Shuyun Yu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Shuyun Yu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Bochao You
School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
2 shared publications - Haili Tian
School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
2 shared publications - Yi Fan
School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
2 shared publications - Zhenghua Cai
School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
2 shared publications - Barbara E. Ainsworth
School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
1 shared publication - Haoyue Zhang
School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
1 shared publication
Latest Publications
- Open Access
Prediction Equations to Estimate Resting Metabolic Rate in Healthy, Community-Dwelling Chinese Older Adults
Background: China’s rapidly aging population demonstrates the importance of conducting an accurate resting metabolic rate (RMR, kcal/day) assessment to mitigate geriatric nutritional imbalances—amid concurrent undernutrition (e.g., ~1/3 with protein insufficiency) and overnutrition (e.g., high obesity and type 2 diabetes rates). While RMR prediction equations exist for other populations, none are specific to Chinese older adults. This study aimed to develop and validate population-specific RMR prediction equations for community-dwelling Chinese older adults. Methods: A total of 189 healthy participants (Aged 69.5 ± 6.3, range: 60–94 years; BMI: 24.0 ± 3.1 kg/m2) were recruited from the Shanghai, China, community. RMR was measured via indirect calorimetry, and body composition via dual-energy X-ray absorptiometry. Results: Two novel prediction equations were derived: Cai1 (fat-free mass [FFM] + age): RMR = 1393.019 − (11.112 × age) + (11.963 × FFM); R2 = 0.572, and Cai2 (sex + age + weight [WT]): RMR = 1537.513 + (91.038 × sex) − (11.515 × age) + (5.436 × WT); R2 = 0.528. Both novel prediction equations achieved 82.5% adequacy (predicted RMR within 90–110% of measured values), minimal systematic bias (%) (−0.72% and −1.08%) and strong positive correlations with measured RMR (r = 0.792 and 0.773, both p < 0.001). Bland–Altman analysis confirmed no systematic bias. In contrast, 11 widely used published prediction equations (e.g., Harris–Benedict, Mifflin–St. Jeor) exhibited significant overestimation (systematic bias +8.39% to +38.03%). Conclusions: The novel population-specific RMR equations outperform published ones, providing a clinically reliable tool for individualized energy prescription in nutritional interventions to support healthy aging in Chinese older adults.
Open AccessPrediction Equations to Estimate Resting Metabolic Rate in Healthy, Community-Dwelling Chinese Older Adults
Background: China’s rapidly aging population demonstrates the importance of conducting an accurate resting metabolic rate (RMR, kcal/day) assessment to mitigate geriatric nutritional imbalances—amid concurrent undernutrition (e.g., ~1/3 with protein insufficiency) and overnutrition (e.g., high obesity and type 2 diabetes rates). While RMR prediction equations exist for other populations, none are specific to Chinese older adults. This study aimed to develop and validate population-specific RMR prediction equations for community-dwelling Chinese older adults. Methods: A total of 189 healthy participants (Aged 69.5 ± 6.3, range: 60–94 years; BMI: 24.0 ± 3.1 kg/m2) were recruited from the Shanghai, China, community. RMR was measured via indirect calorimetry, and body composition via dual-energy X-ray absorptiometry. Results: Two novel prediction equations were derived: Cai1 (fat-free mass [FFM] + age): RMR = 1393.019 − (11.112 × age) + (11.963 × FFM); R2 = 0.572, and Cai2 (sex + age + weight [WT]): RMR = 1537.513 + (91.038 × sex) − (11.515 × age) + (5.436 × WT); R2 = 0.528. Both novel prediction equations achieved 82.5% adequacy (predicted RMR within 90–110% of measured values), minimal systematic bias (%) (−0.72% and −1.08%) and strong positive correlations with measured RMR (r = 0.792 and 0.773, both p < 0.001). Bland–Altman analysis confirmed no systematic bias. In contrast, 11 widely used published prediction equations (e.g., Harris–Benedict, Mifflin–St. Jeor) exhibited significant overestimation (systematic bias +8.39% to +38.03%). Conclusions: The novel population-specific RMR equations outperform published ones, providing a clinically reliable tool for individualized energy prescription in nutritional interventions to support healthy aging in Chinese older adults.
- Open Access
The Muscle–Brain Axis in Aging: Mechanistic and Clinical Perspectives on Resistance Training and Cognitive Function
The global aging population has led to a rising prevalence of cognitive impairment, posing a significant public health challenge. Resistance training (RT) is a non-pharmacological intervention that has been increasingly investigated for its potential to support cognitive function in older adults. Clinical evidence suggests that RT may be associated with benefits in certain cognitive domains, including memory, executive function, processing speed, and visuospatial ability. However, findings across studies remain heterogeneous, with several trials reporting neutral outcomes. Most intervention studies involve structured RT programs conducted at moderate to high intensity and performed multiple times per week. However, optimal training parameters have not yet been clearly established due to variability in study design and the absence of formal dose–response analyses. Emerging evidence suggests that the cognitive effects of RT may be mediated, at least in part, through muscle–brain axis signaling involving exercise-induced myokines. Factors such as irisin, brain-derived neurotrophic factor, interleukin-6, interleukin-15, and insulin-like growth factor-1 have been implicated in processes related to neuroplasticity, neuroinflammatory regulation, and neurovascular function, primarily based on preclinical and translational research. This review synthesizes current evidence on RT-related molecular mechanisms and clinical findings to provide an integrative perspective on the potential role of resistance training in mitigating age-related cognitive decline.
Open AccessThe Muscle–Brain Axis in Aging: Mechanistic and Clinical Perspectives on Resistance Training and Cognitive Function
The global aging population has led to a rising prevalence of cognitive impairment, posing a significant public health challenge. Resistance training (RT) is a non-pharmacological intervention that has been increasingly investigated for its potential to support cognitive function in older adults. Clinical evidence suggests that RT may be associated with benefits in certain cognitive domains, including memory, executive function, processing speed, and visuospatial ability. However, findings across studies remain heterogeneous, with several trials reporting neutral outcomes. Most intervention studies involve structured RT programs conducted at moderate to high intensity and performed multiple times per week. However, optimal training parameters have not yet been clearly established due to variability in study design and the absence of formal dose–response analyses. Emerging evidence suggests that the cognitive effects of RT may be mediated, at least in part, through muscle–brain axis signaling involving exercise-induced myokines. Factors such as irisin, brain-derived neurotrophic factor, interleukin-6, interleukin-15, and insulin-like growth factor-1 have been implicated in processes related to neuroplasticity, neuroinflammatory regulation, and neurovascular function, primarily based on preclinical and translational research. This review synthesizes current evidence on RT-related molecular mechanisms and clinical findings to provide an integrative perspective on the potential role of resistance training in mitigating age-related cognitive decline.