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

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

BACKGROUND

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

METHODS

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

RESULT

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

CONCLUSION

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

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

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

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

Aims

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

Methods

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

Results

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

Conclusions

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

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

AIMS/HYPOTHESIS

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

METHODS

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

RESULTS

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

CONCLUSIONS/INTERPRETATION

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Associations of Insomnia With Insulin Resistance Traits: A Cross-sectional and Mendelian Randomization Study

CONTEXT

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Background

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

Methods

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

Results

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

Conclusion

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

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

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

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

Nutrient regulation of the islet epigenome controls adaptive insulin secretion

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

StocSum: stochastic summary statistics for whole genome sequencing studies

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

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

BACKGROUND AND AIMS

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

METHODS AND RESULTS

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

CONCLUSIONS

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

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

INTRODUCTION

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

Background

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

Methods

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

Results

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

Conclusion

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

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Polygenic risk of type 2 diabetes is associated with incident vascular dementia: a prospective cohort study

Type 2 diabetes and dementia are associated, but it is unclear whether the two diseases have common genetic risk markers that could partly explain their association. It is also unclear whether the association between the two diseases is of a causal nature. Furthermore, few studies on diabetes and dementia have validated dementia end-points with high diagnostic precision. We tested associations between polygenic risk scores for type 2 diabetes, fasting glucose, fasting insulin and haemoglobin A1c as exposure variables and dementia as outcome variables in 29 139 adults (mean age 55) followed for 20-23 years. Dementia diagnoses were validated by physicians through data from medical records, neuroimaging and biomarkers in cerebrospinal fluid. The dementia end-points included all-cause dementia, mixed dementia, Alzheimer's disease and vascular dementia. We also tested causal associations between type 2 diabetes and dementia through two-sample Mendelian randomization analyses. Seven different polygenic risk scores including single-nucleotide polymorphisms with different significance thresholds for type 2 diabetes were tested. A polygenic risk score including 4891 single-nucleotide polymorphisms with a P-value of <5e-04 showed the strongest association with different outcomes, including all-cause dementia (hazard ratio 1.11; Bonferroni corrected P = 3.6e-03), mixed dementia (hazard ratio 1.18; Bonferroni corrected P = 3.3e-04) and vascular dementia cases (hazard ratio 1.28; Bonferroni corrected P = 9.6e-05). The associations were stronger for non-carriers of the Alzheimer's disease risk gene APOE ε4. There was, however, no significant association between polygenic risk scores for type 2 diabetes and Alzheimer's disease. Furthermore, two-sample Mendelian randomization analyses could not confirm a causal link between genetic risk markers of type 2 diabetes and dementia outcomes. In conclusion, polygenic risk of type 2 diabetes is associated with an increased risk of dementia, in particular vascular dementia. The findings imply that certain people with type 2 diabetes may, due to their genetic background, be more prone to develop diabetes-associated dementia. This knowledge could in the future lead to targeted preventive strategies in clinical practice.

Causal Relationships Between Glycemic Traits and Myopia

Purpose

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

Methods

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

Results

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

Conclusions

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

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

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

Genetic predisposition to gestational diabetes mellitus in the Kazakh population

BACKGROUND AND AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

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

Long-term Outcomes Among Young Adults With Type 2 Diabetes Based on Durability of Glycemic Control: Results From the TODAY Cohort Study

OBJECTIVE

To examine the effect of different patterns of durable glycemic control on the development of comorbidities among youth with type 2 diabetes (T2D) and to assess the impact of fasting glucose (FG) variability on the clinical course of T2D.

RESEARCH DESIGN AND METHODS

From the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study, 457 participants (mean age, 14 years) with mean diabetes duration <2 years at entry and a minimum study follow-up of 10 years were included in these analyses. HbA1c, FG concentrations, and β-cell function estimates from oral glucose tolerance tests were measured longitudinally. Prevalence of comorbidities by glycemic control status after 10 years in the TODAY study was assessed.

RESULTS

Higher baseline HbA1c concentration, lower β-cell function, and maternal history of diabetes were strongly associated with loss of glycemic control in youth with T2D. Higher cumulative HbA1c concentration over 4 years and greater FG variability over a year within 3 years of diagnosis were related to higher prevalence of dyslipidemia, nephropathy, and retinopathy progression over the subsequent 10 years. A coefficient of variability in FG ≥8.3% predicted future loss of glycemic control and development of comorbidities.

CONCLUSIONS

Higher baseline HbA1c concentration and FG variability during year 1 accurately predicted youth with T2D who will experience metabolic decompensation and comorbidities. These values may be useful tools for clinicians when considering early intensification of therapy.

Genome-Wide Association Analysis of Over 170,000 Individuals from the UK Biobank Identifies Seven Loci Associated with Dietary Approaches to Stop Hypertension (DASH) Diet

Diet is a modifiable risk factor for common chronic diseases and mental health disorders, and its effects are under partial genetic control. To estimate the impact of diet on individual health, most epidemiological and genetic studies have focused on individual aspects of dietary intake. However, analysing individual food groups in isolation does not capture the complexity of the whole diet pattern. Dietary indices enable a holistic estimation of diet and account for the intercorrelations between food and nutrients. In this study we performed the first ever genome-wide association study (GWA) including 173,701 individuals from the UK Biobank to identify genetic variants associated with the Dietary Approaches to Stop Hypertension (DASH) diet. DASH was calculated using the 24 h-recall questionnaire collected by UK Biobank. The GWA was performed using a linear mixed model implemented in BOLT-LMM. We identified seven independent single-nucleotide polymorphisms (SNPs) associated with DASH. Significant genetic correlations were observed between DASH and several educational traits with a significant enrichment for genes involved in the AMP-dependent protein kinase (AMPK) activation that controls the appetite by regulating the signalling in the hypothalamus. The colocalization analysis implicates genes involved in body mass index (BMI)/obesity and neuroticism (ARPP21, RP11-62H7.2, MFHAS1, RHEBL1). The Mendelian randomisation analysis suggested that increased DASH score, which reflect a healthy diet style, is causal of lower glucose, and insulin levels. These findings further our knowledge of the pathways underlying the relationship between diet and health outcomes. They may have significant implications for global public health and provide future dietary recommendations for the prevention of common chronic diseases.

Genetic variants for prediction of gestational diabetes mellitus and modulation of susceptibility by a nutritional intervention based on a Mediterranean diet

Hypothesis

Gestational diabetes mellitus (GDM) entails a complex underlying pathogenesis, with a specific genetic background and the effect of environmental factors. This study examines the link between a set of single nucleotide polymorphisms (SNPs) associated with diabetes and the development of GDM in pregnant women with different ethnicities, and evaluates its potential modulation with a clinical intervention based on a Mediterranean diet.

Methods

2418 women from our hospital-based cohort of pregnant women screened for GDM from January 2015 to November 2017 (the San Carlos Cohort, randomized controlled trial for the prevention of GDM ISRCTN84389045 and real-world study ISRCTN13389832) were assessed for evaluation. Diagnosis of GDM was made according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Genotyping was performed by IPLEX MassARRAY PCR using the Agena platform (Agena Bioscience, SanDiego, CA). 110 SNPs were selected for analysis based on selected literature references. Statistical analyses regarding patients' characteristics were performed in SPSS (Chicago, IL, USA) version 24.0. Genetic association tests were performed using PLINK v.1.9 and 2.0 software. Bioinformatics analysis, with mapping of SNPs was performed using STRING, version 11.5.

Results

Quality controls retrieved a total 98 SNPs and 1573 samples, 272 (17.3%) with GDM and 1301 (82.7%) without GDM. 1104 (70.2%) were Caucasian (CAU) and 469 (29.8%) Hispanic (HIS). 415 (26.4%) were from the control group (CG), 418 (26.6%) from the nutritional intervention group (IG) and 740 (47.0%) from the real-world group (RW). 40 SNPs (40.8%) presented some kind of significant association with GDM in at least one of the genetic tests considered. The nutritional intervention presented a significant association with GDM, regardless of the variant considered. In CAU, variants rs4402960, rs7651090, IGF2BP2; rs1387153, rs10830963, MTNR1B; rs17676067, GLP2R; rs1371614, DPYSL5; rs5215, KCNJ1; and rs2293941, PDX1 were significantly associated with an increased risk of GDM, whilst rs780094, GCKR; rs7607980, COBLL1; rs3746750, SLC17A9; rs6048205, FOXA2; rs7041847, rs7034200, rs10814916, GLIS3; rs3783347, WARS; and rs1805087, MTR, were significantly associated with a decreased risk of GDM, In HIS, variants significantly associated with increased risk of GDM were rs9368222, CDKAL1; rs2302593, GIPR; rs10885122, ADRA2A; rs1387153, MTNR1B; rs737288, BACE2; rs1371614, DPYSL5; and rs2293941, PDX1, whilst rs340874, PROX1; rs2943634, IRS1; rs7041847, GLIS3; rs780094, GCKR; rs563694, G6PC2; and rs11605924, CRY2 were significantly associated with decreased risk for GDM.

Conclusions

We identify a core set of SNPs in their association with diabetes and GDM in a large cohort of patients from two main ethnicities from a single center. Identification of these genetic variants, even in the setting of a nutritional intervention, deems useful to design preventive and therapeutic strategies.

Identification of sitagliptin binding proteins by affinity purification mass spectrometry

Type 2 diabetes mellitus (T2DM) is recognized as a serious public health concern with increasing incidence. The dipeptidyl peptidase-4 (DPP-4) inhibitor sitagliptin has been used for the treatment of T2DM worldwide. Although sitagliptin has excellent therapeutic outcome, adverse effects are observed. In addition, previous studies have suggested that sitagliptin may have pleiotropic effects other than treating T2DM. These pieces of evidence point to the importance of further investigation of the molecular mechanisms of sitagliptin, starting from the identification of sitagliptin-binding proteins. In this study, by combining affinity purification mass spectrometry (AP-MS) and stable isotope labeling by amino acids in cell culture (SILAC), we discover seven high-confidence targets that can interact with sitagliptin. Surface plasmon resonance (SPR) assay confirms the binding of sitagliptin to three proteins, i. e., LYPLAL1, TCP1, and CCAR2, with binding affinities (K _D) ranging from 50.1 μM to 1490 μM. Molecular docking followed by molecular dynamic (MD) simulation reveals hydrogen binding between sitagliptin and the catalytic triad of LYPLAL1, and also between sitagliptin and the P-loop of ATP-binding pocket of TCP1. Molecular mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis indicates that sitagliptin can stably bind to LYPLAL1 and TCP1 in active sites, which may have an impact on the functions of these proteins. SPR analysis validates the binding affinity of sitagliptin to TCP1 mutant D88A is ~10 times lower than that to the wild-type TCP1. Our findings provide insights into the sitagliptin-targets interplay and demonstrate the potential of sitagliptin in regulating gluconeogenesis and in anti-tumor drug development.

Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10-8) with GDM, mapping to/near MTNR1B (P = 4.3 × 10-54), TCF7L2 (P = 4.0 × 10-16), CDKAL1 (P = 1.6 × 10-14), CDKN2A-CDKN2B (P = 4.1 × 10-9) and HKDC1 (P = 2.9 × 10-8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

A Clinical Update on Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) traditionally refers to abnormal glucose tolerance with onset or first recognition during pregnancy. GDM has long been associated with obstetric and neonatal complications primarily relating to higher infant birthweight and is increasingly recognized as a risk factor for future maternal and offspring cardiometabolic disease. The prevalence of GDM continues to rise internationally due to epidemiological factors including the increase in background rates of obesity in women of reproductive age and rising maternal age and the implementation of the revised International Association of the Diabetes and Pregnancy Study Groups' criteria and diagnostic procedures for GDM. The current lack of international consensus for the diagnosis of GDM reflects its complex historical evolution and pragmatic antenatal resource considerations given GDM is now 1 of the most common complications of pregnancy. Regardless, the contemporary clinical approach to GDM should be informed not only by its short-term complications but also by its longer term prognosis. Recent data demonstrate the effect of early in utero exposure to maternal hyperglycemia, with evidence for fetal overgrowth present prior to the traditional diagnosis of GDM from 24 weeks' gestation, as well as the durable adverse impact of maternal hyperglycemia on child and adolescent metabolism. The major contribution of GDM to the global epidemic of intergenerational cardiometabolic disease highlights the importance of identifying GDM as an early risk factor for type 2 diabetes and cardiovascular disease, broadening the prevailing clinical approach to address longer term maternal and offspring complications following a diagnosis of GDM.

Evidence of genetic overlap and causal relationships between blood-based biochemical traits and human cortical anatomy

Psychiatric disorders such as schizophrenia are commonly associated with structural brain alterations affecting the cortex. Recent genetic evidence suggests circulating metabolites and other biochemical traits play a causal role in many psychiatric disorders which could be mediated by changes in the cerebral cortex. Here, we leveraged publicly available genome-wide association study data to explore shared genetic architecture and evidence for causal relationships between a panel of 50 biochemical traits and measures of cortical thickness and surface area. Linkage disequilibrium score regression identified 191 genetically correlated biochemical-cortical trait pairings, with consistent representation of blood cell counts and other biomarkers such as C-reactive protein (CRP), haemoglobin and calcium. Spatially organised patterns of genetic correlation were additionally uncovered upon clustering of region-specific correlation profiles. Interestingly, by employing latent causal variable models, we found strong evidence suggesting CRP and vitamin D exert causal effects on region-specific cortical thickness, with univariable and multivariable Mendelian randomization further supporting a negative causal relationship between serum CRP levels and thickness of the lingual region. Our findings suggest a subset of biochemical traits exhibit shared genetic architecture and potentially causal relationships with cortical structure in functionally distinct regions, which may contribute to alteration of cortical structure in psychiatric disorders.

Genetic Variation in Targets of Antidiabetic Drugs and Alzheimer Disease Risk: A Mendelian Randomization Study

BACKGROUND AND OBJECTIVES

Previous studies have highlighted antidiabetic drugs as repurposing candidates for Alzheimer disease (AD), but the disease-modifying effects are still unclear.

METHODS

A 2-sample mendelian randomization study design was applied to examine the association between genetic variation in the targets of 4 antidiabetic drug classes and AD risk. Genetic summary statistics for blood glucose were analyzed using UK Biobank data of 326,885 participants, whereas summary statistics for AD were retrieved from previous genome-wide association studies comprising 24,087 clinically diagnosed AD cases and 55,058 controls. Positive control analysis on type 2 diabetes mellitus (T2DM), insulin secretion, insulin resistance, and obesity-related traits was conducted to validate the selection of instrumental variables.

RESULTS

In the positive control analysis, genetic variation in sulfonylurea targets was associated with higher insulin secretion, a lower risk of T2DM, and an increment in body mass index, waist circumference, and hip circumference, consistent with drug mechanistic actions and previous trial evidence. In the primary analysis, genetic variation in sulfonylurea targets was associated with a lower risk of AD (odds ratio [OR] = 0.38 per 1 mmol/L decrement in blood glucose, 95% CI 0.19-0.72, p = 0.0034). These results for sulfonylureas were largely unchanged in the sensitivity analysis using a genetic variant, rs757110, that has been validated to modulate the target proteins of sulfonylureas (OR = 0.35 per 1 mmol/L decrement in blood glucose, 95% CI 0.15-0.82, p = 0.016). An association between genetic variations in the glucagon-like peptide 1 (GLP-1) analogue target and a lower risk of AD was also observed (OR = 0.32 per 1 mmol/L decrement in blood glucose, 95% CI 0.13-0.79, p = 0.014). However, this result should be interpreted with caution because the positive control analyses for GLP-1 analogues did not comply with a weight-loss effect as shown in previous clinical trials. Results regarding other drug classes were inconclusive.

DISCUSSION

Genetic variation in sulfonylurea targets was associated with a lower risk of AD, and future studies are warranted to clarify the underlying mechanistic pathways between sulfonylureas and AD.

Islet Gene View-a tool to facilitate islet research

Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (<i>GCG</i>, 56%), amylin (<i>IAPP</i>, 52%), insulin (<i>INS</i>, 44%), and somatostatin (<i>SST</i>, 24%). Inhibition of two DEGs, <i>UNC5D</i> and <i>SERPINE2</i>, impaired glucose-stimulated insulin secretion and impacted cell survival in a human β-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.

Genetics of Type 2 Diabetes: Past, Present, and Future

Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.

Metabolic Effects of the Waist-To-Hip Ratio Associated Locus GRB14/COBLL1 Are Related to GRB14 Expression in Adipose Tissue

GRB14/COBLL1 locus has been shown to be associated with body fat distribution (FD), but neither the causal gene nor its role in metabolic diseases has been elucidated. We hypothesize that GRB14/COBLL1 may act as the causal genes for FD-related SNPs (rs10195252 and rs6738627), and that they may be regulated by SNP to effect obesity-related metabolic traits. We genotyped rs10195252 and rs6738627 in 2860 subjects with metabolic phenotypes. In a subgroup of 560 subjects, we analyzed GRB14/COBLL1 gene expression in paired visceral and subcutaneous adipose tissue (AT) samples. Mediation analyses were used to determine the causal relationship between SNPs, AT GRB14/COBLL1 mRNA expression, and obesity-related traits. In vitro gene knockdown of Grb14/Cobll1 was used to test their role in adipogenesis. Both gene expressions in AT are correlated with waist circumference. Visceral GRB14 mRNA expression is associated with FPG and HbA1c. Both SNPs are associated with triglycerides, FPG, and leptin levels. Rs10195252 is associated with HbA1c and seems to be mediated by visceral AT GRB14 mRNA expression. Our data support the role of the GRB14/COBLL1 gene expression in body FD and its locus in metabolic sequelae: in particular, lipid metabolism and glucose homeostasis, which is likely mediated by AT GRB14 transcript levels.

Causal Graph Among Serum Lipids and Glycemic Traits: A Mendelian Randomization Study

We systematically investigated the bidirectional causality among HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TGs), fasting insulin (FI), and glycated hemoglobin A1c (HbA1c) based on genome-wide association summary statistics of Europeans (n = 1,320,016 for lipids, 151,013 for FI, and 344,182 for HbA1c). We applied multivariable Mendelian randomization (MR) to account for the correlation among different traits and constructed a causal graph with 13 significant causal effects after adjusting for multiple testing (P < 0.0025). Remarkably, we found that the effects of lipids on glycemic traits were through FI from TGs (β = 0.06 [95% CI 0.03, 0.08] in units of 1 SD for each trait) and HDL-C (β = -0.02 [-0.03, -0.01]). On the other hand, FI had a strong negative effect on HDL-C (β = -0.15 [-0.21, -0.09]) and positive effects on TGs (β = 0.22 [0.14, 0.31]) and HbA1c (β = 0.15 [0.12, 0.19]), while HbA1c could raise LDL-C (β = 0.06 [0.03, 0.08]) and TGs (β = 0.08 [0.06, 0.10]). These estimates derived from inverse-variance weighting were robust when using different MR methods. Our results suggest that elevated FI was a strong causal factor of high TGs and low HDL-C, which in turn would further increase FI. Therefore, early control of insulin resistance is critical to reduce the risk of type 2 diabetes, dyslipidemia, and cardiovascular complications.

Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Genetic Susceptibility to Insulin Resistance and Its Association with Estimated Longevity in the Hungarian General and Roma Populations

Diabetes mellitus is a major public health problem with a wide range of prevalence among different ethnic groups. Early recognition of pre-diabetes is important to prevent the development of the disease, its complications, co-morbidities, and consequently early death. Insulin resistance (IR) is considered a condition that precedes type 2 diabetes; thus, understanding its underlying causes (genetic and non-genetic factors) will bring us closer to preventing it. The present study aimed to investigate the genetic susceptibility to IR and its impact on estimated longevity in populations with different ethnic origins using randomly selected samples of 372 Hungarian general (HG, as a reference with Caucasian origin) and 334 Roma participants (largest ethnic minority in Europe, with a northern India origin). In the present study, we used the Homeostasis Model Assessment—Insulin Resistance (HOMA—IR) to identify people with IR (>3.63) at the population level. To investigate the genetic predisposition to IR, 29 single nucleotide polymorphisms (SNPs) identified in a systematic literature search were selected and genotyped in sample populations. In the analyses, the adjusted p < 0.0033 was considered significant. Of these 29 SNPs, the commutative effects of 15 SNPs showing the strongest association with HOMA—IR were used to calculate an optimized genetic risk score (oGRS). The oGRS was found nominally significantly (p = 0.019) higher in the Roma population compared to HG one, and it was more strongly correlated with HOMA—IR. Therefore, it can be considered as a stronger predictor of the presence of IR among the Roma (AUCRoma = 0.673 vs. AUCHG = 0.528). Furthermore, oGRS also showed a significant correlation with reduced estimated longevity in the Roma population (β = −0.724, 95% CI: −1.230−−0.218; p = 0.005), but not in the HG one (β = 0.065, 95% CI: −0.388−0.518; p = 0.779). Overall, IR shows a strong correlation with a genetic predisposition among Roma, but not in the HG population. Furthermore, the increased genetic risk of Roma is associated with shorter estimated longevity, whereas this association is not observed in the HG one. Increased genetic susceptibility of Roma to IR should be considered in preventive programs targeting the development of type 2 diabetes, which may also reduce the risk of preventable premature death among them.

Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards

Background

Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation. Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

Methods

In this study, we analyzed data from 44,709 nondiabetic UK Biobank participants aged 40-69, predicting the risk of T2D onset within a selected time frame (mean of 7.3 years with an SD of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one nonlaboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes subcohorts, and compared the results to the results of the general cohort. We established the nonlaboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip ratio, and body mass index. For the laboratory model, we used age and sex together with four common blood tests: high-density lipoprotein (HDL), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

Results

The nonlaboratory scorecard model achieved an area under the receiver operating curve (auROC) of 0.81 (95% confidence interval [CI] 0.77-0.84) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (95% CI 5-66). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood test model based on age, sex, and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (95% CI 0.85-0.90) and a deciles' OR of ×48 (95% CI 12-109). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4-7%); 3% (2-4%); 10% (8-12%); and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (95% CI 0.74-0.75). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the anthropometry models.

Conclusions

The four blood test and anthropometric models outperformed the commonly used nonlaboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

Funding

The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk

BACKGROUND

The etiology of colorectal cancer is not fully understood.

METHODS

Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis.

RESULTS

Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer.

CONCLUSIONS

This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data.

IMPACT

The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.