Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Genetic Evidence for Distinct Biological Mechanisms That Link Adiposity to Type 2 Diabetes: Toward Precision Medicine

We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We used MR-Clust to cluster independent genetic variants associated with body fat percentage (388 variants) and BMI (540 variants) based on their impact on type 2 diabetes. We identified five clusters of adiposity-increasing alleles associated with higher type 2 diabetes risk (unfavorable adiposity) and three clusters associated with lower risk (favorable adiposity). We then characterized each cluster based on various biomarkers, metabolites, and MRI-based measures of fat distribution and muscle quality. Analyzing the metabolic signatures of these clusters revealed two primary mechanisms connecting higher adiposity to reduced type 2 diabetes risk. The first involves higher adiposity in subcutaneous tissues (abdomen and thigh), lower liver fat, improved insulin sensitivity, and decreased risk of cardiometabolic diseases and diabetes complications. The second mechanism is characterized by increased body size and enhanced muscle quality, with no impact on cardiometabolic outcomes. Furthermore, our findings unveil diverse mechanisms linking higher adiposity to higher disease risk, such as cholesterol pathways or inflammation. These results reinforce the existence of adiposity-related mechanisms that may act as protective factors against type 2 diabetes and its complications, especially when accompanied by reduced ectopic liver fat.

ARTICLE HIGHLIGHTS

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes

OBJECTIVE

To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD.

RESULTS

Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16).

CONCLUSIONS

The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes

OBJECTIVE

Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D.

RESULTS

Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D.

CONCLUSIONS

Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.

Polygenic Risk for Type 2 Diabetes in African Americans

African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRSAA with other published scores. Results show that a 1-SD increase in the PRSAA was associated with 40-60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37-1.88; OR 1.40, 95% CI 1.16-1.70; and OR 1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population.

ARTICLE HIGHLIGHTS

Impact of periodontitis on type 2 diabetes: a bioinformatic analysis

BACKGROUND

Periodontitis is strongly associated with type 2 diabetes (T2D) that results in serious complications and mortality. However, the pathogenic role of periodontitis in the development of T2D and the underlain mechanism have not been fully elucidated.

METHODS

A Mendelian randomization (MR) was performed to estimate the causality between two diseases. Bioinformatics tools, including gene ontology and pathway enrichment analyses, were employed to analyze the common differentially expressed genes (DEGs) in periodontitis and T2D. MR and colocalization analyses were then utilized to investigate the causal associations between potential pathogenic gene expression and the risk of T2D. Single cell-type expression analysis was further performed to detect the cellular localization of these genes.

RESULTS

Genetically predicted periodontitis was associated with a higher risk of T2D (OR, 1.469; 95% CI, 1.117-1.930; P = 0.006) and insulin resistance (OR 1.034; 95%CI 1.001-1.068; P = 0.041). 79 common DEGs associated with periodontitis and T2D were then identified and demonstrated enrichment mainly in CXC receptor chemokine receptor binding and interleutin-17 signaling pathway. The integration of GWAS with the expression quantitative trait locis of these genes from the peripheral blood genetically prioritized 6 candidate genes, including 2 risk genes (RAP2A, MCUR1) and 4 protective genes (WNK1, NFIX, FOS, PANX1) in periodontitis-related T2D. Enriched in natural killer cells, RAP2A (OR 4.909; 95% CI 1.849-13.039; P = 0.001) demonstrated high risk influence on T2D, and exhibited strong genetic evidence of colocalization (coloc.abf-PPH4 = 0.632).

CONCLUSIONS

This study used a multi-omics integration method to explore causality between periodontitis and T2D, and revealed molecular mechanisms using bioinformatics tools. Periodontitis was associated with a higher risk of T2D. MCUR1, RAP2A, FOS, PANX1, NFIX and WNK1 may play important roles in the pathogenesis of periodontitis-related T2D, shedding light on the development of potential drug targets.

Risk Factors for Pelvic Organ Prolapse: Wide-Angled Mendelian Randomization Analysis

INTRODUCTION AND HYPOTHESIS

We hypothesized that some metabolic factors, lifestyle factors, and socioeconomic factors may have a causal effect on pelvic organ prolapse (POP).

METHODS

We selected instruments from corresponding genome-wide association studies (GWAS), which identified independent single nucleotide polymorphisms strongly associated with 12 potential risk factors. Summary statistics for POP were derived from two GWAS datasets, serving for discovery and replication stage. The primary analysis involved the use of the inverse-variance weighting mendelian randomization (MR) method, with additional sensitivity MR analyses conducted.

RESULTS

The univariable mendelian randomization (UVMR) analysis in both the discovery and replication stage provided evidence for significant causal effects between higher waist-to-hip ratio adjusted for body mass index (WHRadjBMI) levels, lower high-density lipoprotein cholesterol (HDL-C) levels, and lower educational attainment and higher POP risk, as well as a suggestive positive causal effect between triglycerides and POP. The multivariable mendelian randomization (MVMR) analysis showed that only HDL-C among the three blood lipid fractions could reduce the risk of POP. Mediation analysis indicated that HDL-C may partially mediate the effect of WHRadjBMI on POP risk, and the causal effect between educational attainment and POP may be mediated through WHRadjBMI and HDL-C.

CONCLUSIONS

Our study's evidence supported a causal relationship between WHRadjBMI, triglycerides, HDL-C, educational attainment, and POP risk. This highlights that clinicians may guide the general female population to control obesity and blood lipid levels to reduce the risk of POP.

Association of sleep traits with risk of hypertensive disorders of pregnancy: a mendelian randomization study

BACKGROUND

Unhealthy sleep patterns are common during pregnancy and have been associated with an increased risk of developing hypertensive disorders of pregnancy (HDPs) in observational studies. However, the causality underlying these associations remains uncertain. This study aimed to evaluate the potential causal association between seven sleep traits and the risk of HDPs using a two-sample Mendelian randomization study.

METHODS

Genome-wide association study (GWAS) summary statistics were obtained from the FinnGen consortium, UK Biobank, and other prominent consortia, with a focus on individuals of European ancestry. The primary analysis utilized an inverse-variance-weighted MR approach supplemented by sensitivity analyses to mitigate potential biases introduced by pleiotropy. Furthermore, a two-step MR framework was employed for mediation analyses.

RESULTS

The data analyzed included 200 000-500 000 individuals for each sleep trait, along with approximately 15 000 cases of HDPs. Genetically predicted excessive daytime sleepiness (EDS) exhibited a significant association with an increased risk of HDPs [odds ratio (OR) 2.96, 95% confidence interval (95% CI) 1.40-6.26], and the specific subtype of preeclampsia/eclampsia (OR 2.97, 95% CI 1.06-8.3). Similarly, genetically predicted obstructive sleep apnea (OSA) was associated with a higher risk of HDPs (OR 1.27, 95% CI 1.09-1.47). Sensitivity analysis validated the robustness of these associations. Mediation analysis showed that BMI mediated approximately 25% of the association between EDS and HDPs, while mediating up to approximately 60% of the association between OSA and the outcomes. No statistically significant associations were observed between other genetically predicted sleep traits, such as chronotype, daytime napping, sleep duration, insomnia, snoring, and the risk of HDPs.

CONCLUSION

Our findings suggest a causal association between two sleep disorders, EDS and OSA, and the risk of HDPs, with BMI acting as a crucial mediator. EDS and OSA demonstrate promise as potentially preventable risk factors for HDPs, and targeting BMI may represent an alternative treatment strategy to mitigate the adverse impact of sleep disorders.

Coeliac disease and type 2 diabetes risk: a nationwide matched cohort and Mendelian randomisation study

AIMS/HYPOTHESIS

While the association between coeliac disease and type 1 diabetes is well documented, the association of coeliac disease with type 2 diabetes risk remains undetermined. We conducted a nationwide cohort and Mendelian randomisation analysis to investigate this link.

METHODS

This nationwide matched cohort used data from the Swedish ESPRESSO cohort including 46,150 individuals with coeliac disease and 219,763 matched individuals in the comparator group selected from the general population, followed up from 1969 to 2021. Data from 9053 individuals with coeliac disease who underwent a second biopsy were used to examine the association between persistent villous atrophy and type 2 diabetes. Multivariable Cox regression was employed to estimate the associations. In Mendelian randomisation analysis, 37 independent genetic variants associated with clinically diagnosed coeliac disease at p<5×10-8 were used to proxy genetic liability to coeliac disease. Summary-level data for type 2 diabetes were obtained from the DIAGRAM consortium (80,154 cases) and the FinnGen study (42,593 cases).

RESULTS

Over a median 15.7 years' follow-up, there were 6132 (13.3%) and 30,138 (13.7%) incident cases of type 2 diabetes in people with coeliac disease and comparator individuals, respectively. Those with coeliac disease were not at increased risk of incident type 2 diabetes with an HR of 1.00 (95% CI 0.97, 1.03) compared with comparator individuals. Persistent villous atrophy was not associated with an increased risk of type 2 diabetes compared with mucosal healing among participants with coeliac disease (HR 1.02, 95% CI 0.90, 1.16). Genetic liability to coeliac disease was not associated with type 2 diabetes in DIAGRAM (OR 1.01, 95% CI 0.99, 1.03) or in FinnGen (OR 1.01, 95% CI 0.99-1.04).

CONCLUSIONS/INTERPRETATION

Coeliac disease was not associated with type 2 diabetes risk.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

Leveraging data from multiple ancestries can greatly improve fine-mapping power due to differences in linkage disequilibrium and allele frequencies. We propose MultiSuSiE, an extension of the sum of single effects model (SuSiE) to multiple ancestries that allows causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. We evaluated MultiSuSiE via simulations and analyses of 14 quantitative traits leveraging whole-genome sequencing data in 47k African-ancestry and 94k European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr47k+Eur47k was well-calibrated and attained higher power than SuSiE applied to Eur94k; interestingly, higher causal variant PIPs in Afr47k compared to Eur47k were entirely explained by differences in the extent of LD quantified by LD 4th moments. Compared to very recently proposed multi-ancestry fine-mapping methods, MultiSuSiE attained higher power and/or much lower computational costs, making the analysis of large-scale All of Us data feasible. In real trait analyses, MultiSuSiE applied to Afr47k+Eur94k identified 579 fine-mapped variants with PIP > 0.5, and MultiSuSiE applied to Afr47k+Eur47k identified 44% more fine-mapped variants with PIP > 0.5 than SuSiE applied to Eur94k. We validated MultiSuSiE results for real traits via functional enrichment of fine-mapped variants. We highlight several examples where MultiSuSiE implicates well-studied or biologically plausible fine-mapped variants that were not implicated by other methods.

Evaluating the efficacy of GIPR agonists on non-alcoholic fatty liver disease: A Mediation Mendelian Randomization Study

OBJECTIVE

Non-alcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease worldwide while still lacks drugs for treatment or prevention. We aimed to investigate the causal role of glucose-dependent insulinotropic polypeptide receptor agonists (GIPRAs) on NAFLD and identify the mediated risk factors by which GIPRAs exert their therapeutic effects.

METHODS

Genetic proxies of GIPRAs were identified as cis-SNPs of GIPR associated with both the gene expression level and HbA1c and analyses including colocalization and linkage disequilibrium (LD) were performed for validation. We then performed two-sample two-step mendelian randomization to determine the causal effect of GIPRAs on NAFLD.

RESULTS

The MR analysis suggested genetic proxies of GIPRAs were causally associated with reduced risk of NAFLD (Odds ratio (OR): 0.46, 95 % confidence interval (95 % CI): 0.24-0.88, P = 0.02) and T2DM (OR: 0.10, 95 % CI: 0.07-0.13, P < 0.01). In addition, Mediation analysis showed evidence of indirect effect of GIPRAs on NAFLD via TRIG (0.88, [0.85-0.92], P < 0.01) and HDL-C (0.85, [0.80-0.90], P < 0.01).

CONCLUSIONS

Our study provided strong evidence to support the causal role of GIPRAs on reducing the risk of NAFLD probably through improving lipid metabolism, especially TG and HDL-C, providing guidance for future clinical trials.

Association of dietary nutrient intake with type 2 diabetes: A Mendelian randomization study

Observational research suggests that the evidence linking dietary nutrient intake (encompassing minerals, vitamins, amino acids, and unsaturated fatty acids) to type 2 diabetes (T2D) is both inconsistent and limited. This study aims to explore the potential causal relationship between dietary nutrients and T2D. Causal estimation utilized Mendelian randomization techniques. Single nucleotide polymorphisms linked to dietary nutrients were identified from existing genome-wide association studies and used as instrumental variables. Genome-wide association studies data pertinent to T2D were sourced from the DIMANTE consortium and the FinnGen database. Techniques including inverse variance weighting (IVW), weighted mode, weighted median, and Mendelian randomization-Egger were employed for causal inference, complemented by sensitivity analysis. Genetically predicted higher phenylalanine (IVW: odds ratio = 1.10 95% confidence interval 1.04-1.17, P = 1.5 × 10-3, q_pval = 3.4 × 10-2) and dihomo-gamma-linolenic acid (IVW: odds ratio = 1.001 95% confidence interval 1.0006-1.003, P = 3.7 × 10-3, q_pval = 4.1 × 10-2) levels were directly associated with T2D risk. Conversely, no causal relationships between other nutrients and T2D were established. We hypothesize that phenylalanine and dihomo-gamma-linolenic acid contribute to the pathogenesis of T2D. Clinically, the use of foods with high phenylalanine content may pose potential risks for patients with a heightened risk of T2D. Our study provides evidence supporting a causal link between dietary nutrient intake and the development of T2D.

The causal relationship between metabolic syndrome and its components and cardiovascular disease: A mendelian randomization study

AIM

To investigate the causal relationship between metabolic syndrome (MetS) and its components and 14 cardiovascular diseases using Mendelian randomization (MR).

METHODS

We used summary statistics from large-scale genome-wide association studies of MetS, its components, and cardiovascular diseases. We performed a two-sample MR analysis using the inverse-variance weighted method and other sensitivity methods. We also performed multivariate MR to adjust for potential risk factors.

RESULTS

Our study found that MetS was causally associated with an increased risk of ischemic stroke, abdominal aortic aneurysm, pulmonary embolism, coronary heart disease, heart failure, and peripheral artery disease. Waist circumference was causally associated with an increased risk of 6 cardiovascular diseases. Type 2 diabetes mellitus, diastolic blood pressure, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol were all causally associated with coronary heart disease, with varying causal relationships with the remaining 5 cardiovascular diseases. Multivariate MR showed that, except for ischaemic stroke, waist circumference remained causally associated with the remaining five cardiovascular diseases after adjusting for potential confounders.

CONCLUSION

Our study provides evidence that metabolic syndrome is causally associated with 6 cardiovascular diseases. Waist circumference is the most important component of these relationships. These findings have implications for the prevention and management of metabolic syndrome and cardiovascular diseases.

Organoid-guided precision hepatology for metabolic liver disease

Metabolic dysfunction-associated steatotic liver disease affects millions of people worldwide. Progress towards a definitive cure has been incremental and treatment is currently limited to lifestyle modification. Hepatocyte-specific lipid accumulation is the main trigger of lipotoxic events, driving inflammation and fibrosis. The underlying pathology is extraordinarily heterogenous, and the manifestations of steatohepatitis are markedly influenced by metabolic communications across non-hepatic organs. Synthetic human tissue models have emerged as powerful platforms to better capture the mechanistic diversity in disease progression, while preserving person-specific genetic traits. In this review, we will outline current research efforts focused on integrating multiple synthetic tissue models of key metabolic organs, with an emphasis on organoid-based systems. By combining functional genomics and population-scale en masse profiling methodologies, human tissues derived from patients can provide insights into personalised genetic, transcriptional, biochemical, and metabolic states. These collective efforts will advance our understanding of steatohepatitis and guide the development of rational solutions for mechanism-directed diagnostic and therapeutic investigation.

Towards a Global View of Parkinson's Disease Genetics

Parkinson's disease (PD) is a global health challenge, yet historically studies of PD have taken place predominantly in European populations. Recent genetics research conducted in non-European populations has revealed novel population-specific genetic loci linked to PD risk, highlighting the importance of studying PD globally. These insights have broadened our understanding of PD etiology, which is crucial for developing disease-modifying interventions. This review comprehensively explores the global genetic landscape of PD, emphasizing the scientific rationale for studying underrepresented populations. It underscores challenges, such as genotype-phenotype heterogeneity and inclusion difficulties for non-European participants, emphasizing the ongoing need for diverse and inclusive research in PD. ANN NEUROL 2024;95:831-842.

Association between pulp and periapical disease with type 2 diabetes: A bidirectional Mendelian randomization

AIM

This current Mendelian randomization (MR) study aims to comprehensively explore the potential bidirectional link between pulp and periapical disease (PAP) with type 2 diabetes mellitus (T2DM).

METHODOLOGY

Summary level data of European-based population genome-wide association studies (GWASs) were employed to undertake this MR study. With the selection of single nucleotide polymorphisms (SNPs) as the instrumental variable, the radial inverse-variance weighted (radial IVW) method with modified second-order weights was applied as the primary method. Additionally, a range of sensitivity analyses were conducted to investigate pleiotropy. Results from different sources of outcome were pooled by meta-analysis with the fixed model.

RESULTS

The results of this MR analysis did not suggest a significant impact of pulp and periapical disease on type 2 diabetes (combined OR = 1.04, 95% CI: 1.00-1.07, p = .033) and vice versa (OR = 1.04, 95% CI: 0.96-1.14, p = .329). No significant pleiotropy was detected in the final model after the removal of outliers, demonstrating the reliability of the results in our primary analysis.

CONCLUSIONS

With the limitations inherent in the present MR study, there is no significant evidence in either direction to suggest a causal association between pulp and periapical disease and type 2 diabetes mellitus.

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, we examined multiple variants that influence SLC30A8 allele-specific expression. Epigenomic mapping has previously identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighboring genes. Here, we show that deletion of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowers the expression of SLC30A8 and several neighboring genes and improves glucose-stimulated insulin secretion. While downregulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21, or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus

BACKGROUND

The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar.

METHODS

Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment.

RESULTS

We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel.

CONCLUSION

Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.

Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study

BACKGROUND

The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D.

METHODS

We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS.

RESULTS

PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)).

CONCLUSIONS

Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.

LDHB contributes to the regulation of lactate levels and basal insulin secretion in human pancreatic β cells

Using 13C6 glucose labeling coupled to gas chromatography-mass spectrometry and 2D 1H-13C heteronuclear single quantum coherence NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning β cell function. In both mouse and human islets, the contribution of glucose to the tricarboxylic acid (TCA) cycle is similar. Pyruvate fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While the conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is 6-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and β cell-specific LDHB expression. LDHB inhibition amplifies LDHA-dependent lactate generation in mouse and human β cells and increases basal insulin release. Lastly, cis-instrument Mendelian randomization shows that low LDHB expression levels correlate with elevated fasting insulin in humans. Thus, LDHB limits lactate generation in β cells to maintain appropriate insulin release.

Trans-Ancestral Genetic Risk Factors for Treatment-Related Type 2 Diabetes Mellitus in Survivors of Childhood Cancer

PURPOSE

Type 2 diabetes mellitus (T2D) is a prevalent long-term complication of treatment in survivors of childhood cancer, with marked racial/ethnic differences in burden. In this study, we investigated trans-ancestral genetic risks for treatment-related T2D.

PATIENTS AND METHODS

Leveraging whole-genome sequencing data from the St Jude Lifetime Cohort (N = 3,676, 304 clinically ascertained cases), we conducted ancestry-specific genome-wide association studies among survivors of African and European genetic ancestry (AFR and EUR, respectively) followed by trans-ancestry meta-analysis. Trans-/within-ancestry replication including data from the Childhood Cancer Survivor Study (N = 5,965) was required for prioritization. Three external general population T2D polygenic risk scores (PRSs) were assessed, including multiancestry PRSs. Treatment risk effect modification was evaluated for prioritized loci.

RESULTS

Four novel T2D risk loci showing trans-/within-ancestry replication evidence were identified, with three loci achieving genome-wide significance (P < 5 × 10-8). Among these, common variants at 5p15.2 (LINC02112), 2p25.3 (MYT1L), and 19p12 (ZNF492) showed evidence of modifying alkylating agent-related T2D risk in both ancestral groups, but showed disproportionately greater risk in AFR survivors (AFR odds ratios [ORs], 3.95-17.81; EUR ORs, 2.37-3.32). In survivor-specific RNA-sequencing data (N = 207), the 19p12 locus variant was associated with greater ZNF492 expression dysregulation after exposures to alkylators. Elevated T2D risks across ancestry groups were only observed with increasing values for multiancestry T2D PRSs and were especially increased among survivors treated with alkylators (top v bottom quintiles: ORAFR, 20.18; P = .023; OREUR, 13.44; P = 1.3 × 10-9).

CONCLUSION

Our findings suggest therapy-related genetic risks contribute to the increased T2D burden among non-Hispanic Black childhood cancer survivors. Additional study of how therapy-related genetic susceptibility contributes to this disparity is needed.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Extensive long-range polycomb interactions and weak compartmentalization are hallmarks of human neuronal 3D genome

Chromatin architecture regulates gene expression and shapes cellular identity, particularly in neuronal cells. Specifically, polycomb group (PcG) proteins enable establishment and maintenance of neuronal cell type by reorganizing chromatin into repressive domains that limit the expression of fate-determining genes and sustain distinct gene expression patterns in neurons. Here, we map the 3D genome architecture in neuronal and non-neuronal cells isolated from the Wernicke's area of four human brains and comprehensively analyze neuron-specific aspects of chromatin organization. We find that genome segregation into active and inactive compartments is greatly reduced in neurons compared to other brain cells. Furthermore, neuronal Hi-C maps reveal strong long-range interactions, forming a specific network of PcG-mediated contacts in neurons that is nearly absent in other brain cells. These interacting loci contain developmental transcription factors with repressed expression in neurons and other mature brain cells. But only in neurons, they are rich in bivalent promoters occupied by H3K4me3 histone modification together with H3K27me3, which points to a possible functional role of PcG contacts in neurons. Importantly, other layers of chromatin organization also exhibit a distinct structure in neurons, characterized by an increase in short-range interactions and a decrease in long-range ones.

Impact of loss-of-function in angiopoietin-like 4 on the human phenome

BACKGROUND

Carriers of the E40K loss-of-function variant in Angiopoietin-like 4 (ANGPTL4), have lower plasma triglyceride levels as well as lower rates of coronary artery disease (CAD) and type 2 diabetes (T2D). These genetic data suggest ANGPTL4 inhibition as a potential therapeutic target for cardiometabolic diseases. However, it is unknown whether the association between E40K and human diseases is due to linkage disequilibrium confounding. The broader impact of genetic ANGPTL4 inhibition is also unknown, raising uncertainties about the safety and validity of this target.

METHODS

To assess the impact of ANGPLT4 inhibition, we evaluated whether E40K and other loss-of-function variants in ANGPTL4 influenced a wide range of health markers and diseases using 29 publicly available genome-wide association meta-analyses of cardiometabolic traits and diseases, as well as 1589 diseases assessed in electronic health records within FinnGen (n = 309,154). To determine whether these relationships were likely causal, and not driven by other correlated variants, we used the Bayesian fine mapping algorithm CoPheScan.

RESULTS

The CoPheScan posterior probability of E40K being the causal variant for triglyceride levels was 99.99 %, validating the E40K to proxy lifelong lower activity of ANGPTL4. The E40K variant was associated with lower risk of CAD (odds ratio [OR] = 0.84, 95 % CI = 0.81 to 0.87, p=3.6e-21) and T2D (OR = 0.91, 95 % CI = 0.87 to 0.95, p=2.8e-05) in GWAS meta-analyses, with results replicated in FinnGen. These significant results were also replicated using other rare loss-of-function variants identified through whole exome sequencing in 488,278 participants of the UK Biobank. Using a Mendelian randomization study design, the E40K variant effect on cardiometabolic diseases was concordant with lipoprotein lipase enhancement (r = 0.82), but not hepatic lipase enhancement (r = -0.10), suggesting that ANGPTL4 effects on cardiometabolic diseases are potentially mainly mediated through lipoprotein lipase. After correction for multiple testing, the E40K variant did not significantly increase the risk of any of the 1589 diseases tested in FinnGen.

CONCLUSIONS

ANGPTL4 inhibition may represent a potentially safe and effective target for cardiometabolic diseases prevention or treatment.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

A Multivariate Genome-Wide Association Study Reveals Neural Correlates and Common Biological Mechanisms of Psychopathology Spectra

There is considerable comorbidity across externalizing and internalizing behavior dimensions of psychopathology. We applied genomic structural equation modeling (gSEM) to genome-wide association study (GWAS) summary statistics to evaluate the factor structure of externalizing and internalizing psychopathology across 16 traits and disorders among European-ancestry individuals (n's = 16,400 to 1,074,629). We conducted GWAS on factors derived from well-fitting models. Downstream analyses served to identify biological mechanisms, explore drug repurposing targets, estimate genetic overlap between the externalizing and internalizing spectra, and evaluate causal effects of psychopathology liability on physical health. Both a correlated factors model, comprising two factors of externalizing and internalizing risk, and a higher-order single-factor model of genetic effects contributing to both spectra demonstrated acceptable fit. GWAS identified 409 lead single nucleotide polymorphisms (SNPs) associated with externalizing and 85 lead SNPs associated with internalizing, while the second-order GWAS identified 256 lead SNPs contributing to broad psychopathology risk. In bivariate causal mixture models, nearly all externalizing and internalizing causal variants overlapped, despite a genetic correlation of only 0.37 (SE = 0.02) between them. Externalizing genes showed cell-type specific expression in GABAergic, cortical, and hippocampal neurons, and internalizing genes were associated with reduced subcallosal cortical volume, providing insight into the neurobiological underpinnings of psychopathology. Genetic liability for externalizing, internalizing, and broad psychopathology exerted causal effects on pain, general health, cardiovascular diseases, and chronic illnesses. These findings underscore the complex genetic architecture of psychopathology, identify potential biological pathways for the externalizing and internalizing spectra, and highlight the physical health burden of psychiatric comorbidity.

Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence

The 'diabetic bone paradox' suggested that type 2 diabetes (T2D) patients would have higher areal bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both weighted genetic risk score (wGRS) and two-sample Mendelian randomization (MR) analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the T2D-related risk factors for fracture, we observed that the effect of T2D on the risk of fracture decreased when the number of T2D-related risk factors decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect size of T2D on fracture risk decreased if the T2D-related risk factors could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.

A Multivariate Genome-Wide Association Study Reveals Neural Correlates and Common Biological Mechanisms of Psychopathology Spectra

There is considerable comorbidity across externalizing and internalizing behavior dimensions of psychopathology. We applied genomic structural equation modeling (gSEM) to genome-wide association study (GWAS) summary statistics to evaluate the factor structure of externalizing and internalizing psychopathology across 16 traits and disorders among European-ancestry individuals (n's = 16,400 to 1,074,629). We conducted GWAS on factors derived from well-fitting models. Downstream analyses served to identify biological mechanisms, explore drug repurposing targets, estimate genetic overlap between the externalizing and internalizing spectra, and evaluate causal effects of psychopathology liability on physical health. Both a correlated factors model, comprising two factors of externalizing and internalizing risk, and a higher-order single-factor model of genetic effects contributing to both spectra demonstrated acceptable t. GWAS identified 409 lead single nucleotide polymorphisms (SNPs) associated with externalizing and 85 lead SNPs associated with internalizing, while the second-order GWAS identified 256 lead SNPs contributing to broad psychopathology risk. In bivariate causal mixture models, nearly all externalizing and internalizing causal variants overlapped, despite a genetic correlation of only 0.37 (SE = 0.02) between them. Externalizing genes showed cell-type specific expression in GABAergic, cortical, and hippocampal neurons, and internalizing genes were associated with reduced subcallosal cortical volume, providing insight into the neurobiological underpinnings of psychopathology. Genetic liability for externalizing, internalizing, and broad psychopathology exerted causal effects on pain, general health, cardiovascular diseases, and chronic illnesses. These findings underscore the complex genetic architecture of psychopathology, identify potential biological pathways for the externalizing and internalizing spectra, and highlight the physical health burden of psychiatric comorbidity.

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Role of CaMKII in diabetes induced vascular injury and its interaction with anti-diabetes therapy

Diabetes mellitus is a metabolic disorder denoted by chronic hyperglycemia that drives maladaptive structural changes and functional damage to the vasculature. Attenuation of this pathological remodeling of blood vessels remains an unmet target owing to paucity of information on the metabolic signatures of this process. Ca2+/calmodulin-dependent kinase II (CaMKII) is expressed in the vasculature and is implicated in the control of blood vessels homeostasis. Recently, CaMKII has attracted a special attention in view of its chronic upregulated activity in diabetic tissues, yet its role in the diabetic vasculature remains under investigation.This review highlights the physiological and pathological actions of CaMKII in the diabetic vasculature, with focus on the control of the dialogue between endothelial (EC) and vascular smooth muscle cells (VSMC). Activation of CaMKII enhances EC and VSMC proliferation and migration, and increases the production of extracellular matrix which leads to maladaptive remodeling of vessels. This is manifested by activation of genes/proteins implicated in the control of the cell cycle, cytoskeleton organization, proliferation, migration, and inflammation. Endothelial dysfunction is paralleled by impaired nitric oxide signaling, which is also influenced by CaMKII signaling (activation/oxidation). The efficiency of CaMKII inhibitors is currently being tested in animal models, with a focus on the genetic pathways involved in the regulation of CaMKII expression (microRNAs and single nucleotide polymorphisms). Interestingly, studies highlight an interaction between the anti-diabetic drugs and CaMKII expression/activity which requires further investigation. Together, the studies reviewed herein may guide pharmacological approaches to improve health-related outcomes in patients with diabetes.

Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits.

METHODS

We collected non-invasive prenatal test (NIPT) sequencing data along with four glycaemic and 55 biochemical measurements from 30,699 pregnant women during a 2 year period at Shenzhen Baoan Women's and Children's Hospital in China. Genome-wide association studies (GWAS) were conducted between genotypes derived from NIPTs and GDM diagnosis, baseline glycaemic levels and glycaemic levels after glucose challenges. In total, 3317 women were diagnosed with GDM, while 19,565 served as control participants. The results were replicated using two independent cohorts. Additionally, we performed one-sample Mendelian randomisation to explore potential causal associations between the 55 biochemical measurements and risk of GDM and glycaemic levels.

RESULTS

We identified four genetic loci significantly associated with GDM susceptibility. Among these, MTNR1B exhibited the highest significance (rs10830963-G, OR [95% CI] 1.57 [1.45, 1.70], p=4.42×10-29), although its effect on type 2 diabetes was modest. Furthermore, we found 31 genetic loci, including 14 novel loci, that were significantly associated with the four glycaemic traits. The replication rates of these associations with GDM, fasting plasma glucose levels and 0 h, 1 h and 2 h OGTT glucose levels were 4 out of 4, 6 out of 9, 10 out of 11, 5 out of 7 and 4 out of 4, respectively. Mendelian randomisation analysis suggested that a genetically regulated higher lymphocytes percentage and lower white blood cell count, neutrophil percentage and absolute neutrophil count were associated with elevated glucose levels and an increased risk of GDM.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic basis of GDM and glycaemic traits during pregnancy in an East Asian population and highlight the potential role of inflammatory pathways in the aetiology of GDM and variations in glycaemic levels.

DATA AVAILABILITY

Summary statistics for GDM; fasting plasma glucose; 0 h, 1 h and 2h OGTT; and the 55 biomarkers are available in the GWAS Atlas (study accession no.: GVP000001, https://ngdc.cncb.ac.cn/gwas/browse/GVP000001) .

Elucidating the causal landscape: Mendelian randomization analysis of lifestyle and physiological factors in stress urinary incontinence

PURPOSE

To explore the potential causal links between obesity, type 2 diabetes (T2D), and lifestyle choices (such as smoking, alcohol and coffee consumption, and vigorous physical activity) on stress urinary incontinence (SUI), this study employs a Mendelian Randomization approach. This research aims to clarify these associations, which have been suggested but not conclusively established in prior observational studies.

METHODS

Genetic instruments associated with the exposures at the genome-wide significance (p < 5 × 10-8) were selected from corresponding genome-wide association studies. Summary-level data for SUI, was obtained from the UK Biobank. A two-sample MR analysis was employed to estimate causal effects, utilizing the inverse-variance weighted (IVW) method as the primary analytical approach. Complementary sensitivity analyses including MR-PRESSO, MR-Egger, and weighted median methods were performed. The horizontal pleiotropy was detected by using MR-Egger intercept and MR-PRESSO methods, and the heterogeneity was assessed using Cochran's Q statistics.

RESULTS

Our findings demonstrate a significant causal relationship between higher body mass index (BMI) and the risk of SUI, with increased abdominal adiposity (WHRadjBMI) similarly linked to SUI. Smoking initiation is also causally associated with an elevated risk. However, our analysis did not find definitive causal connections for other factors, including T2D, alcohol consumption, coffee intake, and vigorous physical activity.

CONCLUSIONS

These findings provide valuable insights for clinical strategies targeting SUI, suggesting a need for heightened awareness and potential intervention in individuals with higher BMI, WHR, and smoking habits. Further research is warranted to explore the complex interplay between genetic predisposition and lifestyle choices in the pathogenesis of SUI.

Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Multi-ancestry polygenic mechanisms of type 2 diabetes

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Perspectives on genetic studies of type 2 diabetes from the genome-wide association studies era to precision medicine

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

Risk prediction and interaction analysis using polygenic risk score of type 2 diabetes in a Korean population

Joint modelling of genetic and environmental risk factors can provide important information to predict the risk of type 2 diabetes (T2D). Therefore, to predict the genetic risk of T2D, we constructed a polygenic risk score (PRS) using genotype data of one Korean cohort, KARE (745 cases and 2549 controls), and the genome-wide association study summary statistics of Biobank Japan. We evaluated the performance of PRS in an independent Korean cohort, HEXA (5684 cases and 35,703 controls). Individuals with T2D had a significantly higher mean PRS than controls (0.492 vs. - 0.078, p ≈ 0 ). PRS predicted the risk of T2D with an AUC of 0.658 (95% CI 0.651-0.666). We also evaluated interaction between PRS and waist circumference (WC) in the HEXA cohort. PRS exhibited a significant sub-multiplicative interaction with WC (ORinteraction 0.991, 95% CI 0.987-0.995, pinteraction = 4.93 × 10-6) in T2D. The effect of WC on T2D decreased as PRS increased. The sex-specific analyses produced similar interaction results, revealing a decreased WC effect on T2D as the PRS increased. In conclusion, the risk of WC for T2D may differ depending on PRS and those with a high PRS might develop T2D with a lower WC threshold. Our findings are expected to improve risk prediction for T2D and facilitate the identification of individuals at an increased risk of T2D.

SGLT2 inhibition, high-density lipoprotein, and kidney function: a mendelian randomization study

BACKGROUND

Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function.

METHODS

We used a MR study to obtain associations between genetic proxies for SGLT2 inhibition and kidney function. We retrieved the most recent and comprehensive summary statistics from genome-wide association studies (GWAS) that have been previously published and involved kidney function parameters such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and albuminuria. Additionally, we included blood metabolite data from 249 biomarkers in the UK Biobank for a more comprehensive analysis. We performed MR analyses to explore the causal relationships between SGLT2 inhibition and kidney function and two-step MR to discover potential mediating metabolites.

RESULTS

The study found that a decrease in HbA1c levels by one standard deviation, which is genetically expected to result in SGLT2 inhibition, was linked to a decreased likelihood of developing type 2 diabetes mellitus (T2DM) (odds ratio [OR] = 0.55 [95% CI 0.35, 0.85], P = 0.007). Meanwhile, SGLT2 inhibition also protects eGFR (β = 0.05 [95% CI 0.03, 0.08], P = 2.45 × 10- 5) and decreased UACR (-0.18 [95% CI -0.33, -0.02], P = 0.025) and albuminuria (-1.07 [95% CI -1.58, -0.57], P = 3.60 × 10- 5). Furthermore, the study found that of the 249 metabolites present in the blood, only one metabolite, specifically the concentration of small high-density lipoprotein (HDL) particles, was significantly correlated with both SGLT2 inhibition and kidney function. This metabolite was found to play a crucial role in mediating the improvement of renal function through the use of SGLT2 inhibition (β = 0.01 [95% CI 0.005, 0.018], P = 0.001), with a mediated proportion of 13.33% (95% CI [5.71%, 26.67%], P = 0.020).

CONCLUSIONS

The findings of this investigation provide evidence in favor of a genetically anticipated biological linkage between the inhibition of SGLT2, the presence of circulating metabolites, and renal function. The findings demonstrate that the protective effect of SGLT2 inhibition on renal function is mostly mediated by HDL particle concentrations in circulating metabolites. These results offer significant theoretical support for both the preservation of renal function and a better comprehension of the mechanisms underlying SGLT2 inhibition.

The protective role of oily fish intake against type 2 diabetes: insights from a genetic correlation and Mendelian randomization study

Background and aims

Previous research has underscored the association between oily fish intake and type 2 diabetes (T2DM), yet the causality remains elusive.

Methods

A bidirectional univariable Mendelian Randomization (MR) analysis was employed to evaluate the causal effects of oily fish and non-oily fish intake on T2DM. Replication analysis and meta-analysis were conducted to ensure robust results. Multivariable MR analysis was utilized to assess confounders, and further mediation MR analysis discerned mediating effects. Linkage Disequilibrium Score (LDSC) analysis was undertaken to compute genetic correlations. Inverse variance weighted (IVW) was the primary method, complemented by a series of sensitivity analyses.

Results

The LDSC analysis unveiled a significant genetic correlation between oily fish intake and T2DM (Genetic correlation: -0.102, p = 4.43 × 10-4). For each standard deviation (SD) increase in genetically predicted oily fish intake, the risk of T2DM was reduced by 38.6% (OR = 0.614, 95% CI 0.504 ~ 0.748, p = 1.24 × 10-6, False Discovery Rate (FDR) = 3.72 × 10-6). The meta-analysis across three data sources highlighted a persistent causal association (OR = 0.728, 95% CI 0.593 ~ 0.895, p = 0.003). No other causal effects were identified (all p > 0.5, FDR > 0.5). The main outcomes remained consistent in most sensitivity analyses. Both MVMR and mediation MR analyses emphasized the mediating roles of triglycerides (TG), body mass index (BMI), and 25-hydroxyvitamin D (25OHD) levels.

Conclusion

To encapsulate, there's an inverse association between oily fish intake and T2DM risk, suggesting potential benefits of oily fish intake in T2DM prevention.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Causal relationship between diabetes mellitus, glycemic traits and Parkinson's disease: a multivariable mendelian randomization analysis

BACKGROUND

Observational studies have indicated an association between diabetes mellitus (DM), glycemic traits, and the occurrence of Parkinson's disease (PD). However, the complex interactions between these factors and the presence of a causal relationship remain unclear. Therefore, we aim to systematically assess the causal relationship between diabetes, glycemic traits, and PD onset, risk, and progression.

METHOD

We used two-sample Mendelian randomization (MR) to investigate potential associations between diabetes, glycemic traits, and PD. We used summary statistics from genome-wide association studies (GWAS). In addition, we employed multivariable Mendelian randomization to evaluate the mediating effects of anti-diabetic medications on the relationship between diabetes, glycemic traits, and PD. To ensure the robustness of our findings, we performed a series of sensitivity analyses.

RESULTS

In our univariable Mendelian randomization (MR) analysis, we found evidence of a causal relationship between genetic susceptibility to type 1 diabetes (T1DM) and a reduced risk of PD (OR = 0.9708; 95% CI: 0.9466, 0.9956; P = 0.0214). In our multivariable MR analysis, after considering the conditions of anti-diabetic drug use, this correlation disappeared with adjustment for potential mediators, including anti-diabetic medications, insulin use, and metformin use.

CONCLUSION

Our MR study confirms a potential protective causal relationship between genetically predicted type 1 diabetes and reduced risk of PD, which may be mediated by factors related to anti-diabetic medications.

Assessment of the impact of a personalised nutrition intervention in impaired glucose regulation over 26 weeks: a randomised controlled trial

Dietary interventions can reduce progression to type 2 diabetes mellitus (T2DM) in people with non-diabetic hyperglycaemia. In this study we aimed to determine the impact of a DNA-personalised nutrition intervention in people with non-diabetic hyperglycaemia over 26 weeks. ASPIRE-DNA was a pilot study. Participants were randomised into three arms to receive either (i) Control arm: standard care (NICE guidelines) (n = 51), (ii) Intervention arm: DNA-personalised dietary advice (n = 50), or (iii) Exploratory arm: DNA-personalised dietary advice via a self-guided app and wearable device (n = 46). The primary outcome was the difference in fasting plasma glucose (FPG) between the Control and Intervention arms after 6 weeks. 180 people were recruited, of whom 148 people were randomised, mean age of 59 years (SD = 11), 69% of whom were female. There was no significant difference in the FPG change between the Control and Intervention arms at 6 weeks (- 0.13 mmol/L (95% CI [- 0.37, 0.11]), p = 0.29), however, we found that a DNA-personalised dietary intervention led to a significant reduction of FPG at 26 weeks in the Intervention arm when compared to standard care (- 0.019 (SD = 0.008), p = 0.01), as did the Exploratory arm (- 0.021 (SD = 0.008), p = 0.006). HbA1c at 26 weeks was significantly reduced in the Intervention arm when compared to standard care (- 0.038 (SD = 0.018), p = 0.04). There was some evidence suggesting prevention of progression to T2DM across the groups that received a DNA-based intervention (p = 0.06). Personalisation of dietary advice based on DNA did not result in glucose changes within the first 6 weeks but was associated with significant reduction of FPG and HbA1c at 26 weeks when compared to standard care. The DNA-based diet was effective regardless of intervention type, though results should be interpreted with caution due to the low sample size. These findings suggest that DNA-based dietary guidance is an effective intervention compared to standard care, but there is still a minimum timeframe of adherence to the intervention before changes in clinical outcomes become apparent.Trial Registration: www.clinicaltrials.gov.uk Ref: NCT03702465.

Bidirectional relationship between type 2 diabetes mellitus and coronary artery disease: Prospective cohort study and genetic analyses

BACKGROUND

While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD.

METHODS

We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055).

RESULTS

Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively.

CONCLUSION

Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

Overexpression of UBE2E2 in Mouse Pancreatic β-Cells Leads to Glucose Intolerance via Reduction of β-Cell Mass

Genome-wide association studies have identified several gene polymorphisms, including UBE2E2, associated with type 2 diabetes. Although UBE2E2 is one of the ubiquitin-conjugating enzymes involved in the process of ubiquitin modifications, the pathophysiological roles of UBE2E2 in metabolic dysfunction are not yet understood. Here, we showed upregulated UBE2E2 expression in the islets of a mouse model of diet-induced obesity. The diabetes risk allele of UBE2E2 (rs13094957) in noncoding regions was associated with upregulation of UBE2E2 mRNA in the human pancreas. Although glucose-stimulated insulin secretion was intact in the isolated islets, pancreatic β-cell-specific UBE2E2-transgenic (TG) mice exhibited reduced insulin secretion and decreased β-cell mass. In TG mice, suppressed proliferation of β-cells before the weaning period and while receiving a high-fat diet was accompanied by elevated gene expression levels of p21, resulting in decreased postnatal β-cell mass expansion and compensatory β-cell hyperplasia, respectively. In TG islets, proteomic analysis identified enhanced formation of various types of polyubiquitin chains, accompanied by increased expression of Nedd4 E3 ubiquitin protein ligase. Ubiquitination assays showed that UBE2E2 mediated the elongation of ubiquitin chains by Nedd4. The data suggest that UBE2E2-mediated ubiquitin modifications in β-cells play an important role in regulating glucose homeostasis and β-cell mass.

MTNR1B genotype and effects of carbohydrate quantity and dietary glycaemic index on glycaemic response to an oral glucose load: the OmniCarb trial

AIMS/HYPOTHESIS

A type 2 diabetes-risk-increasing variant, MTNR1B (melatonin receptor 1B) rs10830963, regulates the circadian function and may influence the variability in metabolic responses to dietary carbohydrates. We investigated whether the effects of carbohydrate quantity and dietary glycaemic index (GI) on glycaemic response during OGTTs varied by the risk G allele of MTNR1B-rs10830963.

METHODS

This study included participants (n=150) of a randomised crossover-controlled feeding trial of four diets with high/low GI levels and high/low carbohydrate content for 5 weeks. The MTNR1B-rs10830963 (C/G) variant was genotyped. Glucose response during 2 h OGTT was measured at baseline and the end of each diet intervention.

RESULTS

Among the four study diets, carrying the risk G allele (CG/GG vs CC genotype) of MTNR1B-rs10830963 was associated with the largest AUC of glucose during 2 h OGTT after consuming a high-carbohydrate/high-GI diet (β 134.32 [SE 45.69] mmol/l × min; p=0.004). The risk G-allele carriers showed greater increment of glucose during 0-60 min (β 1.26 [0.47] mmol/l; p=0.008) or 0-90 min (β 1.10 [0.50] mmol/l; p=0.028) after the high-carbohydrate/high-GI diet intervention, but not after consuming the other three diets. At high carbohydrate content, reducing GI levels decreased 60 min post-OGTT glucose (mean -0.67 [95% CI: -1.18, -0.17] mmol/l) and the increment of glucose during 0-60 min (mean -1.00 [95% CI: -1.67, -0.33] mmol/l) and 0-90 min, particularly in the risk G-allele carriers (pinteraction <0.05 for all).

CONCLUSIONS/INTERPRETATION

Our study shows that carrying the risk G allele of MTNR1B-rs10830963 is associated with greater glycaemic responses after consuming a diet with high carbohydrates and high GI levels. Reducing GI in a high-carbohydrate diet may decrease post-OGTT glucose concentrations among the risk G-allele carriers.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Associations between type 2 diabetes mellitus and chronic liver diseases: evidence from a Mendelian randomization study in Europeans and East Asians

Objective

Multiple observational studies have demonstrated an association between type 2 diabetes mellitus (T2DM) and chronic liver diseases (CLDs). However, the causality of T2DM on CLDs remained unknown in various ethnic groups.

Methods

We obtained instrumental variables for T2DM and conducted a two-sample mendelian randomization (MR) study to examine the causal effect on nonalcoholic fatty liver disease (NAFLD), hepatocellular carcinoma (HCC), viral hepatitis, hepatitis B virus (HBV) infection, and hepatitis C virus (HCV) infection risk in Europeans and East Asians. The primary analysis utilized the inverse variance weighting (IVW) technique to evaluate the causal relationship between T2DM and CLDs. In addition, we conducted a series of rigorous analyses to bolster the reliability of our MR results.

Results

In Europeans, we found that genetic liability to T2DM has been linked with increased risk of NAFLD (IVW : OR =1.3654, 95% confidence interval [CI], 1.2250-1.5219, p=1.85e-8), viral hepatitis (IVW : OR =1.1173, 95%CI, 1.0271-1.2154, p=0.0098), and a suggestive positive association between T2DM and HCC (IVW : OR=1.2671, 95%CI, 1.0471-1.5333, p=0.0150), HBV (IVW : OR=1.1908, 95% CI, 1.0368-1.3677, p=0.0134). No causal association between T2DM and HCV was discovered. Among East Asians, however, there was a significant inverse association between T2DM and the proxies of NAFLD (ALT: IVW OR=0.9752, 95%CI 0.9597-0.9909, p=0.0021; AST: IVW OR=0.9673, 95%CI, 0.9528-0.9821, p=1.67e-5), and HCV (IVW: OR=0.9289, 95%CI, 0.8852-0.9747, p=0.0027). Notably, no causal association was found between T2DM and HCC, viral hepatitis, or HBV.

Conclusion

Our MR analysis revealed varying causal associations between T2DM and CLDs in East Asians and Europeans. Further research is required to investigate the potential mechanisms in various ethnic groups, which could yield new insights into early screening and prevention strategies for CLDs in T2DM patients.