The effects of alpha-lipoic acid supplementation on inflammatory markers among patients with metabolic syndrome and related disorders: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to determine the effect of alpha-lipoic acid (ALA) supplementation on the inflammatory markers among patients with metabolic syndrome (MetS) and related disorders. We searched the following databases until November 2017: PubMed, MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials. Three reviewers independently assessed study eligibility, extracted data, and evaluated risk of bias of included primary studies. Statistical heterogeneity was assessed using Cochran’s Q test and I-square (I 2 ) statistic. Data were pooled by using the random-effect model and standardized mean difference (SMD) was considered as the summary effect size. Eighteen trials out of 912 potential citations were found to be eligible for our meta-analysis. The findings indicated that ALA supplementation significantly decreased C-reactive protein (CRP) (SMD = − 1.52; 95% CI, − 2.25, − 0.80; P < 0.001), interlokin -6 (IL-6) (SMD = − 1.96; 95% CI, − 2.60, − 1.32; P < 0.001), and tumor necrosis factor alpha levels (TNF-α) (SMD = − 2.62; 95% CI, − 3.70, − 1.55; P < 0.001) in patients diagnosed with metabolic diseases. In summary, the current meta-analysis demonstrated the promising impact of ALA administration on decreasing inflammatory markers such as CRP, IL-6 and TNF-α among patients with MetS and related disorders.
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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.042 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".