From behavioral profiles to neural scaffolds: cortico-striatal integrity predicts longitudinal stability and transition in psychological adaptability during early adulthood.

Obesity and major depressive disorder (MDD) represent two of the most pressing public health challenges worldwide, with profound socioeconomic consequences. Globally, 890 million people worldwide were living with obesity, representing ~16% of the global population (World Health Organization, ), with rates exceeding 40% in countries like the United States and several Pacific Island nations (GBD 2021 Adult BMI Collaborators, ). Concurrently, MDD afflicts ~5% of the world’s population annually, with a lifetime prevalence of 11–15% across different regions (Bromet et al., ). As leading drivers of global morbidity and mortality, high body mass index (BMI) and MDD collectively account for ~160 million DALYs worldwide, alongside an immense economic burden (GBD 2019 Mental Disorders Collaborators, ; Xie et al., ). Epidemiological evidence consistently demonstrates that obesity significantly increases MDD risk, with meta-analyses indicating obese individuals face 1.2–1.6 times higher odds of developing MDD compared to normal-weight counterparts (Luppino et al., ; Mannan, Mamun, Doi, & Clavarino, ; Pereira-Miranda et al., ). When these conditions co-occur, patients experience worse health outcomes, treatment resistance to both conditions, substantially reduced quality of life, and an increased risk of premature mortality, with psychological distress explaining up to 10% of this obesity-related mortality risk (Putra et al., ). This detrimental synergy creates an urgent need for early identification of at-risk obese individuals, as timely intervention could significantly reduce the compounded burden on global healthcare systems and improve patient outcomes. Despite growing recognition of the obesity-MDD link, current predictive approaches for MDD remain inadequate for obese populations. Existing prediction models primarily rely on psychological factors, sociodemographic variables, and general biomarkers developed for the broader population (Ma et al., ; Meng, Speier, Ong, & Arnold, ; Radford-Smith et al., ). Recent efforts have incorporated genetic-risk scores, inflammatory markers (C-reactive protein and apolipoproteins), and basic metabolic indicators, achieving moderate predictive performance (AUCs: 0.66–0.72) in general populations (Radford-Smith et al., ; Wang et al., ). However, these models overlook the unique metabolic landscape of obesity, which fundamentally alters the biological pathways potentially leading to MDD. Most studies have assumed a uniform obesity–MDD association, failing to account for the considerable metabolic heterogeneity among obese individuals. For example, metabolically healthy obese persons may differ significantly from those with dysregulated profiles (Jokela et al., ), rendering general models less applicable. Moreover, previous studies are often limited by small sample sizes, short follow-up periods, and a lack of validation in independent cohorts, while the absence of causal inference frameworks further hinders mechanistic insights, underscoring the need for obesity-specific predictive tools. Investigating metabolic stratification in obesity is crucial for improving the prediction of MDD risk, due to the complex interplay between these conditions. The complex relationship between obesity and MDD extends beyond simple weight metrics, with bidirectional interactions through multiple physiological pathways. Chronic inflammation, hypothalamic–pituitary–adrenal axis dysregulation, insulin resistance, and altered gut microbiota have all been implicated in this relationship (Milaneschi, Simmons, van Rossum, & Penninx, ). Crucially, these mechanisms vary substantially among obese individuals, challenging the outdated view of obesity as a metabolically homogeneous condition. Recent research has identified distinct metabolic phenotypes within obesity, ranging from metabolically healthy to severely dysregulated profiles – that may differentially influence MDD vulnerability (Brandão, Martins, & Monteiro, ). Traditional clinical criteria for metabolically healthy/unhealthy obesity, however, were developed for cardiometabolic outcomes and may inadequately capture neural and psychological risk factors. Blood metabolomics offers a particularly promising approach by providing comprehensive metabolic signatures that reflect both systemic processes and brain-specific pathways relevant to mood regulation (Pan et al., ). This method captures thousands of small molecules representing diverse biological processes, potentially identifying novel predictive markers and therapeutic targets overlooked by conventional clinical measures or isolated biomarkers (Meng et al., ).