Delayed niacin skin flush response and cognitive impairment in Late‐Life Depression
Bibliographic record
Abstract
Abstract Background Late‐life depression (LLD) with cognitive impairment (CI), as a potential subtype of LLD, carries an elevated risk of progressing to Alzheimer's disease (AD), yet the underlying mechanisms remain elusive. While anomalies in niacin skin flushing response (NSFR) have been observed in various neuropsychiatric disorders, there is a paucity of research examining these phenomena in LLD patients. This study aims to elucidate the potential value of the NSFR in predicting CI in LLD patients. Method This study included 86 patients of LLD (46 LLD with CI and 40 LLD without CI), 20 AD and 32 Healthy Controls (HCs). Cognitive functions were estimated through the Chinese version of Montreal Cognitive Assessment (MoCA). NSFR tests were conducted with a modified method. LogEC 50 is utilized to indicate the rate of NSFR. MoCA was retested after six months of treatment. Multivariate analysis of variance (MANOVA) was conducted with demographic variables that differed statistically among the groups to assess differences in NSFR and clinical indexes among groups. Logistic regression models based on NSFR were constructed, and receiver‐operating characteristic (ROC) curve analysis was calculated to evaluate the performance of models. Result The LogEC 50 levels were significantly elevated in LLD with CI group compared to both the LLD without CI ( p <0.05) and HCs ( p <0.05). However, no significant differences were observed between the LLD without CI and the HCs group, nor between the LLD with CI and AD. ROC analysis demonstrated that Log EC 50 can effectively distinguish between LLD with CI and LLD without CI. Six‐month follow‐up data revealed that the baseline LogEC 50 can also effectively predict cognitive outcomes in LLD. Conclusion LLD with CI exhibited a delayed NSFR. Delayed NSFR proved effective in distinguishing cognitive impairment in LLD, suggesting that NSFR could serve as a potential biomarker for LLD with CI. Furthermore, in patients with LLD, a delayed NSFR at baseline predicts poorer cognitive outcomes. These insights open new avenues for research into the mechanisms underlying CI in LLD and offer fresh perspectives on potential therapeutic targets.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".