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

Sun, Yongbing; Qi, Xin; Lin, Xinbei; Zhou, Yang; Lv, Xue; Zhou, Jing; Li, Zhonglin; Wu, Xiaoling; Zou, Zhi; Li, Yongli; Li, Hao

doi:10.1186/s12944-023-01946-5

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Previous studies have shown that higher ALP levels are positively associated with low BMD or osteoporosis, which is consistent with the conclusions we have drawn, probably because alkaline phosphatase activity is increased when skeletal disease is present to meet the demands of bone growth and reconstruction ( 40 , 41 ). There is no clear consensus on the relationship between total cholesterol and BMD, and most studies agree with us ( 42 – 44 ) that there is a negative correlation between the two, however, there are also studies that take the opposite view ( 40 ), and a cross-sectional study from China found that the associations were very different in men and women, with TC positively correlated with BMD in men and In women, the association was U-shaped, with curve inflection points varying by age and BMI ( 45 ). Therefore, the association and mechanisms between TC and BMD need to be explored in further studies.…”

Section: Discussionmentioning

confidence: 54%

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

Xu,

Chen,

Yao

et al. 2024

Front. Public Health

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BackgroundOsteoporosis 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.ObjectiveThis study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data.MethodsUsing 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.ResultsA 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.ConclusionThe machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

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Section: Discussionmentioning

confidence: 54%

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

Xu,

Chen,

Yao

et al. 2024

Front. Public Health

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“…In subgroup analyses, we found that the inverse association between TC levels and lumbar BMD was more pronounced in women and Mexican Americans, underscoring the importance of tailored interventions. However, a study conducted in China found that serum TC was positively correlated with lumbar BMD in men, while a U-shaped correlation was observed between serum TC and lumbar BMD in women [31]. The differences in study results may be related to the different study populations and covariates used.…”

Section: Discussionmentioning

confidence: 91%

Association between lipid biomarkers and bone mineral density: a cross-sectional study from NHANES 2007–2010

Liu,

Zhong,

Peng

et al. 2024

Preprint

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Background: Bone mineral density (BMD) is an established indicator of bone health, with lower BMD associated with increased risk of osteoporosis and fractures. Triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) are acknowledged as novel lipid biomarkers. However, the relationship between lipid biomarkers and BMD remains uncertain. This study examines the association between lipid biomarkers and BMD in a representative US population. Methods: Utilizing cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) 2007–2010, we analyzed BMD measurements at the total femur, femoral neck, and lumbar spine. Serum lipid levels, including TG, TC, LDL and HDL, were assessed. Multivariable regression models adjusted for confounders such as age, gender, race, BMI, laboratory indices, and lifestyle factors were used to explore the association between lipid biomarkers and BMD. In addition, several subgroup analyses were conducted to assess the robustness of the outcomes. Results: Our cross-sectional analysis included a total of 3213 participants from the NHANES 2007–2010 dataset, representing a diverse adult population, identified significant associations between lipid biomarkers and bone mineral density (BMD) at key anatomical sites. This study found that TC and HDL were negatively correlated with femoral BMD, and TG was positively correlated with femoral BMD. After fully adjusting for covariates, the negative association between TC and lumbar BMD remained significant (β= -0.0002,95%CI: -0.0004 to -0.0001, p<0.001). After transforming TC into categorical variables (quartiles), participants in the highest quartile of TC showed significantly lower BMD measurements compared with those in the lowest quartile (β= -0.019,95%CI: -0.033 to -0.005, p=0.007). Subgroup analysis showed that the association between TC and lumbar BMD was more pronounced in women (β= -0.042,95%CI: -0.053 to -0.030, p<0.001) and Mexican Americans (β= -0.093,95%CI: -0.109 to -0.076, p<0.001). Gender and ethnic differences were prominent in the lipid-BMD association, highlighting the importance of considering demographic factors when assessing the risk of osteoporosis. Conclusions: HDL and TC were negatively correlated with total femoral BMD, and TG was positively correlated with femoral BMD. And TC might serve as a potential lipid biomarkers of osteoporosis outperforming HDL, TG and LDL. The complex correlation between the lipid biomarkers and bone metabolism requires further evaluation in large prospective studies. The observed gender and racial differences in these associations underscore the need for personalized approaches in osteoporosis risk assessment and management.

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Association between arteriosclerosis index and lumbar bone mineral density in U.S adults: a cross-sectional study from the NHANES 2011–2018

Xie,

Ren,

et al. 2024

Front. Cardiovasc. Med.

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BackgroundThe arteriosclerosis index, defined as the ratio of non-high density lipoprotein cholesterol to high density lipoprotein cholesterol (NHHR), has emerged as a novel biomarker for various diseases. The relationship between NHHR and lumbar bone mineral density (BMD) has not been previously examined.MethodsThis cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018. NHHR was calculated as (total cholesterol—high-density lipoprotein cholesterol)/high-density lipoprotein cholesterol. Lumbar BMD was calculated to Z scores. Weighted multivariate linear regression, subgroup analysis, interaction analysis, generalized additive model, and two-piecewise linear regression were used.ResultsA total of 8,602 participants were included. The negative association between NHHR and lumbar BMD was consistent and significant (Model 1: β = −0.039, 95% CI: −0.055, −0.023, p < 0.001; Model 2: β = −0.045, 95% CI: −0.062, −0.027, p < 0.001; Model 3: β = −0.042, 95% CI: −0.061, −0.023, p < 0.001). The linear relationship between NHHR and lumbar BMD was significantly influenced by body mass index (p for interaction = 0.012) and hypertension (p for interaction = 0.047). Non-linear associations between NHHR and lumbar BMD Z scores were observed in specific populations, including U-shaped, reverse U-shaped, L-shaped, reverse L-shaped, and U-shaped relationships among menopausal females, underweight participants, those with impaired glucose tolerance, those with diabetes mellitus and those taking anti-hyperlipidemic drugs, respectively.ConclusionsNHHR exhibited a negative association with lumbar BMD, but varying across specific populations. These findings suggest that NHHR should be tailored to individual levels to mitigate bone loss through a personalized approach. Individuals at heightened risk of cardiovascular disease should focus on their bone health.

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

Cited by 3 publications

References 27 publications

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

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

Association between lipid biomarkers and bone mineral density: a cross-sectional study from NHANES 2007–2010

Association between arteriosclerosis index and lumbar bone mineral density in U.S adults: a cross-sectional study from the NHANES 2011–2018

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