Investigating Pace of Biological Aging as a Mental and Physical Health Biomarker in Youth with Perinatally-Acquired HIV
Bibliographic record
Abstract
HIV and its treatment may affect the growth and development of organ function and the mental health of youth who are living with perinatally-acquired HIV (PHIV). Recent research has investigated utilizing biological age as a quantitative tool that can accurately predict a broad range of physical and mental health outcomes. However, few studies have utilized a longitudinal approach to analyze biological age in targeted populations such as those with PHIV. Even fewer studies have investigated the trends of biological aging in children since most biological aging algorithms have been developed with an older population in mind. To fill the gap, we propose a new approach to quantify the pace of biological aging as a weighted combination of the temporal trajectory of each individual biomarker. The resulting metric is called individualized pace of biological aging (IPOA). Compared to existing methods such as Belsky's pace of biological aging (BPOA), and biological age gap (BAG), our proposed IPOA allows researchers to effectively account for each participant's baseline health condition as these serve as weights in the model. Our results show that IPOA was significantly associated with future HIV viral load and multiple health outcomes, outperforming previously validated biological aging and pace of aging methods. Overall, our proposed method allows for a more accurate, data-driven method of calculating pace of aging for adolescents by utilizing a weighted combination of the starting health condition of each individual that may be clinically useful as a predictive tool of whole person health.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".