DISCUSSION: Several treatment options are available for different stages of prostate cancer. Hormone therapy known as androgen deprivation therapy (ADT) is the first line treatment used to treat advanced prostate cancer. Chemical castration by gonadotropin-releasing hormone agonists suppresses lutenizing hormone production, which in turn inhibits the production of testosterone and dihydrotestosterone. This will prevent the growth of prostate cancer cells. However, ADT causes deleterious effects on bone health because the androgens are essential in preserving optimal bone health in men.
CONCLUSION: Various observational studies showed that long-term ADT for advanced or metastatic prostate cancer was associated with decreased bone mineral density, as well as altered body composition that might affect bone health. Considering the potential impact of osteoporotic fracture, interventions to mitigate these skeletal adverse effects should be considered by physicians when initiating ADT on their patients.
METHODS: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.
RESULTS: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.
CONCLUSIONS: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.
PURPOSE: We aimed to examine the role of age-dependent intervention thresholds (ITs) applied to the Fracture Risk Assessment (FRAX) tool in therapeutic decision making for osteoporosis in the Malaysian population.
METHODS: Data were collated from 1380 treatment-naïve postmenopausal women aged 40-85 years who underwent bone mineral density (BMD) measurements for clinical reasons. Age-dependent ITs, for both major osteoporotic fracture (MOF) and hip fracture (HF), were calculated considering a woman with a BMI of 25 kg/m2, aged between 40 and 85years, with a prior fragility fracture, sans other clinical risk factors. Those with fracture probabilities equal to or above upper assessment thresholds (UATs) were considered to have high fracture risk. Those below the lower assessment thresholds (LATs) were considered to have low fracture risk.
RESULTS: The ITs of MOF and HF ranged from 0.7 to 18% and 0.2 to 8%, between 40 and 85years. The LATs of MOF ranged from 0.3 to 11%, while those of HF ranged from 0.1 to 5.2%. The UATs of MOF and HF were 0.8 to 21.6% and 0.2 to 9.6%, respectively. In this study, 24.8% women were in the high-risk category while 30.4% were in the low-risk category. Of the 44.8% (n=618) in the intermediate risk group, after recalculation of fracture risk with BMD input, 38.3% (237/618) were above the ITs while the rest (n=381, 61.7%) were below the ITs. Judged by the Youden Index, 11.5% MOF probability which was associated with a sensitivity of 0.62 and specificity of 0.83 and 4.0% HF probability associated with a sensitivity of 0.63 and a specificity 0.82 were found to be the most appropriate fixed ITs in this analysis.
CONCLUSION: Less than half of the study population (44.8%) required BMD for osteoporosis management when age-specific assessment thresholds were applied. Therefore, in more than half, therapeutic decisions can be made without BMD based on these assessment thresholds.
INTRODUCTION: The International Osteoporosis Foundation and European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis published guidance for the diagnosis and management of osteoporosis in 2019. This manuscript seeks to apply this in an international setting, taking additional account of further categorisation of increased risk of fracture, which may inform choice of therapeutic approach.
METHODS: Clinical perspective and updated literature search.
RESULTS: The following areas are reviewed: categorisation of fracture risk and general pharmacological management of osteoporosis.
CONCLUSIONS: A platform is provided on which specific guidelines can be developed for national use to characterise fracture risk and direct interventions.
METHODS: 93 patients and 78 spousal/sibling controls underwent comprehensive assessment of diet, clinical status, muscle strength/performance, frailty, body composition (using dual-energy X-ray absorptiometry), and serum levels of neurogastrointestinal hormones and inflammatory markers.
RESULTS: PD patients were older than controls (66.0 ± 8.5 vs. 62.4 ± 8.4years, P = 0.003). Mean body mass index (24.0 ± 0.4 vs. 25.6 ± 0.5kg/m2, Padjusted = 0.016), fat mass index (7.4 ± 0.3 vs. 9.0 ± 0.3kg/m2, Padjusted<0.001), and whole-body fat percentage (30.7 ± 0.8 vs. 35.7 ± 0.9%, Padjusted<0.001) were lower in patients, even after controlling for age and gender. There were no between-group differences in skeletal muscle mass index and whole-body bone mineral density. Body composition parameters did not correlate with disease duration or motor severity. Reduced whole-body fat percentage was associated with higher risk of motor response complications as well as higher levels of insulin-growth factor-1 and inflammatory markers. PD patients had a higher prevalence of sarcopenia (17.2% vs. 10.3%, Padjusted = 0.340) and frailty (69.4% vs. 24.2%, Padjusted = 0.010). Older age and worse PD motor severity were predictors of frailty in PD.
CONCLUSIONS: We found reduced body fat with relatively preserved skeletal muscle mass, and a high prevalence of frailty, in PD. Further studies are needed to understand the patho-mechanisms underlying these alterations.