Utilization of Positive and Negative Controls to Examine Comorbid Associations in Observational Database Studies.

BACKGROUND: Opportunities to leverage observational data for precision medicine research are hampered by underlying sources of bias and paucity of methods to handle resulting uncertainty. We outline an approach to account for bias in identifying comorbid associations between 2 rare genetic disorders and type 2 diabetes (T2D) by applying a positive and negative control disease paradigm. RESEARCH DESIGN: Association between 10 common and 2 rare genetic disorders [Hereditary Fructose Intolerance (HFI) and α-1 antitrypsin deficiency] and T2D was compared with the association between T2D and 7 negative control diseases with no established relationship with T2D in 4 observational databases. Negative controls were used to estimate how much bias and variance existed in datasets when no effect should be observed. RESULTS: Unadjusted association for common and rare genetic disorders and T2D was positive and variable in magnitude and distribution in all 4 databases. However, association between negative controls and T2D was 200% greater than expected indicating the magnitude and confidence intervals for comorbid associations are sensitive to systematic bias. A meta-analysis using this method demonstrated a significant association between HFI and T2D but not for α-1 antitrypsin deficiency. CONCLUSIONS: For observational studies, when covariate data are limited or ambiguous, positive and negative controls provide a method to account for the broadest level of systematic bias, heterogeneity, and uncertainty. This provides greater confidence in assessing associations between diseases and comorbidities. Using this approach we were able to demonstrate an association between HFI and T2D. Leveraging real-world databases is a promising approach to identify and corroborate potential targets for precision medicine therapies.

Body Mass Index and Risk of Nonalcoholic Fatty Liver Disease: Two Electronic Health Record Prospective Studies.

CONTEXT: The relationship between rising body mass index (BMI) and prospective risk of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) is virtually absent. OBJECTIVE: Determine the extent of the association between BMI and risk of future NAFLD diagnosis, stratifying by sex and diabetes. DESIGN: Two prospective studies using Humedica and Health Improvement Network (THIN) with 1.54 and 4.96 years of follow-up, respectively. SETTING: Electronic health record databases. PARTICIPANTS: Patients with a recorded BMI measurement between 15 and 60 kg/m(2), and smoking status, and 1 year of active status before baseline BMI. Patients with a diagnosis or history of chronic diseases were excluded. INTERVENTIONS: None. MAIN OUTCOME MEASURE: Recorded diagnosis of NAFLD/NASH during follow-up (Humedica International Classification of Diseases, Ninth Revision code 571.8, and read codes for NAFLD and NASH in THIN). RESULTS: Hazard ratios (HRs) were calculated across BMI categories using BMI of 20-22.5 kg/m(2) as the reference category, adjusting for age, sex, and smoking status. Risk of recorded NAFLD/NASH increased linearly with BMI and was approximately 5-fold higher in Humedica (HR = 4.78; 95% confidence interval, 4.17-5.47) and 9-fold higher in THIN (HR = 8.93; 7.11-11.23) at a BMI of 30-32.5 kg/m(2) rising to around 10-fold higher in Humedica (HR = 9.80; 8.49-11.32) and 14-fold higher in THIN (HR = 14.32; 11.04-18.57) in the 37.5- to 40-kg/m(2) BMI category. Risk of NAFLD/NASH was approximately 50% higher in men and approximately double in those with diabetes. CONCLUSIONS: These data quantify the consistent and strong relationships between BMI and prospectively recorded diagnoses of NAFLD/NASH and emphasize the importance of weight reduction strategies for prevention and management of NAFLD.