Trabecular bone score (TBS) is a gray-level textural index of bone microarchitecture derived from lumbar spine dual-energy X-ray absorptiometry (DXA) images. TBS is a bone mineral density (BMD)-independent predictor of fracture risk. The objective of this metaanalysis was to determine whether TBS predicted fracture risk independently of FRAX probability and to examine their combined performance by adjusting the FRAX probability for TBS. We utilized individual-level data from 17,809 men and women in 14 prospective population-based cohorts. Baseline evaluation included TBS and the FRAX risk variables, and outcomes during follow-up (mean 6.7 years) comprised major osteoporotic fractures. The association between TBS, FRAX probabilities, and the risk of fracture was examined using an extension of the Poisson regression model in each cohort and for each sex and expressed as the gradient of risk (GR; hazard ratio per 1 SD change in risk variable in direction of increased risk). FRAX probabilities were adjusted for TBS using an adjustment factor derived from an independent cohort (the Manitoba Bone Density Cohort). Overall, the GR of TBS for major osteoporotic fracture was 1.44 (95% confidence interval [CI] 1.35-1.53) when adjusted for age and time since baseline and was similar in men and women (p > 0.10). When additionally adjusted for FRAX 10-year probability of major osteoporotic fracture, TBS remained a significant, independent predictor for fracture (GR ¼ 1.32, 95% CI 1.24-1.41). The adjustment of FRAX probability for TBS resulted in a small increase in the GR (1.76, 95% CI 1.65-1.87 versus 1.70, 95% CI 1.60-1.81). A smaller change in GR for hip fracture was observed (FRAX hip fracture probability GR 2.25 vs. 2.22). TBS is a significant predictor of fracture risk independently of FRAX. The findings support the use of TBS as a potential adjustment for FRAX probability, though the impact of the adjustment remains to be determined in the context of clinical assessment guidelines.

Objective To calculate the expected increase in the number of fractures in adults attributable to the predicted increase in the number of elderly Australians. Data sources All fractures in adult residents (> 35 years) of the Barwon Statistical Division (total population, 218000) were identified from radiological reports from February 1994 to February 1996. The Australian Bureau of Statistics supplied predictions of Australia's population (1996 to 2051). Main outcome measure The projected annual number of fractures in Australian adults up to 2051 (based on stable rates of fracture in each age group). Results The number of fractures per year is projected to increase 25% from 1996 to 2006 (from 83000 fractures to 104000). Hip fractures are projected to increase 36% (from 15000 to 21000) because of a substantial rise in the number of elderly aged 85 years and over. Hip fractures are expected to double by 2026 and increase fourfold by 2051. Conclusions In contrast to Europe and North America, where numbers of hip fractures are expected to double by 2026 and then stabilise, in Australia hip fractures will continue to place a growing demand on healthcare resources for many decades. These projections can be used for setting goals and evaluating the costs and benefits of interventions in Australia.

The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR ( ≥ pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9 minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients.

Objective To assess vitamin D intake and casual exposure to sunshine in relation to serum 25‐hydroxyvitamin D (250HD) levels. Design Cross‐sectional study of a population‐based, random sample of women aged 20–92 years, assessed between 1994 and 1997. Setting and participants 861 women from the Barwon Statistical Division (population, 218000), which includes the city of Geelong (latitude 38° south) in Victoria. Main outcome measures Vitamin D intake; serum 250HD level; season of assessment; exposure to sunshine. Results Median intake of vitamin D was 1.2 μg/day (range, 0.0–11.4 μg/day). Vitamin D supplements, taken by 7.9% of participants, increased intake by 8.1% to 1.3 μg/day (range, 0.0–101.2 μg/day) (P< 0.001). A dose–response relationship in serum 250HD levels was observed for sunbathing frequency before and after adjusting for age (P<0.05). During winter (May–October), serum 250HD levels were dependent on vitamin D intake (partial r2=0.01; P<0.05) and were lower than during summer (November–April) (age‐adjusted mean, 59nmol/L [95% CI, 57–62] v 81 nmol/L [95% CI, 78–84]; P<0.05). No association was detected between serum 250HD and vitamin D intake during summer. The prevalences of low concentrations of serum 250HD were, for <28 nmol/L, 7.2% and 11.3% overall and in winter, respectively; and, for < 50 nmol/L, 30.0% and 43.2% overall and in winter, respectively. Conclusions At latitude 38° south, the contribution of vitamin D from dietary sources appears to be insignificant during summer. However, during winter vitamin D status is influenced by dietary intake. Australia has no recommended dietary intake (RDI) for vitamin D, in the belief that adequate vitamin D can be obtained from solar irradiation alone. Our results suggest that an RDI may be needed.

The substantial 60% reduction in fracture risk associated with statin use is greater than would be expected from increases in BMD alone.

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BackgroundThe body mass index (BMI) is commonly used as a surrogate marker for adiposity. However, the BMI indicates weight-for-height without considering differences in body composition and the contribution of body fat to overall body weight.The aim of this cross-sectional study was to identify sex-and-age-specific values for percentage body fat (%BF), measured using whole body dual energy x-ray absorptiometry (DXA), that correspond to BMI 18.5 kg/m2 (threshold for underweight), 25.0 kg/m2 (overweight) and 30.0 kg/m2 (obesity) and compare the prevalence of underweight, overweight and obesity in the adult white Australian population using these BMI thresholds and equivalent values for %BF. These analyses utilise data from randomly-selected men (n = 1446) and women (n = 1045), age 20–96 years, who had concurrent anthropometry and DXA assessments as part of the Geelong Osteoporosis Study, 2001–2008.ResultsValues for %BF cut-points for underweight, overweight and obesity were predicted from sex, age and BMI. Using these cut-points, the age-standardised prevalence among men for underweight was 3.1% (95% CI 2.1, 4.1), overweight 40.4% (95% CI 37.7, 43.1) and obesity 24.7% (95% CI 22.2, 27.1); among women, prevalence for underweight was 3.8% (95% CI 2.6, 5.0), overweight 32.3% (95% CI 29.5, 35.2) and obesity 29.5% (95% CI 26.7, 32.3). Prevalence estimates using BMI criteria for men were: underweight 0.6% (95% CI 0.2, 1.1), overweight 45.5% (95% CI 42.7, 48.2) and obesity 19.7% (95% CI 17.5, 21.9); and for women, underweight 1.4% (95% CI 0.7, 2.0), overweight 30.3% (95% CI 27.5, 33.1) and obesity 28.2% (95% CI 25.4, 31.0).ConclusionsUtilising a single BMI threshold may underestimate the true extent of obesity in the white population, particularly among men. Similarly, the BMI underestimates the prevalence of underweight, suggesting that this body build is apparent in the population, albeit at a low prevalence. Optimal thresholds for defining underweight and obesity will ultimately depend on risk assessment for impaired health and early mortality.

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