What role will physiological resilience play in brown-white fat dynamic in obesity management?

The current obesity prevalence presents a significant paradox: despite notable therapeutic advances, including widespread use of GLP-1 receptor agonists (GLP-1RA) and SGLT2 inhibitors (SGLT2i) valued at $46.7 billion globally, the prevalence is expected to rise. Projections for 2035 estimate economic costs of $4 trillion [1,2]. These medical and socioeconomic challenges, linked to increasing obesity-related cardiovascular and neurodegenerative comorbidities, highlight fundamental limitations in current pharmacotherapeutic approaches [3][4][5][6]. However, the recently introduced adipocentric model for understanding metabolic health and preventing metabolic alterations offers a promising way forward. Over 75 medical societies recognize that the dynamics of brown and white fat (adipose tissue) are key components of the definition and diagnosis of obesity [7]. Today, obesity is an umbrella term encompassing a big group of diseases/with metabolic disorders (Supp. 1). Therefore, the new diagnostic approach based on the adipocentric model for prevention and early detection of preobesity and clinical obesity will help identify body changes that are predictors or signs of metabolic alterations. This promising strategy could slow the emergence of a new pandemic of metabolic diseases and related comorbidities.At the same time, a contemporary understanding of metabolic health through the lens of physiological resilience reveals how personalized responses to external stressors determine health outcomes and can help implement precise preventive interventions. Hans Selye, the 'father of biological stress,' was an academic leader who first introduced the concept of 'stressors' through his pioneering observations on animal models as well as on clinical findings on sudden increases in gastroduodenal ulcer hospitalizations during Nazi rocket attacks and air raids in London (1942)(1943) and involvement of brown fat in stress response [8]. Regarding the WHO report, during the outgoing war in Ukraine, there was an increase in type 2 diabetes mellitus twice, and three-fifths of adults aged 18-69 years in Ukraine were overweight and obese based on BMI (18.5-24.9 kg/m2) [9]. These evidence-based facts reveal that chronic stress induces metabolic diseases.Selye's three-stage stress response involving hypothalamic-pituitary-adrenal (HPA) axis regulation includes alarm, resistance, and exhaustion. His later theory of 'cross-resistance' shows that organisms exposed to mild stressors tend to perform better when faced with more intense versions of the same stressor [10,11]. These findings, along with subsequent evidence on autonomic nervous system (ANS) balance, as reflected in heart rate variability (HRV) indices, and inflammatory tone, are essential for understanding the relationship between physiological resilience and metabolic changes driven by brown-white adipose tissue dynamics [12,13].Physiological resilience, an individual's natural ability to handle stress effectively, depends on various factors, including age, pre-existing ANS flexibility, health conditions, metabolic health, lifestyle choices such as physical activity, sleep quality, and nutrition, and adaptation to cold or heat exposure [14]. Higher physiological resilience raises the threshold at which stressors cause discomfort and damage, while lower resilience makes one more vulnerable to these stressors. Because personal metabolic health is crucial for building physiological resilience, it can help predict and prevent pre-obesity. The bidirectional transformation between energy-storing white fat cells and thermogenic brown/beige fat cells creates a mechanistic link between stress adaptation and metabolic health, forming a resilience-adiposity axis. Its activity refers to metabolic flexibility (the ability to switch between glucose and fat/ketones as fuel sources). Metabolic inflexibility leads to dysregulation of energy metabolism and immune system. These interconnected multisystem loops, or the resilience-adiposity axis, with brown-white fat plasticity and dynamics at their core, represent an adipocentric model for practical assessment and strategies to prevent and manage obesity based on resting and challenge-recovery HRV indices, cortisol awakening response, coldpressor or cold-exposure recovery, perceived stress scale with test-retest reliability (Fig. 1).The dynamic interplay between brown and white fat phenotypes is a fundamental mechanism through which physiological resilience influences metabolic health [15,16]. The process of 'browning' fat involves β-adrenergic signaling cascades that promote mitochondrial biogenesis and UCP1 expression, effectively converting energy-storing white adipocytes into energy-dissipating brown-like cells [17,18]. This brown-white fat dynamic may be characterized by 'whitening' of brown adipose tissue (progressive lipid droplet accumulation, white fat hypertrophy, mitochondrial dysfunction, and loss of thermogenic capacity), decreased cellular defense and development of metaflammation [19,20]. It is often associated with metabolic inflexibility, which is linked to chronic stress, aging, or metabolic disease. These resilience-adiposity axis activities reflect a series of challengerecovery-growth waves across the fat plasticity continuum, with strong wave responses encouraging sustained browning and metabolic health by biofeedback, while disrupted wave patterns accelerate pathological whitening and the development of obesity. Moreover, adipocentric metabolic "personalized signature" has gained renewed clinical relevance in severe COVID-19 cases, where initial cortisol elevation is followed by adrenal exhaustion and glucocorticoid synthesis failure, with synthetic corticosteroid intervention proving lifesaving in many patients [21]. On the other hand, the personalized "fingerprint" of the resilience-adiposity axis, which integrates multiple systems (adipose, autonomic, and inflammatory), has been useful for understanding patients with long COVID, a new disease related to the malfunctioning of adaptive capacity mobilization [22][23][24]. Thus, we hypothesize that subtyping obesity with an adipocentric model based on resilience profiling can predict: (1) preclinical obesity, (2) treatment response before anti-obesity therapy begins, and (3) the effectiveness of GLP-1RA and SGLT2i.Based on our understanding that the prediction of metabolic disorders and the early assessment of obesity subtypes lack consistent correlation with clinical outcomes, several potential solutions can be helpful. Thus, should be considered to add to advanced anthropometric measurements [25][26][27][28], a complex digital biomarker score integrating during standardized cold challenges [29][30][31][32] (Supp. 2). When findings are analyzed using machine learning algorithms, they can detect brown-white fat dynamic dysfunction with higher accuracy in pre-obese individuals (BMI 25-29.9 kg/m²) before clinical obesity manifests. This predictive accuracy exceeds that of traditional biomarkers (fasting glucose, lipid panel, and inflammatory markers) used to diagnose metabolic disorders. This hypothesis directly addresses the need for early detection biomarkers that preceded clinical manifestations. For this reason, it needs to test its comparative predictive accuracy against traditional cardiometabolic risk scores (Framingham, ASCVD). Moreover, it can provide evidence-based, relevant arguments for brown fat-targeted interventions, such as cold exposure therapy and exercise protocols, as well as for the validity of standardized uptake value measurements in PET-CT imaging used to detect and quantify brown fat (Suppl. 3).Since physiological resilience, an individual's natural capacity to manage stress effectively, depends on various factors, including age, pre-existing health conditions, metabolic and immunological status, lifestyle choices, physical activity, sleep quality, nutrition, and acclimatization to cold or heat exposure, autonomic adaptability plays a central role for metabolic health [33][34][35][36][37]. Dysregulation of the HPA axis leads to chronic cortisol elevation, which promotes stress-related obesity, activates ANS, the brain-gut axis, and increases inflammatory markers such as CRP, leading to several malfunctions in energy and glucose metabolism [38][39]. Prolonged cortisol increase specifically promotes the buildup of visceral fat and boosts white adipose tissue lipolysis, releasing fatty acids. Glucocorticoids also directly affect fat distribution and can suppress brown fat activity, increase whitening and ectopic fat formation. The sympathetic nervous system is the principal stimulator of brown adipose tissue thermogenesis through norepinephrine release onto β3-adrenergic receptors. Brown fat activation is under direct control of central sympathetic circuits that respond to cold, metabolic signals, and thermoregulatory demands. Altered HRV reflects autonomic imbalance and is associated with increased inflammation and insulin resistance [13][14]. Findings about lower HRV, which correlates with elevated proinflammatory adipokines levels, and decreased vagal activity impairing anti-inflammatory mechanisms, help in understanding metabolic flexibility and its changes [40].High physiological resilience elevates the threshold at which stressors cause discomfort and harm, whereas low resilience increases vulnerability to them. Since personal metabolic status is vital for developing physiological resilience, autonomic profiling and character of brown-white fat dynamics could be used to predict and prevent pre-obesity. Furthermore, it has potential for effective therapeutic interventions for clinical obesity [41][42][43].Next, adipocentric model introduced for classified individuals by adipocentric criteria into distinct obesity subtypes: metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), sarcopenic obesity, TOFI (Thin Outside, Fat Inside) phenotype, android versus gynoid distribution, or brown adipose tissue deficit obesity and further graded by physiological resilience capacity (assessed through autonomic adaptability metrics, stress tolerance testing, and challenge-recovery-growth wave analysis) will help predict therapeutic response. It will help select efficient nonpharmacological and pharmacological interventions for those who demonstrate significantly different therapeutic responses to matched versus mismatched interventions. Specifically, we hypothesize that based on the proposal adipocentric model with autonomic profiling, subtype-matched therapies (e.g., GLP-1 agonists for MUO, leucine supplementation with resistance training for sarcopenic obesity, cold exposure protocols for BAT deficit obesity) will help achieve greater reductions in cardiometabolic risk markers than standard ("one-size-fits-all approach") at 12-month follow-up. In addition, the current failure of uniform treatment protocols across obesity subtypes suggests that an individualized approach is essential for long-term maintenance of metabolic improvements in the face of obesity heterogeneity. It is therefore the resilience-adiposity axis that is considered a promising adipocentric model for precise, individualized treatment protocols for obesity, matching interventions to autonomic tone, metabolic flexibility, and the inflammatory state. The adipocentric model identifies individuals with a normal BMI (<25 kg/m²) but with excessive visceral and ectopic fat deposition in the liver, pancreas, and skeletal muscle, which can be detected through CT/MRI or elevated liver enzymes. This suggests metabolic-associated fatty liver disease (MAFLD), and these individuals face metabolic risks like those with clinical obesity despite having a normal appearance.WHR differentiation (>0.9 for men; >0.85 for women in the android pattern) identifies upper body obesity associated with higher cardiometabolic risk, increased cortisol reactivity, and reduced brown fat activity, which require cold exposure therapy and stress management, compared to lower body obesity that has relatively preserved metabolic health and different therapeutic priorities [].This diagnostic approach for brown-white fat dynamics helps address individual physiological resilience profiles to achieve personalized functionality, rather than relying on universal therapeutic interventions. Moreover, the adipocentric framework identifies individuals with impaired cold-induced thermogenesis and reduced brown fat activity (assessable through cold tolerance testing or thermal imaging), who benefit specifically from graduated cold exposure protocols (15-18°C for 15 mins until 90 mins daily) and dietary thermogenic compounds (capsaicin, green tea catechins) to restore metabolic flexibility. This precision subtyping enables personalized therapeutic algorithms.