The metabolic signature of salt intake: a cross-sectional analysis from the SCAPIS-study
Bibliographic record
Abstract
Abstract Background Untargeted metabolomic analysis provides novel insights into the relationship between sodium intake and cardiometabolic risk. This study examined cross-sectional associations between estimated sodium intake and plasma metabolite profiles in a large Swedish cohort. Methods This cross-sectional analysis was conducted in the in the SCAPIS cohort (mean age 50–64 years, n = 8,957). Sodium intake was estimated using the Kawasaki formula (est24hNa) from urine samples. Plasma metabolites were measured using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) (Metabolon Inc ® ), identifying 713 metabolites grouped into eight biochemical classes (CC). Principal component analysis (PCA) was conducted for each CC, and the first principal component (PC1) was used as the response variable, with est24hNa, age, sex, and cardiovascular risk factors as predictors in restricted cubic spline models. ANOVA and pathway enrichment analyses were performed to explore associations. Results Est24hNa was significantly associated with the lipid and energy CC. Lower est24hNa was linked to higher concentrations of free fatty acids and citric acid cycle intermediates, suggesting enhanced beta-oxidation. Bonferroni-adjusted analyses revealed 231 metabolites significantly associated with est24hNa, with 2 S,3R-dihydroxybutyrate (β = -0.13, p = 2.28 × 10 − 37 ) showing the strongest association. Lipid subgroups including phosphatidylcholines, lysophospholipids, bile acids, and plasmalogens were positively associated with est24hNa. Pathway enrichment suggested links to branched-chain amino acid metabolism and biosynthesis of unsaturated fatty acids. Conclusions Lower salt intake was associated with a metabolic profile indicative of increased beta-oxidation, while higher salt intake was linked to lipid species previously implicated in atherosclerosis. These findings highlight potential metabolic pathways through which salt intake may influence cardiovascular health and merit further evaluation in longitudinal studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".