Abstract P375: Cardiometabolic Risk Factors and Socioeconomic Status Indices in Young Adults in Latin America: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background: Metabolic syndrome components in children and adolescents have gained attention in low- and middle-income countries due to variability in their effects and measures across socioeconomic status (SES) determinants. Socioeconomic disparities have been studied related to health, and this fact still poses a serious challenge that raises concerns, especially in low-and middle-income countries, where the classification of the population relies on different determinants from wealthier countries. We conducted a systematic review and meta-analysis to illustrate the effects of measures of metabolic syndrome components across SES in Latin American (LA) young adults. Methods: We searched PubMed/MEDLINE, Web of Science, Lilacs (Bireme), Embase and Scopus for LA studies published before September 2023. Included studies were those with participants aged 6-19 years in whom metabolic syndrome (MS) components were analyzed across SES in LA. The quality of observational studies was assessed using the adapted New Castle Ottawa quality tool. Blood pressure, abdominal obesity, body mass index, body fat, glucose levels and insulin resistance data were synthesized, and a random effect model was used to measure the collective effect of overweight/obesity and lipid profile on higher SES levels compared with middle and low SES levels. The protocol of this review was registered with the International Prospective Register of Systematic Reviews database (registration number: CRD42020222378) and was developed following PRISMA statements. Results: Sixty-three eligible articles (n=255, 521 participants) from nine LA countries were selected. SES definitions were used in 41 studies from five countries with similar criteria. Obesity and overweight (odds ratio: 1.57, 95% confidence interval: 1.18-1.96) and altered lipid profiles were significantly associated with higher SES compared to lower SES. However, high-density lipoprotein-cholesterol levels were lower in patients with a lower SES. The heterogeneity observed could be explained by age, sex, nutrition patterns, standardized cardiometabolic measures, and temporally collected data due to rapid transitions in LA countries. Conclusions: Cardiometabolic components may be associated with higher SES in LA countries. As national SES definitions have been used around LA countries to classify populations, this definition needs to be standardized around LA countries to guide national programs to intensify cardiovascular health and prevent detrimental effects in young people.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".