Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy
Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impac...
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creator | Jiang, Yingxin Celia Lai, Kaitao Muirhead, Roslyn Patricia Chung, Long Hoa Huang, Yu James, Elizaveta Liu, Xin Tracy Wu, Julian Atkinson, Fiona S Yan, Shuang Fogelholm, Mikael Raben, Anne Don, Anthony Simon Sun, Jing Brand-Miller, Jennie Cecile Qi, Yanfei |
description | Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk.
This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss.
We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses.
We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance.
Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice.
The PREVIEW lifestyle intervention study w |
doi_str_mv | 10.1016/j.ajcnut.2024.08.015 |
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This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss.
We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses.
We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance.
Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice.
The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).</description><identifier>ISSN: 0002-9165</identifier><identifier>ISSN: 1938-3207</identifier><identifier>EISSN: 1938-3207</identifier><identifier>DOI: 10.1016/j.ajcnut.2024.08.015</identifier><identifier>PMID: 39182617</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Aged ; Biomarkers ; Biomarkers - blood ; Blood Glucose - analysis ; Blood Glucose - metabolism ; Body weight ; Caloric Restriction ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus, Type 2 - blood ; Diet ; Diglycerides ; Europe ; Fatty acids ; Female ; Health risks ; Hemoglobin ; Homeostasis ; Humans ; Insulin ; Insulin resistance ; Intervention ; Lecithin ; Life Style ; Lifestyles ; Lipidomics ; Lipids ; Lipids - blood ; low-energy diet ; Machine learning ; Male ; Middle Aged ; Nutrient deficiency ; Organic acids ; Overweight ; Overweight - blood ; Overweight - diet therapy ; Overweight - therapy ; Parameters ; Phosphatidylcholine ; Phosphatidylethanolamine ; Population studies ; Population-based studies ; prediabetes ; Prediabetic State - blood ; Prediabetic State - diet therapy ; Prediabetic State - therapy ; PREVIEW study ; Serum lipids ; Sphingolipids ; Triglycerides ; Weight Loss</subject><ispartof>The American journal of clinical nutrition, 2024-10, Vol.120 (4), p.864-878</ispartof><rights>2024 American Society for Nutrition</rights><rights>Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright American Society for Clinical Nutrition, Inc. Oct 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c269t-788eb8ad46c4be3984c712d1d42a58ff694106fecaa60dd24176bbe422ed5c8d3</cites><orcidid>0000-0002-6797-8754 ; 0000-0003-1391-4111</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39182617$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jiang, Yingxin Celia</creatorcontrib><creatorcontrib>Lai, Kaitao</creatorcontrib><creatorcontrib>Muirhead, Roslyn Patricia</creatorcontrib><creatorcontrib>Chung, Long Hoa</creatorcontrib><creatorcontrib>Huang, Yu</creatorcontrib><creatorcontrib>James, Elizaveta</creatorcontrib><creatorcontrib>Liu, Xin Tracy</creatorcontrib><creatorcontrib>Wu, Julian</creatorcontrib><creatorcontrib>Atkinson, Fiona S</creatorcontrib><creatorcontrib>Yan, Shuang</creatorcontrib><creatorcontrib>Fogelholm, Mikael</creatorcontrib><creatorcontrib>Raben, Anne</creatorcontrib><creatorcontrib>Don, Anthony Simon</creatorcontrib><creatorcontrib>Sun, Jing</creatorcontrib><creatorcontrib>Brand-Miller, Jennie Cecile</creatorcontrib><creatorcontrib>Qi, Yanfei</creatorcontrib><title>Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy</title><title>The American journal of clinical nutrition</title><addtitle>Am J Clin Nutr</addtitle><description>Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk.
This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss.
We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses.
We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance.
Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice.
The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).</description><subject>Adult</subject><subject>Aged</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Blood Glucose - analysis</subject><subject>Blood Glucose - metabolism</subject><subject>Body weight</subject><subject>Caloric Restriction</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus, Type 2 - blood</subject><subject>Diet</subject><subject>Diglycerides</subject><subject>Europe</subject><subject>Fatty acids</subject><subject>Female</subject><subject>Health risks</subject><subject>Hemoglobin</subject><subject>Homeostasis</subject><subject>Humans</subject><subject>Insulin</subject><subject>Insulin resistance</subject><subject>Intervention</subject><subject>Lecithin</subject><subject>Life Style</subject><subject>Lifestyles</subject><subject>Lipidomics</subject><subject>Lipids</subject><subject>Lipids - blood</subject><subject>low-energy diet</subject><subject>Machine learning</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Nutrient deficiency</subject><subject>Organic acids</subject><subject>Overweight</subject><subject>Overweight - blood</subject><subject>Overweight - diet therapy</subject><subject>Overweight - therapy</subject><subject>Parameters</subject><subject>Phosphatidylcholine</subject><subject>Phosphatidylethanolamine</subject><subject>Population studies</subject><subject>Population-based studies</subject><subject>prediabetes</subject><subject>Prediabetic State - blood</subject><subject>Prediabetic State - diet therapy</subject><subject>Prediabetic State - therapy</subject><subject>PREVIEW study</subject><subject>Serum lipids</subject><subject>Sphingolipids</subject><subject>Triglycerides</subject><subject>Weight Loss</subject><issn>0002-9165</issn><issn>1938-3207</issn><issn>1938-3207</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kktvEzEUhQcEoqHwC0DIEpuymGB7HvF0gYRKgEqVQAjo0vLYdxIHxx78SBV-PZ4ksGDB6sryd47vvT5F8ZzgOcGkfb2Zi420Kc4ppvUcszkmzf1iRrqKlRXFiwfFDGNMy460zVnxOIQNxoTWrH1UnFUdYbQli9m9Z-8ARhTApy0yetTKbbUMSCuwUQ8aAoKdMElEvQMkrEKjB6Xl4dhrtxX-B_iABueRtkrvtErC6F-g0MrsJWQz5CGMzgaYKGPcnbYrlEsJFvxqj5SGWGZtkll0B3q1jvk6hEsk0Ocvy-9TI84iN2RS9BCzT1x7l1br3PAAIe4NoGsbwe9O6KFNNyYjDscQk5oG0RYtk3fjcQ6RLXKJa0C3zhuFLqbHrpe3r1BI_aTZPykeDsIEeHqq58W398uvVx_Lm08frq_e3pSStl0sF4xBz4SqW1n3UHWslgtCFVE1FQ0bhrarCW4HkEK0WClak0Xb91BTCqqRTFXnxcXRd_TuZ8oT8a0OEowRFlwKvMLdgtSsa6qMvvwH3bjkbe6OV4R0tGoIbjJVHynp8yI9DHz0On_VnhPMp_DwDT-Gh0_h4ZjxHJ4se3EyT_0W1F_Rn7Rk4M0RgLyNnQbPg9Rg88dpDzJy5fT_X_gNLBTfmA</recordid><startdate>202410</startdate><enddate>202410</enddate><creator>Jiang, Yingxin Celia</creator><creator>Lai, Kaitao</creator><creator>Muirhead, Roslyn Patricia</creator><creator>Chung, Long Hoa</creator><creator>Huang, Yu</creator><creator>James, Elizaveta</creator><creator>Liu, Xin Tracy</creator><creator>Wu, Julian</creator><creator>Atkinson, Fiona S</creator><creator>Yan, Shuang</creator><creator>Fogelholm, Mikael</creator><creator>Raben, Anne</creator><creator>Don, Anthony Simon</creator><creator>Sun, Jing</creator><creator>Brand-Miller, Jennie Cecile</creator><creator>Qi, Yanfei</creator><general>Elsevier Inc</general><general>American Society for Clinical Nutrition, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7T7</scope><scope>7TS</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6797-8754</orcidid><orcidid>https://orcid.org/0000-0003-1391-4111</orcidid></search><sort><creationdate>202410</creationdate><title>Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy</title><author>Jiang, Yingxin Celia ; Lai, Kaitao ; Muirhead, Roslyn Patricia ; Chung, Long Hoa ; Huang, Yu ; James, Elizaveta ; Liu, Xin Tracy ; Wu, Julian ; Atkinson, Fiona S ; Yan, Shuang ; Fogelholm, Mikael ; Raben, Anne ; Don, Anthony Simon ; Sun, Jing ; Brand-Miller, Jennie Cecile ; Qi, Yanfei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c269t-788eb8ad46c4be3984c712d1d42a58ff694106fecaa60dd24176bbe422ed5c8d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Blood Glucose - analysis</topic><topic>Blood Glucose - metabolism</topic><topic>Body weight</topic><topic>Caloric Restriction</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus, Type 2 - blood</topic><topic>Diet</topic><topic>Diglycerides</topic><topic>Europe</topic><topic>Fatty acids</topic><topic>Female</topic><topic>Health risks</topic><topic>Hemoglobin</topic><topic>Homeostasis</topic><topic>Humans</topic><topic>Insulin</topic><topic>Insulin resistance</topic><topic>Intervention</topic><topic>Lecithin</topic><topic>Life Style</topic><topic>Lifestyles</topic><topic>Lipidomics</topic><topic>Lipids</topic><topic>Lipids - blood</topic><topic>low-energy diet</topic><topic>Machine learning</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Nutrient deficiency</topic><topic>Organic acids</topic><topic>Overweight</topic><topic>Overweight - blood</topic><topic>Overweight - diet therapy</topic><topic>Overweight - therapy</topic><topic>Parameters</topic><topic>Phosphatidylcholine</topic><topic>Phosphatidylethanolamine</topic><topic>Population studies</topic><topic>Population-based studies</topic><topic>prediabetes</topic><topic>Prediabetic State - 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Academic</collection><jtitle>The American journal of clinical nutrition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Yingxin Celia</au><au>Lai, Kaitao</au><au>Muirhead, Roslyn Patricia</au><au>Chung, Long Hoa</au><au>Huang, Yu</au><au>James, Elizaveta</au><au>Liu, Xin Tracy</au><au>Wu, Julian</au><au>Atkinson, Fiona S</au><au>Yan, Shuang</au><au>Fogelholm, Mikael</au><au>Raben, Anne</au><au>Don, Anthony Simon</au><au>Sun, Jing</au><au>Brand-Miller, Jennie Cecile</au><au>Qi, Yanfei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy</atitle><jtitle>The American journal of clinical nutrition</jtitle><addtitle>Am J Clin Nutr</addtitle><date>2024-10</date><risdate>2024</risdate><volume>120</volume><issue>4</issue><spage>864</spage><epage>878</epage><pages>864-878</pages><issn>0002-9165</issn><issn>1938-3207</issn><eissn>1938-3207</eissn><abstract>Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk.
This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss.
We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses.
We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance.
Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice.
The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>39182617</pmid><doi>10.1016/j.ajcnut.2024.08.015</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-6797-8754</orcidid><orcidid>https://orcid.org/0000-0003-1391-4111</orcidid></addata></record> |
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issn | 0002-9165 1938-3207 1938-3207 |
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subjects | Adult Aged Biomarkers Biomarkers - blood Blood Glucose - analysis Blood Glucose - metabolism Body weight Caloric Restriction Diabetes Diabetes mellitus Diabetes Mellitus, Type 2 - blood Diet Diglycerides Europe Fatty acids Female Health risks Hemoglobin Homeostasis Humans Insulin Insulin resistance Intervention Lecithin Life Style Lifestyles Lipidomics Lipids Lipids - blood low-energy diet Machine learning Male Middle Aged Nutrient deficiency Organic acids Overweight Overweight - blood Overweight - diet therapy Overweight - therapy Parameters Phosphatidylcholine Phosphatidylethanolamine Population studies Population-based studies prediabetes Prediabetic State - blood Prediabetic State - diet therapy Prediabetic State - therapy PREVIEW study Serum lipids Sphingolipids Triglycerides Weight Loss |
title | Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy |
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