Person:

Fox, Caroline

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (~26%–27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ~2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10−8) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT–assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.

Objective: The association of familial as compared to genetic factors in the current obesogenic environment, compared to earlier, leaner time periods, is uncertain. Design and Methods Participants from the Framingham Heart Study were classified according to parental obesity status in the Original, Offspring, and Third Generation cohorts; mean BMI levels were estimated and we compared the association of parental history across generations. Finally, a genetic risk score comprised of 32 well-replicated single nucleotide polymorphisms for BMI was examined in association with BMI levels in 1948, 1971, and 2002. Results: BMI was 1.49 kg/m2 higher per each affected parent among the Offspring, and increased to 2.09 kg/m2 higher among the Third Generation participants (p-value for the cohort comparison=0.007). Parental history of obesity was associated with increased weight gain (p<0.0001) and incident obesity (p=0.009). Despite a stronger association of parental obesity with offspring BMI in more contemporary time periods, we observed no change in the effect size of a BMI genetic risk score from 1948 to 2002 (p=0.11 for test of trend across the time periods). Conclusions: The association of parental obesity has become stronger in more contemporary time period, whereas the association of a BMI genetic risk score has not changed.

Background: 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. Methods and Results: 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. Conclusions: 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.

Objective: Perivascular fat may have a local adverse effect on the vasculature. We evaluated whether thoracic periaortic adipose tissue (TAT), a type of perivascular fat, and visceral adipose tissue (VAT) are associated with vascular function. Design and Methods TAT and VAT were quantified in Framingham Heart Study participants using multidetector computed tomography; vascular function was assessed using brachial artery vasodilator function, peripheral arterial tone and arterial tonometry (n= 2735, 48% women, mean age 50 years, mean BMI 27.7 kg/m2). Using multiple linear regression, we examined relations between TAT, VAT, and vascular measures while adjusting for cardiovascular risk factors. Results: Mean TAT and VAT volumes were 13.2 and 1763 cm3. TAT and VAT were associated with multiple vascular function measures after multivariable adjustment. After BMI adjustment, TAT and VAT remained negatively associated with peripheral arterial tone and inverse carotid femoral pulse wave velocity (p<0.02); TAT was negatively associated with hyperemic mean flow velocity (p=0.03). Associations of TAT with vascular function were attenuated after VAT adjustment (all p>0.06). Conclusion: Thoracic periaortic and visceral fat are associated with microvascular function and large artery stiffness after BMI adjustment. These findings support the growing recognition of associations between ectopic fat and vascular function.

Background: Thoracic periaortic adipose tissue (TAT) is associated with atherosclerosis and cardiovascular disease (CVD) risk factors and may play a role in obesity‐mediated vascular disease. We sought to determine the prevalence, distribution, and risk factor correlates of high TAT. Methods and Results: Participants from the Framingham Heart Study (n=3246, 48% women, mean age 51.1 years) underwent multidetector computed tomography; high TAT and visceral adipose tissue (VAT) were defined on the basis of sex‐specific 90th percentiles in a healthy referent sample. The prevalence of high TAT was 38.1% in women and 35.7% in men. Among individuals without high VAT, 10.1% had high TAT. After adjustment for age and VAT, both women and men with high TAT in the absence of high VAT were older and had a higher prevalence of CVD (P<0.0001) compared with those without high TAT. In addition, men in this group were more likely to be smokers (P=0.02), whereas women were more likely to have low high‐density lipoprotein cholesterol (P=0.005). Conclusions: Individuals in our community‐based sample with high TAT in the absence of high VAT were characterized by an adverse cardiometabolic profile. This adipose tissue phenotype may identify a subset of individuals with distinct metabolic characteristics.

Central abdominal fat is a strong risk factor for diabetes and cardiovascular disease. To identify common variants influencing central abdominal fat, we conducted a two-stage genome-wide association analysis for waist circumference (WC). In total, three loci reached genome-wide significance. In stage 1, 31,373 individuals of Caucasian descent from eight cohort studies confirmed the role of FTO and MC4R and identified one novel locus associated with WC in the neurexin 3 gene [NRXN3 (rs10146997, p = 6.4×10^−7)]. The association with NRXN3 was confirmed in stage 2 by combining stage 1 results with those from 38,641 participants in the GIANT consortium (p = 0.009 in GIANT only, p = 5.3×10^−8 for combined analysis, n = 70,014). Mean WC increase per copy of the G allele was 0.0498 z-score units (0.65 cm). This SNP was also associated with body mass index (BMI) [p = 7.4×10^−6, 0.024 z-score units (0.10 kg/m2) per copy of the G allele] and the risk of obesity (odds ratio 1.13, 95% CI 1.07–1.19; p = 3.2×10^−5 per copy of the G allele). The NRXN3 gene has been previously implicated in addiction and reward behavior, lending further evidence that common forms of obesity may be a central nervous system-mediated disorder. Our findings establish that common variants in NRXN3 are associated with WC, BMI, and obesity.

Background: Ectopic fat density is associated with cardiovascular disease (CVD) risk factors above and beyond fat volume. Volumetric measures of ectopic fat have been associated with CVD risk factors and subclinical atherosclerosis. The aim of this study was to investigate the association between fat density and subclinical atherosclerosis. Methods and Results: Participants were drawn from the Multi‐Detector Computed Tomography (MDCT) substudy of the Framingham Heart Study (n=3079; mean age, 50.1 years; 49.2% women). Fat density was indirectly estimated by computed tomography attenuation (Hounsfield Units [HU]) on abdominal scan slices. Visceral fat (VAT), subcutaneous fat (SAT), and pericardial fat HU and volumes were quantified using standard protocols; coronary and abdominal aortic calcium (CAC and AAC, respectively) were measured radiographically. Multivariable‐adjusted logistic regression models were used to evaluate the association between adipose tissue HU and the presence of CAC and AAC. Overall, 17.1% of the participants had elevated CAC (Agatston score [AS]>100), and 23.3% had elevated AAC (AS>age‐/sex‐specific cutoffs). Per 5‐unit decrement in VAT HU, the odds ratio (OR) for elevated CAC was 0.76 (95% confidence interval [CI], 0.65 to 0.89; P=0.0005), even after adjustment for body mass index or VAT volume. Results were similar for SAT HU. With decreasing VAT HU, we also observed an OR of 0.79 (95% CI, 0.67 to 0.92; P=0.004) for elevated AAC after multivariable adjustment. We found no significant associations between SAT HU and AAC. There was no significant association between pericardial fat HU and either CAC or AAC. Conclusions: Lower VAT and SAT HU, indirect estimates of fat quality, are associated with a lower risk of subclinical atherosclerosis.

Background: Prolactin is an anterior pituitary hormone that may modulate the adverse effects of obesity. Prolactin has been associated with cardiovascular disease mortality, but less is known about whether prolactin predicts incidence of cardiovascular disease risk factors. Methods and Results: Our sample (n=3232, mean age 40.4 years, 52.1% women) was drawn from Framingham Heart Study participants who attended 2 examinations an average of 6.1 years apart. After excluding those with elevated prolactin (>30 mg/dL for women, >20 mg/dL for men), multivariable‐adjusted regressions modeled the associations between baseline prolactin and changes in cardiovascular disease risk factors. Models were adjusted for age, sex, baseline value of the risk factor, smoking status, hormone replacement therapy, and menopausal status and additionally for body mass index. Mean prolactin levels were 11.9 mg/dL (SD 5.2) in women and 8.0 mg/dL (SD 2.9) in men. No associations were observed for change in weight, body composition, total cholesterol, triglycerides, or fasting glucose. In women, for example, for each 5‐mg/dL increment in prolactin, odds of incident hypercholesterolemia were 1.06, which was not significant (95% CI 0.91–1.23, P=0.46). Some exceptions were of note. In women, for each 5‐mg/dL increment in prolactin, we observed increased odds of low high‐density lipoprotein cholesterol at follow‐up (odds ratio 1.50, 95% CI 1.18–1.91, P=0.001) that persisted after adjustment for body mass index (P=0.001). In men, a 5‐mg/dL increment in prolactin was associated with increased odds of incident hypertension (odds ratio 1.61, 95% CI 1.18–2.20 P=0.002) and incident diabetes (odds ratio 1.70, 95% CI 1.04–2.78, P=0.03). Conclusions: Prolactin is not associated with a comprehensive panel of incident cardiovascular disease risk factors. Measurement of circulating prolactin levels in the community likely does not provide substantial insight into cardiometabolic risk.