A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Gene-environment interactions in human health

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

A new test for trait mean and variance detects unreported loci for blood-pressure variation

Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci.

Association between a polygenic lipodystrophy genetic risk score and diabetes risk in the high prevalence Maltese population

BACKGROUND

Type 2 diabetes (T2DM) is genetically heterogenous, driven by beta cell dysfunction and insulin resistance. Insulin resistance drives the development of cardiometabolic complications and is typically associated with obesity. A group of common variants at eleven loci are associated with insulin resistance and risk of both type 2 diabetes and coronary artery disease. These variants describe a polygenic correlate of lipodystrophy, with a high metabolic disease risk despite a low BMI.

OBJECTIVES

In this cross-sectional study, we sought to investigate the association of a polygenic risk score composed of eleven lipodystrophy variants with anthropometric, glycaemic and metabolic traits in an island population characterised by a high prevalence of both obesity and type 2 diabetes.

METHODS

814 unrelated adults (n = 477 controls and n = 337 T2DM cases) of Maltese-Caucasian ethnicity were genotyped and associations with phenotypes explored.

RESULTS

A higher polygenic lipodystrophy risk score was correlated with lower adiposity indices (lower waist circumference and body mass index measurements) and higher HOMA-IR, atherogenic dyslipidaemia and visceral fat dysfunction as assessed by the visceral adiposity index in the DM group. In crude and covariate-adjusted models, individuals in the top quartile of polygenic risk had a higher T2DM risk relative to individuals in the first quartile of the risk score distribution.

CONCLUSION

This study consolidates the association between polygenic lipodystrophy risk alleles, metabolic syndrome parameters and T2DM risk particularly in normal-weight individuals. Our findings demonstrate that polygenic lipodystrophy risk alleles drive insulin resistance and diabetes risk independent of an increased BMI.

Zebrafish cobll1a regulates lipid homeostasis via the RA signaling pathway

Background

The COBLL1 gene has been implicated in human central obesity, fasting insulin levels, type 2 diabetes, and blood lipid profiles. However, its molecular mechanisms remain largely unexplored.

Methods

In this study, we established cobll1a mutant lines using the CRISPR/Cas9-mediated gene knockout technique. To further dissect the molecular underpinnings of cobll1a during early development, transcriptome sequencing and bioinformatics analysis was employed.

Results

Our study showed that compared to the control, cobll1a -/- zebrafish embryos exhibited impaired development of digestive organs, including the liver, intestine, and pancreas, at 4 days post-fertilization (dpf). Transcriptome sequencing and bioinformatics analysis results showed that in cobll1a knockout group, the expression level of genes in the Retinoic Acid (RA) signaling pathway was affected, and the expression level of lipid metabolism-related genes (fasn, scd, elovl2, elovl6, dgat1a, srebf1 and srebf2) were significantly changed (p < 0.01), leading to increased lipid synthesis and decreased lipid catabolism. The expression level of apolipoprotein genes (apoa1a, apoa1b, apoa2, apoa4a, apoa4b, and apoea) genes were downregulated.

Conclusion

Our study suggest that the loss of cobll1a resulted in disrupted RA metabolism, reduced lipoprotein expression, and abnormal lipid transport, therefore contributing to lipid accumulation and deleterious effects on early liver development.

A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Caucasian and Latin American Pregnant Women

The pathophysiology of gestational diabetes mellitus (GDM) comprises clinical and genetic factors. In fact, GDM is associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, including 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT) individuals, and 255 (16.1%) had GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed via IPLEX Mass ARRAY PCR, selecting 110 SNPs based on literature references. SNPs showing the strongest likelihood of developing GDM were rs10830963, rs7651090, and rs1371614 in CAU and rs1387153 and rs9368222 in LAT. Clinical variables, including age, pre-pregnancy body mass index, and fasting plasma glucose (FPG) at 12 gestational weeks, predicted the risk of GDM (AUC 0.648, 95% CI 0.601-0.695 in CAU; AUC 0.688, 95% CI 0.628-9.748 in LAT), and adding SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680-0.764 in CAU; AUC 0.769, 95% CI 0.711-0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women.

Association between fatty liver index and controlled attenuation parameters as markers of metabolic dysfunction-associated fatty liver disease and bone mineral density: observational and two-sample Mendelian randomization studies

Previously observational studies did not draw a clear conclusion on the association between fatty liver diseases and bone mineral density (BMD). Our large-scale studies revealed that MAFLD and hepatic steatosis had no causal effect on BMD, while some metabolic factors were correlated with BMD. The findings have important implications for the relationship between fatty liver diseases and BMD, and may help direct the clinical management of MAFLD patients who experience osteoporosis and osteopenia.

PURPOSE

Liver and bone are active endocrine organs with several metabolic functions. However, the link between metabolic dysfunction-associated fatty liver disease (MAFLD) and bone mineral density (BMD) is contradictory.

METHODS

Using the UK Biobank and National Health and Nutrition Examination Survey (NHANES) dataset, we investigated the association between MAFLD, steatosis, and BMD in the observational analysis. We performed genome-wide association analysis to identify single-nucleotide polymorphisms associated with MAFLD. Large-scale two-sample Mendelian randomization (TSMR) analyses examined the potential causal relationship between MAFLD, hepatic steatosis, or major comorbid metabolic factors, and BMD.

RESULTS

After adjusting for demographic factors and body mass index, logistic regression analysis demonstrated a significant association between MAFLD and reduced heel BMD. However, this association disappeared after adjusting for additional metabolic factors. MAFLD was not associated with total body, femur neck, and lumbar BMD in the NHANES dataset. Magnetic resonance imaging-measured steatosis did not show significant associations with reduced total body, femur neck, and lumbar BMD in multivariate analysis. TSMR analyses indicated that MAFLD and hepatic steatosis were not associated with BMD. Among all MAFLD-related comorbid factors, overweight and type 2 diabetes showed a causal relationship with increased BMD, while waist circumference and hyperlipidemia had the opposite effect.

CONCLUSION

No causal effect of MAFLD and hepatic steatosis on BMD was observed in this study, while some metabolic factors were correlated with BMD. This has important implications for understanding the relationship between fatty liver disease and BMD, which may help direct the clinical management of MAFLD patients with osteoporosis.

Exploring the bidirectional relationship between metabolic syndrome and thyroid autoimmunity: a Mendelian randomization study

Background

Observational studies have reported a possible association between metabolic syndrome (MetS) and thyroid autoimmunity. Nevertheless, the relationship between thyroid autoimmunity and MetS remains unclear. The objective of this research was to assess the causal impact of MetS on thyroid autoimmunity through the utilization of Mendelian randomization (MR) methodology.

Methods

We performed bidirectional MR to elucidate the causal relationship between MetS and their components and thyroid autoimmunity (positivity of TPOAb). Single nucleotide polymorphisms (SNPs) of MetS and its components were obtained from the publicly available genetic variation summary database. The Thyroidomics Consortium conducted a genome-wide association analysis, which provided summary-level data pertaining to thyroid autoimmunity. The study included several statistical methods, including the inverse variance weighting method (IVW), weighted median, simple mode, weight mode, and MR-Egger methods, to assess the causal link. In addition, to ensure the stability of the results, a sensitivity analysis was conducted.

Results

IVW showed that MetS reduced the risk of developing thyroid autoimmunity (OR = 0.717, 95% CI = 0.584 - 0.88, P = 1.48E-03). The investigation into the causative association between components of MetS and thyroid autoimmune revealed a statistically significant link between triglycerides levels and the presence of thyroid autoimmunity (IVW analysis, OR = 0.603, 95%CI = 0.45 -0.807, P = 6.82E-04). The reverse analysis did not reveal any causal relationship between thyroid autoimmunity and MetS, including its five components.

Conclusions

We have presented new genetic evidence demonstrating that MetS and its triglyceride components may serve as potential protective factors against thyroid autoimmunity.

Mendelian randomization study on insulin resistance and risk of hypertension and cardiovascular disease

Observational studies have suggested that insulin resistance (IR) is associated with hypertension and various cardiovascular diseases. However, the presence of a causal relationship between IR and cardiovascular disease remains unclear. Here, we applied Mendelian randomization (MR) approaches to address the causal association between genetically determined IR and the risk of cardiovascular diseases. Our primary genetic instruments comprised 53 SNPs associated with IR phenotype from a GWAS of up to 188,577 participants. Genetic association estimates for hypertension and venous thromboembolism (VTE) were extracted from UK Biobank, estimates for atrial fibrillation (AF) were extracted from the hitherto largest GWAS meta-analysis on AF, estimates for heart failure were extracted from HERMES Consortium, estimates for peripheral artery disease (PAD) and aortic aneurysm were extracted from the FinnGen Study. The main analyses were performed using the random-effects inverse-variance weighted approach, and complemented by sensitivity analyses and multivariable MR analyses. Corresponding to 55% higher fasting insulin adjusted for body mass index, 0.46 mmol/L lower high-density lipoprotein cholesterol and 0.89 mmol/L higher triglyceride, one standard deviation change in genetically predicted IR was associated with increased risk of hypertension (odds ratio (OR) 1.06, 95% CI 1.04-1.08; P = 1.91 × 10-11) and PAD (OR 1.90, 95% CI 1.43-2.54; P = 1.19 × 10-5). Suggestive evidence was obtained for an association between IR and heart failure (OR per SD change in IR: 1.19, 95% CI 1.01-1.41, P = 0.041). There was no MR evidence for an association between genetically predicted IR and atrial fibrillation, VTE, and aortic aneurysm. Results were widely consistent across all sensitivity analyses. In multivariable MR, the association between IR and PAD was attenuated after adjustment for lipids (P = 0.347) or BMI (P = 0.163). Our findings support that genetically determined IR increases the risk of hypertension and PAD.

Shared genetic architecture between periodontal disease and type 2 diabetes: a large scale genome-wide cross-trait analysis

PURPOSE

To investigate the relationship between abnormal glucose metabolism, type 2 diabetes (T2D), and periodontal disease (PER) independent of Body Mass Index (BMI), we employed a genome-wide cross-trait approach to clarify the association.

METHODS

Our study utilized the most extensive genome-wide association studies conducted for populations of European ancestry, including PER, T2D, fasting glucose, fasting insulin, 2-hour glucose after an oral glucose challenge, HOMA-β, HOMA-IR (unadjusted or adjusted for BMI) and HbA1c.

RESULTS

With this approach, we were able to identify pleiotropic loci, establish expression-trait associations, and quantify global and local genetic correlations. There was a significant positive global genetic correlation between T2D (rg = 0.261, p = 2.65 × 10-13), HbA1c (rg = 0.182, p = 4.14 × 10-6) and PER, as well as for T2D independent of BMI (rg = 0.158, p = 2.34 × 10-6). A significant local genetic correlation was also observed between PER and glycemic traits or T2D. We also identified 62 independent pleiotropic loci that impact both PER and glycemic traits, including T2D. Nine significant pathways were identified between the shared genes between T2D, glycemic traits and PER. Genetically liability of HOMA-βadjBMI was causally associated with the risk of PER.

CONCLUSION

Our research has revealed a genetic link between T2D, glycemic traits, and PER that is influenced by biological pleiotropy. Notably, some of these links are not related to BMI. Our research highlights an underlying link between patients with T2D and PER, regardless of their BMI.

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Causal links between sedentary behavior, physical activity, and psychiatric disorders: a Mendelian randomization study

BACKGROUND

Studies suggest a correlation between excessive sedentary behavior, insufficient physical activity, and an elevated likelihood of experiencing psychiatric disorder. Nonetheless, the precise influence of sedentary behavior and physical activity on psychiatric disorder remains uncertain. Hence, the objective of this research was to investigate the possible causal relationship between sedentary behavior, physical activity, and the susceptibility to psychiatric disorder (depression, schizophrenia and bipolar disorder), utilizing a two-sample Mendelian randomization (MR) approach.

METHODS

Potential genetic instruments related to sedentary leisure behaviors were identified from the UK Biobank database, specifically a summary-level genome-wide association study (GWAS) involving 422,218 individuals of European descent. The UK Biobank database also provided the GWAS data for physical activity. Primary analysis was performed using inverse variance weighting (IVW) to assess the causal relationship between sedentary behavior, physical activity, and the risk of psychiatric disorder (depression, schizophrenia and bipolar disorder). Sensitivity analysis was conducted using Cochran's Q test, the MR-Egger intercept test, the MR-pleiotropy RESidual sum and outlier test, leave-one-out analysis, and funnel plot analysis.

RESULTS

According to the IVW analysis, there was a significant association between genetically predicted leisure television watching and an increased risk of depression (odds ratio [OR] = 1.027, 95% confidence interval [CI]: 1.001-1.053; P = 0.04). The IVW analysis also indicated that there was a decreased risk of depression associated with fraction accelerations of > 425 milligravities, as measured by accelerometers (OR = 0.951, 95%CI: 0.914-0.989; P = 0.013). The other MR methods obtained consistent but non-significant results in the same direction. However, there was no evidence of a causal association between genetic liability for moderate-to-vigorous physical activity, accelerometer-assessed physical activity, computer use, or driving and the risk of depression. Furthermore, IVW analysis has also found that driving has a slight effect in reducing the risk of schizophrenia (OR = 0.092, 95%CI: 0.010-0.827; P = 0.033), while leisure television viewing has a significant protective effect against the onset of bipolar disorder (OR = 0.719, 95%CI: 0.567-0.912; P = 0.006).

CONCLUSION

The study provides compelling evidence of a link between depression, bipolar disorder, and excessive TV watching. Furthermore, it suggests that higher accelerometer-assessed fraction accelerations of > 425 milligravities can serve as a genetic protective factor against depression. To mitigate the risk of developing depression, it is advisable to reduce sedentary activities, particularly television watching, and prioritize engaging in vigorous physical exercise.

Separating the effects of life course adiposity on diabetic nephropathy: a comprehensive multivariable Mendelian randomization study

Aims

Previous Mendelian randomization (MR) of obesity and diabetic nephropathy (DN) risk used small sample sizes or focused on a single adiposity metric. We explored the independent causal connection between obesity-related factors and DN risk using the most extensive GWAS summary data available, considering the distribution of adiposity across childhood and adulthood.

Methods

To evaluate the overall effect of each obesity-related exposure on DN (Ncase = 3,676, Ncontrol = 283,456), a two-sample univariate MR (UVMR) analysis was performed. The independent causal influence of each obesity-related feature on DN was estimated using multivariable MR (MVMR) when accounting for confounding variables. It was also used to examine the independent effects of adult and pediatric obesity, adjusting for their interrelationships. We used data from genome-wide association studies, including overall general (body mass index, BMI) and abdominal obesity (waist-to-hip ratio with and without adjustment for BMI, i.e., WHR and WHRadjBMI), along with childhood obesity (childhood BMI).

Results

UVMR revealed a significant association between adult BMI (OR=1.24, 95%CI=1.03-1.49, P=2.06×10-2) and pediatric BMI (OR=1.97, 95%CI=1.59-2.45, P=8.55×10-10) with DN risk. At the same time, adult WHR showed a marginally significant increase in DN (OR =1.27, 95%CI = 1.01-1.60, P=3.80×10-2). However, the outcomes were adverse when the influence of BMI was taken out of the WHR (WHRadjBMI). After adjusting for childhood BMI, the causal effects of adult BMI and adult abdominal obesity (WHR) on DN were significantly attenuated and became nonsignificant in MVMR models. In contrast, childhood BMI had a constant and robust independent effect on DN risk(adjusted for adult BMI: IVW, OR=1.90, 95% CI=1.60-2.25, P=2.03×10-13; LASSO, OR=1.91, 95% CI=1.65-2.21, P=3.80×10-18; adjusted for adult WHR: IVW, OR=1.80, 95% CI=1.40-2.31, P=4.20×10-6; LASSO, OR=1.90, 95% CI=1.56-2.32, P=2.76×10-10).

Interpretation

Our comprehensive analysis illustrated the hazard effect of obesity-related exposures for DN. In addition, we showed that childhood obesity plays a separate function in influencing the risk of DN and that the adverse effects of adult obesity (adult BMI and adult WHR) can be substantially attributed to it. Thus, several obesity-related traits deserve more attention and may become a new target for the prevention and treatment of DN and warrant further clinical investigation, especially in childhood obesity.

Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank

Insulin resistance (IR) is a well-established risk factor for metabolic disease. The ratio of triglycerides to high-density lipoprotein cholesterol (TG:HDL-C) is a surrogate marker of IR. We conducted a genome-wide association study of the TG:HDL-C ratio in 402,398 Europeans within the UK Biobank. We identified 369 independent SNPs, of which 114 had a false discovery rate-adjusted P value < 0.05 in other genome-wide studies of IR making them high-confidence IR-associated loci. Seventy-two of these 114 loci have not been previously associated with IR. These 114 loci cluster into five groups upon phenome-wide analysis and are enriched for candidate genes important in insulin signaling, adipocyte physiology and protein metabolism. We created a polygenic-risk score from the high-confidence IR-associated loci using 51,550 European individuals in the Michigan Genomics Initiative. We identified associations with diabetes, hyperglyceridemia, hypertension, nonalcoholic fatty liver disease and ischemic heart disease. Collectively, this study provides insight into the genes, pathways, tissues and subtypes critical in IR.

Associations of diabetes, circulating protein biomarkers, and risk of pancreatic cancer

BACKGROUND

Type 2 diabetes (T2D) is associated with higher risk of pancreatic cancer (PC), but the underlying mechanisms are not fully understood.

METHODS

We conducted a case-subcohort study involving 610 PC cases and 623 subcohort participants with 92 protein biomarkers measured in baseline plasma samples. Genetically-instrumented T2D was derived using 86 single-nucleotide polymorphisms (SNPs), including insulin resistance (IR) SNPs.

RESULTS

In observational analyses of 623 subcohort participants (mean age, 52 years; 61% women), T2D was positively associated with 13 proteins (SD difference: IL6: 0.52 [0.23-0.81]; IL10: 0.41 [0.12-0.70]), of which 8 were nominally associated with incident PC. The 8 proteins potentially mediated 36.9% (18.7-75.0%) of the association between T2D and PC. In MR, no associations were observed for genetically-determined T2D with proteins, but there were positive associations of genetically-determined IR with IL6 and IL10 (SD difference: 1.23 [0.05-2.41] and 1.28 [0.31-2.24]). In two-sample MR, fasting insulin was associated with both IL6 and PC, but no association was observed between IL6 and PC.

CONCLUSIONS

Proteomics were likely to explain the association between T2D and PC, but were not causal mediators. Elevated fasting insulin driven by insulin resistance might explain the associations of T2D, proteomics, and PC.

mTORC1 controls murine postprandial hepatic glycogen synthesis via Ppp1r3b

In response to a meal, insulin drives hepatic glycogen synthesis to help regulate systemic glucose homeostasis. The mechanistic target of rapamycin complex 1 (mTORC1) is a well-established insulin target and contributes to the postprandial control of liver lipid metabolism, autophagy, and protein synthesis. However, its role in hepatic glucose metabolism is less understood. Here, we used metabolomics, isotope tracing, and mouse genetics to define a role for liver mTORC1 signaling in the control of postprandial glycolytic intermediates and glycogen deposition. We show that mTORC1 is required for glycogen synthase activity and glycogenesis. Mechanistically, hepatic mTORC1 activity promotes the feeding-dependent induction of Ppp1r3b, a gene encoding a phosphatase important for glycogen synthase activity whose polymorphisms are linked to human diabetes. Reexpression of Ppp1r3b in livers lacking mTORC1 signaling enhances glycogen synthase activity and restores postprandial glycogen content. mTORC1-dependent transcriptional control of Ppp1r3b is facilitated by FOXO1, a well characterized transcriptional regulator involved in the hepatic response to nutrient intake. Collectively, we identify a role for mTORC1 signaling in the transcriptional regulation of Ppp1r3b and the subsequent induction of postprandial hepatic glycogen synthesis.

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.

FORGEdb: a tool for identifying candidate functional variants and uncovering target genes and mechanisms for complex diseases

The majority of disease-associated variants identified through genome-wide association studies are located outside of protein-coding regions. Prioritizing candidate regulatory variants and gene targets to identify potential biological mechanisms for further functional experiments can be challenging. To address this challenge, we developed FORGEdb ( https://forgedb.cancer.gov/ ; https://forge2.altiusinstitute.org/files/forgedb.html ; and https://doi.org/10.5281/zenodo.10067458 ), a standalone and web-based tool that integrates multiple datasets, delivering information on associated regulatory elements, transcription factor binding sites, and target genes for over 37 million variants. FORGEdb scores provide researchers with a quantitative assessment of the relative importance of each variant for targeted functional experiments.

Multi-tissue transcriptome-wide association study reveals susceptibility genes and drug targets for insulin resistance-relevant phenotypes

AIM

Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study.

MATERIALS AND METHODS

We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis.

RESULTS

We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk.

CONCLUSIONS

Our study provided new insights into IR aetiology and avenues for therapeutic development.

Genetic effect of metformin use on risk of cancers: evidence from Mendelian randomization analysis

BACKGROUND

Increasing number of studies reported the positive effect of metformin on the prevention and treatment of cancers. However, the genetic causal effect of metformin utilization on the risk of common cancers was not completely demonstrated.

METHODS

Two-sample Mendelian Randomization (two-sample MR) analysis was conducted to uncover the genetically predicted causal association between metformin use and 26 kinds of cancers. Besides, two-step Mendelian Randomization (two-step MR) assessment was applied to clarify the mediators which mediated the causal effect of metformin on certain cancer. We utilized five robust analytical methods, in which the inverse variance weighting (IVW) method served as the major one. Sensitivity, pleiotropy, and heterogeneity were assessed. The genetic statistics of exposure, outcomes, and mediators were downloaded from publicly available datasets, including the Open Genome-Wide Association Study (GWAS), FinnGen consortium (FinnGen), and UK Biobank (UKB).

RESULTS

Among 26 kinds of common cancers, HER-positive breast cancer was presented with a significant causal relationship with metformin use [Beta: - 4.0982; OR: 0.0166 (95% CI: 0.0008, 0.3376); P value: 0.0077], which indicated metformin could prevent people from HER-positive breast cancer. Other cancers only showed modest associations with metformin use. Potential mediators were included in two-step MR, among which total testosterone levels (mediating effect: 24.52%) displayed significant mediating roles. Leave-one-out, MR-Egger, and MR-PRESSO analyses produced consistent outcomes.

CONCLUSION

Metformin use exhibited a genetically protective effect on HER-positive breast cancer, which was partially mediated by total testosterone levels.

Interaction between the GCKR rs1260326 variant and serum HDL cholesterol contributes to HOMA-β and ISIMatusda in the middle-aged T2D individuals

This study aims to investigate the correlations between islet function/ insulin resistance and serum lipid levels, as well as to assess whether the strength of such correlations is affected by the GCKR rs1260326 variant in healthy and T2D individuals. We performed an oral glucose tolerance test (OGTT) on 4889 middle-aged adults, including 3135 healthy and 1754 T2D individuals from the REACTION population study in the Nanjing region. We also measured their serum lipid levels and genotyped for rs1260326. We found that serum high-density lipoprotein (HDL) cholesterol and triglyceride (TG) levels were independently correlated with indexes of islet function (HOMA-β and IGI [insulinogenic index]) and insulin resistance (HOMO-IR and ISIMatsuda) in both healthy and T2D individuals. The correlations were significantly decreased in T2D individuals, with significant heterogeneities compared to healthy controls (I2 > 75%, Phet < 0.05). Although no correlation was observed between serum total cholesterol (TC) level and islet function/ insulin resistance in healthy controls, significant correlations were found in T2D individuals, with significant heterogeneity to healthy controls in the correlation with ISIMatsuda(I2 = 85.3%, Phet = 0.009). Furthermore, we found significant interactions of the GCKR rs1260326 variant for the correlations between serum HDL cholesterol and HOMA-β/ISIMatsuda in T2D subjects (P = 0.015 and 0.038, respectively). These findings illustrate that distinct correlations between serum lipid levels and islet function/ insulin resistance occurred in T2D subjects compared to healthy individuals. Common gene variants, such as rs1260326, might interact substantially when studied in specific populations, especially T2D disease status.

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies

MOTIVATION

statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics.

RESULTS

We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies.

AVAILABILITY AND IMPLEMENTATION

REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

Candidate genes related to acute cerebral circulatory disorders in Preeclampsia in the Kazakh Population

BACKGROUND

The purpose of this study is to conduct a comparative analysis of the population frequencies of alleles and genotypes of polymorphic variants of coagulation and fibrinolysis genes SERPINE1 rs1799889, ITGA2 rs1126643, THBD rs1042580, FII rs1799963, FV rs6025, FVII rs6046, angiogenesis and endothelial dysfunction PGF rs12411, FLT1 rs4769612, KDR rs2071559, ACE rs4340, GWAS associated with the development of acute cerebral circulatory disorders in preeclampsia, in an ethnically homogeneous population of Kazakhs with previously studied populations of the world.

METHODS

The genomic database was analysed based on the results of genotyping of 1800 conditionally healthy individuals of Kazakh nationality ∼2.5 million SNPs using OmniChip 2.5 M Illumina chips at the DECODE Iceland Genomic Center as part of the joint implementation of the project "Genetic Studies of Preeclampsia in Populations of Central Asia and Europe" (InterPregGen) within the 7th Framework Programme of the European Commission under Grant Agreement No. 282540.

RESULT

The study discovered a significantly higher population frequency of carrying the unfavorable rs1126643 allele of the ITGA2 gene polymorphism when compared with European populations. The population frequencies of carrying minor alleles of the SERPINE1 (rs179988) and KDR (rs2071559) genes in the Kazakh population were significantly lower when compared with the previously studied populations of Europe and Asia. An intermediate frequency of unfavorable minor alleles between European and Asian populations was found in Kazakhs for gene polymorphisms: FV rs6025, PGF rs12411, and ACE rs4340. The genomic analysis determined the choice of polymorphisms for their further replicative genotyping in patients with ACCD in PE in the Kazakh population.

CONCLUSION

The obtained results will serve as a basis for the development of effective methods of early diagnosis and treatment of PE in pregnant women, carriers of unfavorable genotypes.

Influence of polymorphisms in IRS1, IRS2, MC3R, and MC4R on metabolic and inflammatory status and food intake in Brazilian adults: An exploratory pilot study

Polymorphisms in genes of leptin-melanocortin and insulin pathways have been associated with obesity and type 2 diabetes. We hypothesized that polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory markers and food intake composition in Brazilian subjects. This exploratory pilot study included 358 adult subjects. Clinical, anthropometric, and laboratory data were obtained through interview and access to medical records. The variants IRS1 rs2943634 A˃C, IRS2 rs1865434 C>T, MC3R rs3746619 C>A, and MC4R rs17782313 T>C were analyzed by real-time polymerase chain reaction. Food intake composition was assessed in a group of subjects with obesity (n = 84) before and after a short-term nutritional counseling program (9 weeks). MC4R rs17782313 was associated with increased risk of obesity (P = .034). Multivariate linear regression analysis adjusted by covariates indicated associations of IRS2 rs1865434 with reduced low-density lipoprotein cholesterol and resistin, MC3R rs3746619 with high glycated hemoglobin, and IRS1 rs2943634 and MC4R rs17782313 with increased high-sensitivity C-reactive protein (P < .05). Energy intake and carbohydrate and total fat intakes were reduced after the diet-oriented program (P < .05). Multivariate linear regression analysis showed associations of IRS2 rs1865434 with high basal fiber intake, IRS1 rs2943634 with low postprogram carbohydrate intake, and MC4R rs17782313 with low postprogram total fat and saturated fatty acid intakes (P < .05). Although significant associations did not survive correction for multiple comparisons using the Benjamini-Hochberg method in this exploratory study, polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory status in Brazilian adults. IRS1 and MC4R variants may influence carbohydrate, total fat, and saturated fatty acid intakes in response to a diet-oriented program in subjects with obesity.

An early prediction model for type 2 diabetes mellitus based on genetic variants and nongenetic risk factors in a Han Chinese cohort

Aims

We aimed to construct a prediction model of type 2 diabetes mellitus (T2DM) in a Han Chinese cohort using a genetic risk score (GRS) and a nongenetic risk score (NGRS).

Methods

A total of 297 Han Chinese subjects who were free from type 2 diabetes mellitus were selected from the Tianjin Medical University Chronic Disease Cohort for a prospective cohort study. Clinical characteristics were collected at baseline and subsequently tracked for a duration of 9 years. Genome-wide association studies (GWASs) were performed for T2DM-related phenotypes. The GRS was constructed using 13 T2DM-related quantitative trait single nucleotide polymorphisms (SNPs) loci derived from GWASs, and NGRS was calculated from 4 biochemical indicators of independent risk that screened by multifactorial Cox regressions.

Results

We found that HOMA-IR, uric acid, and low HDL were independent risk factors for T2DM (HR >1; P<0.05), and the NGRS model was created using these three nongenetic risk factors, with an area under the ROC curve (AUC) of 0.678; high fasting glucose (FPG >5 mmol/L) was a key risk factor for T2DM (HR = 7.174, P< 0.001), and its addition to the NGRS model caused a significant improvement in AUC (from 0.678 to 0.764). By adding 13 SNPs associated with T2DM to the GRS prediction model, the AUC increased to 0.892. The final combined prediction model was created by taking the arithmetic sum of the two models, which had an AUC of 0.908, a sensitivity of 0.845, and a specificity of 0.839.

Conclusions

We constructed a comprehensive prediction model for type 2 diabetes out of a Han Chinese cohort. Along with independent risk factors, GRS is a crucial element to predicting the risk of type 2 diabetes mellitus.

Investigating causal relationships between obesity and skin barrier function in a multi-ethnic Asian general population cohort

BACKGROUND

Skin diseases impact significantly on the quality of life and psychology of patients. Obesity has been observed as a risk factor for skin diseases. Skin epidermal barrier dysfunctions are typical manifestations across several dermatological disturbances.

OBJECTIVES

We aim to establish the association between obesity and skin physiology measurements and investigate whether obesity may play a possible causal role on skin barrier dysfunction.

METHODS

We investigated the relationship of obesity with skin physiology measurements, namely transepidermal water loss (TEWL), skin surface moisture and skin pH in an Asian population cohort (n = 9990). To assess for a possible causal association between body mass index (BMI) and skin physiology measurements, we performed Mendelian Randomization (MR), along with subsequent additional analyses to assess the potential causal impact of known socioeconomic and comorbidities of obesity on TEWL.

RESULTS

Every 1 kg/m2 increase in BMI was associated with a 0.221% (95%CI: 0.144-0.298) increase in TEWL (P = 2.82E-08), a 0.336% (95%CI: 0.148-0.524) decrease in skin moisture (P = 4.66E-04) and a 0.184% (95%CI: 0.144-0.224) decrease in pH (P = 1.36E-19), adjusting for age, gender, and ethnicity. Relationships for both TEWL and pH with BMI remained strong (Beta 0.354; 95%CI: 0.189-0.520 and Beta -0.170; 95%CI: -0.253 to -0.087, respectively) even after adjusting for known confounders, with MR experiments further supporting BMI's possible causal relationship with TEWL. Based on additional MR performed, none of the socioeconomic and comorbidities of obesity investigated are likely to have possible causal relationships with TEWL.

CONCLUSION

We establish strong association of BMI with TEWL and skin pH, with MR results suggestive of a possible causal relationship of obesity with TEWL. It emphasizes the potential impact of obesity on skin barrier function and therefore opportunity for primary prevention.

Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort

AIMS/HYPOTHESIS

There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution.

METHODS

Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin.

RESULTS

A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10-8). Two variants associated with low z-insulin (P15, p value <5×10-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin.

CONCLUSIONS/INTERPRETATION

The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.

Age at menarche and polycystic ovary syndrome: A Mendelian randomization study

OBJECTIVE

The authors aimed to use a large two-sample Mendelian randomization (MR) study to reveal the causality between age at menarche (AAM) and polycystic ovary syndrome (PCOS) incidence.

METHODS

The authors collected summary statistics from the hitherto largest genome-wide association studies conducted in AAM and PCOS in the same ancestry. MR with inverse variance weighting was conducted as the main analysis method, while weighted median and MR-Egger regression were used for comprehensive analysis. As for pleiotropy detection, inverse variance weighting, MR-Egger regression, Mendelian Randomization Pleiotropy Residual Sum and Outlier, as well as leave-one-out analysis were used to detect pleiotropy. Risk factor analysis was conducted to investigate the underlying mechanisms linking AAM to PCOS.

RESULTS

Each standard deviation increment in AAM was associated with a significantly lower incidence of PCOS (odds ratio, 0.86 [95% confidence interval, 0.75-0.98]). After adjustment in horizontal pleiotropy by eliminating four outliers, this pathogenic association was still statistically detected. All pleiotropy indexes were without statistical differences, which suggested the conclusions were robust. It showed the causal association between later AAM and lower body mass index, lower fasting insulin level and insulin resistance.

CONCLUSION

Our MR analysis verified that a slightly later onset age (15 to 18 years) at menarche could reduce the risk of PCOS. A more comprehensive investigation in a prospective setting is strongly advised.

Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study

Given the polygenic nature of antipsychotic-induced weight gain (AIWG), we investigated whether polygenic risk scores (PRS) for various psychiatric and metabolic traits were associated with AIWG. We included individuals with schizophrenia (SCZ) of European ancestry from two cohorts (N = 151, age = 40.3 ± 11.8 and N = 138, age = 36.5 ± 10.8). We investigated associations of AIWG defined as binary and continuous variables with PRS calculated from genome-wide association studies of body mass index (BMI), coronary artery disease (CAD), fasting glucose, fasting insulin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, type 1 and 2 diabetes mellitus, and SCZ, using regression models. We observed nominal associations (uncorrected p < 0.05) between PRSs for BMI, CAD, and LDL-C, type 1 diabetes, and SCZ with AIWG. While results became non-significant after correction for multiple testing, these preliminary results suggest that PRS analyses might contribute to identifying risk factors of AIWG and might help to elucidate mechanisms at play in AIWG.

A single-cell CRISPRi platform for characterizing candidate genes relevant to metabolic disorders in human adipocytes

CROP-Seq combines gene silencing using CRISPR interference with single-cell RNA sequencing. Here, we applied CROP-Seq to study adipogenesis and adipocyte biology. Human preadipocyte SGBS cell line expressing KRAB-dCas9 was transduced with a sgRNA library. Following selection, individual cells were captured using microfluidics at different timepoints during adipogenesis. Bioinformatic analysis of transcriptomic data was used to determine the knockdown effects, the dysregulated pathways, and to predict cellular phenotypes. Single-cell transcriptomes recapitulated adipogenesis states. For all targets, over 400 differentially expressed genes were identified at least at one timepoint. As a validation of our approach, the knockdown of PPARG and CEBPB (which encode key proadipogenic transcription factors) resulted in the inhibition of adipogenesis. Gene set enrichment analysis generated hypotheses regarding the molecular function of novel genes. MAFF knockdown led to downregulation of transcriptional response to proinflammatory cytokine TNF-α in preadipocytes and to decreased CXCL-16 and IL-6 secretion. TIPARP knockdown resulted in increased expression of adipogenesis markers. In summary, this powerful, hypothesis-free tool can identify novel regulators of adipogenesis, preadipocyte, and adipocyte function associated with metabolic disease.NEW & NOTEWORTHY Genomics efforts led to the identification of many genomic loci that are associated with metabolic traits, many of which are tied to adipose tissue function. However, determination of the causal genes, and their mechanism of action in metabolism, is a time-consuming process. Here, we use an approach to determine the transcriptional outcome of candidate gene knockdown for multiple genes at the same time in a human cell model of adipogenesis.

Genes of Predisposition to Childhood Beta-Cell Acute Lymphoblastic Leukemia in the Kazakh Population

BACKGROUND

Today, acute lymphoblastic leukemia is one of the most common malignant diseases of the hematopoietic system. The genetic predisposition to ALL is not fully explored in various ethnic populations.

OBJECTIVE

The study aimed to conduct a comparative analysis of the population frequencies of alleles and genotypes of polymorphic gene variants: immune regulation GATA3 (rs3824662); transcription and differentiation of B cells: ARID5B (rs7089424, rs10740055), IKZF1 (rs4132601); differentiation of hematopoietic cells: PIP4K2A (rs7088318); apoptosis: CEBPE (rs2239633), tumor suppressors: CDKN2A (rs3731249), TP53 (rs1042522); carcinogen metabolism: CBR3 (rs1056892), CYP1A1 (rs104894, rs4646903), according to genome-wide association studies analyses associated with the risk of developing pediatric beta-cell acute lymphoblastic leukemia (B-cell ALL), in an ethnically homogeneous population of Kazakhs with studied populations.

METHODS

The genomic database consists of 1800 conditionally healthy persons of Kazakh nationality, genotyped using OmniChip 2.5-8 Illumina chips at the deCODE genetics as part of the InterPregGen 7 project of the European Union (EU) framework program under Grant Agreement No. 282540.

RESULTS

High population frequencies of single nucleotide polymorphism (SNP) minor alleles identified for immune regulation genes - GATA3 rs3824662 - 42.5%; transcription and differentiation of B-cells genes - ARID5B rs7089424 - 33.1% and rs10740055 - 48.5%, which suggests their significant genetic contribution to the risk of development and prognosis of the effectiveness of B-cell ALL therapy in the Kazakh population. The significantly lower population frequency of the minor allele G rs1056892 CBR3 gene - 38.6% in the Kazakhs suggests its significant protective effect in reducing the risk of childhood B-cell ALL and the smaller number of cardiac complications after anthracycline therapy.

CONCLUSION

The obtained results will serve as a basis for developing effective methods for predicting the risk of development, early diagnosis, and effectiveness of treatment of B-cell ALL in children.

Causal association between metabolic syndrome and cholelithiasis: a Mendelian randomization study

Background

Metabolic syndrome (MetS) has been associated with digestive system diseases, and recent observational studies have suggested an association between MetS and cholelithiasis. However, the causal relationship between them remains unclear. This study aimed to assess the causal effect of MetS on cholelithiasis using Mendelian randomization (MR) analysis.

Methods

Single nucleotide polymorphisms (SNPs) of MetS and its components were extracted from the public genetic variation summary database. The inverse variance weighting method (IVW), weighted median method, and MR-Egger regression were used to evaluate the causal relationship. A sensitivity analysis was performed to ensure the stability of the results.

Results

IVW showed that MetS increased the risk of cholelithiasis (OR = 1.28, 95% CI = 1.13-1.46, P = 9.70E-05), and the weighted median method had the same result (OR = 1.49, 95% CI = 1.22-1.83, P = 5.68E-05). In exploring the causal relationship between MetS components and cholelithiasis, waist circumference (WC) was significantly associated with cholelithiasis. IVW analysis (OR = 1.48, 95% CI = 1.34-1.65, P = 1.15E-13), MR-Egger regression (OR = 1.62, 95% CI = 1.15-2.28, P = 0.007), and weighted median (OR = 1.73, 95% CI = 1.47-2.04, P = 1.62E-11) all found the same results.

Conclusion

Our study indicated that MetS increases the incidence of cholelithiasis, especially in MetS patients with abdominal obesity. Control and treatment of MetS can effectively reduce the risk of gallstone formation.

Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis

Patients suffering from mental disorders are at high risk of developing cardiovascular diseases, leading to a reduction in life expectancy. Genetic variants can display greater influence on cardiometabolic features in psychiatric cohorts compared to the general population. The difference is possibly due to an intricate interaction between the mental disorder or the medications used to treat it and metabolic regulations. Previous genome wide association studies (GWAS) on antipsychotic-induced weight gain included a low number of participants and/or were restricted to patients taking one specific antipsychotic. We conducted a GWAS of the evolution of body mass index (BMI) during early (i.e., ≤ 6) months of treatment with psychotropic medications inducing metabolic disturbances (i.e., antipsychotics, mood stabilizers and some antidepressants) in 1135 patients from the PsyMetab cohort. Six highly correlated BMI phenotypes (i.e., BMI change and BMI slope after distinct durations of psychotropic treatment) were considered in the analyses. Our results showed that four novel loci were associated with altered BMI upon treatment at genome-wide significance (p < 5 × 10-8): rs7736552 (near MAN2A1), rs11074029 (in SLCO3A1), rs117496040 (near DEFB1) and rs7647863 (in IQSEC1). Associations between the four loci and alternative BMI-change phenotypes showed consistent effects. Replication analyses in 1622 UK Biobank participants under psychotropic treatment showed a consistent association between rs7736552 and BMI slope (p = 0.017). These findings provide new insights into metabolic side effects induced by psychotropic drugs and underline the need for future studies to replicate these associations in larger cohorts.

Nutrient regulation of the islet epigenome controls adaptive insulin secretion

Adaptation of the islet β cell insulin-secretory response to changing insulin demand is critical for blood glucose homeostasis, yet the mechanisms underlying this adaptation are unknown. Here, we have shown that nutrient-stimulated histone acetylation plays a key role in adapting insulin secretion through regulation of genes involved in β cell nutrient sensing and metabolism. Nutrient regulation of the epigenome occurred at sites occupied by the chromatin-modifying enzyme lysine-specific demethylase 1 (Lsd1) in islets. β Cell-specific deletion of Lsd1 led to insulin hypersecretion, aberrant expression of nutrient-response genes, and histone hyperacetylation. Islets from mice adapted to chronically increased insulin demand exhibited shared epigenetic and transcriptional changes. Moreover, we found that genetic variants associated with type 2 diabetes were enriched at LSD1-bound sites in human islets, suggesting that interpretation of nutrient signals is genetically determined and clinically relevant. Overall, these studies revealed that adaptive insulin secretion involves Lsd1-mediated coupling of nutrient state to regulation of the islet epigenome.

StocSum: stochastic summary statistics for whole genome sequencing studies

Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.

Genetic prediction of age at menarche, age at natural menopause and type 2 diabetes: A Mendelian randomization study

BACKGROUND AND AIMS

The relationship between reproductive factors and type 2 diabetes (T2D) is controversial; therefore, we explored the causal relationship of age at menarche (AAM), age at natural menopause (ANM), with the risk of T2D and glycemic traits using two-sample Mendelian randomization.

METHODS AND RESULTS

We used publicly available data at the summary level of genome-wide association studies, where AAM (N = 329,345), ANM (N = 69,360), T2D (N = 464,389). The inverse variance weighting (IVW) method was employed as the primary method. To demonstrate the robustness of the results, we also conducted various sensitivity analysis methods including the MR-Egger regression, the weighted median (WM) and the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. After excluding IVs associated with confounders, we found a causal association between later AAM and reduced risk of T2D (OR 0.81 [95% CI 0.75, 0.87]; P = 2.20 × 10^-8), lower levels of FI (β -0.04 [95% CI -0.06, -0.01]; P = 2.19 × 10^-3), FPG (β -0.03 [95% CI -0.05, -0.007]; P = 9.67 × 10^-5) and HOMA-IR (β -0.04 [95% CI -0.06, -0.01]; P = 4,95 × 10^-3). As for ANM, we only found a causal effect with HOMA-IR (β -0.01 [95% CI -0.02, -0.005]; P = 1.77 × 10^-3), but not with T2D.

CONCLUSIONS

Our MR study showed a causal relationship between later AAM and lower risk of developing T2D, lower FI, FPG and HOMA-IR levels. This may provide new insights into the prevention of T2D in women.

Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms

INTRODUCTION

Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables.

RESEARCH DESIGN AND METHODS

Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort.

RESULTS

Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively.

CONCLUSIONS

We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

Association between Genotype and the Glycemic Response to an Oral Glucose Tolerance Test: A Systematic Review

The inter-individual variability of metabolic response to foods may be partly due to genetic variation. This systematic review aims to assess the associations between genetic variants and glucose response to an oral glucose tolerance test (OGTT). Three databases (PubMed, Web of Science, Embase) were searched for keywords in the field of genetics, OGTT, and metabolic response (PROSPERO: CRD42021231203). Inclusion criteria were available data on single nucleotide polymorphisms (SNPs) and glucose area under the curve (gAUC) in a healthy study cohort. In total, 33,219 records were identified, of which 139 reports met the inclusion criteria. This narrative synthesis focused on 49 reports describing gene loci for which several reports were available. An association between SNPs and the gAUC was described for 13 gene loci with 53 different SNPs. Three gene loci were mostly investigated: transcription factor 7 like 2 (TCF7L2), peroxisome proliferator-activated receptor gamma (PPARγ), and potassium inwardly rectifying channel subfamily J member 11 (KCNJ11). In most reports, the associations were not significant or single findings were not replicated. No robust evidence for an association between SNPs and gAUC after an OGTT in healthy persons was found across the identified studies. Future studies should investigate the effect of polygenic risk scores on postprandial glucose levels.

Phenome-wide Mendelian randomization study evaluating the association of circulating vitamin D with complex diseases

Background

Circulating vitamin D has been associated with multiple clinical diseases in observational studies, but the association was inconsistent due to the presence of confounders. We conducted a bidirectional Mendelian randomization (MR) study to explore the healthy atlas of vitamin D in many clinical traits and evaluate their causal association.

Methods

Based on a large-scale genome-wide association study (GWAS), the single nucleotide polymorphism (SNPs) instruments of circulating 25-hydroxyvitamin D (25OHD) from 443,734 Europeans and the corresponding effects of 10 clinical diseases and 42 clinical traits in the European population were recruited to conduct a bidirectional two-sample Mendelian randomization study. Under the network of Mendelian randomization analysis, inverse-variance weighting (IVW), weighted median, weighted mode, and Mendelian randomization (MR)–Egger regression were performed to explore the causal effects and pleiotropy. Mendelian randomization pleiotropy RESidual Sum and Outlier (MR-PRESSO) was conducted to uncover and exclude pleiotropic SNPs.

Results

The results revealed that genetically decreased vitamin D was inversely related to the estimated BMD (β = −0.029 g/cm2, p = 0.027), TC (β = −0.269 mmol/L, p = 0.006), TG (β = −0.208 mmol/L, p = 0.002), and pulse pressure (β = −0.241 mmHg, p = 0.043), while positively associated with lymphocyte count (β = 0.037%, p = 0.015). The results did not reveal any causal association of vitamin D with clinical diseases. On the contrary, genetically protected CKD was significantly associated with increased vitamin D (β = 0.056, p = 2.361 × 10−26).

Conclusion

The putative causal effects of circulating vitamin D on estimated bone mass, plasma triglyceride, and total cholesterol were uncovered, but not on clinical diseases. Vitamin D may be linked to clinical disease by affecting health-related metabolic markers.

Association between serum calcium level and type 2 diabetes: An NHANES analysis and Mendelian randomization study

AIMS

This study investigated the association between serum calcium levels and the prevalence of T2D using a cross-sectional study and Mendelian randomization analysis.

METHODS

Cross-sectional data were obtained from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Serum calcium levels were divided into three groups (low, medium and high groups) according to the tertiles. Logistic regression was used to estimate the association between serum calcium levels and T2D prevalence. Instrumental variables for serum calcium levels were obtained from the UK Biobank and a two-sample MR analysis was performed to examine the causal relationship between genetically predicted serum calcium levels and the risk of T2D.

RESULTS

A total of 39,645 participants were available for cross-sectional analysis. After adjusting for covariates, participants in the high serum calcium group had significantly higher odds of T2D (OR = 1.18, 95% CI = 1.07, 1.30, p = 0.001) than those in the moderate group. Restricted cubic spline plots showed a J-shaped curve relationship between serum calcium level and prevalence of T2D. Consistently, Mendelian randomization analysis showed that higher genetically predicted serum calcium levels were causally associated with a higher risk of T2D (OR = 1.16, 95% CI: 1.01, 1.33, p = 0.031).

CONCLUSIONS

The results of this study suggest that higher serum calcium levels are causally associated with a higher risk of T2D. Further studies are needed to clarify whether intervening in high serum calcium could reduce the risk of T2D.

Causal Relationships Between Glycemic Traits and Myopia

Purpose

Little is known about whether sugar intake is a risk factor for myopia, and the influence of glycemic control remains unclear, with inconsistent results reported. This study aimed to clarify this uncertainty by evaluating the link between multiple glycemic traits and myopia.

Methods

We employed a two-sample Mendelian randomization (MR) design using summary statistics from independent genome-wide association studies. A total of six glycemic traits, including adiponectin, body mass index, fasting blood glucose, fasting insulin, hemoglobin A1c (HbA1c), and proinsulin levels, were used as exposures, and myopia was used as the outcome. The inverse-variance-weighted (IVW) method was the main applied analytic tool and was complemented with comprehensive sensitivity analyses.

Results

Out of the six glycemic traits studied, we found that adiponectin was significantly associated with myopia. The genetically predicted level of adiponectin was consistently negatively associated with myopia incidence: IVW (odds ratio [OR] = 0.990; P = 2.66 × 10-3), MR Egger (OR = 0.983; P = 3.47 × 10-3), weighted median method (OR = 0.989; P = 0.01), and weighted mode method (OR = 0.987; P = 0.01). Evidence from all sensitivity analyses further supported these associations. In addition, a higher HbA1c level was associated with a greater risk of myopia: IVW (OR = 1.022; P = 3.06 × 10-5).

Conclusions

Genetic evidence shows that low adiponectin levels and high HbA1c are associated with an increased risk of myopia. Given that physical activity and sugar intake are controllable variables in blood glycemia treatment, these findings provide new insights into potential strategies to delay myopia onset.

Causal associations of sleep apnea and snoring with type 2 diabetes and glycemic traits and the role of BMI

OBJECTIVE

Sleep apnea and snoring have been associated with type 2 diabetes, with BMI playing a role in the pathway, but the directions of causality are unclear. This study examined the causal associations of sleep apnea and snoring with type 2 diabetes while assessing the role of BMI using multiple genetic methods.

METHODS

Five genetic methods were used: two-sample; bidirectional univariable Mendelian randomization (MR) inverse variance-weighted (MR-IVW); multivariable MR-IVW; network MR; and latent causal variable method.

RESULTS

Compared with univariable MR-IVW, the odds ratio (95% CI) of type 2 diabetes for genetically predicted sleep apnea and snoring using the largest genome-wide association study decreased dramatically, from 1.61 (95% CI: 1.16-2.23) to 1.08 (95% CI: 0.59-1.97) and from 1.98 (95% CI: 1.25-3.13) to 1.09 (95% CI: 0.64-1.86) after adjustment for BMI. Network MR showed that BMI accounts for 67% and 62% of the total effect of sleep apnea and snoring on type 2 diabetes, respectively. The latent causal variable suggested that sleep apnea and snoring have no direct causal effect on type 2 diabetes.

CONCLUSIONS

These results first suggest that the associations of sleep apnea and snoring with type 2 diabetes were mainly driven by BMI. The possible indirect effects of sleep apnea and snoring on type 2 diabetes through BMI cannot be ruled out.

Associations of insomnia with insulin resistance traits: a cross-sectional and Mendelian Randomization study

CONTEXT

Insomnia is associated with insulin resistance (IR) in observational studies, however, whether insomnia is causally associated with IR remains unestablished.

OBJECTIVE

This study aims to estimate the causal associations of insomnia with IR and its related traits.

METHODS

In primary analyses, multivariable regression (MVR) and one-sample Mendelian randomization (1SMR) analyses were performed to estimate the associations of insomnia with IR (triglyceride-glucose [TyG] index and triglyceride to high-density lipoprotein cholesterol [TG/HDL-C] ratio) and its related traits (glucose level, TG, and HDL-C) in UK Biobank. Thereafter, two-sample MR (2SMR) analyses were used to validate the findings from primary analyses. Finally, the potential mediating effects of IR on the pathway of insomnia giving rise to T2D were examined using a two-step MR design.

RESULTS

Across the MVR, 1SMR, and their sensitivity analyses, we found consistent evidence suggesting that more frequent insomnia symptoms were significantly associated with higher values of TyG index (MVR: β = 0.024, P < 2.00E-16; 1SMR: β = 0.343, P < 2.00E-16), TG/HDL-C ratio (MVR: β = 0.016, P = 1.75E-13; 1SMR: β = 0.445, P < 2.00E-16), and TG level (MVR: β = 0.019 log mg/dl, P < 2.00E-16; 1SMR: β = 0.289 log mg/dL, P < 2.00E-16) after Bonferroni adjustment. Similar evidence was obtained by using 2SMR, and mediation analysis suggested that about a quarter (25.21%) of the association between insomnia symptoms and T2D was mediated by IR.

CONCLUSIONS

This study provides robust evidence supporting that more frequent insomnia symptoms are associated with IR and its related traits across different angles. These findings indicate that insomnia symptoms can be served as a promising target to improve IR and prevent subsequent T2D.

Finnish-specific AKT2 gene variant leads to impaired insulin signalling in myotubes

Finnish-specific gene variant p.P50T/AKT2 (minor allele frequency (MAF) = 1.1%) is associated with insulin resistance and increased predisposition to type 2 diabetes. Here, we have investigated in vitro the impact of the gene variant on glucose metabolism and intracellular signalling in human primary skeletal muscle cells, which were established from 14 male p.P50T/AKT2 variant carriers and 14 controls. Insulin-stimulated glucose uptake and glucose incorporation into glycogen were detected with 2-[1,2-3H]-deoxy-D-glucose and D-[14C]-glucose, respectively, and the rate of glycolysis was measured with a Seahorse XFe96 analyzer. Insulin signalling was investigated with Western blotting. The binding of variant and control AKT2-PH domains to phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P3) was assayed using PIP StripsTM Membranes. Protein tyrosine kinase and serine-threonine kinase assays were performed using the PamGene® kinome profiling system. Insulin-stimulated glucose uptake and glycogen synthesis in myotubes in vitro were not significantly affected by the genotype. However, the insulin-stimulated glycolytic rate was impaired in variant myotubes. Western blot analysis showed that insulin-stimulated phosphorylation of AKT-Thr308, AS160-Thr642 and GSK3β-Ser9 was reduced in variant myotubes compared to controls. The binding of variant AKT2-PH domain to PI(3,4,5)P3 was reduced as compared to the control protein. PamGene® kinome profiling revealed multiple differentially phosphorylated kinase substrates, e.g. calmodulin, between the genotypes. Further in silico upstream kinase analysis predicted a large-scale impairment in activities of kinases participating, for example, in intracellular signal transduction, protein translation and cell cycle events. In conclusion, myotubes from p.P50T/AKT2 variant carriers show multiple signalling alterations which may contribute to predisposition to insulin resistance and T2D in the carriers of this signalling variant.

Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h² = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

Bidirectional Mendelian randomization study of insulin-related traits and risk of ovarian cancer

BACKGROUND

It is well known that the occurrence and development of ovarian cancer are closely related to the patient's weight and various endocrine factors in the body.

AIM

Mendelian randomization (MR) was used to analyze the bidirectional relationship between insulin related characteristics and ovarian cancer.

METHODS

The data on insulin related characteristics are from up to 5567 diabetes free patients from 10 studies, mainly including fasting insulin level, insulin secretion rate, peak insulin response, etc. For ovarian cancer, UK Biobank data just updated in 2021 was selected, of which the relevant gene data was from 199741 Europeans. Mendelian randomization method was selected, with inverse variance weighting (IVW) as the main estimation, while MR Pleiotropy, MR Egger, weighted median and other methods were used to detect the heterogeneity of data and whether there was multi validity affecting conclusions.

RESULTS

Among all insulin related indicators (fasting insulin level, insulin secretion rate, peak insulin response), the insulin secretion rate was selected to have a causal relationship with the occurrence of ovarian cancer (IVW, P < 0.05), that is, the risk of ovarian cancer increased with the decrease of insulin secretion rate. At the same time, we tested the heterogeneity and polymorphism of this indicator, and the results were non-existent, which ensured the accuracy of the analysis results. Reverse causal analysis showed that there was no causal effect between the two (P>0.05).

CONCLUSION

The impairment of the insulin secretion rate has a causal effect on the risk of ovarian cancer, which was confirmed by Mendel randomization. This suggests that the human glucose metabolism cycle represented by insulin secretion plays an important role in the pathogenesis of ovarian cancer, which provides a new idea for preventing the release of ovarian cancer.

A comparison of the genes and genesets identified by GWAS and EWAS of fifteen complex traits

Identifying genomic regions pertinent to complex traits is a common goal of genome-wide and epigenome-wide association studies (GWAS and EWAS). GWAS identify causal genetic variants, directly or via linkage disequilibrium, and EWAS identify variation in DNA methylation associated with a trait. While GWAS in principle will only detect variants due to causal genes, EWAS can also identify genes via confounding, or reverse causation. We systematically compare GWAS (N > 50,000) and EWAS (N > 4500) results of 15 complex traits. We evaluate if the genes or gene ontology terms flagged by GWAS and EWAS overlap, and find substantial overlap for diastolic blood pressure, (gene overlap P = 5.2 × 10^-6; term overlap P = 0.001). We superimpose our empirical findings against simulated models of varying genetic and epigenetic architectures and observe that in most cases GWAS and EWAS are likely capturing distinct genesets. Our results indicate that GWAS and EWAS are capturing different aspects of the biology of complex traits.

Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.

Genetic predisposition to gestational diabetes mellitus in the Kazakh population

BACKGROUND AND AIMS

The purpose of the study was to conduct a comparative analysis of population frequencies of alleles and genotypes of polymorphic variants of genes for impaired insulin synthesis and associated with insulin signal transduction.

METHODS

This investigation uses a genomic database of 1800 conditionally healthy individuals of Kazakh ethnicity, who underwent full genome genotyping using OmniChip 2.5-8 Illumina chips of ∼2.5 million Single Nucleotide Polymorphism at deCODE Iceland Genomic Centre.

RESULTS

The highest frequency of carriage of minor A allele - 17.6% rs4607517 polymorphism of Glucokinase gene, unfavorable genotypes A/G - 29.5% and A/A - 3.0% in comparison with European and Asian populations, indicates a contribution of hereditary family forms of Maturity-onset diabetes of the young type 2 to gestational diabetes mellitus in Kazakh population.

CONCLUSIONS

The study of the associations of genetic markers of gestational diabetes mellitus will allow timely identification of high-risk groups before and at an early stage of pregnancy, carrying out the necessary effective preventive measures and, in the case of gestational diabetes mellitus development, optimizing the correction of carbohydrate metabolism disorders and predicting outcomes for the mother and the fetus.

Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits

There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.

No evidence for a causal link between Helicobacter pylori infection and nonalcoholic fatty liver disease: A bidirectional Mendelian randomization study

Although clinical studies have shown the possible relationship between Helicobacter pylori (H. pylori) infection and the development of nonalcoholic fatty liver disease (NAFLD), their causal relationship is still unknown. This bidirectional Mendelian randomization (MR) study aimed to investigate the causal link between H. pylori infection and NAFLD. Two previously reported genetic variants SNPs rs10004195 and rs368433 were used as the instrumental variables (IVs) of H. pylori infection. The genetic variants of NAFLD were extracted from the largest genome-wide association study (GWAS) summary data with 1,483 cases and 17,781 controls. The exposure and outcome data were obtained from the publicly available GWAS dataset. Then, a bidirectional MR was carried out to evaluate the causal relationship between H. pylori infection and NAFLD. In addition, the GWAS data were also collected to explore the causal relationship between H. pylori infection and relevant clinical traits of NAFLD, including triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), and body mass index (BMI). Genetically predicted H. pylori infection showed no association with NAFLD both in FinnGen GWAS (OR, 1.048; 95% CI, 0.778-1.411; value of p = 0.759) and the GWAS conducted by Anstee (OR, 0.775; 95% CI, 0.475-1.265; value of p = 0.308). An inverse MR showed no causal effect of NAFLD on H. pylori infection (OR,0.978;95% CI, 0.909-1.052; value of p = 0.543). No significant associations were observed between H. pylori infection and the levels of triglycerides, LDL-C, HDL-C, or FBG, while H. pylori infection was associated with an increase in BMI. These results indicated that there was no genetic evidence for a causal link between H. pylori and NAFLD, suggesting that the eradication or prevention of H. pylori infection might not benefit NAFLD and vice versa.