Genetic Variants Associated With Glycine Metabolism and Their Role in Insulin Sensitivity and Type 2 Diabetes

Circulating metabolites associated with insulin sensitivity may represent useful biomarkers, but their causal role in insulin sensitivity and diabetes is less certain. We previously identified novel metabolites correlated with insulin sensitivity measured by the hyperinsulinemic-euglycemic clamp. The top-ranking metabolites were in the glutathione and glycine biosynthesis pathways. We aimed to identify common genetic variants associated with metabolites in these pathways and test their role in insulin sensitivity and type 2 diabetes. With 1,004 nondiabetic individuals from the RISC study, we performed a genome-wide association study (GWAS) of 14 insulin sensitivity-related metabolites and one metabolite ratio. We replicated our results in the Botnia study (n = 342). We assessed the association of these variants with diabetes-related traits in GWAS meta-analyses (GENESIS [including RISC, EUGENE2, and Stanford], MAGIC, and DIAGRAM). We identified four associations with three metabolites-glycine (rs715 at CPS1), serine (rs478093 at PHGDH), and betaine (rs499368 at SLC6A12; rs17823642 at BHMT)-and one association signal with glycine-to-serine ratio (rs1107366 at ALDH1L1). There was no robust evidence for association between these variants and insulin resistance or diabetes. Genetic variants associated with genes in the glycine biosynthesis pathways do not provide consistent evidence for a role of glycine in diabetes-related traits

Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity.

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp- and oral glucose tolerance test-based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], -0.03 [-0.04, -0.01]; P = 0.004). This score was associated with lower BMI (-0.01 [-0.01, -0.0]; P = 0.02) and gluteofemoral fat mass (-0.03 [-0.05, -0.02; P = 1.4 × 10(-6)) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.The MRC-Ely Study was funded by the Medical Research Council (MC_U106179471) and Diabetes UK. We are grateful to all the volunteers, and to the staff of St. Mary’s Street Surgery, Ely and the study team. The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. DBS and RKS are funded by the Wellcome Trust, the U.K. NIHR Cambridge Biomedical Research Centre and the MRC Centre for Obesity and Related Metabolic Disease. Genotyping in ULSAM was performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se), which is supported by Uppsala University, Uppsala University Hospital, Science for Life Laboratory - Uppsala and the Swedish Research Council (Contracts 80576801 and 70374401). The RISC Study was supported by European Union grant QLG1-CT-2001-01252 and AstraZeneca. The RISC Study Project Management Board: B Balkau, F Bonnet, SW Coppack, JM Dekker, E Ferrannini, A Golay, A Mari, A Natali, J Petrie, M Walker. We thank all EPIC participants and staff for their contribution to the study. We thank the lab team at the MRC Epidemiology Unit for sample management and Nicola Kerrison of the MRC Epidemiology Unit for data management. Funding for the EPIC-InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197).In addition, EPIC-InterAct investigators acknowledge funding from the following agencies: PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung Foundation; LCG: Swedish Research Council; NS: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (Nº 6236); LA: We thank the participants of the Spanish EPIC cohort for their contribution to the study as well as to the team of trained nurses who participated in the recruitment; RK: German Cancer Aid, German Ministry of Research (BMBF); TJK: Cancer Research UK; PMN: Swedish Research Council; KO: Danish Cancer Society; SP: Compagnia di San Paolo; JRQ: Asturias Regional Government; OR: The Västerboten County Council; AMWS and DLvdA: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; IB: Wellcome Trust grant 098051 and United Kingdom NIHR Cambridge Biomedical Research Centre; MIM: InterAct, Wellcome Trust (083270/Z/07/Z), MRC (G0601261); ER: Imperial College Biomedical Research.This is the author accepted manuscript. The final version is available from the American Diabetes Association via http://dx.doi.org/10.2337/db14-031

icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.ISSN:1424-7860ISSN:1424-399

Radiologically Isolated Syndrome: 5-Year Risk for an Initial Clinical Event

OBJECTIVE: To report the 5-year risk and to identify risk factors for the development of a seminal acute or progressive clinical event in a multi-national cohort of asymptomatic subjects meeting 2009 RIS Criteria. METHODS: Retrospectively identified RIS subjects from 22 databases within 5 countries were evaluated. Time to the first clinical event related to demyelination (acute or 12-month progression of neurological deficits) was compared across different groups by univariate and multivariate analyses utilizing a Cox regression model. RESULTS: Data were available in 451 RIS subjects (F: 354 (78.5%)). The mean age at from the time of the first brain MRI revealing anomalies suggestive of MS was 37.2 years (y) (median: 37.1 y, range: 11-74 y) with mean clinical follow-up time of 4.4 y (median: 2.8 y, range: 0.01-21.1 y). Clinical events were identified in 34% (standard error=3%) of individuals within a 5-year period from the first brain MRI study. Of those who developed symptoms, 9.6% fulfilled criteria for primary progressive MS. In the multivariate model, age [hazard ratio (HR): 0.98 (95% CI: 0.96-0.99); p=0.03], sex (male) [HR: 1.93 (1.24-2.99); p=0.004], and lesions within the cervical or thoracic spinal cord [HR: 3.08 (2.06-4.62); p=<0.001] were identified as significant predictors for the development of a first clinical event. INTERPRETATION: These data provide supportive evidence that a meaningful number of RIS subjects evolve to a first clinical symptom. An age <37 y, male sex, and spinal cord involvement appear to be the most important independent predictors of symptom onset