Chronic Systemic Inflammation Moderates the Relationship Between Adiposity and Behavioral and Neuroelectric Indices of Attention
Bibliographic record
Abstract
PURPOSE: Two-thirds of the US population today is living with overweight or obesity, signifying a serious public health concern. One co-morbidity of obesity is chronic inflammation, which contributes to cardiovascular and metabolic disease, and is often indicated by elevated plasma C-reactive protein (CRP) concentrations. Further, adiposity has been linked to decrements in selective aspects of cognitive function. However, the potential interactive effects of adiposity and inflammation on cognitive function are limited. This study aimed to examine the relationships among plasma CRP concentrations, cognitive function, and adiposity. METHODS: 36 adults (25-45 years) underwent a fasted venous blood draw for measurement of CRP and a dual x-ray absorptiometry (DXA) scan for quantification of whole body adiposity. Cognitive function was assessed using a two-stimulus visual oddball paradigm while underlying event-related brain potentials were recorded. Specifically, the latency of the P3 waveform in a central-parietal region of interest (ROI) was used to index attentional resource allocation and information processing speed, respectively. RESULTS: According to bivariate correlations, plasma CRP was positively associated with whole body percent fat (r=0.55, p<0.001). Whole body percent fat and CRP were negatively correlated with target accuracy (r=-0.28, p=0.048; r=-0.44, p=0.003; respectively). Whole body percent fat was correlated with lower peak latency difference (target peak latency - non-target peak latency) in the ROI (r=-0.38, p=0.01), signifying poorer modulation in cognitive processing speed. Yet, adjustment of plasma CRP using partial correlations revealed that the relationship between adiposity and target accuracy (r=-0.13, p=0.23), and P3 peak latency difference (r=-0.24, p=0.09) was mitigated (All, P>0.05). CONCLUSIONS: The cross-sectional relationship between adiposity and cognitive function was moderated by the extent of systemic inflammation in overweight and obese adults. Future studies are needed to determine whether reducing chronic systemic inflammation via exercise and nutritional manipulations prevents the negative implications of adiposity for cognitive function.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".