Circulating Interleukin-17A is associated with executive function in middle aged adults with and without type 2 diabetes
Bibliographic record
Abstract
Midlife cardiovascular risk factors such as Type 2 Diabetes (T2DM) and obesity are associated with the later development of cognitive impairment and dementia. Systemic inflammation is postulated as a crucial mechanism, yet there are few studies examining this at the earliest stages prior to overt cognitive impairment. To assess this, we recruited a cohort of middle-aged cognitively-unimpaired individuals with and without uncomplicated T2DM. Comprehensive neuropsychological assessment was performed at baseline and at 4-year follow-up. Ten serum chemokines and cytokines (Eotaxin, MCP-1, MIP-1β, CXCL10, IL-6, IL-10, IL12p70, IL-17A, IFN-γ and TNF-α) were measured at both baseline and follow-up using high-sensitivity assays. Overall, 136 participants were recruited including 90 with uncomplicated midlife T2DM (age 52.6 ± 8.3; 47% female) and 46 without (age 52.9 ± 8.03; 61% female). Cognitive trajectories were stable over time and did not differ with T2DM. Yet on cross-sectional analyses at both baseline and follow-up, greater circulating IL-17A was consistently associated with poorer performance on tests of executive function/attention (β: 0.21; -0.40, -0.02, p = 0.03 at baseline; β: 0.26; -0.46, -0.05, p = 0.02 at follow-up). Associations persisted on covariate adjustment and did not differ by T2DM status. In summary, we provide evidence that greater circulating IL-17A levels were associated with poorer executive function in midlife, independent of T2DM. Long-term follow-up of this and other cohorts will further elucidate the earliest stages in the relationship between systemic inflammation and cognitive decline to provide further mechanistic insights and potentially identify those at greatest risk for later cognitive decline.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".