Obesity-related Traits Research Articles

Integrative analysis of transcriptome and proteome wide association studies prioritized functional genes for obesity.

Genome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses. We first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability. We prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets. Our study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.

Major depression recurrence is associated with differences in obesity-related traits in women, but not in men.

Obesity-related cardiometabolic comorbidity is common in major depressive disorder (MDD). However, sex differences and MDD recurrence may modify the MDD-obesity-link. Sex-specific associations of MDD recurrence (single [MDDS] or recurrent episodes [MDDR]) and obesity-related traits were analyzed in 4.100 adults (51.6% women) from a cross-sectional population-based cohort in Germany (SHIP-Trend-0). DSM-IV-based lifetime MDD diagnoses and MDD recurrence status were obtained through diagnostic interviews. Obesity-related outcomes included anthropometrics (weight, body mass index, waist- and hip-circumference, waist-to-hip ratio, waist-to-height ratio), bioelectrical impedance analysis of body fat mass and fat-free mass, and subcutaneous (SAT) and visceral adipose tissue (VAT) from abdominal magnetic resonance imaging. Sex-stratified linear regression models predicting obesity-related traits from MDD recurrence status were adjusted for age, education, and current depressive symptoms. 790 participants (19.3%) fulfilled lifetime MDD criteria (23.8% women vs. 14.5% men, p<0.001). In women, MDDS was inversely associated with anthropometric indicators of general and central obesity, while MDDR was positively associated with all obesity-related traits, except waist-to-hip ratio and fat-free mass. In women, MDDR versus MDDS was associated with higher levels of obesity across all outcomes except fat-free mass. In men, MDD was positively associated with SAT regardless of MDD recurrence. Additionally, lifetime MDD was positively associated with VAT in men. Results remained significant in sensitivity analyses after exclusion of participants with current use of antidepressants. The MDD-obesity association is modified by MDD recurrence and sex independent of current depressive symptoms. Accounting for sex and MDD recurrence may identify individuals with MDD at increased cardiometabolic risk.

The Mediating Roles of Lung Function Traits and Inflammatory Factors on the Associations between Measures of Obesity and Risk of Lower Respiratory Tract Infections: A Mendelian Randomization Study.

Identifying mediators between obesity-related traits and lower respiratory tract infections (LRTIs) would inform preventive and therapeutic strategies to reduce the burden of LRITs. We aimed to recognize whether lung function and inflammatory factors mediate their associations. We conducted a two-step, two-sample Mendelian randomization (MR) analysis. Two-sample MR was performed on (1) obesity-related traits (i.e., body mass index [BMI], waist circumference [WC], and waist-to-hip ratio [WHR]) and LRTIs (i.e., acute bronchitis, acute bronchiolitis, bronchiectasis, influenza, and pneumonia), (2) obesity-related traits and potential mediators, and (3) potential mediators and LRTIs. Next, two-step MR was applied to infer whether the mediation effects exist. We found that C-reactive protein (CRP), interleukin-6 (IL-6), and forced expiratory volume in the first second (FEV1) mediated 32.59% (95% CI: 17.90%, 47.27%), 7.96% (95% CI: 1.79%, 14.14%), and 4.04% (95% CI: 0.34%, 7.74%) of the effect of BMI on pneumonia, and they mediated 26.90% (95% CI: 13.98%, 39.83%), 10.23% (95% CI: 2.72%, 17.73%), and 4.67% (95% CI: 0.25%, 9.09%) of the effect of WC on pneumonia, respectively. Additionally, CRP, forced vital capacity (FVC), and FEV1 mediated 18.66% (95% CI: 8.70%, 28.62%), 8.72% (95% CI: 1.86%, 15.58%), and 8.41% (95% CI: 2.77%, 14.06%) of the effect of BMI on acute bronchitis, and they mediated 19.96% (95% CI: 7.44%, 32.48%), 12.19% (95% CI: 2.00%, 22.39%), and 12.61% (95% CI: 2.94%, 22.29%) of the effect of WC on acute bronchitis, respectively. Health interventions linked to reducing inflammation and maintaining normal lung function could help mitigate the risk of obesity-related LRTIs.

Comprehensive Genetic Analysis of Associations between Obesity-Related Parameters and Physical Activity: A Scoping Review.

Genetic epidemiological studies have shown that numerous genetic variants cumulatively increase obesity risk. Although genetically predisposed individuals are more prone to developing obesity, it has been shown that physical activity can modify the genetic predisposition to obesity. Therefore, genetic data obtained from earlier studies, including 30 polymorphisms located in 18 genes, were analyzed using novel methods such as the total genetic score and Biofilter 2.4 software to combine genotypic and phenotypic information for nine obesity-related traits measured before and after the realization of the 12-week training program. The results revealed six genes whose genotypes were most important for post-training changes-LEP, LEPR, ADIPOQ, ADRA2A, ADRB3, and DRD2. Five noteworthy pairwise interactions, LEP × LEPR, ADRB2 × ADRB3, ADRA2A × ADRB3, ADRA2A × ADRB2, ADRA2A × DRD2, and three specific interactions demonstrating significant associations with key parameters crucial for health, total cholesterol (TC), high-density lipoprotein (HDL), and fat-free mass (FFM), were also identified. The molecular basis of training adaptation described in this study would have an enormous impact on the individualization of training programs, which, designed according to a given person's genetic profile, will be effective and safe intervention strategies for preventing obesity and improving health.

Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians.

While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited. To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG). Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ. Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.

A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans.

Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a 'high impact' SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.

Human brain proteome-wide association study provides insights into the genetic components of protein abundance in obesity.

Genome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances. We performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level. By integrating human brain proteomes from discovery (ROSMAP, N = 376) and validation datasets (BANNER, N = 198) with genome-wide summary statistics of obesity-related phenotypes (N ranged from 325,153 to 806,834), we identified 51 genes whose cis-regulated brain protein abundance was associated with obesity. These 51 genes were enriched in 11 metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 51 genes had high drug repurposing value. Ten of the 51 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes. Our study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development.

Birth weight and risk of cerebral aneurysm: A multivariable Mendelian randomization study

BackgroundBirth weight has been linked with various health outcomes. The association between birth weight and cerebral aneurysm remains unknown. MethodsThe two-sample Mendelian randomization (MR) approach was used to evaluate the causal effect of birth weight on cerebral aneurysm based on genome-wide association studies (GWAS), comprising 261,932 UKB participants for birth weight and 204,060 FinnGen participants for cerebral aneurysm. The inverse variance weighted (IVW) method was used as the primary method. Alternative methods were used for comparison. Sensitivity analysis was conducted to evaluate the robustness of the results. Multivariable MR (MVMR) was further conducted to evaluate the direct effect of the birth weight on cerebral aneurysm. ResultsThe IVW detected a causal association between higher birth weight and increased risk of cerebral aneurysm (OR = 0.521, 95% CI = 0.356 ∼ 0.763, P = 7.88 × 10−4), which was supported by alternative MR models. Sensitivity analysis did not find any evidence of heterogeneity or pleiotropy. MVMR further identified a direct effect of birth weight on cerebral aneurysm, independent of obesity-related traits or smoking. ConclusionThis MR study found evidence of the association between birth weight and cerebral aneurysm, providing novel insight into the etiology of cerebral aneurysm, indicating the promising role of birth weight as a marker for screening populations at higher risk of cerebral aneurysm.

Investigating the causal association between obesity and risk of hepatocellular carcinoma and underlying mechanisms

Obesity is a global health concern and independent risk factor for cancers including hepatocellular carcinoma (HCC). However, evidence on the causal links between obesity and HCC is limited and inconclusive. This study aimed to investigate the causal relationship between obesity-related traits and HCC risk and explore underlying mechanisms using bioinformatics approaches. Two-sample Mendelian randomization analysis was conducted leveraging publicly available genome-wide association study summary data on obesity traits (body mass index, body fat percentage, waist circumference, waist-to-hip ratio, visceral adipose tissue volume) and HCC. Associations of obesity with primary mechanisms (insulin resistance, adipokines, inflammation) and their effects on HCC were examined. Differentially expressed genes in obesity and HCC were identified and functional enrichment analyses were performed. Correlations with tumor microenvironment (TME) and immunotherapy markers were analyzed. Genetically predicted higher body mass index and body fat percentage showed significant causal relationships with increased HCC risk. Overall obesity also demonstrated causal links with insulin resistance, circulating leptin levels, C-reactive protein levels and risk of severe insulin resistant type 2 diabetes. Four differentially expressed genes (ESR1, GCDH, FAHD2A, DCXR) were common in obesity and HCC. Enrichment analyses indicated their roles in processes like RNA capping, viral transcription, IL-17 signaling and endocrine resistance. They exhibited negative correlations with immune cell infiltration and immunotherapy markers in HCC. Overall obesity likely has a causal effect on HCC risk in Europeans, possibly via influencing primary mechanisms. The identified differentially expressed genes may be implicated in obesity-induced hepatocarcinogenesis through regulating cell cycle, inflammation and immune evasion. Further research on precise mechanisms is warranted.

Genetic variants of LEAP2 are associated with anthropometric traits and circulating insulin-like growth factor-1 concentration: A UK Biobank study.

To test the hypothesis that liver-expressed antimicrobial peptide 2 (LEAP2) genetic variants might influence the susceptibility to human obesity. Using data from the UK Biobank, we identified independent LEAP2 gene single nucleotide polymorphisms (SNPs) and examined their associations with obesity traits and serum insulin-like growth factor-1 (IGF-1) concentration. These associations were evaluated for both individual SNPs and after combining them into a genetic risk score (GRSLEAP2) using linear and logistic regression models. Sex-stratified analyses were also conducted. Five SNPs showed positive associations with obesity-related traits. rs57880964 was associated with body mass index (BMI) and waist-to-hip ratio adjusted for BMI (WHRadjBMI), in the total population and among women. Four independent SNPs were positively associated with higher serum IGF-1 concentrations in both men and women. GRSLEAP2 was associated with BMI and WHRadjBMI only in women and with serum IGF-1 concentration in both sexes. These findings reveal sex-specific associations between key LEAP2 gene variants and several obesity traits, while also indicating a strong independent association of LEAP2 variants with serum IGF-1 concentration.

Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression

The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.

Phenome-Wide Investigation of the Causal Associations Between Pre-Pregnancy Obesity Traits and Gestational Diabetes: A Two-Sample Mendelian Randomization Analyses.

Pre-pregnancy obesity was associated with gestational diabetes in observational studies, but whether this relationship is causal remains to be determined. To evaluate whether pre-pregnancy obesity traits causally affect gestational diabetes risk, a two-sample Mendelian randomization (MR) analysis was performed utilizing summary-level statistics from published genome-wide association studies (GWAS). Obesity-related traits included body mass index (BMI), overweight, obesity, obesity class 1, obesity class 2, obesity class 3, childhood obesity, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), percent liver fat, visceral adipose tissue volume, abdominal subcutaneous adipose tissue volume. Effect estimates were evaluated using the inverse-variance weighting method. Weighted median, MR-Egger, simple mode, and weighted mode were performed as sensitivity analyses. Genetically predicted pre-pregnancy BMI [odds ratio (OR) = 1.68; 95% confidence interval (CI): 1.45-1.95; P = 9.13 × 10-12], overweight (OR = 1.49; 95% CI: 1.21-1.85; P = 2.06 × 10-4), obesity (OR = 1.25; 95% CI: 1.18-1.33; P = 8.01 × 10-13), obesity class 1 (OR = 1.31; 95% CI: 1.17-1.46; P = 1.49 × 10-6), obesity class 2 (OR = 1.26; 95% CI: 1.16-1.37; P = 5.23 × 10-8), childhood obesity (OR = 1.33; 95% CI: 1.23-1.44; P = 4.06 × 10-12), and WHR (OR = 2.35; 95% CI: 1.44-3.83; P = 5.89 × 10-4) were associated with increased risk of gestational diabetes. No significant association was observed with obesity class 3, WC, HC, percent liver fat, visceral adipose tissue volume, or abdominal subcutaneous adipose tissue volume. Similar results were observed in sensitivity analyses. Therefore, genetically predicted pre-pregnancy obesity traits may increase the risk of gestational diabetes. Weight control before pregnancy may be beneficial to prevent gestational diabetes.

Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis.

Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system. Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis. These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.

Genome-Wide Epistatic Network Analyses of Semantic Fluency in Older Adults.

Semantic fluency impairment has been attributed to a wide range of neurocognitive and psychiatric conditions, especially in the older population. Moderate heritability estimates on semantic fluency were obtained from both twin and family-based studies suggesting genetic contributions to the observed variation across individuals. Currently, effort in identifying the genetic variants underlying the heritability estimates for this complex trait remains scarce. Using the semantic fluency scale and genome-wide SNP genotype data from the Long Life Family Study (LLFS), we performed a genome-wide association study (GWAS) and epistasis network analysis on semantic fluency in 2289 individuals aged over 60 years from the American LLFS cohorts and replicated the findings in 1129 individuals aged over 50 years from the Danish LLFS cohort. In the GWAS, two SNPs with genome-wide significance (rs3749683, p = 2.52 × 10-8; rs880179, p = 4.83 × 10-8) mapped to the CMYAS gene on chromosome 5 were detected. The epistasis network analysis identified five modules as significant (4.16 × 10-5 < p < 7.35 × 10-3), of which two were replicated (p < 3.10 × 10-3). These two modules revealed significant enrichment of tissue-specific gene expression in brain tissues and high enrichment of GWAS catalog traits, e.g., obesity-related traits, blood pressure, chronotype, sleep duration, and brain structure, that have been reported to associate with verbal performance in epidemiological studies. Our results suggest high tissue specificity of genetic regulation of gene expression in brain tissues with epistatic SNP networks functioning jointly in modifying individual verbal ability and cognitive performance.

Obesity-Related Traits Mediate the Effects of Educational Attainment on the Risk of Varicose Veins, Venous Thromboembolism, and Phlebitis.

The extent to which educational attainment (EA) influences the risk of varicose veins (VVs), venous thromboembolism (VTE), and phlebitis occurrence, whether this pathway is mediated by obesity-related traits, and the proportion of their mediation is unknown. A Mendelian randomization (MR) design was used to genetically investigate the causal effects of EA on the risk of VV, VTE, and phlebitis and to assess the mediating effect of obesity-related traits. Causal effects were estimated using primarily the multiplicative random-effects inverse variance-weighted method. This was supplemented by Cochran's Q-statistic, MR-Egger regression, MR funnel plots, and leave-one-out test to evaluate the reliability of the results. For the individual mediation effect, the coefficient product method was mainly utilized to estimate. An increase in genetically predicted EA was associated with a lower risk of VV, VTE, and phlebitis, as well as lower body mass index, basal metabolic rate, hip circumference, and waist circumference. As genetically predicted body mass index, basal metabolic rate, hip circumference, and waist circumference increased, the risk of developing VV, VTE, and phlebitis increased, respectively. Body mass index, basal metabolic rate, hip circumference, and waist circumference were identified as mediators of the protective effects of EA on VV, VTE, and phlebitis. The findings support a causal relationship between higher EA and lower risk of VV, VTE, and phlebitis. Obesity-related traits play a significant mediating role in these pathways, and there are interactions between them, with hip circumference mediating these pathways relatively independently from the other three.

The Effect of Broccoli Glucoraphanin Supplementation on Ameliorating High-Fat-Diet-Induced Obesity through the Gut Microbiome and Metabolome Interface.

Obesity and its metabolic comorbidities pose a major global challenge for public health. Glucoraphanin (GRN) is a natural bioactive compound enriched in broccoli that is known to have potential health benefits against various human chronic diseases. This study investigats the effects of broccoli GRN supplementation on body weight, metabolic parameters, gut microbiome and metabolome associated with obesity. The study is conducted on an obese-related C57BL/6J mouse model through the treatment of normal control diet, high-fat diet (HFD)and GRN-supplemented HFD (HFD-GRN) to determine the metabolic protection of GRN. The results shows that GRN treatment alleviates obesity-related traits leading to improved glucose metabolism in HFD-fed animals. Mechanically, the study noticed that GRN significantly shifts the gut microbial diversity and composition to an eubiosis status. GRN supplement also significantly alters plasma metabolite profiles. Further integrated analysis reveal a complex interaction between the gut microbes and host metabolism that may contribute to GRN-induced beneficial effects against HFD. These results indicate that beneficial effects of broccoli GRN on reversing HFD-induced adverse metabolic parameters may be attributed to its impacts on reprogramming microbial community and metabolites. Identification of the mechanistic functions of GRN further warrants it as a dietary candidate for obesity prevention.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit).

A genome-wide cross-trait analysis identifies shared loci and causal relationships of obesity and lipidemic traits with psoriasis.

Obesity and dyslipidemia, major global health concerns, have been linked to psoriasis, but previous studies faced methodological limitations and their shared genetic basis remains unclear. This study examines various obesity-related and lipidemic traits as potential contributors to psoriasis development, aiming to clarify their genetic associations and potential causal links. Summary statistics from genome-wide association studies (GWAS) conducted for obesity-related traits (body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-hip ratio adjusted for the body mass index (WHRadjBMI)) and lipidemic traits (high-density lipoprotein (HDL), LDL, triglyceride (TG), total Cholesterol (TC), apolipoprotein A1 (apoA1), apolipoprotein B (apoB), and apolipoprotein E (apoE)) and psoriasis, all in populations of European ancestry, were used. We quantified genetic correlations, identified shared loci and explored causal relationship across traits. We found positive genetic correlation between BMI and psoriasis (rg=0.22, p=2.44×10-18), and between WHR and psoriasis (rg=0.19, p=1.41×10-12). We further found the positive genetic correlation between psoriasis and WHRadjBMI(rg=0.07, p=1.81×10-2) the genetic correlation, in while the effect of BMI was controlled for. We identified 14 shared loci underlying psoriasis and obesity-related traits and 43 shared loci between psoriasis and lipidemic traits via cross-trait meta-analysis. Mendelian randomization (MR) supported the causal roles of BMI (IVW OR=1.483, 95%CI=1.333-1.649), WHR (IVW OR=1.393, 95%CI=1.207-1.608) and WHRadjBMI (IVW OR=1.18, 95%CI=1.047-1.329) in psoriasis, but not observe any significant association between lipidemic traits and the risk of psoriasis. Genetic predisposition to psoriasis did not appear to affect the risk of obesity and lipidemic traits. An intrinsic link between obesity-related traits and psoriasis has been demonstrated. The genetic correlation and causal role of obesity-related traits in psoriasis highlight the significance of weight management in both the prevention and treatment of this condition.

Associations of 50 modifiable risk factors with atrial fibrillation using Mendelian randomization analysis.

Substantial focus has been placed on atrial fibrillation (AF) treatment and associated stroke prevention rather than preventing AF itself. We employed Mendelian randomization (MR) approach to examine the causal relationships between 50 modifiable risk factors (RFs) and AF. Instrumental variables for genetically predicted exposures were derived from corresponding genome-wide association studies (GWASs). Summary-level statistical data for AF were obtained from a GWAS meta-analysis (discovery dataset, N = 1,030,836) and FinnGen (validation dataset, N = 208,594). Univariable and multivariable MR analyses were performed, primarily using inverse variance weighted method with a series of robust sensitivity analyses. Genetic predisposition to insomnia, daytime naps, apnea, smoking initiation, moderate to vigorous physical activity and obesity traits, including body mass index, waist-hip ratio, central and peripheral fat/fat-free mass, exhibited significant associations with an increased risk of AF. Coffee consumption and ApoB had suggestive increased risks. Hypertension (odds ratio (OR) 95% confidence interval (CI): 5.26 (4.42, 6.24)), heart failure (HF) (OR 95% CI, 4.77 (2.43, 9.37)) and coronary artery disease (CAD) (OR 95% CI: 1.20 (1.16, 1.24)) were strongly associated with AF, while college degree, higher education attachment and HDL levels were associated with a decreased AF risk. Reverse MR found a bidirectional relationship between genetically predicted AF and CAD, HF and ischemic stroke. Multivariable analysis further indicated that obesity-related traits, systolic blood pressure and lower HDL levels independently contributed to the development of AF. This study identified several lifestyles and cardiometabolic factors that might be causally related to AF, underscoring the importance of a holistic approach to AF management and prevention.

Renal sinus fat is associated with intrarenal hemodynamic abnormalities independent of visceral fat in patients with chronic kidney disease

ObjectiveObesity is a risk factor of chronic kidney disease (CKD), contributing to the rising incidence of cardiometabolic diseases. Renal sinus fat (RSF) is an ectopic fat depot located at the renal cavity that could impair renal function and hemodynamic through compression of renal structures. The major purpose of this study was to explore the relationship between RSF accumulation and renal dysfunction in CKD patients. MethodsWe evaluated the associations between computed tomography measured RSF volume and key clinical and histologic parameters involved in renal function and hemodynamics in 132 well-characterized CKD patients who underwent renal biopsy (median age: 62 years; 63.6% men). ResultsRSF volume normalized by renal volume (RSF%) positively correlated with obesity-related traits such body mass index and visceral fat volume (VFV) (all P < 0.001) whereas it negatively correlated with estimated glomerular filtration rate (eGFR) (ρ = −0.42, P < 0.001) and 24-h urinary creatinine clearance (CCr) (ρ = −0.34, P < 0.001). Notably, we found robust positive correlations between RSF% and renal resistive index (RRI) measured by the Doppler ultrasound (ρ = 0.40, P < 0.001), and the histological severity of global glomerular sclerosis (ρ = 0.48, P < 0.001) and interstitial fibrosis and tubular atrophy (IFTA) (ρ = 0.35, P < 0.001). In the multivariate linear regression models, after accounting for potential confounders including VFV, RSF% remained significantly associated with CCr (β = −0.26, P < 0.001), RRI (β = 0.17, P = 0.022), global glomerular sclerosis (β = 0.21, P = 0.002), and IFTA (β = 0.17, P = 0.012). ConclusionRSF accumulation is associated with renal dysfunction and hemodynamic abnormalities independent of visceral adiposity. Our results suggest that RSF may have a potential unique role in the pathogenesis of CKD.