Association between sleep quality and bone mineral density in Chinese women vary by age and menopausal status

Association Between Changes in Sleep, Nap Duration and Bone Mineral Density in Mexican Adults

Studies have found associations between sleep, nap duration, and bone mineral density (BMD). However, the longitudinal relationship between sleep, nap duration, and BMD has not been explored. We evaluated the association between the change in sleep and nap duration and BMD in Mexican adults. Data come from 1,337 adult participants of the Health Workers Cohort Study (341 were men and 996 were women, including 450 women < 45 years old and 546 ≥ 45 years old), with two study waves. At each wave, sleep and nap duration was assessed using self-administered questionnaires and BMD in g/cm2 was determined by dual X-ray absorptiometry. We used fixed-effect regression models stratified by sex and adjusted for BMI, diet, physical activity, vitamin supplements, and hormone replacement therapy. Women who changed from < 7 to ≥ 7 h/day of sleep from baseline to follow-up were associated with increases in the total hip (β = 0.012 g/cm2; 95% CI: 0.002, 0.022) and lumbar spine BMD (β = 0.024 g/cm2; 95% CI: 0.009, 0.039). Furthermore, most of these associations were observed in women ≥ 45 years. For women, a changing from 0 to > 60 min/day of napping was associated with a significant increase in total hip BMD of 0.012 g/cm2 (95% CI: 0.004, 0.024) and lumbar spine BMD of 0.027 g/cm2 (95% CI: 0.009, 0.045). No significant associations were observed for men. Our results suggest that increased sleep and nap duration are associated with gains in BMD in Mexican women, emphasizing sleep's role in promoting bone health and supporting established recommendations.

Factors Associated With Trabecular Bone Score and Bone Mineral Density; A Machine Learning Approach

INTRODUCTION

Bone indexes including trabecular bone score (TBS) and bone mineral density (BMD) have been shown to be associated with wide spectrum of variables including physical activity, vitamin D, liver enzymes, biochemical measurements, mental and sleep disorders, and quality of life. Here we aimed to determine the most important factors related to TBS and BMD in SUVINA dataset.

METHODS

Data were extracted from the Survey of Ultraviolet Intake by Nutritional Approach (SUVINA study) including all 306 subjects entered this survey. All the available parameters in the SUVINA database were included the analysis. XGBoost modeler software was used to define the most important features associated with bone indexes including TBS and BMD in various sites.

RESULTS

Applying XGBoost modeling for 4 bone indexes indicated that this algorithm could identify the most important variables in relation to bone indexes with an accuracy of 92%, 93%, 90% and 90% respectively for TBS T-score, lumbar Z-score, neck of femur Z-score and Radius Z-score. Serum vitamin D, pro-oxidant-oxidant balance (PAB) and physical activity level (PAL) were the most important factors related to bone indices in different sites of the body.

CONCLUSIONS

Our findings indicated that XGBoost could identify the most important variables with an accuracy of &gt;90% for TBS and BMD. The most important features associated with bone indexes were serum vitamin D, PAB and PAL.

Estimates of the effects of physical activity on osteoporosis using multivariable Mendelian randomization analysis

This study estimates causality of physical activity (PA) on bone mineral density (BMD) by conducting multivariable Mendelian randomization (MR). The findings suggest that habitual vigorous PA increases lumbar spine BMD, and higher overall acceleration average would improve forearm BMD. The results could promote PA intervention targeting individuals with optimized type.

INTRODUCTION

Evidence from epidemiologic studies showed type, frequency, and duration of PA influenced BMD. However, these observational studies may be confounded by many factors, resulting in spurious associations. We aimed to conduct multivariable MR to estimate the causal effect of self-reported and device-measured PA on osteoporosis.

METHODS

Three self-reported and two device-measured PA-related traits were selected as exposures. Outcomes were BMD at different skeletal sites: femoral neck BMD (FN BMD), lumbar spine BMD (LS BMD), and forearm BMD (FA BMD). Exposure datasets were obtained from UK Biobank with total 377,234 subjects. Outcome datasets were obtained from GEFOS consortium with 53,236 subjects. Standard MR analysis and multivariable MR were conducted to assess the total and direct causal effect of PA on BMD.

RESULTS

For self-reported PA, inverse-normalized moderate-to-vigorous had a direct causal effect on FN BMD independently (β = - 1.116 (95% confidence interval, 95%CI: - 2.210, - 0.023), P = 0.045); vigorous PA showed a direct effect (β = 3.592 (95%CI: 0.310, 6.874), P = 0.032) on LS BMD independently. While overall acceleration average and fraction of accelerations both had a direct causal effect on FA BMD independently.

CONCLUSIONS

Habitual vigorous PA could increase LS BMD. Individuals with higher overall acceleration average would have a higher FA BMD.

Influence of Habitual Physical Behavior - Sleeping, Sedentarism, Physical Activity - On Bone Health in Community-Dwelling Older People

Sedentary behavior (SB) has emerged as an independent public-health risk and may contribute to the lower bone mineral density (BMD) in old (>60 years of age) than young adults. The purpose of this study was to quantify SB and habitual physical behavior (PB) in community-dwelling older adults and how this correlates with BMD. In 112 relatively healthy and independent-living individuals aged 72.5 ± 6.4 years, BMD, PB and SB were determined using dual energy X-ray absorptiometry and 7-day three-dimensional accelerometry, respectively. In men, only healthy and osteopenic BMDs were found, whereas in women, osteoporotic BMD classifications also occurred. Our sample spent ∼61%, 7%, 12% and 19% of daily waking hours in SB, standing, LIPA [light intensity physical activity (PA)] and MVPA (medium-to-vigorous intensity PA), respectively. In men, after accounting for covariates (BMI, total fat, android:gynoid ratio), sleeping (hours/day), number of breaks in SB, number of SB ≥ 5 min, number of PA bouts, total duration of PA bouts (min), mean PA bouts duration (min), LIPA (%PA bout time) and MVPA (%PA bout time) were all predictors of BMD. In women, after accounting for covariates (age, BMI, total fat, android:gynoid ratio), SB (hours/day), SB (% waking hours), LIPA (hours/day), LIPA (% waking hours), MVPA (% waking hours) and number of short SB (i.e., <5 min), total time spent in PA (min) significantly correlated with BMD. In conclusion, the PB predictors of bone health in older persons include: night time sleeping duration, number of short bouts of SB, number and duration of bouts of PA relative to total waking hours. While radar graphs of PB patterns for healthy, osteopenic, osteoporotic individuals highlighted significant differences in PB between them, they were not consistent with the expectations from the Mechanostat Theory: i.e., more loading leads to better bone. Rather, our results suggest that a balance of activities must be maintained across the PB spectrum, where certain PB parameters are especially impactful in each sex, supporting the recently coined multifactorial-based variations in the Mechanostat threshold.