Circulating differentiated and senescent lymphocyte subsets and incident diabetes risk in older adults: The Cardiovascular Health Study
Bibliographic record
Abstract
Abstract Introduction Cellular senescence is a feature of aging implicated in the pathophysiology of diabetes mellitus (DM). Whether senescent lymphocytes are associated with the future occurrence of DM is uncertain. Methods We used cryopreserved peripheral blood mononuclear cells collected from 1860 Cardiovascular Health Study participants (average age 80.2 years) and flow cytometry immunophenotyping to evaluate the longitudinal relationships of naive (CD45RA+), memory (CD45RO+), senescent (CD28−), and T effector memory RA+ (TEMRA) (CD28−CD57+CD45RA+) CD4+ and CD8+ T cells, and memory B cells (CD19+CD27+), with the risk of incident DM. In exploratory analyses we evaluated the relationships of 13 additional innate lymphocyte and CD4+ and CD8+ subsets with incident DM risk. Results Over a median follow‐up time of 8.9 years, 155 cases of incident DM occurred. In Cox models adjusted for demographic variables (age, sex, race, study site and flow cytometry analytical batch) or diabetes risk factors (demographic variables plus education, body mass index, smoking status, alcohol use, systolic blood pressure, hypertension medication use and physical activity), no significant associations were observed for any CD4+, CD8+ or CD19+ cell phenotypes with incident DM. Conclusions These results suggest the frequencies of naive, memory and senescent T cells and memory B cells are not strongly associated with incident DM risk in older adults.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".