A newly published study by Sun et al. in Biology of Sex Differences (2025) delivers compelling evidence that metabolic stress emerges early and differs significantly between sexes in a widely used mouse model of Alzheimer’s disease (AD). Using the APP23 transgenic line, the researchers investigated subtle physiological and behavioral differences during the pre-plaque stage of disease development, before classical hallmarks like amyloid beta accumulation take hold.
Background and Study Goals
Alzheimer’s disease disproportionately affects women—both in prevalence and progression—yet many preclinical models fail to address sex as a biological variable. This study sought to understand whether early metabolic changes in AD-prone mice vary by sex, and if so, how those differences contribute to disease vulnerability. Focusing on mice aged 3–4 months, well before plaque formation, the researchers employed a comprehensive metabolic and behavioral profiling approach to capture early-stage alterations in energy balance.
Key Findings
The results were striking. Female APP23 mice displayed a pronounced negative energy balance, driven by lower food intake and higher spontaneous activity levels, despite similar body mass compared to controls. Importantly, only female transgenic mice exhibited enhanced mitochondrial respiration in liver tissue and increased fatty acid oxidation capacity in adipocytes—adaptations likely aimed at meeting elevated energy demands.
However, these compensatory mechanisms were not enough. Female APP23 mice experienced increased mortality during this early stage, suggesting that their metabolic adaptations, while active, were insufficient to maintain homeostasis. In contrast, male APP23 mice showed far less deviation from wild-type controls, both behaviorally and metabolically.
Use of the Tecniplast DVC® System
A key innovation in this study was the integration of the Tecniplast DVC® (Digital Ventilated Cage) system—a home-cage monitoring platform that enables 24/7, non-invasive tracking of mouse activity in their natural housing environment. The researchers used the DVC system to gather high-resolution data on locomotor activity and rest patterns, without handling or environmental disruption. This technology allowed the team to quantify spontaneous in-cage activity in real time and directly correlate these behavioral patterns with metabolic parameters like caloric intake and energy expenditure. The DVC-derived data revealed that female APP23 mice were significantly more active, particularly during the dark cycle, which aligned with their negative energy balance and may have contributed to their early mortality. By pairing traditional physiological assays with automated, stress-free behavioral tracking, the study showcases how home-cage monitoring technologies like the DVC system can enrich preclinical data and reveal dynamics that would otherwise go undetected.
Implications for Alzheimer’s Research
This work reinforces the need to consider sex differences from the earliest stages of disease research and highlights energy balance and mitochondrial function as early indicators of Alzheimer’s progression. It also showcases how integrated, high-throughput monitoring technologies can transform our understanding of disease phenotypes in animal models.
By illuminating the interplay between behavior, metabolism, and sex in Alzheimer’s pathogenesis, this study adds an important dimension to preclinical neurodegeneration research—and sets a high bar for future studies aiming to model complex, multifactorial diseases.
