White blood cell subtypes, insulin resistance and β‐cell dysfunction in high‐risk individuals – the PROMISE cohort
Bibliographic record
Abstract
BACKGROUND: Higher white blood cell count (WBC) is associated with incident type 2 diabetes; however, little is known about the potential relationship of WBC subtypes with metabolic abnormalities underlying diabetes. DESIGN: Cross-sectional analysis. PARTICIPANTS: Six hundred and fifty-six nondiabetic participants in the Prospective Metabolism and Islet Cell Evaluation cohort. MEASUREMENTS: Granulocytes (basophils, neutrophils and eosinophils), lymphocytes and monocytes were measured in fasting blood samples. Neutrophil lymphocyte ratio (NLR) is the ratio of neutrophil to lymphocyte. Insulin resistance was measured by insulin sensitivity index (ISOGTT) and homeostasis model assessment of insulin resistance (HOMA-IR). Beta-cell dysfunction was measured by insulinogenic index (IGI) divided by HOMA-IR (IGI/IR) and Insulin Secretion Sensitivity Index-2 (ISSI-2). RESULTS: All WBC subtypes were inversely associated with ISOGTT [β = -0·12 (-0·15, -0·083) for granulocytes, β = -0·23 (-0·31, -0·15) for lymphocytes, β = -0·67 (-1·00, -0·34) for monocytes] and positively associated with HOMA-IR [β = 0·11 (0·074, 0·15) for granulocytes, β = 0·22 (0·14, 0·30) for lymphocytes, β = 0·64 (0·33, 0·97) for monocytes]. Granulocytes and lymphocytes were inversely associated with IGI/IR [β = -0·10 (-0·15, -0·047), β = -0·23 (-0·35, -0·11), respectively] and ISSI-2 [β = -0·048 (-0·074, -0·022), β = -0·14 (-0·19, -0·089), respectively]. BMI attenuated the associations of monocytes with IGI/IR and ISSI-2, and those of NLR with ISOGTT and HOMA-IR. NLR was not associated with IGI/IR and ISSI-2. CONCLUSIONS: All WBC subtypes were independently associated with insulin resistance, whereas granulocytes and lymphocytes, but not monocytes, were associated with β-cell dysfunction. NLR was not associated with β-cell dysfunction, and its association with insulin resistance was confounded by obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".