Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVIEW sub-study.

PREVIEW study lipidomics low-energy diet prediabetes weight loss

Journal

The American journal of clinical nutrition
ISSN: 1938-3207
Titre abrégé: Am J Clin Nutr
Pays: United States
ID NLM: 0376027

Informations de publication

Date de publication:
23 Aug 2024
Historique:
received: 27 03 2024
revised: 12 07 2024
accepted: 19 08 2024
medline: 26 8 2024
pubmed: 26 8 2024
entrez: 25 8 2024
Statut: aheadofprint

Résumé

Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, more than 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 eight weeks. 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's correlation and Bootstrap Forest of Ensemble Machine Learning model. Baseline lipids, predictive of glycemic parameters changes post-weight loss, were 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 were significantly increased. 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 HOMA-IR. 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 one-size-fits-all lifestyle modification advice. The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).

Sections du résumé

BACKGROUND BACKGROUND
Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, more than 50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk.
OBJECTIVE OBJECTIVE
This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss.
METHODS METHODS
We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over eight weeks. 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's correlation and Bootstrap Forest of Ensemble Machine Learning model. Baseline lipids, predictive of glycemic parameters changes post-weight loss, were using Bootstrap Forest Analyses.
RESULTS RESULTS
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 were significantly increased. 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 HOMA-IR.
CONCLUSIONS CONCLUSIONS
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 one-size-fits-all lifestyle modification advice. The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).

Identifiants

DOI: 10.1016/j.ajcnut.2024.08.015 PMID: 39182617
pubmed: 39182617
pii: S0002-9165(24)00709-3
doi: 10.1016/j.ajcnut.2024.08.015
pii:
doi:

Banques de données

ClinicalTrials.gov
['NCT01777893']

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest ☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Jennie Cecile Brand-Miller reports a relationship with The Glycemic Index Foundation that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yingxin Celia Jiang (YC)

Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.

Kaitao Lai (K)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; ANZAC Research Institute, The University of Sydney, Sydney, NSW 2137, Australia.

Roslyn Patricia Muirhead (RP)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.

Long Hoa Chung (LH)

Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.

Huang Yu (H)

Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.

Elizaveta James (E)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.

Xin Tracy Liu (XT)

Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.

Julian Wu (J)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Barker College, Hornsby, NSW 2077, Australia.

Fiona S Atkinson (FS)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia.

Shuang Yan (S)

Department of Endocrinology and Metabolism Diseases, The 4(th) Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China.

Mikael Fogelholm (M)

Department of Food and Nutrition, University of Helsinki, Helsinki, 00014, Finland.

Anne Raben (A)

Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Copenhagen, Denmark; Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark.

Anthony Simon Don (AS)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.

Jing Sun (J)

Rural Health Research Institute, Charles Sturt University, Leeds Parade, NSW 2800, Australia; School of Medicine and Dentistry, Menzies Health Institute Queensland, Institute for Integrated Intelligence and Systems, Griffith University, Southport, Queensland 4222, Australia. Electronic address: addressjingsun2064@hotmail.com.

Jennie Cecile Brand-Miller (JC)

Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia. Electronic address: addressjennie.brandmiller@sydney.edu.au.

Yanfei Qi (Y)

Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia. Electronic address: j.qi@centenary.org.au.

Classifications MeSH