Conventional genome-wide association studies (GWAS) are designed to assess the effect of a genetic locus on phenotypic mean by genotype. Such loci explain a proportion of phenotypic variance known as narrow-sense heritability. In contrast, variance quantitative trait loci (vQTL) are associated with the phenotypic variance by genotype. These loci explain an additional proportion of phenotypic variance and contribute to broad-sense heritability but not to narrow-sense heritability. Here, a genome-wide vQTL analysis in 22,805 African Americans yielded eight loci for body mass index (BMI). Of these loci, three were replicated in 6002 sub-Saharan Africans. No locus reached genome-wide significance using the standard additive model. Furthermore, no locus showed evidence for natural selection, haplotype effects, or gene × sex or gene × study interactions. Two loci showed evidence for an effect of locus-specific ancestry resulting from admixture and for a gene × gene interaction. One locus showed evidence for interaction with diastolic blood pressure, consistent with this vQTL capturing an unmodeled gene × covariate interaction. These analyses demonstrate that relevant BMI loci can be detected by evaluating vQTL and that these loci contribute to the underexplored broad-sense heritability for this trait.

Diabetes is a rapidly growing global health concern projected to affect one in eight adults by 2045, which translates to roughly 783 million people. The profound metabolic alterations often present in dysglycaemia significantly increase the risk of cardiovascular complications. While genetic susceptibility plays a crucial role in diabetes and its vascular complications, identifying genes and molecular mechanisms that influence both diseases simultaneously has proven challenging. A key reason for this challenge is the pathophysiological heterogeneity underlying these diseases, with multiple processes contributing to different forms of diabetes and specific cardiovascular complications. This molecular heterogeneity has limited the effectiveness of large-scale genome-wide association studies (GWAS) in identifying shared underlying mechanisms. Additionally, our limited knowledge of the causal genes, cell types and disease-relevant states through which GWAS signals operate has hindered the discovery of common molecular pathways. This review highlights recent advances in genetic epidemiology, including studies of causal associations that have uncovered genetic and molecular factors influencing both dysglycaemia and cardiovascular complications. We explore how disease subtyping approaches can be critical in pinpointing the unique molecular signatures underlying both diabetes and cardiovascular complications. Finally, we address critical research gaps and future opportunities to advance our understanding of both diseases and translate these discoveries into tangible benefits for patient care and population health.

Heart failure with preserved ejection fraction (HFpEF) is an increasingly prevalent clinical syndrome with high morbidity and mortality. Although HFpEF frequently coexists with cardiometabolic diseases, the causal mechanisms and potential mediators remain poorly understood.

This study aimed to identify cardiometabolic risk factors specifically driving HFpEF and to determine their underlying circulating mediators.

We used two-sample Mendelian Randomization (MR) to analyze the effects of obesity, Type 2 diabetes, hypertension, chronic kidney disease (CKD), and dyslipidemia on HFpEF and heart failure with reduced ejection fraction (HFrEF) in large European-ancestry GWAS datasets. We then performed mediation MR to identify plasma proteins and metabolites that mediate the transition from each cardiometabolic disease to HFpEF, respectively. We applied multivariable MR to assess the impact of risk confounding on the results. Bioinformatic analyses were conducted to delineate mechanisms.

Cardiometabolic diseases had heterogeneous effects on HFpEF and HFrEF. Obesity and type 2 diabetes showed adjusted causal effects with HFpEF, hypertension showed potential relevance to HFpEF, whereas dyslipidemia and CKD did not. MR analysis identified 5 proteins that mediate obesity to HFpEF; 5 proteins that mediate type 2 diabetes to HFpEF. Further mediation MR analysis of obesity and T2D on HFrEF revealed heterogeneity in circulating mediators between metabolic HFpEF and HFrEF. Comprehensive bioinformatics analyses showed that IL1R1, together with other proteins such as TP53 and FGF19, orchestrates the inflammatory and fibrotic processes underlying HFpEF.

These findings suggest that metabolic HFpEF has distinct etiological features compared with HFrEF and is driven by complex, condition-specific mediators. IL1R1 mediates HFpEF in multiple metabolic risk states, suggesting a potential therapeutic target. Further translational studies are warranted to evaluate anti-inflammatory strategies targeting IL1R1 in HFpEF.

Mendelian randomization (MR) studies using diseases as exposures are increasingly prevalent although any observed associations do not necessarily imply effect of diseases. To illustrate this challenge, we conducted a systematic review of MR studies focusing on COVID-19 consequence. We hypothesized if outcome genome-wide association studies (GWAS) were conducted before COVID-19 pandemic in late 2019, any observed associations in these studies were unlikely to be driven by COVID-19. We systematically searched PubMed, EMBASE, and MEDLINE for all MR studies published between 1 January 2019 and 20 May 2023. Inclusion criteria included MR studies which used COVID-19 as the exposure and designed to assess COVID-19's impact on health outcomes. We extracted relevant information, such as result interpretation and relevance assumption assessment. This review was registered at PROSPERO (CRD42023421079). Amongst 57 included studies, 45 studies used outcome GWAS published prior to 2019 whilst the remaining studies likely used outcome GWAS containing data collected before 2019. Relevance assumption was assessed mainly by p values. A total of 35 studies showed an association of COVID-19 liability with health outcomes. Regardless of the results, 45 studies attributed these as evidence (or lack of evidence) of COVID-19 consequence. In MR studies using disease liability as exposure, relevance assumption should consider the prevalence of the disease in the outcome GWAS in the context of 2 sample Mendelian randomization study rather than p values/F-statistic alone. Even when these are verified, these studies likely suffered from pleiotropy, making corresponding interpretation as effect of disease challenging.

Dual-specific phosphatase-8 (DUSP8), identified as the first gene in a genome-wide association study (GWAS), is implicated in cellular oxidative stress, proliferation, apoptosis, and drug resistance through its negative regulation of the dephosphorylation activities of JNK, ERK, and p38 within the MAPK pathway. Recent studies have shown that DUSP8 plays a pivotal role in the progression of several human diseases, notably colorectal cancer, diabetic kidney disease, and breast cancer. This suggests that DUSP8 may represent a novel target for clinical intervention in these diseases. This review first introduces the biological structure and function of DUSP8, with a focus on its relationship with a series of diseases and the regulatory mechanisms involved. Furthermore, we concentrate on unresolved scientific questions in the current research, aiming to establish a new theoretical foundation for the diagnosis and treatment of related diseases.

Measures of subclinical atherosclerosis, such as coronary artery calcification (CAC) and carotid intima-media thickness (CIMT), reflect the underlying pathophysiology of coronary artery disease (CAD) and are genetically correlated with CAD and related risk factors. Leveraging summary statistics from genome-wide association studies of CAD, CIMT, CAC, type 2 diabetes, low-density lipoprotein cholesterol, and systolic blood pressure, we performed 15 separate multi-trait GWAS to identify shared susceptibility loci and elucidate the pleiotropic architecture underlying atherosclerosis. We identified 442 shared risk loci across all analyses that met an experiment-wide Bonferroni threshold of 3.3 × 10-9, uncovering 195 novel atherosclerosis loci. Multi-trait colocalization confirmed a shared causal signal in 25 shared novel loci for atherosclerosis. Trait-eQTL colocalization identified evidence of a shared causal signal in arterial, subcutaneous adipose, and cardiac tissues, implicating genes such as PRRX2, BNC2, CLIC4, SCAI, and PPP6C, and pathways related to vascular remodeling, inflammation, and metabolic regulation.

GLP-1 analogues are a relatively new class of medications that form the cornerstone of diabetes treatment. They possess invaluable glucose-lowering properties without hypoglycemic effects as well as strong cardioprotective effects. The gut microbiome has become the focus of numerous studies, demonstrating its influence not only on the gut but also on the overall well-being of the entire body. However, the effects of GLP-1 analogs on gut microbiota remain uncertain.

Our systematic review (based on PRISMA guidelines) aimed to gather knowledge on the effects of GLP-1 analogue medications on the composition, richness, and abundance of gut microbiota in both animal and human models.

Thirty-eight studies were included in this systematic review. GLP-1 analogues have demonstrated a notable impact on the composition, richness, and diversity of gut microbiota. We can conclude, following the obtained research results of our study, that liraglutide promotes the growth of beneficial genera relevant for beneficial metabolic functions. Exenatide and exendin-4 administration showed various effects on the microbiome composition in animal and human studies. In animal models, it increased genera associated with improved metabolism; however, in human models, genera linked to better metabolic functions and escalated inflammation increased. Following dulaglutide administration, increases in Bacteroides, Akkermansia, and Ruminococcus, genera connected to an improved metabolic model, were significant. Finally, varied results were obtained after semaglutide treatment, in which A. muciniphila, known for its positive metabolic functions, increased; however, microbial diversity decreased. Semaglutide treatment provided various results indicating many confounding factors in semaglutide's impact on the gut microbiota. Results varied due to dissimilarities in the studied populations and the duration of the studies. Further research is essential to confirm these findings and to better recognize their implications for the clinical outcomes of patients.

Mendelian randomization harnesses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. However, certain genetic variants can affect both the exposure and the outcome through a shared factor. This phenomenon, called correlated horizontal pleiotropy, may result in false-positive causal findings. Here, we propose a Pleiotropic Clustering framework for Mendelian randomization, PCMR. PCMR detects correlated horizontal pleiotropy and extends the zero modal pleiotropy assumption to enhance causal inference in trait pairs with correlated horizontal pleiotropic variants. Simulations show that PCMR can effectively detect correlated horizontal pleiotropy and avoid false positives in the presence of correlated horizontal pleiotropic variants, even when they constitute a high proportion of the variants connecting both traits (e.g., 30-40%). In datasets consisting of 48 exposure-common disease pairs, PCMR detects horizontal correlated pleiotropy in 7 out of the exposure-common disease pairs, and avoids detecting false positive causal links. Additionally, PCMR can facilitate the integration of biological information to exclude correlated horizontal pleiotropic variants, enhancing causal inference. We apply PCMR to study causal relationships between three common psychiatric disorders as examples.

Type 2 diabetes (T2D) is a major risk factor for atherosclerotic cardiovascular disease (ASCVD). This study examined the interplay between watching television and T2D genetic risk for risk of ASCVD.

We included 346 916 White British individuals from UK Biobank. A weighted polygenic risk score for T2D was calculated on the basis of 138 genetic variants associated with T2D. Time spent watching television was self-reported and categorized into 2 groups: ≤1 h/d and ≥2 h/d. Over a median 13.8-year follow-up, 21 265 incident ASCVD events were identified. Models using Cox regression with age as the underlying time scale adjusted for potential confounders (demographic, anthropometric, lifestyle factors, and medication use) were fit.

Compared with watching television for ≤1 h/d, watching ≥2 h/d was associated with 12% (95% CI, 1.07-1.16) higher hazards of ASCVD, independently of T2D genetic risk. Joint analyses (with low T2D genetic risk and ≤1 h/d of television viewing as reference) indicated that medium and high T2D genetic risk was not associated with higher hazards of ASCVD as long as television viewing was ≤1 h/d. The P values for multiplicative and additive interactions between T2D genetic risk and television viewing were 0.050 and 0.038, respectively. The 10-year absolute risk of ASCVD was lower for high T2D genetic risk combined with ≤1 h/d of television viewing (2.13%) than for low T2D genetic risk combined with ≥2 h/d of television viewing (2.46%).

Future clinical trials of lifestyle-modification interventions targeting specific types of screen-based sedentary activities could be implemented to individuals at high genetic risk of T2D for primary prevention of ASCVD.

Polygenic severe obesity (body mass index [BMI] ≥40 kg/m2) has increased, especially in Hispanic/Latino populations, yet we know little about the underlying mechanistic pathways. We analyzed whole-blood multiomics data to identify genes differentially regulated in severe obesity in Mexican Americans from the Cameron County Hispanic Cohort. Our RNA sequencing analysis identified 124 genes significantly differentially expressed between severe obesity cases (BMI ≥40 kg/m2) and controls (BMI <25 kg/m2); 33% replicated in an independent sample from the same population. Our integrative approach identified inflammatory genes, including IL4R, ZNF438, and LILRA5. Several genes displayed transcriptomic effects on severe obesity in subcutaneous adipose tissue. We further showed that the genetic regulation of these genes is associated with several traits in a large biobank, including bone fractures, obstructive sleep apnea, and hyperaldosteronism, illuminating potential risk mechanisms. Our findings furnish a molecular architecture of the severe obesity phenotype across multiple molecular domains.

Glucagon-like peptide-1 receptor agonists (GLP1RAs) are widely used in managing type 2 diabetes mellitus and weight control. Their potential in treating aging-related diseases has been gaining attention in recent years. However, the long-term effects of GLP1RAs on these diseases have yet to be fully revealed.

Using a genetic variant in the GLP1R gene to model the long-term effects of GLP1RAs, this Mendelian randomization (MR) study systematically explored potential causal associations between GLP1R agonism and 12 aging-related diseases and indicators. Genetic summary data sets used in this study were obtained from previous genome-wide association studies.

The primary MR analysis results suggested that GLP1R agonism was potentially positively causally associated with appendicular lean mass (Beta = 0.246, 95% confidence interval [CI] = 0.096-0.396), whole-body fat-free mass (Beta = 0.202, 95% CI = 0.048-0.355), and lung function (forced vital capacity [FVC]; Beta = 0.179, 95% CI = 0.152-0.205; p < .05). Additionally, a potential negative causal association was observed with myocardial infarction (odds ratio = 0.430, 95% CI = 0.249-0.745; p < .05).

The present MR study provides exploratory evidence suggesting potential causal associations between GLP1R agonism and appendicular lean mass, whole-body fat-free mass, lung function (FVC), and myocardial infarction. Given the exploratory nature of these findings and the limitations of the MR methodology, further research is needed to validate these results and investigate the underlying biological mechanisms.

The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, liability and meta, for conducting genome-wide association studies (GWASs) that leverage disease liabilities calculated from deep patient phenotyping. Analyzing 38 common traits in ∼300,000 UK Biobank participants, we identified an increased number of loci in comparison to the number identified by the conventional case-control approach, and there were high replication rates in larger external GWASs. Further analyses confirmed the disease specificity of the genetic architecture; the meta method demonstrated higher robustness when phenotypes were imputed with low accuracy. Additionally, polygenic risk scores based on disease liabilities more effectively predicted newly diagnosed cases in the 2022 dataset, which were controls in the earlier 2019 dataset. Our findings demonstrate that integrating high-dimensional phenotypic data into deep neural networks enhances genetic association studies while capturing disease-relevant genetic architecture.

Type 2 diabetes mellitus (T2D) is a significant health concern. Research using non-human primates, which develop T2D with similar symptoms and pancreatic changes as humans, is crucial but limited by long timelines and low success rates.

We targeted capture sequenced 61 normal and 81 T2D cynomolgus monkeys using a primer panel that captured 269 potential regulatory regions potentially associated with T2D in the cynomolgus monkey genome. 80 variants were identified to be associated with T2D and were used to construct a genetic prediction model. Among 8 machine learning algorithms tested, we found that the best prediction performance was achieve when the model using support vector machine with polynomial kernel as the machine learning algorithm (AUC = 0.933). Including age and sex in this model did not significantly improve the prediction performance. Using the genetic prediction model, we further screened 22 monkeys and found 13 were high risk while 9 were low risk. After feeding the 22 monkeys with high-energy food for 32 weeks, we found all the 9 low risk monkeys did not develop T2D while 4 out of 13 high risk monkeys (31 %) develop T2D.

This method greatly increased the success rate of establishing T2D monkey models while decreased the time needed compared to traditional methods. Therefore, we developed a new high-efficiency method to establish T2D monkey models by combining the genetic prediction model and high-energy diet, which will greatly contribute to the research on the clinical characteristics, pathogenesis, complications and potential new treatments.

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

This study examines the causal relationship between type 2 diabetes (T2D) and peripheral artery disease (PAD) and their potential mechanisms based on the analysis of the Gene Expression Omnibus database and 2-sample Mendelian randomization (MR). The first part involved a 2-sample MR study and a comprehensive meta-analysis. Differences in the results were assessed using inverse-variance weighting. Heterogeneity was examined using the Cochrane Q statistical test. The leave-one-out method was applied for sensitivity analysis. The potential horizontal pleiotropic effect was assessed using the MR-Egger intercept technique. The second part involved differential gene analysis and weighted gene coexpression network analysis. Subsequently, we overlapped and consolidated the results from the 2 parts to identify the key genes between them. MR analysis results suggested a statistically significant correlation between the incidence of PAD and T2D (odds ratio: 1.22, 95% confidence interval: 1.13-1.32, P = 3.74e-07). We anticipated a pivotal role for TCF7L2 in PAD and T2D. T2D was significantly associated with PAD risk. Simultaneously, the study deepened our understanding of the underlying mechanisms of both diseases, proposing TCF7L2 as a promising target.

Aging is a major risk factor for type 2 diabetes (T2D), but individuals of the same chronological age may vary in their biological aging rate. The associations of Phenotypic Age Acceleration (PhenoAgeAccel), a new accelerated biological aging indicator based on clinical chemistry biomarkers, with the risk of dynamic progression remain unclear. We aimed to assess these associations and examine whether these associations varied by genetic risk and lifestyle.

We conducted a prospective cohort study that included 376,083 adults free of T2D and diabetes-related events at baseline in UK Biobank. PhenoAgeAccel > 0 and ≤ 0 were defined as biologically older and younger than chronological age. The outcomes of interest were incident T2D, diabetic complications, and mortality. Hazard ratios (HRs) with 95% confidence intervals (CIs) and population attributable fractions (PAFs) for these associations were calculated using multi-state model.

During a median follow-up of 13.7 years, 17,615 participants developed T2D, of whom, 4,524 subsequently developed complications, and 28,373 died. Being biologically older was associated with increased risks of transitions from baseline to T2D (HR 1.77, 95% CI 1.71-1.82; PAF 24.8 [95% CI 23.5-26.2]), from T2D to diabetic complications (1.10, 1.04-1.17; 4.4 [1.4-7.4]), from baseline to all-cause death (1.53, 1.49-1.57; 17.6 [16.6-18.6]), from T2D to all-cause death (1.14, 1.03-1.26; 7.4 [1.8-13.0]), and from diabetic complications to all-cause death (1.32, 1.15-1.51; 15.4 [7.5-23.2]) than being biologically younger. Additionally, participants with older biological age and high genetic risk had a higher risk of incident T2D (4.76,4.43-5.12;18.2 [17.5-19.0]) than those with younger biological age and low genetic risk. Compared with participants with younger biological age and healthy lifestyle, those with older biological age and unhealthy lifestyle had higher risks of transitions in the T2D trajectory, with HRs and PAFs ranging from 1.34 (1.16-1.55; 3.7 [1.8-5.6]) to 5.39 (5.01-5.79; 13.0 [12.4-13.6]).

PhenoAgeAccel was consistently associated with an increased risk of all transitions in T2D progression. It has the potential to be combined with genetic risk to identify early T2D incidence risk and may guide interventions throughout T2D progression while tracking their effectiveness.

Aminopeptidase N (ANPEP), a membrane-associated ectoenzyme, has been identified as a susceptibility gene for type 2 diabetes (T2D) by genome-wide association and transcriptome studies; however, the mechanisms by which this gene contributes to disease pathogenesis remain unclear. The aim of this study was to determine the comprehensive contribution of ANPEP polymorphisms to T2D risk and annotate the underlying mechanisms. A total of 3206 unrelated individuals including 1579 T2D patients and 1627 controls were recruited for the study. Twenty-three common functional single nucleotide polymorphisms (SNP) of ANPEP were genotyped by the MassArray-4 system. Six polymorphisms, rs11073891, rs12898828, rs12148357, rs9920421, rs7111, and rs25653, were found to be associated with type 2 diabetes (Pperm ≤ 0.05). Common haplotype rs9920421G-rs4932143G-rs7111T was strongly associated with increased risk of T2D (Pperm = 5.9 × 10-12), whereas two rare haplotypes such as rs9920421G-rs4932143C-rs7111T (Pperm = 6.5 × 10-40) and rs12442778A-rs12898828A-rs6496608T-rs11073891C (Pperm = 1.0 × 10-7) possessed strong protection against disease. We identified 38 and 109 diplotypes associated with T2D risk in males and females, respectively (FDR ≤ 0.05). ANPEP polymorphisms showed associations with plasma levels of fasting blood glucose, aspartate aminotransferase, total protein and glutathione (P < 0.05), and several haplotypes were strongly associated with the levels of reactive oxygen species and uric acid (P < 0.0001). A deep literature analysis has facilitated the formulation of a hypothesis proposing that increased plasma levels of ANPEP as well as liver enzymes such as aspartate aminotransferase, alanine aminotransferase and gammaglutamyltransferase serve as an adaptive response directed towards the restoration of glutathione deficiency in diabetics by stimulating the production of amino acid precursors for glutathione biosynthesis.

A wide range of factors contribute to the overlap of hyperglycemia-acute or chronic-and sarcopenia, as well as their associated adverse consequences, which can lead to impaired physical function, reduced quality of life, and increased mortality risk. These factors include malnutrition (both overnutrition and undernutrition) and low levels of physical activity. Hyperglycemia and sarcopenia are interconnected through a vicious cycle of events that mutually reinforce and worsen each other. To explore this association, our review compiles evidence on: (i) the impact of hyperglycemia on motor and muscle function, with a focus on the mechanisms underlying biochemical changes in the muscles of individuals with or at risk of diabetes and sarcopenia; (ii) the importance of the clinical assessment and control of sarcopenia under hyperglycemic conditions; and (iii) the potential benefits of medical nutrition therapy and increased physical activity as muscle-targeted treatments for this population. Based on the reviewed evidence, we conclude that a regular intake of key functional nutrients, together with structured and supervised resistance and/or aerobic physical activity, can help maintain euglycemia and improve muscle status in all patients with hyperglycemia and sarcopenia.

Rare variants in HNF1A cause both maturity onset diabetes of the young 3 (HNF1A-MODY) and reduced serum C-reactive protein (CRP) levels. Common variants of HNF1A are associated with serum CRP and type 2 diabetes mellitus (T2DM), but inconsistently with cardiovascular disease (CVD).

Our study aimed to investigate the association of low CRP alleles in HNF1A with CVD and indirectly evaluate the CVD risk of HNF1A-MODY patients because of unavailability of enough cases to study their clinical outcomes.

A literature search was performed using PubMed, Embase, and Cochrane Library databases from inception to December 2023. All relevant studies concerning the association of HNF1A with CRP, CVD, lipids, and T2DM were included. Odds ratios (ORs), 95% CIs, and study characteristics were extracted.

Three common coding variants of HNF1A (rs1169288, rs2464196, and rs1169289) were examined. The minor alleles of these variants correlated with low CRP levels (OR 0.89; 95% CI, 0.86-0.91; OR 0.89; 95% CI, 0.88-0.91; OR 0.89; 95% CI, 0.88-0.91, respectively). Their low CRP alleles were associated with increased risk of CVD (OR 1.03; 95% CI, 1.03-1.04), higher low-density lipoprotein cholesterol levels (OR 1.07; 95% CI, 1.04-1.10), and elevated risk of T2DM (OR 1.04; 95%, CI 1.01-1.08).

Our study revealed an association between low CRP alleles in HNF1A and a high CVD risk, which indicated that antidiabetic drugs with CV benefits such as glucagon-like peptide-1 receptor agonists should be recommended as a first-line choice for HNF1A-MODY.

Diabetes is rapidly spreading worldwide, affecting millions of individuals. Therefore, it is crucial to have a comprehensive understanding of its complications. The present review discusses the complex subject of diabetes, including the type 1 and type 2 variants. Geographical and population differences highlight the importance of targeted therapies and personalized management strategies. Ongoing research aims to identify the causes and treatment strategies for this disease. Preventive interventions, lifestyle changes and public awareness campaigns are all vital components of diabetes management. Collaboration between the general public and health departments is essential for effective prevention. Early intervention and global management strategies are necessary to reduce the significant impact on healthcare systems. A comprehensive plan from health care departments is required to address the issues caused by diabetes and minimize its effects on individuals and communities worldwide. The present review outlines specific measures which can be used to combat the spread of diabetes for a healthier future world.

This study investigates the gender-specific genetic influence of the single nucleotide polymorphism (SNP) rs1111875 on diabetes risk within the Taiwanese population using data from the Taiwan Biobank. Diabetes mellitus, particularly type 2 diabetes (T2D), is influenced by genetic factors, and the rs1111875 SNP near the hematopoietically expressed homeobox (HHEX) gene has been linked to T2D susceptibility.

The study included 69,272 participants after excluding those from arsenic-polluted areas and those with incomplete data. Logistic regression models were used for analyses.

The analyses revealed that the CT genotype of rs1111875 was associated with an increased risk of diabetes (OR = 1.092, 95% CI = 1.030-1.157, P = 0.003), as was the TT genotype (OR = 1.280, 95% CI = 1.165-1.407, P < 0.001). The effect was more pronounced in women (CT: OR = 1.118, 95% CI = 1.036-1.207, P = 0.004; TT: OR = 1.404, 95% CI = 1.243-1.585, P < 0.001). Men exhibited a higher overall risk of diabetes (OR = 1.565, 95% CI = 1.445-1.694, P < 0.001) and had a higher prevalence (12.71% vs 7.80%, P < 0.001) compared to women.

The findings underscore the importance of considering gender differences in genetic studies of diabetes and suggest that personalized diabetes management strategies should account for both genetic and gender-specific risk factors. This research contributes to the broader understanding of genetic determinants of diabetes and their interaction with gender, aiming to enhance personalized healthcare strategies for diabetes prevention and treatment.

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per region. Using the UK Biobank (UKB) data to simulate genomic regions, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS, particularly when there are only a few causal variants in the fine-mapped region. Using data from 2,940 plasma proteins from the UKB study, we observed that on average FINEMAP identified 2.5 causal variants at an association signal and captured 36% more regional heritability than the variant with the lowest P-value. We estimate that in genomic regions with notable contribution to the total heritability, FINEMAP captures on average 13% and 40% more heritability than BOLT and HESS respectively. Our analysis shows how FINEMAP, BOLT and HESS relate to each other in cases where inference of a variant-level picture of the regional genetic architecture is possible.

Cardiovascular diseases (CVDs) and cerebrovascular diseases (CeVDs) are closely related vascular diseases, sharing common cardiometabolic risk factors (RFs). Although pleiotropic genetic variants of these two diseases have been reported, their underlying pathological mechanisms are still unclear. Leveraging GWAS summary data and using genetic correlation, pleiotropic variants identification, and colocalization analyses, we identified 11 colocalized loci for CVDs-CeVDs-BP (blood pressure), CVDs-CeVDs-LIP (lipid traits), and CVDs-CeVDs-cIMT (carotid intima-media thickness) triplets. No shared causal loci were found for CVDs-CeVDs-T2D (type 2 diabetes) or CVDs-CeVDs-BMI (body mass index) triplets. The 11 loci were mapped to 12 genes, namely CASZ1, CDKN1A, TWIST1, CDKN2B, ABO, SWAP70, SH2B3, LRCH1, FES, GOSR2, RPRML, and LDLR, where both GOSR2 and RPRML were mapped to one locus. They were enriched in pathways related to cellular response to external stimulus and regulation of the phosphate metabolic process and were highly expressed in endothelial cells, epithelial cells, and smooth muscle cells. Multi-omics analysis revealed methylation of two genes (CASZ1 and LRCH1) may play a causal role in the genetic pleiotropy. Notably, these pleiotropic loci are highly enriched in the targets of antihypertensive drugs, which further emphasizes the role of the blood pressure regulation pathway in the shared etiology of CVDs and CeVDs.

The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial organization, morphology, physiology, and connectivity, highlighting the importance of transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus in ten adult neurotypical donors to define molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization (NMF) and label transfer, we integrated these data by defining gene expression patterns within the snRNA-seq data and inferring their expression in the SRT data. We identified NMF patterns that captured transcriptional variation across neuronal cell types and indicated that the response of excitatory and inhibitory postsynaptic specializations were prioritized in different SRT spatial domains. We used the NMF and label transfer approach to leverage existing rodent datasets, identifying patterns of activity-dependent transcription and subpopulations of dentate gyrus granule cells in our SRT dataset that may be predisposed to participate in learning and memory ensembles. Finally, we characterized the spatial organization of NMF patterns corresponding to non-cornu ammonis pyramidal neurons and identified snRNA-seq clusters mapping to distinct regions of the retrohippocampus, to three subiculum layers, and to a population of presubiculum neurons. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.

Crohn's disease (CD) is a complex inflammatory bowel disease resulting from an interplay of genetic, microbial, and environmental factors. Cell-type-specific contributions to CD etiology and genetic risk are incompletely understood. Here we built a comprehensive atlas of cell-type- resolved chromatin accessibility comprising 557,310 candidate cis-regulatory elements (cCREs) in terminal ileum and ascending colon from patients with active and inactive CD and healthy controls. Using this atlas, we identified cell-type-, anatomic location-, and context-specific cCREs and characterized the regulatory programs underlying inflammatory responses in the intestinal mucosa of CD patients. Genetic variants that disrupt binding motifs of cell-type-specific transcription factors significantly affected chromatin accessibility in specific mucosal cell types. We found that CD heritability is primarily enriched in immune cell types. However, using fine- mapped non-coding CD variants we identified 29 variants located within cCREs several of which were accessible in epithelial and stromal cells implicating cell types from additional lineages in mediating CD risk in some loci. Our atlas provides a comprehensive resource to study gene regulatory effects in CD and health, and highlights the cellular complexity underlying CD risk.

Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European.

Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations.

We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development.

Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations.

The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

The aim was to investigate the mediating role of inflammatory biomarkers in the causal effect of body composition on glycaemic traits and type 2 diabetes.

A retrospective observational study and a Mendelian randomization (MR) study were used. Observational analyses were performed using data from 4717 Chinese children and adolescents aged 6-18 years who underwent dual-energy X-ray absorptiometry for body composition. MR analyses were based on summary statistics from UK Biobank, deCODE2021, Meta-Analysis of Glucose and Insulin-Related Traits Consortium (MAGIC) and other large consortiums. Inflammatory biomarkers included leptin, adiponectin, osteocalcin, fibroblast growth factor 23 (FGF23) and parathyroid hormone (PTH).

In a retrospective observational study, increased fat mass had a positive effect on homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of pancreatic beta cell function (HOMA-β) through FGF23, whereas fat-free mass produced the opposite effects. PTH and osteocalcin played significant roles in the association of fat mass and fat-free mass with fasting glucose, fasting insulin and HOMA-IR (all p < 0.05). Mediation MR results indicated that childhood body mass index affected glycaemic traits through leptin and adiponectin. There existed a causal effect of fat-free mass on type 2 diabetes via FGF23 (indirect effect: OR [odds ratio]: 1.14 [95% CI, confidence interval: 1.01-1.28]) and adiponectin (OR: 0.85 [95% CI: 0.77-0.93]). Leptin mediated the causal association of fat mass (indirect effect: β: -0.05 [95% CI: -0.07, -0.02]) and fat-free mass (β: 0.03 [95% CI: 0.01, 0.04]) with fasting glucose.

Our findings suggest that different body compositions have differential influences on glycaemic traits and type 2 diabetes through distinct inflammatory biomarkers. The findings may be helpful in tailoring management of body composition based on inflammatory biomarkers with different glycaemic statuses.

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN's utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

Monitoring biochemical phenotypes during pregnancy is vital for maternal and fetal health, allowing early detection and management of pregnancy-related conditions to ensure safety for both. Here, we conducted a genetic analysis of 104 pregnancy phenotypes in 20,900 Chinese women. The genome-wide association study (GWAS) identified a total of 410 trait-locus associations, with 71.71% reported previously. Among the 116 novel hits for 45 phenotypes, 83 were successfully replicated. Among them, 31 were defined as potentially pregnancy-specific associations, including creatine and HELLPAR and neutrophils and ESR1, with subsequent analysis revealing enrichments in estrogen-related pathways and female reproductive tissues. The partitioning heritability underscored the significant roles of fetal blood, embryoid bodies, and female reproductive organs in pregnancy hematology and birth outcomes. Pathway analysis confirmed the intricate interplay of hormone and immune regulation, metabolism, and cell cycle during pregnancy. This study contributes to the understanding of genetic influences on pregnancy phenotypes and their implications for maternal health.

The pathogenesis of type 2 diabetes mellitus is somewhat associated with lipid metabolism. We aim to assess the impact of lipid-lowering drugs (HMGCR inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors) on type 2 diabetes mellitus and its complications through a two-sample Mendelian randomization (MR) study.

We identified suitable genetic instruments from the GWAS database that represent the expression levels of three genes, interpreting reduced genetically proxied gene expression as indicative of lipid-lowering drug use. We evaluated the causal relationships among these variables employing a two-sample Mendelian randomization approach, with the Inverse Variance Weighted (IVW) analysis serving as the primary method. Coronary artery disease was utilized as a positive control to validate the reliability of the selected genetic instruments.

Increased genetically proxied HMGCR expression is significantly associated with a reduced risk of type 2 diabetes mellitus (OR = 0.64, 95%CI = 0.55-0.74), which was replicated in the FinnGen study with consistent results (OR = 0.65, 95%CI = 0.53-0.80). Increased genetically proxied HMGCR expression is associated with a reduced risk of diabetic retinopathy (OR = 0.23, 95%CI = 0.12-0.44) and diabetic nephropathy (OR = 0.35, 95%CI = 0.17-0.71). In contrast, increased genetically proxied PCSK9 expression is associated with a decreased risk of diabetic coma (OR = 0.70, 95%CI = 0.50-0.98), diabetic neuropathy (OR = 0.24, 95%CI = 0.14-0.42), diabetic retinopathy (OR = 0.67, 95%CI = 0.48-0.96), diabetic cardiovascular diseases (OR = 0.62, 95%CI = 0.38-0.99), and diabetic nephropathy (OR = 0.62, 95%CI = 0.41-0.95).

This Mendelian randomization study suggests an association between HMGCR and the pathogenesis of type 2 diabetes mellitus, with increased genetically proxied HMGCR expression reducing the risk of type 2 diabetes mellitus, while PCSK9 and NPC1L1 show no significant association with type 2 diabetes mellitus. These findings may offer more reasonable lipid-lowering drug options for patients with dyslipidemia.

Genome-wide association studies (GWAS) have significantly advanced the identification of genetic susceptibility variants associated with complex diseases. As of 2023, approximately 800 variants predisposing individuals to the risk of type 2 diabetes (T2D) were identified through GWAS, and the majority of studies were predominantly conducted in European populations. Despite the shared nature of the majority of genetic susceptibility loci across diverse ethnic populations, GWAS in non-European populations, including Japanese and East Asian populations, have revealed population-specific T2D loci. Currently, polygenic risk scores (PRSs), encompassing millions of associated variants, can identify individuals with a higher T2D risk than the general population. However, GWAS focusing on microvascular complications of diabetes have identified a limited number of disease-susceptibility loci. Ongoing efforts are crucial to enhance the applicability of PRS for all ethnic groups and unravel the genetic architecture of microvascular complications of diabetes.

Gene therapy utilizes nucleic acid drugs to treat diseases, encompassing gene supplementation, gene replacement, gene silencing, and gene editing. It represents a distinct therapeutic approach from traditional medications and introduces novel strategies for genetic disorders. Over the past two decades, significant advancements have been made in the field of gene therapy, leading to the approval of various gene therapy drugs. Gene therapy was initially employed for treating genetic diseases and cancers, particularly monogenic conditions classified as orphan diseases due to their low prevalence rates; however, polygenic or complex diseases exhibit higher incidence rates within populations. Extensive research on the etiology of polygenic diseases has unveiled new therapeutic targets that offer fresh opportunities for their treatment. Building upon the progress achieved in gene therapy for monogenic diseases and cancers, extending its application to polygenic or complex diseases would enable targeting a broader range of patient populations. This review aims to discuss the strategies of gene therapy, methods of gene editing (mainly CRISPR-CAS9), and carriers utilized in gene therapy, and highlight the applications of gene therapy in polygenic or complex diseases focused on applications that have either entered clinical stages or are currently undergoing clinical trials.

Background: Total joint arthroplasty (TJA) is an orthopedic procedure commonly used to treat damaged joints. Despite the efficacy of TJA, postoperative complications, including aseptic prosthesis loosening and infections, are common. Moreover, the effects of individual genetic susceptibility and modifiable risk factors on these complications are unclear. This study analyzed these effects to enhance patient prognosis and postoperative management. Methods: We conducted an extensive genome-wide association study (GWAS) and Mendelian randomization (MR) study using UK Biobank data. The cohort included 2964 patients with mechanical complications post-TJA, 957 with periprosthetic joint infection (PJI), and a control group of 398,708 individuals. Genetic loci associated with postoperative complications were identified by a GWAS analysis, and the causal relationships of 11 modifiable risk factors with complications were assessed using MR. Results: The GWAS analysis identified nine loci associated with post-TJA complications. Two loci near the PPP1R3B and RBM26 genes were significantly linked to mechanical complications and PJI, respectively. The MR analysis demonstrated that body mass index was positively associated with the risk of mechanical complications (odds ratio [OR]: 1.42; p < 0.001). Higher educational attainment was associated with a decreased risk of mechanical complications (OR: 0.55; p < 0.001) and PJI (OR: 0.43; p = 0.001). Type 2 diabetes was suggestively associated with mechanical complications (OR, 1.18, p = 0.02), and hypertension was suggestively associated with PJI (OR, 1.41, p = 0.008). Other lifestyle factors, including smoking and alcohol consumption, were not causally related to postoperative complications. Conclusions: The genetic loci near PPP1R3B and RBM26 influenced the risk of post-TJA mechanical complications and infections, respectively. The effects of genetic and modifiable risk factors, including body mass index and educational attainment, underscore the need to perform personalized preoperative assessments and the postoperative management of surgical patients. These results indicate that integrating genetic screening and lifestyle interventions into patient care can improve the outcomes of TJA and patient quality of life.