Body mass index and early <scp>CD</scp>4 <scp>T</scp>‐cell recovery among adults initiating antiretroviral therapy in <scp>N</scp>orth <scp>A</scp>merica, 1998–2010
Bibliographic record
Abstract
OBJECTIVES: Adipose tissue affects several aspects of the cellular immune system, but prior epidemiological studies have differed on whether a higher body mass index (BMI) promotes CD4 T-cell recovery on antiretroviral therapy (ART). The objective of this analysis was to assess the relationship between BMI at ART initiation and early changes in CD4 T-cell count. METHODS: We used the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) data set to analyse the relationship between pre-treatment BMI and 12-month CD4 T-cell recovery among adults who started ART between 1998 and 2010 and maintained HIV-1 RNA levels < 400 copies/mL for at least 6 months. Multivariable regression models were adjusted for age, race, sex, baseline CD4 count and HIV RNA level, year of ART initiation, ART regimen and clinical site. RESULTS: A total of 8381 participants from 13 cohorts contributed data; 85% were male, 52% were nonwhite, 32% were overweight (BMI 25-29.9 kg/m(2) ) and 15% were obese (BMI > 30 kg/m(2) ). Pretreatment BMI was associated with 12-month CD4 T-cell change (P < 0.001), but the relationship was nonlinear (P < 0.001). Compared with a reference of 22 kg/m(2) , a BMI of 30 kg/m(2) was associated with a 36 cells/μL [95% confidence interval (CI) 14, 59 cells/μL] greater CD4 T-cell count recovery among women and a 19 cells/μL (95% CI 9, 30 cells/μL) greater recovery among men at 12 months. At a BMI > 30 kg/m(2) , the observed benefit was attenuated among men to a greater degree than among women, although this difference was not statistically significant. CONCLUSIONS: A BMI of approximately 30 kg/m(2) at ART initiation was associated with greater CD4 T-cell recovery at 12 months compared with higher or lower BMI values, suggesting that body composition may affect peripheral CD4 T-cell recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".