Academic Journal
Predictive Models of Pregnancy Based on Male Partner Data From a Preconception Cohort Study.
| Τίτλος: | Predictive Models of Pregnancy Based on Male Partner Data From a Preconception Cohort Study. |
|---|---|
| Συγγραφείς: | Talaei M; Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts, USA.; Department of Electrical and Computer Engineering, and Division of Systems Engineering, Boston University, Boston, Massachusetts, USA., Yland JJ; Optum Epidemiology, Boston, Massachusetts, USA.; Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA., Zad Z; Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts, USA.; Department of Electrical and Computer Engineering, and Division of Systems Engineering, Boston University, Boston, Massachusetts, USA., Koenig M; Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA., Wesselink AK; Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA., Eisenberg ML; Department of Urology, Stanford University, Stanford, California, USA., Wise LA; Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA., Paschalidis IC; Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts, USA.; Department of Electrical and Computer Engineering, and Division of Systems Engineering, Boston University, Boston, Massachusetts, USA.; Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. |
| Πηγή: | Andrology [Andrology] 2026 Aug 27. Date of Electronic Publication: 2026 Aug 27. |
| Publication Model: | Ahead of Print |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101585129 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2047-2927 (Electronic) Linking ISSN: 20472919 NLM ISO Abbreviation: Andrology Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford : Wiley-Blackwell, 2013- |
| Περίληψη: | Background: Male factors contribute to approximately 50% of couple infertility, yet few studies have used machine learning with comprehensive male partner lifestyle and behavioral data to predict spontaneous conception. Objectives: To develop and validate machine learning models predicting spontaneous conception using detailed self-reported male partner characteristics from a North American prospective preconception cohort. Materials and Methods: We analyzed 2843 couples from the Pregnancy Study Online (PRESTO; 2013-2024). Using 185 male partner variables spanning sociodemographic, lifestyle, dietary, anthropometric, medical, and reproductive history domains (with female age and BMI included as covariates), we developed models for: (1) per-cycle probability of conception (Model I) using a discrete-time proportional hazards model; (2) pregnancy in fewer than 12 cycles (Model II); and (3) pregnancy within 6 cycles (Model III) using regularized logistic regression, gradient boosted trees, and random forests. We evaluated model performance using fivefold cross-validation. Age-stratified analyses (≥30 vs. <30 years) assessed heterogeneity in predictive factors. Results: Among 2843 couples, 87.3% reported pregnancy in fewer than 12 cycles and 69.7% within 6 cycles. Model I achieved a concordance index of ∼0.59 (SD 0.012). The sparse regularized logistic regression model, selected via recursive feature elimination for interpretability, achieved ROC AUCs of 0.653 (SD 0.027) for Model II and 0.639 (SD 0.016) for Model III-comparable to the best full models using all candidate predictors (AUC 0.665 and 0.648, respectively). Prior impregnation history was the strongest predictor (Model II OR: 1.48, 95% CI: 1.34-1.63; Model III OR: 1.34, 95% CI: 1.21-1.47). Additional predictors included sugar-sweetened beverage consumption, environmental tobacco smoke exposure, gastroesophageal reflux disease, and physical activity. Male age emerged as a significant predictor among males ≥30 years, whereas female BMI, cycling ≥ 3 h/week, and tobacco smoke exposure were key predictors among younger males. Discussion and Conclusions: Machine learning models incorporating male partner characteristics demonstrated moderate discrimination for predicting pregnancy within 6-12 cycles. Predictive factors differed by male age, highlighting heterogeneity that may warrant further investigation. Modifiable factors, particularly sugar-sweetened beverage consumption, tobacco smoke exposure, and physical activity, were among the predictors identified and may represent candidate targets for future intervention research. (© 2026 American Society of Andrology and European Academy of Andrology.) |
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| Grant Information: | ECCS-2317079 National Science Foundation; CCF-2200052 National Science Foundation; IIS-1914792 National Science Foundation; R01 GM135930 United States NH NIH HHS; R21 HD072326 United States NH NIH HHS; R01 HD086742 United States NH NIH HHS; TR001430 United States NH NIH HHS |
| Contributed Indexing: | Keywords: fecundability; lifestyle factors; machine learning; male fertility; preconception health; predictive modeling |
| Entry Date(s): | Date Created: 20260828 Latest Revision: 20260828 |
| Update Code: | 20260828 |
| DOI: | 10.1111/andr.70375 |
| PMID: | 42661451 |
| Βάση Δεδομένων: | MEDLINE |
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