, in a genome-wide scan of AA families. The mu-opioid receptor gene (OPRM1) is located within the LOD-1 support interval of this linkage peak. OPRM1 is an attractive positional candidate gene for T2DM susceptibility since agonists of OPRM1 affect glucose-induced insulin release and OPRM1 knockout mice have a more rapid induction of insulin resistance than wild-type. Twenty-two SNPs in this gene, at an average spacing of 3.9 kb, were genotyped in 380 AA T2DM cases and 276 AA controls. In single SNP association analyses, rs648007 demonstrated significant evidence of association with T2DM (P=0.013). Four blocks of high linkage disequilibrium were detected across the OPRM1 gene. Association analyses of haplotypes in each of these blocks revealed two haplotype blocks with significant overall P values (P=0.007 and 0.046). Significant, but rare, risk and protective haplotypes were identified as driving these associations with T2DM (P=0.034-0.047). These associations suggest that the OPRM1 gene plays a role in T2DM susceptibility in African Americans.

Impact of a Community-Based Multiple Risk Factor Intervention on Cardiovascular Risk in Black Families With a History of Premature Coronary Disease

BACKGROUND

Black subjects with a family history of premature coronary heart disease (CHD) have a marked excess risk, yet barriers prevent effective risk reduction. We tested a community-based multiple risk factor intervention (community-based care [CBC]) and compared it with "enhanced" primary care (EPC) to reduce CHD risk in high-risk black families.

METHODS AND RESULTS

Black 30- to 59-year-old siblings of a proband with CHD aged <60 years were randomized for care of BP > or =140/90 mm Hg, LDL cholesterol > or =3.37 mmol/L, or current smoking to EPC (n=168) or CBC (n=196) and monitored for 1 year. EPC and CBC were designed to eliminate barriers to care. The CBC group received care by a nurse practitioner and a community health worker in a community setting. The CBC group was 2 times more likely to achieve goal levels of LDL cholesterol and blood pressure compared with the EPC group (95% CI, 1.11 to 4.20 and 1.39 to 3.88, respectively) with adjustment for baseline levels of age, sex, education, and baseline use of medications. The CBC group demonstrated a significant reduction in global CHD risk, whereas no reduction was seen in the EPC group (P<0.0001).

CONCLUSIONS

Eliminating known barriers may not be sufficient to reduce CHD risk in primary care settings. An alternative community care model that addresses barriers may be a more effective way to ameliorate CHD risk in high-risk black families.

Patients' sibling history was sensitive for hypertension and specific for diabetes

OBJECTIVE

We examined the analytic validity of reported family history of hypertension and diabetes among siblings in the Seychelles.

STUDY DESIGN AND SETTING

Four hundred four siblings from 73 families with at least two hypertensive persons were identified through a national hypertension register. Two gold standards were used prospectively. Sensitivity was the proportion of respondents who indicated the presence of disease in a sibling, given that the sibling reported to be affected (personal history gold standard) or was clinically affected (clinical status gold standard). Specificity was the proportion of respondents who reported an unaffected sibling, given that the sibling reported to be unaffected or was clinically unaffected. Respondents gave information on the disease status in their siblings in approximately two-thirds of instances.

RESULTS

When sibling history could be obtained (n=348 for hypertension, n=404 for diabetes), the sensitivity and the specificity of the sibling history were, respectively, 90 and 55% for hypertension, and 61 and 98% for diabetes, using clinical status and, respectively, 89 and 78% for hypertension, and 53 and 98% for diabetes, using personal history.

CONCLUSION

The sibling history, when available, is a useful screening test to detect hypertension, but it is less useful to detect diabetes.

A genome-wide scan for type 2 diabetes in African-American families reveals evidence for a locus on chromosome 6q

African Americans are at increased risk of type 2 diabetes and many diabetes complications. We have carried out a genome-wide scan for African American type 2 diabetes using 638 affected sibling pairs (ASPs) from 247 families ascertained through impaired renal function to identify type 2 diabetes loci in this high-risk population. Of the 638 ASPs, 210 were concordant for diabetes with impaired renal function. A total of 390 markers, at an average spacing of 9 cM, were genotyped by the Center for Inherited Disease Research (CIDR) as part of the International Type 2 Diabetes Linkage Analysis Consortium. Nonparametric linkage (NPL) analyses conducted using the exponential model implemented in Genehunter Plus provided suggestive evidence for linkage at 6q24-q27 (163.5 cM, logarithm of odds [LOD] 2.26). Multilocus NPL regression analysis identified the 6q locus (D6S1035, LOD 2.67) and two additional regions: 7p (LOD 1.06) and 18q (LOD 0.87) as important in this model. NPL regression-based interaction analyses and ordered subset analyses (OSAs) supported the presence of a locus at chromosome 7p (29-34 cM) in the pedigrees with the earliest mean age of diagnosis of type 2 diabetes (P = 0.009 for interaction, DeltaP = 0.0034 for OSA) and lower mean BMI (P = 0.009 for interaction, DeltaP = 0.070 for OSA). These results provide evidence that genes predisposing African-American individuals to type 2 diabetes are located in the 6q and 7p regions of the genome.

Family-centered approaches to understanding and preventing coronary heart disease

Family history represents the contributions and interactions of unique genomic and ecologic factors that affect the metabolic profile and life course of a family and its members. It is well known that a family history of coronary heart disease (CHD) is a significant predictor of an individual's risk for CHD even after adjusting for an individual's own established risk factors, such as hypertension, smoking, and abnormal lipoprotein levels. The explanation for the observed familial disease aggregation is not well understood except for the general knowledge that genetic and environmental factors predisposing to CHD also aggregate in families. Given the multifactorial nature of an individual's risk, it can be argued that an individual's familial risk of disease may, in fact, be a better indicator of the many complex interactions among predisposing genetic and environmental factors than can be captured by an individual's own risk factors. Issues of how to assess, quantitate, and apply family history information in clinical settings still need to be resolved. Some clinical risk indicators, such as the National Cholesterol Education Program III guidelines, take into account family history, while others, such as the Framingham Risk Score, do not. Moreover, several family-centered intervention studies have demonstrated the particular advantages of focusing on families rather than just individuals. Although there has been tremendous progress in primary prevention of CHD over the last 20 years, substantial advancements may still be achieved by focusing on the family as its own unit of inference and as a specific target for disease prevention.

Genetics of diabetes complications

Long-term exposure to the hyperglycemia characteristic of diabetes patients leads to serious and frequently disabling or fatal complications. Emerging evidence suggests that genes are a significant contributor to an individual's risk of developing complications. This evidence is from evaluations of familial aggregation, differences in incidence in racial and ethnic groups, and statistical analysis of family data. Evidence to date suggests that complication genes are, distinct from the genes contributing to diabetes. Molecular geneticists have taken several approaches to identify genes contributing to complications, ranging from relatively simple analysis of specific candidate genes in small case-control comparisons to systematic evaluations of the human genome using genome scans and linkage analysis in large collections of families. Results suggest that genetic contributions to diabetes complications are diverse and complex in nature, presenting a significant challenge to researchers. Diabetes-affected families are frequently enriched for complications such as cardiovascular disease or nephropathy. In addition to their value in the study of diabetes complications, such families may be valuable resources for understanding cardiovascular disease and nephropathy in the nondiabetic population also.

Relationships between the children and the parents for coronary risk factors

BACKGROUND

We aimed to investigate the relation of coronary risk factors in children to coronary heart disease (CHD) or coronary risk factors in their parents.

METHODS

A sample of 252 parents of 164 children with two or more coronary risk factors were included in this study. The control group consisted of 175 parents of 114 children with no risk factors. Both groups were evaluated for coronary risk factors and CHD. The children in the groups were separated into sex and age groups consisting of 7 to 11-years-olds, 12 to 15-years-old and 16 to 18-years-old.

RESULTS

Many lipid parameters related with coronary risk factors in the mothers of 7 to 11-year-old girls, in the fathers of 7 to 11-year-old girls and 16 to 18-year-old boys were at higher levels than in the control group. Anthropometric parameters, especially those of reflecting body fatness such as skinfold thickness measurements and total bodyfat percentage values were at higher levels in the fathers of 12-15- and 16 to 18-year-old girls. The higher levels of most of the anthropometric and biochemical parameters in the fathers of 16 to 18-year-old boys were striking. Hyperlipidemia prevalences in the fathers of 7 to 11-year-old girls and boys were higher than the control parents. In the study group, the girls positively correlated with both their parents for total cholesterol levels and positively correlated with only their mothers for height, bodyfat percentage, triglycerides and low-density lipoprotein- cholesterol (LDL-C) levels. However, the boys positively correlated with their mothers for weight and with their fathers for bodyfat percentage, diastolic blood pressure (BP) values and serum LDL-C levels. The proportion of individuals who had ischemic findings on treadmill exercise testing was significantly greater than control group in only the fathers of 12 to 15-year-old boys (P<0.05).

CONCLUSIONS

The parents, especially the fathers of children with coronary risk factors have higher levels of coronary risk factors than those in the control group. There is a familial aggregation of body fatness and adverse lipid levels in the families of the children with coronary risk factors. The parents of children recognized as having coronary risk factors should be evaluated for these risk factors too.

Familial risk of high blood pressure in the Canadian population

Familial risk ratios for high blood pressure were estimated in a representative sample of the Canadian population. The sample consisted of 14,069 participants 7-69 years of age from 5,753 families participating in the 1981 Canada Fitness Survey. Resting systolic (SBP) and diastolic (DBP) blood pressures were adjusted for the effects of body mass index using regression procedures. Varying degrees of high blood pressure were defined as the 75(th), 85(th), and 95(th) percentiles of age- and sex-specific values. Age- and sex-standardized risk ratios (SRRs) were calculated comparing the prevalences in the general population to those in spouses and first-degree relatives of probands with high blood pressure. SRRs for the 95(th) percentile were, for SBP and DBP, respectively, 1.37 and 1.45 in spouses and 1.33 and 2.36 in first-degree relatives of probands. SRRs decrease with decreasing percentile cut-offs used to define high blood pressure (95(th) > 85(th) > 75(th)), and SRRs are generally higher in first-degree relatives than in spouses, particularly for DBP. The results indicate significant familial risk for high blood pressure in the Canadian population, and the pattern of SRRs suggests that genetic factors may be responsible for a portion of the risk.

African Americans and high blood pressure: the role of stereotype threat

We examined the effect of stereotype threat on blood pressure reactivity. Compared with European Americans, and African Americans under little or no stereotype threat, African Americans under stereotype threat exhibited larger increases in mean arterial blood pressure during an academic test, and performed more poorly on difficult test items. We discuss the significance of these findings for understanding the incidence of hypertension among African Americans.

Family risk score of coronary heart disease (CHD) as a predictor of CHD: the Atherosclerosis Risk in Communities (ARIC) study and the NHLBI family heart study

Family history of coronary heart disease (CHD) has been found to be a risk factor for CHD in numerous studies. Few studies have addressed whether a quantitative measure of family history of CHD (family risk score, FRS) predicts CHD in African Americans. This study assessed the association between FRS and incident CHD of participants, and the variation of the association by gender and race. Participants in the study were a biracial population-based cohort with 3,958 African Americans and 10,580 Whites aged 45-64 years old in the ARIC baseline survey (1987-1989). They were randomly selected from four U. S. communities. During follow-up (1987-1993), 352 participants experienced the onset of CHD. Incidence density of CHD (per 1,000 person-years) was 7.8 and 3.6 among African-American men (AAM) and women (AAW), and 7.2 and 2.2 among White men (WM) and women (WW). The hazard rate ratio (HRR) of CHD associated with one standard deviation increase of FRS was 1.52 in AAW, 1.46 in AAM, 1.41 in WW, and 1.68 in WM. The HRRs decreased 4.6% in AAW, 1.4% in WW, 5.7% in AAM, and 3.0% in WM, but increased 2.1% in AAM after adjustment for selected covariates. FRS predicts incident CHD in African Americans and Whites, men and women. The relation of FRS to incident CHD can be only partially explained by the selected risk factors in the biological causal pathways: IMT, T-G, LDL, HDL, Lp(a), fibrinogen and hypertension. No significant difference by race has been found in this study.

Familial predisposition and susceptibility to the effect of other risk factors for myocardial infarction

STUDY OBJECTIVES

To assess if familial predisposition to myocardial infarction (MI) is an indicator of increased susceptibility to the effect of other established risk factors. The study assessed whether a family history of MI modifies the effect of arterial blood pressure, plasma cholesterol, high and low density lipoprotein cholesterol, % triglycerides, diabetes mellitus, body mass index, height, smoking habits, alcohol intake, physical activity level, and educational level on the incidence of MI.

DESIGN

Prospective population based cohort study of cardiovascular risk and risk factors with follow up of MI by record linkage with the Cause of Death Register and The National Hospital Discharge Register until 1994.

SETTING

The Copenhagen Centre for Prospective Population Studies, where data from three Danish studies are integrated.

PARTICIPANTS

Subjects were 24,664 people aged 20-93, examined between 1976 and 1987.

MAIN RESULTS

A total of 1763 new cases of MI occurred during 293,559 person years of observation. All risk factors, including family history of MI reported by 4012 subjects, were, as expected, associated with incidence of MI. With a few inconsistent exceptions we found no significant interactions between family history of MI and cardiovascular risk factors in their effect on MI.

CONCLUSIONS

The familial predisposition to MI does not consistently modify the effect of other risk factors on the risk of MI. However, subjects with a family history of MI may still be regarded as an appropriate target group for screening for cardiovascular risk and intervention against other risk factors.

Ionizing radiation and genetic risks. VI. Chronic multifactorial diseases: a review of epidemiological and genetical aspects of coronary heart disease, essential hypertension and diabetes mellitus

This paper provides a broad overview of the epidemiological and genetical aspects of common multifactorial diseases in man with focus on three well-studied ones, namely, coronary heart disease (CHD), essential hypertension (EHYT) and diabetes mellitus (DM). In contrast to mendelian diseases, for which a mutant gene either in the heterozygous or homozygous condition is generally sufficient to cause disease, for most multifactorial diseases, the concepts of genetic susceptibility' and risk factors' are more appropriate. For these diseases, genetic susceptibility is heterogeneous. The well-studied diseases such as CHD permit one to conceptualize the complex relationships between genotype and phenotype for chronic multifactorial diseases in general, namely that allelic variations in genes, through their products interacting with environmental factors, contribute to the quantitative variability of biological risk factor traits and thus ultimately to disease outcome. Two types of such allelic variations can be distinguished, namely those in genes whose mutant alleles have (i) small to moderate effects on the risk factor trait, are common in the population (polymorphic alleles) and therefore contribute substantially to the variability of biological risk factor traits and (ii) profound effects, are rare in the population and therefore contribute far less to the variability of biological risk factor traits. For all the three diseases considered in this review, a positive family history is a strong risk factor. CHD is one of the major contributors to mortality in most industrialized countries. Evidence from epidemiological studies, clinical correlations, genetic hyperlipidaemias etc., indicate that lipids play a key role in the pathogenesis of CHD. The known lipid-related risk factors include: high levels of low density lipoprotein cholesterol, low levels of high density lipoprotein cholesterol, high apoB levels (the major protein fraction of the low density lipoprotein particles) and elevated levels of Lp(a) lipoprotein. Among the risk factors which are not related to lipids are: high levels of homocysteine, low activity of paraoxonase and possibly also elevated plasma fibrinogen levels. In addition to the above, hypertension, diabetes and obesity (which themselves have genetic determinants) are important risk factors for CHD. Among the environmental risk factors are: high dietary fat intake, smoking, stress, lack of exercise etc. About 60% of the variability of the plasma cholesterol is genetic in origin. While a few genes have been identified whose mutant alleles have large effects on this trait (e.g., LDLR, familial defective apoB-100), variability in cholesterol levels among individuals in most families is influenced by allelic variation in many genes (polymorphisms) as well as environmental exposures. A proportion of this variation can be accounted for by two alleles of the apoE locus that increase (&epsi;4) and decrease (&epsi;2) cholesterol levels, respectively. A polymorphism at the apoB gene (XbaI) also has similar effects, but is probably not mediated through lipids. High density lipoprotein cholesterol levels are genetically influenced and are related to apoA1 and hepatic lipase (LIPC) gene functions. Mutations in the apoA1 gene are rare and there are data which suggest a role of allelic variation at or linked LIPC gene in high density lipoprotein cholesterol levels. Polymorphism at the apoA1--C3 loci is often associated with hypertriglyceridemia. The apo(a) gene which codes for Lp(a) is highly polymorphic, each allele determining a specific number of multiple tandem repeats of a unique coding sequence known as Kringle 4. The size of the gene correlates with the size of the Lp(a) protein. The smaller the size of the Lp(a) protein, the higher are the Lp(a) levels. (ABSTRACT TRUNCATED)

Family history of coronary heart disease and pre-clinical carotid artery atherosclerosis in African-Americans and whites: the ARIC study: Atherosclerosis Risk in Communities

The association between family history of coronary heart disease (CHD) and morbidity and mortality due to atherosclerotic sequelae, although well documented in population-based samples of whites, has been little studied in African Americans. Less is known about the relationship between a family history of CHD and pre-clinical atherosclerosis. We report the relation between family history of CHD, summarized in a family risk score (FRS), and asymptomatic atherosclerosis at the extracranial carotid arteries, measured by B-mode ultrasound. The FRS was assessed in relatives of 3,034 African Americans and 9,048 white probands aged 45 to 64 years, in the four community-based cohorts of the ARIC Study. The analyses were restricted to individuals free of clinically manifest CHD. The distribution of CHD FRS by ethnic-gender groups was right skewed, with slightly higher mean values for white than African-American males, and for African-American than white females. In a series of multivariate linear regression models with mean carotid artery intima-media wall thickness (IMT) as the dependent variable, FRS was associated positively with IMT in white and African-American women and white men. In a multiple regression model, approximately one-half of the quantitative statistical relationship of the CHD FRS with IMT in whites was statistically explained by the major risk factors considered as intervening, explanatory variables in this analysis. This association in African-American women was fully explained by the major risk factors. The FRS was not, however, associated with atherosclerosis or major risk factors in African-American males, in the ARIC Study.

Markedly high prevalence of coronary risk factors in apparently healthy African-American and white siblings of persons with premature coronary heart disease

Among persons with a family history of premature coronary heart disease (CHD), siblings bear an excess risk of CHD that is as high as 12 times that of the general population. Aggressive, new, national guidelines for CHD risk reduction have focused on high-risk families, yet little is known about actual remediable risk factors in siblings of persons with premature CHD. To determine the magnitude of the problem relative to the general population, we screened 846 unaffected siblings (ages 30 to 59 years) of persons with documented CHD before age 60 years and compared their risk factor values with population reference norms obtained in the Third National Health and Nutrition Examination Survey (NHANES III) and the National Health Interview Survey (NHIS). Mean levels of low-density lipoprotein cholesterol were 0.52 mmol/L (20 mg/dl) higher in siblings; the prevalence of low-density lipoprotein cholesterol > or =4.14 mmol/L (160 mg/dl) was nearly twice that of race, sex, and age-specific values from NHANES III. Levels of high-density lipoprotein cholesterol <0.91 mmol/L (35 mg/dl) were similar between siblings and NHANES III (11% and 12%, respectively). Only 4% of all siblings had triglyceride levels > or =4.52 mmol/L (400 mg/dl). Hypertension prevalence was twice as high among siblings as among the NHANES III. Current smoking was 33.9% in white siblings and 25.5% in the NHIS, whereas smoking in African-Americans was similar to that in the NHIS (31.1% vs 29.2%). A mere 13% to 29% of siblings were without any major remediable risk factors. The overwhelming need for risk factor modification in this easily identifiable high-risk population supports aggressive national guidelines and demonstrates the lack of adequate treatment of apparently healthy siblings of persons with premature CHD.