Person:

Mora, Samia

Objective: Variation in the fat mass and obesity-associated (FTO) gene is associated with obesity. The extent to which separate and combined effects of physical activity and caloric intake modify this association remains unclear. Research Design and Methods: FTO polymorphism rs8050136 was measured, and physical activity, caloric intake, and anthropometrics were self-reported in 21,675 apparently healthy Caucasian women. Results: The effect of the risk allele (A) on BMI was larger among inactive or higher intake women, with additive effects of inactivity and high intake on the associated genetic risk. Specifically, each A allele was associated with mean BMI difference of +0.73 (SE 0.08) kg/m(^2) among inactive women ((\leq)median, 8.8 MET-hours/week), compared with +0.31 (0.06) kg/m(^2), P < 0.0001, among active women (>8.8 MET-hours/week). Similarly, each A allele was associated with mean BMI difference of +0.65 (0.07) among high intake women (>median, 1,679 kcals/day), compared with +0.38 (0.07) kg/m(^2), P = 0.005, among low intake women ((\leq)1,679 kcals/day). Among inactive/high intake women, each A allele was associated with mean BMI difference of +0.97 (0.11) kg/m(^2) vs. +0.22 (0.08) kg/m(^2) among inactive/low intake women, P < 0.0001. Among inactive/high intake women, each A allele carried increased risk of obesity (odds ratio 1.39, 95% CI 1.27–1.52) and diabetes (odds ratio 1.36, 95% CI 1.07–1.73). Conclusions: In this study, lifestyle factors modified the genetic risk of FTO on obesity phenotypes, particularly among women who were both inactive and had high intake. Healthier lifestyle patterns blunted but did not completely eliminate the associated genetic risk.

Aims To critically evaluate the clinical implications of the use of non-fasting rather than fasting lipid profiles and to provide guidance for the laboratory reporting of abnormal non-fasting or fasting lipid profiles. Methods and results Extensive observational data, in which random non-fasting lipid profiles have been compared with those determined under fasting conditions, indicate that the maximal mean changes at 1–6 h after habitual meals are not clinically significant [+0.3 mmol/L (26 mg/dL) for triglycerides; −0.2 mmol/L (8 mg/dL) for total cholesterol; −0.2 mmol/L (8 mg/dL) for LDL cholesterol; +0.2 mmol/L (8 mg/dL) for calculated remnant cholesterol; −0.2 mmol/L (8 mg/dL) for calculated non-HDL cholesterol]; concentrations of HDL cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein(a) are not affected by fasting/non-fasting status. In addition, non-fasting and fasting concentrations vary similarly over time and are comparable in the prediction of cardiovascular disease. To improve patient compliance with lipid testing, we therefore recommend the routine use of non-fasting lipid profiles, while fasting sampling may be considered when non-fasting triglycerides >5 mmol/L (440 mg/dL). For non-fasting samples, laboratory reports should flag abnormal concentrations as triglycerides ≥2 mmol/L (175 mg/dL), total cholesterol ≥5 mmol/L (190 mg/dL), LDL cholesterol ≥3 mmol/L (115 mg/dL), calculated remnant cholesterol ≥0.9 mmol/L (35 mg/dL), calculated non-HDL cholesterol ≥3.9 mmol/L (150 mg/dL), HDL cholesterol ≤1 mmol/L (40 mg/dL), apolipoprotein A1 ≤1.25 g/L (125 mg/dL), apolipoprotein B ≥1.0 g/L (100 mg/dL), and lipoprotein(a) ≥50 mg/dL (80th percentile); for fasting samples, abnormal concentrations correspond to triglycerides ≥1.7 mmol/L (150 mg/dL). Life-threatening concentrations require separate referral when triglycerides >10 mmol/L (880 mg/dL) for the risk of pancreatitis, LDL cholesterol >13 mmol/L (500 mg/dL) for homozygous familial hypercholesterolaemia, LDL cholesterol >5 mmol/L (190 mg/dL) for heterozygous familial hypercholesterolaemia, and lipoprotein(a) >150 mg/dL (99th percentile) for very high cardiovascular risk. Conclusion: We recommend that non-fasting blood samples be routinely used for the assessment of plasma lipid profiles. Laboratory reports should flag abnormal values on the basis of desirable concentration cut-points. Non-fasting and fasting measurements should be complementary but not mutually exclusive.

Low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and total cholesterol are heritable, modifiable, risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,578 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5×10−8, including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian, and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipids are often associated with cardiovascular and metabolic traits including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio, and body mass index. Our results illustrate the value of genetic data from individuals of diverse ancestries and provide insights into biological mechanisms regulating blood lipids to guide future genetic, biological, and therapeutic research.

Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiologic studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P<5×10−8 for each) to examine the role of triglycerides on risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglycerides, and show that the direction and magnitude of both are factors in determining CAD risk. Second, we consider loci with only a strong magnitude of association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol, a polymorphism's strength of effect on triglycerides is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

A recent genome-wide association study associated 62 single nucleotide polymorphisms (SNPs) from 43 genomic loci, with fasting lipoprotein subfractions in European–Americans (EAs) at genome-wide levels of significance across three independent samples. Whether these associations are consistent across ethnicities with a non-European ancestry is unknown. We analyzed 15 lipoprotein subfraction measures, on 1677 African–Americans (AAs), 1450 Hispanic–Americans (HAs), and 775 Chinese–Americans (CHN) participating in the multi-ethnic study of atherosclerosis (MESA). Genome-wide data were obtained using the Affymetrix 6.0 and Illumina HumanOmni chips. Linear regression models between genetic variables and lipoprotein subfractions were adjusted for age, gender, body mass index, smoking, study center, and genetic ancestry (based on principal components), and additionally adjusted for Mexican/Non-Mexican status in HAs. A false discovery rate correction was applied separately within the results for each ethnicity to correct for multiple testing. Power calculations revealed that we did not have the power for SNP-based measures of association, so we analyzed phenotype-specific genetic risk scores (GRSs), constructed as in the original genome-wide analysis. We successfully replicated all 15 GRS–lipoprotein associations in 2527 EAs. Among the 15 significant GRS–lipoprotein associations in EAs, 11 were significant in AAs, 13 in HAs, and 1 in CHNs. Further analyses revealed that ethnicity differences could not be explained by differences in linkage disequilibrium, lipid lowering drugs, diabetes, or gender. Our study emphasizes the importance of ethnicity (here indexing genetic ancestry) in genetic risk for CVD and highlights the need to identify ethnicity-specific genetic variants associated with CVD risk. Electronic supplementary material The online version of this article (doi:10.1007/s00439-017-1782-y) contains supplementary material, which is available to authorized users.

We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log10Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns - both in our original data and in a replication cohort - consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log10Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html.

Background: Glycosylated proteins partake in multiple cellular processes including inflammation. We hypothesized that GlycA, a novel biomarker of protein glycan N‐acetyl groups, is related to incident cardiovascular disease (CVD), and we compared it with high‐sensitivity C‐reactive protein (hsCRP). Methods and Results: In 27 491 initially healthy women, baseline GlycA was quantified by nuclear magnetic resonance spectroscopy and hsCRP by an immunoturbidimetric assay. During median follow‐up of 17.2 years, 1648 incident CVD events occurred (myocardial infarction, ischemic stroke, coronary revascularization, and CVD death). GlycA and hsCRP were moderately correlated (Spearman r=0.61, P<0.0001). In Cox regression models that included age, ethnicity, smoking, blood pressure, medications, menopausal status, body mass index, and diabetes, hazard ratios for CVD across quartiles 1 to 4 of GlycA were 1.00, 1.10 (95% CI, 0.92 to 1.30), 1.34 (95% CI, 1.13 to 1.58), and 1.64 (95% CI, 1.39 to 1.93), similar to hsCRP, for which hazard ratios were 1.00, 1.18 (95% CI, 0.99 to 1.41), 1.35 (95% CI, 1.14 to 1.61), and 1.75 (95% CI, 1.47 to 2.09) (both Ptrend<0.0001). Associations were attenuated after additionally adjusting for lipids: the hazard ratio of quartile 4 versus 1 for GlycA was 1.23 (95% CI, 1.04 to 1.46; Ptrend=0.002) and for hsCRP was 1.44 (95% CI, 1.20 to 1.72; Ptrend<0.0001). Further adjustment for the other biomarker resulted in a hazard ratio of quartile 4 versus 1 for GlycA of 1.03 (95% CI, 0.85 to 1.24; Ptrend=0.41) and for hsCRP of 1.29 (95% CI, 1.06 to 1.56; Ptrend=0.001). Conclusions: In this prospective study of initially healthy women, baseline GlycA was associated with incident CVD, consistent with a possible role for protein glycans in inflammation and CVD. Clinical Trial Registration URL: http//clinicaltrials.gov/. Unique identifier NCT00000479.

Background: GlycA, a novel protein glycan biomarker of N‐acetyl side chains of acute‐phase proteins, was recently associated with incident cardiovascular disease (CVD) in healthy women. Whether GlycA predicts CVD events in the setting of statin therapy in men and women without CVD but with evidence of chronic inflammation is unknown. Methods and Results: In the Justfication for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial (NCT00239681), participants with low‐density lipoprotein cholesterol <130 mg/dL and high‐sensitivity C‐reactive protein (hsCRP) ≥2 mg/L were randomized to rosuvastatin 20 mg/day or placebo. GlycA was quantified by nuclear magnetic resonance spectroscopy in 12 527 before randomization and 10 039 participants at 1 year. A total of 310 first primary CVD events occurred during maximum follow‐up of 5.0 years (median, 1.9). GlycA changed minimally after 1 year on study treatment: 6.8% and 4.7% decrease in the rosuvastatin and placebo groups, respectively. Overall, baseline GlycA levels were associated with increased risk of CVD: multivariable‐adjusted hazard ratio (HR) per SD increment, 1.20 (95% CI, 1.08–1.34; P=0.0006). After additionally adjusting for hsCRP, this was slightly attenuated (HR, 1.18; 95% CI, 1.04–1.35; P=0.01). On‐treatment GlycA levels were also associated with CVD; corresponding multivariable‐adjusted HRs per SD before and after additionally adjusting for hsCRP: 1.27 (95% CI, 1.13–1.42; P<0.0001) and 1.24 (95% CI, 1.07–1.44; P=0.004), respectively. Tests for heterogeneity by treatment arm were not significant (P for interaction, >0.20). Conclusion: In the JUPITER trial, increased levels of GlycA were associated with an increased risk of CVD events independent of traditional risk factors and hsCRP. Clinical Trials Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT00239681.

Background: Acute phase proteins highlight the dynamic interaction between inflammation and oncogenesis. GlycA, a novel nuclear magnetic resonance (NMR) inflammatory marker that identifies primarily circulating N-acetyl glycan groups attached to acute phase proteins, may be a future CRC risk biomarker. Methods: We examined the association between GlycA and incident CRC and mortality in two prospective cohorts (N = 34,320); Discovery cohort: 27,495 participants from Women's Health Study (WHS); Replication cohort: 6,784 participants from Multi-Ethnic Study of Atherosclerosis (MESA). Multivariable Cox models were adjusted for clinical risk factors and compared GlycA to acute phase proteins (high-sensitivity C-reactive protein [hsCRP], fibrinogen, and soluble intercellular adhesion molecule-1 [sICAM-1]). Results: In WHS (median follow-up 19 years, 337 cases, 103 deaths), adjusted HRs (95% CIs) per SD increment of GlycA for CRC incidence and mortality were 1.19 (1.06–1.35; p = 0.004) and 1.24 (1.00–1.55; p = 0.05), respectively. We replicated findings in MESA (median follow-up 11 years, 70 cases, 23 deaths); HRs (95% CIs) per SD of GlycA for CRC incidence and mortality were 1.32 (1.06–1.65; p = 0.01) and 1.54 (1.06–2.23; p = 0.02), respectively, adjusting for age, sex, and race. Pooled analysis, adjusted HR (95% CI) per SD of GlycA for CRC incidence and mortality was 1.26 (1.15–1.39; p = 1 x 10−6). Other acute phase proteins (hsCRP, fibrinogen, and sICAM-1) had weaker or no association with CRC incidence, while only fibrinogen and GlycA were associated with CRC mortality. Conclusions: The clinical utility of GlycA to personalize CRC therapies or prevention warrants further study. Trial Registration ClinicalTrials.gov: WHS NCT00000479, MESA NCT00005487

Context: Whether subclinical hypothyroidism (SCH) is associated with cardiometabolic abnormalities is uncertain. Objective: To examine diverse cardiometabolic biomarkers across euthyroid, SCH, and overt hypothyroidism (HT) in women free of cardiovascular disease. Design: Cross-sectional adjusted associations for lipids, lipoprotein subclasses, lipoprotein insulin resistance score, inflammatory, coagulation, and glycemic biomarkers by analysis of covariance for thyroid categories or thyroid stimulating hormone (TSH) quintiles on a Women’s Health Study subcohort. Setting: Outpatient. Patients or Other Participants: Randomly sampled 3914 middle-aged and older women for thyroid function analysis (TSH, free T4), of whom 3321 were not on lipid-lowering therapy. Intervention: None. Main Outcome Measure: Associations of SCH and HT with cardiometabolic markers. Results: Going from euthyroid to HT, the lipoprotein subclass profiles were indicative of insulin resistance (respective values and P for trend): larger very-low-density lipoprotein size (nm) (51.5 [95% confidence interval (CI), 51.2, 51.8] to 52.9 [51.8, 54.1], P = 0.001); higher low-density lipoprotein (LDL) particle concentration (nmol/L) [1283 (95% CI, 1267, 1299) to 1358 (1298, 1418), P = 0.004], and smaller LDL size. There was worsening lipoprotein insulin resistance score from euthyroid (49.2; 95% CI, 48.3, 50.2) to SCH (52.1; 95% CI, 50.1, 54.0) and HT (52.1; 95% CI, 48.6, 55.6); P for trend of 0.008. Of the other biomarkers, SCH and HT were associated with higher high-sensitivity C-reactive protein and hemoglobin A1c. For increasing TSH quintiles, results were overall similar. Conclusions: In apparently healthy women, SCH cardiometabolic profiles indicated worsening insulin resistance and higher cardiovascular disease risk markers compared with euthyroid individuals, despite similar LDL and total cholesterol. These findings suggest that cardiometabolic risk may increase early in the progression toward SCH and overt HT.