ALT/AST as an Independent Risk Factor of Gestational Diabetes Mellitus Compared with TG/HDL-C

Song, Shen; Zhang, Y; Xiaolin, Qiao; Y, Duo; Xu, Jing; Peng, Zhenyao; Zhang, J; Chen, Y; Nie, Xin; Sun, Qing‐Yuan; Yang, Xianchun; Wang, A; Zhang, Lu; Sun, Wei; Fu, Yue; Dong, Yingyue; Yuan, Tao; Zhao, Weigang

doi:10.2147/ijgm.s332946

Cited by 13 publications

(16 citation statements)

References 31 publications

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“…However, only one study investigated the relationship between AST/ALT levels in the first trimester and GDM. Consistent with our study, they reported that lower AST/ALT in early pregnancy, even within the normal range, was an independent risk factor for GDM ( 17 ). Compared to this study, our study included and adjusted for more confounding variables to make the results more reliable.…”

Section: Discussionsupporting

confidence: 91%

“…Since the pathogenesis of GDM is similar to that of T2DM, it is suggested that AST/ALT ratio may correlate with the risk of GDM. To date, only one study has focused on the association between AST/ALT in early pregnancy and subsequent GDM ( 17 ), which reported that ALT/AST was independently associated with the incidence of GDM. However, some potential confounding factors were not adjusted in this study, and AST/ALT predictive value in GDM have not valued.…”

Section: Introductionmentioning

confidence: 99%

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AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus

Zhang

et al. 2022

Front. Endocrinol.

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BackgroundAspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) has been reported affect the risk of type 2 diabetes (T2DM), but it is uncertain if it has relationship with gestational diabetes mellitus (GDM).ObjectivesOur study aimed to investigate the association between AST/ALT ratio in the first trimester and the risk of subsequent development of GDM.MethodThis prospective cohort study enrolling 870 pregnant women, 204 pregnant women with missing data or liver diseases were excluded, 666 pregnant women were included in this study containing 94 GDM women. Blood samples were collected in the first trimester. Univariate analysis and multivariate logistic regression were used to evaluate the association between AST/ALT and GDM. Nomogram was established based on the results of multivariate logistic analysis. Receiver Operating Characteristic (ROC) curves and calibration curves were used to evaluate the predictive ability of this nomogram model for GDM. Decision curve analysis (DCA) was used to examine the clinical net benefit of predictive model.ResultsAST/ALT ratio (RR:0.228; 95% CI:0.107-0.488) was associated with lower risk of GDM after adjusting for confounding factors. Indicators used in nomogram including AST/ALT, maternal age, preBMI, waist circumference, glucose, triglycerides, high density lipoprotein cholesterol and parity. The area under the ROC curve (AUC) value of this predictive model was 0.778, 95% CI (0.724, 0.832). Calibration curves for GDM probabilities showed acceptable agreement between nomogram predictions and observations. The DCA curve demonstrated a good positive net benefit in the predictive model.ConclusionsThe early AST/ALT level of pregnant women negatively correlated with the risk of GDM. The nomogram including AST/ALT at early pregnancy shows good predictive ability for the occurrence of GDM.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus

Zhang

et al. 2022

Front. Endocrinol.

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“…In several previous studies, serum ALT and AST values did not change during pregnancy or remain within the normal range compared with women who are not pregnant (38,42). A previous report analysing the association between GDM and the ratio of ALT/AST showed that ALT/AST was higher in the GDM group compared with that in the control group (43). The relationship between transaminases and macrosomia has also been analysed previously, where a positive correlation between infant birth weight and ALT levels in macrosomic babies has been observed (44).…”

Section: Discussionmentioning

confidence: 90%

New born macrosomia in gestational diabetes mellitus

Bernea¹,

Uyy²,

Mihai³

et al. 2022

Exp Ther Med

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Gestational diabetes mellitus (GDM) is a metabolic complication of pregnancy. The pathogenesis of GDM is considered to involve β-cell dysfunction and insulin resistance (IR). GDM is associated with a significant risk of macrosomia in addition to a high probability of metabolic complications for the offspring. The precise mechanism underlying GDM remains unclear. The aim of the present study was to analyse the factors associated with insulin resistance and β-cell dysfunction involved in the pathophysiology of GDM complicated with macrosomia compared with GDM without macrosomia. In addition, another aim of the present study was to assess the relationship between GDM complicated with macrosomia and anthropometric, clinical and paraclinical parameters. The following group of patients were recruited as part of a case-control study: Patients with GDM without macrosomia, patients with GDM complicated with macrosomia and healthy gestational controls. Blood samples were collected at the third trimester of pregnancy and tested for adiponectin, leptin, insulin, proinsulin and C-peptide. Homeostatic model assessment-IR (HOMA-IR), steady state β-cell function (HOMA%B), insulin sensitivity (HOMA%S) and body mass index (BMI) were also calculated. All patients diagnosed with GDM showed an impairment in HOMA%B and a decrease in C-peptide maternal serum concentration. Additionally, diabetic status leading to the birth of offspring with macrosomia did not induce changes in the maternal serum levels of insulin, proinsulin, adiponectin or leptin, which was also the case in patients with GDM but not macrosomia. HOMA%B presented a stronger positive correlation with pre-pregnancy BMI and maternal weight gain, and a stronger negative correlation with adiponectin. Furthermore, HOMA%S in this group exhibited strong positive correlations with maternal serum levels of high-density lipoprotein cholesterol (HDL) and aspartate aminotransferase, and a strong negative correlation with pre-pregnancy BMI. In the same patients, HOMA-IR was also found to have a high negative correlation with HDL levels, and highly positive correlations with gestational age and triglyceride levels. In conclusion, the present study suggests that the different correlations among the factors involved in the pathogenesis of GDM may explain the evolution of GDM pregnancy to macrosomia.

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“…In contrast, PCa in its early clinical stage is often insidious, and most patients only seek treatment when elevated PSA is detected during routine physical examination. In previous studies, LSR has been applied in the evaluation of gestational diabetes ( 21 ), diagnosis of cirrhosis ( 22 ), and prognosis of different cancers ( 39 , 40 ). The levels of alanine aminotransferase and aspartate aminotransferase may be affected by obesity ( 41 , 42 ), and fluctuations in these two indicators may influence the diagnosis of PCa in a similar way that BMI influences PCa.…”

Section: Discussionmentioning

confidence: 99%

“…For example, PSA-derived indices, such as free-to-total PSA (F/T) values and prostate-specific antigen density (PSAD), have been found to exhibit greater diagnostic accuracy than PSA alone ( 12 – 14 ). In addition, several inflammatory and neurotrophic markers, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio ( 15 , 16 ), gamma-glutamyl transpeptidase-to-lymphocyte count ratio ( 17 , 18 ), red cell distribution width-to-platelet ratio ( 19 , 20 ), and alanine transaminase-to-aspartate transaminase ratio ( 21 , 22 ), have previously been shown to have predictive value not only as inflammatory markers but also as indicators for the diagnosis and prognosis of malignancies. To date, studies focusing on the role of these composite indicators in the differential diagnosis of PCa are limited.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Deng

et al. 2022

Front. Oncol.

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ObjectiveThe aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.MethodsA total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.ResultsProstate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.ConclusionThe RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

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ALT/AST as an Independent Risk Factor of Gestational Diabetes Mellitus Compared with TG/HDL-C

Cited by 13 publications

References 31 publications

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus

New born macrosomia in gestational diabetes mellitus

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

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