Fatty acids as biomarkers of total dairy and dairy fat intakes: a systematic review and meta-analysis
Bibliographic record
Abstract
Context: Dairy intake in humans is commonly assessed using questionnaires, but the data collected are often biased. As a result, there is increasing interest in biomarkers of dairy fat. To date, there has been no overview of the fatty acids suitable for use as biomarkers of dairy fat intake. Objective: This systematic review and meta-analysis of observational studies was performed to identify circulating fatty acids as biomarkers of total dairy and dairy fat intakes in the general population. Data Sources: MEDLINE, Embase, and Web of Knowledge databases were searched for eligible studies published until June 2017. Study Selection: Articles were included when a correlation between circulating dairy fatty acids and intakes of total dairy and dairy fat was found, as measured by dietary assessment tools. Data Extraction: Two authors extracted data independently and assessed the risk of bias. An adapted form of the Newcastle-Ottawa Scale was used for quality assessment. Results: Data were pooled using the random-effects model. Meta-analysis revealed that the fatty acids in plasma/serum were significantly correlated with intakes of total dairy (C14:0 [r = 0.15; 95%CI, 0.11 - 0.18], C15:0 [r = 0.20; 95%CI, 0.13 - 0.27], and C17:0 [r = 0.10; 95%CI, 0.03 - 0.16] and dairy fat (C14:0 [r = 0.16; 95%CI, 0.10 - 0.22], C15:0 [r = 0.33; 95%CI, 0.27 - 0.39], C17:0 [r = 0.19; 95%CI, 0.14 - 0.25], and trans-C16:1n-7 [r = 0.21; 95%CI, 0.14 - 0.29). Conclusions: C14:0, C15:0, C17:0, and trans-C16:1n-7 were identified as biomarkers of total dairy and dairy fat intakes in the general population. In light of the suboptimal measurement techniques used in some studies, correlations with trans-C18:1n-7 and conjugated linoleic acid require further investigation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".