James B Meigs

Harvard Medical School, Boston, Massachusetts, United States

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Publications (411)3289.97 Total impact

  • Han Chen, James B Meigs, Josée Dupuis
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    ABSTRACT: Objectives: The incorporation of gene-environment interactions could improve the ability to detect genetic associations with complex traits. For common genetic variants, single-marker interaction tests and joint tests of genetic main effects and gene-environment interaction have been well-established and used to identify novel association loci for complex diseases and continuous traits. For rare genetic variants, however, single-marker tests are severely underpowered due to the low minor allele frequency, and only a few gene-environment interaction tests have been developed. We aimed at developing powerful and computationally efficient tests for gene-environment interaction with rare variants. Methods: In this paper, we propose interaction and joint tests for testing gene-environment interaction of rare genetic variants. Our approach is a generalization of existing gene-environment interaction tests for multiple genetic variants under certain conditions. Results: We show in our simulation studies that our interaction and joint tests have correct type I errors, and that the joint test is a powerful approach for testing genetic association, allowing for gene-environment interaction. We also illustrate our approach in a real data example from the Framingham Heart Study. Conclusion: Our approach can be applied to both binary and continuous traits, it is powerful and computationally efficient. © 2014 S. Karger AG, Basel.
    Human Heredity 07/2014; 78(2):81-90. · 1.57 Impact Factor
  • Soo Lim, James B Meigs
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    ABSTRACT: The average of overweight individual can have differential fat depots in target organs or specific compartments of the body. This ectopic fat distribution may be more of a predictive factor for cardiovascular risk than obesity. Abdominal visceral obesity, a representative ectopic fat, is robustly associated with insulin resistance and cardiovascular risk. Fat depots in the liver and muscle tissue cause adverse cardiometabolic risk by affecting glucose and lipid metabolism. Pericardial fat and perivascular fat affect coronary atherosclerosis, cardiac function, and hemodynamics. Fat around the neck is associated with systemic vascular resistance. Fat around the kidney may increase blood pressure and induce albuminuria. Fat accumulation in or around the pancreas alters glucose metabolism, conferring cardiovascular risk. Ectopic fat may act as an active endocrine and paracrine organ that releases various bioactive mediators that influence insulin resistance, glucose and lipid metabolism, coagulation, and inflammation, which all contribute to cardiovascular risk. Because both obese and apparently lean individuals can have ectopic fat, regional fat distribution may play an important role in the development of cardiovascular diseases in both nonobese and obese people.
    Arteriosclerosis Thrombosis and Vascular Biology 07/2014; · 6.34 Impact Factor
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    ABSTRACT: Abstract Background: Multiple abnormal metabolic traits are found together or "cluster" within individuals more often than is predicted by chance. The individual and combined role of adiposity and insulin resistance (IR) on metabolic trait clustering is uncertain. We tested the hypothesis that change in trait clustering is a function of both baseline level and change in these measures. Methods: In 2616 nondiabetic Framingham Offspring Study participants, body mass index (BMI) and fasting insulin were related to a within-person 7-year change in a trait score of 0-4 Adult Treatment Panel III metabolic syndrome traits (hypertension, high triglycerides, low high-density lipoprotein cholesterol, hyperglycemia). Results: At baseline assessment, mean trait score was 1.4 traits, and 7-year mean (SEM) change in trait score was +0.25 (0.02) traits, P<0.0001. In models with BMI predictors only, for every quintile difference in baseline BMI, the 7-year trait score increase was 0.14 traits, and for every quintile increase in BMI during 7-year follow-up, the trait score increased by 0.3 traits. Baseline level and change in fasting insulin were similarly related to trait score change. In models adjusted for age-sex-baseline cluster score, 7-year change in trait score was significantly related to both a 1-quintile difference in baseline BMI (0.07 traits) and fasting insulin (0.18 traits), and to both a 1-quintile 7-year increase in BMI (0.21 traits) and fasting insulin (0.18 traits). Conclusions: Change in metabolic trait clustering was significantly associated with baseline levels and changes in both BMI and fasting insulin, highlighting the importance of both obesity and IR in the clustering of metabolic traits.
    Metabolic syndrome and related disorders. 07/2014;
  • Diabetes 07/2014; 63(7):e13. · 7.90 Impact Factor
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    ABSTRACT: To test among diabetes-free urban community-dwelling adults the hypothesis that the proportion of African genetic ancestry is positively associated with glycaemia, after accounting for other continental ancestry proportions, BMI and socioeconomic status (SES).
    Diabetologia 06/2014; · 6.49 Impact Factor
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    ABSTRACT: A genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits.RESEARCH DESIGN AND METHODS: Diabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC).RESULTS: Two hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002).CONCLUSIONS: Metabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors.
    Diabetes care. 06/2014;
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    ABSTRACT: Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study, we sequenced 5 gene regions at 11p11.2 to identify rare, potentially functional variants influencing fasting glucose or FI levels.
    Circulation Cardiovascular Genetics 06/2014; 7(3):374-82. · 6.73 Impact Factor
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    ABSTRACT: Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.
    Molecular Genetics and Metabolism 05/2014; · 2.83 Impact Factor
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    ABSTRACT: Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets, but none have yet been described for type 2 diabetes (T2D). Through sequencing or genotyping of ~150,000 individuals across 5 ancestry groups, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8) and harbors a common variant (p.Trp325Arg) associated with T2D risk and glucose and proinsulin levels. Collectively, carriers of protein-truncating variants had 65% reduced T2D risk (P = 1.7 × 10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34Serfs*50) demonstrated reduced glucose levels (−0.17 s.d., P = 4.6 × 10−4). The two most common protein-truncating variants (p.Arg138* and p.Lys34Serfs*50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested that reduced zinc transport increases T2D risk, and phenotypic heterogeneity was observed in mouse Slc30a8 knockouts. In contrast, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, suggesting ZnT8 inhibition as a therapeutic strategy in T2D prevention.
    Nature Genetics 03/2014; · 35.21 Impact Factor
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    ABSTRACT: To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.
    Nature Genetics 03/2014; 46(3):234-244. · 35.21 Impact Factor
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    ABSTRACT: Tissue plasminogen activator (tPA), a serine protease, catalyzes the conversion of plasminogen to plasmin, the major enzyme responsible for endogenous fibrinolysis. In some populations, elevated plasma levels of tPA have been associated with myocardial infarction and other cardiovascular diseases. We conducted a meta-analysis of genome-wide association studies to identify novel correlates of circulating levels of tPA. Fourteen cohort studies with tPA measures (N=26 929) contributed to the meta-analysis. Three loci were significantly associated with circulating tPA levels (P<5.0×10(-8)). The first locus is on 6q24.3, with the lead single nucleotide polymorphism (SNP; rs9399599; P=2.9×10(-14)) within STXBP5. The second locus is on 8p11.21. The lead SNP (rs3136739; P=1.3×10(-9)) is intronic to POLB and <200 kb away from the tPA encoding the gene PLAT. We identified a nonsynonymous SNP (rs2020921) in modest linkage disequilibrium with rs3136739 (r(2)=0.50) within exon 5 of PLAT (P=2.0×10(-8)). The third locus is on 12q24.33, with the lead SNP (rs7301826; P=1.0×10(-9)) within intron 7 of STX2. We further found evidence for the association of lead SNPs in STXBP5 and STX2 with expression levels of the respective transcripts. In in vitro cell studies, silencing STXBP5 decreased the release of tPA from vascular endothelial cells, whereas silencing STX2 increased the tPA release. Through an in silico lookup, we found no associations of the 3 lead SNPs with coronary artery disease or stroke. We identified 3 loci associated with circulating tPA levels, the PLAT region, STXBP5, and STX2. Our functional studies implicate a novel role for STXBP5 and STX2 in regulating tPA release.
    Arteriosclerosis Thrombosis and Vascular Biology 02/2014; · 6.34 Impact Factor
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    ABSTRACT: Objective: This study examines the relationship between usual sugar-sweetened beverage (SSB) consumption and prevalence of abnormal metabolic health across BMI categories. Design and Methods: Using cross-sectional data from the Framingham Heart Study Offspring (1998-2001) and Third Generation (2002-2005) cohorts, we classified the metabolic health of 6,842 non-diabetic adults. Adults were classified as normal weight, overweight or obese and, within these categories, metabolic health was defined based on five criteria - hypertension, elevated fasting glucose, elevated triglycerides, low HDL cholesterol, and insulin resistance. Individuals without metabolic abnormalities were considered metabolically healthy. Logistic regression was used to examine the associations between categories of SSB consumption and risk of metabolic health after stratification by BMI. Results: Comparing the highest category of SSB consumers (median of 7 SSB per week) to the lowest category (non-consumers), odds ratios (95% confidence intervals) for metabolically abnormal phenotypes, compared to the metabolically normal, were 1.9 (1.1-3.4) among the obese, 2.0 (1.4-2.9) among the overweight, and 1.9 (1.4-2.6) among the normal weight individuals. Conclusions: In this cross-sectional analysis, we observed that, irrespective of weight status, consumers of SSB were more likely to display metabolic abnormalities compared to non-consumers in a dose-dependent manner.
    Obesity 02/2014; · 3.92 Impact Factor
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    ABSTRACT: Knowledge of the genetics of type 2 diabetes mellitus (T2DM) has evolved tremendously over the past few years. Following advances in technology and analytical approaches, collaborative case-control genome-wide association studies have revealed up to 65 loci credibly associated with T2DM. Prospective population studies have demonstrated that aggregated genetic risk scores, so-called because they sum the genetic risk attributed to each locus, can predict incident T2DM among individuals of various age ranges and diverse ethnic backgrounds. With each set of T2DM loci discovered, increasing the number of loci in these scores has improved their predictive ability, although a prediction plateau may already have been reached. The current literature shows that intensive lifestyle interventions are effective for preventing T2DM at any level of genetic risk and might be particularly efficacious among individuals with high genetic susceptibility. By contrast, counselling to inform patients about their personal T2DM genetic risk profiles does not seem to improve motivation or attitudes that lead to positive lifestyle behaviour changes. Future studies should investigate the role of genetics for both T2DM prediction and prevention in young populations in the hope of reducing disease burden for future generations.
    Nature Reviews Endocrinology 02/2014; · 11.03 Impact Factor
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    ABSTRACT: Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared to previous less inclusive GRSt; 2) separate β-cell and insulin resistance GRS (GRSβ and GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively, p<0.001); it did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ, but not GRSIR, predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods including less common as well as functional variants.
    Diabetes 02/2014; · 7.90 Impact Factor
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    ABSTRACT: Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. The study includes 4 population-based cohorts with 2047 first incident strokes from 22 720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.
    Stroke 01/2014; · 6.16 Impact Factor
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    ABSTRACT: Obesity is associated with altered atrial electrophysiology and a prominent risk factor for atrial fibrillation. Body mass index, the most widely used adiposity measure, has been related to atrial electrical remodeling. We tested the hypothesis that pericardial fat is independently associated with electrocardiographic measures of atrial conduction. We performed a cross-sectional analysis of 1946 Framingham Heart Study participants (45% women) to determine the relation between pericardial fat and atrial conduction as measured by P wave indices (PWI): PR interval, P wave duration (P-duration), P wave amplitude (P-amplitude), P wave area (P-area), and P wave terminal force (P-terminal). We performed sex-stratified linear regression analyses adjusted for relevant clinical variables and ectopic fat depots. Each 1-SD increase in pericardial fat was significantly associated with PR interval (β=1.7 ms, P=0.049), P-duration (β=2.3 ms, P<0.001), and P-terminal (β=297 μV·ms, P<0.001) among women; and P-duration (β=1.2 ms, P=0.002), P-amplitude (β=-2.5 μV, P<0. 001), and P-terminal (β=160 μV·ms, P=0.002) among men. Among both sexes, pericardial fat was significantly associated with P-duration in analyses additionally adjusting for visceral fat or intrathoracic fat; a similar but non-significant trend existed with P-terminal. Among women, pericardial fat was significantly associated with P wave area after adjustment for visceral and intrathoracic fat. Pericardial fat is associated with atrial conduction as quantified by PWI, even with adjustment for extracardiac fat depots. Further studies are warranted to identify the mechanisms through which pericardial fat may modify atrial electrophysiology and promote subsequent risk for arrhythmogenesis.
    Journal of the American Heart Association. 01/2014; 3(2):e000477.
  • Marco Dauriz, James B Meigs
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    ABSTRACT: The large-scale genome-wide association studies conducted so far identified numerous allelic variants associated with type 2 diabetes (T2D), coronary heart disease (CHD) and related cardiometabolic traits. Many T2D- and some CHD-risk loci are also linked with metabolic traits that are hallmarks of insulin resistance (lipid profile, abdominal adiposity). Chromosome 9p21.3 and 2q36.3 are the most consistently replicated loci appearing to share genetic risk for both T2D and CHD. Although many glucose- or insulin-related trait variants are also linked with T2D risk, none of them is associated with CHD. Hence, while T2D and CHD are strongly clinically linked together, further ongoing analyses are needed to clarify the existence of a shared underlying genetic signature of these complex traits. The present review summarizes an updated picture of T2D-CHD genetics as of 2013, aiming to provide a platform for targeted studies dissecting the contribution of genetics to the phenotypic heterogeneity of T2D and CHD.
    Current Cardiovascular Risk Reports 01/2014; 8(1):368.
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    ABSTRACT: Performing genetic studies in multiple human populations can identify disease risk alleles that are common in one population but rare in others, with the potential to illuminate pathophysiology, health disparities, and the population genetic origins of disease alleles. Here we analysed 9.2 million single nucleotide polymorphisms (SNPs) in each of 8,214 Mexicans and other Latin Americans: 3,848 with type 2 diabetes and 4,366 non-diabetic controls. In addition to replicating previous findings, we identified a novel locus associated with type 2 diabetes at genome-wide significance spanning the solute carriers SLC16A11 and SLC16A13 (P = 3.9 × 10−13; odds ratio (OR) = 1.29). The association was stronger in younger, leaner people with type 2 diabetes, and replicated in independent samples (P = 1.1 × 10−4; OR = 1.20). The risk haplotype carries four amino acid substitutions, all in SLC16A11; it is present at ~50% frequency in Native American samples and ~10% in east Asian, but is rare in European and African samples. Analysis of an archaic genome sequence indicated that the risk haplotype introgressed into modern humans via admixture with Neanderthals. The SLC16A11 messenger RNA is expressed in liver, and V5-tagged SLC16A11 protein localizes to the endoplasmic reticulum. Expression of SLC16A11 in heterologous cells alters lipid metabolism, most notably causing an increase in intracellular triacylglycerol levels. Despite type 2 diabetes having been well studied by genome-wide association studies in other populations, analysis in Mexican and Latin American individuals identified SLC16A11 as a novel candidate gene for type 2 diabetes with a possible role in triacylglycerol metabolism.
    Nature 12/2013; · 38.60 Impact Factor
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    ABSTRACT: Based upon evidence in animal and in-vitro studies, we tested the hypothesis that higher serum concentrations of the cytokines interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), and the inflammatory marker C-reactive protein (CRP) would be inversely associated with BMD in a community-based cohort of men and women, with the strongest associations among post-menopausal women not using menopausal hormone therapy (MHT). We ascertained fasting serum concentrations of IL-6, TNF-α, and CRP and measured BMD at the femoral neck, trochanter, total femur, and spine (L2-L4) using dual energy X-ray absorptiometry in 2,915 members of the Framingham Offspring cohort (1996 to 2001). We used multivariable linear regression to estimate the difference (β) in BMD at each bone site associated with a one-unit increase in log-transformed serum concentrations of IL-6, TNF-α, and CRP separately for men (n=1,293), pre-menopausal women (n=231), post-menopausal women using MHT (n=498), and post-menopausal women not using MHT (n=893). Inflammatory biomarkers were not associated with BMD in men. Among pre-menopausal women, there were statistically significant, modest inverse associations between IL-6 and trochanter BMD (β=-0.030, p<0.01), and between CRP and femoral neck (β=-0.015, p=0.05) and trochanter BMD (β=-0.014, p=0.04). TNF-α, was positively associated with spine BMD (β=0.043, p=0.01). In post-menopausal MHT users, CRP was positively associated with femoral neck BMD (β=0.011, p=0.04). There were no associations among post-menopausal women not using MHT. The lack of consistency in our results suggests that elevated circulating concentration of inflammatory biomarkers may not be a risk factor for low BMD. © 2013 American College of Rheumatology.
    Arthritis care & research. 12/2013;
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    ABSTRACT: Patients with established type 2 diabetes display both beta-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci and indices of proinsulin processing, insulin secretion and insulin sensitivity. We included data from up to 58,614 non-diabetic subjects with basal measures, and 17,327 with dynamic measures. We employed additive genetic models with adjustment for sex, age and BMI, followed by fixed-effects inverse variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (including TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without detectable change in fasting glucose. The final group contained twenty risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.
    Diabetes 12/2013; · 7.90 Impact Factor

Publication Stats

31k Citations
3,289.97 Total Impact Points

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Institutions

  • 1998–2014
    • Harvard Medical School
      • • Department of Medicine
      • • Department of Population Medicine
      Boston, Massachusetts, United States
  • 1996–2014
    • Massachusetts General Hospital
      • • Department of Medicine
      • • Division of Gastroenterology
      • • Hospital Medicine Unit
      Boston, Massachusetts, United States
  • 2013
    • Seoul National University Bundang Hospital
      • Department of Radiology
      Sŏul, Seoul, South Korea
    • Karl Jaspers Society of North America
      United States
  • 2006–2013
    • National Heart, Lung, and Blood Institute
      • Division of Cardiovascular Sciences (DCVS)
      Maryland, United States
    • Rutgers, The State University of New Jersey
      New Brunswick, New Jersey, United States
    • Morehouse School of Medicine
      Atlanta, Georgia, United States
  • 2012
    • University of Oxford
      • Wellcome Trust Centre for Human Genetics
      Oxford, ENG, United Kingdom
    • Beverly Hospital, Boston MA
      Beverly, Massachusetts, United States
    • Kaiser Permanente
      Oakland, California, United States
    • University of Southern California
      • Department of Preventive Medicine
      Los Angeles, CA, United States
    • Northwestern University
      • Department of Preventive Medicine
      Evanston, IL, United States
  • 2005–2012
    • Boston University
      • • Department of Biostatistics
      • • Genetics and Genomics
      Boston, MA, United States
    • German Institute of Human Nutrition
      • Department of Epidemiology
      Potsdam, Brandenburg, Germany
    • Medical University of South Carolina
      Charleston, South Carolina, United States
    • Simmons College
      Boston, Massachusetts, United States
  • 2011
    • Université de Sherbrooke
      • Division of Endocrinology
      Sherbrooke, Quebec, Canada
    • Karolinska University Hospital
      Tukholma, Stockholm, Sweden
  • 2004–2011
    • University of Verona
      • Department of Medicine and Public Health
      Verona, Veneto, Italy
    • Countess Of Chester Hospital NHS Foundation Trust
      Chester, England, United Kingdom
  • 2002–2011
    • Tufts University
      • Tufts Center for Conservation Medicine
      Medford, MA, United States
  • 2010
    • Centers for Disease Control and Prevention
      • National Office of Public Health Genomics
      Druid Hills, GA, United States
    • University of Texas Health Science Center at Houston
      • Division of Epidemiology, Human Genetics and Environmental Sciences
      Houston, TX, United States
    • Wellcome Trust Sanger Institute
      Cambridge, England, United Kingdom
    • McMaster University
      Hamilton, Ontario, Canada
    • University of Washington Seattle
      • Department of Epidemiology
      Seattle, WA, United States
    • Karolinska Institutet
      • Institutionen för medicinsk epidemiologi och biostatistik
      Solna, Stockholm, Sweden
  • 2009
    • Brigham and Women's Hospital
      • Division of Endocrinology, Diabetes and Hypertension
      Boston, MA, United States
    • University of Kentucky
      Lexington, Kentucky, United States
  • 2003–2009
    • Harvard University
      • • Department of Nutrition
      • • Department of Epidemiology
      Boston, MA, United States
  • 2008
    • National Institutes of Health
      Maryland, United States
    • University of Massachusetts Boston
      Boston, Massachusetts, United States
    • University Medical Center Utrecht
      • Julius Center for Health Sciences and Primary Care
      Utrecht, Provincie Utrecht, Netherlands
    • Biogen Idec
      Weston, Massachusetts, United States
  • 2007–2008
    • Emory University
      Atlanta, Georgia, United States
    • Partners HealthCare
      Boston, Massachusetts, United States
    • Hospital De Clínicas De Porto Alegre
      Pôrto de São Francisco dos Casaes, Rio Grande do Sul, Brazil
  • 2006–2008
    • The University of Manchester
      • Institute of Cardiovascular Sciences
      Manchester, ENG, United Kingdom
  • 2005–2008
    • Beth Israel Deaconess Medical Center
      • Division of Endocrinology, Diabetes and Metabolism
      Boston, Massachusetts, United States
  • 2002–2004
    • University of Chicago
      Chicago, Illinois, United States