Association between homocysteine and obesity: A meta‐analysis
Bibliographic record
Abstract
Abstract According to previous studies of obesity, we found that the association between homocysteine concentrations and obesity was reported controversially. Thus, we carried out this meta‐analysis to investigate this association. We searched PubMed, The Cochrane library, and EMBASE database for studies that evaluate the relationship between homocysteine concentrations and obesity from inception to March, 2019. The quality of all included studies was assessed by the Newcastle Ottawa Scale (NOS) and the Agency for Healthcare Research Quality (AHRQ). The RevMan5.3 software and Stata12.0 software were used for conducting all data analyses. Standardized mean differences (SMD) with the corresponding 95% confidence intervals (95% CIs) were used as a measure of effect size to assess the relationship between homocysteine concentrations and obesity through a meta‐analysis. The level of significance was set at P < .05. A total of 14 studies were ultimately included in our meta‐analysis. Meta‐analysis of the 14 studies found remarkable lower homocysteine concentrations in controls than in obese patients (SMD = 0.76, 95% CI = 0.25‐1.27, P < .01; I 2 = 94% and P < .01 for heterogeneity), regardless of nutritional status, dietary habit, insulin resistance (IR) status, special disease history, history of medicine taken, genetic background, and so on. Homocysteine concentrations in nonobese patients with polycystic ovarian syndrome (PCOS) were lower than obese patients with PCOS (SMD = 0.48, 95% CI = 0.20‐0.77, P < .01; I 2 = 39% and P = .18 for heterogeneity). The result of our meta‐analysis showed that homocysteine concentrations were significantly elevated among obese patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".