Lipid Screening for Cardiovascular Risk in Youth: A Narrative Review of Current Guidelines and Potential Use of Nonfasting Measures
Bibliographic record
Abstract
<h2>Abstract</h2> Early identification of dyslipidemia in children and youth allows for the prevention of cardiovascular diseases (CVD). Emerging evidence in adults suggests that nonfasting remnant cholesterol (RC) is a significant independent risk factor for CVD. Understanding the applicability of nonfasting RC in youth and children may provide greater options for routine and early identification of CVD risk. The purpose of this review is to provide an overview of guidelines for assessing dyslipidemia in practice and to explore whether nonfasting lipid biomarkers and/or RC have been incorporated for children and youth. The Medline database was used to identify clinical practice guidelines published between 2011 and August 2025 in English for children and/or youth aged 2-20 years. Of an initial 2537 citations, 41 met the inclusion criteria for data extraction. Although there is controversy regarding assessing fasting or nonfasting biomarkers in youth, definitions of normal ranges of lipoproteins were less controversial and are typically defined based on the National Heart, Lung, and Blood Institute recommendations. Most guidelines recommend measuring fasting lipid biomarkers (especially low-density lipoprotein cholesterol) in youth. In contrast, a select number of guidelines and statements recommend assessing nonfasting non–high-density lipoprotein cholesterol as the priority. Currently, no guidelines discuss measuring RC in youth. Evidence suggests that nonfasting non–high-density lipoprotein cholesterol is a reliable, practical biomarker for lipid screening in children and youth; however, most guidelines still suggest fasting measurement of low-density lipoprotein cholesterol. We note that nonfasting RC is an important knowledge gap not addressed in the current guidelines, and increasing awareness of nonfasting RC may be valuable.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".