Identification of Chinese dietary patterns and their relationships with health outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: China has been undergoing a rapid nutrition transition in the past few decades. This review aims to characterise commonly reported dietary patterns in Chinese populations and their associations with health outcomes. DESIGN: We searched PubMed, Embase and CNKI from inception to June 2020 to identify observational studies reporting dietary patterns or the associations between dietary patterns and health outcomes. Information regarding dietary patterns, their association with health outcomes and other related items was collected. SETTING: Chinese population and Chinese immigrants. PARTICIPANTS: Not applicable. RESULTS: Results from 130 studies with over 900 000 participants were included. Six dietary patterns were identified: traditional whole-grain diet (Traditional WG), traditional non-whole-grain diet (Traditional NWG), plant-based diet (Plant-based), animal food diet (Animal-food), Western energy-dense diet (Western) and other unclassified diets (Unclassified). The Plant-based diet was associated with a reduced risk of CVD and cancer from prospective studies, reduced risk of diabetes, hypertension, cognitive impairment and depressive symptoms from all study designs. The Traditional WG diet was associated with a reduced risk of diabetes and hypertension. Animal-food diet is associated with a range of metabolic diseases, and Western diet was associated with increased risks of obesity and depressive symptoms. CONCLUSION: Multiple dietary patterns identified reflect the diversity and transitioning of the Chinese diet. A healthy Chinese diet, comprising both the Traditional WG and Plant-based diets, was associated with reduced risks of specific undesirable health outcomes. Promoting this healthy diet will improve public health among the Chinese populations.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".