Table 1.

AIC and BIC values for linear mixed effects models

Formulae when modelling the lme ( ) function		AIC	BIC
lm1 = lme(FPG_log2 ~ time + HOMA-β + HbA1c + HOMA-IR + Urinary sugar + Insulin + BMI + Waist + weight + Age + Group, random = ~ 0 + time|new_id, data = data, method = “ML”)	Random slope model	4004.980	4138.897
lm11 = lme(FPG_log2 ~ time + HOMA-β + HbA1c + HOMA-IR + Urinary sugar + Insulin + BMI + Group, random = ~ 0 + time|new_id, data = data, method = “ML”)	Random slope model	4000.553	4117.002
lm2 = lme(FPG_log2 ~ time + HOMA-β + HbA1c + HOMA-IR + Urinary sugar + Insulin + BMI + Waist + weight + Age + Group, random = ~ 1|new_id, data = data, method = “ML”)	Random intercept model	3735.778	3869.695
lm22 = lme(FPG_log2 ~ time + HOMA-β + HbA1c + HOMA-IR + Urinary sugar + Insulin, random = ~ 1|new_id, data = data, method = “ML”)	Random intercept model	3736.811	3812.503
lm3 = lme(FPG_log2 ~ time + HOMA-β + HbA1c + HOMA-IR + Urinary sugar + Insulin + BMI + Waist + weight + Age + Group, random = ~ 1 + time|new_id, data = data, method = “ML”)	Random intercept + random slope model	3729.655	3875.217
lm33 = lme(FPG_log2 ~ time + HOMA-β + HbA1c + HOMA-IR + Urinary sugar + Insulin, random = ~ 1 + time|new_id, data = data, method = “ML”)	Random intercept + random slope model	3731.114	3818.451