Dietary management improves sleep quality in patients with metabolic syndrome: the mediating roles of metabolic, inflammatory, and oxidative stress changes
Bibliographic record
Abstract
Background Sleep disorders frequently co-occur with metabolic syndrome (MetS), yet effective strategies targeting both conditions remain limited. Inflammation and oxidative stress represent shared mechanisms, suggesting dietary management as a promising dual-target intervention. This study aimed to evaluate whether structured dietary management could improve sleep quality and metabolic, inflammatory, and oxidative stress parameters in patients with MetS. Methods We conducted a single-arm prospective interventional study including 158 patients with MetS and sleep disorders [Pittsburgh Sleep Quality Index (PSQI) > 7] between August and October 2024. Participants received a structured dietary management program. Clinical characteristics, metabolic parameters, and inflammatory and oxidative stress biomarkers were assessed before and after intervention. Paired tests evaluated pre–post changes, and stepwise multivariate linear regression was performed to identify independent predictors of sleep quality. Results Dietary intervention significantly improved liver enzymes, lipid profile (triglycerides, LDL-C, HDL-C), glucose metabolism (fasting glucose, fasting insulin, HOMA-IR), and uric acid levels (all P < 0.05). TNF-α and hsCRP were markedly reduced ( P < 0.001), while IL-6 showed a non-significant trend ( P = 0.075). Oxidative stress improved, with lower MDA and higher SOD levels ( P < 0.05). Regression analysis identified smoking status and insulin resistance as independent predictors of PSQI scores, underscoring the interplay between lifestyle factors and metabolic dysfunction in sleep health. Conclusion Structured dietary management improves metabolic, inflammatory, and oxidative stress profiles while enhancing sleep quality in patients with MetS. The findings highlight dietary and lifestyle modifications as integral to comprehensive management strategies for MetS with sleep disturbances.
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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 | 0.000 | 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".