A negative association between total cholesterol and bone mineral density in US adult women

Non-linear association of the platelet/high-density lipoprotein cholesterol ratio with bone mineral density a cross-sectional study

BACKGROUND

Numerous studies have demonstrated shared risk factors and pathophysiologic mechanisms between osteoporosis and cardiovascular disease. High-density lipoprotein cholesterol (HDL-C) and platelets have long been recognized as crucial factors for cardiovascular health. The platelet to HDL-C ratio (PHR) combines platelet count and high-density lipoprotein cholesterol (HDL-C) level, It is a novel biomarker for metabolic syndrome and cardiovascular disease. The platelet to HDL-C ratio (PHR) possibly reflects the balance between proinflammatory and anti-inflammatory states in the body. Therefore, we hypothesized that changes in PHR ratios may predict a predisposition to pro-inflammatory and increased bone resorption. However, the relationship between the platelet to HDL-C ratio (PHR) and bone mineral density (BMD) remains insufficiently understood. This study aimed to elucidate the relationship between the platelet to HDL-C ratio (PHR) index and bone mineral density (BMD).

METHODS

Data from the NHANES 2005-2018 were analyzed, excluding adults with missing key variables and specific conditions. Nonlinear relationships were explored by fitting smoothed curves and generalized additive models, with threshold effects employed to calculate inflection points. Additionally, subgroup analyses and interaction tests were conducted.

RESULTS

The study included 13,936 individuals with a mean age of 51.19 ± 16.65 years. Fitted smoothed curves and generalized additive models revealed a nonlinear, inverted U-shaped relationship between the two variables. Threshold effect analysis showed a significant negative association between PHR and total femur bone mineral density (BMD) beyond the inflection point of platelet to HDL-C ratio (PHR) 33.301. Subgroup analyses showed that a significant interaction between these two variables was observed only in the age and sex subgroups (P-interaction < 0.05).

CONCLUSIONS

Our study identified a complex, nonlinear, inverted U-shaped relationship between platelet to HDL-C ratio (PHR) and total femur bone mineral density (BMD). These findings underscore the importance of maintaining optimal PHR levels to support bone health, especially in high-risk populations.

Remnant cholesterol is associated with hip BMD and low bone mass in young and middle-aged men: a cross-sectional study

PURPOSE

Remnant cholesterol (RC) is a contributor to cardiovascular diseases, obesity, diabetes, and metabolic syndrome. However, the specific relationship between RC and bone metabolism remains unexplored. Therefore, we aimed to investigate the relationships of RC with hip bone mineral density (BMD) and the risk of low bone mass.

METHODS

Physical examination data was collected from men aged < 60 years as part of the Kailuan Study between 2014 and 2018. The characteristics of the participants were compared between RC quartile groups. A generalized linear regression model was used to evaluate the relationship between RC and hip BMD and a logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for low bone mass. Additional analyses were performed after stratification by body mass index (BMI) (≥ or < 24 kg/m2). Sensitivity analyses were performed by excluding individuals who were taking lipid-lowering therapy or had cancer, cardiovascular diseases, or diabetes.

RESULTS

Data from a total of 7,053 participants were included in the analysis. After adjustment for confounding factors, RC negatively correlated with hip BMD (β =  - 0.0079, 95% CI: - 0.0133, - 0.0025). The risk of low bone mass increased from the lowest to the highest RC quartile, with ORs of 1 (reference), 1.09 (95% CI: (0.82, 1.44), 1.35 (95%CI: 1.02, 1.77), and 1.43 (95% CI: 1.09, 1.89) for Q1, Q2, Q3, and Q4, respectively (P for trend = 0.004) in the fully adjusted model. Compared to RC < 0.80 mmol/l group, the risk of low bone mass increased 39% in RC ≥ 0.80 mmol/l group (P < 0.001). The correlation between RC and hip BMD was stronger in participants with BMI ≥ 24 kg/m2 group (β =  - 0.0159, 95% CI: - 0.0289, - 0.0029). The results of sensitivity analyses were consistent with the main results.

CONCLUSION

We have identified a negative correlation between serum RC and hip BMD, and a higher RC concentration was found to be associated with a greater risk of low bone mass in young and middle-aged men.

Application of machine learning algorithms to identify people with low bone density

Background

Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis.

Objective

This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data.

Methods

Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance.

Results

A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase.

Conclusion

The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

Development and Validation of a Nomogram Model for Individualizing the Risk of Osteopenia in Abdominal Obesity

OBJECTIVE

This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity.

METHODS

Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013-2014 and 2017-2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689-0.738) and 0.701 (95 % CI: 0.662-0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact.

CONCLUSION

Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.

TC and LDL-C are negatively correlated with bone mineral density in patients with osteoporosis

OBJECTIVE

To investigate the relationships of multiple lipid metabolism indicators and bone turnover markers (BTMs) with bone mineral density (BMD) and osteoporosis, in order to identify high-risk populations.

METHODS

A total of 380 patients were recruited and their general information was collected. Linear and logistic regression models were used to analyze the correlation of these indicators with BMD and osteoporosis.

RESULTS

Lipid metabolism indices and BTMs exhibited varying degrees of positive or negative correlation with BMD. Elevated levels of triglycerides (r = -0.204, P = 0.004), total cholesterol (TC) (r = -0.244, P < 0.001), low-density lipoprotein cholesterol (LDL-C) (r = -0.256, P < 0.001), apoprotein B (r = -0.292, P < 0.001) and lipoprotein-associated phospholipase A2 (Lp-PLA2) (r = -0.221, P = 0.002) in women were associated with a reduction in BMD. This relationship persisted even after adjusting for confounding factors and in the subgroup analysis of elderly women. In males, TC (r = 0.159, P = 0.033), LDL-C (r = 0.187, P = 0.012), apoprotein B (r = 0.157, P = 0.035), and Lp-PLA2 (r = 0.168, P = 0.024) exhibited a positive correlation with BMD, while free fatty acid (FFA) (r = -0.153, P = 0.041) was negatively correlated with BMD. However, after adjusting for confounding factors, only FFA remained negatively correlated with BMD, which was not observed in the age subgroup analysis. Furthermore, elevated levels of TC and LDL-C in elderly women were positively associated with the risk of osteoporosis or low bone mass.

CONCLUSION

Elevated levels of TC and LDL-C not only indicate a decrease in BMD in females but also positively correlate with the occurrence of osteoporosis and low bone mass in elderly females.

The association of lipid metabolism with bone metabolism and the role of human traits: a Mendelian randomization study

Background

The impact of lipid metabolism on bone metabolism remains controversial, and the extent to which human traits mediate the effects of lipid metabolism on bone metabolism remains unclear.

Objective

This study utilized mendelian randomization to investigate the effects of blood lipids on bone mineral density (BMD) at various skeletal sites and examined the mediating role of human traits in this process.

Methods

We leveraged genetic data from large-scale genome-wide association studies on blood lipids (n=1,320,016), forearm bone mineral density (FA-BMD) (n=10,805), lumbar spine bone mineral density (LS-BMD) (n=44,731), and femoral neck bone mineral density (FN-BMD) (n=49,988) to infer causal relationships between lipid and bone metabolism. The coefficient product method was employed to calculate the indirect effects of human traits and the proportion of mediating effects.

Results

The results showed that a 1 standard deviation(SD) increase in HDL-C, LDL-C and TC was associated with a decrease in LS-BMD of 0.039 g/cm2, 0.045 g/cm2 and 0.054 g/cm2, respectively. The proportion of mediating effects of systolic blood pressure (SBP) on HDL-C to LS-BMD was 3.17%, but suppression effects occurred in the causal relationship of LDL-C and TC to LS-BMD. Additionally, the proportion of mediating effects of hand grip strength (HGS) on the TC to LS-BMD pathway were 6.90% and 4.60% for the left and right hands, respectively.

Conclusion

In conclusion, a negative causal relationship was established between lipid metabolism and bone metabolism. Our results indicated that SBP and HGS served as mediators for the effects of lipid metabolism on bone metabolism.

Association between total cholesterol and lumbar bone density in Chinese: a study of physical examination data from 2018 to 2023

BACKGROUND

The impact of total cholesterol (TC) on lumbar bone mineral density (BMD) is a topic of interest. However, empirical evidence on this association from demographic surveys conducted in China is lacking. Therefore, this study aimed to examine the relationship between serum TC and lumbar BMD in a sample of 20,544 Chinese adults between the ages of 20 and 80 years over a period of 5 years, from February 2018 to February 2023. Thus, we investigated the effect of serum TC level on lumbar BMD and its relationship with bone reduction in a Chinese adult population.

METHODS

This cross-sectional study used data obtained from the Department of Health Management at Henan Provincial People's Hospital between February 2018 and February 2023. The aim of this study was to examine the correlation between serum TC and lumbar BMD in individuals of different sexes. The research methodology encompassed population description, analysis of stratification, single-factor and multiple-equation regression analyses, smooth curve fitting, and analysis of threshold and saturation effects. The R and EmpowerStats software packages were used for statistical analysis.

RESULTS

After adjusting for confounding variables, a multiple linear regression model revealed a significant correlation between TC and lumbar BMD in men. In subgroup analysis, serum TC was found to have a positive association with lumbar BMD in men, specifically those aged 45 years or older, with a body mass index (BMI) ranging from 24 to 28 kg/m2. A U-shaped correlation arose between serum TC and lumbar BMD was detected in women of different ages and BMI, the inflection point was 4.27 mmol/L for women aged ≥ 45 years and 4.35 mmol/L for women with a BMI of ≥ 28 kg/m2.

CONCLUSION

In this study, Chinese adults aged 20-80 years displayed different effects of serum TC on lumbar BMD in sex-specific populations. Therefore, monitoring BMI and serum TC levels in women of different ages could prevent osteoporosis and osteopenia.

TRIAL REGISTRATION

The research protocol was approved by the Ethics Committee of Beijing Jishuitan Hospital, in accordance with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are part of the China Health Quantitative CT Big Data Research team, which has been registered at clinicaltrials.gov (code: NCT03699228).

The Effects of Claw Health and Bone Mineral Density on Lameness in Duroc Boars

To investigate the effects of claw lesion types and bone mineral density on lameness in boars, the data of claw lesion score, gait score, and bone mineral density, measured by a Miniomin ultrasound bone densitometer, were collected from a total of 739 Duroc boars. Firstly, we discovered that the prevalence of claw lesions was as high as 95.26% in boars. The percentage of lameness of boars with SWE was higher than those with other claw lesions. Meanwhile, the results showed that the probability of lameness was higher in boars with lower bone mineral density (p < 0.05). Logistic regression models, including variables of boar age, body weight, serum mineral level, and housing type, were used to identify the influencing factors of bone mineral density in this study. The results found that bone mineral density increases with age before reaching a maximum value at 43 months of age, and begins to decrease after 43 months of age. Elevated serum Ca levels were significantly associated with an increase in bone mineral density (p < 0.05). Aside from the above findings, we also made an interesting discovery that boars in the individual pen model significantly increased bone mineral density compared to those in the individual stall model. In conclusion, claw lesions and bone mineral density were significantly associated with lameness. Age, serum Ca, and housing type are the potential influencing factors for bone mineral density in boars.