Application of Penalized Mixed Model in Identification of Most Associated Factors with Hemoglobin A1c Level in Type 2 Diabetes

BACKGROUND: The effect of controlling blood sugar on decreasing diabetes complications and their fatality has been investigated in many cross-sectional studies, but instability of blood sugar and some of the potential effective factors on it during the time render these studies imprecise and unreliable. Exploring among a big number of possible covariates is another challenging issue which renders the traditional methods inefficient. Therefore, we aimed to determine factors which are mostly associated with HbA1c level, among a large number of potential covariates using penalized linear mixed model in a longitudinal study

METHOD: The participants consisted of diabetic patients referred to Endocrine and Metabolism Research Center of Isfahan from 2000 to 2012 who were measured 4-12 times. Linear mixed model with LASSO penalty was used to investigate the relationship between HbA1c and the factors which potentially affect HbA1c. SPSS version 18 and glmmLassopackage in R. 3.1.3 software were used for statistical analysis.

RESULTS: Most of the 360 patients, (62.5%) were female. Their mean age was 52.2 years (SD=9.24) and median number of their visit was 5 with inter-quartile range of 4 to 6. The simple mixed model revealed that all of the covariates had significant effects on HbA1c, but using LMMLASSO led to elimination of 8 redundant covariates from the final model.

CONCLUSION: By Appling linear mixed model with LASSO penalty retinopathy, hypertension, cholesterol, HDL and TG had the most significant association with HbA1c level.