The STRIDE weight loss and lifestyle intervention for individuals taking antipsychotic medications: a randomized trial.

OBJECTIVES: The STRIDE study assessed whether a lifestyle intervention, tailored for individuals with serious mental illnesses, reduced weight and diabetes risk. The authors hypothesized that the STRIDE intervention would be more effective than usual care in reducing weight and improving glucose metabolism. METHOD: The study design was a multisite, parallel two-arm randomized controlled trial in community settings and an integrated health plan. Participants who met inclusion criteria were ≥18 years old, were taking antipsychotic agents for ≥30 days, and had a body mass index ≥27. Exclusions were significant cognitive impairment, pregnancy/breastfeeding, recent psychiatric hospitalization, bariatric surgery, cancer, heart attack, or stroke. The intervention emphasized moderate caloric reduction, the DASH (Dietary Approaches to Stop Hypertension) diet, and physical activity. Blinded staff collected data at baseline, 6 months, and 12 months. RESULTS: Participants (men, N=56; women, N=144; mean age=47.2 years [SD=10.6]) were randomly assigned to usual care (N=96) or a 6-month weekly group intervention plus six monthly maintenance sessions (N=104). A total of 181 participants (90.5%) completed 6-month assessments, and 170 (85%) completed 12-month assessments, without differential attrition. Participants attended 14.5 of 24 sessions over 6 months. Intent-to-treat analyses revealed that intervention participants lost 4.4 kg more than control participants from baseline to 6 months (95% CI=-6.96 kg to -1.78 kg) and 2.6 kg more than control participants from baseline to 12 months (95% CI=-5.14 kg to -0.07 kg). At 12 months, fasting glucose levels in the control group had increased from 106.0 mg/dL to 109.5 mg/dL and decreased in the intervention group from 106.3 mg/dL to 100.4 mg/dL. No serious adverse events were study-related; medical hospitalizations were reduced in the intervention group (6.7%) compared with the control group (18.8%). CONCLUSIONS: Individuals taking antipsychotic medications can lose weight and improve fasting glucose levels. Increasing reach of the intervention is an important future step.

Misclassification in assessment of diabetogenic risk using electronic health records.

PURPOSE: Suspected diabetogenic effects or drug indication may increase testing for diabetes mellitus (DM), resulting in measurement bias when evaluating diabetogenic drug effects. We sought to evaluate the validity of electronic health record data in determining DM risk. METHODS: We used time-dependent Cox proportional hazard models within a retrospective cohort design to assess associations between use of antihypertensives, statins, atypical antipsychotics, and antidepressants, and two endpoints: (i) DM onset defined as fasting blood glucose (BG) ≥126 mg/dl, random BG ≥200 mg/dl, HbA1c ≥7.0%, or antidiabetic drug initiation; and (ii) first negative DM test. We used Poisson regression to assess the influence of these drugs on DM testing rates. Patients aged 35-64 years enrolled in Kaiser Permanente Northwest between 1997 and 2010 entered the cohort at the first negative BG test after ≥6 months without manifest DM. RESULTS: All drug classes showed significant associations not only with DM onset but also with first negative BG test and with DM testing rates. Antipsychotics had the greatest diabetogenic risk (adjusted hazard ratio [HR] = 1.73 [1.44-2.08]), the greatest propensity for a first negative test (adjusted HR = 1.87 [1.74-2.01]), and the highest testing rate (adjusted rate ratio = 1.76 [1.72-1.81]. Although renin-angiotensin system blockers and calcium channel blockers have shown no diabetogenic risk in clinical trials, both were associated with DM (HR = 1.19 [1.12-1.26] and 1.27 [1.17-1.38]), a negative glucose test (1.38 [1.35-1.41] and 1.24 [1.20-1.28]), and increased testing rates (rate ratio = 1.26 [1.24-1.27] and 1.27 [1.25-1.28]). CONCLUSION: Caution should be used when diabetogenic risk is evaluated using data that rely on DM testing in general practice.

Diabetes and asthma case identification, validation, and representativeness when using electronic health data to construct registries for comparative effectiveness and epidemiologic research.

BACKGROUND: Advances in health information technology and widespread use of electronic health data offer new opportunities for development of large scale multisite disease-specific patient registries. Such registries use existing data, can be constructed at relatively low cost, include large numbers of patients, and once created can be used to address many issues with a short time between posing a question and obtaining an answer. Potential applications include comparative effectiveness research, public health surveillance, mapping and improving quality of clinical care, and others. OBJECTIVE AND DISCUSSION: This paper describes selected conceptual and practical challenges related to development of multisite diabetes and asthma registries, including development of case definitions, validation of case identification methods, variation in electronic health data sources; representativeness of registry populations, including the impact of attrition. Specific challenges are illustrated with data from actual registries.

Association of cardiometabolic risk factors and prevalent cardiovascular events.

BACKGROUND: Although cardiovascular disease causes substantial morbidity and mortality, how individual and groups of risk factors contribute to cardiovascular outcomes is incompletely understood. This study evaluated cardiometabolic risk factors and their relationship to prevalent diagnosis of acute myocardial infarction (AMI) and stroke. METHODS: We used retrospective data from 3 integrated health-care systems that systematically collect and store detailed patient-level data. Adult enrollees were eligible for inclusion if they had all of the following clinical measurements: weight, height, blood pressure, high density lipoproteins, triglycerides, and fasting blood glucose or evidence of diabetes from July 1, 2003, to June 30, 2005. We used National Cholesterol Education Program Adult Treatment Panel III guidelines to determine qualifying levels for cardiometabolic risk factors. RESULTS: A total of 170,648 persons met the inclusion/exclusion criteria; 11,757 had no qualifying risk factors, 25,684 had 1, 38,176 had 2, and 95,031 had 3 or more risk factors. Compared to those without risk factors, persons with any 1 risk factor were 2.21 (95% confidence interval [CI], 1.78-2.74) times more likely to have had a diagnosis of AMI or stroke. The risk increased to 2.79 (95% CI, 2.26-3.42) for persons with 2, 3.45 (95% CI, 2.80-4.24) for persons with 3, 4.35 (95% CI, 3.54-5.35) for persons with 4, and 5.73 (95% CI, 4.65-7.07) for persons with 5 risk factors. The highest risk was conferred by having the combination of risk factors of diabetes, hypertension, and dyslipidemia, with or without weight risk. CONCLUSIONS: This study demonstrates a direct association between an increasing number of cardiometabolic risk factors and prevalent diagnosis of AMI and stroke. The combination of risk factors conferring the highest risk was diabetes, hypertension, and dyslipidemia.