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Zhong Z, An R, Ma S, Zhang N, Zhang X, Chen L, Wu X, Lin H, Xiang T, Tan H, Chen M. Association between the Maternal Gut Microbiome and Macrosomia. BIOLOGY 2024; 13:570. [PMID: 39194508 DOI: 10.3390/biology13080570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/13/2024] [Accepted: 07/26/2024] [Indexed: 08/29/2024]
Abstract
Fetal macrosomia is defined as a birthweight ≥4000 g and causes harm to pregnant women and fetuses. Studies reported that the maternal intestinal microbiome plays a key role in the establishment, growth, and development of the fetal intestinal microbiome. However, whether there is a relationship between maternal gut microbiota and macrosomia remains unclear. Our study aimed to identify gut microbiota that may be related to the occurrence of macrosomia, explore the possible mechanisms by which it causes macrosomia, and establish a prediction model to determine the feasibility of predicting macrosomia by early maternal gut microbiota. We conducted a nested case-control study based on an early pregnancy cohort (ChiCTR1900020652) in the Maternity and Child Health Hospital of Hunan Province on fecal samples of 93 women (31 delivered macrosomia as the case group and 62 delivered normal birth weight newborns as the control group) collected and included in this study. We performed metagenomic analysis to compare the composition and function of the gut microbiome between cases and controls. Correlation analysis was used to explore the association of differential species and differential functional pathways. A random forest model was used to construct an early pregnancy prediction model for macrosomia. At the species level, there were more Bacteroides salyersiae, Bacteroides plebeius, Ruminococcus lactaris, and Bacteroides ovatus in the intestinal microbiome of macrosomias' mothers compared with mothers bearing fetuses that had normal birth weight. Functional pathways of the gut microbiome including gondoate biosynthesis, L-histidine degradation III, cis-vaccenate biosynthesis, L-arginine biosynthesis III, tRNA processing, and mannitol cycle, which were more abundant in the macrosomia group. Significant correlations were found between species and functional pathways. Bacteroides plebeius was significantly associated with the pathway of cis-vaccenate biosynthesis (r = 0.28, p = 0.005) and gondoate biosynthesis (r = 0.28, p < 0.001) and Bacteroides ovatus was positively associated with the pathway of cis-vaccenate biosynthesis (r = 0.29, p = 0.005) and gondoate biosynthesis (r = 0.32, p = 0.002). Bacteroides salyersiae was significantly associated with the pathway of cis-vaccenate biosynthesis (r = 0.24, p = 0.018), gondoate biosynthesis (r = 0.31, p = 0.003), and L-histidine degradation III (r = 0.22, p = 0.291). Finally, four differential species and four clinical indicators were included in the random forest model for predicting macrosomia. The areas under the working characteristic curves of the training and validation sets were 0.935 (95% CI: 0.851~0.979) and 0.909 (95% CI: 0.679~0.992), respectively. Maternal gut microbiota in early pregnancy may play an important role in the development of macrosomia and can be used as potential predictors to prevent macrosomia.
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Affiliation(s)
- Zixin Zhong
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Rongjing An
- Chaoyang District Center for Diseases Prevention and Control of Beijing, Beijing 100020, China
| | - Shujuan Ma
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Reproductive and Genetic Hospital of CITIC-Xiangya, Clinical Research Center for Reproduction and Genetics in Hunan Province, Changsha 410008, China
| | - Na Zhang
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Xian Zhang
- Department of Occupational and Environment Health, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Lizhang Chen
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Xinrui Wu
- School of Medicine, Jishou University, Jishou 416000, China
| | - Huijun Lin
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Tianyu Xiang
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Hongzhuan Tan
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
| | - Mengshi Chen
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410013, China
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410013, China
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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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Ewington L, Black N, Leeson C, Al Wattar BH, Quenby S. Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review. BJOG 2024. [PMID: 38465451 DOI: 10.1111/1471-0528.17802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/08/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies. OBJECTIVES To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice. SEARCH STRATEGY MEDLINE, EMBASE and Cochrane Library were searched until June 2022. SELECTION CRITERIA We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance. DATA COLLECTION AND ANALYSIS Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool). MAIN RESULTS A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias. CONCLUSIONS There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.
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Affiliation(s)
- Lauren Ewington
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Naomi Black
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Charlotte Leeson
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Bassel H Al Wattar
- Beginnings Assisted Conception Unit, Epsom and St Helier University Hospitals, London, UK
- Comprehensive Clinical Trials Unit, Institute for Clinical Trials and Methodology, University College London, London, UK
| | - Siobhan Quenby
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
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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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Park JK, Park JE, Bang S, Jeon HJ, Kim JW, Lee WS. Development and validation of a nomogram for predicting ongoing pregnancy in single vitrified-warmed blastocyst embryo transfer cycles. Front Endocrinol (Lausanne) 2023; 14:1257764. [PMID: 38075065 PMCID: PMC10702135 DOI: 10.3389/fendo.2023.1257764] [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: 07/12/2023] [Accepted: 10/10/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The global adoption of the "freeze-all strategy" has led to a continuous increase in utilization of single vitrified-warmed blastocyst embryo transfer (SVBT) owing to its clinical effectiveness. Accurate prediction of clinical pregnancy is crucial from a patient-centered perspective. However, this remains challenging, with inherent limitations due to the absence of precise and user-friendly prediction tools. Thus, this study primarily aimed to develop and assess a nomogram based on quantitative clinical data to optimize the efficacy of personalized prognosis assessment. Materials and methods We conducted a retrospective cohort analysis of ongoing pregnancy data from 658 patients with infertility who underwent SVBT at our center between October 17, 2017, and December 18, 2021. Patients were randomly assigned to the training (n=461) or validation (n=197) cohort for nomogram development and testing, respectively. A nomogram was constructed using the results of the multivariable logistic regression (MLR), which included clinical covariates that were assessed for their association with ongoing pregnancy. Results The MLR identified eight significant variables that independently predicted ongoing pregnancy outcomes in the study population. These predictors encompassed maternal physiology, including maternal age at oocyte retrieval and serum anti-Müllerian hormone levels; uterine factors, such as adenomyosis; and various embryo assessment parameters, including the number of fertilized embryos, blastocyst morphology, blastulation day, blastocyst re-expansion speed, and presence of embryo string. The area under the receiver operating characteristic curve in our prediction model was 0.675 (95% confidence interval [CI], 0.622-0.729) and 0.656 (95% CI, 0.573-0.739) in the training and validation cohorts, respectively, indicating good discrimination performance in both cohorts. Conclusions Our individualized nomogram is a practical and user-friendly tool that can provide accurate and useful SVBT information for patients and clinicians. By offering this model to patients, clinical stakeholders can alleviate uncertainty and confusion about fertility treatment options and enhance patients' confidence in making informed decisions.
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Affiliation(s)
| | | | | | | | - Ji Won Kim
- *Correspondence: Ji Won Kim, ; Woo Sik Lee,
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Wang H, Wang X, Xu L. Chromosome 1p36 candidate gene ZNF436 predicts the prognosis of neuroblastoma: a bioinformatic analysis. Ital J Pediatr 2023; 49:145. [PMID: 37904225 PMCID: PMC10617224 DOI: 10.1186/s13052-023-01549-x] [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: 04/25/2023] [Accepted: 10/16/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Genetic 1p deletion is reported in 30% of all neuroblastoma and is associated with the unfavorable prognosis of neuroblastoma. The expressions and prognosis of 1p candidate genes in neuroblastoma are unclear. METHODS Public neuroblastoma cohorts were obtained for secondary analysis. The prognosis of 1p candidate genes in neuroblastoma was determined using Kaplan-Meier and cox regression analysis. The prediction of the nomogram model was determined using timeROC. RESULTS First, we confirmed the bad prognosis of 1p deletion in neuroblastoma. Moreover, zinc finger protein 436 (ZNF436) located at 1p36 region was down-regulated in 1p deleted neuroblastoma and higher ZNF436 expression was associated with the longer event free survival and overall survival of neuroblastoma. The expression levels of ZNF436 were lower in neuroblastoma patients with MYCN amplification or age at diagnosis ≥ 18months, or with stage 4 neuroblastoma. ZNF436 had robust predictive values of MYCN amplification and overall survival of neuroblastoma. Furthermore, the prognostic significance of ZNF436 in neuroblastoma was independent of MYCN amplification and age of diagnosis. Combinations of ZNF436 with MYCN amplification or age of diagnosis achieved better prognosis. At last, we constructed a nomogram risk model based on age, MYCN amplification and ZNF436. The nomogram model could predict the overall survival of neuroblastoma with high specificity and sensitivity. CONCLUSIONS Chromosome 1p36 candidate gene ZNF436 was a prognostic maker of neuroblastoma.
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Affiliation(s)
- Haiwei Wang
- Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
| | - Xinrui Wang
- Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Liangpu Xu
- Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
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Zhang L, Bi X, Chen X, Zhang L, Xiong Q, Cao W, Lin Y, Yang L, Jiang T, Deng W, Wang S, Wu S, Liu R, Gao Y, Shen G, Chang M, Hao H, Xu M, Hu L, Lu Y, Li M, Xie Y. A nomogram based on HBeAg, AST, and age to predict non-minimal liver inflammation in CHB patients with ALT <80 U/L. Front Immunol 2023; 13:1119124. [PMID: 36741383 PMCID: PMC9892180 DOI: 10.3389/fimmu.2022.1119124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/28/2022] [Indexed: 01/21/2023] Open
Abstract
Objective Precise assessment of liver inflammation in untreated hepatitis B e antigen (HBeAg)-positive patients with chronic hepatitis B virus (HBV) infection can determine when to initiate antiviral therapy. The aim of this study was to develop and validate a nomogram model for the prediction of non-minimal liver inflammation based on liver pathological injuries combined with age and alanine aminotransferase (ALT), aspartate aminotransferase (AST), hepatitis B surface antigen (HBsAg), HBeAg, and HBV DNA quantification. Methods We retrospectively included 735 HBeAg-positive chronic hepatitis B (CHB) patients with ALT < 80 U/L as the primary cohort and prospectively enrolled 196 patients as the validation cohort. Multivariate logistic regression analysis identified independent impact factors. A nomogram to predict significant liver inflammation was developed and validated. Results Multivariate logistic regression analysis showed that HBeAg, AST, and age were independent risk factors for predicting non-minimal liver inflammation in untreated CHB patients. The final formula for predicting non-minimal liver inflammation was Logit(P) = -1.99 - 0.68 × Log10HBeAg + 0.04 × Age + 0.06 × AST. A nomogram for the prediction of non-minimal liver inflammation was established based on the results from the multivariate analysis. The predicted probability of the model being consistent with the actual probability was validated by the calibration curves, showing the best agreement in both the primary and validation cohorts. The C-index was 0.767 (95%CI = 0.734-0.802) in the primary cohort and 0.749 (95%CI = 0.681-0.817) in the prospective validation cohort. Conclusions The nomogram based on HBeAg, AST, and age might help predict non-minimal liver inflammation in HBeAg-positive CHB patients with ALT < 80 U/L, which is practical and easy to use for clinicians.
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Affiliation(s)
- Lu Zhang
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Xiaoyue Bi
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Xiaoxue Chen
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Luxue Zhang
- Infectious Disease Department, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Qiqiu Xiong
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Weihua Cao
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China,Department of Infectious Diseases, Miyun Teaching Hospital, Capital Medical University, Beijing, China
| | - Yanjie Lin
- Department of Hepatology Division 2, Peking University Ditan Teaching Hospital, Beijing, China
| | - Liu Yang
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Tingting Jiang
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Wen Deng
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Shiyu Wang
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Shuling Wu
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Ruyu Liu
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yuanjiao Gao
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Ge Shen
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Min Chang
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Hongxiao Hao
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Mengjiao Xu
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Leiping Hu
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yao Lu
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Minghui Li
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China,Department of Hepatology Division 2, Peking University Ditan Teaching Hospital, Beijing, China,*Correspondence: Minghui Li, ; Yao Xie, ,
| | - Yao Xie
- Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing, China,Department of Hepatology Division 2, Peking University Ditan Teaching Hospital, Beijing, China,*Correspondence: Minghui Li, ; Yao Xie, ,
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Hufnagel A, Grant ID, Aiken CEM. Glucose and oxygen in the early intrauterine environment and their role in developmental abnormalities. Semin Cell Dev Biol 2022; 131:25-34. [PMID: 35410716 DOI: 10.1016/j.semcdb.2022.03.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/02/2022] [Accepted: 03/31/2022] [Indexed: 12/14/2022]
Abstract
The early life environment can have profound impacts on the developing conceptus in terms of both growth and morphogenesis. These impacts can manifest in a variety of ways, including congenital fetal anomalies, placental dysfunction with subsequent effects on fetal growth, and adverse perinatal outcomes, or via effects on long-term health outcomes that may not be detected until later childhood or adulthood. Two key examples of environmental influences on early development are explored: maternal hyperglycaemia and gestational hypoxia. These are increasingly common pregnancy exposures worldwide, with potentially profound impacts on population health. We explore what is known regarding the mechanisms by which these environmental exposures can impact early intrauterine development and thus result in adverse outcomes in the immediate, short, and long term.
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Affiliation(s)
- Antonia Hufnagel
- University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Imogen D Grant
- Department of Obstetrics and Gynaecology, University of Cambridge, Box 223, The Rosie Hospital and NIHR Cambridge Comprehensive Biomedical Research Centre, Cambridge CB2 0SW, UK
| | - Catherine E M Aiken
- Department of Obstetrics and Gynaecology, University of Cambridge, Box 223, The Rosie Hospital and NIHR Cambridge Comprehensive Biomedical Research Centre, Cambridge CB2 0SW, UK; University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
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Jing G, Huwei S, Chao C, Lei C, Ping W, Zhongzhou X, Sen Y, Jiayuan C, Ruiyao C, Lu L, Shuqing L, Kaixiang Y, Jie X, Weiwei C. A predictive model of macrosomic birth based upon real-world clinical data from pregnant women. BMC Pregnancy Childbirth 2022; 22:651. [PMID: 35982421 PMCID: PMC9386989 DOI: 10.1186/s12884-022-04981-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction. METHODS In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software. RESULTS We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model. CONCLUSIONS Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.
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Affiliation(s)
- Gao Jing
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, 200040, China.,Shanghai Municipal Key Clinical Specialty, Shanghai, 200030, China
| | - Shi Huwei
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Chen Chao
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China.,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, 200040, China.,Shanghai Municipal Key Clinical Specialty, Shanghai, 200030, China
| | - Chen Lei
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China
| | - Wang Ping
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China
| | - Xiao Zhongzhou
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Yang Sen
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Chen Jiayuan
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Chen Ruiyao
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Luo Shuqing
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China
| | - Yang Kaixiang
- The Second Affiliated Hospital of Nanjing Medical University, Nanjing, 210003, Jiangsu, China
| | - Xu Jie
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China.
| | - Cheng Weiwei
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, No. 910 Hengshan Road, Shanghai, 200030, China. .,Shanghai Key Laboratory of Embryo Original Disease, Shanghai, 200040, China. .,Shanghai Municipal Key Clinical Specialty, Shanghai, 200030, China.
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Wang Y, Liu H, Wang J, Hu X, Wang A, Nie Z, Xu H, Li J, Xin H, Zhang J, Zhang H, Wang Y, Lyu Z. Development and validation of a new predictive model for macrosomia at late-term pregnancy: A prospective study. Front Endocrinol (Lausanne) 2022; 13:1019234. [PMID: 36465629 PMCID: PMC9713232 DOI: 10.3389/fendo.2022.1019234] [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: 08/14/2022] [Accepted: 11/01/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE Fetal macrosomia is defined as a birth weight more than 4,000 g and is associated with maternal and fetal complications. This early metabolic disease may influence the entire life of the infant. Currently, macrosomia is predicted by using the estimated fetal weight (EFW). However, the EFW is inaccurate when the gestational week is gradually increasing. To assess precisely the risk of macrosomia, we developed a new predictive model to estimate the risk of macrosomia. METHODS We continuously collected data on 655 subjects who attended regular antenatal visits and delivered at the Second Hospital of Hebei Medical University (Shijiazhuang, China) from November 2020 to September 2021. A total of 17 maternal features and 2 fetal ultrasonographic features were included at late-term pregnancy. The 655 subjects were divided into a model training set and an internal validation set. Then, 450 pregnant women were recruited from Handan Central Hospital (Handan, China) from November 2021 to March 2022 as the external validation set. The least absolute shrinkage and selection operator method was used to select the most appropriate predictive features and optimize them via 10-fold cross-validation. The multivariate logistical regressions were used to build the predictive model. Receiver operating characteristic (ROC) curves, C-indices, and calibration plots were obtained to assess model discrimination and accuracy. The model's clinical utility was evaluated via decision curve analysis (DCA). RESULTS Four predictors were finally included to develop this new model: prepregnancy obesity (prepregnancy body mass index ≥ 30 kg/m2), hypertriglyceridemia, gestational diabetes mellitus, and fetal abdominal circumference. This model afforded moderate predictive power [area under the ROC curve 0.788 (95% confidence interval [CI] 0.736, 0.840) for the training set, 0.819 (95% CI 0.744,0.894) for the internal validation set, and 0.773 (95% CI 0.713,0.833) for the external validation set]. On DCA, the model evidenced a good fit with, and positive net benefits for, both the internal and external validation sets. CONCLUSIONS We developed a predictive model for macrosomia and performed external validation in other regions to further prove the discrimination and accuracy of this predictive model. This novel model will aid clinicians in easily identifying those at high risk of macrosomia and assist obstetricians to plan accordingly.
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Affiliation(s)
- Yuhan Wang
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Hongzhou Liu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Department of Endocrinology, First Hospital of Handan City, Handan, Hebei, China
| | - Jincheng Wang
- Department of Epidemiology, The George Washington University, Washington, DC, United States
| | - Xiaodong Hu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Anning Wang
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Zhimei Nie
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Huaijin Xu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Jiefei Li
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Hong Xin
- Department of Obstetrics, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jiamei Zhang
- Department of Ultrasound Diagnosis, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Han Zhang
- Department of Ultrasound Diagnosis, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yueheng Wang
- Department of Ultrasound Diagnosis, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhaohui Lyu
- Department of Endocrinology, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Zhaohui Lyu,
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