The effect of thyroid hormone therapy on muscle function, strength and mass in older adults with subclinical hypothyroidism-an ancillary study within two randomized placebo controlled trials.

BACKGROUND: loss of skeletal muscle function, strength and mass is common in older adults, with important socioeconomic impacts. Subclinical hypothyroidism is common with increasing age and has been associated with reduced muscle strength. Yet, no randomized placebo-controlled trial (RCT) has investigated whether treatment of subclinical hypothyroidism affects muscle function and mass. METHODS: this is an ancillary study within two RCTs conducted among adults aged ≥65 years with persistent subclinical hypothyroidism (thyrotropin (TSH) 4.60-19.99 mIU/l, normal free thyroxine). Participants received daily levothyroxine with TSH-guided dose adjustment or placebo and mock titration. Primary outcome was gait speed at final visit (median 18 months). Secondary outcomes were handgrip strength at 1-year follow-up and yearly change in muscle mass. RESULTS: we included 267 participants from Switzerland and the Netherlands. Mean age was 77.5 years (range 65.1-97.1), 129 (48.3%) were women, and their mean baseline TSH was 6.36 mIU/l (standard deviation [SD] 1.9). At final visit, mean TSH was 3.8 mIU/l (SD 2.3) in the levothyroxine group and 5.1 mIU/l (SD 1.8, P < 0.05) in the placebo group. Compared to placebo, participants in the levothyroxine group had similar gait speed at final visit (adjusted between-group mean difference [MD] 0.01 m/s, 95% confidence interval [CI] -0.06 to 0.09), similar handgrip strength at one year (MD -1.22 kg, 95% CI -2.60 to 0.15) and similar yearly change in muscle mass (MD -0.15 m2, 95% CI -0.49 to 0.18). CONCLUSIONS: in this ancillary analysis of two RCTs, treatment of subclinical hypothyroidism did not affect muscle function, strength and mass in individuals 65 years and older.

Stopping Denosumab.

PURPOSE OF REVIEW: Denosumab discontinuation is associated with a rebound effect manifesting by an increased risk of multiple spontaneous vertebral fractures. The purpose of this review is to (1) better characterize this risk and (2) find solutions to avoid it. RECENT FINDINGS: In the absence of a potent bisphosphonate prescription at denosumab discontinuation, the frequency of multiple vertebral fractures is common or frequent (≥ 1/100 and < 1/10). In five recent case series, the median number of vertebral fractures was 5 within 7 to 20 months (median 11) after the last denosumab injection. Prescribing bisphosphonate before starting denosumab and/or after stopping denosumab may reduce this risk. However, only small case series have evaluated these strategies. After the second denosumab dose, there is a rebound effect with an increased risk of multiple vertebral fractures. A potent bisphosphonate prescribed at denosumab discontinuation could reduce this risk. As denosumab discontinuation is characterized by many uncertainties, denosumab is a second-line treatment for osteoporosis. Studies are urgently needed to define the management of denosumab discontinuation.

Standardization of body composition parameters between GE Lunar iDXA and Hologic Horizon A and their clinical impact.

Body composition (BC) measured by DXA differs between devices. We aimed to compare regional and total BC measurements assessed by the Hologic Horizon A and the GE Lunar iDXA devices; to determine device-specific calibration equations for each BC parameter; and to assess the impact of this standardization procedure on the assessment of sarcopenia, lipedema, obesity, and cardiovascular risk with DXA. A total of 926 postmenopausal women (aged 72.9 ± 6.9 yr, height 160.3 ± 6.6 cm, weight 66.1 ± 12.7 kg) underwent BC assessment on each device within 1 h, following the ISCD guidelines. The included sample was split into 80% train and 20% test datasets stratified by age, height, and weight. Inter-device differences in BC parameters were assessed with Bland-Altman analysis, Pearson or Spearman correlation coefficients, and t-tests or Wilcoxon tests. The equations were developed in the train dataset using backward stepwise multiple linear regressions and were evaluated in the test dataset with the R-squared and mean absolute error. We compared the abovementioned BC-derived health conditions before and after standardization in the test set with respect to relative risk, accuracy, Kappa score, and McNemar tests. Total and regional body masses were similar (p>.05) between devices. BMC was greater for all regions in the Lunar device (p<.05), while fat and lean masses differed among regions. Regression equations showed high performance metrics in both datasets. The BC assessment from Hologic classified 2.13 times more sarcopenic cases (McNemar: p<.001), 1.39 times more lipedema (p<.001), 0.40 times less high cardiovascular risk (p<.001), and similarly classified obesity (p>.05), compared to Lunar. After standardization, the differences disappeared (p>.05), and the classification metrics improved. This study discusses how hardware and software differences impact BC assessments. The provided standardization equations address these issues and improve the agreement between devices. Future studies and disease definitions should consider these differences.

Development and reporting of artificial intelligence in osteoporosis management.

An abundance of medical data and enhanced computational power have led to a surge in Artificial Intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17th, 2020, to February 1st, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the MI-CLAIM checklist. The systematic search yielded 97 articles that fell into five areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles) and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A 6th area, AI-driven clinical decision support, identified the studies from the five preceding areas which aimed to improve clinician efficiency, diagnostic accuracy and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.

This review covers the recent advancements in artificial intelligence (AI) for managing osteoporosis, an increasingly prevalent condition that weakens bone tissues and increases fracture risk. Analyzing 97 studies from December 2020 to February 2023, the present work highlights how AI enhances bone properties assessment, osteoporosis classification, fracture detection and classification, risk prediction, and bone segmentation. A systematic qualitative assessment of the studies revealed improvements in study quality compared with the earlier review period, supported by innovative and more explainable AI approaches. AI shows promise in clinical decision support by offering novel screening tools that can help in the earlier identification of the disease, improve clinical workflows and patient prognosis. New pre-processing strategies and advanced model architectures have played a critical role in these improvements. Researchers have enhanced the accuracy and predictive performance of traditional methods by integrating clinical data with imaging data through advanced multi-factorial AI techniques. These innovations, paired with standardized development and validation processes, promise to personalize medicine and enhance patient care in osteoporosis management.

Association of adiposity evaluated by anthropometric, BIA, and DXA measures with cardiometabolic risk factors in nonobese postmenopausal women: the CoLaus/OsteoLaus cohort.

OBJECTIVE: After menopause, body composition changes with body fat accumulation, and an increase in cardiometabolic risk factors. Total fat mass, regional fat mass, and visceral adipose tissue (VAT) may be estimated with anthropometric measures, bioelectrical impedance analysis (BIA), and dual-energy X-ray absorptiometry (DXA). The aim of our study was to assess which measurement correlated best with cardiometabolic risk factors in healthy nonobese postmenopausal women. METHODS: The CoLaus/OsteoLaus cohort included 1,500 postmenopausal women (age range 50-80). We analyzed correlations between: 1) measurements of body composition assessed by anthropometric measures, BIA, and DXA and 2) these measurements and different selected cardiometabolic risk factors, such as blood pressure, lipid markers (cholesterol subtypes and triglycerides), and metabolic markers (glucose, insulin, adiponectin, and leptin). Spearman correlation coefficient, stepwise forward regression, and linear regression analyses were used to determine association between anthropometric measurements and cardiometabolic risk factors. RESULTS: In the 803 included participants (mean age 62.0 ± 7.1 y, mean body mass index 25.6 kg/m2 ± 4.4), correlations between total fat mass measured by BIA and total fat mass, android fat, gynoid fat, or VAT measured by DXA are very strong (from r = 0.531, [99% confidence interval (CI), 0.443-0.610] to r = 0.704, [99% CI, 0.640-0.758]). Body mass index and waist circumference have a higher correlation with VAT (r = 0.815, [99% CI, 0.772-0.851] and r = 0.823 [99% CI, 0.782-0.858], respectively) than BIA (r = 0.672 [99% CI, 0.603-0.731]). Among the anthropometric measurement and the measurements derived from DXA and BIA, VAT is the parameter most strongly associated with cardiometabolic risk factors. VAT better explains the variation of most of the cardiometabolic risk factors than age and treatment. For example, nearly 5% of the variability of the diastolic blood pressure (9.9 vs 4.9), nearly 15% of the variability of high-density lipoprotein cholesterol (20.3 vs 3.8) and triglyceride (21.1 vs 6.5), 25.3% of the variability of insulin (33.3 vs 8.1), and 37.5% of the variability of leptin (37.7 vs 1.1) were explained by VAT. CONCLUSIONS: BIA seems not to be a good tool to assess VAT. At the population level, waist circumference and body mass index seem to be good tools to estimate VAT. VAT measured by DXA is the parameter most correlated with cardiometabolic risk factors and could become a component of the cardiometabolic marker on its own.

The glucocorticoid-induced leucine zipper (gilz/Tsc22d3-2) gene locus plays a crucial role in male fertility.

The glucocorticoid-induced leucine zipper (Tsc22d3-2) is a widely expressed dexamethasone-induced transcript that has been proposed to be important in immunity, adipogenesis, and renal sodium handling based on in vitro studies. To address its function in vivo, we have used Cre/loxP technology to generate mice deficient for Tsc22d3-2. Male knockout mice were viable but surprisingly did not show any major deficiencies in immunological processes or inflammatory responses. Tsc22d3-2 knockout mice adapted to a sodium-deprived diet and to water deprivation conditions but developed a subtle deficiency in renal sodium and water handling. Moreover, the affected animals developed a mild metabolic phenotype evident by a reduction in weight from 6 months of age, mild hyperinsulinemia, and resistance to a high-fat diet. Tsc22d3-2-deficient males were infertile and exhibited severe testis dysplasia from postnatal d 10 onward with increases in apoptotic cells within seminiferous tubules, an increased number of Leydig cells, and significantly elevated FSH and testosterone levels. Thus, our analysis of the Tsc22d3-2-deficient mice demonstrated a previously uncharacterized function of glucocorticoid-induced leucine zipper protein in testis development.