Lipid profile and plasma atherogenic index in postmenopausal osteoporosis

Association of HDL and LDL levels with osteoporosis in rheumatoid arthritis: a retrospective cohort study

OBJECTIVES

A correlation exists between lipids and osteoporosis (OP), as well as between lipids and rheumatoid arthritis (RA). However, lipids, the relationship between RA and OP is still unclear. This study mainly investigates the relationship between lipid levels and OP risk in RA patients.

METHODS

Retrospective collection of RA patient data from July 2017 to May 2022, encompassing baseline demographics, treatment regimens, laboratory results, and bone mineral density (BMD) measurements. Analyses, stratified by BMD subgroups, were conducted using propensity score matching (PSM) based on age, sex, and baseline duration, and binary logistic regression to examine the interplay between lipoprotein levels and other risk factors. The relationship between continuous variables and OP risk was assessed using restricted cubic spline (RCS), followed by a reanalysis of the correlation between varying lipoprotein levels and different factors, segmented according to RCS-determined cutoffs.

RESULTS

The study included 2673 RA patients. Binary logistic regression revealed significant associations between high-density lipoprotein (HDL), low-density lipoprotein (LDL), and RA-OP (p < 0.01). Specifically, HDL emerged as a protective factor against OP (OR = 0.40, 95% CI 0.250-0.629; p < 0.001), whereas LDL was identified as a risk factor (OR = 1.56, 95% CI 1.290-1.890; p < 0.001). Furthermore, HDL (RCS cutoff point 1.28 mmol/L) showed a negative, linear correlation with RA-related OP, while LDL (RCS cutoff point 2.63 mmol/L) demonstrated a positive, linear correlation.

CONCLUSIONS

The levels of HDL and LDL may be indicators of OP occurrence in RA patients.

Association between the atherogenic index of plasma and bone mineral density among adult women: NHANES (2011-2018)

Background

Studies on the relationship between the atherogenic index of plasma (AIP) and bone mineral density (BMD) among adult women in the United States are limited. The purpose of this study was to explore this association using a sizable, nationally representative sample.

Methods

Data from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES) were used in this observational study. The AIP was computed as log10 (triglycerides/high-density lipoprotein cholesterol). Total BMD was measured via dual-energy X-ray densitometry. We constructed multiple linear regression models to evaluate the correlation between the AIP and BMD. The non-linear relationship was characterized by smooth curve fitting and generalized additive models. We also conducted subgroup and interaction analyses.

Results

In this study, we included 2,362 adult women with a mean age of 38.13 ± 12.42 years. The results of multiple linear regression analysis, the AIP and total BMD showed a negative association (β = -0.021, 95%CI: -0.037, -0.006). The curve fitting analysis and threshold effect analysis showed a non-linear relationship between the two variables, and the inflection point of the AIP was found to be -0.61. The total BMD decreased significantly when the AIP reached this value (β = -0.03, 95%CI: -0.04, -0.01). The results of the subgroup analysis showed that AIP and total BMD had a strong negative relationship in participants who were below 45 years old (β = -0.023; 95% CI: -0.041, -0.004), overweight (BMI ≥ 25 kg/m2) (β = -0.022; 95% CI: -0.041, -0.002), had a higher education level (β = -0.025; 95% CI: -0.044, -0.006), and had no partners (β = -0.014; 95% CI: -0.06, -0.009).

Conclusions

We found a negative correlation between the AIP and total BMD. Clinicians should pay attention to patients with high AIP, which might indicate a low BMD and has reference significance in preventing osteoporosis.

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study

Purpose

Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients.

Patients and Methods

Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters.

Results

In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444-1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups.

Conclusion

Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

Typing characteristics of metabolism-related genes in osteoporosis

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning.

Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method.

Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9.

Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.

Association between lipid biomarkers and osteoporosis: a cross-sectional study

BACKGROUND

Osteoporosis and cardiovascular diseases (CVDs) are 2 major public health problems. Osteoporosis and CVDs may be linked but the association between lipid profile and osteoporosis is still controversial. The purpose of this study was to examine the associations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) with osteoporosis.

METHODS

Using inpatients' and outpatients' electronic medical records (EMR) and dual X-ray absorptiometry (DXA) database stored at The Second Hospital of Jilin University, we included 481 individuals with complete and valid lipid and bone mineral density (BMD) data in 2017. Serum samples were used to measure TC, LDL-C, HDL-C and TG. Femoral neck and total hip BMD were measured by DXA; osteoporosis was defined as femoral neck or total hip T-score ≤ -2.5. Multivariable logistic regression models were used to test the associations of TC, LDL-C, HDL-C and TG with osteoporosis.

RESULTS

The mean age for included individuals was 62.7 years (SD = 8.6 years); 60.1 % of them were female. Each standard deviation (SD) increase in TC (Odds Ratio [OR]: 1.48; 95 % Confidence Interval [CI]: 1.06-2.07) and TG (OR: 1.67; 95 % CI: 1.16-2.39) were associated with increased risk of osteoporosis; LDL-C and HDL-C levels were not associated with osteoporosis. Age, sex and body mass index (BMI) did not interact with the relationships of TC and TG with osteoporosis (all P > 0.10).

CONCLUSIONS

Higher TC and TG levels were associated with greater risk of osteoporosis in this cross-sectional study.

Blood lipid levels in patients with osteopenia and osteoporosis:a systematic review and meta-analysis

INTRODUCTION

Considering the controversial relationship between blood lipid levels and osteopenia and osteoporosis (OP), we performed this meta-analysis.

MATERIALS AND METHODS

Using specific keywords and related words, we searched PubMed, Embase, and Cochrane Library databases. The Newcastle-Ottawa Scale form was used to evaluate the quality of the literature. According to the inclusion and exclusion criteria, we systematically screened the literature to extract relevant information and data. ReVman 5.3 and Stata 13.0 software were used for statistical analysis. Results were expressed as the mean difference (MD) and 95% confidence interval (95% CI). The heterogeneity test was conducted according to I² and Q tests. Egger's test was used to quantitatively evaluate publication bias.

RESULTS

This analysis involved 12 studies (12,395 subjects). The quality of the literature was acceptable. Among subjects who were not taking lipid-lowering drugs, total cholesterol (TC) (MD = 0.11 mmol/L, 95%CI: - 0.03, 0.25; I² = 21%; P = 0.36), triglycerides (TG) (MD =  - 0.01 mmol/L, 95%CI: - 0.09, 0.07; I² = 6%; P = 0.34), and low-density lipoprotein cholesterol (LDL-C) (MD = 0.10 mmol/L, 95%CI: 0.00, 0.19; I² = 0%; P = 0.74) in the osteopenia were not significantly increased/decreased. There were no significant differences in LDL-C (MD = 0.02 mmol/L, 95%CI: - 0.09, 0.13; I² = 0%; P = 0.74) in postmenopausal women in osteopenia. TG (MD =  - 0.04 mmol/L, 95%CI: - 0.14,0.07; I² = 49%; P = 0.07) was unchanged in the osteoporosis (OP) group in subjects without taking lipid-lowering drugs. HDL-C was elevated in OP group (MD = 0.05 mmol/L, 95%CI: 0.03, 0.07; I² = 31%; P = 0.15) but not in osteopenia group (MD = 0.01 mmol/L, 95%CI: - 0.01, 0.02; I² = 38%; P = 0.14) in all subjects.

CONCLUSION

HDL-C was elevated in patients with OP.

Hypertension is associated with osteoporosis: a case-control study in Chinese postmenopausal women

BACKGROUND

Osteoporosis and cardiovascular disease (CVD) are age-related diseases. It is reported that patients with CVD have a higher risk of bone loss. This retrospective study sought to reveal the association between osteoporosis and CVD in Chinese women. Although epidemiological evidence has indicated a relationship between the two, clinical data in southeast China are lacking.

METHODS

In total, 2873 participants completed the baseline survey from January 2007 to October 2019, and 2039 were included in this retrospective study. We divided all subjects into an osteoporosis group and a non-osteoporosis group based on their bone mineral density (BMD). Dual-energy X-ray absorptiometry (DXA) was used to examine BMD. The general information came from the questionnaire survey. Cardiovascular diseases were defined by asking participants at the first visit and checking relevant medical records if they had suffered from hypertension, coronary heart disease, or cerebral infarction.

RESULTS

According to the criterion, the osteoporosis group had 678 subjects, and the non-osteoporosis group had 1361 subjects. Subjects in the osteoporosis group had a significantly higher prevalence of hypertension and coronary heart disease. Besides, the proportion of subjects who drank tea and drank milk were relatively higher in the osteoporosis group. The odds ratio (OR) for suffering from osteoporosis was high if the patients had hypertension.

CONCLUSIONS

This study indicated that Chinese postmenopausal women with osteoporosis had a higher prevalence of hypertension. Hypertension was significantly associated with osteoporosis.

Association Between Bone Mineral Density and Lipid Profile in Chinese Women

Purpose

Atherosclerotic cardiovascular disease may share the risk factors for low bone mineral density (BMD), one of which is dyslipidemia. The association between serum cholesterol and BMD remains controversial. Thus, the correlation between serum lipids and BMD in women was explored in the current study.

Materials and Methods

This cross-sectional study included 1116 Chinese female participants. Serum samples were collected to evaluate total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and other laboratory markers. Dual-energy X-ray absorptiometry was used to assess lumbar spine, femoral neck, and total hip BMD.

Results

In the postmenopausal women, a non-linear relationship was detected between TC, LDL-C, HDL-C, and lumbar spine BMD. Using segmented linear regression, the inflection points were 5.86 mmol/L, 3.52 mmol/L, and 2.37 mmol/L, respectively. To the left of the inflection point, the higher the serum lipid level, the lower the value for lumbar spine BMD. To the right of the inflection point, the higher the serum level of TC and LDL-C, the higher the value for lumbar spine BMD. In the premenopausal women, the association between HDL-C and femoral neck BMD was non-linear. In addition, LDL-C had a positive association with BMD of the femoral neck and HDL-C had an inverse association with BMD of the femoral neck in postmenopausal women.

Conclusion

In postmenopausal women, the relationship between TC, LDL-C, HDL-C, and lumbar spine BMD was non-linear. TC, LDL-C, and HDL-C were negatively associated with lumbar spine BMD when the values were less than 5.86 mmol/L, 3.52 mmol/L, and 2.37 mmol/L, respectively. The mechanisms of the association were unclear, and further research is warranted to clarify the relationship.

The Clinical Importance of the Plasma Atherogenic Index, Other Lipid Indexes, and Urinary Sodium and Potassium Excretion in Patients with Stroke

Objective

Cardiovascular complications are still the primary reason for high mortality rates worldwide. The determination of risk factors is important to prevent stroke. The aim of the present study was to analyze the importance of serum lipid indexes and urinary sodium (Na)/potassium (K) excretion in patients with stroke together with sex differences.

Materials and Methods

A total of 50 (28 male and 22 female, mean age 65.9±14.6 years) patients with acute stroke were included in the study group, and 32 body mass index-matched healthy subjects were included in the control group. Lipid profiles [(cholesterol, triglyceride, very low-density lipoprotein, low-density lipoprotein, and high-density lipoprotein (HDL)], serum creatinine (Cre), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and Na, K, and Cre excretion in spot urine samples of the patients were recorded.

Results

Systolic blood pressure (p=0.021), ESR (p=0.044), and CRP (p=0.042) were significantly higher in all patients in the stroke group; urinary Tanaka (K) (p=0.033), Kawazaki (K) (p=0.028), urinary spot Cre (p=0.012), and Na excretion (p=0.036) levels were found to be significantly lower in only male patients with stroke. The mean plasma atherogenic indexes were 0.57±0.24 in the study (stroke) group and 0.54±0.22 in the control group (p=0.61). Other lipid indexes, such as Castelli's risk index (CRI)-I (p=0.29), CRI-II (p=0.24), atherogenic coefficient (p=0.29), and non-HDL cholesterol (p=0.69), were not statistically different from the controls.

Conclusion

Urinary Na, K, and Cre excretion was significantly lower in male patients with stroke, and acute phase reactants were significantly higher in the entire stroke group than in controls. These parameters can be used as auxiliary biomarkers in the risk assessment of stroke.