Sarcopenia is the loss of skeletal muscle mass and function with advancing age. It involves both complex genetic and modifiable risk factors, such as lack of exercise, malnutrition and reduced neurological drive. Cognitive decline refers to diminished or impaired mental and/or intellectual functioning. Contracting skeletal muscle is a major source of neurotrophic factors, including brain-derived neurotrophic factor, which regulate synapses in the brain. Furthermore, skeletal muscle activity has important immune and redox effects that modify brain function and reduce muscle catabolism. The identification of common risk factors and underlying mechanisms for sarcopenia and cognition may allow the development of targeted interventions that slow or reverse sarcopenia and also certain forms of cognitive decline. However, the links between cognition and skeletal muscle have not been elucidated fully. This review provides a critical appraisal of the literature on the relationship between skeletal muscle health and cognition. The literature suggests that sarcopenia and cognitive decline share pathophysiological pathways. Ageing plays a role in both skeletal muscle deterioration and cognitive decline. Furthermore, lifestyle risk factors, such as physical inactivity, poor diet and smoking, are common to both disorders, so their potential role in the muscle–brain relationship warrants investigation.

Background: We aimed to examine the relationship between musculoskeletal deterioration and all‐cause mortality in a cohort of women studied prospectively over a decade.Methods: A cohort of 750 women aged 50–94 years was followed for a decade after femoral neck bone mineral density (BMD) and appendicular lean mass (ALM) were measured using dual energy X‐ray absorptiometry, in conjunction with comorbidities, health behaviour data, and other clinical measures. The outcome was all‐cause mortality identified from the Australian National Deaths Index. Using Cox proportional hazards models and age as the time variable, mortality risks were estimated according to BMD groups (ideal‐BMD, osteopenia, and osteoporosis) and ALM groups (T‐scores > −1.0 high, −2.0 to −1.0 medium, <−2.0 low).Results: During 6712 person years of follow‐up, there were 190 deaths, the proportions increasing with diminishing BMD: 10.7% (23/215) ideal‐BMD, 23.5% (89/378) osteopenia, 49.7% (78/157) osteoporosis; and with diminishing ALM: 17.0% (59/345) high, 26.2% (79/301) medium, 50.0% (52/104) low. In multivariable models adjusted for smoking, polypharmacy, and mobility, compared with those with ideal BMD, mortality risk was greater for those with osteopenia [hazard ratio (HR) 1.77, 95% confidence interval (CI) 1.11–2.81] and osteoporosis (HR 2.61, 95%CI 1.60–4.24). Similarly, compared with those with high ALM, adjusted mortality risk was greater for medium ALM (HR 1.36, 95%CI 0.97–1.91) and low ALM (HR 1.65, 95%CI 1.11–2.45). When BMD and ALM groups were tested together in the model, BMD remained a predictor of mortality (HR 1.74, 95%CI 1.09–2.78; HR 2.82, 95%CI 1.70–4.70; respectively), and low ALM had borderline significance (HR 1.52, 95%CI 1.00–2.31), which was further attenuated after adjusting for smoking, polypharmacy, and mobility.Conclusions: Poor musculoskeletal health increased the risk for mortality independent of age. This appears to be driven mainly by a decline in bone mass. Low lean mass independently exacerbated mortality risk, and this appeared to operate through poor health exposures.

Background: The extent of muscle deterioration associated with ageing or disease can be quantified by comparison with appropriate reference data. The objective of this study is to present normative data for lower-limb muscle strength and quality for 573 males and 923 females aged 20-97 yr participating in the Geelong Osteoporosis Study in southeastern Australia. Methods: In this cross-sectional study, measures of muscle strength for hip flexors and hip abductors were obtained using a Nicholas manual muscle tester, a hand-held dynamometer (HHD; kg). Leg lean mass was measured by dual energy x-ray absorptiometry (DXA; kg), and muscle quality calculated as strength/mass (N/kg). Results: For both sexes, muscle strength and quality decreased with advancing age. Age explained 12.9–25.3% of the variance in muscle strength in males, and 20.8–24.6% in females; age explained less of the variance in muscle quality. Means and standard deviations for muscle strength and quality for each muscle group are reported by age-decade for each sex, and cutpoints equivalent to T-scores of − 2.0 and − 1.0 were derived using data from young males (n = 89) and females (n = 148) aged 20–39 years. Conclusions: These data will be useful for quantifying the extent of dynapenia and poor muscle quality among adults in the general population in the face of frailty, sarcopenia and other age-related muscle dysfunction.

Background: Low handgrip strength (HGS) is a measure of poor skeletal muscle performance and a marker of ill health and frailty. Muscle quality (MQ) is a measure of muscle strength relative to muscle mass. We aimed to develop normative data for HGS and MQ, report age‐related prevalence of low HGS and MQ, and determine the relationship with age, anthropometry, and body composition for women in Australia.Methods: This cross‐sectional analysis included data from 792 women (ages 28–95 years) assessed by the Geelong Osteoporosis Study. Duplicate measures of HGS were performed for each hand with a dynamometer (Jamar) and the mean of maximum values used for analyses. Dual energy X‐ray absorptiometry‐derived lean mass for the arms was used to calculate MQ as HGS/lean mass (kg/kg). Body mass index (BMI) was categorized as normal (BMI < 25.0 kg/m²), overweight (25.0–29.9 kg/m²), and obese (>30.0 kg/m²). Fat mass index (FMI) was calculated as whole body fat/height² (kg/m²) and appendicular lean mass index (ALMI) as lean mass of arms and legs/height² (kg/m²).Results: Mean (±SD) of HGS values for normal BMI, overweight, and obese groups were 25 (±7), 24 (±7), and 24 (±7) kg, P = 0.09, and for MQ, 12 (±3), 11 (±3), and 10 (±3) kg/kg, P < 0.001. Our data indicated a quadratic relationship between age and HGS or MQ. Mean HGS and MQ remained stable until the fifth age decade then declined steadily with increasing age; therefore, we used data for women (n = 283) aged 28–49 years as the young adult reference group, with mean (SD) values for HGS 28 (±6) kg and MQ 12 (±3) kg/kg. The prevalence of low (T‐score < −2) HGS and MQ for women 80 years and older was 52.2% and 39.6%, respectively. In multivariable models, age‐adjusted HGS was associated with FMI (B = −0.13, P = 0.004) and ALMI (1.03, <0.001) while age‐adjusted MQ was associated with BMI (−0.15, <0.001) but not with FMI. In a sensitivity analysis, the same pattern remained after the removal of 129 women who reported hand and/or arm pain.Conclusions: Mean HGS and MQ declined with advancing age in older women. Our data suggest that while mean HGS increased with appendicular lean mass and decreased with body fat mass, there was no association with BMI. By contrast, MQ decreased with increasing BMI, but not with increasing adiposity.