Reliable biomarkers for decreased spatial navigation in the Young People with Obesity: Increased serum CRP and QUIN levels
Bibliographic record
Abstract
Abstract Background: Obesity is increasing morbidity and mortality. Obesity and cognitive impairment research have focused on the negative consequences of obesity-related medical diseases on cognition. This study aimed to examine the effect of obesity on spatial navigation, and the relationship between changes in tryptophan kynurenine metabolites and spatial navigation in the people with obesity between 18 and 35 years of age. Methods: In 29 adults with obesity and 25 normal weight adults, we examined plasma levels of CRP, leptin, kynurenine (KYN), tryptophan (TRP), kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), and quinolinic acid (QUIN), as well as the TRP/KYN, KYNA/3-HK, and KYNA/QUIN ratios. Body and abdominal fat composition (AFC) were also examined. The EAT-26 was used to assess eating attitudes. We used Montreal cognitive assessment (MOCA), Reaction Time (RT), Rey-Osterrieth Complex Figure Test (RCFT), and Virtual-Reality-Based Route-Learning Test with subtests Route repetition task (RPT), Route retraction task (RRT), Directional-approach task (DAT) to measure cognitive abilities. Results: In participants with obesity, the EAT-26 score was higher (p= 0.006), but the MOCA total score (p=0.03) and RCFT copy subscale score (p=0.03), as well as the RPT (p< 0,001), RRT p= 0,004), and DAT (p< 0,001) percentage of correct answers, was lower than normal-weight participants. The QUIN was found to be a negative predictor of RRT (B=-7.29, CI: -12.98, -1.59, -0.31, p=0.01) and DAT (B=-6.15, CI: -9.83, -2.46, p=0.002), while AFC was a negative predictor of RPT (B=-1.01, CI: -1.47, -0.55, p< 0.001). CRP was likewise greater in participants with obesity and a negative predictor of RRT (B=-7.96, CI: -14.30, -1.62, p=0.02) and DAT (B=-9.25, CI: -16.34, -2.17, p=0.012). Discussion: The performance of participants with obesity without comorbidities was worse on visuospatial tests than healthy controls. QUIN and CRP may also help identify new serum biomarkers of poor visuospatial cognition in young adults with 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".