Progress in osteoporosis and fracture prevention: focus on postmenopausal women

In the past decade, we have witnessed a revolution in osteoporosis diagnosis and therapeutics. This includes enhanced understanding of basic bone biology, recognizing the severe consequences of fractures in terms of morbidity and short-term re-fracture and mortality risk and case finding based on clinical risks, bone mineral density, new imaging approaches, and contributors to secondary osteoporosis. Medical interventions that reduce fracture risk include sufficient calcium and vitamin D together with a wide spectrum of drug therapies (with antiresorptive, anabolic, or mixed effects). Emerging therapeutic options that target molecules of bone metabolism indicate that the next decade should offer even greater promise for further improving our diagnostic and treatment approaches

Serum urate, menopause, and postmenopausal hormone use: from eminence to evidence-based medicine

The relationship between serum urate, menopause, and aging has not been clearly defined by scientific evidence. In the present issue of Arthritis Research and Therapy, Hak and Choi present a cross-sectional analysis to clarify the effect of menopause and hormone replacement therapy on serum urate in women within the Third National Health and Nutritional Examination Survey. Menopause increased serum urate and hormone replacement therapy significantly decreased serum urate, although the overall level of change was small. The implications of these urate changes on gout and cardiovascular disease outcomes require further study

Gout. Hyperuricemia and cardiovascular disease: how strong is the evidence for a causal link?

An association between high levels of serum urate and cardiovascular disease has been proposed for many decades. However, it was only recently that compelling basic science data, small clinical trials, and epidemiological studies have provided support to the idea of a true causal effect. In this review we present recently published data that study the association between hyperuricemia and selected cardiovascular diseases, with a final conclusion about the possibility of this association being causal

Integrated safety studies of the urate reabsorption inhibitor lesinurad in treatment of gout.

ObjectiveLesinurad (LESU) is a selective urate reabsorption inhibitor approved at 200 mg daily for use with a xanthine oxidase inhibitor (XOI) to treat hyperuricaemia in gout patients failing to achieve target serum urate on XOI. The aim of the study was to investigate the long-term safety of LESU + XOI therapy.MethodsSafety data were pooled from three 12-month phase III (core) trials evaluating LESU 200 and 400 mg/day combined with an XOI (LESU200+XOI and LESU400+XOI), and two 12-month extension studies using descriptive statistics. To adjust for treatment duration, treatment-emergent adverse events (TEAEs) were expressed as exposure-adjusted incidence rates (patients with events per 100 person-years).ResultsIn the core studies, exposure-adjusted incidence rates for total and total renal-related TEAEs were comparable for XOI alone and LESU200+XOI but higher with LESU400+XOI. Exposure-adjusted incidence rates for serum creatinine (sCr) elevations ⩾1.5×baseline were 2.9, 7.3 and 18.7, respectively. Resolution (sCr ⩽1.2×baseline) occurred in 75-90% of all events, with 66-75% occurring without any study medication interruption. Major adverse cardiovascular events were 3, 4 and 9 with XOI, LESU200+XOI and LESU400+XOI, respectively. Longer exposure in core+extension studies did not increase rates for any safety signals.ConclusionAt the approved dose of 200 mg once-daily combined with an XOI, LESU did not increase renal, cardiovascular or other adverse events compared with XOI alone, except for sCr elevations. With extended exposure in the core+extension studies, the safety profile was consistent with that observed in the core studies, and no new safety concerns were identified

A multi-modal intervention for Activating Patients at Risk for Osteoporosis (APROPOS): Rationale, design, and uptake of online study intervention material

OBJECTIVE: To develop an innovative and effective educational intervention to inform patients about the need for osteoporosis treatment and to determine factors associated with its online uptake. METHODS: Postmenopausal women with a prior fracture and not currently using osteoporosis therapy were eligible to be included in the Activating Patients at Risk for OsteoPOroSis (APROPOS). Four nominal groups with a total of 18 racially/ethnically diverse women identified osteoporosis treatment barriers. We used the Information, Motivation, Behavior Skills conceptual model to develop a direct-to-patient intervention to mitigate potentially modifiable barriers to osteoporosis therapy. The intervention included videos tailored by participants\u27 race/ethnicity and their survey responses: ranked barriers to osteoporosis treatment, deduced barriers to treatment, readiness to behavior change, and osteoporosis treatment history. Videos consisted of storytelling narratives, based on osteoporosis patient experiences and portrayed by actresses of patient-identified race/ethnicity. We also delivered personalized brief phone calls followed by an interactive voice-response phone messages aimed to promote uptake of the videos. RESULTS: To address the factors associated with online intervention uptake, we focused on participants assigned to the intervention arm (n = 1342). These participants were 92.9% Caucasian, with a mean (SD) age 74.9 (8.0) years and the majority (77.7%) had some college education. Preference for natural treatments was the barrier ranked #1 by most (n = 130; 27%), while concern about osteonecrosis of the jaw was the most frequently reported barrier (at any level; n = 322; 67%). Overall, 28.1% (n = 377) of participants in the intervention group accessed the videos online. After adjusting for relevant covariates, the participants who provided an email address had 6.07 (95% CI 4.53-8.14) higher adjusted odds of accessing their online videos compared to those who did not. CONCLUSION: We developed and implemented a novel tailored multi-modal intervention to improve initiation of osteoporosis therapy. An email address provided on the survey was the most important factor independently associated with accessing the intervention online. The design and uptake of this intervention may have implications for future studies in osteoporosis or other chronic diseases

SToRytelling to Improve Disease outcomes in Gout (STRIDE-GO): A multicenter, randomized controlled trial in African American veterans with gout

BACKGROUND: Urate-lowering therapy (ULT) adherence is low in gout, and few, if any, effective, low-cost, interventions are available. Our objective was to assess if a culturally appropriate gout-storytelling intervention is superior to an attention control for improving gout outcomes in African-Americans (AAs). METHODS: In a 1-year, multicenter, randomized controlled trial, AA veterans with gout were randomized to gout-storytelling intervention vs. a stress reduction video (attention control group; 1:1 ratio). The primary outcome was ULT adherence measured with MEMSCap™, an electronic monitoring system that objectively measured ULT medication adherence. RESULTS: The 306 male AA veterans with gout who met the eligibility criteria were randomized to the gout-storytelling intervention (n = 152) or stress reduction video (n = 154); 261/306 (85%) completed the 1-year study. The mean age was 64 years, body mass index was 33 kg/m CONCLUSIONS: A culturally appropriate gout-storytelling intervention was not superior to attention control for improving gout outcomes in AAs with gout. TRIAL REGISTRATION: Registered at ClinicalTrials.gov NCT02741700

Sex differences in gout characteristics: tailoring care for women and men

BACKGROUND: To characterize the differences between women and men with gout. METHODS: We analyzed a US national cohort of gout patients cared for by rheumatologists. RESULTS: Compared with the 1012 men with gout, women with gout (n = 262) were older (71 vs. 61 years, p \u3c 0.001) and had a greater burden of comorbid conditions (p \u3c 0.001 for hypertension, diabetes, renal disease and obesity). Risk factors for gout differed with women more often taking diuretics (p \u3c 0.001), while men more frequently had dietary triggers (p \u3c 0.05). CONCLUSIONS: The profiles of women and men with gout are markedly different, suggesting a need to tailor treatment recommendations

Machine Learning Approaches for the Prediction of Bone Mineral Density by Using Genomic and Phenotypic Data of 5130 Older Men

The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance. When using GRS and phenotypic covariates as the predictors, all ML models’ performance and linear regression in BMD prediction were similar. However, when replacing GRS with the 1103 individual SNPs in the model, ML models performed significantly better than linear regression (with lasso regularization), and the gradient boosting model performed the best. Our study suggested that ML models, especially gradient boosting, can improve BMD prediction in genomic data

Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions

Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies. Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. Results: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models. Conclusion: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented