Does the Inclusion of Free Sugars as Opposed to Total Sugars in Nutrient Profiling Models Improve Their Performance? A Cross-sectional Analysis From the PREDISE Study
Bibliographic record
Abstract
BACKGROUND: Nutrient profiling (NP) models characterize the healthfulness of foods. Few NP models have been validated, and nutrients included in their algorithm do not always reflect the most recent scientific evidence. OBJECTIVES: This study aimed 1) to evaluate the validity of NP models against a diet quality measure and cardiometabolic risk factors in French-Canadians and 2) to compare the validity of each model when replacing total sugars by free sugars in their algorithm. METHODS: The PRÉDicteurs Individuels, Sociaux et Environnementaux cross-sectional study was used to test original and modified versions (i.e., including total or free sugars, respectively) of 3 NP models: Health Star Rating (HSR) system, Nutri-Score, and Nutrient-Rich Food index 6.3. Data from web-based self-administered 24-h recalls completed by 1019 adults were used to calculate energy-weighted NP-derived individual scores for both versions of each model. Associations between individual scores and the Healthy Eating Food Index 2019, as well as 14 biomarkers covering anthropometry, blood pressure, blood lipids, glucose homeostasis, and inflammatory biomarkers, were assessed using multivariable linear models. RESULTS: ; P ≤ 0.0001), diastolic blood pressure (β: -0.08 to +0.30 mm Hg; P ≤ 0.04) and triglycerides (β: -0.01 to +0.02 mmol/L; P ≤ 0.002). Original HSR and Nutri-Score were also associated with lower waist circumference and HOMA-IR, lower insulin (HSR only), and higher HDL cholesterol (Nutri-Score only). Replacing total sugars by free sugars in each model only slightly increased the number of associations observed with biomarkers. CONCLUSIONS: All 3 models are associated with diet quality and some biomarkers of health status in French-Canadians, although no model outranks the others. Replacing total sugars by free sugars has little to no effect on NP models' performance, therefore not supporting this approach for now.
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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 | 0.047 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".