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Plasma Metabolomics and Lipidomics Differentiate Obese Individuals by Peripheral Neuropathy Status
Author(s) -
Kai Guo,
Masha G. Savelieff,
Amy E. Rumora,
Fadhl Alakwaa,
Brian C. Callaghan,
Junguk Hur,
Eva L. Feldman
Publication year - 2021
Publication title -
the journal of clinical endocrinology and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.206
H-Index - 353
eISSN - 1945-7197
pISSN - 0021-972X
DOI - 10.1210/clinem/dgab844
Subject(s) - lipidomics , metabolomics , peripheral neuropathy , peripheral , medicine , bioinformatics , endocrinology , biology , diabetes mellitus
Context Peripheral neuropathy (PN) is a frequent prediabetes and type 2 diabetes (T2D) complication. Multiple clinical studies reveal that obesity and dyslipidemia can also drive PN progression, independent of glycemia, suggesting a complex interplay of specific metabolite and/or lipid species may underlie PN. Objective This work aimed to identify the plasma metabolomics and lipidomics signature that underlies PN in an observational study of a sample of individuals with average class 3 obesity. Methods We performed plasma global metabolomics and targeted lipidomics on obese participants with (n = 44) and without PN (n = 44), matched for glycemic status, vs lean nonneuropathic controls (n = 43). We analyzed data by Wilcoxon, logistic regression, partial least squares–discriminant analysis, and group-lasso to identify differential metabolites and lipids by obesity and PN status. We also conducted subanalysis by prediabetes and T2D status. Results Lean vs obese comparisons, regardless of PN status, identified the most significant differences in gamma-glutamyl and branched-chain amino acid metabolism from metabolomics analysis and triacylglycerols from lipidomics. Stratification by PN status within obese individuals identified differences in polyamine, purine biosynthesis, and benzoate metabolism. Lipidomics found diacylglycerols as the most significant subpathway distinguishing obese individuals by PN status, with additional contributions from phosphatidylcholines, sphingomyelins, ceramides, and dihydroceramides. Stratifying the obese group by glycemic status did not affect discrimination by PN status. Conclusion Obesity may be as strong a PN driver as prediabetes or T2D in a sample of individuals with average class 3 obesity, at least by plasma metabolomics and lipidomics profile. Metabolic and complex lipid pathways can differentiate obese individuals with and without PN, independent of glycemic status.

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