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. 2025 Feb;16(2):246-256.
doi: 10.1111/jdi.14355. Epub 2024 Nov 16.

Blood metabolomic profile in patients with type 2 diabetes mellitus with diabetic peripheral neuropathic pain

Hung-Chou Kuo  1   2 , Chia-Ni Lin  3   4 , Sung-Sheng Tsai  2   5 , Chiung-Mei Chen  1   2 , Rong-Kuo Lyu  1   2 , Chun-Che Chu  1   2 , Long-Sun Ro  1   2 , Ming-Feng Liao  1   2 , Hong-Shiu Chang  1   2 , Yi-Ching Weng  1   2 , Jawl-Shan Hwang  2   5
Affiliations

Blood metabolomic profile in patients with type 2 diabetes mellitus with diabetic peripheral neuropathic pain

Hung-Chou Kuo et al. J Diabetes Investig. 2025 Feb.

Abstract

Aims: This study aimed to identify metabolic markers for diabetic peripheral neuropathic pain (DPNP) in patients with type 2 diabetes mellitus (T2DM).

Materials and methods: Blood metabolite levels in the amino acid, biogenic amine, sphingomyelin, phosphatidylcholine (PC), carnitines, and hexose classes were analyzed in nondiabetic control (n = 27), T2DM without DPNP (n = 58), and T2DM with DPNP (n = 29) using liquid chromatography tandem mass spectrometry. Variable importance projection (VIP) evaluation by partial least squares discriminant analysis was performed on clinical parameters and metabolites.

Results: Sixteen variables with VIP > 1.0 (P < 0.05) were identified across all patient groups, and 5 variables were identified to discriminate between the two T2DM groups. DPNP patients showed elevated fasting blood glucose, glutamate, PC aa C36:1, lysoPC a C18:1, and lysoPC a C18:2, while low-density lipoprotein cholesterol, phenylalanine, and tryptophan were reduced. Glutamate, lysoPC a C18:1, and lysoPC a C18:2 discriminated T2DM with DPNP from those without DPNP with an AUC of 0.671. The AUC was improved to 0.765 when ratios of metabolite pairs were considered.

Interpretation: Blood metabolites include glutamate, and phospholipid-related metabolites implicated in neuropathic pain may have the potential as biomarkers for DPNP. Further investigation is required to understand the mechanism of action of these altered metabolites in DPNP.

Keywords: Diabetic peripheral neuropathic pain; Metabolomics; Type 2 diabetes mellitus.

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Conflict of interest statement

The authors declare no conflict of interest.

Approval of the research protocol: Approval was obtained from the ethics committee of Chang Gung Memorial Hospital (IRB No. 201700994B0, 201702035B0, and 201902169B0). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Informed consent: Individual consent for this retrospective analysis was waived.

Approval date of registry and the registration no. of the study/trial: N/A.

Animal studies: N/A.

Figures

Figure 1
Figure 1
Targeted metabolites and clinical variables in separating nondiabetic control, T2DM without DPNP, and T2DM with DPNP patients. A total of 10 clinical variables* and 130 metabolites were included in the PCA (a) and PLS‐DA (b) in separating nondiabetic control subjects (n = 27), T2DM patients without DPNP (n = 58), and T2DM patients with DPNP (n = 29). (c) the metabolites, and clinical variables with VIP score >1.0, indicating their contribution to patient separation in the PLS‐DA model. *, age, BMI, AC, Hb‐Alc, T‐CHOL, triglyceride, HDL‐C, LDL‐C, creatinine, and eGFR. AC, fasting blood glucose; BMI, body mass index; DPNP, diabetic peripheral neuropathic pain; eGFR: estimated glomerular filtration rate; HDL‐C high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; PC, phosphatidylcholine; PCA, principal component analysis; PLS‐DA, partial least squares discriminant analysis; SM, sphingomyelin; T2DM, type 2 diabetes mellitus; T‐CHOL, total cholesterol; VIP, variable importance in the projection.
Figure 2
Figure 2
Targeted metabolites and clinical variables in discriminating T2DM without patients with and without DPNP. A total of 14 clinical variables* and 130 metabolites were included in the PCA (a) and PLS‐DA (b) in discriminating T2DM patients without DPNP (n = 58) and T2DM patients with DPNP (n = 29). (c) the metabolites and clinical variables with VIP score >1.0, indicating their contribution to patient separation in the PLS‐DA model. *, age, BMI, AC, Hb‐Alc, T‐CHOL, triglyceride, HDL‐C, LDL‐C, B12, creatinine, hemoglobin, eGFR, mMNSI, and NCS total score. AC, fasting blood glucose; B12, vitamin B12; BMI, body mass index; DPNP, diabetic peripheral neuropathic pain; eGFR: estimated glomerular filtration rate; HDL‐C high‐density lipoprotein cholesterol; LDL‐C low‐density lipoprotein cholesterol; lysoPC, lysophosphatidylcholine; mMNSI, modified Michigan Neuropathy Screening Instrument; NCS, nerve conduction studies; PC, phosphatidylcholine; PCA, principal component analysis; PLS‐DA, partial least squares discriminant analysis; SM, sphingomyelin; T2DM, type 2 diabetes mellitus; T‐CHOL, total cholesterol; VIP, variable importance in the projection.
Figure 3
Figure 3
ROC analysis of potential metabolite biomarker pairs to discriminate T2DM with or without DPNP using logistic regression. The marker features included in the model were: Glutamate/Sarcosine, PC aa C36:0, Sarcosine/PC ae C40:1, LDL/C14:1. LDL/lysoPC a C18:2, Citrulline/lysoPC a C18:2, Citrulline/C14:1, LDL/PC ae C40:1. The area under ROC curve (AUC) with 95% CI is presented.
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