Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

IGF-1 Interacted With Obesity in Prognosis Prediction in HER2-Positive Breast Cancer Patients

Purpose: Dysmetabolism and high circulating insulin-like growth factor 1 (IGF-1) would increase breast cancer risk, but its association with survival in HER2+ breast cancer patients has not been well-studied. Herein, we aim to evaluate the prognostic value of IGF-1 and metabolic abnormalities in HER2+ population.

Patients and Methods: HER2+ breast cancer patients treated in Ruijin Hospital between November 2012 and June 2017 were retrospectively analyzed. Median value of circulating IGF-1 was adopted to classify low or high IGF-1 group. Metabolic syndrome (MetS) was defined using AHA/NHLBI criteria. Overweight was defined by body mass index (BMI) ≥ 24.0 kg/m² in Chinese population.

Results: Overall, 679 patients were included and 209 had synchronous MetS. High IGF-1 level was more common in pre/peri-menopausal women (P < 0.001) and high IGFBP-3 patients (P < 0.001). After a median follow-up of 36 months, 52 patients had disease recurrences. IGF-1 level was not associated with recurrence-free survival (RFS, P = 0.620) in the whole population. However, exploratory subgroup analysis found that BMI and IGF-1 interacted in predicting RFS (P = 0.009). For non-overweight patients, high IGF-1 showed a superior 4-years RFS (91.1 vs. 85.0%; HR 0.53, 95% CI 0.27–1.00, P = 0.049) compared with patients with low IGF-1 level. In contrast, for overweight patients, high IGF-1 was associated with an impaired 4-years RFS (88.3 vs. 95.7%, HR 3.20, 95% CI 1.00–10.21, P = 0.038). Furthermore, high IGF-1 level was independently associated with better OS in the whole (HR 0.26, 95% CI 0.08–0.82, P = 0.044) as well as non-overweight population (HR 0.15, 95% CI 0.03–0.68, P = 0.005).

Conclusions: IGF-1 level was not associated with RFS in HER2+ breast cancer patients. However, IGF-1 and BMI had significant interaction in disease outcome prediction in HER2+ patients. High IGF-1 was protective in non-overweight patients, but risk factor for those overweight, which deserves further evaluation.

Association of Educational Attainment With Adiposity, Type 2 Diabetes, and Coronary Artery Diseases: A Mendelian Randomization Study

Background: Observational studies showed that educational attainment (EA) is associated with cardiometabolic diseases, but the long interval between exposure and outcome makes it difficult to infer causality. We herein used Mendelian randomization (MR) to examine the causal effects of EA on adiposity, type 2 diabetes (T2D), and coronary artery disease (CAD).

Methods: A two-sample MR analysis was conducted using genome-wide association study (GWAS) summary statistics. Seventy-four instrumental variables (IVs) were used to determine the causal effect of EA on cardiometabolic diseases. Sensitivity analyses were also performed to detect the pleiotropy of the IVs.

Results: Using the MR approach, we found that each additional year in EA is associated with a reduction in the body mass index (BMI) (β −0.17 [95% CI −0.23, −0.10], P = 8.85 × 10⁻⁷), a 39% reduction in the odds of having T2D (OR 0.61 [95% CI 0.50, 0.75], P = 1.16 × 10⁻⁶), and a 36% reduction in the odds of having CAD (OR 0.64 [95% CI 0.55, 0.75], P = 2.38 × 10⁻⁸) at the Bonferroni-adjusted level of significance.

Conclusion: Our findings suggest a causal role of EA on the cardiometabolic diseases including adiposity, T2D, and CAD.

Mendelian Randomization Study of Obesity and Cerebrovascular Disease

OBJECTIVE

To systematically investigate causal relationships between obesity and cerebrovascular disease and the extent to which hypertension and hyperglycemia mediate the effect of obesity on cerebrovascular disease.

METHODS

We used summary statistics from genome-wide association studies for body mass index (BMI), waist-to-hip ratio (WHR), and multiple cerebrovascular disease phenotypes. We explored causal associations with 2-sample Mendelian randomization (MR) accounting for genetic covariation between BMI and WHR, and we assessed what proportion of the association between obesity and cerebrovascular disease was mediated by systolic blood pressure (SBP) and blood glucose levels, respectively.

RESULTS

Genetic predisposition to higher BMI did not increase the risk of cerebrovascular disease. In contrast, for each 10% increase in WHR there was a 75% increase (95% confidence interval [CI] = 44-113%) in risk for large artery ischemic stroke, a 57% (95% CI = 29-91%) increase in risk for small vessel ischemic stroke, a 197% increase (95% CI = 59-457%) in risk of intracerebral hemorrhage, and an increase in white matter hyperintensity volume (β = 0.11, 95% CI = 0.01-0.21). These WHR associations persisted after adjusting for genetic determinants of BMI. Approximately one-tenth of the observed effect of WHR was mediated by SBP for ischemic stroke (proportion mediated: 12%, 95% CI = 4-20%), but no evidence of mediation was found for average blood glucose.

INTERPRETATION

Abdominal adiposity may trigger causal pathological processes, partially independent from blood pressure and totally independent from glucose levels, that lead to cerebrovascular disease. Potential targets of these pathological processes could represent novel therapeutic opportunities for stroke. ANN NEUROL 2020;87:516-524.

Impact of Glucose Level on Micro- and Macrovascular Disease in the General Population: A Mendelian Randomization Study

OBJECTIVE

To evaluate whether high glucose levels in the normoglycemic range and higher have a causal genetic effect on risk of retinopathy, neuropathy, nephropathy, chronic kidney disease (CKD), peripheral arterial disease (PAD), and myocardial infarction (MI; positive control) in the general population.

RESEARCH DESIGN AND METHODS

This study applied observational and one-sample Mendelian randomization (MR) analyses to individual-level data from 117,193 Danish individuals, and validation by two-sample MR analyses on summary-level data from 133,010 individuals from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), 117,165 from the CKDGen Consortium, and 452,264 from the UK Biobank.

RESULTS

Observationally, glucose levels in the normoglycemic range and higher were associated with high risks of retinopathy, neuropathy, diabetic nephropathy, PAD, and MI (all P for trend <0.001). In genetic causal analyses, the risk ratio for a 1 mmol/L higher glucose level was 2.01 (95% CI 1.18-3.41) for retinopathy, 2.15 (1.38-3.35) for neuropathy, 1.58 (1.04-2.40) for diabetic nephropathy, 0.97 (0.84-1.12) for estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m², 1.19 (0.90-1.58) for PAD, and 1.49 (1.02-2.17) for MI. Summary-level data from the MAGIC, the CKDGen Consortium, and the UK Biobank gave a genetic risk ratio of 4.55 (95% CI 2.26-9.15) for retinopathy, 1.48 (0.83-2.66) for peripheral neuropathy, 0.98 (0.94-1.01) for eGFR <60 mL/min/1.73 m², and 1.23 (0.57-2.67) for PAD per 1 mmol/L higher glucose level.

CONCLUSIONS

Glucose levels in the normoglycemic range and higher were prospectively associated with a high risk of retinopathy, neuropathy, diabetic nephropathy, eGFR <60 mL/min/1.73 m², PAD, and MI. These associations were confirmed in genetic causal analyses for retinopathy, neuropathy, diabetic nephropathy, and MI, but they could not be confirmed for PAD and seemed to be refuted for eGFR <60 mL/min/1.73 m².

FAM13A affects body fat distribution and adipocyte function

Genetic variation in the FAM13A (Family with Sequence Similarity 13 Member A) locus has been associated with several glycemic and metabolic traits in genome-wide association studies (GWAS). Here, we demonstrate that in humans, FAM13A alleles are associated with increased FAM13A expression in subcutaneous adipose tissue (SAT) and an insulin resistance-related phenotype (e.g. higher waist-to-hip ratio and fasting insulin levels, but lower body fat). In human adipocyte models, knockdown of FAM13A in preadipocytes accelerates adipocyte differentiation. In mice, Fam13a knockout (KO) have a lower visceral to subcutaneous fat (VAT/SAT) ratio after high-fat diet challenge, in comparison to their wild-type counterparts. Subcutaneous adipocytes in KO mice show a size distribution shift toward an increased number of smaller adipocytes, along with an improved adipogenic potential. Our results indicate that GWAS-associated variants within the FAM13A locus alter adipose FAM13A expression, which in turn, regulates adipocyte differentiation and contribute to changes in body fat distribution.

Influence of Genetic Ancestry on Human Serum Proteome

Disease risk varies significantly between ethnic groups, however, the clinical significance and implications of these observations are poorly understood. Investigating ethnic differences within the human proteome may shed light on the impact of ancestry on disease risk. We used admixture mapping to explore the impact of genetic ancestry on 237 cardiometabolic biomarkers in 2,216 Latin Americans within the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) study. We developed a variance component model in order to determine the proportion of variance explained by inter-ancestry differences, and we applied it to the biomarker panel. Multivariable linear regression was used to identify and localize genetic loci affecting biomarker variability between ethnicities. Variance component analysis revealed that 5% of biomarkers were significantly impacted by genetic admixture (p < 0.05/237), including C-peptide, apolipoprotein-E, and intercellular adhesion molecule 1. We also identified 46 regional associations across 40 different biomarkers (p < 1.13 × 10^-6). An independent analysis revealed that 34 of these 46 regions were associated at genome-wide significance (p < 5 × 10^-8) with their respective biomarker in either Europeans or Latin populations. Additional analyses revealed that an admixture mapping signal associated with increased C-peptide levels was also associated with an increase in diabetes risk (odds ratio [OR] = 6.07 per SD, 95% confidence interval [CI] 1.44 to 25.56, p = 0.01) and surrogate measures of insulin resistance. Our results demonstrate the impact of ancestry on biomarker levels, suggesting that some of the observed differences in disease prevalence have a biological basis, and that reference intervals for those biomarkers should be tailored to ancestry. Specifically, our results point to a strong role of ancestry in insulin resistance and diabetes risk.

Interplay of Placental DNA Methylation and Maternal Insulin Sensitivity in Pregnancy

The placenta participates in maternal insulin sensitivity changes during pregnancy; however, mechanisms remain unclear. We investigated associations between maternal insulin sensitivity and placental DNA methylation markers across the genome. We analyzed data from 430 mother-offspring dyads in the Gen3G cohort. All women underwent 75-g oral glucose tolerance tests at ∼26 weeks of gestation; we used glucose and insulin measures to estimate insulin sensitivity (Matsuda index). At delivery, we collected samples from placenta (fetal side) and measured DNA methylation using Illumina EPIC arrays. Using linear regression models to quantify associations at 720,077 cytosine-guanine dinucleotides (CpGs), with adjustment for maternal age, gravidity, smoking, BMI, child sex, and gestational age at delivery, we identified 188 CpG sites where placental DNA methylation was associated with Matsuda index (P < 6.94 × 10^-8). Among genes annotated to these 188 CpGs, we found enrichment in targets for miRNAs, in histone modifications, and in parent-of-origin DNA methylation including the H19/MIR675 locus (paternally imprinted). We identified 12 known placenta imprinted genes, including KCNQ1 Mendelian randomization analyses revealed five loci where placenta DNA methylation may causally influence maternal insulin sensitivity, including the maternally imprinted gene DLGAP2. Our results suggest that placental DNA methylation is fundamentally linked to the regulation of maternal insulin sensitivity in pregnancy.

Transcription Factor RREB1: from Target Genes towards Biological Functions

The Ras-responsive element binding protein 1(RREB1) is a member of zinc finger transcription factors, which is widely involved in biological processes including cell proliferation, transcriptional regulation and DNA damage repair. New findings reveal RREB1 functions as both transcriptional repressors and transcriptional activators for transcriptional regulation of target genes. The activation of RREB1 is regulated by MAPK pathway. We have summarized the target genes of RREB1 and discussed RREB1 roles in the cancer development. In addition, increasing evidences suggest that RREB1 is a potential risk gene for type 2 diabetes and obesity. We also review the current clinical application of RREB1 as a biomarker for melanoma detection. In conclusion, RREB1 is a promising diagnostic biomarker or new drug target for cancers and metabolic diseases.

The Importance of Precision Medicine in Type 2 Diabetes Mellitus (T2DM): From Pharmacogenetic and Pharmacoepigenetic Aspects

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) is a worldwide disorder as the most important challenges of health-care systems. Controlling the normal glycaemia greatly profit long-term prognosis and gives explanation for early, effective, constant, and safe intervention.

MATERIAL AND METHODS

Finding the main genetic and epigenetic profile of T2DM and the exact molecular targets of T2DM medications can shed light on its personalized management. The comprehensive information of T2DM was earned through the genome-wide association study (GWAS) studies. In the current review, we represent the most important candidate genes of T2DM like CAPN10, TCF7L2, PPAR-γ, IRSs, KCNJ11, WFS1, and HNF homeoboxes. Different genetic variations of a candidate gene can predict the efficacy of T2DM personalized strategy medication.

RESULTS

SLCs and AMPK variations are considered for metformin, CYP2C9, KATP channel, CDKAL1, CDKN2A/2B and KCNQ1 for sulphonylureas, OATP1B, and KCNQ1 for repaglinide and the last but not the least ADIPOQ, PPAR-γ, SLC, CYP2C8, and SLCO1B1 for thiazolidinediones response prediction.

CONCLUSION

Taken everything into consideration, there is an extreme need to determine the genetic status of T2DM patients in some known genetic region before planning the medication strategies.

Genome-wide meta-analysis associates GPSM1 with type 2 diabetes, a plausible gene involved in skeletal muscle function

Genome-wide association studies (GWASs) have identified many genetic variations associated with type 2 diabetes mellitus (T2DM) in Asians, but understanding the functional genetic variants that influence traits is often a complex process. In this study, fine mapping and other analytical strategies were performed to investigate the effects of G protein signaling modulator 1 (GPSM1) on insulin resistance in skeletal muscle. A total of 128 single-nucleotide polymorphisms (SNPs) within GPSM1 were analysed in 21,897 T2DM cases and 32,710 healthy controls from seven GWASs. The SNP rs28539249 in intron 9 of GPSM1 showed a nominally significant association with T2DM in Asians (OR = 1.07, 95% CI = 1.04-1.10, P < 10^-4). The GPSM1 mRNA was increased in skeletal muscle and correlated with T2DM traits across obese mice model. An eQTL for the cis-acting regulation of GPSM1 expression in human skeletal muscle was identified for rs28539249, and the increased GPSM1 expression related with T2DM traits within GEO datasets. Another independent Asian cohort showed that rs28539249 is associated with the skeletal muscle expression of CACFD1, GTF3C5, SARDH, and FAM163B genes, which are functionally enriched for endoplasmic reticulum stress (ERS) and unfolded protein response (UPR) pathways. Moreover, rs28539249 locus was predicted to disrupt regulatory regions in human skeletal muscle with enriched epigenetic marks and binding affinity for CTCF. Supershift EMSA assays followed luciferase assays demonstrated the CTCF specifically binding to rs28539249-C allele leading to decreased transcriptional activity. Thus, the post-GWAS annotation confirmed the Asian-specific association of genetic variant in GPSM1 with T2DM, suggesting a role for the variant in the regulation in skeletal muscle.

Genetic Determinants of Clustering of Cardiometabolic Risk Factors in U.K. Biobank

Objective: The metabolic syndrome (MetS) is a description of a clustering of cardiometabolic risk factors in the same individual. This study searched for genetic loci associated with all five prespecified components of MetS to find a common pathophysiological link for this risk factor clustering. Methods: Using data from 291,107 individuals in the U.K. biobank, a genome-wide association study (GWAS) was performed versus each of the five components of the syndrome as continuous variables (glucose, systolic blood pressure, triglycerides, waist circumference, and high-density lipoprotein-cholesterol). Results: Using false discovery rate <0.05, three loci were related to all five MetS components (rs7575523; nearest gene LINC0112, rs3936511; intron of C5orf67, and rs111970447; intron of GIP). Of those, C5orf67 seems the most interesting candidate for clustering of risk factors, since previous GWASs in other samples have identified this locus as being related to all five risk factors. Also, genetic loci being related to the different combinations of four or three MetS components were presented. Generally, each MetS component combination was related to a unique genetic profile, and the genetic overlap between these combinations was low. Conclusion: A genetic locus was discovered being related to each of the five MetS components, being a candidate for a common pathophysiological link for risk factor clustering. In addition, genetic loci being related to different combinations of four or three MetS components were presented, and the genetic overlap between those combinations of MetS was low.

Causal association of type 2 diabetes with amyotrophic lateral sclerosis: new evidence from Mendelian randomization using GWAS summary statistics

BACKGROUND

Associations between type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) were discovered in observational studies in both European and East Asian populations. However, whether such associations are causal remains largely unknown.

METHODS

We employed a two-sample Mendelian randomization approach to evaluate the causal relationship of T2D with the risk of ALS in both European and East Asian populations. Our analysis was implemented using summary statistics obtained from large-scale genome-wide association studies with ~660,000 individuals for T2D and ~81,000 individuals for ALS in the European population, and ~191,000 individuals for T2D and ~4100 individuals for ALS in the East Asian population. The causal relationship between T2D and ALS in both populations was estimated using the inverse-variance-weighted methods and was further validated through extensive complementary and sensitivity analyses.

RESULTS

Using multiple instruments that were strongly associated with T2D, a negative association between T2D and ALS was identified in the European population with the odds ratio (OR) estimated to be 0.93 (95% CI 0.88-0.99, p = 0.023), while a positive association between T2D and ALS was observed in the East Asian population with OR = 1.28 (95% CI 0.99-1.62, p = 0.058). These results were robust against instrument selection, various modeling misspecifications, and estimation biases, with the Egger regression and MR-PRESSO ruling out the possibility of horizontal pleiotropic effects of instruments. However, no causal association was found between T2D-related exposures (including glycemic traits) and ALS in the European population.

CONCLUSION

Our results provide new evidence supporting the causal neuroprotective role of T2D on ALS in the European population and provide empirically suggestive evidence of increasing risk of T2D on ALS in the East Asian population. Our results have an important implication on ALS pathology, paving ways for developing therapeutic strategies across multiple populations.

Bioinformatics analysis of hepatic gene expression profiles in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by hyperglycemia. The liver has a critical role in regulating glucose homeostasis. The present study aimed to analyze hepatic gene expression profiles and to identify the key genes and pathways involved in T2DM. Gene expression profiles of 10 patients with T2DM and 7 subjects with normal glucose tolerance were downloaded from the Gene Expression Omnibus database. Subsequently, differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed. In addition, a protein-protein interaction network was built and hub genes were identified. In total, 1,320 DEGs were identified, including 698 up- and 622 downregulated genes, and these were mainly enriched in positive regulation of transcription from RNA polymerase II promoter, cell adhesion, inflammatory response, positive regulation of apoptotic process, signal transduction and the Tolllike receptor signaling pathway. A total of 8 hub genes (G-protein subunit gamma transducin 2, ubiquitinconjugating enzyme E2 D1, glutamate metabotropic receptor 1, G-protein signaling modulator 1, C-X-C motif chemokine ligand 9, neurotensin, purinergic receptor P2Y1 and ring finger protein 41) were screened from the network. The present study may contribute to the elucidation of the hepatic pathology of T2DM.

A Phenome-Wide Mendelian Randomization Study of Pancreatic Cancer Using Summary Genetic Data

BACKGROUND

The 5-year mortality rate for pancreatic cancer is among the highest of all cancers. Greater understanding of underlying causes could inform population-wide intervention strategies for prevention. Summary genetic data from genome-wide association studies (GWAS) have become available for thousands of phenotypes. These data can be exploited in Mendelian randomization (MR) phenome-wide association studies (PheWAS) to efficiently screen the phenome for potential determinants of disease risk.

METHODS

We conducted an MR-PheWAS of pancreatic cancer using 486 phenotypes, proxied by 9,124 genetic variants, and summary genetic data from a GWAS of pancreatic cancer (7,110 cancer cases, 7,264 controls). ORs and 95% confidence intervals per 1 SD increase in each phenotype were generated.

RESULTS

We found evidence that previously reported risk factors of body mass index (BMI; 1.46; 1.20-1.78) and hip circumference (1.42; 1.21-1.67) were associated with pancreatic cancer. We also found evidence of novel associations with metabolites that have not previously been implicated in pancreatic cancer: ADpSGEGDFXAEGGGVR*, a fibrinogen-cleavage peptide (1.60; 1.31-1.95), and O-sulfo-l-tyrosine (0.58; 0.46-0.74). An inverse association was also observed with lung adenocarcinoma (0.63; 0.54-0.74).

CONCLUSIONS

Markers of adiposity (BMI and hip circumference) are potential intervention targets for pancreatic cancer prevention. Further clarification of the causal relevance of the fibrinogen-cleavage peptides and O-sulfo-l-tyrosine in pancreatic cancer etiology is required, as is the basis of our observed association with lung adenocarcinoma.

IMPACT

For pancreatic cancer, MR-PheWAS can augment existing risk factor knowledge and generate novel hypotheses to investigate.

Epigenome-Wide Association Study of Incident Type 2 Diabetes in a British Population: EPIC-Norfolk Study

Epigenetic changes may contribute substantially to risks of diseases of aging. Previous studies reported seven methylation variable positions (MVPs) robustly associated with incident type 2 diabetes mellitus (T2DM). However, their causal roles in T2DM are unclear. In an incident T2DM case-cohort study nested within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort, we used whole blood DNA collected at baseline, up to 11 years before T2DM onset, to investigate the role of methylation in the etiology of T2DM. We identified 15 novel MVPs with robust associations with incident T2DM and robustly confirmed three MVPs identified previously (near to TXNIP, ABCG1, and SREBF1). All 18 MVPs showed directionally consistent associations with incident and prevalent T2DM in independent studies. Further conditional analyses suggested that the identified epigenetic signals appear related to T2DM via glucose and obesity-related pathways acting before the collection of baseline samples. We integrated genome-wide genetic data to identify methylation-associated quantitative trait loci robustly associated with 16 of the 18 MVPs and found one MVP, cg00574958 at CPT1A, with a possible direct causal role in T2DM. None of the implicated genes were previously highlighted by genetic association studies, suggesting that DNA methylation studies may reveal novel biological mechanisms involved in tissue responses to glycemia.

Genome-Wide Association Study on the Early-Phase Insulin Response to a Liquid Mixed Meal: Results From the NEO Study

Early-phase insulin secretion is a determinant of postprandial glucose homeostasis. In this study, we aimed to identify novel genetic variants associated with the early-phase insulin response to a liquid mixed meal by a genome-wide association study using a discovery and replication design embedded in the Netherlands Epidemiology of Obesity (NEO) study. The early-phase insulin response was defined as the difference between the natural logarithm-transformed insulin concentrations of the postprandial state at 30 min after a meal challenge and the fasting state (Δinsulin). After Bonferroni correction, rs505922 (β: -6.5% [minor allele frequency (MAF) 0.32, P = 3.3 × 10^-8]) located in the ABO gene reached genome-wide significant level (P < 5 × 10^-8) and was also replicated successfully (β: -7.8% [MAF 0.32, P = 7.2 × 10^-5]). The function of the ABO gene was assessed using in vitro shRNA-mediated knockdown of gene expression in the murine pancreatic β-cell line MIN6. Knocking down the ABO gene led to decreased insulin secretion in the murine pancreatic β-cell line. These data indicate that the previously identified elevated risk of type 2 diabetes for carriers of the ABO rs505922:C allele may be caused by decreased early-phase insulin secretion.

Pancreatic Islet Transcriptional Enhancers and Diabetes

PURPOSE OF REVIEW

Common genetic variants that associate with type 2 diabetes risk are markedly enriched in pancreatic islet transcriptional enhancers. This review discusses current advances in the annotation of islet enhancer variants and their target genes.

RECENT FINDINGS

Recent methodological advances now allow genetic and functional mapping of diabetes causal variants at unprecedented resolution. Mapping of enhancer-promoter interactions in human islets has provided a unique appreciation of the complexity of islet gene regulatory processes and enabled direct association of noncoding diabetes risk variants to their target genes. The recently improved human islet enhancer annotations constitute a framework for the interpretation of diabetes genetic signals in the context of pancreatic islet gene regulation. In the future, integration of existing and yet to come regulatory maps with genetic fine-mapping efforts and in-depth functional characterization will foster the discovery of novel diabetes molecular risk mechanisms.

Functional Screening of Candidate Causal Genes for Insulin Resistance in Human Preadipocytes and Adipocytes

Rationale: Genome-wide association studies have identified genetic loci associated with insulin resistance (IR) but pinpointing the causal genes of a risk locus has been challenging. Objective: To identify candidate causal genes for IR, we screened regional and biologically plausible genes (16 in total) near the top 10 IR-loci in risk-relevant cell types, namely preadipocytes and adipocytes. Methods and Results: We generated 16 human Simpson-Golabi-Behmel syndrome preadipocyte knockout lines each with a single IR-gene knocked out by lentivirus-mediated CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 system. We evaluated each gene knockout by screening IR-relevant phenotypes in the 3 insulin-sensitizing mechanisms, including adipogenesis, lipid metabolism, and insulin signaling. We performed genetic analyses using data on the genotype-tissue expression portal expression quantitative trait loci database and accelerating medicines partnership type 2 diabetes mellitus Knowledge Portal to evaluate whether candidate genes prioritized by our in vitro studies were expression quantitative trait loci genes in human subcutaneous adipose tissue, and whether expression of these genes is associated with risk of IR, type 2 diabetes mellitus, and cardiovascular diseases. We further validated the functions of 3 new adipose IR genes by overexpression-based phenotypic rescue in the Simpson-Golabi-Behmel syndrome preadipocyte knockout lines. Twelve genes, PPARG, IRS-1, FST, PEPD, PDGFC, MAP3K1, GRB14, ARL15, ANKRD55, RSPO3, COBLL1, and LYPLAL1, showed diverse phenotypes in the 3 insulin-sensitizing mechanisms, and the first 7 of these genes could affect all the 3 mechanisms. Five out of 6 expression quantitative trait loci genes are among the top candidate causal genes and the abnormal expression levels of these genes (IRS-1, GRB14, FST, PEPD, and PDGFC) in human subcutaneous adipose tissue could be associated with increased risk of IR, type 2 diabetes mellitus, and cardiovascular disease. Phenotypic rescue by overexpression of the candidate causal genes (FST, PEPD, and PDGFC) in the Simpson-Golabi-Behmel syndrome preadipocyte knockout lines confirmed their function in adipose IR. Conclusions: Twelve genes showed diverse phenotypes indicating differential roles in insulin sensitization, suggesting mechanisms bridging the association of their genomic loci with IR. We prioritized PPARG, IRS-1, GRB14, MAP3K1, FST, PEPD, and PDGFC as top candidate genes. Our work points to novel roles for FST, PEPD, and PDGFC in adipose tissue, with consequences for cardiometabolic diseases.

Genetic predisposition to gestational glucose metabolism and gestational diabetes mellitus risk in a Chinese population

BACKGROUND

Genome-wide association studies (GWAS) have identified several genetic variants affecting gestational glucose metabolism. However, information regarding their known associations with gestational diabetes mellitus (GDM) risk remains scarce.

METHODS

This study examined the associations of 12 gestational glucose metabolism-related variants with GDM risk in a Chinese population (964 GDM cases, 1021 controls). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression analysis.

RESULTS

Rs10830963 in melatonin receptor 1B (MTNR1B) was found to be associated with an increased risk of GDM, after adjusting for age, prepregnancy body mass index, parity, abnormal pregnancy history, and family history of diabetes (OR 1.20; 95% CI 1.05-1.36; P = 0.007). Compared with women with a family history of diabetes, there was a significant association of rs7936247 with GDM risk among pregnant women without a family history of diabetes (OR 1.20; 95% CI 1.04-1.38; P = 0.014; P_{heterogeneity}  = 0.035). Further functional annotations showed that rs10830963 and rs7936247 fell in the functional elements of human pancreatic islets. Genotype-phenotype associations indicated that the variants may contribute to GDM risk by affecting the expression of nearby or distant genes.

CONCLUSIONS

The findings of this study suggest that rs10830963 and rs7936247 may be markers for susceptibility to GDM in a Chinese population. Additional studies are warranted to validate our findings and clarify the underlying mechanism.

In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study

AIMS/HYPOTHESIS

The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome 'diabetes' and whether these associations were causal.

METHODS

In 2467 individuals of the population-based, cross-sectional EpiHealth study (45-75 years, 50% women), 249 plasma proteins were analysed by the proximity extension assay technique. DNA was genotyped using the Illumina HumanCoreExome-12 v1.0 BeadChip. Diabetes was defined as taking glucose-lowering treatment or having a fasting plasma glucose of ≥7.0 mmol/l. The associations between proteins and diabetes were assessed using logistic regression. To investigate causal relationships between proteins and diabetes, a bidirectional two-sample Mendelian randomisation was performed based on large, genome-wide association studies belonging to the DIAGRAM and MAGIC consortia, and a genome-wide association study in the EpiHealth study.

RESULTS

Twenty-six proteins were positively associated with diabetes, including cathepsin D, retinal dehydrogenase 1, α-L-iduronidase, hydroxyacid oxidase 1 and galectin-4 (top five findings). Three proteins, lipoprotein lipase, IGF-binding protein 2 and paraoxonase 3 (PON-3), were inversely associated with diabetes. Fourteen of the proteins are novel discoveries. The Mendelian randomisation study did not disclose any significant causal effects between the proteins and diabetes in either direction that were consistent with the relationships found between the protein levels and diabetes.

CONCLUSIONS/INTERPRETATION

The 29 proteins associated with diabetes are involved in several physiological pathways, but given the power of the study no causal link was identified for those proteins tested in Mendelian randomisation. Therefore, the identified proteins are likely to be biomarkers for type 2 diabetes, rather than representing causal pathways.

Transethnic meta-analysis of metabolic syndrome in a multiethnic study

Genome-wide association studies (GWAS) have been used to establish thousands of genetic associations across numerous phenotypes. To improve the power of GWAS and generalize associations across ethnic groups, transethnic meta-analysis methods are used to combine the results of several GWAS from diverse ancestries. The goal of this study is to identify genetic associations for eight quantitative metabolic syndrome (MetS) traits through a meta-analysis across four ethnic groups. Traits were measured in the GENetics of Noninsulin dependent Diabetes Mellitus (GENNID) Study which consists of African-American (families = 73, individuals = 288), European-American (families = 79, individuals = 519), Japanese-American (families = 17, individuals = 132), and Mexican-American (families = 113, individuals = 610) samples. Genome-wide association results from these four ethnic groups were combined using four meta-analysis methods: fixed effects, random effects, TransMeta, and MR-MEGA. We provide an empirical comparison of the four meta-analysis methods from the GENNID results, discuss which types of loci (characterized by allelic heterogeneity) appear to be better detected by each of the four meta-analysis methods in the GENNID Study, and validate our results using previous genetic discoveries. We specifically compare the two transethnic methods, TransMeta and MR-MEGA, and discuss how each transethnic method's framework relates to the types of loci best detected by each method.

HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.

A Proteomics-Based Approach Reveals Differential Regulation of Urine Proteins between Metabolically Healthy and Unhealthy Obese Patients

Metabolic dysfunction associated with obesity threatens to inundate health care resources by increasing the incidences of obesity-related diseases. The aim of the present study was to investigate the changes in the urinary proteome of 18 individuals classified into metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO) patients. Proteome analysis was performed using the two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with mass spectrometry (MS). Upon analysis, a total of 54 proteins were found to be affected with ≥1.5-fold change (ANOVA, p ≤ 0.05), of which 44 proteins were upregulated and 10 proteins were downregulated. These differentially abundant proteins were related to nuclear factor κB (NF-κB) and p38 mitogen-activated protein (MAP) kinase pathways and were involved in cellular compromise, inflammatory response, and cancer. Proteins involved in inflammation (fibrinogen alpha (FIBA), serotransferrin (TRFE, and kininogen-1 (KNG1)) and insulin resistance (ADP-ribosylation factor (ARF)-like protein 15 (ARL15) and retinol-binding protein 4 (RET4)) were found to be significantly increased in the urine samples of MUHO compared to MHO patients. Investigating the effects of obesity on urinary proteins can help in developing efficient diagnostic procedures for early detection and prevention of obesity-related complications.

Gene-diet quality interactions on haemoglobin A1c and type 2 diabetes risk: The Airwave Health Monitoring Study

INTRODUCTION

Type 2 diabetes (T2D) is multifactorial involving lifestyle, environmental and genetic risk factors. This study aims to investigate the impact of genetic interactions with alcohol and diet quality on glycated haemoglobin A1c (HbA1c) independent of obesity, in a British population.

METHODS

Cross-sectional study of 14 089 white British participants from Airwave Health Monitoring Study and a subsample of 3733 participants with dietary data. A T2D genetic risk score (GRS) was constructed, and its interactions with diet on HbA1c were assessed.

RESULTS

GRS was associated with a higher HbA1c% (β = 0.03, P < 0.0001) and a higher risk of prediabetes (OR = 1.09, P < 0.0001) and T2D (OR = 1.14, P = 0.006). The genetic effect on HbA1c% was significantly higher in obese participants (β = 1.88, P interaction = 0.03). A high intake of wholegrain attenuated the effect on HbA1c% in high-risk individuals P interaction = 0.04.

CONCLUSION

The genetic effect on HbA1c was almost doubled in obese individuals, compared with those with a healthy weight, and independent of weight, there was a modest offset on HbA1c in high-genetic-risk individuals consuming a diet high in wholegrain. This supports the importance of a healthy diet high in wholegrains and along with maintaining a healthy weight in controlling HbA1c among high-genetic-risk groups.

Genetic Correlations Between Diabetes and Glaucoma: An Analysis of Continuous and Dichotomous Phenotypes

PURPOSE

A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits.

DESIGN

Cross-sectional study.

METHODS

We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure [IOP], central corneal thickness [CCT], corneal hysteresis [CH], corneal resistance factor [CRF], cup-to-disc ratio [CDR], and primary open-angle glaucoma [POAG]). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank, and the International Glaucoma Genetics Consortium. We calculated genetic correlation (rg) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT, and select diabetes-related traits based on individual level phenotype data in 2 Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines.

RESULTS

Overall, there was little rg between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (rg = -0.14; P = .16). Using Sequential Oligogenic Linkage Analysis Routines, the genetic correlations between measured IOP, CCT, FBS, fasting insulin, and hemoglobin A1c were null. In contrast, genetic correlations between IOP and POAG (rg ≥ 0.45; P ≤ 3.0 × 10-4) and between CDR and POAG were high (rg = 0.57; P = 2.8 × 10-10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (rg range -0.18 to 0.11) and nonsignificant (P ≥ .07).

CONCLUSION

These analyses suggest that there is limited genetic correlation between diabetes- and glaucoma-related traits.

The effect of mid-life insulin resistance and type 2 diabetes on older-age cognitive state: the explanatory role of early-life advantage

AIMS/HYPOTHESIS

Type 2 diabetes, hyperglycaemia and insulin resistance are associated with cognitive impairment and dementia, but causal inference studies using Mendelian randomisation do not confirm this. We hypothesised that early-life cognition and social/educational advantage may confound the relationship.

METHODS

From the population-based British 1946 birth cohort, a maximum number of 1780 participants had metabolic variables (type 2 diabetes, insulin resistance [HOMA2-IR] and HbA1c) assessed at age 60-64 years, and cognitive state (Addenbrooke's Cognitive Examination III [ACE-III]) and verbal memory assessed at age 69 years. Earlier-life measures included socioeconomic position (SEP), cognition at age 8 years and educational attainment. Polygenic risk scores (PRSs) for type 2 diabetes were calculated. We first used a PRS approach with multivariable linear regression to estimate associations between PRSs and metabolic traits and later-life cognitive state. Second, using a path model approach, we estimated the interrelationships between earlier-life measures, features of mid-life type 2 diabetes and cognitive state at age 69 years. All models were adjusted for sex.

RESULTS

The externally weighted PRS for type 2 diabetes was associated with mid-life metabolic traits (e.g. HOMA2-IR β = 0.08 [95% CI 0.02, 0.16]), but not with ACE-III (β = 0.04 [-0.02, 0.90]) or other cognitive outcomes. While there was an association between HOMA2-IR and subsequent ACE-III (β = -0.09 [-0.15, -0.03]), path modelling showed no direct effect (β = -0.01 [-0.06, 0.03]) after accounting for the association between childhood SEP and education with HOMA2-IR. The same pattern was observed for later-life verbal memory.

CONCLUSIONS/INTERPRETATION

Associations between type 2 diabetes and mid-life metabolic traits with subsequent cognitive state do not appear causal, and instead they may be explained by SEP in early life, childhood cognition and educational attainment. Therefore, glucose-lowering medication may be unlikely to combat cognitive impairment in older age.

Sex-specific Mendelian randomization study of genetically predicted insulin and cardiovascular events in the UK Biobank

Insulin drives growth and reproduction which trade-off against longevity. Genetically predicted insulin, i.e., insulin proxied by genetic variants, is positively associated with ischemic heart disease, but sex differences are unclear, despite different disease rates and reproductive strategies by sex. We used Mendelian randomization in 392,010 white British from the UK Biobank to assess the sex-specific role of genetically predicted insulin in myocardial infarction (MI) (14,442 cases, 77% men), angina (21,939 cases, 65% men) and heart failure (5537 cases, 71% men). Genetically predicted insulin was associated with MI (odds ratio (OR) 4.27 per pmol/L higher insulin, 95% confidence interval (CI) 1.60 to 11.3) and angina (OR 2.93, 1.27 to 6.73) in men, but not women (MI OR 0.80, 95% CI 0.23 to 2.84, angina OR 1.10, 95% CI 0.38 to 3.18). Patterns were similar for insulin resistance and heart failure. Mitigating the effects of insulin might address sexual disparities in health.

Association of Birth Weight With Type 2 Diabetes and Glycemic Traits: A Mendelian Randomization Study

IMPORTANCE

Observational studies have shown associations of birth weight with type 2 diabetes (T2D) and glycemic traits, but it remains unclear whether these associations represent causal associations.

OBJECTIVE

To test the association of birth weight with T2D and glycemic traits using a mendelian randomization analysis.

DESIGN, SETTING, AND PARTICIPANTS

This mendelian randomization study used a genetic risk score for birth weight that was constructed with 7 genome-wide significant single-nucleotide polymorphisms. The associations of this score with birth weight and T2D were tested in a mendelian randomization analysis using study-level data. The association of birth weight with T2D was tested using both study-level data (7 single-nucleotide polymorphisms were used as an instrumental variable) and summary-level data from the consortia (43 single-nucleotide polymorphisms were used as an instrumental variable). Data from 180 056 participants from 49 studies were included.

MAIN OUTCOMES AND MEASURES

Type 2 diabetes and glycemic traits.

RESULTS

This mendelian randomization analysis included 49 studies with 41 155 patients with T2D and 80 008 control participants from study-level data and 34 840 patients with T2D and 114 981 control participants from summary-level data. Study-level data showed that a 1-SD decrease in birth weight due to the genetic risk score was associated with higher risk of T2D among all participants (odds ratio [OR], 2.10; 95% CI, 1.69-2.61; P = 4.03 × 10-5), among European participants (OR, 1.96; 95% CI, 1.42-2.71; P = .04), and among East Asian participants (OR, 1.39; 95% CI, 1.18-1.62; P = .04). Similar results were observed from summary-level analyses. In addition, each 1-SD lower birth weight was associated with 0.189 SD higher fasting glucose concentration (β = 0.189; SE = 0.060; P = .002), but not with fasting insulin, 2-hour glucose, or hemoglobin A1c concentration.

CONCLUSIONS AND RELEVANCE

In this study, a genetic predisposition to lower birth weight was associated with increased risk of T2D and higher fasting glucose concentration, suggesting genetic effects on retarded fetal growth and increased diabetes risk that either are independent of each other or operate through alterations of integrated biological mechanisms.

Kahn S, Boyko EJ, Bray GA, Horton ES, Hivert MF, Goldberg R, Chen L, Mercader J, Harden M, Florez JC, Diabetes Prevention Program Research Group. A Polygenic Lipodystrophy Genetic Risk Score Characterizes Risk Independent of BMI in the Diabetes Prevention Program

CONTEXT

There is substantial heterogeneity in insulin sensitivity, and genetics may suggest possible mechanisms by which common variants influence this trait.

OBJECTIVES

We aimed to evaluate an 11-variant polygenic lipodystrophy genetic risk score (GRS) for association with anthropometric, glycemic and metabolic traits in the Diabetes Prevention Program (DPP). In secondary analyses, we tested the association of the GRS with cardiovascular risk factors in the DPP.

DESIGN

In 2713 DPP participants, we evaluated a validated GRS of 11 common variants associated with fasting insulin-based measures of insulin sensitivity discovered through genome-wide association studies that cluster with a metabolic profile of lipodystrophy, conferring high metabolic risk despite low body mass index (BMI).

RESULTS

At baseline, a higher polygenic lipodystrophy GRS was associated with lower weight, BMI, and waist circumference measurements, but with worse insulin sensitivity index (ISI) values. Despite starting at a lower weight and BMI, a higher GRS was associated with less weight and BMI reduction at one year and less improvement in ISI after adjusting for baseline values but was not associated with diabetes incidence. A higher GRS was also associated with more atherogenic low-density lipoprotein peak-particle-density at baseline but was not associated with coronary artery calcium scores in the Diabetes Prevention Program Outcomes Study.

CONCLUSIONS

In the DPP, a higher polygenic lipodystrophy GRS for insulin resistance with lower BMI was associated with diminished improvement in insulin sensitivity and potential higher cardiovascular disease risk. This GRS helps characterize insulin resistance in a cohort of individuals at high risk for diabetes, independent of adiposity.

Genomics of body fat percentage may contribute to sex bias in anorexia nervosa

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = -0.17), suggesting that the female preponderance in AN may, in part, be explained by sex-specific anthropometric and metabolic genetic factors increasing liability to AN.

Assessment of Causal Direction Between Gut Microbiota-Dependent Metabolites and Cardiometabolic Health: A Bidirectional Mendelian Randomization Analysis

We examined the causal direction between gut microbiota-dependent metabolite trimethylamine N-oxide (TMAO) or its predecessors and cardiometabolic diseases, such as risk of type 2 diabetes mellitus (T2DM), coronary artery disease (CAD), myocardial infarction (MI), stroke, atrial fibrillation (AF), and chronic kidney disease (CKD). We used genetic variants as instruments to test the causal associations. Genetically predicted higher TMAO and carnitine were not associated with higher odds of T2DM, AF, CAD, MI, stroke, and CKD after Bonferroni correction (P ≤ 0.0005). However, we observed that genetically increased choline showed a suggestive association with higher risk of T2DM (odds ratio 1.84 [95% CI 1.00-3.42] per 10 units, P = 0.05). In contrast, genetically predicted higher betaine (0.68 [0.48-0.95] per 10 units, P = 0.023) was suggestively associated with a lower risk of T2DM. We observed a suggestive association of genetically increased choline with a lower level of body fat percentage (β ± SE -0.28 ± 0.11, P = 0.013) but a higher estimated glomerular filtration rate (0.10 ± 0.05, P = 0.034). We further found that T2DM (0.130 ± 0.036, P < 0.0001) and CKD (0.483 ± 0.168, P = 0.004) were causally associated with higher TMAO levels. Our Mendelian randomization findings support that T2DM and kidney disease increase TMAO levels and that observational evidence for cardiovascular diseases may be due to confounding or reverse causality.

GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

Circulating vitamin E and cardiometabolic measures: a Mendelian randomization analysis

Although a large body of literature reported that high intake of vitamin E played a possible role in reducing risk of cardiometabolic diseases, conflicting results were also found in some observational studies due to confounding factors. Hence, we used a Mendelian randomization study as an alternative way to examine the causality between circulating vitamin E and cardiometabolic diseases. Summary-level data were extracted from consortia and three single nucleotide polymorphisms were used as instrumental variables. Our study showed that a one-SD increase in circulating vitamin E levels was causally associated with an increased risk of coronary artery disease [odds ratio (OR) 3.16 (95%CI 1.74, 5.73); p = 1.91 × 10^-3] at the Bonferroni-adjusted level of significance (p<0.005). Moreover, a one-SD increase in circulating vitamin E levels was associated with a 0.572-SD increase in low density lipoprotein cholesterol (mg/dl), a 0.693-SD increase in total cholesterol (mg/dl), and a 1.45-SD increase in triglyceride (mg/dl), but a 0.502-SD decrease in high density lipoprotein cholesterol (mg/dl) at the Bonferroni-adjusted level of significance (p<0.0028). Our findings indicated that genetically elevated vitamin E was associated with increased risk of coronary artery disease, suggesting an adverse causality between circulating vitamin E and coronary artery disease.

Powerful statistical method to detect disease-associated genes using publicly available genome-wide association studies summary data

Genome-wide association studies (GWAS) have thus far achieved substantial success. In the last decade, a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker-based tests, that is, testing one single-nucleotide polymorphism (SNP) at a time. Generally, the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene-level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test genetic association for both continuous and binary traits through not only one study but also multiple studies, which would be helpful to overcome the limitation of existing methods that can only be applied to a specific type of data. We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS meta-analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in β -cell function, glucose homeostasis, and lipids traits.

Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large-scale genome-wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease-related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the "single trait" association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well-characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome-phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual-level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P-value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta-analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta-analysis. All the proposed methods are implemented in a publicly available R package.

Liver Function and Risk of Type 2 Diabetes: Bidirectional Mendelian Randomization Study

Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from, or is merely correlated with T2D due to confounding. We used Mendelian randomization to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D case and 607,012 control subjects. Several biomarkers were used as proxies of liver function (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], and γ-glutamyl transferase [GGT]). Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetic predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of nonalcoholic fatty liver disease (NAFLD), these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10^-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r² > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10^-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.

Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals

Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10-8) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.

Genetic Regulation of Enoyl-CoA Hydratase Domain-Containing 3 in Adipose Tissue Determines Insulin Sensitivity in African Americans and Europeans

Insulin resistance (IR) is a harbinger of type 2 diabetes (T2D) and partly determined by genetic factors. However, genetically regulated mechanisms of IR remain poorly understood. Using gene expression, genotype, and insulin sensitivity data from the African American Genetics of Metabolism and Expression (AAGMEx) cohort, we performed transcript-wide correlation and expression quantitative trait loci (eQTL) analyses to identify IR-correlated cis-regulated transcripts (cis-eGenes) in adipose tissue. These IR-correlated cis-eGenes were tested in the European ancestry individuals in the Metabolic Syndrome in Men (METSIM) cohort for trans-ethnic replication. Comparison of Matsuda index-correlated transcripts in AAGMEx with the METSIM study identified significant correlation of 3,849 transcripts, with concordant direction of effect for 97.5% of the transcripts. cis-eQTL for 587 Matsuda index-correlated genes were identified in both cohorts. Enoyl-CoA hydratase domain-containing 3 (ECHDC3) was the top-ranked Matsuda index-correlated cis-eGene. Expression levels of ECHDC3 were positively correlated with Matsuda index, and regulated by cis-eQTL, rs34844369 being the top cis-eSNP in AAGMEx. Silencing of ECHDC3 in adipocytes significantly reduced insulin-stimulated glucose uptake and Akt Ser⁴⁷³ phosphorylation. RNA sequencing analysis identified 691 differentially expressed genes in ECHDC3-knockdown adipocytes, which were enriched in γ-linolenate biosynthesis, and known IR genes. Thus, our studies elucidated genetic regulatory mechanisms of IR and identified genes and pathways in adipose tissue that are mechanistically involved in IR.

Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks

Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

The Burmese cat as a genetic model of type 2 diabetes in humans

The recent extension of genetic tools to the domestic cat, together with the serendipitous consequences of selective breeding, have been essential to the study of the genetic diseases that affect them. Cats are increasingly presented for veterinary surveillance and share many of human's heritable diseases, allowing them to serve as natural models of these conditions. Feline diabetes mellitus is a common condition in domestic cats that bears close pathological and clinical resemblance to type 2 diabetes in humans, including pancreatic β-cell dysfunction and peripheral insulin resistance. In Australia, New Zealand and Europe, diabetes mellitus is almost four times more common in cats of the Burmese breed than in other breeds. This geographically based breed predisposition parallels familial and population clustering of type 2 diabetes in humans. As a genetically isolated population, the Australian Burmese breed provides a spontaneous, naturally occurring genetic model of type 2 diabetes. Genetically isolated populations typically exhibit extended linkage disequilibrium and increased opportunity for deleterious variants to reach high frequencies over many generations due to genetic drift. Studying complex diseases in such populations allows for tighter control of confounding factors including environmental heterogeneity, allelic frequencies and population stratification. The homogeneous genetic background of Australian Burmese cats may provide a unique opportunity to either refine genetic signals previously associated with type 2 diabetes or identify new risk factors for this disease.

Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis

BACKGROUND

In addition to the established association between general obesity and breast cancer risk, central obesity and circulating fasting insulin and glucose have been linked to the development of this common malignancy. Findings from previous studies, however, have been inconsistent, and the nature of the associations is unclear.

METHODS

We conducted Mendelian randomization analyses to evaluate the association of breast cancer risk, using genetic instruments, with fasting insulin, fasting glucose, 2-h glucose, body mass index (BMI) and BMI-adjusted waist-hip-ratio (WHRadj BMI). We first confirmed the association of these instruments with type 2 diabetes risk in a large diabetes genome-wide association study consortium. We then investigated their associations with breast cancer risk using individual-level data obtained from 98 842 cases and 83 464 controls of European descent in the Breast Cancer Association Consortium.

RESULTS

All sets of instruments were associated with risk of type 2 diabetes. Associations with breast cancer risk were found for genetically predicted fasting insulin [odds ratio (OR) = 1.71 per standard deviation (SD) increase, 95% confidence interval (CI) = 1.26-2.31, p  =  5.09  ×  10-4], 2-h glucose (OR = 1.80 per SD increase, 95% CI = 1.3 0-2.49, p  =  4.02  ×  10-4), BMI (OR = 0.70 per 5-unit increase, 95% CI = 0.65-0.76, p  =  5.05  ×  10-19) and WHRadj BMI (OR = 0.85, 95% CI = 0.79-0.91, p  =  9.22  ×  10-6). Stratified analyses showed that genetically predicted fasting insulin was more closely related to risk of estrogen-receptor [ER]-positive cancer, whereas the associations with instruments of 2-h glucose, BMI and WHRadj BMI were consistent regardless of age, menopausal status, estrogen receptor status and family history of breast cancer.

CONCLUSIONS

We confirmed the previously reported inverse association of genetically predicted BMI with breast cancer risk, and showed a positive association of genetically predicted fasting insulin and 2-h glucose and an inverse association of WHRadj BMI with breast cancer risk. Our study suggests that genetically determined obesity and glucose/insulin-related traits have an important role in the aetiology of breast cancer.

Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality

BACKGROUND

Global dietary recommendations for and cardiovascular effects of linoleic acid, the major dietary omega-6 fatty acid, and its major metabolite, arachidonic acid, remain controversial. To address this uncertainty and inform international recommendations, we evaluated how in vivo circulating and tissue levels of linoleic acid (LA) and arachidonic acid (AA) relate to incident cardiovascular disease (CVD) across multiple international studies.

METHODS

We performed harmonized, de novo, individual-level analyses in a global consortium of 30 prospective observational studies from 13 countries. Multivariable-adjusted associations of circulating and adipose tissue LA and AA biomarkers with incident total CVD and subtypes (coronary heart disease, ischemic stroke, cardiovascular mortality) were investigated according to a prespecified analytic plan. Levels of LA and AA, measured as the percentage of total fatty acids, were evaluated linearly according to their interquintile range (ie, the range between the midpoint of the first and fifth quintiles), and categorically by quintiles. Study-specific results were pooled using inverse-variance-weighted meta-analysis. Heterogeneity was explored by age, sex, race, diabetes mellitus, statin use, aspirin use, omega-3 levels, and fatty acid desaturase 1 genotype (when available).

RESULTS

In 30 prospective studies with medians of follow-up ranging 2.5 to 31.9 years, 15 198 incident cardiovascular events occurred among 68 659 participants. Higher levels of LA were significantly associated with lower risks of total CVD, cardiovascular mortality, and ischemic stroke, with hazard ratios per interquintile range of 0.93 (95% CI, 0.88-0.99), 0.78 (0.70-0.85), and 0.88 (0.79-0.98), respectively, and nonsignificantly with lower coronary heart disease risk (0.94; 0.88-1.00). Relationships were similar for LA evaluated across quintiles. AA levels were not associated with higher risk of cardiovascular outcomes; in a comparison of extreme quintiles, higher levels were associated with lower risk of total CVD (0.92; 0.86-0.99). No consistent heterogeneity by population subgroups was identified in the observed relationships.

CONCLUSIONS

In pooled global analyses, higher in vivo circulating and tissue levels of LA and possibly AA were associated with lower risk of major cardiovascular events. These results support a favorable role for LA in CVD prevention.

HNF1A gene p.I27L is associated with early-onset, maturity-onset diabetes of the young-like diabetes in Turkey

BACKGROUND

The molecular basis of the Turkish population with suspected maturity-onset diabetes of the young (MODY) has not been identified. This is the first study to investigate the association between HNF1A-gene single-nucleotide polymorphisms (SNPs) and having early-onset, MODY-like diabetes mellitus in the Turkish population.

METHODS

All diabetic patients (N = 565) who presented to our clinic between 2012 and 2015 with a clinical suspicion of MODY were included in the study. Analysis of HNF1A, HNFB, HNF4A, GCK gene mutations was performed using real-time polymerase chain reaction sequencing. After genetic analysis, diabetics (n = 46) with HNF1A, HNF1B, HNF4A, GCK gene mutations (diagnosed as MODY) and diabetics (n = 30) with HNF1B, HNF4A, GCK gene SNPs were excluded. Patients with early-onset, MODY-like diabetes (n = 486) and non-diabetic controls (n = 263) were included. Genetic analyses for the HNF1A gene p.S487 N (rs2464196), p.A98V (rs1800574) and p.I27L (rs1169288) SNPs were performed using Sanger-based DNA sequencing among the control group.

RESULTS

p.S487 N and p.A98V was similar between the diabetics and controls in dominant and recessive models with no association (each, p > 0.05). p.I27L GT/TT carriers (GT/TT vs. GG, OR = 1.68, 95% CI: [1. 21-2.13]; p = 0.035) and p.I27L TT carriers had increased risk of having MODY-like diabetes (GT/GG vs. TT, OR = 1.56, 95% CI: [1. 14-2.57]; p = 0.048). Family inheritance of diabetes was significantly more common in patients with the p.I27L TT genotype. The p.I27L SNP was modestly associated with having diabetes after adjusting for body mass index and age (β = 1.45, 95% CI: [1. 2-4.2]; p = 0.036).

CONCLUSIONS

The HNF1A gene p.I27L SNP was modestly associated with having early-onset, MODY-like diabetes in the Turkish population. HNF1A gene p.I27L SNP might contribute to age at diabetes diagnosis and family inheritance.

Does sex hormone-binding globulin cause insulin resistance during pubertal growth?

BACKGROUND

The directional influences between serum sex hormone-binding globulin (SHBG), adiposity and insulin resistance during pubertal growth remain unclear. The aim of this study was to investigate bidirectional associations between SHBG and insulin resistance (HOMA-IR) and adiposity from childhood to early adulthood.

METHODS

Participants were 396 healthy girls measured at baseline (age 11.2 years) and at 1, 2, 4 and 7.5 years. Serum concentrations of estradiol, testosterone and SHBG were determined by ELISA, glucose and insulin by enzymatic photometry, insulin-like growth factor 1 (IGF-1) by time-resolved fluoroimmunoassays, whole-body fat mass by dual-energy X-ray absorptiometry and HOMA-IR were determined by homeostatic model assessment. The associations were examined using cross-lagged path models.

RESULTS

In a cross-lagged path model, SHBG predicted HOMA-IR before menarche β = -0.320 (95% CI: -0.552 to -0.089), P = 0.007, independent of adiposity and IGF-1. After menarche, no directional effect was found between SHBG and insulin resistance or adiposity.

CONCLUSIONS

Our results suggest that in early puberty, decline in SHBG predicts development of insulin resistance, independent of adiposity. However, after menarche, no directional influences between SHBG, adiposity and insulin resistance were found, suggesting that observational associations between SHBG, adiposity and insulin resistance in pubertal children may be subject to confounding. Further research is needed to understand the underlying mechanisms of the associations between SHBG and cardiometabolic risk markers in peripubertal children.