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Zhu YT, Xiang LL, Chen YJ, Zhong TY, Wang JJ, Zeng Y. Developing and validating a predictive model of delivering large-for-gestational-age infants among women with gestational diabetes mellitus. World J Diabetes 2024; 15:1242-1253. [PMID: 38983822 PMCID: PMC11229959 DOI: 10.4239/wjd.v15.i6.1242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/05/2024] [Accepted: 04/25/2024] [Indexed: 06/11/2024] Open
Abstract
BACKGROUND The birth of large-for-gestational-age (LGA) infants is associated with many short-term adverse pregnancy outcomes. It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus (GDM) is significantly higher than that born to healthy pregnant women. However, traditional methods for the diagnosis of LGA have limitations. Therefore, this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants. AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA. METHODS The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was < 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value < 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses. RESULTS After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P < 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram's prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit. CONCLUSION Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.
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Affiliation(s)
- Yi-Tian Zhu
- Department of Clinical Laboratory, Jinling Clinical Medical College of Nanjing Medical University, Nanjing 210002, Jiangsu Province, China
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210003, Jiangsu Province, China
| | - Lan-Lan Xiang
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210003, Jiangsu Province, China
| | - Ya-Jun Chen
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210003, Jiangsu Province, China
| | - Tian-Ying Zhong
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210003, Jiangsu Province, China
| | - Jun-Jun Wang
- Department of Clinical Laboratory, Jinling Clinical Medical College of Nanjing Medical University, Nanjing 210002, Jiangsu Province, China
| | - Yu Zeng
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing 210003, Jiangsu Province, China
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Yin JL, Yang J, Song XJ, Qin X, Chang YJ, Chen X, Liu FH, Li YZ, Xu HL, Wei YF, Cao F, Bai XL, Wu L, Tao T, Du J, Gong TT, Wu QJ. Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies. Cardiovasc Diabetol 2024; 23:177. [PMID: 38783270 PMCID: PMC11118729 DOI: 10.1186/s12933-024-02241-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 04/20/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Numerous meta-analyses have explored the association between the triglyceride-glucose (TyG) index and diverse health outcomes, yet the comprehensive assessment of the scope, validity, and quality of this evidence remains incomplete. Our aim was to systematically review and synthesise existing meta-analyses of TyG index and health outcomes and to assess the quality of the evidence. METHODS A thorough search of PubMed, EMBASE, and Web of Science databases was conducted from their inception through to 8 April 2024. We assessed the quality of reviews using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. This study was registered with PROSPERO (CRD: 42024518587). RESULTS Overall, a total of 95 associations from 29 meta-analyses were included, investigating associations between TyG index and 30 health outcomes. Of these, 83 (87.4%) associations were statistically significant (P < 0.05) according to the random effects model. Based on the AMSTAR tool, 16 (55.2%) meta-analyses were high quality and none was low quality. The certainty of the evidence, assessed by the GRADE framework, showed that 6 (6.3%) associations were supported by moderate-quality evidence. When compared with the lowest category of the TyG index, the risk of contrast-induced nephropathy (CIN) [relative risk (RR) = 2.25, 95%CI 1.82, 2.77], the risk of stroke in patients with diabetes mellitus (RR = 1.26, 95%CI 1.18, 1.33) or with acute coronary syndrome disease (RR = 1.56, 95%CI 1.06, 2.28), the prognosis of coronary artery disease (CAD)-non-fatal MI (RR = 2.02, 95%CI 1.32, 3.10), and the severity of CAD including coronary artery stenosis (RR = 3.49, 95%CI 1.71, 7.12) and multi-vessel CAD (RR = 2.33, 95%CI 1.59, 3.42) increased with high TyG index. CONCLUSION We found that the TyG index was positively associated with many diseases including the risk of CIN and stroke, the prognosis of CAD, and the severity of CAD which were supported by moderate-quality evidence. TyG index might be useful to identify people at high-risk for developing these diseases.
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Affiliation(s)
- Jia-Li Yin
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Jing Yang
- Department of Endocrinology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Xin-Jian Song
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Xue Qin
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu-Jiao Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xing Chen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yi-Zi Li
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yi-Fan Wei
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fan Cao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xue-Li Bai
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Tao Tao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Jian Du
- Department of Endocrinology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China.
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China.
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China.
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility, (China Medical University), National Health Commission, Shenyang, China.
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Ma N, Bai L, Lu Q. First-Trimester Triglyceride-Glucose Index and Triglyceride/High-Density Lipoprotein Cholesterol are Predictors of Gestational Diabetes Mellitus Among the Four Surrogate Biomarkers of Insulin Resistance. Diabetes Metab Syndr Obes 2024; 17:1575-1583. [PMID: 38616992 PMCID: PMC11015049 DOI: 10.2147/dmso.s454826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/23/2024] [Indexed: 04/16/2024] Open
Abstract
Purpose This study seeks to assess the potential of early pregnancy Triglyceride Glucose Index (TyG), triglyceride to High-Density Lipoprotein Cholesterol ratio (TG/HDL-c), Low-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol ratio (LDL-C/HDL-C), and Total Cholesterol to High-Density Lipoprotein Cholesterol ratio (TC/HDL-C) in predicting Gestational Diabetes Mellitus (GDM). Patients and Methods A total of 1073 adults singleton pregnant women were enrolled from June 2017 to September 2019. Complete anthropometric data and lipid profiles were measured in the first trimester (before 12 weeks gestation) and a 75g oral glucose tolerance test (OGTT) at 24-28 weeks was performed. Based on OGTT results, participants were categorised into Normal Glucose Tolerance (NGT) group (n=872) and GDM group (n=201). General data, laboratory test results, and surrogate insulin resistance indicators such as TyG index, TG/HDL-C, LDL-C/HDL-C, and TC/HDL-C were documented and compared. To compare differences between the two groups, t-test was used, Spearman correlation analysis and linear regression analysis were performed to establish associations between these indicators and insulin resistance in GDM. Receiver Operating Characteristic (ROC) curves were generated to compare the thresholds of these indicators for predicting GDM during pregnancy and to quantify overall diagnostic accuracy. Results Individuals with GDM had higher TyG, TG/HDL-C, and LDL-C/HDL-C levels (P < 0.001), but with no significant difference observed in TC/HDL-C. All four ratios were positively correlated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), yet only TyG emerged as an independent risk factor for HOMA-IR. The Area under the Curve (AUC) of TyG index (0.692) was comparable to that of HOMA-IR (0.703). The cut-off points for TyG index, TG/HDL-C, and HOMA-IR in predicting GDM were 7.088, 0.831, and 1.8, respectively. HOMA-IR exhibited the highest sensitivity (79.1%), while TyG index (64.3%) and TG/HDL-C ratio (64.3%) demonstrated better specificity compared to HOMA-IR (56.3%). LDL-C/HDL-C and TC/HDL-C offered no discernible predictive advantage. Conclusion Early pregnancy TyG index and TG/HDL-C can aid in identifying pregnant women at risk for GDM, potentially facilitating early and effective intervention to improve prognosis. TyG index exhibited superior predictive capability compared to TG/HDL-C.
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Affiliation(s)
- Ning Ma
- Department of Endocrinology, First Hospital of Qinhuangdao, Hebei, Qinhuangdao, 066000, People’s Republic of China
| | - Liwei Bai
- Qinhuangdao Hospital for Maternal and Child Health, Hebei, Qinhuangdao, 066000, People’s Republic of China
| | - Qiang Lu
- Department of Endocrinology, First Hospital of Qinhuangdao, Hebei, Qinhuangdao, 066000, People’s Republic of China
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Mo Z, Cao C, Han Y, Hu H, He Y, Zuo X. Relationships between triglyceride-glucose index and incident gestational diabetes mellitus: a prospective cohort study of a Korean population using publicly available data. Front Public Health 2024; 12:1294588. [PMID: 38414896 PMCID: PMC10896852 DOI: 10.3389/fpubh.2024.1294588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/29/2024] [Indexed: 02/29/2024] Open
Abstract
Background The connection between the triglyceride-glucose index (TyG index) and gestational diabetes mellitus (GDM) is currently debated. Our study aimed to investigate the connection between the TyG index and GDM within the Korean population. Methods Using publically accessible data in Korea, we performed a secondary study on a sample of 589 pregnant women who were carrying a single fetus. The analysis employed a binary logistic regression model, some sensitivity analyses, and subgroup analysis to investigate the association between the TyG index and the occurrence of GDM. To assess the TyG index's potential to predict GDM, a receiver operating characteristic (ROC) study was also carried out. Results The mean age of the pregnant women was 32.065 ± 3.798 years old, while the mean TyG index was 8.352 ± 0.400. The prevalence rate of GDM was found to be 6.112%. Upon adjusting for potential confounding variables, a positive association was detected between the TyG index and incident GDM (OR = 12.923, 95%CI: 3.581-46.632, p = 0.00009). The validity of this connection was further confirmed by subgroup analysis and sensitivity analyses. With an area under the ROC curve of 0.807 (95%CI: 0.734-0.879), the TyG index showed strong predictive power for GDM. The TyG index's ideal cutoff value for detecting GDM was found to be 8.632, with a sensitivity of 78.7% and a specificity of 72.2%. Conclusion The findings of our study provide evidence that an increased TyG index is significantly associated with the occurrence of GDM. Utilizing the TyG index during the 10-14 week gestational period may be a valuable tool in identifying pregnant individuals at a heightened risk for developing GDM. Early detection enables timely and efficacious interventions, thereby enhancing the prognosis of affected individuals.
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Affiliation(s)
- Zihe Mo
- Department of Physical Examination, DongGuan Tungwah Hospital, Dongguan, Guangdong, China
| | - Changchun Cao
- Department of Rehabilitation, Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, Guangdong, China
| | - Yong Han
- Department of Emergency, Shenzhen Second People's Hospital and The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Haofei Hu
- Department of Nephrology, Shenzhen Second People's Hospital and The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Yongcheng He
- Department of Nephrology, Shenzhen Hengsheng Hospital, Shenzhen, Guangdong, China
- Department of Nephrology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Xin Zuo
- Department of Endocrinology, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China
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Cao Y, Sheng J, Zhang D, Chen L, Jiang Y, Cheng D, Su Y, Yu Y, Jia H, He P, Wang L, Xu X. The role of dietary fiber on preventing gestational diabetes mellitus in an at-risk group of high triglyceride-glucose index women: a randomized controlled trial. Endocrine 2023; 82:542-549. [PMID: 37737931 DOI: 10.1007/s12020-023-03478-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/01/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND Pregnant women with a high triglyceride-glucose (TyG) index during early pregnancy may increase the risk of gestational diabetes mellitus (GDM), and dietary fiber could play an important role in glucose and lipid metabolism. However, no trials have tested the effects of dietary fiber on preventing GDM in women with a high TyG index. This study aims to investigate whether GDM can be prevented by dietary fiber supplementation in women with a TyG index ≥8.5 during early pregnancy (<20 weeks). METHODS A randomized clinical trial was performed among 295 women with a TyG index ≥8.5 before 20 weeks of gestation, divided into a fiber group (24 g dietary fiber powder/day) or a control group (usual care). The intervention was conducted from 20 to 24+6 gestational weeks, and both groups received guidance on exercise and diet. The primary outcomes were the incidence of GDM diagnosed by a 75 g oral glucose tolerance test at 25-28 gestational weeks, and levels of maternal blood glucose, lipids. Secondary outcomes include gestational hypertension, postpartum hemorrhage, preterm birth, and other maternal and neonatal complications. RESULTS GDM occurred at 11.2% (10 of 89) in the fiber group, which was significantly lower than 23.7 (44 of 186) in the control group (P = 0.015). The mean gestational weeks increased dramatically in the fiber group compared with the control group (39.07 ± 1.08 vs. 38.58 ± 1.44 weeks, P = 0.006). The incidence of preterm birth was 2.3% (2 of 86) of women randomized to the fiber group compared with 9.4% (17 of 181) in the control group (P = 0.032). The concentrations of 2 h postprandial blood glucose showed statistically higher in the control group compared with the intervention group (6.69 ± 1.65 vs. 6.45 ± 1.25 mmol/L, P = 0.026). There were no other significant differences between groups in lipid profile values, or other secondary outcomes. CONCLUSION An intervention with dietary fiber supplementation during pregnancy may prevent GDM and preterm birth in women with a TyG index ≥8.5 before 20 weeks of gestation.
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Affiliation(s)
- Yannan Cao
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Jing Sheng
- Department of Radiology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, China
| | - Dongyao Zhang
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
- Department of Obstetrics and Gynecology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Li Chen
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Ying Jiang
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
- Nursing Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Decui Cheng
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yao Su
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yuexin Yu
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Haoyi Jia
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Pengyuan He
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Li Wang
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Xianming Xu
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China.
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Lin Q, Fang ZJ. Establishment and evaluation of a risk prediction model for gestational diabetes mellitus. World J Diabetes 2023; 14:1541-1550. [PMID: 37970129 PMCID: PMC10642414 DOI: 10.4239/wjd.v14.i10.1541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/21/2023] [Accepted: 09/14/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM. AIM To develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention. METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified. RESULTS Pre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM (P < 0.05). According to the nomogram model's prediction of GDM in pregnancy, the area under the curve (AUC) was determined to be 0.883 [95% confidence interval (CI): 0.846-0.921], and the sensitivity and specificity were 74.1% and 87.6%, respectively. The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin, fasting blood glucose in early pregnancy, pre-pregnancy BMI, Hb level and triglyceride level. The random forest model achieved an AUC of 0.950 (95%CI: 0.927-0.973), the sensitivity was 84.8%, and the specificity was 91.4%. The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model (P < 0.05). CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.
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Affiliation(s)
- Qing Lin
- Department of Obstetrics, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, Fujian Province, China
| | - Zhuan-Ji Fang
- Department of Obstetrics, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, Fujian Province, China
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Zeng Y, Yin L, Yin X, Zhao D. Association of triglyceride-glucose index levels with gestational diabetes mellitus in the US pregnant women: a cross-sectional study. Front Endocrinol (Lausanne) 2023; 14:1241372. [PMID: 37881497 PMCID: PMC10597685 DOI: 10.3389/fendo.2023.1241372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
Objective This investigation aimed to assess the correlation between the triglyceride-glucose (TyG) index and gestational diabetes mellitus (GDM) in pregnant women in the United States. Methods We calculated the TyG index utilizing data from pregnant women who participated in the National Health and Nutrition Examination Survey (NHANES) through 1999 to March 2020, and then employed multivariate logistic regression, smoothed curve fitting, and subgroup analysis to investigate the association between the TyG index and gestational diabetes during pregnancy. Results Logistic regression models revealed a positive association between the TyG index and GDM, remaining significant even after adjusting for all confounding variables (OR=3.43, 95% CI: 1.20-9.85, P = 0.0216). Subgroup analysis demonstrated consistent correlations and showed that there is no difference in the TyG index among first trimester subgroup. The TyG index had limited diagnostic efficacy for GDM (AUC=0.57, 95% CI: 0.50-0.63). Conclusion The TyG index correlates positively with the GDM, however its diagnostic efficacy is limited. Further research on the TyG index as an early predictor of GDM is required.
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Affiliation(s)
- Yan Zeng
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Guiyang Maternal and Child Health Care Hospital, Guiyang Children’s Hospital, Guizhou Medical University, Guiyang, China
| | - Li Yin
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xiaoping Yin
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Danqing Zhao
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
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Kavurt S, Uzlu SE, Bas AY, Tosun M, Çelen Ş, Üstün YE, Demirel N. Can the triglyceride-glucose index predict insulin resistance in LGA newborns? J Perinatol 2023; 43:1119-1124. [PMID: 36564472 DOI: 10.1038/s41372-022-01586-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/07/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The aim of this study is to investigate the TyG index and TG/HDL-C ratio and their relationships with insulin resistance in LGA infants. METHODS A prospective controlled study was conducted including 65 LGA and gestational age, gender-matched appropriate for gestational age (AGA) neonates. Serum TG, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), insulin and glucose levels were measured within two hours after birth, TyG index and HOMA-IR values were calculated. RESULTS TyG index and TG/HDL- C ratio were higher in LGA neonates compared to AGA ones (p = 0.03; p = 0.00, respectively). Compared with AGA newborns, LGA newborns had higher levels of insulin and HOMA-IR (p = 0.00; p = 0.00, respectively). TyG index and TG/HDL-C ratio showed moderate correlation with HOMA-IR (r = 0.59 R2 = 0.35 p < 0.001; r = 0.5 R2 = 0.25 p < 0.001, respectively). CONCLUSıON: The results of this study show that LGA newborns have increased levels of TyG index and TG/HDL-C associated with insulin resistance.
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Affiliation(s)
- Sumru Kavurt
- Department of Neonatology, Etlik Zubeyde Hanım Women's Health Training and Research Hospital, University of Health Sciences, Ankara, Turkey.
| | - Safiye Elif Uzlu
- Department of Neonatology, Etlik Zubeyde Hanım Women's Health Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Ahmet Yagmur Bas
- Department of Neonatology, Ankara Yıldırım Beyazıt University, Ankara, Turkey
| | - Mehtap Tosun
- Department of Neonatology, Etlik Zubeyde Hanım Women's Health Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Şevki Çelen
- Department of Perinatology, Etlik Zubeyde Hanım Women's Health Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Yaprak Engin Üstün
- Department of Obstetrics and Gynecology, Etlik Zubeyde Hanım Women's Health Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Nihal Demirel
- Department of Neonatology, Ankara Yıldırım Beyazıt University, Ankara, Turkey
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Salvatori B, Linder T, Eppel D, Morettini M, Burattini L, Göbl C, Tura A. TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy. Cardiovasc Diabetol 2022; 21:215. [PMID: 36258194 PMCID: PMC9580191 DOI: 10.1186/s12933-022-01649-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
Background The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy. Methods At early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition. Results The relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R2 > 0.64, p < 0.0001). Bland–Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG. Conclusions We developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01649-8.
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Affiliation(s)
| | - Tina Linder
- Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria
| | - Daniel Eppel
- Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica Delle Marche, 60131, Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica Delle Marche, 60131, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127, Padua, Italy.
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Liu Y, Chi R, Jiang Y, Chen B, Chen Y, Chen Z. Triglyceride glycemic index as a biomarker for gestational diabetes mellitus: a systemic review and meta-analysis. Endocr Connect 2021; 10:1420-1427. [PMID: 34636743 PMCID: PMC8630762 DOI: 10.1530/ec-21-0234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/11/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Triglyceride glycemic (TyG) index is a novel tool for assessing insulin resistance (IR). Recently, TyG index as a potential biomarker for gestational diabetes mellitus (GDM) has been studied, but its performance is yet inconclusive. Thus, we performed this systemic review and meta-analysis to evaluate the performance of TyG index in predicting GDM. METHODS Studies published before March 1, 2021, with comparison of TyG index between GDM patients and healthy controls were retrieved from multiple databases (PubMed, Web of Science, The Cochrane Library, and Embase). The mean difference (MD) of TyG index in GDM patients and healthy controls was pooled using random-effect models. RESULTS Differentiation of TyG index between patients with GDM and controls showed significant results. Overall, there is a four-fold increase in TyG index in GDM patients compared with controls (MD: 0.22, 95% CI: 0.07-0.36, P = 0.003; I2 = 71%, P = 0.009). In subgroup analyses according to gestational time, TyG index in the second trimester predicted GDM with low heterogeneity (MD: 0.26, 95% CI: 0.15-0.37, P < 0.001; I2 = 0%, P = 0.54), while no such correlation was found in the first trimester. CONCLUSION TyG index, especially in the second trimester, could be a promising biomarker for predicting GDM.
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Affiliation(s)
- Yusen Liu
- Department of Cooperation and Communication, The First Clinical Medical College & The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China
| | - Ruiwen Chi
- Department of Cooperation and Communication, The First Clinical Medical College & The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China
| | - Yujie Jiang
- School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, China
| | - Bicheng Chen
- Department of Cooperation and Communication, The First Clinical Medical College & The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China
| | - Youli Chen
- Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Correspondence should be addressed to Z Chen or Y Chen: or
| | - Zengrui Chen
- Intensive Care Unit, The People’s Hospital of Yuhuan, Yuhuan, China
- Correspondence should be addressed to Z Chen or Y Chen: or
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