In model 3, smoking status, waist circumference, medical conditions, and health care use were added.
In men, the predicting equation included fasting glucose, smoking status, waist circumference, and GGT and in women included fasting glucose, BMI, diabetes in the family, and triglycerides.
A second model included age, sex, education, income, smoking status, waist circumference, diabetes, history of stroke, history of myocardial infarction, total cholesterol, HDL-cholesterol, c-reactive protein, statin use, and antihypertensive medication use.
Other goals were improvement of HbA1c, blood pressure, BodyMassIndex (BMI), cholesterol, smoking status, waist circumference and in the long-term prevention of T2DM and lowering the incidence of complications.
Independent t-tests or one-way ANOVAs using covariance were conducted to determine differences in PA/ST between age, BMI, body fat percentage, language ability, migration status, waist circumference, and IMD rank.
Additional variables that were considered for inclusion but did not meet the a priori criteria were race, Hispanic ethnicity, years of follow-up, smoking status, waist circumference, waist-to-hip ratio, intra-abdominal fat, subcutaneous fat, hypertension, retinopathy, nephropathy, total cholesterol, HDL, and triglycerides.
Insulin resistance in probands was assessed using eGDR, a measure derived from clamp studies in 24 NHW adults with long-standing type 1 diabetes using hypertension status, waist-to-hip ratio, and glycemic control (5).
Cluster groupings were validated against students' measured weight status and waist circumference as indicators of health risk.
bAdjusted for traditional risk factors (age; sex; race; smoking, alcohol and exercising status; diabetes; waist; systolic blood pressure; total cholesterol; high-density lipoprotein cholesterol; triglycerides).
In a multivariate logistic regression analysis; age above 60 years, female gender, low educational status, increased waist circumference, hypertension and diabetes were associated with low eGFR.
The predictors used in Cox proportional hazards models including baseline demographic and metabolic profiles (age at diabetes onset, sex, education, diabetes duration, smoking status, BMI, waist circumference, triglycerides and HDL cholesterol, urine ACR, HbA1c, and hypertension status).
