Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes.

The number of people affected by Type 2 diabetes mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. Given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs. 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than 5 years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM. We confirmed the causality of such association for leucine by 2-sample Mendelian randomisation (MR) based on independent data. Our MR approach further identified new metabolites potentially playing a causal role on T2D, including betaine, lysine and mannose. Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. In addition, our study revealed a reverse causal effect of metabolites such as glutamic acid and alanine. Collectively, these findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers

The cost of a single concussion in American high school football: A retrospective cohort study

Aim: The potential financial burden of American football-related concussions (FRC) is unknown. Our objective was to describe the healthcare costs associated with an FRC and determine factors associated with increased costs. Methodology/results: A retrospective cohort study of concussed high school football players presenting between November 2017 and March 2020 was undertaken; 144 male high school football players were included. Total costs were about $115,000, for an average direct healthcare cost of$800.10/concussion. Visiting the emergency department (β = 502.29, 95% CI: 105.79-898.61; p = 0.01), the initial post-concussion symptom scale score (β = 0.39, 95% CI: 0.11-0.66; p = 0.01) and a post-concussion syndrome diagnosis (β = 670.37, 95% CI: 98.96-1241.79; p = 0.02) were each independently associated with total costs. Conclusion: A granular understanding of cost-driving factors associated with FRC is the first step in understanding the cost-effectiveness of prevention and treatment methods

KAT2B Is Required for Pancreatic Beta Cell Adaptation to Metabolic Stress by Controlling the Unfolded Protein Response.

The endoplasmic reticulum (ER) unfolded protein response (UPR(er)) pathway plays an important role in helping pancreatic β cells to adapt their cellular responses to environmental cues and metabolic stress. Although altered UPR(er) gene expression appears in rodent and human type 2 diabetic (T2D) islets, the underlying molecular mechanisms remain unknown. We show here that germline and β cell-specific disruption of the lysine acetyltransferase 2B (Kat2b) gene in mice leads to impaired insulin secretion and glucose intolerance. Genome-wide analysis of Kat2b-regulated genes and functional assays reveal a critical role for Kat2b in maintaining UPR(er) gene expression and subsequent β cell function. Importantly, Kat2b expression is decreased in mouse and human diabetic β cells and correlates with UPR(er) gene expression in normal human islets. In conclusion, Kat2b is a crucial transcriptional regulator for adaptive β cell function during metabolic stress by controlling UPR(er) and represents a promising target for T2D prevention and treatment

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

This is the final version. Available from Public Library of Science via the DOI in this record. Data Availability: The research utilised data from the UK Biobank resource carried out under UK Biobank application number 9072. UK Biobank protocols were approved by the National Research Ethics Service Committee. Individual-level data cannot be shared publicly because of data access policies of the UK Biobank. Data are available from the UK Biobank for researchers who meet the criteria for access to datasets to UK Biobank (http://www.ukbiobank.ac.uk). The weights used to calculate the polygenic score for height is available in Table C in S1 Data. The weights used to calculate the polygenic score for LDL-cholesterol, calculated in a meta-analysis excluding UK Biobank, are available from the Global Lipids Genetics Consortium at https://csg.sph.umich.edu/willer/public/glgc-lipids2021/.Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.Innovative Medicines Initiative 2 Joint UndertakingAcademy of Medical SciencesMedical Research Council (MRC)Australian Research Council (ARC