Lipids As Key Biomarkers in Unravelling the Pathophysiology of Obesity-related Metabolic Dysregulation

Overview

Journal Heliyon

Specialty Social Sciences

Date 2025 Feb 25

PMID 39995923

Authors

Anis Adibah Osman

Siok-Fong Chin

Lay-Kek Teh

Noraidatulakma Abdullah

Nor Azian Abdul Murad

Rahman Jamal

Affiliations

Soon will be listed here.

Abstract

Background And Objective: Obesity is intricately linked with metabolic disturbances. The comprehensive exploration of metabolomes is important in unravelling the complexities of obesity development. This study was aimed to discern unique metabolite signatures in obese and lean individuals using liquid chromatography-mass spectrometry quadruple time-of-flight (LC-MS/Q-TOF), with the goal of elucidating their roles in obesity.

Methods: A total of 160 serum samples (Discovery, n = 60 and Validation, n = 100) of obese and lean individuals with stable Body Mass Index (BMI) values were retrieved from The Malaysian Cohort biobank. Metabolic profiles were obtained using LC-MS/Q-TOF in dual-polarity mode. Metabolites were identified using a molecular feature and chemical formula algorithm, followed by a differential analysis using MetaboAnalyst 5.0. Validation of potential metabolites was conducted by assessing their presence through collision-induced dissociation (CID) using a targeted tandem MS approach.

Results: A total of 85 significantly differentially expressed metabolites (-value <0.05; -1.5 < FC > 1.5) were identified between the lean and the obese individuals, with the lipid class being the most prominent. A stepwise logistic regression revealed three metabolites associated with increased risk of obesity (14-methylheptadecanoic acid, 4'-apo-beta,psi-caroten-4'al and 6E,9E-octadecadienoic acid), and three with lower risk of obesity (19:0(11Me), 7,8-Dihydro-3b,6a-dihydroxy-alpha-ionol 9-[apiosyl-(1->6)-glucoside] and 4Z-Decenyl acetate). The model exhibited outstanding performance with an AUC value of 0.95. The predictive model underwent evaluation across four machine learning algorithms consistently demonstrated the highest predictive accuracy of 0.821, aligning with the findings from the classical logistic regression statistical model. Notably, the presence of 4'-apo-beta,psi-caroten-4'-al showed a statistically significant difference between the lean and obese individuals among the metabolites included in the model.

Conclusions: Our findings highlight the significance of lipids in obesity-related metabolic alterations, providing insights into the pathophysiological mechanisms contributing to obesity. This underscores their potential as biomarkers for metabolic dysregulation associated with obesity.

References

. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004; 363(9403):157-63. DOI: 10.1016/S0140-6736(03)15268-3. View

Jamal R, Syed Zakaria S, Kamaruddin M, Abd Jalal N, Ismail N, Mohd Kamil N . Cohort Profile: The Malaysian Cohort (TMC) project: a prospective study of non-communicable diseases in a multi-ethnic population. Int J Epidemiol. 2014; 44(2):423-31. PMC: 4469794. DOI: 10.1093/ije/dyu089. View

Telle-Hansen V, Christensen J, Formo G, Holven K, Ulven S . A comprehensive metabolic profiling of the metabolically healthy obesity phenotype. Lipids Health Dis. 2020; 19(1):90. PMC: 7211343. DOI: 10.1186/s12944-020-01273-z. View

Wang W, Yang J, Yang H, Sanidad K, Hammock B, Kim D . Effects of high-fat diet on plasma profiles of eicosanoid metabolites in mice. Prostaglandins Other Lipid Mediat. 2016; 127:9-13. PMC: 5628263. DOI: 10.1016/j.prostaglandins.2016.11.003. View

Shabana , Shahid S, Sarwar S . The abnormal lipid profile in obesity and coronary heart disease (CHD) in Pakistani subjects. Lipids Health Dis. 2020; 19(1):73. PMC: 7158030. DOI: 10.1186/s12944-020-01248-0. View

Chen S, Zong G, Wu Q, Yun H, Niu Z, Zheng H . Associations of plasma glycerophospholipid profile with modifiable lifestyles and incident diabetes in middle-aged and older Chinese. Diabetologia. 2021; 65(2):315-328. DOI: 10.1007/s00125-021-05611-3. View

. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2001; 894:i-xii, 1-253. View

Ferrara P, Rong X, Maschek J, Verkerke A, Siripoksup P, Song H . Lysophospholipid acylation modulates plasma membrane lipid organization and insulin sensitivity in skeletal muscle. J Clin Invest. 2021; 131(8). PMC: 8262507. DOI: 10.1172/JCI135963. View

Jeyakumar S, Vajreswari A, Giridharan N . Chronic dietary vitamin A supplementation regulates obesity in an obese mutant WNIN/Ob rat model. Obesity (Silver Spring). 2006; 14(1):52-9. DOI: 10.1038/oby.2006.7. View

10.

Guijas C, Montenegro-Burke J, Warth B, Spilker M, Siuzdak G . Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol. 2018; 36(4):316-320. PMC: 5937131. DOI: 10.1038/nbt.4101. View

11.

Vanweert F, Schrauwen P, Phielix E . Role of branched-chain amino acid metabolism in the pathogenesis of obesity and type 2 diabetes-related metabolic disturbances BCAA metabolism in type 2 diabetes. Nutr Diabetes. 2022; 12(1):35. PMC: 9356071. DOI: 10.1038/s41387-022-00213-3. View

12.

Duft R, Castro A, Bonfante I, Lopes W, da Silva L, Chacon-Mikahil M . Altered metabolomic profiling of overweight and obese adolescents after combined training is associated with reduced insulin resistance. Sci Rep. 2020; 10(1):16880. PMC: 7547065. DOI: 10.1038/s41598-020-73943-y. View

13.

Schiffman C, Petrick L, Perttula K, Yano Y, Carlsson H, Whitehead T . Filtering procedures for untargeted LC-MS metabolomics data. BMC Bioinformatics. 2019; 20(1):334. PMC: 6570933. DOI: 10.1186/s12859-019-2871-9. View

14.

Yao N, Yan S, Guo Y, Wang H, Li X, Wang L . The association between carotenoids and subjects with overweight or obesity: a systematic review and meta-analysis. Food Funct. 2021; 12(11):4768-4782. DOI: 10.1039/d1fo00004g. View

15.

Sohn M, Chae W, Ko J, Cho J, Kim J, Choi J . Metabolomic Signatures for the Effects of Weight Loss Interventions on Severe Obesity in Children and Adolescents. Metabolites. 2022; 12(1). PMC: 8778282. DOI: 10.3390/metabo12010027. View

16.

Hanamatsu H, Ohnishi S, Sakai S, Yuyama K, Mitsutake S, Takeda H . Altered levels of serum sphingomyelin and ceramide containing distinct acyl chains in young obese adults. Nutr Diabetes. 2014; 4:e141. PMC: 4217001. DOI: 10.1038/nutd.2014.38. View

17.

Marcelino G, Machate D, Freitas K, Hiane P, Maldonade I, Pott A . β-Carotene: Preventive Role for Type 2 Diabetes Mellitus and Obesity: A Review. Molecules. 2020; 25(24). PMC: 7763535. DOI: 10.3390/molecules25245803. View

18.

Pakiet A, Wilczynski M, Rostkowska O, Korczynska J, Jablonska P, Kaska L . The Effect of One Anastomosis Gastric Bypass on Branched-Chain Fatty Acid and Branched-Chain Amino Acid Metabolism in Subjects with Morbid Obesity. Obes Surg. 2019; 30(1):304-312. DOI: 10.1007/s11695-019-04157-z. View

19.

Albuquerque D, Nobrega C, Manco L, Padez C . The contribution of genetics and environment to obesity. Br Med Bull. 2017; 123(1):159-173. DOI: 10.1093/bmb/ldx022. View

20.

Kiriyama H, Kaneko H, Itoh H, Kamon T, Mizuno Y, Fujiu K . Association between changes in body weight and lipid profile in the general population: a community-based cohort study. Eur Heart J Qual Care Clin Outcomes. 2020; 7(1):109-110. DOI: 10.1093/ehjqcco/qcaa017. View