Dietary Patterns in Early Young Adulthood Predicted Risks of Abnormal Blood Lipids in Later Young Adults: Evidence From a Prospective Cohort Study
Bibliographic record
Abstract
Background and aims: The extent to which dietary patterns influence the risk of abnormal blood lipids throughout young adulthood remains unclear. the aim of this is study is to investigate whether early young adulthood dietary patterns predict the risk of abnormal blood lipids during later young adulthood. We used data from a long running birth cohort study in Australia. Western dietary pattern rich in meats, processed foods and high-fat dairy products and prudent pattern rich in fruit, vegetables, fish, nuts, whole grains and low-fat dairy products were derived using principal component analysis at the 21-year follow-up from dietary data obtained using a food frequency questionnaire. After 9-years, fasting blood samples of all participants were collected and their total, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterols and triglyceride (TG) levels were measured. Abnormal blood lipids were based on clinical cut-offs for total, LDL and HDL cholesterols, and TG and relative distributions for total: HDL and TG: HDL cholesterols ratios. Log-binomial models were used to estimate risk of each outcome in relation to dietary patterns Greater adherence to the Western pattern predicted increased risks of high LDL (RR: 1.47; 95%CI: 1.06, 2.03) and TG (1.90; 1.25, 2.86), and high ratios of total: HDL (1.48; 1.00, 2.19) and TG: HDL (1.78; 1.18, 2.70) cholesterols in fully adjusted models. Conversely, a prudent pattern predicted reduced risks of low HDL (0.58; 0.42, 0.78) and high TG (0.66; 0.47, 0.92) and high total: HDL (0.71; 0.51, 0.98) and TG: HDL (0.61; 0.45, 0.84) cholesterols ratios. This is the first prospective study to show greater adherence to an unhealthy Western diet predicted increased risks of abnormal blood lipids, whereas a healthy prudent diet predicted lower risks in young adults. Addressing diets in the early course may improve the cardiovascular health of young adults. The Original cohort (Mater-University of Queensland Study of Pregnancy (MUSP)) is supported by the National and Medical Research Council (NHMRC), Australia. However, the works in this particular abstract did not receive any funding.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".