{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:15:18Z","timestamp":1788963318956,"version":"build-2803163510"},"reference-count":76,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Obesity is a major public health concern worldwide and contributes to multiple metabolic disorders. Low carbohydrate diets (LCDs) have been proposed as an effective strategy for weight management and metabolic improvement; however, the mechanisms underlying LCD-induced molecular changes in individuals with obesity remain incompletely understood. This study aimed to assess the impact of a three-month LCD on the proteomic profile of adults with obesity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Thirty adults (18\u201350\u202fyears) with obesity (BMI\u202f\u2265\u202f30\u202fkg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ) followed an LCD (60\u2013110\u202fg\/day) for 3\u202fmonths. Anthropometric and laboratory measurements were obtained, and blood samples were collected at baseline and endpoint. Changes in the plasma proteomic profile were evaluated using an untargeted label-free quantitative proteomics approach (LC\u2013MS\/MS), complemented by bioinformatics and pathway network analyses. Anthropometric and biochemical data were analyzed using paired-sample\n                      <jats:italic>t<\/jats:italic>\n                      -tests and Wilcoxon signed-rank tests in IBM SPSS\u00ae.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall, the LCD was associated with significant reductions in body weight, BMI, waist and hip circumferences, fat mass and percentage, GGT, triglycerides, and HbA1c (paired\n                      <jats:italic>t<\/jats:italic>\n                      -test,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). Participants were stratified by total weight loss percentage into responders (\u22655%;\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f15) and non-responders (&amp;lt;5%;\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f15). Label-free proteomics identified 90 significantly dysregulated proteins in responders (53 upregulated, 37 downregulated) and 183 in non-responders (111 upregulated, 72 downregulated) between baseline and endpoint. Significantly dysregulated proteins in the responder group included beta-enolase (ENO3) and CREG1 (both upregulated), and tenascin (downregulated). In the non-responder group, insulin-like growth factor-binding protein 7, acyl-CoA-binding protein, and talin-1 were significantly downregulated. Pathway network analysis in responders was centered around dysregulation of TNF, IL6, IFNG, APP, DEGS1, and ENO3 and was related to cell-mediated immune response, cell-to-cell signaling and interaction, and cellular movement. In non-responders, the top network was centered around PGAM1, PDIA3, PHGDH, and EGFR and was associated with cardiovascular disease, dermatological diseases and conditions, and organismal injury and abnormalities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A three-month LCD significantly improved anthropometric and metabolic parameters in adults with obesity and was associated with marked changes in the plasma proteome. Distinct proteomic signatures between responders and non-responders highlight inter-individual variability in biological response, supporting the potential of plasma proteomics for biomarker discovery and precision nutrition in obesity management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1856332","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:50:56Z","timestamp":1788961856000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The impact of low carbohydrate diet on the plasma proteomic profile of individuals with obesity"],"prefix":"10.3389","volume":"13","author":[{"given":"Nora A.","family":"Alfadda","sequence":"first","affiliation":[{"name":"Department of Community Health Sciences, Clinical Nutrition, College of Applied Medical Sciences, King Saud University","place":["Riyadh, Saudi 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Obesity Research Center, College of Medicine, King Saud University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Othman Mahmoud","family":"Othman","sequence":"additional","affiliation":[{"name":"Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osama Z.","family":"Alzeer","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, King Khalid University Hospital, King Saud University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Abdulaziz","family":"Al Wabel","sequence":"additional","affiliation":[{"name":"University Diabetes Center, King Saud University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Assim 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