Background Early‐life exposures can increase the risk of both obesity and high blood pressure in adulthood. In this paper, we identify exposures across five pre‐hypothesised childhood domains, explore them as predictors of obesity and high blood pressure comorbidity using the 1958 National Child Development Study (NCDS), and discuss these results in comparison to a similar approach using another birth cohort (1970 British Cohort Study (BCS70)). Methods The analytical sample included 9150 participants. The outcome was obesity (BMI of ≥ 30) and high blood pressure (blood pressure > 140/90 mm Hg) comorbidity at age 44. Domains included: ‘prenatal, antenatal, neonatal and birth’, ‘developmental attributes and behaviour’, ‘education and academic ability’, ‘socioeconomic factors’ and ‘parental and family environment’. Stepwise backward elimination selected variables for inclusion for each domain, and predicted risk scores of obesity–high blood pressure for each cohort member within each domain were calculated. We performed multivariable logistic regression analysis including domain‐specific risk scores, sex and ethnicity to assess how well the outcome could be predicted taking all domains into account. In additional analysis, we included potential adult factors. Results Including all domain‐specific risk scores, sex and ethnicity in the same prediction model the area under the curve was 0.70 (95% CI 0.67–0.72). The strongest domain predictor was the socioeconomic factors domain (OR 1.28 95% CI 1.19–1.38), similar to the BCS70 results. The parental and family environment domain was not a significant predictor (OR 1.08 95% CI 0.94–1.24) unlike the BCS70 results. After considering adult predictors, robust associations remained to the socioeconomic, education and academic abilities, development and behaviour, and prenatal, antenatal, neonatal and birth domains. Conclusions In the NCDS, some early‐life course domains were significant predictors of obesity–high blood pressure comorbidity, supporting previous findings. Shared early‐life characteristics could have a role in predicting obesity–high blood pressure comorbidity, particularly for those who faced socioeconomic disadvantage.
Obesity remains a complex global public health crisis driven by environmental, biochemical, and behavioral factors, leading to severe metabolic comorbidities. Digital therapeutics (DTx) has emerged as a scalable, data‐driven intervention, utilizing wearable sensors, mobile applications, and artificial intelligence (AI) to deliver personalized weight‐management strategies. Rather than relying solely on traditional approaches, true DTx distinguishes it through rigorous clinical validation and targeted behavioral modification. Current evidence from clinical trials and real‐world data indicates that while DTx effectively enhances patient engagement and promotes short‐term behavioral change, sustaining long‐term adherence and significant weight loss remains a critical challenge. Furthermore, the integration of AI models offers advanced personalization, though their efficacy must be balanced against potential algorithmic biases and traditional statistical approaches. Successful implementation of DTx into standard obesity care requires overcoming substantial barriers, particularly digital divide, data privacy concerns, and fragmented regulatory frameworks. Ultimately, realizing the full potential of DTx necessitates collaborative efforts to establish standardized regulations, ensure equitable access, and design evidence‐based interventions that prioritize long‐term patient engagement and clinical efficacy.
Background and Objectives Binge‐spectrum eating disorders (BSEDs) are clinically impactful, yet the psychological mechanisms underlying their persistence and heterogeneity remain insufficiently understood. Clarifying these mechanisms is essential for refining diagnostic models and improving treatment. This study focused on young to early‐middle adults, a developmental period with elevated risk for the onset and aggravation of BSED, to identify empirical subgroups based on eating‐ and psychological‐related variables. Methods A two‐step cluster analysis was performed in 187 patients (men and women, aged 20–45 years) diagnosed with different BSED subtypes. Variables considered included emotion‐related eating behaviors, impulsivity, emotion dysregulation, decision‐making, stress, anxiety–depression, and obsessive–compulsive symptoms. Results A three‐cluster solution provided the best fit, yielding distinct patient profiles. Anxiety–depression and hunger emerged as the most influential variables for the clustering. No significant differences between the clusters were found in sociodemographic characteristics and eating disorder subtype. Clear gradients were observed in clinical severity and psychological functioning, ranging from high psychopathological burden and dysregulation in Cluster 1 (10.2% of the sample) to markedly more adaptive functioning and reduced distress in Cluster 3 (56.7%). The largest between‐cluster differences involved hunger, emotion regulation deficits, and anxiety–depression levels. Conclusions The findings delineate clinically meaningful BSED subgroups, spanning severely dysregulated to more adaptive profiles. This differentiation may enhance diagnostic classification and inform process‐based treatment approaches, enabling personalized interventions targeting core features (particularly anxiety–depression, emotion regulation, and hunger) to optimize clinical outcomes.
