Abstract
Diet plays a crucial role in health, with low-carbohydrate diets often proposed to exert metabolic benefits. We aim to investigate metabolomic adaptations in 164 adults with overweight or obesity who were randomly assigned to high- (n = 54), moderate- (n = 53), or low-carbohydrate (n = 57) diets during a 20-week weight-loss maintenance phase of the Framingham State Food Study [(FS)2], a controlled, parallel feeding trial (ClinicalTrials.gov: NCT02068885). We measure fasting plasma metabolites by liquid chromatography-tandem mass spectrometry using samples from 147 participants who completed the study (n = 45, 48, and 54 in the high-, moderate-, and low-carbohydrate diet groups, respectively). Significant associations (False Discovery Rate<0.05) are identified between carbohydrate-to-fat ratio (CFR) and diet-induced changes in 148 of 479 metabolites at 20 weeks, with nearly all showing consistent trends at 10 and 20 weeks. Phosphatidylcholines plasmanyls/plasmalogens, phosphatidylethanolamines plasmanyls/plasmalogens, and sphingomyelins generally decrease with higher CFR, whereas lysophosphatidylcholines, lysophosphatidylethanolamines, and triglycerides generally increase. Our findings are largely reproducible in an independent feeding trial involving diets with similar CFR (Popular Diets Study, ClinicalTrials.gov: NCT00315354). Eleven triglyceride species (≤3 double bonds), linked to type 2 diabetes risk, increase with higher CFR. Our findings demonstrate metabolomic changes caused by varying CFR dietary patterns, offering potential insights into mechanisms that could guide targeted dietary intervention strategies.
Similar content being viewed by others
Data availability
The full trial protocol, demographic characteristics of participants, and cardiometabolic outcomes from the (FS)2 trial are publicly available through Open Science Framework (https://osf.io/rvbuy/). All data supporting the findings described in this manuscript are available in the article and in the Supplementary Information and Supplementary Data. De-identified metabolomics data are available under controlled access to qualified investigators for non-commercial research purposes. The raw data are protected and not publicly available due to consent restrictions. Access to metabolomics data can be requested by contacting the corresponding authors (joel.hirschhorn@childrens.harvard.edu; cara.ebbeling@childrens.harvard.edu). Requests will be reviewed in accordance with IRB approval and applicable ethical and legal requirements. Eligible investigators will be required to sign a Data Use Agreement (DUA). Upon execution of the DUA, requests will be fulfilled within 4 weeks, and data will remain available for the period specified in the DUA (at least one year). Trials were registered at ClinicalTrials.gov: (FS)2, NCT02068885, registered on 21 February 2014 (https://clinicaltrials.gov/study/NCT02068885); and the Popular Diets Study (validation study), NCT00315354, registered on 18 April 2006 (https://clinicaltrials.gov/study/NCT00315354). Source data are provided in this paper.
Code availability
The code and documentation for PAIRUP-MS, which was previously developed and published and applied in the present paper, are publicly available on GitHub (https://github.com/yuhanhsu/PAIRUP-MS). No new custom or unique computational code was developed for this study. All packages used are publicly available and have been cited in the manuscript.
References
Micha, R. et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: Systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE 12, e0175149 (2017).
Schwingshackl, L. et al. Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur. J. Epidemiol. 32, 363–375 (2017).
Sacks, F. M. et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N. Engl. J. Med. 360, 859–873 (2009).
GBD 2017 Diet Collaborators Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 393, 1958–1972 (2019).
Hu, Y. et al. Low-Carbohydrate Diet Scores and Mortality Among Adults With Incident Type 2 Diabetes. Diabetes Care 46, 874–884 (2023).
Lopez-Garcia, E. et al. The Mediterranean-style dietary pattern and mortality among men and women with cardiovascular disease. Am. J. Clin. Nutr. 99, 172–180 (2014).
Bhupathiraju, S. N. et al. Glycemic index, glycemic load, and risk of type 2 diabetes: results from 3 large US cohorts and an updated meta-analysis. Am. J. Clin. Nutr. 100, 218–232 (2014).
Ebbeling, C. B. et al. Effects of a low carbohydrate diet on energy expenditure during weight loss maintenance: randomized trial. BMJ 363, k4583 (2018).
Ludwig, D. S. et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am. J. Clin. Nutr. 114, 1873–1885 (2021).
Hengist, A. et al. Ketogenic diet but not free-sugar restriction alters glucose tolerance, lipid metabolism, peripheral tissue phenotype, and gut microbiome: RCT. Cell Rep. Med. 5, 101667 (2024).
Vogelzangs, N. et al. Metabolic profiling of tissue-specific insulin resistance in human obesity: results from the Diogenes study and the Maastricht Study. Int. J. Obes. 44, 1376–1386 (2020).
Meyer, A. et al. Plasma metabolites and lipids predict insulin sensitivity improvement in obese, nondiabetic individuals after a 2-phase dietary intervention. Am. J. Clin. Nutr. 108, 13–23 (2018).
Guasch-Ferré, M., Bhupathiraju, S. N. & Hu, F. B. Use of metabolomics in improving assessment of dietary intake. Clin. Chem. 64, 82–98 (2018).
Ulaszewska, M. M. et al. Nutrimetabolomics: an integrative action for metabolomic analyses in human nutritional studies. Mol. Nutr. Food Res. 63, e1800384 (2019).
Posma, J. M. et al. Nutriome-metabolome relationships provide insights into dietary intake and metabolism. Nat. Food 1, 426–436 (2020).
Esko, T. et al. Metabolomic profiles as reliable biomarkers of dietary composition. Am. J. Clin. Nutr. 105, 547–554 (2017).
Ebbeling, C. B. et al. Effects of dietary composition on energy expenditure during weight-loss maintenance. JAMA 307, 2627–2634 (2012).
Morze, J. et al. Metabolomics and type 2 diabetes risk: an updated systematic review and meta-analysis of prospective cohort studies. Diabetes Care 45, 1013–1024 (2022).
Ebbeling, C. B. et al. A randomized study of dietary composition during weight-loss maintenance: Rationale, study design, intervention, and assessment. Contemp. Clin. Trials 65, 76–86 (2018).
Hsu, Y.-H. H. et al. PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data. PLoS Comput. Biol. 15, e1006734 (2019).
Adams, S. H. et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. J. Nutr. 139, 1073–1081 (2009).
Audzeyenka, I. et al. β-Aminoisobutyric acid (L-BAIBA) is a novel regulator of mitochondrial biogenesis and respiratory function in human podocytes. Sci. Rep. 13, 766 (2023).
Sandoval-Salazar, C., Ramírez-Emiliano, J., Trejo-Bahena, A., Oviedo-Solís, C. I. & Solís-Ortiz, M. S. A high-fat diet decreases GABA concentration in the frontal cortex and hippocampus of rats. Biol. Res. 49, 15 (2016).
Wang, Q., Ren, L., Wan, Y. & Prud’homme, G. J. GABAergic regulation of pancreatic islet cells: Physiology and antidiabetic effects. J. Cell Physiol. 234, 14432–14444 (2019).
Ma, X. et al. ACE2 modulates glucose homeostasis through GABA signaling during metabolic stress. J. Endocrinol. 246, 223–236 (2020).
Geng, J., Ni, Q., Sun, W., Li, L. & Feng, X. The links between gut microbiota and obesity and obesity related diseases. Biomed. Pharmacother. 147, 112678 (2022).
Tuomainen, M. et al. Associations of serum indolepropionic acid, a gut microbiota metabolite, with type 2 diabetes and low-grade inflammation in high-risk individuals. Nutr. Diabetes 8, 35 (2018).
Cipryan, L. et al. A lipidomic and metabolomic signature of a very low-carbohydrate high-fat diet and high-intensity interval training: an additional analysis of a randomized controlled clinical trial. Metabolomics 20, 10 (2023).
Dambrova, M. et al. Acylcarnitines: nomenclature, biomarkers, therapeutic potential, drug targets, and clinical trials. Pharmacol. Rev. 74, 506–551 (2022).
Strand, E. et al. Serum acylcarnitines and risk of cardiovascular death and acute myocardial infarction in patients with stable anginapectoris. J. Am. Heart Assoc. 6, e003620 (2017).
An, J. et al. Hepatic expression of malonyl-CoA decarboxylase reverses muscle, liver and whole-animal insulin resistance. Nat. Med. 10, 268–274 (2004).
Lu, J. et al. High-coverage targeted lipidomics reveals novel serum lipid predictors and lipid pathway dysregulation antecedent to type 2 diabetes onset in normoglycemic Chinese adults. Diabetes Care 42, 2117–2126 (2019).
Rhee, E. P. et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J. Clin. Invest. 121, 1402–1411 (2011).
Hornburg, D. et al. Dynamic lipidome alterations associated with human health, disease and ageing. Nat. Metab. 5, 1578–1594 (2023).
Wang, F. et al. Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals. Nat. Commun. 14, 5744 (2023).
van der Veen, J. N. et al. The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. Biochim. Biophys. Acta Biomembr. 1859, 1558–1572 (2017).
Zhong, J. et al. Specific triacylglycerol, diacylglycerol, and lyso-phosphatidylcholine species for the prediction of type 2 diabetes: a ~ 16-year prospective study in Chinese. Cardiovasc. Diabeto.l 21, 234 (2022).
Meikle, P. J. & Summers, S. A. Sphingolipids and phospholipids in insulin resistance and related metabolic disorders. Nat. Rev. Endocrinol. 13, 79–91 (2017).
Dean, J. M. & Lodhi, I. J. Structural and functional roles of ether lipids. Protein Cell 9, 196–206 (2018).
Pietiläinen, K. H. et al. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects–a monozygotic twin study. PLoS ONE 2, e218 (2007).
Chang, W., Hatch, G. M., Wang, Y., Yu, F. & Wang, M. The relationship between phospholipids and insulin resistance: From clinical to experimental studies. J. Cell Mol. Med. 23, 702–710 (2019).
Holland, W. L. & Summers, S. A. Sphingolipids, insulin resistance, and metabolic disease: new insights from in vivo manipulation of sphingolipid metabolism. Endocr. Rev. 29, 381–402 (2008).
Meikle, P. J. et al. Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes. PLoS ONE 8, e74341 (2013).
Barber, M. N. et al. Plasma lysophosphatidylcholine levels are reduced in obesity and type 2 diabetes. PLoS ONE 7, e41456 (2012).
Wang, W. et al. Dietary patterns and cardiometabolic health: Clinical evidence and mechanism. MedComm 4, e212 (2023).
Goldenberg, J. Z. et al. Efficacy and safety of low and very low carbohydrate diets for type 2 diabetes remission: systematic review and meta-analysis of published and unpublished randomized trial data. BMJ 372, m4743 (2021).
Ludwig, D. S., Dickinson, S. L., Henschel, B., Ebbeling, C. B. & Allison, D. B. Do lower-carbohydrate diets increase total energy expenditure? an updated and reanalyzed meta-analysis of 29 controlled-feeding studies. J. Nutr. 151, 482–490 (2021).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
Ebbeling, C. B. et al. Effects of a low-carbohydrate diet on insulin-resistant dyslipoproteinemia-a randomized controlled feeding trial. Am. J. Clin. Nutr. 115, 154–162 (2022).
Acknowledgements
We thank the study investigators and participants of the (FS)2 for their time and effort. We thank Jakub Morze, the first author of the meta-analysis Metabolomics and Type 2 Diabetes, for generously providing data for analysis in this study. We would like to thank the Consortium of METabolomics Studies (COMETS) for promoting collaborations among the investigators. M.Y.M. was supported by NIH grant HL171855. N.ET. and C.W. were supported by the SciLifeLab & Wallenberg Data-Driven Life Science Program (grant: KAW 2020.0239). Analyses for this paper were supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125273), and additional support was provided by R01DK075787. Implementation of the Framingham State Food Study was supported by grants from Nutrition Science Initiative (made possible by gifts from Arnold Ventures and Robert Lloyd Corkin Charitable Foundation), New Balance Foundation, Many Voices Foundation, and Blue Cross Blue Shield. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation and writing of the manuscript.
Author information
Authors and Affiliations
Contributions
C.B.E. and J.N.H. designed and coordinated the study; D.S.L. and C.B.E. designed and implemented the (FS)2; A.M.A. analyzed data and designed the figures with input from E.B., Y.H., O.A.Z., and J.N.H.; A.M.A. wrote the first draft; A.M.A., C.B.C., C.B.E., and J.N.H. wrote the paper; C.B.C. conducted the metabolomics measurements; J.LS, C.B.E., and J.N.H. supervised the project. All authors (A.M.A., E.B., Y.H., O.A.Z., N.ET., M.Y.M., S.N.B., R.S.K., C.W., J.L.-S., C.B.C., D.S.L., C.B.E., and J.N.H.) contributed to the discussion and interpretation of results, critically reviewed the paper, and agreed to submit the paper for publication.
Corresponding authors
Ethics declarations
Competing interests
D.S.L. received royalties for books on obesity and nutrition that recommend a low-glycemic load diet. J.L.-S. is a scientific advisor to TruDiagnostic and Precion Inc. The rest of the authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Armand Valsesia, Stefania Noerman and Amir Asiaee for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Angelidi, A.M., Bartell, E., Huang, Y. et al. Weight-independent effects of dietary carbohydrate-to-fat ratio on metabolomic profiles: secondary outcomes of a 5-month randomized controlled feeding trial. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68353-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-68353-z
