ObjectivesObesity is a known risk factor for type 2 diabetes (T2D). We conducted a case–control study to assess the association between body mass index (BMI) and the risk of being diagnosed with T2D in the United States.MethodsWe selected adults (≥ 18 years old) who were diagnosed with T2D (defined by ICD-9-CM diagnosis codes or use of anti-diabetic medications) between January 2004 and October 2011 (“cases”) from an electronic health records database provided by an integrated health system in the Middle Atlantic region. Twice as many individuals enrolled in the health system without a T2D diagnosis during the study period (“controls”) were selected based on age, sex, history of cardiac comorbidities or hyperinflammatory state (defined by C-reactive protein and erythrocyte sedimentation rate), and use of psychiatric or beta blocker medications. BMI was measured during one year prior to the first observed T2D diagnosis (for cases) or a randomly assigned date (for controls); individuals with no BMI measure or BMI < 18.5 kg/m2 were excluded. We assessed the impact of increased BMI (overweight: 25–29.9 kg/m2; Obesity Class I: 30–34.9 kg/m2; Obesity Class II: 35–39.9 kg/m2; Obesity Class III: ≥40 kg/m2), relative to normal BMI (18.5–24.9 kg/m2), on a T2D diagnosis using odds ratios (OR) and relative risks (RR) estimated from multiple logistic regression results.ResultsWe included 12,179 cases (mean age: 55, 43% male) and 25,177 controls (mean age: 56, 45% male). We found a positive association between BMI and the risk of a T2D diagnosis. The strength of this association increased with BMI category (RR [95% confidence interval]: overweight, 1.5 [1.4–1.6]; Obesity Class I, 2.5 [2.3–2.6]; Obesity Class II, 3.6 [3.4–3.8]; Obesity Class III, 5.1 [4.7–5.5]).ConclusionsBMI is strongly and independently associated with the risk of being diagnosed with T2D. The incremental association of BMI category on the risk of T2D is stronger for people with a higher BMI relative to people with a lower BMI.

BackgroundNonadherence to antipsychotic treatment increases the likelihood of relapse and progressive symptomatology in patients with schizophrenia. Atypical long-acting injectables, including paliperidone palmitate (PP), may increase adherence and improve symptoms. This study compared and assessed predictors of treatment patterns and symptom remission among schizophrenia patients treated with PP versus atypical oral antipsychotic therapy (OAT) in community behavioral health organizations (CBHOs).MethodsThis retrospective cohort analysis evaluated 763 patients with schizophrenia and new (PP-N; N = 174) or continuing (PP-C; N = 308) users of PP, or new users of OAT (N = 281) at enrollment in the REACH-OUT study (2010–2013). Treatment outcomes assessed at 1 year were discontinuation, and adherence, measured by proportion of days covered (PDC) or medication possession ratio (MPR). Remission status was assessed using the Structured Clinical Interview for Symptoms of Remission (SCI-SR). A machine learning platform, Reverse Engineering and Forward Simulation (REFS™), was used to identify predictors of study outcomes. Multivariate Cox and generalized linear regressions estimated the adjusted hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals.ResultsAmong PP-N users, 27% discontinued their initial treatment regimen versus 51% (p < 0.001) of OAT users. PP-N (vs OAT; HR = 0.49 [0.31–0.76]) users and males (HR = 0.65 [0.46–0.92]) had significantly lower rates of discontinuation. Relative to OAT, PP-N had a 36% [31%–42%] higher MPR and a 10-fold increased achievement of PDC ≥80% (OR = 10.46 [5.72–19.76]). PP users were significantly more likely to achieve remission in follow-up (PP-N vs OAT: OR = 2.65 [1.39–5.05]; PP-C vs OAT: OR = 1.83 [1.03–3.25]).ConclusionsRelative to OAT, PP was associated with improved adherence, less frequent treatment discontinuation, and improved symptom remission in this CBHO study population.

Introduction: To identify predictors of hypoglycemia and five other clinical and economic outcomes among treated patients with type 2 diabetes (T2D) using machine learning and structured data from a large, geographically diverse administrative claims database. Methods: A retrospective cohort study design was applied to Optum Clinformatics claims data indexed on first antidiabetic prescription date. A hypothesis-free, Bayesian machine learning analytics platform (GNS Healthcare REFS TM : Reverse Engineering and Forward Simulation) was used to build ensembles of generalized linear models to predict six outcomes defined in patients' 1-year post-index claims history, including hypoglycemia, antidiabetic class persistence, glycated hemoglobin (HbA1c) target Enhanced Digital Features To view enhanced digital features for this article go to https://doi.org/10.6084/ m9.figshare.11527902.

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