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Pediatrics

A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother–newborn–offspring study in a mild-to-moderate iodine deficiency area

International Journal of Obesity (2025)Cite this article

Subjects

Abstract

Background

Childhood obesity and iodine deficiency are prevalent in developed countries and are linked to adverse health outcomes in adulthood. Mild-to-moderate iodine deficiency and insufficient maternal iodine intake during pregnancy may increase the risk of large-for-gestational-age newborns, which are associated with childhood obesity. Despite this, predicting childhood obesity during pregnancy remains a challenge. We assessed and evaluated machine learning algorithms predicting childhood obesity risk using maternal anthropometrics, thyroid function and iodine intake; and identified key prenatal factors contributing to childhood obesity.

Methods

A diagnostic accuracy study was conducted based on 87 parameters collected from a mother-newborn-offspring prospective cohort (N = 191) in a mild-to-moderate iodine deficiency region. Maternal iodine status and thyroid function, including serum free tri-iodo-thyronine (FT3) concentrations, were assessed during the second half of pregnancy. Iodine intake was evaluated using a semi-quantitative food frequency questionnaire. Anthropometric measurements were obtained from mothers during pregnancy, from newborns at birth, and from children at 2 years of age. An outcome of overweight at 2 years was defined as a gender-adjusted weight percentile >85%. The dataset was split into training (80%) and test (20%) sets. Synthetic datasets were created to evaluate the performance of six machine learning models, including artificial neural networks (Nnet) that trained and evaluated the model using 5-fold cross-validation.

Results

The best-performing model was Nnet, which achieved the highest accuracy (1500 instances with a balanced predicted outcome). On the unseen test data, accuracy, Kappa, outcome F1-score and weighted F1 were 0.743, 0.347, 0.500 and 0.769 (respectively). Significant predictors included gravidity, maternal-newborn anthropometrics (height and head circumference, respectively), maternal consumption and dietary intake of iodine-rich foods (popsicle, selected fish, and yogurt) and FT3.

Conclusions

Machine learning approaches show promise in predicting childhood obesity risk using maternal and dietary factors during pregnancy. If validated, these findings could support interventions to reduce childhood obesity rates.

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Fig. 1: Flow chart of source data generation.
Fig. 2
Fig. 3: Mean absolute SHAP values for the top 15 most influential features identified by Random Forest.
Fig. 4: Ranked feature importance for offspring overweight at 2 years of age by SHAP for top 15 predictive features in the model.
Fig. 5: Exampled individual risk profile for offspring overweight at 2 years of age illustrated by Waterfall plot.

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Data availability

The anonymized source data used in this study are available from the corresponding author upon request.

Code availability

The code implementing the described methods is available from the corresponding author upon request.

References

  1. Chong B, Jayabaskaran J, Kong G, Chan YH, Chin YH, Goh R, et al. Trends and predictions of malnutrition and obesity in 204 countries and territories: an analysis of the Global Burden of Disease Study 2019. EClinicalMedicine. 2023;57:101850. https://doi.org/10.1016/j.eclinm.2023.101850.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gao L, Peng W, Xue H, Wu Y, Zhou H, Jia P, et al. Spatial-temporal trends in global childhood overweight and obesity from 1975 to 2030: a weight mean center and projection analysis of 191 countries. Glob Health. 2023;19:53. https://doi.org/10.1186/s12992-023-00954-5.

    Article  Google Scholar 

  3. Twig G, Yaniv G, Levine H, Leiba A, Goldberger N, Derazne E, et al. Body-mass index in 2.3 million adolescents and cardiovascular death in adulthood. N Engl J Med. 2016;374:2430–40. https://doi.org/10.1056/NEJMoa1503840.

    Article  PubMed  Google Scholar 

  4. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med. 1993;22:167–77. https://doi.org/10.1006/pmed.1993.1014.

    Article  CAS  PubMed  Google Scholar 

  5. van der Baan-Slootweg O, Benninga MA, Beelen A, van der Palen J, Tamminga-Smeulders C, Tijssen JGP, et al. Inpatient treatment of children and adolescents with severe obesity in the Netherlands: a randomized clinical trial. JAMA Pediatr. 2014;168:807–14. https://doi.org/10.1001/jamapediatrics.2014.521.

    Article  PubMed  Google Scholar 

  6. Blake-Lamb TL, Locks LM, Perkins ME, Woo Baidal JA, Cheng ER, Taveras EM. Interventions for childhood obesity in the first 1000 days a systematic review. Am J Prev Med. 2016;50:780–9. https://doi.org/10.1016/j.amepre.2015.11.010.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Alexander EK, Pearce EN, Brent GA, Brown RS, Chen H, Dosiou C, et al. 2017 Guidelines of the American Thyroid Association for the diagnosis and management of thyroid disease during pregnancy and the postpartum. Thyroid. 2017;27:315–89. https://doi.org/10.1089/thy.2016.0457.

    Article  PubMed  Google Scholar 

  8. Ministry of Health and Population, GAIN, UNICEF. National survey of iodine status and household iodised salt use among primary school children and pregnant women: Egypt 2014/2015. Cairo, Egypt (2015).

  9. Perrine CG, Herrick KA, Gupta PM, Caldwell KL. Iodine status of pregnant women and women of reproductive age in the United States. Thyroid. 2019;29:153–4. https://doi.org/10.1089/thy.2018.0345.

    Article  PubMed  Google Scholar 

  10. Ittermann T, Albrecht D, Arohonka P, Bilek R, de Castro JJ, Dahl L, et al. Standardized map of iodine status in Europe. Thyroid. 2020;30:1346–54. https://doi.org/10.1089/thy.2019.0353.

    Article  CAS  PubMed  Google Scholar 

  11. Cannas A, Rayman MP, Kolokotroni O, Bath SC. Iodine status of pregnant women from the Republic of Cyprus. Br J Nutr. 2023;129:126–34. https://doi.org/10.1017/S0007114522000617.

    Article  CAS  PubMed  Google Scholar 

  12. Vural M, Koc E, Evliyaoglu O, Acar HC, Aydin AF, Kucukgergin C, et al. Iodine status of Turkish pregnant women and their offspring: a national cross-sectional survey. J Trace Elem Med Biol. 2021;63:126664. https://doi.org/10.1016/j.jtemb.2020.126664.

    Article  CAS  PubMed  Google Scholar 

  13. McMullan P, Hamill L, Doolan K, Hunter A, McCance D, Patterson C, et al. Iodine deficiency among pregnant women living in Northern Ireland. Clin Endocrinol. 2019;91:639–45. https://doi.org/10.1111/cen.14065.

    Article  CAS  Google Scholar 

  14. Ovadia YS, Arbelle JE, Gefel D, Brik H, Wolf T, Nadler V, et al. First Israeli National Iodine Survey demonstrates iodine deficiency among school-aged children and pregnant women. Thyroid. 2017;27:1083–91. https://doi.org/10.1089/thy.2017.0251.

    Article  CAS  PubMed  Google Scholar 

  15. Ovadia YS, Gefel D, Toledano Y, Rosen SR, Avrahami-Benyounes Y, Groisman L, et al. Does iodine intake modify the effect of maternal dysglycemia on birth weight in mild-to-moderate iodine-deficient populations? A mother-newborn prospective cohort study. Nutrients. 2023;15:2914. https://doi.org/10.3390/nu15132914.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hynes K, Otahal P, Burgess J, Oddy W, Hay I. Reduced educational outcomes persist into adolescence following mild iodine deficiency in utero, despite adequacy in childhood: 15-year follow-up of the gestational iodine cohort investigating auditory processing speed and working memory. Nutrients. 2017;9:1354. https://doi.org/10.3390/nu9121354.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Threapleton DE, Waiblinger D, Snart CJP, Taylor E, Keeble C, Ashraf S, et al. Prenatal and postpartum maternal iodide intake from diet and supplements, urinary iodine and thyroid hormone concentrations in a region of the united kingdom with mild-to-moderate iodine deficiency. Nutrients. 2021;13:230. https://doi.org/10.3390/nu13010230.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Greenwood DC, Webster J, Keeble C, Taylor E, Hardie LJ. Maternal iodine status and birth outcomes: a systematic literature review and meta-analysis. Nutrients. 2023;15:387. https://doi.org/10.3390/nu15020387.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shehab M, Abualigah L, Shambour Q, Abu-Hashem MA, Shambour MKY, Alsalibi AI, et al. Machine learning in medical applications: a review of state-of-the-art methods. Comput Biol Med. 2022;145:105458. https://doi.org/10.1016/j.compbiomed.2022.105458.

    Article  PubMed  Google Scholar 

  20. Eliyati N, Faruk A, Kresnawati ES, Arifieni I. Support vector machines for classification of low birth weight in Indonesia. J Phys Conf Ser. 2019;1282:012010. https://doi.org/10.1088/1742-6596/1282/1/012010.

    Article  Google Scholar 

  21. LeCroy MN, Kim RS, Stevens J, Hanna DB, Isasi CR. Identifying key determinants of childhood obesity: a narrative review of machine learning studies. Child Obes. 2021;17:153–9. https://doi.org/10.1089/chi.2020.0324.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shenhav S, Benbassat C, Gefel D, Zangen S, Rosen SR, Avrahami-Benyounes Y, et al. Can mild-to-moderate iodine deficiency during pregnancy alter thyroid function? Lessons from a mother-newborn cohort. Nutrients. 2022;14:5336. https://doi.org/10.3390/nu14245336.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hampl SE, Hassink SG, Skinner AC, Armstrong SC, Barlow SE, Bolling CF, et al. Clinical practice guideline for the evaluation and treatment of children and adolescents with obesity. Pediatrics. 2023;151:e2022060640.

    Article  PubMed  Google Scholar 

  24. Domingos P. A few useful things to know about machine learning. Commun ACM. 2012;55:78–87. https://doi.org/10.1145/2347736.2347755.

    Article  Google Scholar 

  25. Patro SGK, Sahu KK. Normalization: a preprocessing stage, IARJSET. arXiv Prepr. 2015;arXiv:1503.06462.

  26. Barmpas P, Tasoulis S, Vrahatis AG, Georgakopoulos SV, Anagnostou P, Prina M, et al. A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large-scale population studies: the ATHLOS project. Health Inf Sci Syst. 2022;10:6. https://doi.org/10.1007/s13755-022-00171-1.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C. Interpretable machine learning: fundamental principles and 10 grand challenges. Stat Surv. 2022;16:1–85. https://doi.org/10.1214/21-SS133.

    Article  Google Scholar 

  28. Rogers J, Gunn S. Identifying feature relevance using a random forest. In: Proc. international statistical and optimization perspectives workshop on “subspace, latent structure and feature selection”. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006:173–84. https://doi.org/10.1007/11752790_12

  29. Han H, Guo X, Yu H. Variable selection using mean decrease accuracy and mean decrease Gini based on random forest. In: Proc. 7th IEEE international conference on software engineering and service science (ICSESS). IEEE; 2016: 219–24. https://doi.org/10.1109/ICSESS.2016.7883053

  30. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765–74. https://doi.org/10.5555/3295222.3295230.

    Article  Google Scholar 

  31. Mayer M, Watson D, Biecek P. Package ‘kernelshap’. CRAN. Available at: https://cran.r-project.org/web/packages/kernelshap/index.html. [Last accessed: 6 March 2025].

  32. Mayer M, Stando A. Package ‘shapviz’. CRAN. Available at: http://cran.r-project.org/web/packages/shapviz/index.html. [Last accessed: 6 March 2025].

  33. Lunardon N, Menardi G, Torelli N. ROSE: a package for binary imbalanced learning. R J. CRAN. Available at: https://cran.r-project.org/web/packages/ROSE/index.html. [Last accessed: 6 March 2025].

  34. Bishop, CM Neural networks for pattern recognition. Oxford University Press; 1995. ISBN: 9780198538493.

  35. Ripley B, Venables W. Package ‘nnet’. CRAN. Available at: https://cran.r-project.org/web/packages/nnet/index.html. [Last accessed: 6 March 2025].

  36. Friedman J, Hastie T, Tibshirani R, Narasimhan B, Tay K, Simon N et al. Package ‘glmnet’. CRAN R Repository. 2021; 595. Available at: https://cran.r-project.org/web/packages/glmnet/index.html. [Last accessed: 6 March 2025].

  37. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–97. https://doi.org/10.1007/BF00994018.

    Article  Google Scholar 

  38. Kuhn M. Caret: classification and regression training [Internet]. 2020. Available from: https://cran.r-project.org/package=caret [Last accessed: 6 March 2025].

  39. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proc. 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016:785-94. https://doi.org/10.1145/2939672.2939785.

  40. Breiman L. Random forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.

    Article  Google Scholar 

  41. Breiman L, Cutler A, Liaw A, Wiener M. Package ‘randomForest’. CRAN. Available at: https://cran.r-project.org/web/packages/randomForest/index.html. [Last accessed: 6 March 2025].

  42. Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006;15:651–74. https://doi.org/10.1198/106186006X133933.

    Article  Google Scholar 

  43. Hothorn T, Seibold H, Zeileis A. Package ‘partykit’. CRAN. Available at: https://cran.r-project.org/web/packages/partykit/index.html [Last accessed: 6 March 2025].

  44. Lunardon N, Menardi G, Torelli N. ROSE: a package for binary imbalanced learning. R J. 2014;6:79–89.

    Article  Google Scholar 

  45. Steur M, Smit HA, Schipper CMA, Scholtens S, Kerkhof M, de Jongste JC, et al. Predicting the risk of newborn children to become overweight later in childhood: the PIAMA birth cohort study. Int J Pediatr Obes. 2011;6:e170–8.

    Article  PubMed  Google Scholar 

  46. Weng SF, Redsell SA, Nathan D, Swift JA, Yang M, Glazebrook C. Estimating overweight risk in childhood from predictors during infancy. Pediatrics. 2013;132:e414–21. Aug.

    Article  PubMed  Google Scholar 

  47. Santorelli G, Petherick ES, Wright J, Wilson B, Samiei H, Cameron N, et al. Developing prediction equations and a mobile phone application to identify infants at risk of obesity. PLoS One. 2013;8:e71183.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Robson JO, Verstraete SG, Shiboski S, Heyman MB, Wojcicki JM. A risk score for childhood obesity in an urban Latino cohort. J Pediatr. 2016;172:29–34.e1. May.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Colmenarejo G. Machine learning models to predict childhood and adolescent obesity: a review. Nutrients. 2020;12:2466 Aug 16.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Lee I, Bang KS, Moon H, Kim J. Risk factors for obesity among children aged 24 to 80 months in Korea: a decision tree analysis. J Pediatr Nurs. 2019;46:e15–e23. May-Jun.

    Article  PubMed  Google Scholar 

  51. Kitsantas P, Gaffney KF. Risk profiles for overweight/obesity among preschoolers. Early Hum Dev. 2010;86:563–8. Sep.

    Article  PubMed  Google Scholar 

  52. Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform. 2015;06:506–20.

    Article  CAS  Google Scholar 

  53. Ovadia YS, Gefel D, Weizmann N, Raizman M, Goldsmith R, Mabjeesh SJ, et al. Low iodine intake from dairy foods despite high milk iodine content in Israel. Thyroid. 2018;28:1042–51. https://doi.org/10.1089/thy.2017.0654.

    Article  CAS  PubMed  Google Scholar 

  54. Horan MK, Donnelly JM, McGowan CA, Gibney ER, McAuliffe FM. The association between maternal nutrition and lifestyle during pregnancy and 2-year-old offspring adiposity: analysis from the ROLO study. Z Gesundh Wiss. 2016;24:427–36. https://doi.org/10.1007/s10389-016-0740-9.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Larqué E, Labayen I, Flodmark CE, Lissau I, Czernin S, Moreno LA, et al. From conception to infancy—early risk factors for childhood obesity. Nat Rev Endocrinol. 2019;15:456–78. https://doi.org/10.1038/s41574-019-0219-1.

    Article  PubMed  Google Scholar 

  56. HAPO Study Cooperative Research Group, Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002. https://doi.org/10.1056/NEJMoa0707943.

    Article  Google Scholar 

  57. Kalter-Leibovici O, Freedman LS, Olmer L, Liebermann N, Heymann A, Tal O, et al. Screening and diagnosis of gestational diabetes mellitus: critical appraisal of the new International Association of Diabetes in Pregnancy Study Group recommendations on a national level. Diabetes Care. 2012;35:1894–6. https://doi.org/10.2337/dc12-0041.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhang L, Shang F, Liu C, Zhai X. The correlation between iodine and metabolism: a review. Front Nutr. 2024;11:1346452. https://doi.org/10.3389/fnut.2024.1346452.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Jin M, Zhang Z, Li Y, Teng D, Shi X, Ba J, et al. U-shaped associations between urinary iodine concentration and the prevalence of metabolic disorders: a cross-sectional study. Thyroid. 2020;30:1053–65. https://doi.org/10.1089/thy.2019.0516.

    Article  CAS  PubMed  Google Scholar 

  60. Shan X, Liu C, Luo X, Zou Y, Huang L, Zhou W, et al. Iodine nutritional status and related factors among chinese school-age children in three different areas: a cross-sectional study. Nutrients. 2021;13:1404. https://doi.org/10.3390/nu13051404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lecube A, Zafon C, Gromaz A, Fort JM, Caubet E, Baena JA, et al. Iodine deficiency is higher in morbid obesity in comparison with late after bariatric surgery and non-obese women. Obes Surg. 2015;25:85–9. https://doi.org/10.1007/s11695-014-1313-z.

    Article  PubMed  Google Scholar 

  62. Godoy GAF, Korevaar TIM, Peeters RP, Hofman A, de Rijke YB, Bongers-Schokking JJ, et al. Maternal thyroid hormones during pregnancy, childhood adiposity and cardiovascular risk factors: the generation R study. Clin Endocrinol. 2014;81:117–25. https://doi.org/10.1111/cen.12399.

    Article  CAS  Google Scholar 

  63. Kemkem Y, Nasteska D, de Bray A, Bargi-Souza P, Peliciari-Garcia RA, Guillou A, et al. Maternal hypothyroidism in mice influences glucose metabolism in adult offspring. Diabetologia. 2020;63:1822–35. https://doi.org/10.1007/s00125-020-05172-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Farahani H, Ghasemi A, Roghani M, Zahediasl S. The effect of maternal hypothyroidism on the carbohydrate metabolism and insulin secretion of isolated islets in adult male offspring of rats. Horm Metab Res. 2010;42:792–7. https://doi.org/10.1055/s-0030-1262826.

    Article  CAS  PubMed  Google Scholar 

  65. Karbalaei N, Ghasemi A, Hedayati M, Godini A, Zahediasl S. The possible mechanisms by which maternal hypothyroidism impairs insulin secretion in adult male offspring in rats. Exp Physiol. 2014r;99:701–14. https://doi.org/10.1113/expphysiol.2013.073825.

    Article  CAS  PubMed  Google Scholar 

  66. Harris SE, De Blasio MJ, Davis MA, Kelly AC, Davenport HM, Wooding FBP, et al. Hypothyroidism in utero stimulates pancreatic beta cell proliferation and hyperinsulinaemia in the ovine fetus during late gestation. J Physiol. 2017;595:3331–43. https://doi.org/10.1113/JP273555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Glinoer D. Maternal and fetal impact of chronic iodine deficiency. Clin Obstet Gynecol. 1997;40:102–16. https://doi.org/10.1097/00003081-199703000-00011.

    Article  CAS  PubMed  Google Scholar 

  68. Fernández-Real JM, López-Bermejo A, Castro A, Casamitjana R, Ricart W. Thyroid function is intrinsically linked to insulin sensitivity and endothelium-dependent vasodilation in healthy euthyroid subjects. J Clin Endocrinol Metab. 2006;91:3337–43. https://doi.org/10.1210/jc.2006-0841.

    Article  CAS  PubMed  Google Scholar 

  69. Roos A, Bakker SJL, Links TP, Gans ROB, Wolffenbuttel BHR. Thyroid function is associated with components of the metabolic syndrome in euthyroid subjects. J Clin Endocrinol Metab. 2007;92:491–6. https://doi.org/10.1210/jc.2006-1718.

    Article  CAS  PubMed  Google Scholar 

  70. Fontenelle L, Feitosa M, Severo J, Freitas T, Morais J, Torres-Leal F, et al. Thyroid function in human obesity: underlying mechanisms. Horm Metab Res. 2016;48:787–94. https://doi.org/10.1055/s-0042-121421.

    Article  CAS  PubMed  Google Scholar 

  71. Cai L, Tao J, Li X, Lin L, Ma J, Jing J, et al. Association between the full range of birth weight and childhood weight status: by gestational age. Eur J Clin Nutr. 2019;73:1141–8. https://doi.org/10.1038/s41430-018-0343-3.

    Article  PubMed  Google Scholar 

  72. Standing Committee on the Scientific Evaluation of Dietary Reference Intakes, Institute of Health; The United States of America. Iodine. National Academy Press; 2001.

  73. Erkan N. Iodine content of cooked and processed fish in Turkey. Int J Food Sci Technol. 2011;46:1734–8. https://doi.org/10.1111/j.1365-2621.2011.02684.x.

    Article  CAS  Google Scholar 

  74. Øyen J, Aadland EK, Liaset B, Fjære E, Dahl L, Madsen L. Lean-seafood intake increases urinary iodine concentrations and plasma selenium levels: a randomized controlled trial with crossover design. Eur J Nutr. 2021;60:1679–89. https://doi.org/10.1007/s00394-020-02366-2.

    Article  CAS  PubMed  Google Scholar 

  75. Reynolds RM, Osmond C, Phillips DIW, Godfrey KM. Maternal BMI, parity, and pregnancy weight gain: influences on offspring adiposity in young adulthood. J Clin Endocrinol Metab. 2010;95:5365–9. https://doi.org/10.1210/jc.2010-0697.

    Article  CAS  PubMed  Google Scholar 

  76. Sha T, Gao X, Chen C, Li L, He Q, Wu X, et al. Associations of pre-pregnancy BMI, gestational weight gain and maternal parity with the trajectory of weight in early childhood: a prospective cohort study. Int J Environ Res Public Health. 2019;16:1110. https://doi.org/10.3390/ijerph16071110.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Alkhawaldeh IM, Albalkhi I, Naswhan AJ. Challenges and limitations of synthetic minority oversampling techniques in machine learning. World J Methodol. 2023;13:373–8. https://doi.org/10.5662/wjm.v13.i5.373.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials—a practical guide with flowcharts. BMC Med Res Methodol. 2017;17:162. https://doi.org/10.1186/s12874-017-0442-1.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Reiner Benaim A, Almog R, Gorelik Y, Hochberg I, Nassar L, Mashiach T, et al. Analysing medical research results based on synthetic data and their relation to real data results: systematic comparison from five observational studies. JMIR Med Inform. 2020;8:e16492. https://doi.org/10.2196/16492.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are indebted to all the study participants. We thank Dr Arie Budovsky from BUMCA’s research authority for discussing and reviewing this manuscript. We extend our gratitude to Prof. Bruce Rosen from the Paul Baerwald School of Social Work and Social at the Hebrew University of Jerusalem, whose expertise enriched this article. We also acknowledge Mrs. Ruhama Kremer and Mrs. Hagit Afuta of BUMCA’s Department of Obstetrics and Gynecology for their assistance with the study’s ethical and administrative procedures.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Obstetrics and Gynecology Division, Barzilai University Medical Center, Ashkelon, Israel

    Yaniv S. Ovadia, Tatiana Ketslakh, Eyal Y. Anteby, Dov Gefel & Simon Shenhav

  2. Medical Office of Southern District, Ministry of Health, Ashkelon, Israel

    Natalya Bilenko

  3. Faculty of Health Sciences, Ben-Gurion University of Negev, Beersheba, Israel

    Natalya Bilenko, Eyal Y. Anteby & Simon Shenhav

  4. Loewenstein Rehabilitation Medical Center, Ra’anana, Israel

    Orit Mazza

  5. Clalit Health Services, Dan Petah-Tiqwa District, Petah Tikva, Israel

    Orit Mazza

  6. The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, Petach Tikva, Israel

    Naama Fisch-Shvalb

  7. Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Naama Fisch-Shvalb

  8. Department of Information Systems, The Max Stern Emek Yezreel College, Emek Yezreel, Israel

    Abigail Paradise Vit

  9. School of Nutritional Science; Institute of Biochemistry, Food Science and Nutrition; Robert H. Smith Faculty of Agriculture, Food and Environment; The Hebrew University of Jerusalem, Rehovot, Israel

    Shani R. Rosen

  10. Women’s Health Center, Maccabi Healthcare Services, Southern Region, Beersheba, Israel

    Yael Avrahami-Benyounes

  11. National Public Health Laboratory, Ministry of Health, Tel Aviv, Israel

    Ludmila Groisman & Efrat Rorman

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Contributions

YSO was responsible for conceptualization, resources, data curation, investigation, writing - original draft and revised, manuscript, visualization, supervision and project administration; NB was responsible for resources, data curation; DG was responsible for conceptualization and Resources; OM was responsible for methodology, software, formal analysis, writing—original draft; APV was responsible for methodology, software, formal analysis, writing - revised manuscript; NFS was responsible for investigation and writing—original draft; SRR was responsible for investigation and writing—review & editing; YAB was responsible for investigation; LG was responsible for validation and investigation; ER was responsible for validation and investigation; TK was responsible for data curation; EYA was responsible for conceptualization, resources and supervision; SS was responsible for resources, data curation, investigation, writing—review & editing and project administration; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Yaniv S. Ovadia.

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Competing interests

YSO is a named inventor on a pending provisional patent application (U.S. Application No. 63/911,629) describing systems and methods for predicting and mitigating offspring health risks (including predictive models and methods presented in this manuscript). The remaining authors declare that they have no competing financial interests.

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Ovadia, Y.S., Bilenko, N., Mazza, O. et al. A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother–newborn–offspring study in a mild-to-moderate iodine deficiency area. Int J Obes (2025). https://doi.org/10.1038/s41366-025-01988-y

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