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Table 3 Applying logistic regression to assess the significance of relationship between Independent demographic and genetic variables and metabolic syndrome

From: Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

Variables B Odds ratio (OR) P value
Age 0.025 1.025  < 0.001
Gender (female = 0) 0.864 2.373  < 0.001
Schooling years − 0.021 0.978 0.009
BMI 0.207 1.230  < 0.001
Physical activity 0.0001 1.000 0.005
Smoking status Current smoker(reference)    
Never smoker 0.005 1.005 0.962
Former smoker 0.072 1.075 0.698
Second hand 0.646 1.066 0.593
Marital status Divorced (reference)    
Married − 0.087 0.916 0.808
Single − 0.198 0.820 0.595
Widowed − 0.010 0.989 0.981
rs1260326 CC(reference)    
TC 0.206 1.229 0.347
TT 0.472 1.603 0.133
rs780094 CC(reference)    
TC 0.149 1.161 0.664
TT − 1.211 0.298 0.008
rs780093 CC(reference)    
TC − 0.122 0.884 0.664
TT 1.066 2.903 0.002
  1. BMI Body Mass Index; logistic regression is used to predict the metabolic syndrome status of the participants in TCGS. The metabolic syndrome was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05)