Associations Between Aldosterone-Renin-Ratio and Bone Parameters Derived from Peripheral Quantitative Computed Tomography and Impact Microindentation in Men.

Components of the renin-angiotensin-aldosterone system (RAAS) are present on bone cells. One measure of RAAS activity, the aldosterone-renin-ratio (ARR), is used to screen for primary aldosteronism. Associations between ARR and bone mineral density are conflicting. This study investigated associations between ARR and peripheral quantitative computed tomography (pQCT) and impact microindentation (IMI). Male participants (n = 431) were from the Geelong Osteoporosis Study. "Likely" primary aldosteronism was defined as ARR ≥ 70 pmol/mIU. Another group, "possible" primary aldosteronism, was defined as either ARR ≥ 70 pmol/mIU or taking a medication that affects the RAAS, but not a beta blocker, and renin < 15 mU/L. Using pQCT, images at 4% and 66% of radial (n = 365) and tibial (n = 356) length were obtained. Using IMI measurements, bone material strength index (BMSi; n = 332) was determined. Associations between ARR or likely/possible primary aldosteronism and IMI or pQCT-derived bone parameters were tested using median regression. ARR and aldosterone values were not associated with any of the pQCT-derived bone variables in either unadjusted or adjusted analyses. Men with likely primary aldosteronism (n = 16), had lower adjusted total bone area (radial 66% site, - 12.5%). No associations were observed for men with possible primary aldosteronism (unadjusted or adjusted). No associations with BMSi were observed (p > 0.05). There were no associations between ARR or aldosterone and pQCT-derived bone parameters. Men with likely primary aldosteronism had lower bone area, suggesting clinically high levels of ARR may have a negative impact on bone health.

Peripheral quantitative computed tomography-derived bone parameters in men with impaired fasting glucose and diabetes.

INTRODUCTION: Individuals with type 2 diabetes mellitus (T2DM) are at higher risk of fracture, but paradoxically do not have reduced bone mineral density. We investigated associations between peripheral quantitative computed tomography (pQCT) and glycaemia status. MATERIALS AND METHODS: Participants were men (n = 354, age 33-92 year) from the Geelong Osteoporosis Study. Diabetes was defined by fasting plasma glucose (FPG) ≥ 7.0 mmol/L, self-report of diabetes and/or antihyperglycaemic medication use and impaired fasting glucose (IFG) as FPG 5.6-6.9 mmol/L. Bone measures were derived using pQCT (XCT2000) at 4% and 66% radial and tibial sites. Linear regression was used, adjusting for age, body mass index and socio-economic status. RESULTS: At the 4% site, men with T2DM had lower adjusted bone total area, trabecular area and cortical area at the radius (all - 6.2%) and tibia (all - 6.4%) compared to normoglycaemia. Cortical density was higher for T2DM at the radius (+ 5.8%) and tibia (+ 8.0%), as well as adjusted total bone density at the tibial site (+ 6.1%). At the 66% site, adjusted total bone area and polar stress strain index were lower for T2DM at the radius (- 4.3% and - 8.0%). Total density was also higher for T2DM (+ 1.2%). Only cortical density at the 4% tibial site was different between IFG and normoglycaemia in adjusted analyses (+ 4.5%). CONCLUSION: Men with T2DM had lower total bone area, trabecular area, cortical area and polar stress strain index than the other two groups; however, total density and cortical density were higher. Only one difference was observed between IFG and normoglycaemia; increased tibial cortical density.

Fatty Liver Index and Skeletal Muscle Density.

Accumulation of fat in the liver and skeletal muscle is associated with obesity and poor health outcomes. Liver steatosis is a characteristic of non-alcoholic fatty liver disease (NAFLD) and myosteatosis, of poor muscle quality in sarcopenia. In this study of 403 men (33-96 years), we investigated associations between the fatty liver index (FLI) and muscle density, as markers of fat accumulation in these organs. We also investigated associations between the FLI and parameters of sarcopenia, including DXA-derived appendicular lean mass (ALM) and handgrip strength by dynamometry. Muscle density was measured using pQCT at the radius and tibia. FLI was calculated from BMI, waist circumference, and levels of triglycerides and gamma-glutamyltransferase. There was a pattern of decreasing muscle density across increasing quartiles of FLI. After adjusting for age and lifestyle, mean radial muscle density in Q4 was 2.1% lower than Q1 (p < 0.001) and mean tibial muscle density was 1.8% lower in Q3 and 3.0% lower in Q4, compared to Q1 (p = 0.022 and < 0.001, respectively). After adjusting for age and sedentary lifestyle, participants in the highest FLI quartile were sixfold more likely to have sarcopenia. In conclusion, our results suggest that fat accumulation in the liver co-exists with fat infiltration into skeletal muscle.

Musculoskeletal Deficits and Cognitive Impairment: Epidemiological Evidence and Biological Mechanisms.

PURPOSE OF REVIEW: Cognitive impairment is associated with obesity, sarcopenia, and osteoporosis. However, no critical appraisal of the literature on the relationship between musculoskeletal deficits and cognitive impairment, focusing on the epidemiological evidence and biological mechanisms, has been published to date. Herein, we critically evaluate the literature published over the past 3 years, emphasizing interesting and important new findings, and provide an outline of future directions that will improve our understanding of the connections between the brain and the musculoskeletal system. RECENT FINDINGS: Recent literature suggests that musculoskeletal deficits and cognitive impairment share pathophysiological pathways and risk factors. Cytokines and hormones affect both the brain and the musculoskeletal system; yet, lack of unified definitions and standards makes it difficult to compare studies. Interventions designed to improve musculoskeletal health are plausible means of preventing or slowing cognitive impairment. We highlight several musculoskeletal health interventions that show potential in this regard.

Associations Between Muscle Quality and Cognitive Function in Older Men: Cross-Sectional Data From the Geelong Osteoporosis Study.

Sarcopenia-related declines appear to be adversely associated with cognition in the elderly. Poor muscle quality is a marker for sarcopenia, yet little research has examined the concurrence of poor muscle quality and poor cognition. The aim of this study was to investigate the association between muscle quality and cognitive function, overall and in specific domains, in older men. This study involved 342 men from the Geelong Osteoporosis Study (ages 60-96 years). Handgrip strength (HGS, kg) was measured by dynamometry (Vernier, LoggerPro3), and lean mass of arms (kg) and appendicular lean mass (ALM, kg) by dual-energy X-ray absorptiometry (Lunar). Muscle quality was expressed as HGS/(arm lean mass) (kg/kg) as well as HGS/ALM (kg/kg). Cognitive function was assessed in 4 domains: visual attention, psychomotor function, working memory and visual learning. Overall cognitive function scores were calculated. Higher scores represent poorer cognitive performance in attention, psychomotor function and working memory, but better performance for visual memory/learning and overall cognitive function. Additionally, cognitive impairment was determined by the mini-mental state exam (score ≤ 24). Linear regression analyses and logistic regression were performed. There were age-related declines observed for all measures relating to muscle and cognition. Muscle quality (HGS/arm lean mass) was associated with all cognition assessments before and after adjusting for age, except for age-adjusted working memory. Muscle quality (HGS/arm lean mass) was associated with psychomotor function (B -0.01, 95% CI -0.02, -0.005) and overall cognitive function (b + 0.07, 95% CI 0.03, 0.11) after adjusting for age and education. Greater muscle quality was also associated with the likelihood of cognitive impairment OR 0.64 (95%CI 0.46-0.88) after adjusting for age; associations with attention and visual memory/learning were attenuated after further adjustment for confounders. Similar patterns were observed when muscle quality was determined as HGS/ALM. Our data support an association between muscle quality and cognitive function. Further research is needed to examine temporal changes between the Two.

Skeletal Muscle Density and Cognitive Function: A Cross-Sectional Study in Men.

We aimed to investigate cross-sectional associations between skeletal muscle density, a proxy measure for fatty infiltration into muscle, and cognition. Contributions from body fat mass, systemic inflammation and lifestyle were explored, as these factors have been identified in both muscle and cognitive deterioration. For 281 men (60-95 year) from the Geelong Osteoporosis Study, radial and tibial muscle density were measured using peripheral quantitative computed tomography. Body fat and appendicular lean mass were measured using dual-energy X-ray absorptiometry. Cognitive function was assessed for psychomotor function (DET), visual identification/attention (IDN), visual learning (OCL) and working memory (OBK) (CogState Brief Battery). Composite scores signified overall cognitive function (OCF). Higher scores represent poorer performance except for OCL and OCF. Regression analyses examined associations between muscle density and cognition; potential confounders included age, muscle cross-sectional area (CSA), body composition, lifestyle and serum markers of inflammation. Negative associations with age were evident for muscle density, all cognitive domains and OCF. Muscle density at both sites was positively associated with DET, OCL and OCF. After adjustment for age, the association persisted for DET (radius: B = - 0.006, p = 0.02; tibia: B = - 0.003, p = 0.04) and OCL (radius B = + 0.004, p = 0.02; tibia: B = + 0.005, p < 0.001). At the radius, further adjustment for serum TNF-α explained the association between muscle density (B = - 0.002, p = 0.66) and DET. Education and physical activity contributed to the model for radial muscle density and DET. There were no contributions from muscle CSA, appendicular lean mass, body fat mass, other markers of inflammation or other potential confounders. At the tibia, the association between muscle density and DET (B = - 0.003, p = 0.04) was independent of TNF-α. There was an age-adjusted association between muscle density and OCL at both sites (radius: B = + 0.004, p = 0.02; tibia: B = + 0.005, p < 0.001). None of the potential confounders contributed to the models. Muscle density was associated with cognitive function in the DET and OCL domains. However, there was little evidence that this was explained by inflammation or body fat mass. No associations were identified between muscle density and IDN or OBK.

The contribution of musculoskeletal factors to physical frailty: a cross-sectional study.

BACKGROUND: Musculoskeletal conditions and physical frailty have overlapping constructs. We aimed to quantify individual contributions of musculoskeletal factors to frailty. METHODS: Participants included 347 men and 360 women aged ≥60 yr (median ages; 70.8 (66.1-78.6) and 71.0 (65.2-77.5), respectively) from the Geelong Osteoporosis Study. Frailty was defined as ≥3, pre-frail 1-2, and robust 0, of the following; unintentional weight loss, weakness, low physical activity, exhaustion, and slowness. Measures were made of femoral neck BMD, appendicular lean mass index (ALMI, kg/m2) and whole-body fat mass index (FMI, kg/m2) by DXA (Lunar), SOS, BUA and SI at the calcaneus (Lunar Achilles Insight) and handgrip strength by dynamometers. Binary and ordinal logistic regression models and AUROC curves were used to quantify the contribution of musculoskeletal parameters to frailty. Potential confounders included anthropometry, smoking, alcohol, prior fracture, FMI, SES and comorbidities. RESULTS: Overall, 54(15.6%) men and 62(17.2%) women were frail. In adjusted-binary logistic models, SI, ALMI and HGS were associated with frailty in men (OR = 0.73, 95%CI 0.53-1.01; OR=0.48, 0.34-0.68; and OR = 0.11, 0.06-0.22; respectively). Muscle measures (ALMI and HGS) contributed more to this association than did bone (SI) (AUROCs 0.77, 0.85 vs 0.71, respectively). In women, only HGS was associated with frailty in adjusted models (OR = 0.30 95%CI 0.20-0.45, AUROC = 0.83). In adjusted ordinal models, similar results were observed in men; for women, HGS and ALMI were associated with frailty (ordered OR = 0.30 95%CI 0.20-0.45; OR = 0.56, 0.40-0.80, respectively). CONCLUSION: Muscle deficits appeared to contribute more than bone deficits to frailty. This may have implications for identifying potential musculoskeletal targets for preventing or managing the progression of frailty.

Prevalence of Frailty in Older Men and Women: Cross-Sectional Data from the Geelong Osteoporosis Study.

Few studies have investigated the prevalence of frailty in the Australian general population. This study determined the prevalence of frailty in a population-based sample of older adults and examined the relationship between frailty and comorbid conditions. Men (n = 347) and women (n = 360) aged ≥ 60 year from the Geelong Osteoporosis Study (GOS) were assessed between 2016-2019 and 2011-2014, respectively. Frailty was identified using a modified Fried frailty phenotype. Prevalence estimates were standardised to the 2011 Australian population. Kruskal-Wallis test and χ2 test were used to analyse data. For women, mean standardised prevalence estimates were 18.3% (14.1-22.5) for frail, 54.1% (47.3-60.8) pre-frail and 22.9% (18.9-26.8) robust. Corresponding estimates for men were 13.1% (9.8-16.3) frail, 47.8% (42.0-53.6) pre-frail and 27.3% (22.7-31.8) robust. Women who were frail were older, shorter, tended to have a higher body mass index (BMI) and used more medications compared to other groups. Compared to robust women, those who were frail were more likely to have cardio-metabolic (OR 3.5 (0.7-20.0)), pulmonary (OR 3.5 (1.5-8.4)) and musculoskeletal (OR 10.1 (2.1-48.0)) conditions. Frail men were older, had a higher BMI and were more likely to have musculoskeletal conditions (OR 5.8 (2.8-12.3)) and tended to be from a lower SES. No further associations were observed. This study reported the prevalence of frail and pre-frail individuals in a population-based sample of Australian men and women. Frailty was associated with musculoskeletal conditions for both men and women; however, associations with cardio-metabolic and pulmonary comorbidities were evident in women only.

The association between a fracture risk tool and frailty: Geelong Osteoporosis Study.

BACKGROUND: Frailty is characterised by age-related declines in physical, psychological and social functioning. Features of frailty overlap with risk factors for fragility fractures. The aim of this study was to investigate the association between the fracture risk assessment tool (FRAX®) and frailty. METHODS: In cross-sectional analysis, frailty status was determined for participants aged 60-90 yr at 15-year follow-up of the Geelong Osteoporosis Study, using a modified Fried frailty phenotype. Using the FRAX on-line tool, scores for hip and major osteoporotic fracture (MOF) were calculated with and without bone mineral density (BMD). Using the area under Receiver Operating Characteristic (AUROC) curves, and FRAX scores calculated at the baseline visit for these participants, we investigated the association of FRAX and frailty 15 years later. RESULTS: Forty-seven of 303 women (15.5%) and 41 of 282 men (14.5%) were frail at the 15-year visit. There was a gradient of increasing median FRAX scores from robust to frail. For example, for women, median MOF-FRAX without BMD increased from 5.9 for the robust to 7.5 for the pre-frail and 14.0 for the frail (p < 0.001). In secondary analyses, an association was observed between FRAX and frailty over 15 years, with the highest AUROC for women being 0.72 for MOF-FRAX with BMD, and for men, 0.76 hip-FRAX without BMD. CONCLUSION: An association was observed between FRAX and frailty where frail men and women had higher FRAX-scores compared to the other groups. Preliminary data suggest that FRAX, with or without BMD, may be useful in enhancing the information on frailty. Further research using larger datasets will be required to explore this.

Lower-limb muscle strength: normative data from an observational population-based study.

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.

Skeletal Muscle Health and Cognitive Function: A Narrative Review.

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.

Obesity and Brain Function: The Brain-Body Crosstalk.

Dementia comprises a wide range of progressive and acquired neurocognitive disorders. Obesity, defined as excessive body fat tissue, is a common health issue world-wide and a risk factor for dementia. The adverse effects of obesity on the brain and the central nervous system have been the subject of considerable research. The aim of this review is to explore the available evidence in the field of body-brain crosstalk focusing on obesity and brain function, to identify the major research measurements and methodologies used in the field, to discuss the potential risk factors and biological mechanisms, and to identify the research gap as a precursor to systematic reviews and empirical studies in more focused topics related to the obesity-brain relationship. To conclude, obesity appears to be associated with reduced brain function. However, obesity is a complex health condition, while the human brain is the most complicated organ, so research in this area is difficult. Inconsistency in definitions and measurement techniques detract from the literature on brain-body relationships. Advanced techniques developed in recent years are capable of improving investigations of this relationship.

Normative Data for Lean Mass Using FNIH Criteria in an Australian Setting.

Recommendations from the FNIH Sarcopenia Project are that appendicular lean mass (ALM, kg) adjusted for body mass index (BMI, kg/m2) be used for identifying low lean mass, with ALM/BMI cutpoints of < 0.789 m2 for men and < 0.512 m2 for women. We report normative ALM/BMI values for Australian adults, and compare the performance of cutpoints derived from reference values for this population with FNIH values for identifying low lean mass. Body composition was measured by DXA (Lunar) for 1411 men and 960 women, aged 20-93 years, from the Geelong Osteoporosis Study, a population-based study in Australia. Sex-stratified means and standard deviations for DXA-derived ALM/BMI were generated for each age-decade, and cutpoints equivalent to T-scores of - 2.0 were derived using reference data for 374 men and 308 women aged 20-39 years. Mean ALM/BMI values were greater for men than women, and decreased with age in both sexes. Cutpoints for ALM/BMI corresponding to T-scores of - 2.0 were 0.827 m2 for men and 0.518 m2 for women. For individuals aged 65+ years, cross-classification of low lean mass according to FNIH criteria (ALM/BMI < 0.789 m2 men and < 0.512 m2 women) in comparison with our cutpoints for ALM/BMI showed overall agreement of 94.6% for men and 99.0% for women (κ 0.73 and 0.89, respectively). We report good agreement for low ALM indexed to BMI, particularly for women, between classifications based on recommendations from the FNIH Sarcopenia Project for identifying clinically significant weakness, with low values identified within our population distribution of ALM/BMI.

Psychiatric symptoms, associated pharmacological treatments and cognitive function: A population-based study of men.

BACKGROUND: Psychiatric symptomatology and medications used in their treatment may be modifiable risk factors associated with cognitive function, although findings from population-based studies spanning the full adult age range are lacking. This study aimed to investigate associations between psychiatric symptomatology, psychotropic medication use and cognitive function in a population-based sample of men. METHODS: Data for 537 men were drawn from the Geelong Osteoporosis Study. Cognitive function (psychomotor function, attention, working memory and visual learning) was determined using the Cog-State Brief Battery. Current depressive and anxiety symptomatology was determined using the Hospital Anxiety and Depression Scale, and psychotropic medication use was self-reported. Linear regression models were developed to determine associations between psychiatric symptomatology and psychotropic medication use with each cognitive measure. RESULTS: Depressive symptomatology was associated with lower overall cognitive function (b-0.037 ± 0.010, η2 = 0.025, p < 0.001), psychomotor function (b 0.006 ± 0.002, η2 = 0.028 p < 0.001) and attention (b 0.004 ± 0.001, η2 = 0.021, p < 0.001), whereas psychotropic use was associated with lower overall cognitive function (b - 0.174 ± 0.075, η2 = 0.010, p = 0.021), attention (b 0.017 ± 0.008, η2 = 0.008, p = 0.038 and working memory (b 0.031 ± 0.012, η2 = 0.010, p = 0.010). Anticonvulsant use was associated with lower overall cognitive function (b - 0.723 ± 0.172, η2 = 0.032, p < 0.001), attention (b 0.065 ± 0.018, η2 = 0.029, p < 0.001) and working memory (b 0.088 ± 0.026, η2 = 0.022, p < 0.001). All relationships were found to have a small effect. There were no significant associations between anxiety symptomatology and antidepressant and anxiolytic use with any of the cognitive domains. CONCLUSION: Depressive symptomatology and anticonvulsant use were associated with lower cognitive function. Understanding the underlying mechanisms involved in these relationships can advance knowledge on the heterogeneity in cognitive ageing and aid in prevention initiatives.

A plasma protein signature associated with cognitive function in men without severe cognitive impairment.

BACKGROUND: A minimally invasive blood-based assessment of cognitive function could be a promising screening strategy to identify high-risk groups for the incidence of Alzheimer's disease. METHODS: The study included 448 cognitively unimpaired men (mean age 64.1 years) drawn from the Geelong Osteoporosis Study. A targeted mass spectrometry-based proteomic assay was performed to measure the abundance levels of 269 plasma proteins followed by linear regression analyses adjusted for age and APOE ε4 carrier status to identify the biomarkers related to overall cognitive function. Furthermore, two-way interactions were conducted to see whether Alzheimer's disease-linked genetic variants or health conditions modify the association between biomarkers and cognitive function. RESULTS: Ten plasma proteins showed an association with overall cognitive function. This association was modified by allelic variants in genes ABCA7, CLU, BDNF and MS4A6A that have been previously linked to Alzheimer's disease. Modifiable health conditions such as mood disorders and poor bone health, which are postulated to be risk factors for Alzheimer's disease, also impacted the relationship observed between protein marker levels and cognition. In addition to the univariate analyses, an 11-feature multianalyte model was created using the least absolute shrinkage and selection operator regression that identified 10 protein features and age associated with cognitive function. CONCLUSIONS: Overall, the present study revealed plasma protein candidates that may contribute to the development of a blood-based screening test for identifying early cognitive changes. This study also highlights the importance of considering other risk factors in elucidating the relationship between biomarkers and cognition, an area that remains largely unexplored.

Social determinants of health and cognitive function: A cross-sectional study among men without dementia.

BACKGROUND: Certain age-related and medical factors have been associated with cognitive dysfunction; however, less is known regarding social determinants of health. The current study aimed to investigate associations between social determinants of health and cognitive function in a population-based sample of men without dementia. METHODS: Data were drawn from the ongoing Geelong Osteoporosis Study (n = 536). Cognitive function was determined using the Cog-State Brief Battery. Area-based socioeconomic status (SES) was determined using the Index of Relative Socioeconomic Advantage and Disadvantage, marital status by self-report, and social support by the Multidimensional Scale of Perceived Social Support, which considers family, friends, and significant others. RESULTS: Belonging to a higher SES group, being in a relationship (married/de-facto) and perceived social support from a significant other and friends were each associated with better overall cognitive function. In regard to the specific cognitive domains, higher SES was associated with better psychomotor function and visual learning, being in a relationship was associated with better working memory, and perceived social support from a significant other was associated with better attention and working memory, with perceived social support from friends associated with better psychomotor function. There were no associations detected between social support from family and any of the cognitive domains. CONCLUSION: Higher SES, being in a relationship, and greater perceived social support from a significant other and friends were associated with better cognitive function. Further studies identifying underlying mechanisms linking social factors with cognition are needed to establish prevention strategies and enhance cognitive health.

How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study.

We aimed to examine associations between skeletal muscle deficits and indices of poor health. Cut-points for skeletal muscle deficits were derived using data from the Geelong Osteoporosis Study and definitions from the revised European Consensus on Definition and Diagnosis and the Foundation for the National Institutes of Health. Participants (n = 665; 323 women) aged 60-96 year had handgrip strength measured by dynamometry and appendicular lean mass by whole-body dual-energy X-ray absorptiometry. Physical performance was assessed using the Timed Up and Go test. Sex-specific cut-points were equivalent to two standard deviations below the mean young reference range from the Geelong Osteoporosis Study. Indices of poor health included fractures, falls, and hospitalisations. Low trauma fractures since age 50 year (excluding skull, face, digits) were self-reported and confirmed using radiological reports. Falls (≥1 in the past 12 months) and hospitalisations (past month) were self-reported. Logistic regression models (age- and sex-adjusted) were used to examine associations. Receiver Operating Characteristic curves were applied to determine optimal cut-points for handgrip strength, Timed Up and Go, appendicular lean mass/height2, and appendicular lean mass/body mass index that discriminated poor health outcomes. There were 48 participants (6.9%) with hospitalisations, 94 (13.4%) with fractures, and 177 (25.3%) with at least one fall (≥1). For all cut-points, low handgrip strength was consistently associated with falls. There was little evidence to support an association between low appendicular lean mass, using any cut-point, and indices of poor health. Optimal cut-offs for predicting falls (≥1) were: handgrip strength 17.5 kg for women and 33.5 kg for men; Timed Up and Go 8.6 s for women and 9.9 s for men; appendicular lean mass/height2 6.2 kg/m2 for women and 7.46 kg/m2 for men; and appendicular lean mass/body mass index 0.6 m2 for women and 0.9 m2 for men. In conclusion, muscle strength and function performed better than lean mass to indicate poor health. These findings add to the growing evidence base to inform decisions regarding the selection of skeletal muscle parameters and their optimal cut-points for identifying sarcopenia.

A Review of the Measurement of the Neurology of Gait in Cognitive Dysfunction or Dementia, Focusing on the Application of fNIRS during Dual-Task Gait Assessment.

Poor motor function or physical performance is a predictor of cognitive decline. Additionally, slow gait speed is associated with poor cognitive performance, with gait disturbances being a risk factor for dementia. Parallel declines in muscular and cognitive performance (resulting in cognitive frailty) might be driven primarily by muscle deterioration, but bidirectional pathways involving muscle-brain crosstalk through the central and peripheral nervous systems are likely to exist. Following screening, early-stage parallel declines may be manageable and modifiable through simple interventions. Gait-brain relationships in dementia and the underlying mechanisms are not fully understood; therefore, the current authors critically reviewed the literature on the gait-brain relationship and the underlying mechanisms and the feasibility/accuracy of assessment tools in order to identify research gaps. The authors suggest that dual-task gait is involved in concurrent cognitive and motor activities, reflecting how the brain allocates resources when gait is challenged by an additional task and that poor performance on dual-task gait is a predictor of dementia onset. Thus, tools or protocols that allow the identification of subtle disease- or disorder-related changes in gait are highly desirable to improve diagnosis. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, cost-effective, safe, simple, portable, and non-motion-sensitive neuroimaging technique, widely used in studies of clinical populations such as people suffering from Alzheimer's disease, depression, and other chronic neurological disorders. If fNIRS can help researchers to better understand gait disturbance, then fNIRS could form the basis of a cost-effective means of identifying people at risk of cognitive dysfunction and dementia. The major research gap identified in this review relates to the role of the central/peripheral nervous system when performing dual tasks.

Associations between parameters of peripheral quantitative computed tomography and bone material strength index.

BACKGROUND: Bone material strength index (BMSi) is measured in vivo using impact microindentation (IMI). However, the associations between BMSi and other bone measures are not clear. This study investigated whether bone parameters derived by peripheral quantitative computed tomography (pQCT) are associated with BMSi. METHODS: Participants were men (n = 373, ages 34-96 yr) from the Geelong Osteoporosis Study. BMSi was measured using an OsteoProbe (Active Life Scientific, USA). Bone measures were obtained at both the radius (n = 348) and tibia (n = 342) using pQCT (XCT 2000 Stratec Medizintechnik, Germany). Images were obtained at 4% and 66% of radial and tibial length. Associations between pQCT parameters and BMSi were tested using Spearman's correlation and multivariable regression used to determine independent associations after adjustment for potential confounders. Models were checked for interaction terms. RESULTS: Weak associations were observed between total bone density (radius 4%; r = +0.108, p = 0.046, tibia 4%; r = +0.115, p = 0.035), cortical density (tibia 4%; r = +0.123, p = 0.023) and BMSi. The associations were independent of weight, height, and glucocorticoid use (total bone density: radius 4%; ß = 0.020, p = 0.006, tibia 4%; ß = 0.020, p = 0.027 and cortical density: radius 4%; ß = 4.160, p = 0.006, tibia 4%; ß = 0.038, p = 0.010). Associations with bone mass were also observed at the 66% radial and tibial site, independent of age, weight, and glucocorticoid use (ß = 4.160, p = 0.053, ß = 1.458, p = 0.027 respectively). Total area at the 66% tibial site was also associated with BMSi (ß = 0.010, p = 0.012), independent of weight and glucocorticoid use. No interaction terms were identified. CONCLUSION: There were weak associations detected between some pQCT-derived bone parameters and BMSi.

Are Sarcopenia and Cognitive Dysfunction Comorbid after Stroke in the Context of Brain-Muscle Crosstalk?

Stroke is a leading cause of death and disability and is responsible for a significant economic burden. Sarcopenia and cognitive dysfunction are common consequences of stroke, but there is less awareness of the concurrency of these conditions. In addition, few reviews are available to guide clinicians and researchers on how to approach sarcopenia and cognitive dysfunction as comorbidities after stroke, including how to assess and manage them and implement interventions to improve health outcomes. This review synthesises current knowledge about the relationship between post-stroke sarcopenia and cognitive dysfunction, including the physiological pathways, assessment tools, and interventions involved.