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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.

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.

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.

Summary Assessment of phylogenetic positions of predicted gene and protein sequences is a routine step in any genome project, useful for validating the species' taxonomic position and for evaluating hypotheses about genome evolution and function. Several recent eukaryotic genome projects have reported multiple gene sequences that were much more similar to homologues in bacteria than to any eukaryotic sequence. In the spirit of the times, horizontal gene transfer from bacteria to eukaryotes has been invoked in some of these cases. Here, we show, using comparative sequence analysis, that some of those bacteria-like genes indeed appear likely to have been horizontally transferred from bacteria to eukaryotes. In other cases, however, the evidence strongly indicates that the eukaryotic DNA sequenced in the genome project contains a sample of non-integrated DNA from the actual bacteria, possibly providing a window into the host microbiome. Recent literature suggests also that common reagents, kits and laboratory equipment may be systematically contaminated with bacterial DNA, which appears to be sampled by metagenome projects non-specifically. We review several bioinformatic criteria that help to distinguish putative horizontal gene transfers from the admixture of genes from autonomously replicating bacteria in their hosts' genome databases or from the reagent contamination.

Assessment of phylogenetic positions of predicted gene and protein sequences is a routine step in any genome project, useful for validating the species' taxonomic position and for evaluating hypotheses about genome evolution and function. Several recent eukaryotic genome projects have reported multiple gene sequences that were much more similar to homologues in bacteria than to any eukaryotic sequence. In the spirit of the times, horizontal gene transfer from bacteria to eukaryotes has been invoked in some of these cases. Here, we show, using comparative sequence analysis, that some of those bacteria-like genes indeed appear likely to have been horizontally transferred from bacteria to eukaryotes. In other cases, however, the evidence strongly indicates that the eukaryotic DNA sequenced in the genome project contains a sample of non-integrated DNA from the actual bacteria, possibly providing a window into the host microbiome. Recent literature suggests also that common reagents, kits and laboratory equipment may be systematically contaminated with bacterial DNA, which appears to be sampled by metagenome projects non-specifically. We review several bioinformatic criteria that help to distinguish putative horizontal gene transfers from the admixture of genes from autonomously replicating bacteria in their hosts' genome databases or from the reagent contamination.