Male Sapap3 knockout mice show threat bias under conflict during platform-mediated avoidance task: effects of extinction with response prevention and implications for obsessive compulsive disorder.

Sarcopenia, a progressive, age-related skeletal muscle disorder, has emerged as a critical public health challenge amid global population aging []. Worldwide, its prevalence among individuals aged 60 years and older ranges from 10% to 27% []. This condition is strongly associated with adverse outcomes, including an increased risk of falls, functional disability, hospitalization, and mortality [, ]. Recognizing the urgent need for early identification and intervention, the Asian Working Group for Sarcopenia (AWGS) 2025 Consensus Update has introduced a paradigm shift in the definition and management of sarcopenia. This update represents a pivotal advancement in the field []. The AWGS 2025 Consensus redefines sarcopenia as a disease characterized by the concurrent presence of low muscle mass and low muscle strength. This revision simplifies the diagnostic criteria by removing physical performance as a mandatory component; it is now classified as an outcome indicator. Critically, this adjustment to the diagnostic workflow does not diminish the pathophysiological or clinical value of physical performance metrics in sarcopenia prevention: while they are no longer required for definitive diagnosis, declines in walking speed, lower body mobility, and other physical performance parameters are well-documented early, preclinical manifestations of skeletal muscle deterioration. These changes often occur before the loss of muscle mass and strength reaches the diagnostic threshold for sarcopenia, making them ideal candidate markers for early risk prediction. This updated definition also extends coverage to middle-aged adults (aged 50–64 years) using population-specific cutoff values, thereby emphasizing early detection and intervention across the life course. A key highlight of the AWGS 2025 Consensus is its explicit emphasis on the close relationship between intrinsic capacity (IC) and sarcopenia. Defined by the World Health Organization (WHO) as the composite of an individual’s physical and mental capacities across cognitive, locomotor, vitality, sensory, and psychological domains, IC forms the foundation for healthy aging and functional independence []. Physical performance metrics are the direct, objective quantitative measures of the locomotor domain of IC. For our target population of adults with declined IC, these metrics not only reflect the core impairment of the locomotor domain, but also serve as early sentinels of the bidirectional vicious cycle between IC decline and sarcopenia progression highlighted in the AWGS 2025 Consensus. The AWGS 2025 Consensus posits that declined IC is not only a key risk factor for sarcopenia but also engages in a bidirectional relationship with muscle health. Specifically, declined IC compromises the maintenance of muscle mass and strength, whereas sarcopenia, in turn, exacerbates IC deterioration. This interaction forms a vicious cycle that accelerates functional decline []. Consequently, IC is positioned as a central target for sarcopenia risk stratification and prevention, aligning with the WHO’s Integrated Care for Older People (ICOPE) framework [, ]. Despite the advancements outlined in the AWGS 2025 Consensus, critical gaps remain in translating these insights into clinically practical risk stratification tools. Traditional statistical methods (e.g., conventional logistic regression) currently used for sarcopenia risk prediction are limited in their ability to capture the complex, nonlinear relationships among IC domains, demographic factors, and sarcopenia development [–]. However, machine learning (ML) algorithms, which excel at handling multidimensional data and identifying latent feature interactions, have demonstrated great potential in medical prediction. Despite this, ML remains underutilized in sarcopenia research, particularly for models based on the latest AWGS 2025 criteria. Second, existing prediction models lack specificity for populations with declined IC, a high-risk subgroup explicitly prioritized in the AWGS 2025 Consensus. Most available models are developed for general older adult populations, with IC status only included as an auxiliary covariate, rather than targeting this high-risk subgroup specifically. The pathophysiological mechanisms and risk profiles of sarcopenia differ fundamentally between individuals with compromised IC and those with preserved IC: for adults with already declined IC, sarcopenia development is driven by the bidirectional vicious cycle between multi-domain functional impairment and muscle deterioration, rather than the physiological age-related muscle loss that predominates in the general population. This means that the weight and predictive value of risk factors vary significantly between the two populations, and general population models often fail to achieve sufficient sensitivity and specificity in the IC-declined subgroup, leading to unacceptably high missed diagnosis rates in this high-risk population. Therefore, a dedic