Insulin resistance is associated with nonalcoholic fatty liver disease (NAFLD) and is a major factor in the pathogenesis of type 2 diabetes. The development of hepatic insulin resistance has been ascribed to multiple causes, including inflammation, endoplasmic reticulum (ER) stress, and accumulation of hepatocellular lipids in animal models of NAFLD. However, it is unknown whether these same cellular mechanisms link insulin resistance to hepatic steatosis in humans. To examine the cellular mechanisms that link hepatic steatosis to insulin resistance, we comprehensively assessed each of these pathways by using flash-frozen liver biopsies obtained from 37 obese, nondiabetic individuals and correlating key hepatic and plasma markers of inflammation, ER stress, and lipids with the homeostatic model assessment of insulin resistance index. We found that hepatic diacylglycerol (DAG) content in cytoplasmic lipid droplets was the best predictor of insulin resistance (R = 0.80, P < 0.001), and it was responsible for 64% of the variability in insulin sensitivity. Hepatic DAG content was also strongly correlated with activation of hepatic PKCε (R = 0.67, P < 0.001), which impairs insulin signaling. In contrast, there was no significant association between insulin resistance and other putative lipid metabolites or plasma or hepatic markers of inflammation. ER stress markers were only partly correlated with insulin resistance. In conclusion, these data show that hepatic DAG content in lipid droplets is the best predictor of insulin resistance in humans, and they support the hypothesis that NAFLD-associated hepatic insulin resistance is caused by an increase in hepatic DAG content, which results in activation of PKCε.

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

PurposeGeisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007 Geisinger launched MyCode®, a system-wide biobanking program to link samples and EHR data for broad research use.MethodsPatient-centered input into MyCode® was obtained using participant focus groups. Participation in MyCode® is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR, and, since 2013, the return of clinically actionable results to participants. MyCode® leverages Geisinger’s technology and clinical infrastructure for participant tracking and sample collection.ResultsMyCode® has a consent rate of >85% with more than 90,000 participants currently, with ongoing enrollment of ~4,000 per month. MyCode® samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations.ConclusionThe MyCode® project has created resources that enable a new model for translational research that is faster, more flexible, and more cost effective than traditional clinical research approaches. The new model is scalable, and will increase in value as these resources grow and are adopted across multiple research platforms.

Opioid misuse and dependence among prescription opioid patients in the United States may be higher than expected. A small number of factors, many documented in the medical record, predicted opioid dependence among the out-patients studied. These preliminary findings should be useful in future research efforts.

BACKGROUND Type 2 diabetes (T2D) is a metabolic disease with significant medical complications. Roux-en-Y gastric bypass (RYGB) surgery is one of the few interventions that remit T2D in ~60% of patients. However, there is no accurate method for predicting preoperatively the probability for T2D remission. METHODS A retrospective cohort of 2,300 RYGB patients at Geisinger Clinic was used to identify 690 patients with T2D and complete electronic data. Two additional T2D cohorts (N=276, and N=113) were used for replication at 14 months following RYGB. Kaplan-Meier analysis was used in the primary cohort to create survival curves until remission. A Cox proportional hazards model was used to estimate the hazard ratios on T2D remission. FINDINGS Using 259 preoperative clinical variables, four (use of insulin, age, HbA1c, and type of antidiabetic medication) were sufficient to develop an algorithm that produces a type 2 diabetes remission (DiaRem) score over five years. The DiaRem score spans from 0 to 22 and was divided into five groups corresponding to five probability-ranges for T2D remission: 0–2 (88%–99%), 3–7 (64%–88%), 8–12 (23%–49%), 13–17 (11%–33%), 18–22 (2%–16%). The DiaRem scores in the replication cohorts, as well as under various definitions of diabetes remission, conformed to the DiaRem score of the primary cohort. INTERPRETATION The DiaRem score is a novel preoperative method for predicting the probability (from 2% to 99%) for T2D remission following RYGB surgery. FUNDING This research was supported by the Geisinger Health System and the National Institutes of Health.