More than 45% of UK women of reproductive age are overweight or obese, yet doctors do not know whether the two most powerful weight-loss treatments—bariatric surgery and GLP-1 drugs like Wegovy—pass health risks or benefits to their children. This matters because lifestyle changes during pregnancy rarely work, and the number of women using these interventions before conception is rising rapidly. Existing data on bariatric surgery’s effects on offspring are inconsistent, and no studies have examined the transgenerational impact of GLP-1 receptor agonists, which many women stop taking only shortly before becoming pregnant. The researchers will follow two groups: a new cohort of women who had surgery or took GLP-1 drugs before pregnancy, tracking their children’s cardiometabolic health from the womb to 18 months old; and an existing post-surgery cohort whose children are now 5–9 years old. By analysing maternal metabolic changes during pregnancy and using machine learning to link them to offspring outcomes, the team aims to identify which weight-loss strategies are safest for the next generation. If successful, the findings could directly reshape pre-conception counselling and pregnancy care for millions of women, potentially reducing childhood obesity and lifelong cardiovascular risk.
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Context and the challenge the project addresses Over 45% of women of reproductive age in the UK are overweight or obese. Maternal obesity has a significant impact on the cardiometabolic risk of offspring. Lifestyle interventions for obesity during pregnancy are ineffective. The prevalence of surgical and pharmacological interventions for managing obesity before pregnancy is increasing. However, their effects on offspring’s cardiometabolic risk remain poorly understood, creating a knowledge gap that has substantial implications for the lifelong health of these children. Bariatric surgery (BS) is the most effective treatment for morbid obesity. Limited evidence suggests that pre-pregnancy maternal BS affects offspring cardiometabolic risk factors, but the data are inconsistent. To better understand the impact of maternal BS on offspring health, we initiated a post-BS pregnancy cohort in 2015, which is currently ongoing. From this cohort, we have observed significant associations between maternal serum and urine metabolic changes and offspring birth weight, indicating a potential biological link between these maternal metabolites and offspring growth. However, comprehensive investigations into the nature of these associations have not yet been undertaken and further research into the short- and long-term transgenerational effects of BS is crucial. In contrast to invasive surgical interventions, new pharmacological obesity treatments, such as Glucagon-like peptide-1 receptor agonists (GLP-1RAs), which induce significant weight loss with health benefits, are becoming popular for many people living with obesity. Since many women with obesity may become pregnant soon after stopping or even while taking these medications, it is important to investigate the transgenerational impact of GLP-1RAs and evaluate the effects of both BS and GLP-1RAs maternal weight loss strategies on the offspring cardiometabolic risk factors. Aims and objectives We aim to study the effects of pre-pregnancy maternal BS and GLP-1RA therapy on the offspring cardiometabolic risk factors, and to investigate associations between these effects and maternal metabolic changes resulting from pre-pregnancy obesity treatment. To address these aims, we will study two cohorts: (1) We will establish a cohort of pregnant participants with a history of pre-pregnancy BS or GLP-1RA therapy, for weight loss, and their offspring. We will compare the cardiometabolic risk factors of those offspring (in utero and birth to 12-18 months) to control groups without maternal BS or GLP-1RA therapy. (2) We will use our existing post-BS cohort, initiated in 2015, to study the longer-term impact of BS on the cardiometabolic risk factors of offspring (5-9 years old). In both cohorts, metabolic phenotyping approaches will be applied to investigate weight loss strategies-associated metabolic changes during pregnancy. Subsequently, machine learning methods will be used to explore associations between these metabolic changes and offspring cardiometabolic risk factors. Potential applications and benefits This study has significant clinical implications. The knowledge gained will benefit pregnant people living with obesity and those planning for pregnancy by providing evidence on the transgenerational impacts of different obesity management strategies. The study has the potential to improve pre-conception and pregnancy care for obese women, promote better health outcomes of their children, and may contribute to early childhood obesity prevention.
Abbas Dehghan (Co-Investigator)Alexander Miras (Co-Investigator)Elaine Holmes (Co-Investigator)Gary Frost (Co-Investigator)Jia Li (Principal Investigator)Makrina Savvidou (Co-Investigator)Mark Johnson (Co-Investigator)Saji Alexander (Co-Investigator)
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