Correlation between Acylcarnitine and Peripheral Neuropathy in Type 2 Diabetes Mellitus

An, Zhen-ni; Zheng, Danmeng; Wei, David; Jiang, Ding-wen; Xing, Xuejiao; Fu, Weili

doi:10.1155/2022/8115173

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…DPN is one of the most common chronic complications of diabetes [ 15 , 16 ]. It is characterized by insidiousness and difficulty to detect.…”

Section: Discussionmentioning

confidence: 99%

A Retrospective Study on the Time in Range of Blood Glucose and Type 2 Diabetic Peripheral Neuropathy

Chang

et al. 2022

BioMed Research International

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Background. Time in range (TIR) is one of the basic indicators to assess glycemic control. In this study, the TIR of DPN patients was used as the observation index to further evaluate the correlation between TIR and DPN, so as to provide new ideas for preventing the occurrence of DPN and delaying its disease progression. Methods. A total of 120 patients with T2DM (T2DM) who were hospitalized in the Endocrinology Department of our hospital from October 2018 to February 2020 were included and divided into two groups according to whether the nerve conduction velocity was normal or not, the diabetic peripheral neuropathy group (DPN) and the other groups. No diabetic peripheral neuropathy group (NDPN). According to the corresponding inclusion and exclusion criteria, the baseline data were recorded, and test indicators such as homocysteine and blood lipids were collected at the same time, and TIR was collected by a transient blood glucose meter. To explore the relationship between TIR and other indicators and peripheral neuropathy in T2DM. Results. A total of 120 T2DM patients participated in the study, including 82 in the DPN group and 38 in the NDPN group. There were no statistically significant differences in basic indicators such as age, height, and weight between the two groups. Glycated hemoglobin (HbA1c) and homocysteine (Hcy) in DPN group were higher than those in NDPN group, while TIR and HDL-C were lower than those in NDPN group ( P < 0.05 ). Logistic regression analysis showed that HbA1c and Hcy were risk factors for DPN, and TIR and HDL-C were protective factors for DPN, with statistical significance ( P < 0.05 ). The prediction results of TIR, Hcy, HDL-C, and HbA1c on diabetic peripheral neuropathy were analyzed by ROC curve, and the prediction results of the five variables were all statistically significant ( P < 0.05 ) and have a better prediction effect. Conclusion. (1) The results of TIR level suggest that the longer the blood sugar is in the good control range, the more beneficial it is to reduce the occurrence of DPN. (2) TIR and HDL-C are protective factors for DPN, and HbA1c and Hcy are risk factors for DPN. (3) The results of ROC curve analysis showed that TIR, Hcy, HbA1c, and HDL-C had a good predictive effect on the occurrence of DPN.

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“…DPN is one of the most common chronic complications of diabetes [ 15 , 16 ]. It is characterized by insidiousness and difficulty to detect.…”

Section: Discussionmentioning

confidence: 99%

A Retrospective Study on the Time in Range of Blood Glucose and Type 2 Diabetic Peripheral Neuropathy

Chang

et al. 2022

BioMed Research International

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“…A 6-year prospective study of 2103 community-dwelling individuals aged 50-70 years in Beijing and Shanghai, China, with type 2 diabetes as the observed outcome found higher plasma concentrations of short-, medium-, and long-chain acylcarnitines at baseline, but only long-chain acylcarnitines were significantly associated with the risk of type 2 diabetes [34]. A previous study found that elevated levels of short-and medium-chain acylcarnitines in blood were associated with the risk of developing cardiovascular disease in T2DM [35]. A study on diabetic peripheral neuropathy (DPN) claimed that C4DC and C24 concentrations in non-DPN plasma were significantly higher than in DPN patients and that factors containing C2, C3, C4, and C5 short-chain acylcarnitines were positively associated with the risk of DPN in T2DM [36].…”

Section: Discussionmentioning

confidence: 81%

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

Yin,

Li,

Xue

et al. 2024

Journal of Diabetes Research

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The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

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“…Acylcarnitines are involved in fatty acid β-oxidation and the branched-chain amino acid metabolism, including valine, leucine, and isoleucine, associated with the progression of AIDs ( 31 , 32 ). Observational research has established a positive correlation between blood acylcarnitine levels and peripheral neuropathy in type 2 diabetes patients ( 33 ). It is also suggested that IVC may accelerate the progression of rheumatoid arthritis ( 4 ).…”

Section: Discussionmentioning

confidence: 99%

Causality of metabolites and metabolic pathways on cholestatic liver diseases: a Mendelian randomization study

Wei,

Liu,

Xiong

et al. 2024

Front. Med.

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Background and AimsBlood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC).MethodsUnivariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination.ResultsA genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471–6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949–7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068–0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801–16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621–17.399, p = 7.64 × 10−5), X-11847 (OR = 0.478, 95%CI: 0.352–0.650, p = 2.28 × 10−6), and X-12405 (OR = 3.765, 95%CI: 1.771–8.005, p = 5.71 × 10−4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs.ConclusionThe findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.

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Correlation between Acylcarnitine and Peripheral Neuropathy in Type 2 Diabetes Mellitus

Cited by 4 publications

References 41 publications

A Retrospective Study on the Time in Range of Blood Glucose and Type 2 Diabetic Peripheral Neuropathy

A Retrospective Study on the Time in Range of Blood Glucose and Type 2 Diabetic Peripheral Neuropathy

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

Causality of metabolites and metabolic pathways on cholestatic liver diseases: a Mendelian randomization study

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