Cerebrospinal Fluid Biomarkers Profiling in Older Brazilians reveals that CSF leptin is associated with obesity but not with cognitive decline
Bibliographic record
Abstract
BACKGROUND: Forty-five percent of dementia cases are potentially preventable through changes in individuals' lifestyles (for example, reducing sedentarism) or by treating associated disorders (such as hypertension and obesity). However, there is no consensus on the global importance of CSF analytes and these potential lifestyle risk modifiers (LRM) for dementia onset, ultimately impacting diagnosis, prognosis, and efficient treatments. We aim to identify potential CSF therapeutical targets associated with dementia and its LRMs. METHOD: Our cross-sectional study included individuals cognitively unimpaired ([CU] N = 25, 67.8 ± 4.8 y/o), and with cognitive decline ([CD] N = 37, 73 ± 6.5 y/o). 28 CSF analytes candidates were profiled and included in our dataset, along with LRM (hypertension, obesity, physical exercise, insulin resistance, dyslipidemia). Feature selection through nested logistic regression (LR) models regularized by elastic net was employed to select optimal predictors for CD and associated LRM. Linear regression models were built with selected CSF analytes to evaluate their direct impact on LRM and CD. RESULT: CSF Aβ42/Aβ40, LXA4/cysLT (Lipoxin A4/cysteinyl leukotriene, oxytocin, age, and obesity were identified as predictors for CD, and subsequently only CSF leptin for obesity. The direct impact of obesity, age, and CD in the selected CSF analytes was estimated by regression models. Lower ratios of CSF Aβ42/Aβ40 and LXA4/cys-LT, along with higher CSF oxytocin levels, are associated with CD, and obesity was inversely correlated with cognitive decline. However, there is no association between CSF leptin levels and CD, as leptin is only associated with obesity and age. CONCLUSION: Obesity was primarily related to changes in CSF leptin and negatively associated with CD. However, the effects of obesity in CD decline are not associated with CSF analytes evaluated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".