James B Meigs

Harvard University, Cambridge, Massachusetts, United States

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Publications (430)3484.96 Total impact

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    ABSTRACT: Homozygosity has long been associated with rare, often devastating, Mendelian disorders1, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3, 4. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10−300, 2.1 × 10−6, 2.5 × 10−10 and 1.8 × 10−10, respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months’ less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5, 6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.
    Nature 07/2015; DOI:10.1038/nature14618 · 42.35 Impact Factor
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    ABSTRACT: In observational studies, type-2 diabetes (T2D) is associated with an increased risk of coronary heart disease (CHD), yet interventional trials have shown no clear effect of glucose-lowering on CHD. Confounding may have therefore influenced these observational estimates. Here we use Mendelian randomization to obtain unconfounded estimates of the influence of T2D and fasting glucose (FG) on CHD risk. Using multiple genetic variants associated with T2D and FG, we find that risk of T2D increases CHD risk (odds ratio (OR)=1.11 (1.05-1.17), per unit increase in odds of T2D, P=8.8 × 10(-5); using data from 34,840/114,981 T2D cases/controls and 63,746/130,681 CHD cases/controls). FG in non-diabetic individuals tends to increase CHD risk (OR=1.15 (1.00-1.32), per mmol·per l, P=0.05; 133,010 non-diabetic individuals and 63,746/130,681 CHD cases/controls). These findings provide evidence supporting a causal relationship between T2D and CHD and suggest that long-term trials may be required to discern the effects of T2D therapies on CHD risk.
    Nature Communications 05/2015; 6:7060. DOI:10.1038/ncomms8060 · 10.74 Impact Factor
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    ABSTRACT: -Type 2 diabetes (T2D) and cardiovascular disease (CVD) share risk factors and subclinical atherosclerosis (SCA) predicts events in those with and without diabetes. T2D genetic risk may predict both T2D and SCA. We hypothesized that greater T2D genetic risk is associated with higher extent of SCA. -In a cross-sectional analysis including up to 9,210 European Americans, 3,773 African Americans, 1,446 Hispanic Americans and 773 Chinese Americans without known CVD and enrolled in the FHS, CARDIA, MESA and GENOA studies, we tested a 62 T2D-loci genetic risk score (GRS62) for association with measures of SCA, including coronary artery (CACS) or abdominal aortic calcium score, common (CCA-IMT) and internal carotid artery intima-media thickness, and ankle-brachial index (ABI). We used ancestry-stratified linear regression models, with random effects accounting for family relatedness when appropriate, applying a genetic-only (adjusted for sex) and a full SCA risk factors adjusted model (significance = p<0.01 = 0.05/5, number of traits analyzed). An inverse association with CACS in MESA Europeans (fully-adjusted p=0.004) and with CCA-IMT in FHS (p=0.009) was not confirmed in other study cohorts, either separately or in meta-analysis. Secondary analyses showed no consistent associations with β-cell and insulin resistance sub-GRS in FHS and CARDIA. -SCA does not have a major genetic component linked to a burden of 62 T2D loci identified by large genome-wide association studies. A shared T2D-SCA genetic basis, if any, might become apparent from better functional information about both T2D and CVD risk loci.
    Circulation Cardiovascular Genetics 03/2015; DOI:10.1161/CIRCGENETICS.114.000740 · 5.34 Impact Factor
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    ABSTRACT: Type 2 diabetes mellitus in parents is a strong determinant of diabetes risk in their offspring. We hypothesise that offspring diabetes risk associated with parental diabetes is mediated by metabolic risk factors. We studied initially non-diabetic participants of the Framingham Offspring Study. Metabolic risk was estimated using beta cell corrected insulin response (CIR), HOMA-IR or a count of metabolic syndrome components (metabolic syndrome score [MSS]). Dietary risk and physical activity were estimated using questionnaire responses. Genetic risk score (GRS) was estimated as the count of 62 type 2 diabetes risk alleles. The outcome of incident diabetes in offspring was examined across levels of parental diabetes exposure, accounting for sibling correlation and adjusting for age, sex and putative mediators. The proportion mediated was estimated by comparing regression coefficients for parental diabetes with (β adj) and without (β unadj) adjustments for CIR, HOMA-IR, MSS and GRS (percentage mediated = 1 - β adj / β unadj). Metabolic factors mediated 11% of offspring diabetes risk associated with parental diabetes, corresponding to a reduction in OR per diabetic parent from 2.13 to 1.96. GRS mediated 9% of risk, corresponding to a reduction in OR per diabetic parent from 2.13 to 1.99. Metabolic risk factors partially mediated offspring type 2 diabetes risk conferred by parental diabetes to a similar magnitude as genetic risk. However, a substantial proportion of offspring diabetes risk associated with parental diabetes remains unexplained by metabolic factors, genetic risk, diet and physical activity, suggesting that important familial influences on diabetes risk remain undiscovered.
    Diabetologia 01/2015; 58(5). DOI:10.1007/s00125-015-3498-7 · 6.88 Impact Factor
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    ABSTRACT: Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5x10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
    PLoS Genetics 01/2015; 11(1). DOI:10.1371/journal.pgen.1004876 · 8.52 Impact Factor
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    ABSTRACT: Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
    Nature Communications 01/2015; 6:5897. DOI:10.1038/ncomms6897 · 10.74 Impact Factor
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    ABSTRACT: Increasing access to care may be insufficient to improve the health of patients with diabetes mellitus and unmet basic needs (hereinafter referred to as material need insecurities). How specific material need insecurities relate to clinical outcomes and the use of health care resources in a setting of near-universal access to health care is unclear. To determine the association of food insecurity, cost-related medication underuse, housing instability, and energy insecurity with control of diabetes mellitus and the use of health care resources. Cross-sectional data were collected from June 1, 2012, through October 31, 2013, at 1 academic primary care clinic, 2 community health centers, and 1 specialty center for the treatment of diabetes mellitus in Massachusetts. A random sample of 411 patients, stratified by clinic, consisted of adults (aged ≥21 years) with diabetes mellitus (response rate, 62.3%). The prespecified primary outcome was a composite indicator of poor diabetes control (hemoglobin A1c level, >9.0%; low-density lipoprotein cholesterol level, >100 mg/dL; or blood pressure, >140/90 mm Hg). Prespecified secondary outcomes included outpatient visits and a composite of emergency department (ED) visits and acute care hospitalizations (ED/inpatient visits). Overall, 19.1% of respondents reported food insecurity; 27.6%, cost-related medication underuse; 10.7%, housing instability; 14.1%, energy insecurity; and 39.1%, at least 1 material need insecurity. Poor diabetes control was observed in 46.0% of respondents. In multivariable models, food insecurity was associated with a greater odds of poor diabetes control (adjusted odds ratio [OR], 1.97 [95% CI, 1.58-2.47]) and increased outpatient visits (adjusted incident rate ratio [IRR], 1.19 [95% CI, 1.05-1.36]) but not increased ED/inpatient visits (IRR, 1.00 [95% CI, 0.51-1.97]). Cost-related medication underuse was associated with poor diabetes control (OR, 1.91 [95% CI, 1.35-2.70]) and increased ED/inpatient visits (IRR, 1.68 [95% CI, 1.21-2.34]) but not outpatient visits (IRR, 1.07 [95% CI, 0.95-1.21]). Housing instability (IRR, 1.31 [95% CI, 1.14-1.51]) and energy insecurity (IRR, 1.12 [95% CI, 1.00-1.25]) were associated with increased outpatient visits but not with diabetes control (OR, 1.10 [95% CI, 0.60-2.02] and OR, 1.27 [95% CI, 0.96-1.69], respectively) or with ED/inpatient visits (IRR, 1.49 [95% CI, 0.81-2.73] and IRR, 1.31 [95% CI, 0.80-2.13], respectively). An increasing number of insecurities was associated with poor diabetes control (OR for each additional need, 1.39 [95% CI, 1.18-1.63]) and increased use of health care resources (IRR for outpatient visits, 1.09 [95% CI, 1.03-1.15]; IRR for ED/inpatient visits, 1.22 [95% CI, 0.99-1.51]). Material need insecurities were common among patients with diabetes mellitus and had varying but generally adverse associations with diabetes control and the use of health care resources. Material need insecurities may be important targets for improving care of diabetes mellitus.
    JAMA Internal Medicine 12/2014; 175(2). DOI:10.1001/jamainternmed.2014.6888 · 13.25 Impact Factor
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    ABSTRACT: -Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies (GWAS) have reported numerous SNPs to be associated with CVD, the role of most of these variants in disease processes remains unknown. -We built a CVD network using 1,512 SNPs associated with 21 CVD traits in GWAS (at p≤5×10(-8)) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5,257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-eQTLs (SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples where the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in GWAS, may exert their function by altering expression of eQTL genes (e.g. LDLR, and PCSK7) that in turn may promote inter-individual variation in phenotypes. -Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.
    Circulation 12/2014; 131(6). DOI:10.1161/CIRCULATIONAHA.114.010696 · 14.95 Impact Factor
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    ABSTRACT: Human immunodeficiency virus (HIV) infection is associated with increased risk of myocardial infarction (MI). The use of aspirin for primary and secondary MI prevention in HIV infection has not been extensively studied. We performed a cross-sectional study of 4037 patients infected with HIV and 36 338 demographics-matched control patients in the Partners HealthCare System HIV cohort. We developed an algorithm to ascertain rates of nonepisodic acetylsalicylic acid (ASA) use using medication and electronic health record free text data. We assessed rates of ASA use among HIV-infected and HIV-uninfected (negative) patients with and without coronary heart disease (CHD). Rates of ASA use were lower among HIV-infected compared with HIV-uninfected patients (12.4% vs 15.3%, P < .001), with a relatively greater difference among patients with ≥2 CHD risk factors (22.1% vs 42.4%, P < .001). This finding was present among men and among patients in the 30-39 and 40-49 age groups. Among patients with prevalent CHD using ASA for secondary prevention, rates of ASA use were also lower among HIV-infected patients compared with HIV-uninfected patients (51.6% vs 65.4%, P < .001). Rates of ASA use were lower among HIV-infected patients compared with controls, with a greater relative difference among those with elevated CHD risk and those with known CHD. Further studies are needed to investigate the optimal strategies for ASA use among patients infected with HIV.
    12/2014; 1(3):ofu076. DOI:10.1093/ofid/ofu076
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    ABSTRACT: Heritability measures the proportion of phenotypic variation attributable to genetic factors. In addition to a shared nuclear genetic component, a number of additional variance components, such as spousal correlation, sibship, household and maternal effects, may have strong contributions to inter-individual phenotype variation. In humans, the confounding effects of these components on heritability have not been studied thoroughly. We sought to obtain unbiased heritability estimates for complex traits in the presence of multiple variance components and also to estimate the contributions of these variance components to complex traits. We compared regression and variance component methods to estimate heritability in simulations when additional variance components existed. We then revisited heritability for several traits in Framingham Heart Study (FHS) participants. Using simulations, we found that failure to account for or misclassification of necessary variance components yielded biased heritability estimates. The direction and magnitude of the bias varied depending on a variance structure and an estimation method. Using the best fitted models to account for necessary variance components, we found that heritability estimates for most FHS traits were overestimated, ranging from 4 to 47 %, when we compared models that considered necessary variance components to models that only considered familial relationships. Spousal correlation explained 14-36 % of phenotypic variation in several anthropometric and lifestyle traits. Maternal and sibling effects also contributed to phenotypic variation, ranging from 3 to 5 % and 4 to 7 %, respectively, in several anthropometric and metabolic traits. Our findings may explain, in part, the missing heritability for some traits.
    Human Genetics 11/2014; 134(2). DOI:10.1007/s00439-014-1505-6 · 4.52 Impact Factor
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    ABSTRACT: Background Recent studies suggested that insulin glargine use could be associated with increased risk of cancer. We compared the incidence of cancer in new users of glargine versus new users of NPH in a longitudinal clinical cohort with diabetes for up to 6 years. Methods and Findings From all patients who had been regularly followed at Massachusetts General Hospital from 1/01/2005 to 12/31/2010, 3,680 patients who had a medication record for glargine or NPH usage were obtained from the electronic medical record (EMR). From those we selected 539 new glargine users (age: 60.1±13.6 years, BMI: 32.7±7.5 kg/m2) and 343 new NPH users (61.5±14.1 years, 32.7±8.3 kg/m2) who had no prevalent cancer during 19 months prior to glargine or NPH initiation. All incident cancer cases were ascertained from the EMR requiring at least 2 ICD-9 codes within a 2 month period. Insulin exposure time and cumulative dose were validated. The statistical analysis compared the rates of cancer in new glargine vs. new NPH users while on treatment, adjusted for the propensity to receive one or the other insulin. There were 26 and 28 new cancer cases in new glargine and new NPH users for 1559 and 1126 person-years follow-up, respectively. There were no differences in the propensity-adjusted clinical characteristics between groups. The adjusted hazard ratio for the cancer incidence comparing glargine vs. NPH use was 0.65 (95% CI: 0.36–1.19). Conclusions Insulin glargine is not associated with development of cancers when compared with NPH in this longitudinal and carefully retrieved EMR data.
    PLoS ONE 10/2014; 9(10):e109433. DOI:10.1371/journal.pone.0109433 · 3.53 Impact Factor
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    ABSTRACT: To compare rates of intracerebral hemorrhage (ICH) in HIV-infected and uninfected individuals in a large clinical care cohort and to assess risk factors associated with ICH.
    Neurology 10/2014; 83(19). DOI:10.1212/WNL.0000000000000958 · 8.30 Impact Factor
  • Canadian Journal of Diabetes 10/2014; 38(5):S61. DOI:10.1016/j.jcjd.2014.07.171 · 0.46 Impact Factor
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    ABSTRACT: The genetic basis of type 2 diabetes remains incompletely defined despite the use of multiple genetic strategies. Multiparental populations such as heterogeneous stocks (HS) facilitate gene discovery by allowing fine mapping to only a few megabases, significantly decreasing the number of potential candidate genes compared to traditional mapping strategies. In the present work, we employed expression and sequence analysis in HS rats (Rattus norvegicus) to identify Tpcn2 as a likely causal gene underlying a 3.1-Mb locus for glucose and insulin levels. Global gene expression analysis on liver identified Tpcn2 as the only gene in the region that is differentially expressed between HS rats with glucose intolerance and those with normal glucose regulation. Tpcn2 also maps as a cis-regulating expression QTL and is negatively correlated with fasting glucose levels. We used founder sequence to identify variants within this region and assessed association between 18 variants and diabetic traits by conducting a mixed-model analysis, accounting for the complex family structure of the HS. We found that two variants were significantly associated with fasting glucose levels, including a nonsynonymous coding variant within Tpcn2. Studies in Tpcn2 knockout mice demonstrated a significant decrease in fasting glucose levels and insulin response to a glucose challenge relative to those in wild-type mice. Finally, we identified variants within Tpcn2 that are associated with fasting insulin in humans. These studies indicate that Tpcn2 is a likely causal gene that may play a role in human diabetes and demonstrate the utility of multiparental populations for positionally cloning genes within complex loci.
    Genetics 09/2014; 198(1):17-29. DOI:10.1534/genetics.114.162982 · 4.87 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.
    08/2014; 66(8). DOI:10.1002/acr.22270
  • 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. DOI:10.1159/000363347 · 1.64 Impact Factor
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    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; 34(9). DOI:10.1161/ATVBAHA.114.303035 · 5.53 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; DOI:10.1089/met.2013.0148 · 1.92 Impact Factor
  • Diabetes 07/2014; 63(7):e13. DOI:10.2337/db14-0449 · 8.47 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; 37(9). DOI:10.2337/dc14-0560 · 8.57 Impact Factor

Publication Stats

42k Citations
3,484.96 Total Impact Points

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Institutions

  • 1999–2015
    • Harvard University
      • Department of Nutrition
      Cambridge, Massachusetts, United States
  • 1998–2015
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
  • 1996–2014
    • Massachusetts General Hospital
      • • Department of Medicine
      • • Hospital Medicine Unit
      • • Diabetes Unit
      Boston, Massachusetts, United States
  • 2013
    • University of Lausanne
      Lausanne, Vaud, Switzerland
  • 1997–2013
    • Boston University
      • • Department of Medicine
      • • Division of Mathematics
      • • Department of Mathematics and Statistics
      Boston, Massachusetts, United States
  • 2012
    • University of Oxford
      • Wellcome Trust Centre for Human Genetics
      Oxford, ENG, United Kingdom
  • 2005–2012
    • Beverly Hospital, Boston MA
      BVY, Massachusetts, United States
    • Uppsala University
      • Department of Public Health and Caring Sciences
      Uppsala, Uppsala, Sweden
    • German Institute of Human Nutrition
      • Department of Epidemiology
      Potsdam, Brandenburg, Germany
    • Simmons College
      • Program in Nutrition and Dietetics
      Boston, Massachusetts, United States
  • 2011
    • Karolinska University Hospital
      Tukholma, Stockholm, Sweden
  • 2010
    • McMaster University
      Hamilton, Ontario, Canada
    • Wellcome Trust Sanger Institute
      Cambridge, England, United Kingdom
    • University of Texas Health Science Center at Houston
      • Division of Epidemiology, Human Genetics and Environmental Sciences
      Houston, TX, United States
    • Karolinska Institutet
      • Institutionen för medicinsk epidemiologi och biostatistik
      Solna, Stockholm, Sweden
  • 2009
    • University of Massachusetts Medical School
      Worcester, Massachusetts, United States
  • 2002–2009
    • Tufts University
      • Department of Medicine
      Georgia, United States
  • 2008
    • Johns Hopkins University
      • Welch Center for Prevention, Epidemiology, and Clinical Research
      Baltimore, Maryland, United States
    • American Heart Association
      Dallas, Texas, United States
  • 2006–2008
    • Beth Israel Deaconess Medical Center
      • Division of Endocrinology, Diabetes and Metabolism
      Boston, Massachusetts, United States
  • 2004–2007
    • University of Verona
      Verona, Veneto, Italy
    • University of Chicago
      Chicago, Illinois, United States