Predictive and Explanatory Factors of Change in HbA1c in a 24-Week Observational Study of 66,726 People With Type 2 Diabetes Starting Insulin Analogs

Abstract

OBJECTIVE Individualization of therapy choices requires the prediction of likely response. Predictor and explanatory factors of change in HbA1c were studied using data from a large observational study of starting insulin analog therapy (the A1chieve study). RESEARCH DESIGN AND METHODS Univariate analyses were performed for insulin-naive people and prior insulin users in the A1chieve study. Statistically significant factors were carried forward to baseline factor–only multivariate analyses (“predictor” analysis), and separately using all significant factors (“explanatory” analysis). Power was considered in terms of the variance explained. RESULTS Geographical region, baseline HbA1c level, lipid levels, and baseline insulin dose were the most powerful predictors of HbA1c change (mean change −2.1% [−23 mmol/mol]) observed in the univariate analysis (r2 > 0.010, P < 0.001). However, although the predictor and explanatory multivariate models explained 62–82% of the variance in HbA1c change, this was mainly associated with baseline HbA1c (r2 = 0.544–0.701) and region (r2 = 0.014–0.037). Other factors were statistically significant but had low predictive power (r2 < 0.010); in the explanatory analysis, this included end-of-study hypoglycemia (insulin-naive group), insulin dose, and health-related quality of life (r2 < 0.001–0.006, P ≤ 0.007). CONCLUSIONS Many factors can guide clinicians in predicting the response to starting therapy with insulin analogs, but many are interdependent and thus of poor utility. The factor explaining most of the variance in HbA1c change is baseline HbA1c level, with each increase of 1.0%-units (11 mmol/mol) providing a 0.7–0.8%-units (8–9 mmol/mol) greater fall. Other factors do not explain much of the remaining variance, even when including all end-of-trial measures.