Score evaluation for the consumption of ultra-processed foods in children and its relationship with cardiometabolic risk
Bibliographic record
Abstract
Ultra-processed foods (UPF) are industrial formulations nutritionally unbalanced and highly palatable. The high consumption of UPF is associated with development of metabolic alterations in children. However, considering that there is a diversity of UPF frequently consumed by children, it is necessary to evaluate a score specific for these foods, identifying their subgroups and their relationship with cardiometabolic risk. This study aimed to evaluate the score of ultra-processed food consumption in children and its relationship with cardiometabolic risk. Firstly, it was conducted a systematic review with the longitudinal evidence on the association between consumption of UPF and cardiometabolic risk. Data extraction of this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality and risk of bias were assessed with the Newcastle-Ottawa Scale. Scopus, Science direct, Scielo, PubMed, and Google scholar databases were searched without any restriction on publication dates. Ten longitudinal studies were selected for this review, being four conducted with children and six with adults. The findings showed a positive association between the high consumption of UPF and cardiometabolic risk a long term, independent of the age (PROSPERO registration no: CRD42022327714). For the original investigation, it was carried out a cross-sectional study with 378 children aged 8 and 9 years attending in all urban public and private schools in Viçosa, Minas Gerais Brazil. A semi-structured questionnaire was applied to obtain sociodemographic and lifestyle information. The cardiometabolic risk was evaluated according to total and android body fat, lipid profile, blood pressure, uric acid, fasting glucose, and HOMA-IR. From the application of three 24-hour recalls, a score of UPF consumption was created with 24 items. From this list, three subgroups of UPF were obtained: "sugary foods and beverages", "fatty foods" and "salty foods and processed meats". Multivariate linear regression models were used to assess the association of the UPF score and its subgroups with cardiometabolic risk markers. The “sugary foods and beverages” score was positively associated with LDL-c and uric acid. Every 1 SDof "sugary foods and beverages” score was associated to 3.1 (95%CI: 0.8, 5.3) and 0.1 (95%CI: 0.1, 0.2) units higher in LDL-c and uric acid, respectively. The “salty foods and meat products” score was positively associated with total cholesterol and LDL-c. Every 1 SD of “salty foods and meat products” score was associated to 3.0 (95%CI: 0.2, 5.8) and 3.3 (95%CI: 1.0, 5.6) units higher in total cholesterol and LDL-c, respectively. Finally, UPF score was positively associated with LDL-c. Every 1 SD of UPF score was associated to 2.8 (95%CI: 0.6, 4.9) units higher in LDL-c. In conclusion of both investigations, it is important to implement effective strategies in the public health to prevent the excessive consumption of UPF and protect their effect in the long- term health, independent of age. In addition, the use of the score for the UPF consumption in childhood can be an easy and quick method to identify unhealthy eating habits and evaluate their associations (at whole and subgroups) with cardiometabolic risk at early age. Keywords: Eating. Cardiometabolic risk factors. Nutritional epidemiology.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".