Table 4. Effect of baseline body composition on HIV disease progression outcomes in HIV+ adults in Botswana during a follow-up period of 18 months† (Hazard ratios and 95% confidence intervals).
| Outcome | Continuous BMI | BMI >25 kg/m2 | Continuous fat mass (%) | ||||||
|---|---|---|---|---|---|---|---|---|---|
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| HR | 95% CI | P | HR | 95% CI | P | HR | 95% CI | P | |
| ≥25 % Decline in CD4 cell count | 0.963 | 0.919, 1.009 | 0.113 | 0.744 | 0.489, 1.131 | 0.166 | 0.974 | 0.945, 1.003 | 0.083 |
| CD4 cell count ≤250/μl | 1.043 | 0.932, 1.167 | 0.462 | 1.021 | 0.381, 2.740 | 0.966 | 0.984 | 0.909, 1.065 | 0.793 |
| AIDS defining conditions | 0.218 | 0.068, 0.701 | 0.011*‡ | 0.500 | 0.047, 4.465 | 0.500 | 0.855 | 0.741, 0.987 | 0.033* |
| CD4 cell count ≤250/μl and AIDS defining conditions | 0.904 | 0.796, 1.028 | 0.124 | 1.089 | 0.453, 2.619 | 0.849 | 0.918 | 0.847, 0.994 | 0.036* |
CD4, cluster of differentiation 4; CD8, cluster of differentiation 8.
Statistically significant (P<0-05).
Cox proportional hazards model were used to examine the effect of baseline continuous BMI, the effect of BMI groups (0 = BMI 18.0–24.9 kg/m2 and 1 = BMI ≥25 kg/m2), and baseline continuous fat mass % on individual HIV disease progression outcomes. All individual HIV disease progression outcomes were analysed as separate models and adjusted for age, sex, marriage, children and baseline CD4 count and viral load.
Model adjusted for age, sex, children and baseline CD4 cell count and viral load.