The effects of ginger intake on weight loss and metabolic profiles among overweight and obese subjects: 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 performed to summarize the effect of ginger intake on weight loss, glycemic control and lipid profiles among overweight and obese subjects. We searched the following databases through November 2017: MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials. The relevant data were extracted and assessed for quality of the studies according to the Cochrane risk of bias tool. Data were pooled using the inverse variance method and expressed as Standardized Mean Difference (SMD) with 95% Confidence Intervals (95% CI). Heterogeneity between studies was assessed by the Cochran Q statistic and I-squared tests (I2). Overall, 14 studies were included in the meta-analyses. Fourteen RCTs with 473 subjects were included in our meta-analysis. The results indicated that the supplementation with ginger significantly decreased body weight (BW) (SMD −0.66; 95% CI, −1.31, −0.01; P = 0.04), waist-to-hip ratio (WHR) (SMD −0.49; 95% CI, −0.82, −0.17; P = 0.003), hip ratio (HR) (SMD −0.42; 95% CI, −0.77, −0.08; P = 0.01), fasting glucose (SMD −0.68; 95% CI, −1.23, −0.05; P = 0.03) and insulin resistance index (HOMA-IR) (SMD −1.67; 95% CI, −2.86, −0.48; P = 0.006), and significantly increased HDL-cholesterol levels (SMD 0.40; 95% CI, 0.10, 0.70; P = 0.009). We found no detrimental effect of ginger on body mass index (BMI) (SMD −0.65; 95% CI, −1.36, 0.06; P = 0.074), insulin (SMD −0.54; 95% CI, −1.43, 0.35; P = 0.23), triglycerides (SMD −0.27; 95% CI, −0.71, 0.18; P = 0.24), total- (SMD −0.20; 95% CI, −0.58, 0.18; P = 0.30) and LDL-cholesterol (SMD −0.13; 95% CI, −0.51, 0.24; P = 0.48). Overall, the current meta-analysis demonstrated that ginger intake reduced BW, WHR, HR, fasting glucose and HOMA-IR, and increased HDL-cholesterol, but did not affect insulin, BMI, triglycerides, total- and LDL-cholesterol levels.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.042 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".