SNPs in apolipoproteins contribute to sex-dependent differences in blood lipids before and after a high-fat dietary challenge in healthy U.S. adults

Gene clusters linked to insulin resistance identified in a genome-wide study of the Taiwan Biobank population

This pioneering genome-wide association study examined surrogate markers for insulin resistance (IR) in 147,880 Taiwanese individuals using data from the Taiwan Biobank. The study focused on two IR surrogate markers: the triglyceride to high-density lipoprotein cholesterol (TG:HDL-C) ratio and the TyG index (the product of fasting plasma glucose and triglycerides). We identified genome-wide significance loci within four gene clusters: GCKR, MLXIPL, APOA5, and APOC1, uncovering 197 genes associated with IR. Transcriptome-wide association analysis revealed significant associations between these clusters and TyG, primarily in adipose tissue. Gene ontology analysis highlighted pathways related to Alzheimer's disease, glucose homeostasis, insulin resistance, and lipoprotein dynamics. The study identified sex-specific genes associated with TyG. Polygenic risk score analysis linked both IR markers to gout and hyperlipidemia. Our findings elucidate the complex relationships between IR surrogate markers, genetic predisposition, and disease phenotypes in the Taiwanese population, contributing valuable insights to the field of metabolic research.

Fine-Scale Dietary Polyphenol Intake Is Associated with Systemic and Gastrointestinal Inflammation in Healthy Adults

BACKGROUND

Polyphenols are dietary bioactive compounds, many of which have anti-inflammatory properties. However, information on the intake of dietary polyphenols at the class and compound levels and their associations with gastrointestinal (GI) and systemic inflammation is lacking.

OBJECTIVES

Estimate dietary polyphenol intake in healthy adults and examine its relationship with GI and systemic inflammation markers.

METHODS

Healthy adults (n = 350) completed the United States Department of Agriculture Nutritional Phenotyping Study, an observational, cross-sectional study balanced for age, sex, and body mass index. Dietary intake, assessed via multiple 24-h recalls, was ingredientized and mapped to FooDB, a comprehensive food composition database. Dietary polyphenol intake (total, class, compound) was estimated and examined for its relationship to GI and systemic inflammation markers using linear models and random forest regressions.

RESULTS

Mean total polyphenol intake was ∼914 mg/1000 kcal/d with flavonoids as the greatest class contributor (495 mg/1000 kcal/d). Tea, coffee, and fruits were among the largest food contributors to polyphenol intake. Total polyphenol intake was negatively associated with the GI inflammation marker, fecal calprotectin (β = -0.004, P = 0.04). At the class level, polyphenols categorized as prenol lipids (β = -0.94, P < 0.01) and phenylpropanoic acids (β = -0.92, P < 0.01) were negatively associated with plasma lipopolysaccharide-binding protein, a proxy for GI permeability. Food sources of these two classes included mainly olive products. We further detected a positive association between C-reactive protein and polyphenols in the "cinnamic acids and derivatives" class using hierarchical feature engineering and random forest modeling.

CONCLUSIONS

Even in healthy adults, dietary polyphenol intake was negatively associated with GI inflammation and intake of prenol lipids and phenylpropanoic acids was negatively associated with GI permeability. Relationships between polyphenol intake and inflammatory outcomes varied with the resolution-total, class, compound-of polyphenol intake, suggesting a nuanced impact of polyphenols on GI and systemic inflammation. This trial was registered at clinicaltrials.gov as NCT02367287.

Detecting gene-environment interactions from multiple continuous traits

MOTIVATION

Genetic variants present differential effects on humans according to various environmental exposures, the so-called "gene-environment interactions" (GxE). Many diseases can be diagnosed with multiple traits, such as obesity, diabetes, and dyslipidemia. I developed a multivariate scale test (MST) for detecting the GxE of a disease with several continuous traits. Given a significant MST result, I continued to search for which trait and which E enriched the GxE signals. Simulation studies were performed to compare MST with the univariate scale test (UST).

RESULTS

MST can gain more power than UST because of (1) integrating more traits with GxE information and (2) the less harsh penalty on multiple testing. However, if only few traits account for GxE, MST may lose power due to aggregating non-informative traits into the test statistic. As an example, MST was applied to a discovery set of 93 708 Taiwan Biobank (TWB) individuals and a replication set of 25 200 TWB individuals. From among 2 570 487 SNPs with minor allele frequencies ≥5%, MST identified 18 independent variance quantitative trait loci (P < 2.4E-9 in the discovery cohort and P < 2.8E-5 in the replication cohort) and 41 GxE signals (P < .00027) based on eight trait domains (including 29 traits).

AVAILABILITY AND IMPLEMENTATION

https://github.com/WanYuLin/Multivariate-scale-test-MST.

Causal effects of circulating lipids and lipid-lowering drugs on the risk of urinary stones: a Mendelian randomization study

Background

Previous studies have yielded conflicting findings regarding the association between circulating lipids and lipid-lowering drugs with urinary stones, and the causal relationship between the two remains inconclusive.

Objective

This study aimed to assess the causal relationship between circulating lipids (Triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], apolipoprotein A [APOA], apolipoprotein B [APOB] and Pure hypercholesterolaemia), lipid-lowering drugs (HMGCR [HMG-CoA reductase] inhibitors and PCSK9[Proprotein Convertase Subtilisin/Kexin Type 9] inhibitors) and the risk of urinary stones, using genetic data.

Methods

Genetic instrumental variables (GIVs) for circulating lipids and lipid-lowering drugs were obtained from the UK Biobank and existing literature. Outcome data were extracted from a genetic association database with 3,625 urinary stone cases and 459,308 controls. Two-sample MR analysis, employing the TwoSampleMR software package in R 4.2.3, was conducted to assess the associations between multiple exposures. The primary outcome was determined using the inverse variance weighted (IVW) method for the causal relationship between exposure and outcome, while additional methods such as MR-Egger, weighted median, simple mode, and weighted mode were utilized as supplementary analyses. Robustness of the Mendelian Randomization (MR) analysis results was assessed through leave-one-out analysis and funnel plots.

Results

The MR analysis revealed a significant association between elevated TG levels per 1 standard deviation and the occurrence of urinary stones (odds ratio [OR]: 1.002, 95% confidence interval [CI]: 1.000-1.003, P = 0.010). However, no significant association was observed between factors other than TG exposure and the risk of urinary stone occurrence across all methods(LDL-C: [OR], 1.001; 95% [CI], 1.000-1.003, P=0.132;HDL-C: [OR], 0.999; 95% [CI], 0.998-1.000, P=0.151;APOA:[OR] being 1.000 (95% [CI], 0.999-1.001, P=0.721;APOB: [OR] of 1.001 (95% [CI], 1.000-1.002, P=0.058;Pure hypercholesterolaemia: [OR] of 1.015 (95% [CI], 0.976-1.055, P=0.455) and lipid-lowering drugs (HMGCR inhibitors: [OR], 0.997; 95% [CI], 0.990-1.003, P=0.301 and PCSK9 inhibitors:[OR], 1.002; 95% [CI], 1.000-1.005, P=0.099).

Conclusion

Our findings provide conclusive evidence supporting a causal relationship between an increased risk of urinary stones and elevated serum TG levels. However, we did not find a significant association between urinary stone occurrence and the levels of LDL-C, HDL-C, APOA, APOB, Pure hypercholesterolaemia and lipid-lowering drugs.

Trimethylamine N-Oxide Response to a Mixed Macronutrient Tolerance Test in a Cohort of Healthy United States Adults

Plasma trimethylamine n-oxide (TMAO) concentration increases in responses to feeding TMAO, choline, phosphatidylcholine, L-carnitine, and betaine but it is unknown whether concentrations change following a mixed macronutrient tolerance test (MMTT) with limited amounts of TMAO precursors. In this proof-of-concept study, we provided healthy female and male adults (n = 97) ranging in age (18-65 years) and BMI (18-44 kg/m2) a MMTT (60% fat, 25% sucrose; 42% of a standard 2000 kilo calorie diet) and recorded their metabolic response at fasting and at 30 min, 3 h, and 6 h postprandially. We quantified total exposure to TMAO (AUC-TMAO) and classified individuals by the blood draw at which they experienced their maximal TMAO concentration (TMAO-response groups). We related AUC-TMAO to the 16S rRNA microbiome, to two SNPs in the exons of the FMO3 gene (rs2266782, G>A, p.Glu158Lys; and rs2266780, A>G, p.Glu308Gly), and to a priori plasma metabolites. We observed varying TMAO responses (timing and magnitude) and identified a sex by age interaction such that AUC-TMAO increased with age in females but not in males (p-value = 0.0112). Few relationships between AUC-TMAO and the fecal microbiome and FMO3 genotype were identified. We observed a strong correlation between AUC-TMAO and TNF-α that depended on TMAO-response group. These findings promote precision nutrition and have important ramifications for the eating behavior of adults who could benefit from reducing TMAO exposure, and for understanding factors that generate plasma TMAO.