Relationship Between Acylcarnitine and the Risk of Retinopathy in Type 2 Diabetes Mellitus

Wang, Wanying; Liu, Xu; Gao, Xiaoqian; Li, Xin; Fang, Zhong‐Ze

doi:10.3389/fendo.2022.834205

Cited by 6 publications

(7 citation statements)

References 44 publications

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“…Notably, in our study, after adjusting for the total ACars level, the odds ratio between majority of long-chain ACars and DR changed from less than 1 to greater than 1. Therefore, the negative association of long-chain ACars and DR in the previous study ( 14 ) should be interpreted with caution. In contrast, the inverse association between medium-chain ACars and the risk of DR is robust with or without correction of the total ACar level.…”

Section: Discussionmentioning

confidence: 74%

“…Wang et al. ( 14 ) found that individuals with DR were more likely to had higher levels of plasma Acars than those without DR. However, an untargeted metabolomics study showed that increased levels of ACars were related to the higher risk of DR ( 6 ).…”

Section: Discussionmentioning

confidence: 99%

“…An untargeted metabolomics study via high-resolution mass spectrometry found that plasma levels of deoxynivalenol in patients with diabetic retinopathy were higher than in diabetic controls ( 6 ). Another more intensive study from northern China found that multiple ACars (C2, C14DC, C16, C18:1OH, C18:1) were negatively associated with risk of retinopathy in patients with type 2 diabetes ( 14 ). However, the evidence based on population remains limited and the relationship of ACars with DR is controversial.…”

Section: Introductionmentioning

confidence: 99%

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Plasma acylcarnitine and diabetic retinopathy: A study from Eastern China

Jin

Zhao²,

Li³

et al. 2022

Front. Endocrinol.

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Background and purposeAcylcarnitines (ACars) are important for insulin resistance and type 2 diabetes (T2D). However, their roles in diabetic retinopathy (DR) remain controversial. In this study, we aimed to investigate the association of ACars with DR and their values in DR detection.MethodsThis was a two-center case-control study based on the propensity score matching approach between August 2017 to June 2018 in Eastern China. Multivariable logistic regression models were applied to estimate the association of plasma ACars with DR. Differential ACars were screened by models of least absolute shrinkage and selection operator, elastic net, and weighted quantile sum regression, and their roles in DR identification were further evaluated by the area under the receiver operating curve (AUC).ResultsEight of twenty plasma ACars (8:0, 12:0, 12:1, 14:1, 16:2, 18:0, 18:2 and 18:3) were associated with DR, while only ACar 8:0 was selected by three variable selection methods. As compared to those with the 1st tertile of ACar 8:0, the adjusted odds ratio (OR) and 95% confidence interval (CI) of DR were 0.22 (0.08, 0.59) and 0.12 (0.04, 0.36) for subjects in the 2nd and 3rd tertiles, respectively (P for trend < 0.001). Consistent associations were also observed in both restricted cubic spline regression models and subgroup analyses. AUC (95% CI) were 0.74 (0.66, 0.82) for ACar 8:0 alone and 0.77 (0.70, 0.85) for ACar 8:0 combined with covariates.ConclusionsOur findings suggest higher ACar 8:0 is significantly associated with a decreased risk of DR, which provides a unique window for early identification of DR.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Plasma acylcarnitine and diabetic retinopathy: A study from Eastern China

Jin

Zhao²,

Li³

et al. 2022

Front. Endocrinol.

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“…Glucose is the main energetic substrate; however, when glucose is not available as an energetic substrate in insulin resistance states, the cell uses free fatty acids, lipids, and amino acids as alternative substrates, causing an imbalance between acylcarnitines and amino acids [ 56 ]. This metabolic shift causes the accumulation of intermediary substrates, such as acylcarnitines that could be implied to interfere with insulin sensitivity, causing insulin resistance and the onset of diabetes.…”

Section: Discussionmentioning

confidence: 99%

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

Razo-Azamar,

Nambo-Venegas,

Meraz-Cruz

et al. 2023

Diabetol Metab Syndr

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Background Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM. Methods A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM. Results Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873–0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015–0.25 μmol/L and of C5:1 with a range of 0.015–0.19 μmol/L. Conclusion Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

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“…For proliferative diabetic retinopathy (PDR) in diabetic patients, the AA and acylcarnitine (AcylCN) profiles were different from non-PDR patients [ 4 , 6 , 7 ]. In our previous studies, tyrosine, phenylalanine, and several long-chain AcylCN were also found to be correlated with DR [ 8 , 9 ]. Thus, AA and AcylCN may have potential as new biomarkers for predicting DR.…”

Section: Introductionmentioning

confidence: 99%

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes

Zong,

Wang,

Zheng

et al. 2023

Journal of Diabetes Research

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The burden of diabetic retinopathy (DR) is increasing, and the sensitive biomarkers of the disease were not enough. Studies have found that the metabolic profile, such as amino acid (AA) and acylcarnitine (AcylCN), in the early stages of DR patients might have changed, indicating the potential of metabolites to become new biomarkers. We are amid to construct a metabolite-based prediction model for DR risk. This study was conducted on type 2 diabetes (T2D) patients with or without DR. Logistic regression and extreme gradient boosting (XGBoost) prediction models were constructed using the traditional clinical features and the screening features, respectively. Assessing the predictive power of the models in terms of both discrimination and calibration, the optimal model was interpreted using the Shapley Additive exPlanations (SHAP) to quantify the effect of features on prediction. Finally, the XGBoost model incorporating AA and AcylCN variables had the best comprehensive evaluation ( ROCAUC = 0.82 , PRAUC = 0.44 , Brier score = 0.09 ). C18 : 1OH lower than 0.04 μmol/L, C18 : 1 lower than 0.70 μmol/L, threonine higher than 27.0 μmol/L, and tyrosine lower than 36.0 μmol/L were associated with an increased risk of developing DR. Phenylalanine higher than 52.0 μmol/L was associated with a decreased risk of developing DR. In conclusion, our study mainly used AAs and AcylCNs to construct an interpretable XGBoost model to predict the risk of developing DR in T2D patients which is beneficial in identifying high-risk groups and preventing or delaying the onset of DR. In addition, our study proposed possible risk cut-off values for DR of C18 : 1OH, C18 : 1, threonine, tyrosine, and phenylalanine.

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Relationship Between Acylcarnitine and the Risk of Retinopathy in Type 2 Diabetes Mellitus

Cited by 6 publications

References 44 publications

Plasma acylcarnitine and diabetic retinopathy: A study from Eastern China

Plasma acylcarnitine and diabetic retinopathy: A study from Eastern China

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes

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