Table 4. Final regression model for coronary artery calcification.

Independent Variable (increment)	lnCAC (n = 434)
	β-Coefficient (95% CI)
Age (10 yr. increase)	0.82 (0.65, 1.0)
Gender (male vs. female)	0.70 (0.35, 1.0)
Lipid-lowering medication	0.65 (0.31, 1.0)
Diabetes Status (vs. normal)
Impaired fasting glucose	0.46 (0.06, 0.86)
Diabetes (untreated)	ns
Diabetes (treated)	ns
CD4+ Memory cells (15.2%)	0.20 (0.03, 0.37)
Model R2	0.21

Backward elimination regression was used to develop multivariate models for coronary artery calcification (CAC) level. CAC was analyzed using the ln-agatston score in individuals with a score >0. Independent variables were divided by their standard deviations (shown in parentheses). The candidate starting variables were: age, gender, race/ethnicity, IL-6, BMI, systolic BP, use of BP lowering medication, smoking status, total-cholesterol, HDL-cholesterol, use of lipid lowering medication, type 2 diabetes status, CMV and H. pylori titers, and CD4⁺ memory cell proportions or, in separate analyses, CD4⁺ naive cell proportions. Only significant variables (p<0.05) were retained in the final model to obtain the model's R². ns: non-significant.