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Obesity and type 2 diabetes (T2D) are associated with increased risk of pancreatic cancer. Here we assessed the relationship between pancreatic cancer and two distinct measures of obesity, namely total adiposity, using BMI, versus abdominal adiposity, using BMI adjusted waist-to-hip ratio (WHRadjBMI) by utilising polygenic scores (PGS) and Mendelian randomisation (MR) analyses. We constructed z-score weighted PGS for BMI and WHRadjBMI using publicly available data and tested for their association with pancreatic cancer defined in UK biobank (UKBB). Using publicly available summary statistics, we then performed bi-directional MR analyses between the two obesity traits and pancreatic cancer. PGSBMI was significantly (multiple testing-corrected) associated with pancreatic cancer (OR[95%CI] = 1.0804[1.025–1.14], P = 0.0037). The significance of association declined after T2D adjustment (OR[95%CI] = 1.073[1.018–1.13], P = 0.00904). PGSWHRadjBMI association with pancreatic cancer was at the margin of statistical significance (OR[95%CI] = 1.047[0.99–1.104], P = 0.086). T2D adjustment effectively lost any suggestive association of PGSWHRadjBMI with pancreatic cancer (OR[95%CI] = 1.039[0.99–1.097], P = 0.14). MR analyses showed a nominally significant causal effect of WHRadjBMI on pancreatic cancer (OR[95%CI] = 1.00095[1.00011–1.0018], P = 0.027) but not for BMI on pancreatic cancer. Overall, we show that abdominal adiposity measured using WHRadjBMI, may be a more important causal risk factor for pancreatic cancer compared to total adiposity, with T2D being a potential driver of this relationship.

OBJECTIVEDepression is a common co-morbidity of type 2 diabetes. However, the causality and underlying mechanisms remain unclear.RESEARCH DESIGN AND METHODSWe applied bi-directional Mendelian randomization (MR) to assess causality between type 2 diabetes and self-reported depression. Using the UK biobank, we performed 1) GWAS, separately, and 2) multi-phenotype GWAS (MP-GWAS) of type 2 diabetes (cases=19,344, controls=463,641) and depression, using two depression definitions–clinically diagnosed major depressive disorder (MDD, cases=5,262, controls=86,275) and self-reported depressive symptoms (PHQ-9, n=153,079). The FinnGen study was used for replication for MDD (n=23,424) and type 2 diabetes (n=32,469). Based on the results, we analyzed expression quantitative trait loci (eQTL) data from public databases to identify target genes in relevant tissues.RESULTSMR demonstrated a significant causal effect of depression on type 2 diabetes (OR=1.18[1.06-1.32], p=0.0024), but not in the reverse direction. GWAS of type 2 diabetes and depressive symptoms did not identify any shared loci between them, whereas MP-GWAS identified seven shared loci mapped toTCF7L2, CDKAL1, IGF2BP2, SPRY2, CCND2-AS1, IRS1, CDKN2B-AS1. MDD did not yield genome-wide significant loci in either GWAS or MP-GWAS. We found that most MP-GWASlocihad an eQTL, including SNPs implicating the cell cycle geneCCND2in pancreatic islets and brain, and key insulin signaling geneIRS1in adipose tissue, suggesting a multi-tissue and pleiotropic underlying mechanism.CONCLUSIONOur study reveals the complexity in the depression-diabetes relationship and our results have important implications for a more efficient prevention of type 2 diabetes from early adulthood when depressive symptoms usually occur.

Obesity and type 2 diabetes (T2D) are associated with increased risk of pancreatic cancer. Here we assessed the relationship between pancreatic cancer and two distinct measures of obesity, namely total adiposity, using BMI, versus abdominal, using BMI adjusted waist-to-hip ratio (WHRadjBMI) using polygenic scores (PGS) and Mendelian randomization (MR). We constructed z-score weighted PGS for BMI and WHRadjBMI using publicly available data and tested for their association with pancreatic cancer defined in UK biobank (UKBB). Using publicly available summary statistics we then performed bi-directional MR analyses between the two obesity traits and pancreatic cancer. PGSBMI was significantly (multiple testing-corrected) associated with pancreatic cancer (OR[95%CI] = 1.077[1.023–1.13], P = 0.0052). The significance of association declined after T2D adjustment (OR[95%CI] = 1.069[1.015–1.079], P = 0.012). MR analyses showed a nominally significant causal effect of WHRadjBMI on pancreatic cancer (OR[95%CI] = 1.00095[1.00011–1.0018], P = 0.027). Overall, we show that abdominal adiposity measured using WHRadjBMI, is a more important causal risk factor for pancreatic cancer compared to total adiposity, with T2D being a potential driver of this relationship.

Polycystic ovary syndrome (PCOS) is a very common endocrine condition in women in India. Gut microbiome alterations were shown to be involved in PCOS, yet it is remarkably understudied in Indian women who have a higher incidence of PCOS as compared to other ethnic populations. During the regional PCOS screening program among young women, we recruited 19 drug naive women with PCOS and 20 control women at the Sher-i-Kashmir Institute of Medical Sciences, Kashmir, North India. We profiled the gut microbiome in faecal samples by 16S rRNA sequencing and included 40/58 operational taxonomic units (OTUs) detected in at least 1/3 of the subjects with relative abundance (RA) ≥ 0.1%. We compared the RAs at a family/genus level in PCOS/non-PCOS groups and their correlation with 33 metabolic and hormonal factors, and corrected for multiple testing, while taking the variation in day of menstrual cycle at sample collection, age and BMI into account. Five genera were significantly enriched in PCOS cases: Sarcina, Megasphaera, and previously reported for PCOS Bifidobacterium, Collinsella and Paraprevotella confirmed by different statistical models. At the family level, the relative abundance of Bifidobacteriaceae was enriched, whereas Peptococcaceae was decreased among cases. We observed increased relative abundance of Collinsella and Paraprevotella with higher fasting blood glucose levels, and Paraprevotella and Alkalibacterium with larger hip, waist circumference, weight, and Peptococcaceae with lower prolactin levels. We also detected a novel association between Eubacterium and follicle-stimulating hormone levels and between Bifidobacterium and alkaline phosphatase, independently of the BMI of the participants. Our report supports that there is a relationship between gut microbiome composition and PCOS with links to specific reproductive health metabolic and hormonal predictors in Indian women.

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, PFDR = 3.40 × 10−5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10−4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10−5), and CCL2 rs1024611 (OR = 1.38, PFDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7–87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10−6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = −17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

Background Genome-wide association studies have captured a large proportion of genetic variation related to type 1 diabetes mellitus (T1D). However, most of these studies are performed in populations of European ancestry and therefore the disease risk estimations can be inaccurate when extrapolated to other world populations. Methods We conducted a case-control study in 1866 individuals from the three major populations of the Republic of Bashkortostan (Russians, Tatars, and Bashkirs) in Russian Federation, using single-locus and multilocus approach to identify genetic predictors of T1D. Results We found that LTA rs909253 and TNF rs1800629 polymorphisms were associated with T1D in the group of Tatars. Meta-analysis of the association study results in the three ethnic groups has confirmed the association between the T1D risk and LTA rs909253 genetic variant. LTA rs909253 and TNF rs1800629 loci were also featured in combinations most significantly associated with T1D. Conclusion Our findings suggest that LTA rs909253 and TNF rs1800629 polymorphisms are associated with the risk of T1D both independently and in combination with polymorphic markers in other inflammatory genes, and the analysis of multi-allelic combinations provides valuable insight in the study of polygenic traits.

Epidemic obesity is the most important risk factor for prediabetes and type 2 diabetes (T2D) in youth as it is in adults. Obesity shares pathophysiological mechanisms with T2D and is likely to share part of the genetic background. We aimed to test if weighted genetic risk scores (GRSs) for T2D, fasting glucose (FG) and fasting insulin (FI) predict glycaemic traits and if there is a causal relationship between obesity and impaired glucose metabolism in children and adolescents. Genotyping of 42 SNPs established by genome-wide association studies for T2D, FG and FI was performed in 1660 Italian youths aged between 2 and 19 years. We defined GRS for T2D, FG and FI and tested their effects on glycaemic traits, including FG, FI, indices of insulin resistance/beta cell function and body mass index (BMI). We evaluated causal relationships between obesity and FG/FI using one-sample Mendelian randomization analyses in both directions. GRS-FG was associated with FG (beta = 0.075 mmol/l, SE = 0.011, P = 1.58 × 10−11) and beta cell function (beta = −0.041, SE = 0.0090 P = 5.13 × 10−6). GRS-T2D also demonstrated an association with beta cell function (beta = −0.020, SE = 0.021 P = 0.030). We detected a causal effect of increased BMI on levels of FI in Italian youths (beta = 0.31 ln (pmol/l), 95%CI [0.078, 0.54], P = 0.0085), while there was no effect of FG/FI levels on BMI. Our results demonstrate that the glycaemic and T2D risk genetic variants contribute to higher FG and FI levels and decreased beta cell function in children and adolescents. The causal effects of adiposity on increased insulin resistance are detectable from childhood age.

Background\ud The prevalence of depression is higher among those with diabetes than in the general population. The Patient Health Questionnaire (PHQ-9) is commonly used to assess depression in people with diabetes, but measurement invariance of the PHQ-9 across groups of people with and without diabetes has not yet been investigated.\ud \ud Methods\ud Data from three independent cohorts from the USA (n=1,886 with diabetes, n=4,153 without diabetes), Quebec, Canada (n= 800 with diabetes, n= 2,411 without diabetes), and the UK (n=4,981 with diabetes, n=145,570 without diabetes), were used to examine measurement invariance between adults with and without diabetes. A series of multiple group confirmatory factor analyses were performed, with increasingly stringent model constraints applied to assess configural, equal thresholds, and equal thresholds and loadings invariance, respectively. One-factor and two-factor (somatic and cognitive-affective items) models were examined.\ud \ud Results\ud Results demonstrated that the most stringent models, testing equal loadings and thresholds, had satisfactory model fit in the three cohorts for one-factor models (RMSEA = .063 or below and CFI = .978 or above) and two-factor models (RMSEA = .042 or below and CFI = .989 or above).\ud \ud Limitations\ud Data were from Western countries only and we could not distinguish between type of diabetes.\ud \ud Conclusions\ud Results provide support for measurement invariance between groups of people with and without diabetes, using either a one-factor or a two-factor model. While the two-factor solution has a slightly better fit, the one-factor solution is more parsimonious. Depending on research or clinical needs, both factor structures can be used.

Genome-wide association studies (GWAS) have contributed to a major leap forward in the unravelling of the genetic basis of complex disorders, and identified over 900 genetic loci for blood pressure (BP) regulation, 160 for coronary artery disease (CAD) and 140 for type 2 diabetes (T2D). The ubiquitousness of genetic pleiotropy and the fact that cardiovascular diseases, T2D, and other metabolic disorders share a number of risk factors, suggest that these conditions may have common underlying molecular mechanisms. We aimed to dissect shared genetic factors contributing to the comorbidity of these diseases using multi-phenotype approach. Using summary statistics from published GWAS available in public domain, including those from GWAS consortia DIAGRAM/DIAMANTE (T2D), MAGIC/ENGAGE (glycaemic), ICBP (blood pressure, BP), CARDIOGRAM (cardiovascular), GLGC/ENGAGE (lipids), GIANT (obesity), we analysed 3429 single nucleotide variants (SNVs) associated with 63 cardiometabolic phenotypes (defined as related to T2D, cardiovascular and metabolic disorders). We identified 301 unique association signals, and found 426 CAD/BP SNVs overlapping with 1194 SNVs reported for other phenotypes and located within 100kb from one another. Further analysis revealed 49 linkage patterns for cardiometabolic phenotypes (D'>0.9, r2>0.8). Specifically, nine SNVs in SH2B3-ATXN2 locus are associated with CAD, systolic and diastolic BP, stroke, ICAM-1 and diabetes; three SNVs in ATP5G1- UBE2Z influence CAD risk, T2D, and anthropometric traits; nine SNVs in TCF7L2 are associated with pulse pressure (PP), body mass index (BMI), glycaemic traits and T2D risk; four SNVs in ADAMTS9 and three in BCL2 are associated with BP traits, T2D, and abdominal obesity; twelve SNVs in IRS1 gene are related to hypertension, T2D risk, glycaemic and lipid traits; ten SNVs in MTNR1B affect PP, T2D susceptibility, and glycaemic traits; six SNVs in KLF14 influence systolic BP, T2D, and high density lipoproteins (HDL) level; nine SNVs in ABO affect CAD, T2D, and stroke susceptibility, as well as lipids and ICAM-1 level; eight SNVs in CDKAL1 are associated with systolic BP, increased T2D risk, and glycaemic traits; two in SLC39A8 affect BP traits, lipid levels, and BMI. Our results highlight the pathways that influence multiple cardiometabolic phenotypes, including circadian rhythm regulation (MTNR1B), pancreatic islet function and insulin signalling (CDKAL1 and IRS1), appetite control and nutritional regulation (NUCB2 and ATXN2), immune and inflammatory signaling (SH2B3 and SLC39A8), cell cycle regulation and apoptosis (UBE2Z and BCL2), angiogenesis (ADAMTS9), suggesting that similar disease-driving biological processes are implicated in the pathogenesis of these diseases.

Polycystic ovary syndrome (PCOS) is a common endocrine condition in women of reproductive age understudied in non-European populations. In India, PCOS affects the life of up to 19.4 million women of age 14-25 years. Gut microbiome composition might contribute to PCOS susceptibility. We profiled the microbiome in DNA isolated from faecal samples by 16S rRNA sequencing in 19/20 women with/without PCOS from Kashmir, India. We assigned genera to sequenced species with an average 121k reads depth and included bacteria detected in at least 1/3 of the subjects or with average relative abundance ≥0.1%. We compared the relative abundances of 40/58 operational taxonomic units in family/genus level between cases and controls, and in relation to 33 hormonal and metabolic factors, by multivariate analyses adjusted for confounders, and corrected for multiple testing. Seven genera were significantly enriched in PCOS cases:Sarcina, AlkalibacteriumandMegasphaera, and previously reported for PCOSBifidobacterium, Collinsella, ParaprevotellaandLactobacillus. We identified significantly increased relative abundance ofBifidobacteriaceae(median 6.07% vs. 2.77%) andAerococcaceae(0.03% vs. 0.004%), whereas we detected lower relative abundancePeptococcaceae(0.16% vs. 0.25%) in PCOS cases. For the first time, we identified a significant direct association between butyrate producingEubacteriumand follicle-stimulating hormone levels. We observed increased relative abundance ofCollinsellaandParaprevotellawith higher fasting blood glucose levels, andParaprevotellaandAlkalibacteriumwith larger hip and waist circumference, and weight. We show a relationship between gut microbiome composition and PCOS linking it to specific reproductive health metabolic and hormonal predictors in Indian women.

BackgroundMulti-phenotype genome-wide association studies (MP-GWAS) of correlated traits have greater power to detect genotype–phenotype associations than single-trait GWAS. However, no multi-phenotype analysis method exists for epigenome-wide association studies (EWAS).ResultsWe extended the SCOPA approach developed by us to “methylSCOPA” software in C++ by ‘reversely’ regressing DNA hyper/hypo-methylation information on a linear combination of phenotypes. We evaluated two models of association between DNA methylation and fasting glucose (FG) and insulin (FI) levels: Model 1, including FG, FI, and three measured potential confounders (body mass index [BMI], fasting serum triglyceride levels [TG], and waist/hip ratio [WHR]), and Model 2, including FG and FI corrected for the effects of BMI, TG, and WHR. Both models were additionally corrected for participant sex and smoking status (current/ever/never). We meta-analyzed the cohort-specific MP-EWAS results with our novel software META-methylSCOPA, mapped genomic locations to CGCh37/hg19, and adoptedP−7to denote epigenome-wide significance. We used the Illumina Infinium HumanMethylation450K BeadChip array data from the Northern Finland Birth Cohorts (NFBC) 1966/1986. We quality-controlled the data, regressed out the effects of measured potential confounders, and normalized the methylation signal intensity and FI data. The MP-EWAS included data for 643/457 individuals from NFBC1966 and NFBC1986, respectively (total N=1,100).In Model 1, we detected epigenome-wide significant association in the MP-EWAS meta-analysis at cg13708645 (chr12:121,974,305;P=1.2×10−8) withinKDM2Bgene. Single-trait effects withinKDM2Bwere on FI, BMI, and WHR. Model with effect on BMI and WHR showed the strongest association at this locus, while effect on FI in single-phenotype analysis was driven by the effect of adiposity. In Model 2, the strongest association was at cg05063096 (chr3:143,689,810;P=2.3×10−7) annotated toC3orf58with strongest effect on FI in single-trait analysis and multi-phenotype effect on FI and WHI within Model 1.We characterized the effects of established EWAS loci for diabetes and its risk factors and detected suggestive (pPHGDH, TXNIP, SLC7A11, CPT1A, MYO5CandABCG1, through the dissection of the multi-phenotype effects in Model 1.ConclusionsWe implemented MP-EWAS in methylSCOPA and demonstrated its enhanced power over single-trait EWAS for correlated phenotypes in large-scale data.

PurposeHomeostasis Model Assessment of β-cell function and Insulin Resistance (HOMA-B/-IR) indices are informative about the pathophysiological processes underlying type 2 diabetes (T2D). Data on both fasting glucose and insulin levels are required to calculate HOMA-B/-IR, leading to underpowered Genome-Wide Association studies (GWAS) of these traits.MethodsWe overcame such power loss issues by implementing Genome-Wide Inferred Statistics (GWIS) approach and subsequent dense genome-wide imputation of HOMA-B/-IR summary statistics with SS-imp to 1000 Genomes project variant density, reaching an analytical sample size of 75,240 European individuals without diabetes. We dissected mechanistic heterogeneity of glycaemic trait/T2D loci effects on HOMA-B/-IR and their relationships with 36 inflammatory and cardiometabolic phenotypes.ResultsWe identified one/three novel HOMA-B (FOXA2)/HOMA-IR (LYPLAL1, PER4,PPP1R3B) loci. We detected novel strong genetic correlations between HOMA-IR/-B and Plasminogen Activator Inhibitor 1 (PAI-1, rg=0.92/0.78, P=2.13×10-4/2.54×10-3). HOMA-IR/-B were also correlated with C-Reactive Protein (rg=0.33/0.28, P=4.67×10-3/3.65×10-3). HOMA-IR was additionally correlated with T2D (rg=0.56, P=2.31×10-9), glycated haemoglobin (rg=0.28, P=0.024) and adiponectin (rg=-0.30, P=0.012).ConclusionUsing innovative GWIS approach for composite phenotypes we report novel evidence for genetic relationships between fasting indices of insulin resistance/beta-cell function and inflammatory markers, providing further support for the role of inflammation in T2D pathogenesis.

Type 2 diabetes (T2D) is a global health burden that will benefit from personalised risk prediction and targeted prevention programmes. Omics data have enabled more detailed risk prediction; however, most studies have focussed on directly on the ability of DNA variants predicting T2D onset with less attention given to epigenetic regulation and glycaemic trait variability. By applying machine learning to the longitudinal Northern Finland Birth Cohort 1966 (NFBC 1966) at 31 (T1) and 46 (T2) years old, we predicted fasting glucose (FG) and insulin (FI), glycated haemoglobin (HbA1c) and 2-hour glucose and insulin from oral glucose tolerance test (2hGlu, 2hIns) at T2 in 513 individuals from 1,001 variables at T1 and T2, including anthropometric, metabolic, metabolomic and epigenetic variables. We further tested whether the information obtained by the machine learning models in NFBC could be used to predict glycaemic traits in the independent French study with 48 matching predictors (DESIR, N=769, age range 30-65 years at recruitment, interval between data collections: 9 years). In this study, FG and FI were best predicted, with average R2 values of 0.38 and 0.53. Sex, branched-chain and aromatic amino acids, HDL-cholesterol, glycerol, ketone bodies, blood pressure at T2 and measurements of adiposity at T1, as well as multiple methylation marks at both time points were amongst the top predictors. In the validation analysis, we reached R2 values of 0.41/0.55 for FG/FI when trained and tested in NFBC1966 and 0.17/0.30 when trained in NFBC1966 and tested in DESIR. We identified clinically relevant sets of predictors from a large multi-omics dataset and highlighted the potential of methylation markers and longitudinal changes in prediction.

Background. Obesity mainly caused by overeating is one of the most important risk factor of type 2 diabetes mellitus (T2DM). The aim of the present study was to investigate eating behavior types of patients with T2DM and obesity. Methods. We compared samples of patients with T2DM from the UK ( N =113, 64 men) and Russia ( N =200; 61 men) whom we asked to complete the Dutch Eating Behaviour Questionnaire (DEBQ). Mean age was 61.2±9.7 years old (range 39-85 years). Physical assessment included height, weight, body mass index and obesity degree according World Health Organization. Statistical analyses were performed using SPSS 21.0. Means (SD), 95% confidence interval (CI) were calculated. Results. One hundred seventy-nine patients were classified as obese (range 30.0 – 53.9). Mean values for restraint, emotional and external eating for this obese group were 2.8±0.07 (95% CI 2.7-3.0), 3.3±0.08 (95% CI 3.2-3.5) and 3.0±0.05 (95% CI 2.9-3.2), respectively. Russian patients showed higher levels of external eating ( p