Skip to main content
Download PDF
Download ePub

Association between high-density lipoprotein cholesterol and lumbar bone mineral density in Chinese: a large cross-sectional study

Lipids in Health and Disease volume 23, Article number: 27 (2024) Cite this article

Abstract

Background

The association between lipid and bone metabolism, particularly the role of high-density lipoprotein cholesterol (HDL-C) in regulating bone mineral density (BMD), is of significant interest. Despite numerous studies, findings on this relationship remain inconclusive, especially since evidence from large, sexually diverse Chinese populations is sparse. This study, therefore, investigates the correlation between HDL-C and lumbar BMD in people of different genders using extensive population-based data from physical examinations conducted in China.

Methods

Data from a cross-sectional survey involving 20,351 individuals aged > = 20 years drawn from medical records of health check-ups at the Health Management Centre of the Henan Provincial People’s Hospital formed the basis of this study. The primary objective was to determine the correlation between HDL-C levels and lumbar BMD across genders. The analysis methodology included demographic data analysis, one-way ANOVA, subgroup analyses, multifactorial regression equations, smoothed curve fitting, and threshold and saturation effect analyses.

Results

Multifactorial regression analysis revealed a significant inverse relationship between HDL-C levels and lumbar BMD in both sexes, controlling for potential confounders (Male: β = -8.77, 95% CI -11.65 to -5.88, P < 0.001; Female: β = -4.77, 95% CI -8.63 to -0.90, P = 0.015). Subgroup and threshold saturation effect analyses indicated a stronger association in males, showing that increased HDL-C correlates with reduced lumbar BMD irrespective of age and body mass index (BMI). The most significant effect was observed in males with BMI > 28 kg/m2 and HDL-C > 1.45 mmol/L and in females with a BMI between 24 and 28 kg/m2.

Conclusion

Elevated HDL-C is associated with decreased bone mass, particularly in obese males. These findings indicate that individuals with high HDL-C levels should receive careful clinical monitoring to mitigate osteoporosis risk.

Trial registration

The research protocol received ethics approval from the Ethics Committee at Beijing Jishuitan Hospital, in conformity with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are a contribution of the China Health Quantitative CT Big Data Research team, registered at clinicaltrials.gov (code: NCT03699228).

Introduction

Osteoporosis (OP) is a systemic skeletal condition characterized by reduced bone density and deterioration of bone tissue microarchitecture, resulting in increased bone fragility and a higher fracture risk [1]. It affects approximately 200 million people worldwide [2]. According to 2019 survey data, OP exhibits a high prevalence in China, affecting 6.46% of males and 29.13% of females over the age of 50 [3]. With the increasing aging population, the societal significance of OP can no longer be overlooked, making it a global public health concern. Reduced bone density serves as a diagnostic indicator for OP, with lumbar bone mineral density (BMD) being one of its commonly utilized measures [4].

In recent years, the interplay between lipid metabolism and bone metabolism has attracted significant attention. Studies have indicated that individuals with OP and decreased bone mass often exhibit abnormalities in lipid metabolism, particularly in serum high-density lipoprotein cholesterol metabolism [5], with a specific focus on serum HDL-C abnormalities [6]. Nevertheless, prior investigations have highlighted the influence of numerous confounding factors on the relationship between HDL-C and BMD [7, 8]. The variation in findings can be attributed to small sample sizes, regional disparities, and differences in control for confounding factors. Consequently, it is crucial to examine the correlation between HDL-C and lumbar BMD using a large, homogeneous dataset.

Lipids play a pivotal role in various metabolic disorders [9]. HDL-C, which is responsible for transporting excess cholesterol from peripheral sites to the liver, contributes to reducing the risk of cardiovascular diseases [10]. However, recent research has indicated that elevated HDL-C levels in adults ( > = 135 mg/dl in females and > = 116 mg/dl in males) may lead to increased all-cause mortality [11]. Another extensive study demonstrated a positive association between HDL-C levels and high mortality rates [12]. These findings have prompted a reevaluation of HDL-C’s role in metabolic processes. The relationship between HDL-C and bone metabolism has long been a subject of intrigue. Jiang et al. identified elevated HDL-C levels as an independent risk factor for bone loss in both men and women based on comprehensive multicenter data collected in China [13]. However, some studies have reported positive correlations [14], while others have found no association [15] between HDL-C and BMD. Consequently, the link between HDL-C and BMD remains a subject of significant controversy. Investigating the effect of HDL-C on BMD could establish a new theoretical basis for preventing and managing bone loss, as well as addressing the emergence of OP, which holds substantial clinical relevance.

Therefore, the present study acquired data from five consecutive years of physical examinations at the Health Screening Center of Henan Provincial People’s Hospital. The primary objective was to investigate the relationship between HDL-C and lumbar BMD in Chinese populations of different sexes. Multiple analytical methods were employed, and various factors potentially influencing lipid and bone metabolism were included as covariates to enhance the study’s robustness.

Materials and methods

Participants and inclusion criterion

The data for this study were sourced from the medical records of individuals undergoing medical examinations at the Health Management Center of Henan Provincial People’s Hospital, spanning from February 2018 to February 2023. This dataset is an integral component of the China Health Quantitative CT Big Data Research Project Group and has been duly registered with clinicaltrials.gov (code: NCT03699228).

The inclusion criteria for participants included the following: (1) individuals aged between 20 and 80 years, (2) those possessing comprehensive demographic information, and (3) individuals who underwent routine low-dose chest CT scans and lipid assessments. Exclusion criteria were as follows: (1) a history of any form of cancer, (2) prior or current endocrine-related ailments, (3) previous or existing metabolic conditions associated with the liver or kidneys, and (4) previous or ongoing usage of medications designed for OP prevention and blood lipid regulation. Thorough data collection, including age, gender, ethnicity, medical history, and medication history, was carried out on all participants by trained research personnel through on-site surveys.

Initially, a total of 23,653 participants were enrolled in this research initiative. However, 123 subjects were deemed ineligible due to age constraints, while 2,762 participants were excluded due to the absence of lumbar BMD, HDL-C, or body mass index (BMI) data. Additionally, 417 participants were disqualified owing to disparate medical histories that failed to meet the stipulated inclusion criteria. Consequently, the final cohort for this study comprised 20,351 participants, including 13,235 males and 7,116 females. The process of subject screening is visually depicted in Fig. 1.

Fig. 1
figure 1

Flowchart of participants selection

Full size image

Methods of research

To ensure data accuracy and impartiality, all researchers underwent standardized training before conducting the surveys. A uniform questionnaire was employed to collect essential information from the participants. This questionnaire included details of the participants’ medical history, including any prior or current occurrences of various cancers, liver and kidney ailments, thyroid and endocrine disorders, and the utilization of anti-osteoporotic medications and lipid metabolism modifiers. The completion of the questionnaire was followed by data compilation, summary, and verification.

Subsequently, following a 12 h fasting period, the participants’ height, weight, and blood pressure were measured in the morning by the research personnel. Each participant underwent two measurements, and the average of these two measurements was calculated to minimize potential errors. BMI was defined as the quotient of weight divided by height squared (kg/m2).

Measurements in the laboratory

Fasting blood samples were systematically collected at 8 am to measure HDL-C and various other laboratory parameters, which included total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total protein (TP), albumin (Alb), hemoglobin (Hb), globulin, total bilirubin (TB), direct bilirubin, indirect bilirubin, alanine aminotransferase (ALT), aspartate transaminase (AST), glutamine transaminase, alkaline phosphatase (ALP), fasting blood glucose (FBG), blood potassium, blood chloride, blood calcium, blood creatinine, uric acid (UA), blood phosphorus, and glycosylated hemoglobin (GH). Blood glucose and lipid levels were assessed using an Olympus® AU 5400 automated biochemistry analyzer (Olympus Corporation, Japan, Shizuoka Prefecture). The remaining indicators were evaluated using standard laboratory techniques.

BMD measurement

Low-dose chest CT scans constituted a component of the participants’ routine health assessments, and measurements of BMD were derived from standard examination images, thus obviating any additional radiation exposure for the participants. The CT scans for each participant were executed using uniform parameters.

Volumetric BMD (vBMD) measurements were conducted employing Mindways quantitative computed tomography (QCT) Pro (Mindways Software, Inc., Austin, TX, USA). Specifically, trabecular vBMD (mg/cm3) of the lumbar vertebrae (L1-L2) was assessed utilizing asynchronous BMD calibration and QCT Pro analysis (Mindways Software Inc., Austin, United States). Each vertebra underwent three measurements, and the resultant average of these three measurements was deemed the BMD for each individual vertebra. The final BMD value was computed as the average of the measurements for both vertebrae. Radiologists who had received specialized training conducted all analyses using QCT software. It is worth noting that a previously published study validated the suitability of this approach for Chinese individuals [16].

To ensure consistent quality control throughout the research, routine calibration and cross-comparisons among systems were upheld, employing the European Spinal Prosthesis-145 (ESP-145) as a benchmark.

Variables

In this study, BMD was employed as the dependent variable, with HDL-C serving as the independent variable. The covariates taken into consideration included ethnicity, marital status, age, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), TC, LDL-C, TG, TP, Alb, Hb, globulin, TB, direct bilirubin, indirect bilirubin, ALT, AST, glutamine aminotransferase, ALP, FBG, potassium, chloride, calcium, creatinine, UA, phosphorus, and GH.

Statistical analysis

The statistical analysis was carried out using two software packages, namely, R and Empower. All data were tested for normality, with means ± standard deviations indicating normally distributed continuous variables and medians indicating non-normally distributed continuous variables. In this assessment, we initially characterized the participants’ traits by presenting means or medians with quartiles for continuous variables and proportions for categorical variables. To address significant variations within the dataset, both chi-square tests and variance estimation were employed. The investigation of the correlation between HDL-C levels and lumbar BMD was conducted using a multiple linear regression model. Independent variables were scrutinized through a stepwise regression method, with the exclusion of those exhibiting severe square collinearity. Furthermore, a multivariate linear regression model was applied to perform subgroup analyses of the linear association between HDL-C and lumbar BMD across different sex groups, categorized by age and BMI. To discern potential nonlinear relationships between HDL-C levels and lumbar BMD, a smooth curve fitting technique and a generalized additive model were utilized. Objective calculations were employed to identify inflection points in the correlation between HDL-C and lumbar BMD, and a two-stage linear regression model was established on either side of these inflection points. Statistical significance was established at a two-tailed P < 0.05. The graphic representation depicting the distribution of serum HDL-C levels was generated using Origin software (Origin Lab, USA, version 2021b).

Result

Baseline characteristics of participants

A total of 20,351 individuals participated in this study, comprising 13,235 men and 7,116 women. The HDL-C levels were categorized into quartiles based on sex, with the following ranges: (male: Q1: 0.45–1.07; Q2: 1.08–1.22; Q3: 1.23–1.40; Q4: 1.41–3.42. Female: Q1: 0.44–1.25; Q2: 1.26–1.45; Q3: 1.46–1.66; Q4: 1.67–3.01). The distribution of BMD across HDL-C quartiles is depicted in Fig. 2.

Fig. 2
figure 2

The distribution of HDL-C in different gender groups was quartered. (A) males; (B) females; HDL-C, high-density lipoprotein cholesterol, BMD, bone mineral density

Full size image

Tables 1 and 2 illustrate substantial variations in baseline characteristics among HDL-C quartiles in both men and women. Men in the Q4 HDL-C group tended to be older, married, and exhibited lower height, weight, BMI, DBP, TG, Hb, ALT, ALP, FBG, UA, GH, and BMD and higher TC, LDL-C, Alb, TB, direct bilirubin, and indirect bilirubin than those in the other subgroups.

Table 1 Characteristics of the male study population
Full size table
Table 2 Characteristics of the female study population
Full size table

In contrast, women in the Q4 HDL-C group tended to be older, married, taller, and had higher TC, TP, Alb, TB, direct bilirubin, indirect bilirubin, serum creatinine and lumbar BMD, whereas body weight, BMI, DBP, SBP, LDL-C, TG, Hb, globulin, ALT, AST, transglutaminase, ALP, FBG and GH. The distribution of HDL-C among the participants is visualized in Fig. 3.

Fig. 3
figure 3

Distribution histogram of HDL-C. (A) all participants; (B) all males; (C) all females; HDL-C, high-density lipoprotein cholesterol

Full size image

Univariate analysis

The results of the univariate analysis demonstrated that in the male cohort, lumbar BMD exhibited a negative correlation with age > 45, marital status, SBP, globulin, direct bilirubin, ALP, FBG, and GH. Conversely, most of the other factors demonstrated a positive correlation (Table 3). Conversely, in the female cohort, lumbar BMD displayed a positive correlation with height and Alb. In contrast, the remaining significant factors exhibited a negative correlation with lumbar BMD (Table 4).

Table 3 Results of univariate analysis of male
Full size table
Table 4 Results of univariate analysis of female
Full size table

Relationship between HDL-C and lumbar BMD

The results of the multiple regression analysis, as presented in Table 5, indicated that in the male cohort, there was a negative correlation with lumbar BMD in the unadjusted model (β = -7.03, 95% CI -9.18 to -4.84, P < 0.001). After adjusting for covariates, this negative correlation remained highly significant, with significant associations observed in both Model I (β = -7.33, 95% CI -9.46 to -5.19, P < 0.001) and Model II (β = -8.77, 95% CI -11.65 to -5.88, P < 0.001). The negative correlation persisted even after the conversion of HDL-C from a continuous variable to quartiles in all models, except for participants in Q4 in Model II.

Table 5 Relationship between HDL-C and lumbar BMD
Full size table

In contrast, the female cohort exhibited a positive association between HDL-C and lumbar BMD in the unadjusted model (β = 4.19, 95% CI 1.01 to 7.36, P = 0.009). However, after adjusting for all covariates in Model II, this association became negative (β = -4.77, 95% CI -8.63 to -0.90, P = 0.015). Following the conversion of HDL-C into quartiles, a significant positive correlation was observed only for participants in Q4 in the unadjusted model.

Subgroup analysis

Table 6 presents the results of the subgroup analyses. Stratified analyses based on age revealed that the relationship between HDL-C and lumbar BMD was influenced by age exclusively in the male cohort. HDL-C exhibited a negative correlation with lumbar BMD in men aged > 45 (β = -7.47, 95% CI -13.22 to -1.72, P = 0.010) and < = 45 (β = -7.45, 95% CI -11.13 to -3.77, P < 0.001). When stratifying by BMI, with cut off values of 24 and 28 kg/m2, it was evident that male HDL-C displayed a negative association with lumbar BMD across all BMI strata. Notably, this effect was most pronounced among individuals with a BMI greater than 28 kg/m2 (β = -12.14, 95% CI -19.18 to -5.10, P < 0.001). Conversely, in the female cohort, HDL-C exhibited a significantly negative association with lumbar BMD only within the BMI range of 24–28 kg/m2 (β = -8.47, 95% CI -15.29 to -1.64, P = 0.015).

Table 6 HDL-C and lumbar BMD were subgroup analyzed and stratified by age and BMI
Full size table

Nonrectilinear relationship analysis

In this study, the results of age- and BMI-stratified analyses were subjected to segmented linear regression and smoothed curve fitting (Table 7; Fig. 4). In the male cohort, a negative effect of HDL-C on lumbar BMD was observed, both in individuals under 45 years of age (HDL-C inflection point: 0.86 mmol/L) and those aged 45 or older (HDL-C inflection point: 1.12 mmol/L). Notably, the most pronounced negative effect occurred to the left of the inflection point (Fig. 5A). The greatest negative effect was observed when BMI was within the range of 24–28 kg/m2, and HDL-C was to the left of the inflection point (1.09 mmol/L) (β = -24.60, 95% CI -35.10 to -14.11) (Fig. 5B). Conversely, in women aged over 45, a segmented U-shaped relationship was identified between HDL-C and lumbar BMD, with an inflection point at 1.35 mmol/L for HDL-C. To the left of the inflection point, a negative correlation was observed, while to the right of the inflection point, a positive correlation was noted (Fig. 5C). Among women, the most significant negative effect of HDL-C on lumbar BMD was observed within the BMI range of 24–28 kg/m2 (β = -8.47, 95% CI -15.29 to -1.64) (Fig. 5D).

Table 7 Multivariate regression analysis of the effect of HDL-C on BMD in different gender populations
Full size table
Fig. 4
figure 4

Relationship between HDL-C and lumbar BMD. A-C for male, D-F for female. A and D: Each black hollow point exhibits one participant. B, C, E, and F: Solid red line illustrates the fitted smooth curve among variables. Age, nationality, marital status, high, weight, BMI, DBP, SBP, TC, LDL-C, TG, TP, Alb, Hb, globulin, TB, direct bilirubin, ALT, AST, transglutaminase, ALP, FBG, serum calcium, serum creatinine, UA, serum phosphorus, and GH were adjusted

Full size image
Fig. 5
figure 5

Association between HDL-C and lumbar BMD stratified by tertiles of age and BMI. Nationality, marital status, high, weight, DBP, SBP, TC, LDL-C, TG, TP, Alb, Hb, globulin, TB, direct bilirubin, ALT, AST, transglutaminase, ALP, FBG, serum calcium, serum creatinine, UA, serum phosphorus, and GH were adjusted. A-B for male, C-D for female. A and C: The relationship between HDL-C and lumbar BMD stratified by age. B and D: The relationship between HDL-C and lumbar BMD stratified by BMI

Full size image

Discussion

This study investigated the correlation between HDL-C and lumbar BMD within the Chinese population utilizing a substantial sample of consecutive 5-year data from the same health management center across genders. Upon adjusting for confounding variables, the research findings reveal that higher levels of HDL-C are associated with reduced lumbar BMD in men, regardless of age and BMI. Notably, the detrimental effect of HDL-C on lumbar BMD is most pronounced when BMI exceeds 28 kg/m2 and HDL-C levels are above 1.45 mmol/L. Conversely, when BMI falls within the range of 24–28 kg/m2, HDL-C exhibits a negative association with lumbar BMD in the female cohort. This study marks the first to demonstrate the gender-specific association between HDL-C and lumbar BMD in a Chinese population without overt health issues.

The interplay between lipid metabolism and bone metabolism has attracted substantial interest. A growing body of biological and epidemiological evidence has underscored the connection between cardiovascular disease and OP, with lipid metabolism serving as an intermediary factor between these two conditions [17]. It is noteworthy that osteoblasts, chondrocytes, and adipocytes all originate from a common precursor cell, mesenchymal stem cells. The direction of differentiation is governed by the Wnt/β-catenin signaling pathway, and when this pathway is obstructed, bone marrow stromal cells tend to differentiate into adipocytes. This mechanism forms the physiological basis for the interdependence of lipid metabolism and bone metabolism [18].

Cholesterol and its metabolites play a pivotal role in regulating osteoblast and osteoclast differentiation and activation, thereby influencing bone homeostasis [19]. HDL-C, as one of the body’s key lipids, is implicated in various metabolic diseases [20] and is known for its role in reducing the incidence of cardiovascular disease through reverse cholesterol transport [21]. Previous studies have suggested a potential link between HDL-C and OP [8]. For instance, Kha et al. [22] reported that certain oxysterols can stimulate osteogenic differentiation of mesenchymal stem cells, and high levels of HDL-C may remove oxysterols from peripheral tissues, thereby negatively affecting osteogenic differentiation. In addition, some of the apolipoproteins contained in HDL-C influence both adipose tissue and bone. An increase in bone marrow adipocytes also deteriorates the osteogenic function of bone, thus affecting the process of osteosynthesis [23]. However, the relationship between HDL-C and BMD remains unclear due to the heterogeneity of studies, stemming from differing study designs, sampling methods, controlled confounding variables, and ethnic distributions that have yet to be harmonized. In this context, investigating the effect of HDL-C on BMD serves as a crucial step toward providing a fresh foundation for OP prevention and treatment, as well as offering guidance to individuals with high HDL-C levels to mitigate OP risk.

After adjusting for covariates, this investigation revealed a correlation between elevated HDL-C levels and reduced lumbar BMD within the male cohort, as observed in the multiple linear regression model. This negative correlation was most pronounced when BMI > 28 kg/m2 and HDL-C > 1.45 mmol/L. Previous research has established BMI as a critical intermediary variable when examining the connection between lipids and BMD [24]. Obesity, typically assessed by BMI, frequently intersects with lipid metabolism, resulting in decreased HDL-C levels [25]. Moreover, there exists a positive correlation between BMI and BMD [26]. Consequently, elevated HDL-C levels in obese males frequently lead to diminished BMD. Cui et al. observed a higher incidence of OP in individuals with a BMI < 18.5 kg/m2 than in those with a BMI ≥ 18.5 kg/m2, irrespective of sex [27]. Alternatively, the inverse relationship between BMD and HDL-C concentration could be attributed to reduced oxysterol availability at heightened HDL-C levels, which could impede the osteogenic differentiation process of mesenchymal stem cells [22]. A study conducted by Yang et al., involving 1,040 Chinese men, unveiled a negative correlation between total lumbar BMD and HDL-C levels [28]. These findings align with the conclusions drawn by Kuipers et al. [29] and Choi et al. [30]. Furthermore, an analysis including men aged 20 to 59 based on the 2011–2018 National Health and Nutrition Examination Survey (NHANES) reported a U-shaped relationship between HDL-C and lumbar BMD in males [8]. Another noteworthy result of this investigation is the persistence of the inverse association between HDL-C and BMD in age-stratified analyses, with 45 years as the pivotal point. This suggests that age exerts minimal influence on BMD alterations in males. This suggests that estrogen may mediate the relationship between HDL-C and lumbar BMD, and estrogen plays an important role in the regulation of lipid metabolism and bone resorption. Importantly, estrogen fluctuations are less pronounced in males throughout their lifespan [31]. Hence, the influence of age on BMD is deemed insignificant.

The most intriguing findings within the female cohort of this study indicate that, after adjusting for confounding factors, multiple linear regression models demonstrate an adverse correlation between elevated HDL-C levels and reduced lumbar BMD levels. Stratified analyses unveiled a significant negative correlation between HDL-C and lumbar BMD within the BMI range of 24–28 kg/m2. Nevertheless, when considering the stratified variable of probable age at menopause (45 years) in the analysis, no discernible correlation emerged between HDL-C and lumbar BMD. Zhang et al. conducted a cross-sectional study examining the association between lipid profiles and BMD in approximately 1,116 Chinese women aged approximately 30 years. Their findings revealed a curvilinear relationship between HDL-C and lumbar BMD, characterized by a negative correlation on the left side of the inflection point, which was identified at 2.37 mmol/L [32]. A substantial dataset study from China identified higher levels of HDL-C as an independent risk factor for OP; however, it noted that increasing age exerts a more substantial adverse effect on BMD in women [13]. Adami et al. investigated a cohort of 481 women, aged 68–75 years, recruited from the community, and observed a negative association between their lumbar BMD and HDL-C levels [33]. Conversely, data based on 958 postmenopausal Korean women exhibited no significant association between serum lipid profiles, including HDL-C, and BMD [34]. Similarly, another study involving 170 women aged 50–70 years in Iran also reported no significant correlation between HDL-C and BMD [35]. While prior research has indicated that the use of hormone replacement therapy in postmenopausal women is linked to elevated HDL-C and BMD levels [36], the present study’s data did not provide support for this association, possibly due to the hereditary characteristics shared by HDL-C and BMD [37]. In contrast, Xie’s study reported a positive association between HDL-C and lumbar BMD in American women aged 20 to 59 years [8].

Divergent conclusions regarding the association between HDL-C and lumbar BMD in women may be attributed to variations in researchers’ choice of study variables. Additionally, interpreting this relationship in the female population is challenging due to their unique physiological attributes. Notably, the advantage of a larger sample size in the current study lends greater credibility to the evidence regarding the association between HDL-C and lumbar BMD. However, the causal relationship between HDL-C and lumbar BMD cannot be known in this study, which is also the direction of our future research.

Study strengths and limitations

In contrast to preceding investigations, this study elected to include individuals categorized as apparently healthy, rendering the findings notably applicable to the broader population. Additionally, the study’s substantial sample size facilitated subgroup analyses based on age and BMI within diverse gender cohorts. While the current study diligently considered and effectively adjusted for confounding factors, certain limitations persist: First, the inherent constraints of cross-sectional studies preclude the establishment of a causal relationship between HDL-C and lumbar BMD in this investigation. Second, the unavailability of endocrine indicators, including sex hormones, for most participants can be attributed to variations in the participants’ health screening programs. Consequently, this facet of the results could not be integrated into the covariate analysis. Last, it is worth noting that the study’s reliance on a sample derived solely from a single health management center resulted in a high degree of homogeneity within the sample. However, this homogeneity diminishes the generalizability of the study’s conclusions. Therefore, it is advisable to corroborate the findings of this study through comparative analyses conducted in a multicenter health management context.

Conclusion

Our study has identified that elevated levels of HDL-C can influence bone loss in a Chinese population aged 20 to 80 years. This decrease in BMD was particularly prominent among obese men. Furthermore, women with a BMI ranging from 24 to 28 kg/m2 also exhibited a significant reduction in BMD. Consequently, individuals with elevated HDL-C levels should undergo regular clinical monitoring to reduce the risk of OP.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Zhou J, Ye Z, Wei P, Yi F, Ouyang M, et al. Effect of basal metabolic rate on osteoporosis: a mendelian randomization study. Front Public Health. 2023;11:1096519.

    Article  PubMed  PubMed Central  Google Scholar 

  2. de Sire A, de Sire R, Curci C, Castiglione F, Wahli W. Role of Dietary Supplements and Probiotics in Modulating Microbiota and Bone Health: The Gut-Bone Axis. Cells 2022;11.

  3. Zeng Q, Li N, Wang Q, Feng J, Sun D, Zhang Q, et al. The prevalence of osteoporosis in China, a Nationwide, Multicenter DXA Survey. J Bone Miner Res. 2019;34:1789–97.

    Article  PubMed  Google Scholar 

  4. Yang TL, Shen H, Liu A, Dong SS, Zhang L, Deng FY, et al. A road map for understanding molecular and genetic determinants of osteoporosis. Nat Rev Endocrinol. 2020;16:91–103.

    Article  PubMed  Google Scholar 

  5. Hu X, Ma S, Yang C, Wang W, Chen L. Relationship between senile osteoporosis and cardiovascular and cerebrovascular diseases. Exp Ther Med. 2019;17:4417–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. D’Amelio P, Pescarmona GP, Gariboldi A, Isaia GC. High density lipoproteins (HDL) in women with postmenopausal osteoporosis: a preliminary study. Menopause. 2001;8:429–32.

    Article  PubMed  Google Scholar 

  7. Tang Y, Wang S, Yi Q, Xia Y, Geng B. High-density lipoprotein cholesterol is negatively correlated with bone Mineral density and has potential predictive value for bone loss. Lipids Health Dis. 2021;20:75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Xie R, Huang X, Liu Q, Liu M. Positive association between high-density lipoprotein cholesterol and bone mineral density in U.S. adults: the NHANES 2011–2018. J Orthop Surg Res. 2022;17:92.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ghosh A, Nishtala K. Biofluid lipidome: a source for potential diagnostic biomarkers. Clin Transl Med. 2017;6:22.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chiesa ST, Charakida M. High-density lipoprotein function and dysfunction in Health and Disease. Cardiovasc Drugs Ther. 2019;33:207–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Madsen CM, Varbo A, Nordestgaard BG. Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J. 2017;38:2478–86.

    Article  CAS  PubMed  Google Scholar 

  12. Hamer M, O’Donovan G, Stamatakis E. High-density lipoprotein cholesterol and mortality: too much of a good thing? Arterioscler Thromb Vasc Biol. 2018;38:669–72.

    Article  CAS  PubMed  Google Scholar 

  13. Jiang J, Qiu P, Wang Y, Zhao C, Fan S, Lin X. Association between serum high-density lipoprotein cholesterol and bone health in the general population: a large and multicenter study. Arch Osteoporos. 2019;14:36.

    Article  PubMed  Google Scholar 

  14. Zolfaroli I, Ortiz E, García-Pérez M, Hidalgo-Mora JJ, Tarín JJ, Cano A. Positive association of high-density lipoprotein cholesterol with lumbar and femoral neck bone mineral density in postmenopausal women. Maturitas. 2021;147:41–6.

    Article  CAS  PubMed  Google Scholar 

  15. Kan B, Zhao Q, Wang L, Xue S, Cai H, Yang S. Association between lipid biomarkers and osteoporosis: a cross-sectional study. BMC Musculoskelet Disord. 2021;22:759.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cheng X, Zhao K, Zha X, Du X, Li Y, Chen S, et al. Opportunistic screening using low-dose CT and the prevalence of osteoporosis in China: a Nationwide, Multicenter Study. J Bone Miner Res. 2021;36:427–35.

    Article  CAS  PubMed  Google Scholar 

  17. den Uyl D, Nurmohamed MT, van Tuyl LH, Raterman HG, Lems WF. (sub)clinical cardiovascular disease is associated with increased bone loss and fracture risk; a systematic review of the association between cardiovascular disease and osteoporosis. Arthritis Res Ther. 2011;13:R5.

    Article  Google Scholar 

  18. Katzmarzyk PT, Barreira TV, Harrington DM, Staiano AE, Heymsfield SB, Gimble JM. Relationship between abdominal fat and bone mineral density in white and African American adults. Bone. 2012;50:576–9.

    Article  PubMed  Google Scholar 

  19. Yin W, Li Z, Zhang W. Modulation of Bone and Marrow Niche by Cholesterol. Nutrients 2019;11.

  20. Wang HH, Lee DK, Liu M, Portincasa P, Wang DQ. Novel insights into the Pathogenesis and management of the metabolic syndrome. Pediatr Gastroenterol Hepatol Nutr. 2020;23:189–230.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Rádiková Ž, Penesová A, Vlček M, Havranová A, Siváková M, Šiarnik P, et al. Lipoprotein profiling in early multiple sclerosis patients: effect of chronic inflammation? Lipids Health Dis. 2020;19:49.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kha HT, Basseri B, Shouhed D, Richardson J, Tetradis S, Hahn TJ, et al. Oxysterols regulate differentiation of mesenchymal stem cells: pro-bone and anti-fat. J Bone Miner Res. 2004;19:830–40.

    Article  CAS  PubMed  Google Scholar 

  23. Gkastaris K, Goulis DG, Potoupnis M, Anastasilakis AD, Kapetanos G. Obesity, osteoporosis and bone metabolism. J Musculoskelet Neuronal Interact. 2020;20:372–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Ge B, Lu SQ, Lei SF. The obesity indices mediate the relationships of blood lipids and bone mineral density in Chinese elders. Mol Cell Probes. 2021;56:101705.

    Article  CAS  PubMed  Google Scholar 

  25. Rashid S, Genest J. Effect of obesity on high-density lipoprotein metabolism. Obes (Silver Spring). 2007;15:2875–88.

    Article  CAS  Google Scholar 

  26. Stevens K, Hultin H, Hellman P, Sundbom M. Low bone mineral density following gastric bypass is not explained by lifestyle and lack of exercise. BMC Surg. 2021;21:282.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cui R, Zhou L, Li Z, Li Q, Qi Z, Zhang J. Assessment risk of osteoporosis in Chinese people: relationship among body mass index, serum lipid profiles, blood glucose, and bone mineral density. Clin Interv Aging. 2016;11:887–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yang Y, Liu G, Zhang Y, Xu G, Yi X, Liang J, et al. Association between Bone Mineral density, bone turnover markers, and serum cholesterol levels in type 2 diabetes. Front Endocrinol (Lausanne). 2018;9:646.

    Article  PubMed  Google Scholar 

  29. Kuipers AL, Miljkovic I, Evans R, Bunker CH, Patrick AL, Zmuda JM. Optimal serum cholesterol concentrations are associated with accelerated bone loss in African ancestry men. Osteoporos Int. 2016;27:1577–84.

    Article  CAS  PubMed  Google Scholar 

  30. Choi HS, Kim KJ, Kim KM, Hur NW, Rhee Y, Han DS, et al. Relationship between visceral adiposity and bone mineral density in Korean adults. Calcif Tissue Int. 2010;87:218–25.

    Article  CAS  PubMed  Google Scholar 

  31. Pietschmann P, Rauner M, Sipos W, Kerschan-Schindl K. Osteoporosis: an age-related and gender-specific disease–a mini-review. Gerontology. 2009;55:3–12.

    Article  PubMed  Google Scholar 

  32. Zhang Q, Zhou J, Wang Q, Lu C, Xu Y, Cao H, et al. Association between Bone Mineral Density and lipid Profile in Chinese women. Clin Interv Aging. 2020;15:1649–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Adami S, Braga V, Zamboni M, Gatti D, Rossini M, Bakri J, et al. Relationship between lipids and bone mass in 2 cohorts of healthy women and men. Calcif Tissue Int. 2004;74:136–42.

    Article  CAS  PubMed  Google Scholar 

  34. Go JH, Song YM, Park JH, Park JY, Choi YH. Association between Serum Cholesterol Level and bone Mineral density at Lumbar Spine and Femur Neck in Postmenopausal Korean Women. Korean J Fam Med. 2012;33:166–73.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ghadiri-Anari A, Mortezaii-Shoroki Z, Modarresi M, Dehghan A. Association of lipid profile with bone mineral density in postmenopausal women in Yazd province. Int J Reprod Biomed. 2016;14:597–602.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jirapinyo M, Theppisai U, Manonai J, Suchartwatnachai C, Jorgensen LN. Effect of combined oral estrogen/progestogen preparation (Kliogest) on bone mineral density, plasma lipids and postmenopausal symptoms in HRT-naïve Thai women. Acta Obstet Gynecol Scand. 2003;82:857–66.

    PubMed  Google Scholar 

  37. Weissglas-Volkov D, Pajukanta P. Genetic causes of high and low serum HDL-cholesterol. J Lipid Res. 2010;51:2032–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (82071884); Henan Province young and middle-aged health science and technology innovation talents leading talent training project (YXKC2020004); National key research and development plan (2022YFC2010000, 2022YFC2010001); Medical Science and Technology Research Project of Henan Province (SBGJ202302011, LHGJ20210054). At the meantime, we would also like to express our appreciation for the language editing services (https://m.essaystar.com/service.html) provided by the American journal experts.

Funding

This study was supported by the National Natural Science Foundation of China (82071884); Henan Province young and middle-aged health science and technology innovation talents leading talent training project (YXKC2020004); National key research and development plan (2022YFC2010000, 2022YFC2010001); Medical Science and Technology Research Project of Henan Province (SBGJ202302011, LHGJ20210054).

Author information

Authors and Affiliations

  1. Department of Medical Imaging, People’s Hospital of Zhengzhou University, #7 Wei Wu Road, Zhengzhou, Henan, 450003, China

    Yongbing Sun, Xinbei Lin, Yang Zhou, Ao Liu, Zhonglin Li & Zhi Zou

  2. Department of Medical Imaging, Henan Provincial People’s Hospital, Xinxiang Medical College, Zhengzhou, Henan, 450003, China

    Xin Qi

  3. Department of Medical Imaging, The Third Affiliated Hospital of Zhengzhou University, #7 Kungfu Street, Zhengzhou, Henan, 450052, China

    Xuan Wang

  4. Department of Medical Imaging, Henan Provincial People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan, 450003, China

    Yawei Du

  5. Henan Provincial People’s Hospital, Zhengzhou, Henan, 450003, China

    Xue Lv

  6. Department of Health Management, Chronic Health Management Laboratory, Henan Provincial People’s Hospital, Zhengzhou, Henan, 450003, China

    Jing Zhou, Jiadong Zhu, Feifei Shang & Yongli Li

  7. Department of Nuclear Medicine, Henan Provincial People’s Hospital, Zhengzhou, Henan, 450003, China

    Xiaoling Wu

  8. Sevenoaks Health Management Center, Canada-Canada Institute of Health Engineering, University of Manitoba, Winnipeg, Canada

    Michael Zhang

  9. Fuwaihua Central Vascular Disease Hospital, #1 Fuwai Avenue, Zhengzhou, Henan, 451464, China

    Hao Li

Authors
  1. Yongbing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinbei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yawei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xue Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zhonglin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xiaoling Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhi Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Michael Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jiadong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Feifei Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yongli Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Hao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YS and YL contributed the central idea, YS and XQ analyzed most of the data. YS wrote the initial draft of the paper. XW helped in revising the manuscript. XL, YZ, YD, AL, JZ, and FS contributed to the data collection, and Xue Lv, JZ, ZL, ZZ, Michael Zhang, and HL contributed to the opinion refinement, supplementary analysis, and finalization of this paper. The author(s) read and approved the final manuscript.

Corresponding authors

Correspondence to Yongli Li or Hao Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The research protocol received ethics approval from the Ethics Committee at Beijing Jishuitan Hospital, in conformity with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are a contribution of the China Health Quantitative CT Big Data Research team, registered at clinicaltrials.gov (code: NCT03699228).

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Qi, X., Wang, X. et al. Association between high-density lipoprotein cholesterol and lumbar bone mineral density in Chinese: a large cross-sectional study. Lipids Health Dis 23, 27 (2024). https://doi.org/10.1186/s12944-024-02023-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-024-02023-1

Keywords

Lipids in Health and Disease

ISSN: 1476-511X