scholarly journals Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Nancy McBride ◽  
Paul Yousefi ◽  
Sara L. White ◽  
Lucilla Poston ◽  
Diane Farrar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fatty Acids ◽  
Nuclear Magnetic Resonance ◽  
Risk Factor ◽  
Magnetic Resonance ◽  
Gestational Age ◽  
Maternal Age ◽  
Prediction Models ◽  
Independent Validation ◽  
Pregnancy Smoking

Abstract Background Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders. Methods We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24–28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women. Results Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24–28 weeks and 15–18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated. Conclusions Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.

scholarly journals Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders?

2020 ◽  
Author(s):  
Nancy McBride ◽  
Sara L. White ◽  
Lucilla Poston ◽  
Diane Farrar ◽  
Jane West ◽  
...  
Keyword(s):  
Risk Factors ◽  
Pregnant Women ◽  
Gestational Age ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Large For Gestational Age ◽  
List Type ◽  
Independent Validation ◽  
Pregnancy Smoking

AbstractBackgroundPrediction of pregnancy-related disorders is mostly done based on established and easily measured risk factors. However, these measures are at best moderate at discriminating between high and low risk women. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.Methods and FindingsWe used data collected from women in the Born in Bradford (BiB; n=8,212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n=859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of 1) risk factors (maternal age, pregnancy smoking status, body mass index, ethnicity and parity) to 2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and 3) risk factors and metabolites combined. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a study of obese pregnant women of multiple ethnicities.In BiB, discrimination for GDM, HDP, LGA and SGA was improved with the addition of metabolites to the risk factors only model. Risk factors area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56,0.63)). Combined AUC 95% (CI)): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63,0.70)). For GDM, HDP, LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUC were attenuated. A key limitation was our inability to identify a large general pregnancy population for independent validation.ConclusionsOur results suggest metabolomics combined with established risk factors improves prediction GDM, HDP and LGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.Author SummaryBackgroundCurrent methods used to predict pregnancy-related disorders exhibit modest discrimination and calibration.Metabolomics may enable improved prediction of pregnancy-related disorders.Why Was This Study Done?We require tools to identify women with high-risk pregnancies earlier on, so that antenatal care can be more appropriately targeted at women who need it most and tailored to women’s needs and to facilitate early intervention.It has been suggested that metabolomic markers might improve prediction of future pregnancy-related disorders. Previous studies tend to be small and rarely undertake external validation.What Did the Researchers Do and Find?Using BiB (8,212 pregnant women of multiple ethnicities), we created prediction models, using established risk factors and 156 NMR-derived metabolites, for five pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We sought external validation in UPBEAT (859 obese pregnant women).We compared the predictive discrimination (area under the curve - AUC) and calibration (calibration slopes) of the models. The prediction models we compared were 1) established risk factors (pregnancy smoking, maternal age, body mass index (BMI), maternal ethnicity and parity) 2) NMR-derived metabolites measured in the second trimester and 3) a combined model of risk factors and metabolites.Inclusion of metabolites with risk factors improved prediction of GDM, HDP, LGA and SGA in BiB. Prediction of PTB was poor with all models. Result patterns were similar in validation using UPBEAT, particularly for GDM and HDP, but AUC were attenuated.What Do These Findings Mean?These findings indicate that combining current risk factor and metabolomic data could improve the prediction of GDM, HDP, LGA and SGA. These findings need to be validated in larger, general populations of pregnant women.

scholarly journals Hubungan Faktor Risiko dengan Kejadian Pre-Eklampsia Berat di RSUP Dr. M. Djamil Padang

2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Nurulia Muthi Karima ◽  
Rizanda Machmud ◽  
Yusrawati Yusrawati
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Gestational Age ◽  
Maternal Age ◽  
Case Control Study ◽  
Case Control ◽  
Chi Square ◽  
Adequate Treatment ◽  
History Of ◽  
Control Study

AbstrakPre-eklampsia Berat (PEB) masih merupakan salah satu penyebab morbiditas dan mortalitas ibu apabila tidak ditangani secara adekuat. Ada banyak hal yang mempengaruhi terjadinya PEB, beberapa diantaranya adalah usia ibu, paritas, usia kehamilan, jumlah janin, jumlah kunjungan ANC, dan riwayat hipertensi. Tujuan penelitian ini adalah untuk mengetahui hubungan antara faktor risiko dengan pre-eklampsia berat di RSUP Dr. M. Djamil Padang.Penelitian ini menggunakan rancangan case-control study dengan metode analitik observasional. Pengumpulan data dilakukan pada Januari 2013 dengan menggunakan data sekunder, yakni data rekam medik ibu melahirkan dengan pre-eklampsia berat dan tanpa pre-eklampsia di bagian obstetrik dan ginekologi RSUP Dr. M. Djamil, periode 1 Januari 2010 – 31 Desember 2011. Dari 148 data sampel didapatkan angka distribusi pada variabel riwayat hipertensi yang hanya didapatkan pada ibu dengan PEB. Hasil analisis bivariat dengan menggunakan uji chi-square diperoleh hasil tidak terdapat hubungan signifikan antara faktor risiko (usia ibu, paritas, usia kehamilan, jumlah janin, jumlah kunjungan ANC) dengan masing-masing nilai p > 0,05. Analisis multivariat dengan menggunakan uji regresi logistik didapatkan bahwa usia ibu > 35 tahun merupakan faktor risiko terhadap kejadian PEB dengan nilai p = 0,034. Jadi, usia ibu > 35 tahun dan riwayat hipertensi memiliki hubungan terhadap kejadian pre-eklampsia berat.Kata kunci: pre-eklampsia berat, faktor risiko, hipertensi AbstractSevere Pre-eclampsia is one of the contributors of maternal morbidity and mortality if not getting an adequate treatment. There are many things that affect it, such as maternal age, parity, gestational age, number of fetuses, the number of ANC visits, and history of hypertension. The objective of this study was to determine relationship between the risk factors and the incidence of severe pre-eclampsia The design of this research is case-control study with observational analytic methods. The data was collected in January 2013 by using secondary data, maternal medical record data with severe pre-eclampsia and without pre-eclampsia of the obstetrics and gynecology department Dr. M. Djamil, period 1 January 2010-31 December 2011. From 148 samples obtained figures the variable history of hypertension which is only found in women with severe pre-eclampsia. The results of the bivariate analysis using chi square test results obtained there was no significant relationship between risk factors (maternal age, parity, gestational age, number of fetuses, the number of ANC visits) with each p value > 0.05. While the results of the multivariate analysis using logistic regression found that maternal age> 35 years was a risk factor for the incidence of severe pre-eclampsia with p = 0.034. Maternal age > 35 years and history of hypertension had a relationship to the incidence of severe pre-eclampsia. Keywords: severe pre-eclampsia, risk factor, hypertension

scholarly journals Factors Influencing Maternal Mortality from Severe Preeclampsia and Eclampsia

2016 ◽  
Author(s):  
Denny Khusen
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Maternal Mortality ◽  
Maternal Death ◽  
Gestational Age ◽  
Post Partum ◽  
Severe Preeclampsia ◽  
Maternal Complications ◽  
History Of ◽  
Post Partum Bleeding

Objective: To analyze risk factor, both clinical and laboratory findings, associated with maternal mortality from severe preeclampsia and eclampsia in Atma Jaya Hospital. Methods: This was a retrospective case control study. All medical records of maternal death associated with severe preeclampsia and eclampsia between 1st January 2009 and 31st December 2011 were obtained and then information about risk factors were collected and tabulated. Risk factor analyzed were maternal age, gestational age, parity, coexisting medical illness (hypertension), antenatal examination status, maternal complications, systolic and diastolic blood pressure at admission, and admission laboratory data. Results: There were 19 maternal deaths associated with severe preeclampsia and eclampsia during period of study (Consisted of 6 cases of eclampsia and 13 cases of severe preeclampsia). Maternal mortality rate for severe preeclampsia and eclampsia were 16.7% and 33.3% respectively. Multivariate analysis identified the following risk factors associated with maternal death: gestation age <32 week, history of hypertension, thrombocyte count < 100.0000/μl, post partum bleeding, acute pulmonary edema, HELLP syndrome, and sepsis. Conclusion: In this study, we found that gestational age, history of hypertension, and platelet count are the cause of maternal mortality. Maternal complications associated with maternal mortality are post partum bleeding, acute pulmonary edema, HELLP syndrome, and sepsis. [Indones J Obstet Gynecol 2012; 36-2: 90-4] Keywords: eclampsia, maternal mortality, preeclampsia

scholarly journals Risk factors of childhood leukemia

2014 ◽  
Vol 54 (6) ◽  
pp. 358
Author(s):  
Paulina K. Bangun ◽  
Bidasari Lubis ◽  
Sri Sofyani ◽  
Nelly Rosdiana ◽  
Olga R. Siregar
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Birth Weight ◽  
Maternal Age ◽  
Childhood Leukemia ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
High Birth Weight ◽  
Parental Age ◽  
Prenatal Risk Factors

Background The incidence of childhood leukemia has increasedannually. Recent studies have shown that childhood leukemia isinitiated in utero, and have focused on prenatal risk factors suchas birth weight and parental age. Exposure to pesticides andradiation, as well as parental smoking, breastfeeding, and thenumber of older siblings have also been sugges ted as risk factorsfor childhood leukemia.Objective To evaluate possible risk factors for childhood leukemia,including birth weight, parental age, and other risk factors.Methods This case-con trol study was conducted from October2011 to February 2012 in Haji Adam Malik Hospital, Medan .Case subjects were children aged below 18 years and diagnosedwith leukemia. Control subjects were children aged below 18years who were diagnosed with any non-cancerous acute illnessesin this hospital, and individually matched for age and gen der tothe case subject group. Patients and parents were asked to fill astructured questionnaire. Data was analyzed using conditionallogistic regression .Results A total of 140 subjects were eligible, with 70 subjects ineach group. Birth weight 2: 4000 g and maternal age 2:35 yearswere significant risk factors with OR 10.13 (95%CI 1.124 to 91.2 7)and OR 4.98 (95%CI 1.276 to 19.445), respectively. Paternal ageof 2:35 years was not a significant risk factor. Exposure to pesticideswas also noted as another significant risk factor (OR= 6.66; 95%CI2.021 to 21.966) .Conclusion High birth weight, advan ced maternal age, andexposure to pesticides are risk factors of childhood leukemia.

Early postnatal hyperglycaemia is a risk factor for treatment-demanding retinopathy of prematurity

2017 ◽  
Vol 102 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Carina Slidsborg ◽  
Louise Bering Jensen ◽  
Steen Christian Rasmussen ◽  
Hans Callø Fledelius ◽  
Gorm Greisen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Preterm Infants ◽  
Gestational Age ◽  
Retinopathy Of Prematurity ◽  
Study Groups ◽  
Multivariate Logistic Regression Model ◽  
Multiple Birth ◽  
Retrospective Case ◽  
Matching Criteria

BackgroundTo investigate whether neonatal hyperglycaemia in the first postnatal week is associated with treatment-demanding retinopathy of prematurity (ROP).MethodsThis is a Danish national, retrospective, case–control study of premature infants (birth period 2003–2006). Three national registers were searched, and data were linked through a unique civil registration number. The study sample consisted of 106 cases each matched with two comparison infants. Matching criteria were gestational age (GA) at birth, ROP not registered and born at the same neonatal intensive care unit. Potential ‘new’ risk factors were analysed in a multivariate logistic regression model, while adjusted for previously recognised risk factors (ie, GA at birth, small for gestational age, multiple birth and male sex).ResultsHospital records of 310 preterm infants (106 treated; 204 comparison infants) were available. Nutrition in terms of energy (kcal/kg/week) and protein (g/kg/week) given to the preterm infants during the first postnatal week were statistically insignificant between the study groups (Mann-Whitney U test; p=0.165/p=0.163). Early postnatal weight gain between the two study groups was borderline significant (t-test; p=0.047). Hyperglycaemic events (indexed value) were statistically significantly different between the two study groups (Mann-Whitney U test; p<0.001). Hyperglycaemia was a statistically independent risk factor (OR: 1.022; 95% CI 1.002 to 1.042; p=0.031).ConclusionAn independent association was found between the occurrence of hyperglycaemic events during the first postnatal week and later development of treatment-demanding ROP, when adjusted for known risk factors.

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