Osteoporotic Fractures In Men Research Articles

Association of back pain with all-cause and cause-specific mortality among older men: a cohort study.

We evaluated whether more severe back pain phenotypes-persistent, frequent or disabling back pain-are associated with higher mortality among older men. In this secondary analysis of a prospective cohort, the Osteoporotic Fractures in Men (MrOS) study, we evaluated mortality rates by back pain phenotype among 5215 older community-dwelling men (mean age, 73 years, SD = 5.6) from six U.S. sites. The primary back pain measure used baseline and year five back pain questionnaire data to characterize participants as having: no back pain; non-persistent back pain; infrequent persistent back pain; or frequent persistent back pain. Secondary measures of back pain from year five questionnaire included disabling back pain phenotypes. The main outcomes measured were all-cause and cause-specific mortality. After the year five exam, during up to 18 years of follow-up (mean follow-up=10.3 years), there were 3513 deaths (1218 cardiovascular, 764 cancer, 1531 other). A higher proportion of men with frequent persistent back pain versus no back pain died (78% versus 69%; sociodemographic-adjusted HR = 1.27, 95%CI=1.11-1.45). No association was evident after further adjusting for health-related factors such as self-reported general health and comorbid chronic health conditions (fully-adjusted HR = 1.00; 95%CI=0.86-1.15). Results were similar for cardiovascular mortality and other mortality, but we observed no association of back pain with cancer mortality. Secondary back pain measures including back-related disability were associated with increased mortality risk that remained statistically significant in fully-adjusted models. While frequent persistent back pain was not independently associated with mortality in older men, additional secondary disabling back pain phenotypes were independently associated with increased mortality. Future investigations should evaluate whether improvements in disabling back pain effect general health and well-being or mortality.

Higher high-density lipoprotein cholesterol levels increased vertebral osteoporotic fracture, but reduced hip fracture in men based on the National Health Insurance Service-National Health Screening Cohort

Background ContextLipids are currently known to play an important role in bone metabolism. PurposeThis study aimed to investigate the effect of high-density lipoprotein cholesterol (HDL-C) on osteoporotic fractures beyond its beneficial effects on the cardiovascular system. Study Design/SettingThis was a retrospective, observational study that used data from the National Health Insurance Service-Health Screening cohort database. Patient SampleThis study included 318,237 participants who were 50 years or older and with HDL-C levels of 10 to 200 mg/dL. Outcomes MeasuresPhysiologic measure - Diagnosis of osteoporotic fracture during the follow-up period. MethodsThe study participants were categorized into four quartiles according to baseline HDL-C levels. The Cox proportional hazards model was used to assess osteoporotic fracture risk according to HDL-C levels. ResultsAfter full adjustment and with the Q1 group as the reference group, estimates of hazard ratios (HRs; 95% confidence intervals [CIs]) for any osteoporotic fracture in men were 1.03 (0.94–1.12), 1.02 (0.93–1.11), and 1.07 (0.98–1.18) for the Q2, Q3, and Q4 groups, respectively. After classifying osteoporotic fractures according to the body location, the fully adjusted HRs for vertebral and hip fractures in the men's Q4 groups were 1.16 (1.02–1.31) and 0.74 (0.57–0.96), respectively. In women, fully adjusted HRs (95% CIs) of the female Q4 group for any osteoporotic, vertebral, and hip fractures were 1.03 (0.95–1.11), 0.96 (0.86–1.07), and 1.06 (0.80–1.41), respectively. ConclusionIn this study, HDL-C levels were positively associated with vertebral fractures in both men and women but inversely related to hip fractures in men. Therefore, monitoring the lipid profiles of patients with osteoporosis may be beneficial for the prevention of osteoporotic fractures.

Hybrid Optimization Model Integrating Gradient Descent and Stochastic Descent for Enhanced Osteoporosis and Osteopenia Recognition

Osteoporosis and osteopenia, prevalent bone diseases affecting millions of people globally, necessitate accurate early diagnosis for effective treatment and fracture prevention. This paper proposes a novel hybrid optimization algorithm tailored for classifying these conditions based on Bone Mineral Density (BMD) measurements. The algorithm, a customized Mini-Batch Gradient Descent (MBGD), blends the advantages of Gradient Descent (GD) and Stochastic Gradient Descent (SGD), addressing specific needs for osteoporosis and osteopenia classification. Utilizing a dataset comprising BMD measurements and clinical risk factors from the Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures (SOF), and Fracture Risk Assessment (FRAX), the model achieves an impressive accuracy of 99.01%. The proposed model outperforms existing methods, demonstrating superior accuracy compared to the accuracy obtained in Gradient Descent of 97.26%, Stochastic Gradient Descent of 97.23%, and other optimization algorithms such as Adam of 96.45% and the RMSprop of 96.23%. This hybrid model presents a robust framework for early diagnosis of Osteoporosis and osteopenia, and hence there is an enhancement in quality of life.

Replacing sedentary time for physical activity: Does intensity matter for body composition in oldest-old adults?

ABSTRACT To assess the independent and combined relationships among objectively measured sedentary time (ST), light intensity PA (LPA), and moderate-to-vigorous intensity PA (MVPA) with muscle mass and fat mass (FM) and how theoretical displacement of these inter-dependent behaviours relates to body composition in oldest-old men. A total of 1046 men participating in the year 14 visit of the prospective Osteoporotic Fractures in Men (MrOS) cohort study with complete data for accelerometry, dual x-ray absorptiometry, and deuterated creatine dilution (D3Cr) muscle mass were included in the analysis (84.0 ± 3.8 yrs.). Single, partition, and isotemporal substitution models were used to assess the interrelationships between PA intensities and ST with body composition measures, while controlling for relevant confounders. Replacing 30-min of ST with 30-min of MVPA was associated with lower FM (β =-0.17, p < 0.001) and higher D3Cr muscle mass, although this was of borderline significance (β = 0.07, p = 0.05). Replacing 30-min of ST for LPA was associated with lower FM (β =-0.15, p < 0.001), but there was no effect on D3Cr muscle mass (p > 0.05). Exchanging ST with any intensity of PA is associated with benefits for FM in oldest-old adult men, although substitution with MVPA may be more beneficial than LPA for maintaining/improving skeletal muscle mass.

Androgen deprivation therapy-related fracture risk in prostate cancer: an insurance claims database study in Japan

IntroductionAndrogen deprivation therapy (ADT) is widely used for the treatment of prostate cancer. ADT is associated with reduced bone density leading to an increased risk of osteoporotic fracture. The objective of this retrospective cohort study was to quantify fracture risk in men treated with ADT for prostate cancer in real-world practice in Japan.Materials and methodsData were extracted from the Japanese Medical Data Vision (MDV) database. Men initiating ADT for treatment of prostate cancer between April 2010 and March 2021 were identified and matched to a cohort of prostate cancer patients not taking ADT using a propensity score. Fracture rates were estimated by a cumulative incidence function and compared between cohorts using a Cox cause-specific hazard model. Information was extracted on demographics, comorbidities and bone densitometry.Results30,561 men with PC starting ADT were matched to 30,561 men with prostate cancer not treated with ADT. Following ADT initiation, <5% of men underwent bone densitometry. Prescription of ADT was associated with an increased fracture risk compared to not taking ADT (adjusted hazard ratio: 1.63 [95% CI 1.52–1.75]).ConclusionADT is associated with a 1.6-fold increase in the risk of osteoporotic fracture in men with prostate cancer. Densitometry in this population is infrequent and monitoring urgently needs to be improved in order to implement effective fracture prevention.

More rapid bone mineral density loss in older men with diabetes: The Osteoporotic Fractures in Men (MrOS) Study.

Type 2 diabetes mellitus (T2D) is associated with more rapid bone loss in women, but less evidence is available for men or those with prediabetes. To determine whether bone loss rate is affected by diabetes status in older men, we analyzed data from the Osteoporotic Fractures in Men (MrOS) study. The multisite MrOS study enrolled 5,994 men aged ≥65 years. Diabetes status was defined by self-report, diabetes medication use, or elevated fasting serum glucose at baseline. Hip bone mineral density (BMD) was measured by dual energy x-ray absorptiometry (DXA) at baseline and a follow-up visit after 4.6 ± 0.4 years. This analysis included 4095 men, excluding those without a follow-up DXA or with unknown diabetes status. Changes in hip BMD in participants with normoglycemia (NG), prediabetes, or T2D, excluding thiazolidinedione (TZD) users, were evaluated using generalized linear models (GLM). Diabetes medication use and BMD loss among those with T2D were also evaluated with GLM. In adjusted models, loss in hip BMD was greater in men with T2D (- 2.23%: 95% CI: -2.54 to -1.91; p<0.001) but not in men with prediabetes (-1.45%; 95% CI -1.63 to -1.26; p=0.33) compared to NG (-1.57%: 95% CI -1.73 to -1.41). Among men with T2D, TZD, insulin and sulfonylurea use were associated with greater hip BMD loss. Men with T2D, but not prediabetes, experienced an accelerated bone loss compared to participants with normoglycemia. More rapid bone loss predicts increased risk of fractures and mortality in broader populations.

The Vitamin D Metabolite Ratio Is Associated With Volumetric Bone Density in Older Men.

Serum 25-hydroxyvitamin D (25(OH)D) is the current marker of vitamin D adequacy, but its relationship with bone health has been inconsistent. The ratio of 24,25-dihydroxyvitamin D3 to 25(OH)D3 (vitamin D metabolite ratio or VMR) is a marker of vitamin D that has been associated with longitudinal changes in bone mineral density (BMD) and fracture risk. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides information on bone health beyond standard dual-energy x-ray absorptiometry, in that it measures volumetric BMD (vBMD) as well bone strength. The relationship of the VMR with vBMD and bone strength remains unknown. We evaluated the associations of the VMR and 25(OH)D3 with vBMD and bone strength in the distal radius and tibia, assessed by HR-pQCT in 545 older men participating in the Osteoporotic Fractures in Men (MrOS) Study. Primary outcomes were vBMD and estimated failure load (EFL, a marker of bone strength) at the distal radius and tibia. The mean age was 84 ± 4 years, 88.3% were White, and 32% had an estimated glomerular filtration rate <60 mL/min/1.73 m2. In adjusted models, each twofold higher VMR was associated with a 9% (3%, 16%) higher total vBMD and a 13% (5%, 21%) higher EFL at the distal radius. Results were similar at the distal tibia. 25(OH)D3 concentrations were not associated with any of the studied outcomes. Among older men, a higher VMR was associated with greater vBMD and bone strength while 25(OH)D3 was not. The VMR may serve as a valuable marker of skeletal health in older men.

Redefining Comorbid Insomnia and Sleep Apnea: The Association of Sleep Breathing Impairment and Insomnia with Incident Diabetes.

Rationale: Obstructive sleep apnea (OSA) is a prevalent sleep disorder that is frequently comorbid with insomnia and often accompanied by metabolic diseases such as type 2 diabetes. Although the apnea-hypopnea index (AHI) is currently the diagnostic criterion for gauging the severity of OSA, the AHI has not consistently predicted incident diabetes. Objectives: To test whether a combined insomnia-OSA (COMISA) phenotype based on comorbid insomnia and sleep breathing impairment index (COMISA-SBII) predicts incident diabetes and to compare the association with an AHI definition of COMISA (COMISA-AHI) in the MrOS (Osteoporotic Fractures in Men) study. Methods: The study samples came from participants in the MrOS sleep study without diabetes at their baseline examination. The SBII was derived as the product of the duration of each respiratory event (apnea and hypopnea) and the accompanying desaturation area from baseline unattended polysomnography. A subgroup of individuals classified as having comorbid insomnia (difficulties falling asleep, waking up in the middle of the night and/or early morning awakenings >15 times per month, and daytime impairments) and sleep breathing impairment (greater than 50th percentile of SBII) were identified at baseline. The primary outcome was incident diabetes during the follow-up visits. Cox proportional models were built to assess the adjusted hazard ratios of COMISA-AHI and COMISA-SBII. Prediction model performances of incident diabetes were compared across different models. Results: A total of 2,365 men (mean age, 76 yr) without diabetes at baseline were included. During a median follow-up of 10.0 years, diabetes developed in 181. After adjusting for demographic characteristics, comorbidities, and behavioral risk factors, participants with COMISA-SBII had a higher risk of incident diabetes (hazard ratio, 1.82; 95% confidence interval, 1.15-2.89) than those without sleep disorders (those with an SBII ⩽13.17 and no insomnia). The result remained significant in the risk competing model. Compared with COMISA-AHI, the addition of COMISA-SBII to a crude model with established risk factors significantly improved the predictive value of incident diabetes. Conclusions: COMISA-SBII, but not COMISA-AHI, predicted incident diabetes after accounting for multiple covariates in a cohort of older men. A comorbid insomnia phenotype based on SBII plus insomnia symptoms may be an important clinical subtype.

3D Finite Element Models Reconstructed From 2D Dual-Energy X-Ray Absorptiometry (DXA) Images Improve Hip Fracture Prediction Compared to Areal BMD in Osteoporotic Fractures in Men (MrOS) Sweden Cohort.

Bone strength is an important contributor to fracture risk. Areal bone mineral density (aBMD) derived from dual-energy X-ray absorptiometry (DXA) is used as a surrogate for bone strength in fracture risk prediction tools. 3D finite element (FE) models predict bone strength better than aBMD, but their clinical use is limited by the need for 3D computed tomography and lack of automation. We have earlier developed a method to reconstruct the 3D hip anatomy from a 2D DXA image, followed by subject-specific FE-based prediction of proximal femoral strength. In the current study, we aim to evaluate the method's ability to predict incident hip fractures in a population-based cohort (Osteoporotic Fractures in Men [MrOS] Sweden). We defined two subcohorts: (i) hip fracture cases and controls cohort: 120 men with a hip fracture (<10 years from baseline) and two controls to each hip fracture case, matched by age, height, and body mass index; and (ii) fallers cohort: 86 men who had fallen the year before their hip DXA scan was acquired, 15 of which sustained a hip fracture during the following 10 years. For each participant, we reconstructed the 3D hip anatomy and predicted proximal femoral strength in 10 sideways fall configurations using FE analysis. The FE-predicted proximal femoral strength was a better predictor of incident hip fractures than aBMD for both hip fracture cases and controls (difference in area under the receiver operating characteristics curve, ΔAUROC = 0.06) and fallers (ΔAUROC = 0.22) cohorts. This is the first time that FE models outperformed aBMD in predicting incident hip fractures in a population-based prospectively followed cohort based on 3D FE models obtained from a 2D DXA scan. Our approach has potential to notably improve the accuracy of fracture risk predictions in a clinically feasible manner (only one single DXA image is needed) and without additional costs compared to the current clinical approach. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria

Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool. We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV). Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%. Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Sleep Apnea Physiological Burdens and Cardiovascular Morbidity and Mortality.

Rationale: Obstructive sleep apnea is characterized by frequent reductions in ventilation, leading to oxygen desaturations and/or arousals. Objectives: In this study, association of hypoxic burden with incident cardiovascular disease (CVD) was examined and compared with that of "ventilatory burden" and "arousal burden." Finally, we assessed the extent to which the ventilatory burden, visceral obesity, and lung function explain variations in hypoxic burden. Methods: Hypoxic, ventilatory, and arousal burdens were measured from baseline polysomnograms in the Multi-Ethnic Study of Atherosclerosis (MESA) and the Osteoporotic Fractures in Men (MrOS) studies. Ventilatory burden was defined as event-specific area under ventilation signal (mean normalized, area under the mean), and arousal burden was defined as the normalized cumulative duration of all arousals. The adjusted hazard ratios for incident CVD and mortality were calculated. Exploratory analyses quantified contributions to hypoxic burden of ventilatory burden, baseline oxygen saturation as measured by pulse oximetry, visceral obesity, and spirometry parameters. Measurements and Main Results: Hypoxic and ventilatory burdens were significantly associated with incident CVD (adjusted hazard ratio [95% confidence interval] per 1 SD increase in hypoxic burden: MESA, 1.45 [1.14, 1.84]; MrOS, 1.13 [1.02, 1.26]; ventilatory burden: MESA, 1.38 [1.11, 1.72]; MrOS, 1.12 [1.01, 1.25]), whereas arousal burden was not. Similar associations with mortality were also observed. Finally, 78% of variation in hypoxic burden was explained by ventilatory burden, whereas other factors explained only <2% of variation. Conclusions: Hypoxic and ventilatory burden predicted CVD morbidity and mortality in two population-based studies. Hypoxic burden is minimally affected by measures of adiposity and captures the risk attributable to ventilatory burden of obstructive sleep apnea rather than a tendency to desaturate.

0435 Machine-learned combination of ventilatory, hypoxic, and arousal burdens classifies daytime sleepiness better than AHI

Abstract Introduction The apnea-hypopnea index (AHI), the current severity metric used clinically for diagnosing obstructive sleep apnea (OSA), does not correlate well to daytime sleepiness measured via the Epworth Sleepiness Scale (ESS). Here, we assessed whether a machine-learned combination of possibly independent metrics across ventilatory/hypoxic/arousal domains would be better associated with ESS than the AHI using data from 3 large cohorts. Methods Polysomnography data were analyzed from The Sleep Heart Health Study (SHHS), The Multi-Ethnic Study of Atherosclerosis (MESA), and The Osteoporotic Fractures in Men (MrOS) Study. A total of N=6618 (39.9% female; age 68.7±6.6) subjects had valid data (ESS and good quality airflow/EEG/SpO2). Ventilatory burden was evaluated using a derived flow signal that utilized the sum of thoracic and abdominal effort signals for SHHS and was evaluated using the Nasal Cannula/Pressure Transducer signal for the MrOS and MESA data. Hypoxic burden was calculated as the area between the baseline and the SpO2 trace for any episode with &amp;gt;= 3% desaturation. Arousal burden was defined as the manually scored arousal index (number/hr.). Based on a cut-off of ESS(ESS&amp;gt;= 10), sleepiness was coded present or absent and was the primary outcome. Data were analyzed in two ways: using all 3 cohorts as train (70%) and test (30%), and by permutations and combinations of the 3 cohorts (70/30 split; e.g., SHHS for training, MESA and MrOS for test). Model performance metrics were the area under the receiver operating characteristic curve (AUROC) and %accuracy. For comparison, a logistic regression model using AHI3a (3% desaturation and/or EEG arousal) was fit. Results The logistic regression model (AHI3a) classified sleepiness with an AUROC of 0.51±0.07. The random forest model trained on 70% of all 3 cohorts achieved the highest AUROC of 0.88±0.07 (mean accuracy of 85.1± 2.13%), whereas the permutations and combinations of the 3 datasets resulted in an average AUROC of 0.63 ±0.12 (mean accuracy of 76.4±6.57%). Conclusion The machine-learned combination of ventilatory/hypoxic/arousal burdens classifies daytime sleepiness in OSA better than AHI3a across data from 3 large cohorts. These results suggest that OSA severity measured using machine-learned combination of ventilatory/hypoxic/arousal burdens better explains the variability in daytime sleepiness. Support (if any)

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Schmorl's nodes are associated with prevalent osteoporotic vertebral fracture and low bone mineral density: a population-based thoracic spine MRI study in older men and women.

Schmorl's node (SN) corresponds to nucleus pulposus herniation into the vertebral spongy bone with thickened trabeculae around the formed node. We hypothesize that a pathway may exist that: osteoporosis → weakened endplate → SN development ↔ endplate fracture of an osteoporotic vertebra. For osteoporotic fractures in men (MrOS) and in women (MsOS) Hong Kong studies, at 14-year follow-up, thoracic spine magnetic resonance imaging (MRI) was sampled in 270 males (mean: 82.9±3.7 years) and 150 females (mean: 81.5±4.3 years). SN and Modic change were assessed as existed or not existed. For posterior disc protrusion, ligamentum flavum ossification, and spinal canal stenosis, semi-quantitative gradings were applied. For each vertebra in women, a score of 0, 0.5, 1, 1.5, 2, 2.5, 3 was assigned for no osteoporotic vertebral fracture (OVF) or OVF of <1/5, ≥1/5-1/4, ≥1/4-1/3, ≥1/3-2/5, ≥2/5-2/3, and ≥2/3 vertebral height loss, respectively, and a summed score was calculated by summing up the scores of vertebrae T1 to T12. For men, those of minimal grade were not considered as OVF and assigned a '0' score. SN prevalence in women (55.5%) almost doubled that in men (25.9%). SN was statistically significantly correlated with lower bone mineral density (BMD) derived femoral neck T-score, while the other four spine degeneration changes were not statistically significantly correlated with the T-score. SN were statistically significantly correlated with OVF score. Subjects with SN were more likely to have OVF, with odds ratio for men of 4.32 [95% confidence interval (CI): 1.70-11.00, P=0.002] and odds ratio for women of 3.28 (95% CI: 1.23-8.74, P=0.018). Among older population, many features of SN parallel those of OVF.

Sarcopenia prevalence and incidence in older men - a MrOs Sweden study.

The European Working Group on Sarcopenia in Older People (EWGSOP) published a revised definition of sarcopenia in 2018. There are few incidence studies of sarcopenia following the latest definition. To study prevalence, incidence proportion and incidence rate of sarcopenia in a simple random sample of older Swedish men using the EWGSOP2 definition. Men aged 69-81 were invited to participate in the Osteoporotic Fractures in Men (MrOs) Sweden study. Of 2,004 included participants, 1,266 participants (mean age 75.1, SD 3.1 years) completed baseline and 5-year follow-up measurements. We assessed muscle strength by measuring grip strength and chair stands test, lean mass by dual energy X-ray absorptiometry and physical performance by gait speed at baseline and follow-up. Sarcopenia prevalence and incidence were calculated according to the EWGSOP2 definition. Sarcopenia prevalence increased from 5.6% at baseline to 12.0% at follow-up. During the mean 5.2-year follow-up period, 9.1% developed sarcopenia (incidence proportion), corresponding to an incidence rate of 1.8 per 100 person-years at risk while 39.4% of the participants with sarcopenia at baseline participating in follow-up reversed to no longer having confirmed sarcopenia at 5-year follow-up. The prevalence of sarcopenia defined along EWGSOP2 criteria doubled within 5 years in older men, and more than a third of the study participants with sarcopenia at baseline did not have sarcopenia at follow-up. We conclude that sarcopenia is not a static condition.

The Association of Metabolic Syndrome and Obesity With Clinical Hip Osteoarthritis in the Study of Osteoporotic Fractures and the Osteoporotic Fractures in Men Study Cohorts.

Metabolic dysregulation frequently co-occurs with obesity, which has been shown to be a risk factor for lower extremity osteoarthritis (OA). We evaluated the association between metabolic syndrome (MetS), alone and in combination with obesity, and hip OA. In two parallel cross-sectional analyses, we studied 403 women from the Study of Osteoporotic Fractures (SOF) and 2354 men from the Osteoporotic Fractures in Men (MrOS) study. We used multivariable logistic regression to evaluate associations of obesity (body mass index ≥30 kg/m2 ) and/or MetS (three of five National Cholesterol Education Program Adult Treatment Panel III criteria) with clinical hip OA, defined as a modified Croft score of 2 or more or total hip replacement, and pain or limited range of motion. Our analysis adjusted for demographics. Approximately 3.5% of SOF women and 5.4% of MrOS men had clinical hip OA. Among women, obesity was not associated with hip OA, yet those with MetS had a 365% higher odds of hip OA (95% CI: 1.37-15.83). Among men, those who had obesity had a 115% higher odds of hip OA (95% CI: 1.39-3.32), yet MetS was not associated with hip OA. There was no interaction between MetS, obesity, and hip OA in either women or men. In women, but not in men, MetS was associated with hip OA. In men, but not in women, obesity was associated with hip OA. These findings suggest that mechanical effects of obesity may predominate in the pathogenesis of hip OA in men, whereas metabolic effects predominate in women.

Association of Subjective and Objective Measures of Sleep With Gut Microbiota Composition and Diversity in Older Men: The Osteoporotic Fractures in Men Study.

Growing evidence suggests bidirectional links between gut microbiota and sleep quality as shared contributors to health. Little is known about the relationship between microbiota and sleep among older persons. We used 16S rRNA sequencing to characterize stool microbiota among men (n=606, mean [standard deviation] age=83.9 [3.8]) enrolled in the Osteoporotic Fractures in Men (MrOS) study from 2014 to 2016. Sleep was assessed concurrently by a questionnaire (Pittsburgh Sleep Quality index [PSQI]), and activity monitor to examine timing (acrophase) and regularity of patterns (F-statistic). Alpha diversity was measured using Faith's phylogenetic diversity (PD). Beta diversity was calculated with robust Aitchison distance with matrix completion (RPCA) and phylogenetic-RPCA (PRPCA). Their association with sleep variables was tested with partial distance-based redundancy analysis (dbRDA). Predictive-ratio biomarkers associated with sleep measurements were identified with CoDaCoRe. In unadjusted analyses, men with poor sleep (PSQI >5) tended to have lower alpha diversity compared to men with normal sleep (Faith's PD, beta=-0.15; 95% confidence interval [CI]: -0.30 to 0.01, p=.06). Sleep regularity was significantly associated with RPCA and PRPCA, even after adjusting for site, batch, age, ethnicity, body mass index, diabetes, antidepressant and sleep medication use, and health behaviors (RPCA/PRPCA dbRDA; p=.033/.002). In taxonomic analysis, ratios of 7:6 bacteria for better regularity (p=.0004) and 4:7 for worse self-reported sleep (p=.005) were differentially abundant: some butyrate-producing bacteria were associated with better sleep characteristics. Subjective and objective indicators of sleep quality suggest that older men with better sleep patterns are more likely to harbor butyrate-producing bacteria associated with better health.

The association between daily sedentary and active bout frequency with mortality risk in older men using accelerometry.

Time awake with advancing age is increasingly spent sedentary and has several negative health consequences. We examined associations between the frequency of daily sedentary and active bouts with all-cause mortality. Data are from 2816 men in the Osteoporotic Fractures in Men (MrOS) Study (mean age ± SD: 79.1 ± 5.2 years) with free-living activity monitor (SenseWear® Pro3 Armband) data (5.1 ± 0.3 days worn >90%) at the Year 7 visit (2007-2009). Sedentary bout frequency was defined as the number of sedentary bouts per day lasting 5+ min to activity of any intensity. Active bout frequency was defined as the number of active bouts per day lasting 5+ min to sedentary behavior. Sleep time was excluded from the analysis. Deaths were centrally adjudicated using death certificates. Cox proportional hazard models were used to separately examine associations between quartiles of sedentary (Q1 referent, <13.6 bouts/day) or active (Q1 referent, <5 bouts/day) bout frequency with mortality. After 9.3 ± 3.8 years of follow-up, 1487 (52.8%) men died. Men averaged 16.8 ± 5.1 and 8.2 ± 4.2 sedentary and active bouts/day, respectively. After full covariate adjustment, each quartile reflecting more frequent sedentary bouts (Q4 vs. Q1 HR: 0.69, 95%CI: 0.58, 0.81, p-trend <0.001) was associated with lower mortality risk. Likewise, each quartile reflecting more frequent active bouts (Q4 vs. Q1 HR: 0.58, 95%CI: 0.49, 0.70, p-trend <0.001) was associated with lower mortality risk. Results for the sedentary bouts model remained significant after adjusting for total minutes per day in sedentary behavior (Q4 vs. Q1 HR: 0.63, 95%CI: 0.61, 0.86, p-trend=0.001). The association between active bout frequency with mortality was attenuated after adjusting for total minutes per day active. Regardless of total time spent sedentary, reducing duration of sedentary bouts with more frequent and shorter bouts may be a simple and feasible method to delay mortality risk among community-dwelling older men.

Recent sarcopenia definitions-prevalence, agreement and mortality associations among men: Findings from population-based cohorts.

The 2019 European Working Group on Sarcopenia in Older People (EWGSOP2) and the Sarcopenia Definitions and Outcomes Consortium (SDOC) have recently proposed sarcopenia definitions. However, comparisons of the performance of these approaches in terms of thresholds employed, concordance in individuals and prediction of important health-related outcomes such as death are limited. We addressed this in a large multinational assembly of cohort studies that included information on lean mass, muscle strength, physical performance and health outcomes. White men from the Health Aging and Body Composition (Health ABC) Study, Osteoporotic Fractures in Men (MrOS) Study cohorts (Sweden, USA), the Hertfordshire Cohort Study (HCS) and the Sarcopenia and Physical impairment with advancing Age (SarcoPhAge) Study were analysed. Appendicular lean mass (ALM) was ascertained using DXA; muscle strength by grip dynamometry; and usual gait speed over courses of 2.4-6m. Deaths were recorded and verified. Definitions of sarcopenia were as follows: EWGSOP2 (grip strength <27kg and ALM index <7.0kg/m2 ), SDOC (grip strength <35.5kg and gait speed <0.8m/s) and Modified SDOC (grip strength <35.5kg and gait speed <1.0m/s). Cohen's kappa statistic was used to assess agreement between original definitions (EWGSOP2 and SDOC). Presence versus absence of sarcopenia according to each definition in relation to mortality risk was examined using Cox regression with adjustment for age and weight; estimates were combined across cohorts using random-effects meta-analysis. Mean (SD) age of participants (n=9170) was 74.3 (4.9) years; 5929 participants died during a mean (SD) follow-up of 12.1 (5.5) years. The proportion with sarcopenia according to each definition was EWGSOP2 (1.1%), SDOC (1.7%) and Modified SDOC (5.3%). Agreement was weak between EWGSOP2 and SDOC (κ=0.17). Pooled hazard ratios (95% CI) for mortality for presence versus absence of each definition were EWGSOP2 [1.76 (1.42, 2.18), I2 : 0.0%]; SDOC [2.75 (2.28, 3.31), I2 : 0.0%]; and Modified SDOC [1.93 (1.54, 2.41), I2 : 58.3%]. There was low prevalence and poor agreement among recent sarcopenia definitions in community-dwelling cohorts of older white men. All indices of sarcopenia were associated with mortality. The strong relationship between sarcopenia and mortality, regardless of the definition, illustrates that identification of appropriate management and lifecourse intervention strategies for this condition is of paramount importance.

ASSOCIATION OF LOWER URINARY TRACT SYMPTOMS WITH INCIDENT FRAILTY AND MORTALITY AMONG OLDER COMMUNITY-DWELLING MEN

Abstract Lower urinary tract symptoms (LUTS) are associated with increased risk of new mobility and functional limitations among older men. Our objective was to evaluate the longitudinal relationship between baseline LUTS severity and incident frailty or all-cause mortality among 3667 community-dwelling non-frail men age &amp;gt;70 years from the Osteoporotic Fractures in Men (MrOS) study. LUTS severity at analytic baseline was defined using the American Urologic Association Symptom Index (AUASI). Phenotypic frailty was defined at baseline and 2-year follow-up visits using modified Fried criteria and classified as robust (0), intermediate stage (1-2), or frail (3-5); men classified as frail at analytic baseline were excluded. Vital status was assessed every 4 months. Since the proportional odds assumption was not met, we used multivariable multinomial logistic regression to estimate odds ratios (OR) for the association between baseline LUTS severity and incident frailty or death at follow-up compared to robust. OR were adjusted for demographics, health-behaviors, comorbidities, and cognition. After a mean follow-up of 2.3 years, 37% of men were categorized as robust, 46% were intermediate stage, 9.2% developed incident frailty, and 7.9% had died. Per 4 point higher AUASI, the adjusted odds incident frailty versus robust was 1.23 (95% CI 1.12, 1.34). Odds of death versus robust was not statistically significant (OR=1.05, 95% CI 0.94, 1.18). In conclusion, non-frail men with greater LUTS severity at baseline have slightly greater odds of incident frailty within 2 years. Clinicians should be aware that LUTS severity is a prognostic marker for developing frailty in older men.