Jonas Wuopio, Lin Yi-Ting, Koen F Dekkers, Tove Fall, J Gustav Smith, Anders Larsson, Gunnar Engström, Marju Orho-Melander, Linda S Johnson, Johan Ärnlöv
{"title":"盐摄入的代谢特征：来自scapis研究的横断面分析。","authors":"Jonas Wuopio, Lin Yi-Ting, Koen F Dekkers, Tove Fall, J Gustav Smith, Anders Larsson, Gunnar Engström, Marju Orho-Melander, Linda S Johnson, Johan Ärnlöv","doi":"10.1186/s12986-025-00997-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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<sup>®</sup>), 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.</p><p><strong>Results: </strong>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<sup>- 37</sup>) 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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":19196,"journal":{"name":"Nutrition & Metabolism","volume":"22 1","pages":"104"},"PeriodicalIF":4.1000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12406461/pdf/","citationCount":"0","resultStr":"{\"title\":\"The metabolic signature of salt intake: a cross-sectional analysis from the SCAPIS-study.\",\"authors\":\"Jonas Wuopio, Lin Yi-Ting, Koen F Dekkers, Tove Fall, J Gustav Smith, Anders Larsson, Gunnar Engström, Marju Orho-Melander, Linda S Johnson, Johan Ärnlöv\",\"doi\":\"10.1186/s12986-025-00997-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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<sup>®</sup>), 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.</p><p><strong>Results: </strong>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<sup>- 37</sup>) 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.</p><p><strong>Conclusions: </strong>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. 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The metabolic signature of salt intake: a cross-sectional analysis from the SCAPIS-study.
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.
期刊介绍：
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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