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

OBJECTIVEIndividualization of therapy choices requires the prediction of likely response. Predictor and explanatory factors of change in HbA(1c) were studied using data from a large observational study of starting insulin analog therapy (the A(1)chieve study).RESEARCH DESIGN AND METHODSUnivariate analyses were performed for insulin-naive people and prior insulin users in the A(1)chieve 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.RESULTSGeographical region, baseline HbA(1c) level, lipid levels, and baseline insulin dose were the most powerful predictors of HbA(1c) change (mean change -2.1% [-23 mmol/mol]) observed in the univariate analysis (r(2) > 0.010, P < 0.001). However, although the predictor and explanatory multivariate models explained 62-82% of the variance in HbA(1c) change, this was mainly associated with baseline HbA(1c) (r(2) = 0.544-0.701) and region (r(2) = 0.014-0.037). Other factors were statistically significant but had low predictive power (r(2) < 0.010); in the explanatory analysis, this included end-of-study hypoglycemia (insulin-naive group), insulin dose, and health-related quality of life (r(2) < 0.001-0.006, P 0.007).CONCLUSIONSMany 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 HbA(1c) change is baseline HbA(1c) 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.