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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. [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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Francis EC, Powe CE, Lowe WL, White SL, Scholtens DM, Yang J, Zhu Y, Zhang C, Hivert MF, Kwak SH, Sweeting A. Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis. COMMUNICATIONS MEDICINE 2023; 3:185. [PMID: 38110524 PMCID: PMC10728189 DOI: 10.1038/s43856-023-00393-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/25/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM. METHODS Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA). RESULTS A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers. CONCLUSIONS Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.
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Affiliation(s)
- Ellen C Francis
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA.
| | - Camille E Powe
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sara L White
- Department of Women and Children's Health, King's College London, London, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jiaxi Yang
- Global Center for Asian Women's Health (GloW), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Cuilin Zhang
- Global Center for Asian Women's Health (GloW), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marie-France Hivert
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Arianne Sweeting
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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Maternal AA/EPA Ratio and Triglycerides as Potential Biomarkers of Patients at Major Risk for Pharmacological Therapy in Gestational Diabetes. Nutrients 2022; 14:nu14122502. [PMID: 35745231 PMCID: PMC9231064 DOI: 10.3390/nu14122502] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 06/08/2022] [Accepted: 06/13/2022] [Indexed: 02/04/2023] Open
Abstract
Gestational diabetes mellitus (GD) is characterized by glycemic and lipid metabolism alterations in an environment of low-grade inflammation. Our trial aimed to assess the effect of nutraceutical supplements (omega-3 fatty acids, anthocyanins, and alpha-cyclodextrins) in GD patients and evaluate the role of anthropometric, metabolic, and inflammatory parameters as biomarkers to identify subjects who require pharmacological hypoglycemic treatment during gestation. Pregnant women with GD at 24-28 weeks of gestation were enrolled in a double-blind trial and randomized to receive either nutraceutical supplements or a placebo for 12 weeks. No statistically significant differences were observed between the two groups in blood and urine measurements of metabolic, inflammatory, and antioxidant parameters. In the whole cohort, pre-pregnancy BMI and anthropometric measurements were significantly different in patients who required pharmacological intervention. These patients showed higher triglycerides, CRP, and insulin levels and gave birth to newborns with significantly higher weights. Subjects with a greater AA/EPA ratio had higher PAF levels and gave birth four days earlier. In conclusion, one-to-one nutritional coaching and poor compliance with nutraceutical supplementation might have outweighed the impact of this intervention. However, triglyceride concentration and the AA/EPA ratio seems to be a biomarker for higher inflammatory levels and GD candidates for pharmacological treatment. An adequate assumption of omega-3 in women with GD, either by a controlled diet or by nutraceutical supplementation, reduces the need for pharmacological therapy.
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Kwapong YA, Boakye E, Wang G, Hong X, Lewey J, Mamas MA, Wu P, Blaha MJ, Nasir K, Hays AG, Blumenthal RS, Wang X, Sharma G. Maternal Glycemic Spectrum and Adverse Pregnancy and Perinatal Outcomes in a Multiracial US Cohort. J Cardiovasc Dev Dis 2022; 9:179. [PMID: 35735808 PMCID: PMC9224544 DOI: 10.3390/jcdd9060179] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/31/2022] [Accepted: 06/02/2022] [Indexed: 02/04/2023] Open
Abstract
Diabetes mellitus (pregestational (PDM) and gestational (GDM)) is associated with adverse pregnancy outcomes (APOs). However, studies exploring the association of APOs with maternal glycemia among women without PDM/GDM are limited. We utilized data from 4119 women (307-PDM; 582-GDM; 3230-non-PDM/GDM) in the Boston Birth Cohort (1998-2016). Women in the non-PDM/GDM group were subdivided by tertiles of 1 h, 50 g oral glucose load test at 24-32 weeks: T1: 50-95 mg/dL (n = 1166), T2: 96-116 mg/dL (n = 1151), T3: 117-201 mg/dL (n = 913). Using multivariable logistic regression, we examined the association of maternal glycemia with APOs-preterm birth (PTB) and hypertensive disorders of pregnancy (HDP)-and adverse perinatal outcomes-high birth weight (HBW), cesarean section (CS), and sub-analyses by race-ethnicity. Compared to women in T1, women in T2 and T3 had a higher prevalence of pre-existing hypertension (T1: 2.8% vs. T2: 5.2% vs. T3: 6.3%) and obesity (T1: 13.3% vs. T2: 18.1% vs. T3: 22.9%). Women in T2 and T3 had higher odds of HBW (adjusted odds ratio aOR T2: 1.47 [1.01-2.19] T3: 1.68 [1.13-2.50]) compared to women in T1. Additionally, women in T2, compared to T1, had higher odds of HDP (aOR 1.44 [1.10-1.88]). Among non-Hispanic Black (NHB) women, those in T2 and T3 had higher odds of HDP compared to T1 (aOR T2 1.67 [1.13-2.51]; T3: 1.68 [1.07-2.62]). GDM and PDM were associated with higher odds of HBW, CS, PTB, and HDP, compared to women in T1. In this predominantly NHB and Hispanic cohort, moderate maternal glycemia without PDM/GDM was associated with higher odds of HBW and HDP, even more strongly among NHB women. If confirmed, a review of current guidelines of glucose screening and risk stratification in pregnancy may be warranted.
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Affiliation(s)
- Yaa Adoma Kwapong
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
| | - Ellen Boakye
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
| | - Guoying Wang
- Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Xiumei Hong
- Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Jennifer Lewey
- Division of Cardiology, Hospital of University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mamas Andreas Mamas
- Keele Cardiovascular Research Group, School of Medicine, Keele University, Keele ST5 5BG, UK
| | - Pensee Wu
- Division of Maternal and Fetal Medicine, Keele University, Keele ST5 5BG, UK
| | - Michael Joseph Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
| | - Khurram Nasir
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
- Center for Outcomes Research, Houston Methodist Hospital and DeBakey Heart & Vascular Center, Houston, TX 77030, USA
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist Debakey Heart and Vascular Center, Houston, TX 77030, USA
| | - Allison Gamboa Hays
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
| | - Roger Scott Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
| | - Xiaobin Wang
- Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Garima Sharma
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Diseases, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, USA
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Furtado RL, Martins JER, Oliveira MAF, Guerreiro DD, de Sá NAR, Ferraz ASM, Ceccatto VM, Rodrigues APR, Araújo VR. Acute effect of high-intensity interval training exercise on redox status in the ovaries of rats fed a high-fat diet. Reprod Fertil Dev 2021; 33:713-724. [PMID: 34437833 DOI: 10.1071/rd20326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 05/05/2021] [Indexed: 11/23/2022] Open
Abstract
This study demonstrates the effect of a single high-intensity interval training (HIIT) session on the redox status of rat ovaries with excess adiposity. Forty Wistar female rats (mean (±s.e.m.) weight 94.40 ± 13.40 g) were divided into two groups and fed either a standard diet (SD) or a high-fat diet (HFD) for 62 days. At the end of this period, the rats were subjected to a single HIIT session and were killed 24 h after exercise. Both groups subjected to exercise (SDex and HFDex) generated a significantly higher antioxidant environment by presenting a higher thiol content, which represents a lower oxidation rate of GSH than their respective controls (SD and HFD). The percentage of morphologically normal primary follicles decreased, whereas that of antral follicles increased, in the SDex group. In addition, the HFD group had a higher percentage of degenerated antral follicles than the SD and SDex groups. Cells immunoreactive for α-smooth muscle actin were seen in the cortical stroma and thecal layer enclosing late secondary and tertiary follicles in all groups. Moreover, heme oxygenase and cytochrome P450 family 19 subfamily A member 1 (Cyp19A1) labelling was seen in all antral follicles. Progesterone concentrations were significantly higher in the HFDex than SDex group. In conclusion, this study indicates that a single session of HIIT may result in an improvement in ovary redox status because of metabolic muscle activity by inducing physiological adaptation after exercise in a paracrine manner.
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Affiliation(s)
- Rodrigo L Furtado
- Graduate Program in Physiological Sciences, Higher Institute of Biomedical Sciences, State University of Ceará, Fortaleza, CE, 60714-903, Brazil
| | - Jonathan Elias R Martins
- Institute of Physical Education and Sports, Federal University of Ceará, Fortaleza, CE, 60455760, Brazil
| | - Maria Alice F Oliveira
- Microscopy Laboratory of Health Sciences Center, State University of Ceará, Fortaleza, CE, 60714-903, Brazil
| | - Denise D Guerreiro
- Laboratory of Manipulation of Oocytes and Preantral Follicles, Veterinary Faculty, State University of Ceará, Fortaleza, CE, 60714-903, Brazil
| | - Naiza A R de Sá
- Laboratory of Manipulation of Oocytes and Preantral Follicles, Veterinary Faculty, State University of Ceará, Fortaleza, CE, 60714-903, Brazil
| | - Alex S M Ferraz
- Institute of Physical Education and Sports, Federal University of Ceará, Fortaleza, CE, 60455760, Brazil
| | - Vânia M Ceccatto
- Laboratory of Biochemistry and Gene Expression, Higher Institute of Biomedical Sciences, State University of Ceará, Fortaleza, CE, 60714-903, Brazil
| | - Ana Paula R Rodrigues
- Laboratory of Manipulation of Oocytes and Preantral Follicles, Veterinary Faculty, State University of Ceará, Fortaleza, CE, 60714-903, Brazil
| | - Valdevane R Araújo
- Graduate Program in Physiological Sciences, Higher Institute of Biomedical Sciences, State University of Ceará, Fortaleza, CE, 60714-903, Brazil; and Microscopy Laboratory of Health Sciences Center, State University of Ceará, Fortaleza, CE, 60714-903, Brazil; and Corresponding author.
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Zou Y, Zhang Y, Yin Z, Wei L, Lv B, Wu Y. Establishment of a nomogram model to predict macrosomia in pregnant women with gestational diabetes mellitus. BMC Pregnancy Childbirth 2021; 21:581. [PMID: 34420518 PMCID: PMC8381578 DOI: 10.1186/s12884-021-04049-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/12/2021] [Indexed: 11/19/2022] Open
Abstract
AIM To establish a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus in China. METHODS We retrospectively collected the medical records of 783 pregnant women with gestational diabetes who underwent prenatal examinations and delivered at the Affiliated Hospital of Qingdao University from October 2019 to October 2020. The pregnant women were randomly divided into two groups in a 4:1 ratio to generate and validate the model. The independent risk factors for macrosomia in pregnant women with gestational diabetes mellitus were analyzed by multivariate logistic regression, and the nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus was established and verified by R software. RESULTS Logistic regression analysis showed that prepregnancy body mass index, weight gain during pregnancy, fasting plasma glucose, triglycerides, biparietal diameter and amniotic fluid index were independent risk factors for macrosomia (P < 0.05). The areas under the ROC curve for internal and external validation of the model were 0.813 (95 % confidence interval 0.754-0.862) and 0.903 (95 % confidence interval 0.588-0.967), respectively. The calibration curve was a straight line with a slope close to 1. CONCLUSIONS In this study, we constructed a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus. The model has good discrimination and calibration abilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women with gestational diabetes mellitus.
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Affiliation(s)
- Yujiao Zou
- School of Nursing, Qingdao University, Qingdao, China
| | - Yan Zhang
- Nursing Department, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhenhua Yin
- School of Public Health, Qingdao University, Qingdao, China
| | - Lili Wei
- Nursing Department, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bohan Lv
- School of Nursing, Qingdao University, Qingdao, China
| | - Yili Wu
- School of Public Health, Qingdao University, Qingdao, China
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Liu H, Li J, Leng J, Wang H, Liu J, Li W, Liu H, Wang S, Ma J, Chan JC, Yu Z, Hu G, Li C, Yang X. Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China. Diabetes Metab Res Rev 2021; 37:e3397. [PMID: 32845061 DOI: 10.1002/dmrr.3397] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 07/21/2020] [Accepted: 08/01/2020] [Indexed: 12/18/2022]
Abstract
AIMS This study aimed to develop a machine learning-based prediction model for gestational diabetes mellitus (GDM) in early pregnancy in Chinese women. MATERIALS AND METHODS We used an established population-based prospective cohort of 19,331 pregnant women registered as pregnant before the 15th gestational week in Tianjin, China, from October 2010 to August 2012. The dataset was randomly divided into a training set (70%) and a test set (30%). Risk factors collected at registration were examined and used to construct the prediction model in the training dataset. Machine learning, that is, the extreme gradient boosting (XGBoost) method, was employed to develop the model, while a traditional logistic model was also developed for comparison purposes. In the test dataset, the performance of the developed prediction model was assessed by calibration plots for calibration and area under the receiver operating characteristic curve (AUR) for discrimination. RESULTS In total, 1484 (7.6%) women developed GDM. Pre-pregnancy body mass index, maternal age, fasting plasma glucose at registration, and alanine aminotransferase were selected as risk factors. The machine learning XGBoost model-predicted probability of GDM was similar to the observed probability in the test data set, while the logistic model tended to overestimate the risk at the highest risk level (Hosmer-Lemeshow test p value: 0.243 vs. 0.099). The XGBoost model achieved a higher AUR than the logistic model (0.742 vs. 0.663, p < 0.001). This XGBoost model was deployed through a free, publicly available software interface (https://liuhongwei.shinyapps.io/gdm_risk_calculator/). CONCLUSION The XGBoost model achieved better performance than the logistic model.
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Affiliation(s)
- Hongwei Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Jing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Junhong Leng
- Tianjin Women and Children's Health Center, Tianjin, China
| | - Hui Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Jinnan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Weiqin Li
- Tianjin Women and Children's Health Center, Tianjin, China
| | - Hongyan Liu
- Tianjin Women and Children's Health Center, Tianjin, China
| | - Shuo Wang
- Tianjin Women and Children's Health Center, Tianjin, China
| | - Jun Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Juliana Cn Chan
- Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
- International Diabetes Federation Centre of Education, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zhijie Yu
- Population Cancer Research Program and Department of Pediatrics, Dalhousie University, Halifax, Canada
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Changping Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xilin Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
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Scavini M, Formoso G, Festa C, Sculli MA, Succurro E, Sciacca L, Torlone E. Follow-up of women with a history of gestational diabetes in Italy: Are we missing an opportunity for primary prevention of type 2 diabetes and cardiovascular disease? Diabetes Metab Res Rev 2021; 37:e3411. [PMID: 32979283 DOI: 10.1002/dmrr.3411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/29/2020] [Accepted: 08/26/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Marina Scavini
- Diabetes Research Institute, San Raffaele Scientific Institute, Milan, Italy
| | - Gloria Formoso
- Department of Medicine and Aging Sciences, Centre for Advanced Studies and Technology (CAST, Ex CeSIMet) G. d'Annunzio University Chieti-Pescara, Chieti, Italy
| | - Camilla Festa
- Sant'Andrea Hospital, Sapienza University, Rome, Italy
| | - Maria Angela Sculli
- Grande Ospedale Metropolitano Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Elena Succurro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Laura Sciacca
- Department of Clinical and Experimental Medicine, Endocrinology Section, University of Catania Medical School, Catania, Italy
| | - Elisabetta Torlone
- Department of Internal Medicine Endocrine and Metabolic Sciences, University of Perugia, Perugia, Italy
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Lizárraga D, García-Gasca A. The Placenta as a Target of Epigenetic Alterations in Women with Gestational Diabetes Mellitus and Potential Implications for the Offspring. EPIGENOMES 2021; 5:epigenomes5020013. [PMID: 34968300 PMCID: PMC8594713 DOI: 10.3390/epigenomes5020013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/23/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is a pregnancy complication first detected in the second or third trimester in women that did not show evident glucose intolerance or diabetes before gestation. In 2019, the International Diabetes Federation reported that 15.8% of live births were affected by hyperglycemia during pregnancy, of which 83.6% were due to gestational diabetes mellitus, 8.5% were due to diabetes first detected in pregnancy, and 7.9% were due to diabetes detected before pregnancy. GDM increases the susceptibility to developing chronic diseases for both the mother and the baby later in life. Under GDM conditions, the intrauterine environment becomes hyperglycemic, while also showing high concentrations of fatty acids and proinflammatory cytokines, producing morphological, structural, and molecular modifications in the placenta, affecting its function; these alterations may predispose the baby to disease in adult life. Molecular alterations include epigenetic mechanisms such as DNA and RNA methylation, chromatin remodeling, histone modifications, and expression of noncoding RNAs (ncRNAs). The placenta is a unique organ that originates only in pregnancy, and its main function is communication between the mother and the fetus, ensuring healthy development. Thus, this review provides up-to-date information regarding two of the best-documented (epigenetic) mechanisms (DNA methylation and miRNA expression) altered in the human placenta under GDM conditions, as well as potential implications for the offspring.
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Cui D, Yang W, Shao P, Li J, Wang P, Leng J, Wang S, Liu E, Chan JC, Yu Z, Hu G, Yang X. Interactions between Prepregnancy Overweight and Passive Smoking for Macrosomia and Large for Gestational Age in Chinese Pregnant Women. Obes Facts 2021; 14:520-530. [PMID: 34419951 PMCID: PMC8546448 DOI: 10.1159/000517846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 06/07/2021] [Indexed: 01/02/2023] Open
Abstract
INTRODUCTION Previous analysis showed that passive smoking and overweight were associated with an increased risk of gestational diabetes mellitus (GDM) in a synergistic manner, while GDM increased the risk of macrosomia/large for gestational age (LGA). This study aimed to examine any interactive effects between passive smoking and overweight/obesity on risk of macrosomia/LGA. METHODS From 2010 to 2012, 22,302 pregnant women registered for pregnancy at a primary hospital in Tianjin, China. Data were collected longitudinally; that is, from their first antenatal care visit, at the glucose challenge test (GCT) time (24-28 weeks of gestation) and at delivery. Passive smoking was self-reported. Macrosomia was defined as birth weight ≥4,000 g. Binary logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction was used to test the synergistic effect. RESULTS Passive smokers accounted for 57.4% of women (n = 8,230). Using nonpassive smoking and prepregnancy body mass index (BMI) <24.0 kg/m2 as the reference, the adjusted ORs of overweight alone and passive smoking alone for macrosomia were 2.39 (95% CI: 2.11-2.71) and 1.17 (95% CI: 1.04-1.32). Copresence of passive smoking and prepregnancy BMI ≥24.0 kg/m2 increased the OR to 2.70 (95% CI: 2.28-3.20), with a significant additive interaction. After further adjustment for GDM or GCT, the OR of copresence of both risk factors was slightly attenuated to 2.52 (2.13-3.00) and 2.51 (2.11-2.98), with significant additive interaction. However, the additive interaction between prepregnancy overweight/obesity and passive smoking for LGA was nonsignificant. CONCLUSIONS Prepregnancy overweight/obesity was associated with an increased risk of macrosomia in Chinese women synergistically with passive smoking during pregnancy, and most of the association was not modified by hyperglycemia during pregnancy.
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Affiliation(s)
- Dingyu Cui
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Wen Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Ping Shao
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Jing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
| | - Peng Wang
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Junhong Leng
- Department of Child Health, Tianjin Women and Children's Health Center, Tianjin, China
| | - Shuo Wang
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Enqing Liu
- Department of Child Health, Tianjin Women and Children's Health Center, Tianjin, China
| | - Juliana C.N. Chan
- Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity and The Chinese University of Hong Kong-Prince of Wales Hospital-International Diabetes Federation Centre of Education, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zhijie Yu
- Population Cancer Research Program, Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Xilin Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
- *Xilin Yang,
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11
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Chen ZG, Xu YT, Ji LL, Zhang XL, Chen XX, Liu R, Wu C, Wang YL, Hu HY, Wang L. The combination of symphysis-fundal height and abdominal circumference as a novel predictor of macrosomia in GDM and normal pregnancy. BMC Pregnancy Childbirth 2020; 20:461. [PMID: 32787792 PMCID: PMC7425134 DOI: 10.1186/s12884-020-03157-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/05/2020] [Indexed: 12/23/2022] Open
Abstract
Background Macrosomia is a major adverse pregnancy outcome of gestational diabetes mellitus (GDM). Although BMI, symphysis-fundal height (SFH) and abdominal circumference (AC) are associated with foetal weight, there are some limitations to their use, especially for the prediction of macrosomia. This study aimed to identify a novel predictive methodology to improve the prediction of high-risk macrosomia. Methods Clinical information was collected from 3730 patients. The association between the ISFHAC (index of the SFH algorithm multiplied by the square of AC) and foetal weight was determined and validated. A new index, the ISFHAC, was evaluated by area under the curve (AUC) analysis. Results A total of 1087 GDM and 657 normal singleton pregnancies were analysed. The ISFHAC was positively correlated with foetal weight in GDM pregnancies and normal pregnancies (NPs). The AUCs of the ISFHAC were 0.815 in the GDM group and 0.804 in the NP group, which were higher than those of BMI, SFH, AC and GA. The ISFHAC cut-off points were 41.7 and 37 in the GDM and NP groups, respectively. The sensitivity values for the prediction of macrosomia with high ISFHAC values were 75.9 and 81.3% in the GDM and NP groups, respectively, which were higher than those with BMI. Regarding the validation data, the sensitivity values for prediction with high ISFHAC values were 78.9% (559 GDM pregnancies) and 78.3% (1427 NPs). Conclusions The ISFHAC can be regarded as a new predictor of and risk factor for macrosomia in GDM pregnancy and NP.
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Affiliation(s)
- Zhi Guo Chen
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China.,Department of Human Anatomy, Basic Medical sciences of Xinxiang Medical University, Xinxiang, 453003, China
| | - Ya Ting Xu
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Lu Lu Ji
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Xiao Li Zhang
- Department of Ultrasound Imaging, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Xiao Xing Chen
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Rui Liu
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Chao Wu
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Yan Ling Wang
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Han Yang Hu
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China
| | - Lin Wang
- Department of Histology and Embryology, Wuhan University, School of Basic Medical sciences, 185 East Lake Road, Wuhan, Hubei, 430071, People's Republic of China. .,Hubei Provincial Key Laboratory of Developmentally Originated Disease, Wuhan, 430071, China.
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12
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Al-Musharaf S, Aljuraiban GS, Danish Hussain S, Alnaami AM, Saravanan P, Al-Daghri N. Low Serum Vitamin B12 Levels Are Associated with Adverse Lipid Profiles in Apparently Healthy Young Saudi Women. Nutrients 2020; 12:E2395. [PMID: 32785129 PMCID: PMC7468727 DOI: 10.3390/nu12082395] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 12/14/2022] Open
Abstract
An abnormal lipid profile is an independent risk factor for cardiovascular diseases. The relationship between vitamin B12 deficiency and lipid profile is inconclusive, with most studies conducted in unhealthy populations. In this study, we aimed to assess the relationship between serum vitamin B12 levels and lipid profiles in a cross-sectional study that included 341 apparently healthy Saudi women, aged 19-30 years, from different colleges at King Saud University, Saudi Arabia. Sociodemographic, anthropometric, biochemical, and lifestyle data were collected, including diet and physical activity. Serum vitamin B12 deficiency was defined as serum B12 level of <148 pmol/L. The prevalence of vitamin B12 deficiency was approximately 0.6%. Using multivariable linear regression models, serum vitamin B12 levels were found to be inversely associated with total cholesterol (B = -0.26; p < 0.001), low-density lipoprotein cholesterol levels (B = -0.30; p < 0.001), and triglyceride (B = -0.16; p < 0.01) after adjusting for potential confounders, while obesity indices of body mass index, central obesity, and fat percentage showed no association. Therefore, we conclude that low serum vitamin B12 levels are independently associated with abnormal lipid profiles in healthy young Saudi women. Further interventional studies are needed to determine whether improving serum vitamin B12 levels in a healthy population can improve lipid profiles.
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Affiliation(s)
- Sara Al-Musharaf
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
- Chair for Biomarkers of Chronic Diseases, Riyadh Biochemistry Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (S.D.H.); (A.M.A.); (N.A.-D.)
| | - Ghadeer S. Aljuraiban
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Syed Danish Hussain
- Chair for Biomarkers of Chronic Diseases, Riyadh Biochemistry Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (S.D.H.); (A.M.A.); (N.A.-D.)
| | - Abdullah M. Alnaami
- Chair for Biomarkers of Chronic Diseases, Riyadh Biochemistry Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (S.D.H.); (A.M.A.); (N.A.-D.)
| | - Ponnusamy Saravanan
- Population, Evidence and Technologies, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV2 2 DX, UK
- Academic Department of Diabetes, Endocrinology and Metabolism, George Eliot Hospital, Nuneaton CV10 7DJ, UK
| | - Nasser Al-Daghri
- Chair for Biomarkers of Chronic Diseases, Riyadh Biochemistry Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (S.D.H.); (A.M.A.); (N.A.-D.)
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