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Table 3 Multiple Cox regression analysis on significant factors of developing dyslipidaemia in training set

From: Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

Variables β S.E. Wald P HR (95%CI)
Conventional model
Age 0.005 0.003 3.017 0.082 1.005(0.999, 1.010)
Family history of diabetes 0.194 0.125 2.429 0.119 1.215(0.951, 1.551)
Physical activity
 Low Reference
 Moderate 0.793 0.080 99.087 < 0.001 2.210(1.890, 2.583)
 High 0.324 0.071 20.810 < 0.001 1.383(1.203, 1.590)
BMI 0.016 0.010 2.777 0.096 1.016(0.997, 1.036)
TG 0.292 0.074 15.609 < 0.001 1.339(1.158, 1.548)
HDL-C −2.103 0.196 114.907 < 0.001 0.122(0.083, 0.179)
LDL-C 0.284 0.052 29.792 < 0.001 1.329(1.200, 1.472)
Conventional + GRS model
Age 0.005 0.003 2.887 0.089 1.005(0.999, 1.010)
Family history of diabetes 0.198 0.125 2.517 0.113 1.219(0.954, 1.557)
Physical activity
 Low Reference
 Moderate 0.802 0.080 101.097 < 0.001 2.230(1.907, 2.607)
 High 0.328 0.071 21.347 < 0.001 1.389(1.208, 1.596)
BMI 0.017 0.010 2.998 0.083 1.017(0.998, 1.037)
TG 0.281 0.074 14.410 < 0.001 1.325(1.146, 1.532)
HDL-C −2.095 0.195 114.889 < 0.001 0.123(0.084, 0.180
LDL-C 0.286 0.052 29.968 < 0.001 1.330(1.201, 1.474)
Weighted GRS 0.276 0.088 9.925 0.002 1.318(1.110, 1.565)
  1. Note: The predictors of the conventional model are variables that are significantly associated with dyslipidaemia in simple Cox regression analysis. GRS is added to the conventional model to construct the conventional+GRS model
  2. Abbreviations: BMI body mass index, TG triglyceride, HDL-C high density lipoprotein, LDL-C low density lipoprotein, GRS genetic risk score
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