Nocturnal sleep duration and bone mineral density: a cross-sectional study of the National Health and Nutrition Examination Survey (NHANES) 2007-2014

Association Between Sleep Duration and Low Bone Mineral Density and Osteoporosis: A Systematic Review and Meta-analysis

Short and long sleep duration have been linked with adverse health outcomes. However, it is unclear if sleep duration affects the risk of low bone mineral density (BMD) or osteoporosis. We systematically reviewed evidence examining the association between short and long sleep with BMD/osteoporosis. PUBMED, Embase, CENTRAL, Web of Science, and Scopus were examined for studies up to July 15, 2024. We pooled adjusted odds ratio (OR) for the association between sleep and osteoporosis and adjusted linear regression coefficients (β) for BMD. A separate analysis was conducted for males, females, postmenopausal females, and the elderly. 14 studies were included. Three were cohort, while the rest were cross-sectional. The definition of short and long sleep varied among studies. Meta-analysis showed that long (OR 1.19 95% CI 1.05, 1.35 I2 = 72%) but not short (OR 1.11 95% CI 0.95, 1.29 I2 = 80%) sleep duration was associated with osteoporosis. Similar results were obtained for females and postmenopausal females. In males, both short and long sleep was associated osteoporosis while no such association was noted in the elderly. Meta-analysis showed that short sleep did not have any significant association with BMD (β - 0.002 95% CI - 0.007, 0.004 I2 = 0), while long sleep duration was associated with a reduction in BMD (β - 0.017 95% CI - 0.031, - 0.004 I2 = 0). Separate analyses for males and females revealed non-significant results. Evidence from mostly cross-sectional data suggests that long sleep duration may be a related to BMD and osteoporosis. Short sleep was not found to be related to BMD and osteoporosis, except for males where a significant effect was noted. Given the low-quality evidence, results must be interpreted with caution.

The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up

The prevalence of osteoporosis has drastically increased recently. It is not only the most frequent but is also a major global public health problem due to its high morbidity. There are many risk factors associated with osteoporosis were identified. However, most studies have used the traditional multiple linear regression (MLR) to explore their relationships. Recently, machine learning (Mach-L) has become a new modality for data analysis because it enables machine to learn from past data or experiences without being explicitly programmed and could capture nonlinear relationships better. These methods have the potential to outperform conventional MLR in disease prediction. In the present study, we enrolled a Chinese post-menopause cohort followed up for 4 years. The difference of T-score (δ-T score) was the dependent variable. Information such as demographic, biochemistry and life styles were the independent variables. Our goals were: (1) Compare the prediction accuracy between Mach-L and traditional MLR for δ-T score. (2) Rank the importance of risk factors (independent variables) for prediction of δ T-score. Totally, there were 1698 postmenopausal women were enrolled from MJ Health Database. Four different Mach-L methods namely, Random forest (RF), eXtreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and stochastic gradient boosting (SGB), to construct predictive models for predicting δ-BMD after four years follow-up. The dataset was then randomly divided into an 80% training dataset for model building and a 20% testing dataset for model testing. A 10-fold cross-validation technique for hyperparameter tuning was used. The model with the lowest root mean square error for the validation dataset was viewed as the best model for each ML method. The averaged metrics of the RF, SGB, NB, and XGBoost models were used to compare the model performance of the benchmark MLR model that used the same training and testing dataset as the Mach-L methods. We defined that the priority demonstrated in each model ranked 1 as the most critical risk factor and 22 as the last selected risk factor. For Pearson correlation, age, education, BMI, HDL-C, and TSH were positively and plasma calcium level, and baseline T-score were negatively correlated with δ-T score. All four Mach-L methods yielded lower prediction errors than the MLR method and were all convincing Mach-L models. From our results, it could be noted that education level is the most important factor for δ-T Score, followed by DBP, smoking, SBP, UA, age, and LDL-C. All four Mach-L outperformed traditional MLR. By using Mach-L, the most important six risk factors were selected which are, from the most important to the least: DBP, SBP, UA, education level, TG and sleeping hour. δ T score was positively related to SBP, education level, UA and TG and negatively related to DBP and sleeping hour in postmenopausal Chinese women.

Life's Essential 8 and osteoporosis in adults aged 50 years or older: data from the National Health and Nutrition Examination Survey

In this cross-sectional study, we examined the association between Life's Essential 8 (LE8) and bone mineral density (BMD) as well as osteoporosis risk among adults aged 50 and over. The findings of this study revealed that higher LE8 scores were associated with higher BMD and reduced osteoporosis risk.

PURPOSE

The objective of the present study was to evaluate the association between Life's Essential 8 (LE8) and bone mineral density (BMD), as well as osteoporosis risk, in adults aged 50 years or over.

METHODS

This cross-sectional study recruited individuals who were 50 years old or older from the National Health and Nutrition Examination Survey. LE8 scores were evaluated and calculated according to the scoring algorithm based on the American Heart Association recommendations, which were further categorized into health behaviors (LE8-HB) and health factors (LE8-HF) scores. Furthermore, the present study utilized multivariate linear regression models to examine the correlations between BMD and LE8 scores. In addition, ordinal logistic regression models were employed to determine the associations between the risk of osteoporosis (normal BMD, osteopenia, and osteoporosis) and LE8 scores.

RESULTS

The final analysis included a total of 2910 participants, whose mean age was 64.49 ± 9.28 years. LE8 and LE8-HF scores exhibited a negative association with BMD and a positive association with osteoporosis risk in unadjusted models. Nevertheless, after adjustment for covariates, LE8 and LE8-HB scores exhibited a positive association with BMD and a negative association with osteoporosis risk, regardless of age, sex, or menopausal status.

CONCLUSIONS

Scoring systems based on multiple lifestyle and behavior factors, similar to LE8, have the potential to become a novel option and be used for osteoporosis risk assessment.

Sleep characteristics and parameters of bone turnover and strength in the adult population: results from the Study of Health in Pomerania-TREND

Poor sleep quality or sleep deprivation may be related to decreased bone mineral density. We aimed to assess whether associations of sleep characteristics and bone turnover or strength are present in adults from the general population and whether these are independent of common risk factors such as sex, age, and obesity. A total of 1037 participants from the Study of Health in Pomerania-TREND underwent laboratory-based polysomnography and quantitative ultrasound measurements at the heel. Of these participants, 804 completed standardised questionnaires to assess daytime sleepiness, insomnia, and sleep quality. Serum concentrations of two bone turnover markers, intact amino-terminal propeptide of type 1 procollagen (P1NP) and carboxy-terminal telopeptide of type 1 collagen (CTX) were measured. Cross-sectional associations of polysomnography variables (total sleep time, sleep efficiency, time spent wake after sleep onset, oxygen desaturation index, apnea-hypopnea index, and obstructive sleep apnea [OSA]), as well as sleep questionnaire scores with the bone turnover markers and the ultrasound-based stiffness index were assessed in linear regression models. In adjusted models, higher insomnia scores and lower sleep quality scores were related to a higher bone turnover in women but not in men. However, associations between polysomnography variables or questionnaire scores and the stiffness index were absent. Our study provides limited evidence for relationships between sleep characteristics and bone turnover and strength independent of common risk factors for OSA and osteoporosis. Nevertheless, women reporting poor sleep or insomnia in combination with risk factors for osteoporosis might benefit from an evaluation of bone health.

Serum Vitamin D3 Concentration, Sleep, and Cognitive Impairment among Older Adults in China

BACKGROUND

Cognitive decline in older adults has become one of the critical challenges to global health. This study aims to examine both cross-sectional and longitudinal associations of levels of serum 25-hydroxyvitamin D3 (25(OH)D3) (briefed as VD3) concentration and sleep quality/duration, especially their interactions, with risk of cognitive impairment among older adults in China.

METHODS

We utilized a special subsample of adults aged 65-105 years (individuals = 3412, observations = 4816) from eight provinces in China derived from the 2011/2012 and 2014 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive impairment was measured by the Mini-Mental State Examination scale. Sleep quality was classified as good versus fair/poor, and sleep duration was classified into short (<7 h), normal (≥7 but <9 h), and long (≥9 h). The VD3 concentration was divided into three levels: deficiency (VD3 < 25 nmol/L), insufficiency (25 nmol/L ≤ VD3 < 50 nmol/L), and sufficiency (VD3 ≥ 50 nmol/L). A wide set of covariates that include demographics, socioeconomic status, family support, health practice, and health conditions was adjusted for robust findings. Multilevel random intercept logit regression models were used to examine the cross-sectional associations between VD3, sleep, and cognitive impairment, whereas logit regression models were applied to investigate the longitudinal associations.

RESULTS

In the cross-sectional analyses, when all covariates were adjusted, VD3 sufficiency was significantly associated with a 33% lower risk of cognitive impairment compared with VD3 deficiency; good sleep quality was associated with 34% lower odds of cognitive impairment compared with fair/poor sleep quality; sleep hours were not associated with cognitive impairment, although a long sleep duration (≥9 h) was associated with 30% higher odds of being cognitively impaired when baseline health was not controlled. Interaction analyses reveal that VD3 sufficiency could help to additionally reduce the risk of cognitive impairment for good sleep quality and normal sleep hours. In the longitudinal analyses, the association of VD3 sufficiency remains significant, whereas sleep quality and sleep duration were not significant associates.

CONCLUSIONS

Good sleep quality, normal sleep hours, and VD3 sufficiency are positively associated with good cognitive function. VD3 sufficiency could enhance the associations between sleep and cognitive impairment.