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Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985-2015 Swedish birth cohort.

ABSTRACT: The goal is to examine the risk of conception mode-type-1 diabetes using different survival analysis modelling approaches and examine if there are differentials in the risk of type-1 diabetes between children from fresh and frozen-thawed embryo transfers. We aimed to compare the performances and fitness of different survival analysis regression models with the Cox proportional hazard (CPH) model used in an earlier study. The effect of conception modes and other prognostic factors on type-1 diabetes among children conceived either spontaneously or by assisted reproductive technology (ART) and its sub-groups was modelled in the earlier study. We used the information on all singleton children from the Swedish Medical Birth Register hosted by the Swedish National Board of Health and Welfare, 1985 to 2015. The main explanatory variable was the mode of conception. We applied the CPH, parametric and flexible parametric survival regression (FPSR) models to the data at 5% significance level. Loglikelihood, Akaike and Bayesian information criteria were used to assess model fit. Among the 3,138,540 singletons, 47,938 (1.5%) were conceived through ART (11,211 frozen-thawed transfer and 36,727 fresh embryo transfer). In total, 18,118 (0.58%) of the children had type-1 diabetes, higher among (0.58%) those conceived spontaneously than the ART-conceived (0.42%). The median (Interquartile range (IQR)) age at onset of type-1 diabetes among spontaneously conceived children was 10 (14-6) years, 8(5-12) for ART, 6 (4-10) years for frozen-thawed embryo transfer and 9 (5-12) years for fresh embryo transfer. The estimates from the CPH, FPSR and parametric PH models are similar. There was no significant difference in the risk of type-1 diabetes among ART- and spontaneously conceived children; FPSR: (adjusted Hazard Ratio (aHR) = 1.070; 95% Confidence Interval (CI):0.929-1.232, p = 0.346) vs CPH: (aHR = 1.068; 95%CI: 0.927-1.230, p = 0.361). A sub-analysis showed that the adjusted hazard of type-1 diabetes was 37% (aHR = 1.368; 95%CI: 1.013-1.847, p = 0.041) higher among children from frozen-thawed embryo transfer than among children from spontaneous conception. The hazard of type-1 diabetes was higher among children whose mothers do not smoke (aHR = 1.296; 95%CI:1.240-1.354, p<0.001) and of diabetic mothers (aHR = 6.419; 95%CI:5.852-7.041, p<0.001) and fathers (aHR = 8.808; 95%CI:8.221-9.437, p<0.001). The estimates from the CPH, parametric models and the FPSR model were close. This is an indication that the models performed similarly and any of them can be used to model the data. We couldn't establish that ART increases the risk of type-1 diabetes except when it is subdivided into its two subtypes. There is evidence of a greater risk of type-1 diabetes when conception is through frozen-thawed transfer.

SUBMITTER: Fagbamigbe AF

PROVIDER: S-EPMC8232413 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Study questionIs there a relation between ART and DNA methylation (DNAm) patterns in cord blood, including any differences between IVF and ICSI?Summary answerDNAm at 19 CpGs was associated with conception via ART, with no difference found between IVF and ICSI.What is known alreadyPrior studies on either IVF or ICSI show conflicting outcomes, as both widespread effects on DNAm and highly localized associations have been reported. No study on both IVF and ICSI and genome-wide neonatal DNAm has been performed.Study design, size, durationThis was a cross-sectional study comprising 87 infants conceived with IVF or ICSI and 70 conceived following medically unassisted conception. The requirement for inclusion in the study was an understanding of the Swedish language and exclusion was the use of donor gametes.Participants/materials, setting, methodsParticipants were from the UppstART study, which was recruited from fertility and reproductive health clinics, and the Born into Life cohort, which is recruited from the larger LifeGene study. We measured DNAm from DNA extracted from cord blood collected at birth using a micro-array (450k array). Group differences in DNAm at individual CpG dinucleotides (CpGs) were determined using robust linear models and post-hoc Tukey's tests.Main results and the role of chanceWe found no association of ART conception with global methylation levels, imprinted loci and meta-stable epialleles. In contrast, we identify 19 CpGs at which DNAm was associated with being conceived via ART (effect estimates: 0.5-4.9%, PFDR < 0.05), but no difference was found between IVF and ICSI. The associated CpGs map to genes related to brain function/development or genes connected to the plethora of conditions linked to subfertility, but functional annotation did not point to any likely functional consequences.Limitations, reasons for cautionWe measured DNAm in cord blood and not at later ages or in other tissues. Given the number of tests performed, our study power is limited and the findings need to be replicated in an independent study.Wider implications of the findingsWe find that ART is associated with DNAm differences in cord blood when compared to non-ART samples, but these differences are limited in number and effect size and have unknown functional consequences in adult blood. We did not find indications of differences between IVF and ICSI.Study funding/competing interest(s)E.W.T. was supported by a VENI grant from the Netherlands Organization for Scientific Research (91617128) and JPI-H2020 Joint Programming Initiative a Healthy Diet for a Healthy Life (JPI HDHL) under proposal number 655 (PREcisE Project) through ZonMw (529051023). Financial support was provided from the European Union's Seventh Framework Program IDEAL (259679), the Swedish Research Council (K2011-69X-21871-01-6, 2011-3060, 2015-02434 and 2018-02640) and the Strategic Research Program in Epidemiology Young Scholar Awards, Karolinska Institute (to A.N.I.) and through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) framework grant no 340-2013-5867, grants provided by the Stockholm County Council (ALF-projects), the Strategic Research Program in Epidemiology at Karolinska Institutet and the Swedish Heart-Lung Foundation and Danderyd University Hospital (Stockholm, Sweden). The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors declare no competing interests.Trial registration numberN/A.

Project description:BackgroundWhile previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied different machine learning models to not only predict birth outcomes but systematically quantify the impacts of pre- and post-conception serum thyroid-stimulating hormone (TSH) levels and other predictive characteristics on birth outcomes.MethodsWe used data from women who gave birth in Shanghai First Maternal and Infant Hospital from 2014 to 2015. We included 14,110 women with the measurement of preconception TSH in the first analysis and 3,428 out of 14,110 women with both pre- and post-conception TSH measurement in the second analysis. Synthetic Minority Over-sampling Technique (SMOTE) was applied to adjust the imbalance of outcomes. We randomly split (7:3) the data into a training set and a test set in both analyses. We compared Area Under Curve (AUC) for dichotomous outcomes and macro F1 score for categorical outcomes among four machine learning models, including logistic model, random forest model, XGBoost model, and multilayer neural network models to assess model performance. The model with the highest AUC or macro F1 score was used to quantify the importance of predictive features for adverse birth outcomes with the loss function algorithm.ResultsThe XGBoost model provided prominent advantages in terms of improved performance and prediction of polytomous variables. Predictive models with abnormal preconception TSH or not-well-controlled TSH, a novel indicator with pre- and post-conception TSH levels combined, provided the similar robust prediction for birth outcomes. The highest AUC of 98.7% happened in XGBoost model for predicting low Apgar score with not-well-controlled TSH adjusted. By loss function algorithm, we found that not-well-controlled TSH ranked 4th, 6th, and 7th among 14 features, respectively, in predicting birthweight, induction, and preterm birth, and 3rd among 19 features in predicting low Apgar score.ConclusionsOur four machine learning models offered valid predictions of birth outcomes in women during pre- and post-conception. The predictive features panel suggested the combined TSH indicator (not-well-controlled TSH) could be a potentially competitive biomarker to predict adverse birth outcomes.

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Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985-2015 Swedish birth cohort.

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