Risk Factors Analysis of Bone Mineral Density Based on Lasso and Quantile Regression in America during 2015-2018

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.

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.

There is a significantly inverse relationship between dietary riboflavin intake and prevalence of osteoporosis in women but not in men: Results from the TCLSIH cohort study

Background

Epidemiological evidence for the relationship between riboflavin intake and bone health is inconsistent, and this relationship has not been examined in Chinese population. This study aimed to investigate the relationship between dietary intake of riboflavin and prevalence of osteoporosis in a Chinese adult population.

Methods

A total of 5,607 participants (mean age, 61.2 years; males, 34.4%) were included in this cross-sectional study. We calculated the riboflavin intake by using the food frequency questionnaire (FFQ) in combination with Chinese food composition database. Bone mineral density (BMD) was detected by an ultrasound bone densitometer. Multivariable logistic regression models were used to evaluate the relationship between dietary riboflavin intake and prevalence of osteoporosis.

Results

In this population, the dietary intake of riboflavin ranged from 0.13 to 1.99 mg/d, and the proportion of abnormal BMD was 36.6%. The prevalence of osteoporosis decreased gradually with increasing quartiles of riboflavin intake, before and after adjustment for a range of confounding factors. In the final model, the multivariate-adjusted ORs (95% CI) across the quartiles of riboflavin intake were 1.00 (reference), 0.84 (0.54, 1.31), 0.59 (0.34, 1.04), and 0.47 (0.22, 0.96), respectively (P for trend < 0.05). In sex-disaggregated analysis, similar results to the total population were observed in women, while no significant results were found in men.

Conclusion

The dietary riboflavin intake was negatively associated with the prevalence of osteoporosis. However, the association was significant in women but not in men. Our findings indicated that women are more sensitive to riboflavin intake in maintaining a normal BMD.

Metabolomic patterns, redox-related genes and metals, and bone fragility endpoints in the Hortega Study

BACKGROUND

The potential joint influence of metabolites on bone fragility has been rarely evaluated. We assessed the association of plasma metabolic patterns with bone fragility endpoints (primarily, incident osteoporosis-related bone fractures, and, secondarily, bone mineral density BMD) in the Hortega Study participants. Redox balance plays a key role in bone metabolism. We also assessed differential associations in participant subgroups by redox-related metal exposure levels and candidate genetic variants.

MATERIAL AND METHODS

In 467 participants older than 50 years from the Hortega Study, a representative sample from a region in Spain, we estimated metabolic principal components (mPC) for 54 plasma metabolites from NMR-spectrometry. Metals biomarkers were measured in plasma by AAS and in urine by HPLC-ICPMS. Redox-related SNPs (N = 341) were measured by oligo-ligation assay.

RESULTS

The prospective association with incident bone fractures was inverse for mPC1 (non-essential and essential amino acids, including branched-chain, and bacterial co-metabolites, including isobutyrate, trimethylamines and phenylpropionate, versus fatty acids and VLDL) and mPC4 (HDL), but positive for mPC2 (essential amino acids, including aromatic, and bacterial co-metabolites, including isopropanol and methanol). Findings from BMD models were consistent. Participants with decreased selenium and increased antimony, arsenic and, suggestively, cadmium exposures showed higher mPC2-associated bone fractures risk. Genetic variants annotated to 19 genes, with the strongest evidence for NCF4, NOX4 and XDH, showed differential metabolic-related bone fractures risk.

CONCLUSIONS

Metabolic patterns reflecting amino acids, microbiota co-metabolism and lipid metabolism were associated with bone fragility endpoints. Carriers of redox-related variants may benefit from metabolic interventions to prevent the consequences of bone fragility depending on their antimony, arsenic, selenium, and, possibly, cadmium, exposure levels.

Humoral and cellular immune response after severe acute respiratory syndrome coronavirus 2 messenger ribonucleic acid vaccination in heart transplant recipients: An observational study in France

INTRODUCTION

Heart transplant (HT) recipients have a high risk of developing severe COVID-19. Immunoglobulin G antibodies are considered to provide protective immunity and T-cell activity is thought to confer protection from severe disease. However, data on T-cell response to mRNA vaccination in a context of HT remains limited.

METHODS

In 96 HT patients, a IFN-γ release assay and an anti-Spike antibody test were used to evaluate the ability of SARS-CoV-2 mRNA vaccines to generate cellular and humoral immune response. Blood samples were collected few weeks to 7 months after vaccination. Multiple fractional polynomial and LASSO regression models were used to define predictors of T-cell response.

RESULTS

Three to five months after vaccination, three doses of vaccine induced a positive SARS-CoV-2 T-cell response in 47% of recipients and a positive humoral response in 83% of recipients, 11.1% of patients remained negative for both T and B cell responses. Three doses were necessary to reach high IgG response levels (>590 BAU/mL), which were obtained in a third of patients. Immunity was greatly amplified in the group who had three vaccine doses plus COVID-19 infection.

CONCLUSION

Our study revealed that T and B immunity decreases over time, leading us to suggest the interest of a booster vaccination at 5 months after the third dose. Moreover, a close follow-up of immune response following vaccination is needed to ensure ongoing immune protection. We also found that significant predictors of higher cellular response were infection and active smoking, regardless of immunosuppressive treatment with mycophenolate mofetil (MMF).

How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach

This decoupling between carbon dioxide emissions and the heavy industry is one of the main topics of government managers. This paper uses the quantile regression approach to investigate the carbon intensity of China's heavy industry, based on 2005-2019 panel data. The main findings are as follows: (1) incentive-based environmental regulations have the greater impact on the carbon intensity in Jiangsu, Shandong, Zhejiang, Henan, Liaoning, and Shaanxi, because these provinces invest more in environmental governance and levy higher resource taxes; (2) the impact of mandatory environmental regulations on carbon intensity in Beijing, Tianjin, and Guangdong provinces is smaller, since these three provinces have the fewest enacted environmental laws and rely mainly on market incentives; (3) conversely, foreign direct investment has contributed most to carbon intensity reduction in Tianjin, Beijing, and Guangdong provinces, because these three have attracted more technologically advanced foreign-funded enterprises; (4) technological progress contributes more to the carbon intensity in the low quantile provinces, because these provinces have more patented technologies; (5) the carbon intensity of Shaanxi, Shanxi, and Inner Mongolia provinces is most affected by energy consumption structures because of their over-reliance on highly polluting coal.

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

Background

The association between serum total cholesterol (TC) and bone mineral density (BMD) is still controversial. We aimed to evaluate the association of serum TC with BMD in general US adult women.

Methods

A cross-sectional study consisting of 7,092 (age range 20–85) participants from the National Health and Nutrition Examination Survey (NHANES) database was conducted. Weighted multivariate linear regression analyses were performed to evaluate association between serum TC and lumbar spine BMD. In addition, subgroup and interaction analysis were used in this study.

Results

The serum TC was negatively correlated with lumbar spine BMD after adjusting for confounders. Subgroup analysis found that the strongest negative association mainly exists in women aged over 45 years with body mass index (BMI) < 24.9 kg/m², and this association is not significant in other groups.

Conclusions

This study found that serum TC exhibit an inverse association with lumbar spine BMD in Us women aged over 45 years. The measurement of serum TC may provide information for predicting poor bone health outcomes in these women.

Analysing the influencing factors on caregivers' burden among amyotrophic lateral sclerosis patients in China: a cross-sectional study based on data mining

OBJECTIVES

There is significant burden on caregivers of patients with amyotrophic lateral sclerosis (ALS). However, only a few studies have focused on caregivers, and traditional research methods have obvious shortcomings in dealing with multiple influencing factors. This study was designed to explore influencing factors on caregiver burden among ALS patients and their caregivers from a new perspective.

DESIGN

Cross-sectional study.

SETTING

The data were collected at an affiliated hospital in Guangzhou, Guangdong, China.

PARTICIPANTS

Fifty-seven pairs of patients with ALS and their caregivers were investigated by standardised questionnaires.

MAIN OUTCOME MEASURES

This study primarily assessed the influencing factor of caregiver burden including age, gender, education level, economic status, anxiety, depression, social support, fatigue, sleep quality and stage of disease through data mining. Statistical analysis was performed using SPSS 24.0, and least absolute shrinkage and selection operator (LASSO) regression model was established by Python 3.8.1 to minimise the effect of multicollinearity.

RESULTS

According to LASSO regression model, we found 10 variables had weights. Among them, Milano-Torinos (MITOS) stage (0-1) had the highest weight (-12.235), followed by younger age group (-3.198), lower-educated group (2.136), fatigue (1.687) and social support (-0.455). Variables including sleep quality, anxiety, depression and sex (male) had moderate weights in this model. Economic status (common), economic status (better), household (city), household (village), educational level (high), sex (female), age (older) and MITOS stage (2-4) had a weight of zero.

CONCLUSIONS

Our study demonstrates that the severity of ALS patients is the most influencing factor in caregiver burden. Caregivers of ALS patients may suffer less from caregiver burden when the patients are less severe, and the caregivers are younger. Low educational status could increase caregiver burden. Caregiver burden is positively correlated with the degree of fatigue and negatively correlated with social support. Hopefully, more attention should be paid to caregivers of ALS, and effective interventions can be developed to relieve this burden.

Transcriptome Profiles Reveal a 12-Signature Metabolic Prediction Model and a Novel Role of Myo-Inositol Oxygenase in the Progression of Prostate Cancer

Prostate adenocarcinoma (PRAD) is an extremely common type of cancer in the urinary system. Here, we aimed to establish a metabolic signature to identify novel targets in a predictive model of PRAD patients. A total of 133 metabolic differentially expressed genes (MDEGs) were identified with significant prognostic value. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a 12-mRNA signature model, a metabolic prediction model (MPM), in 491 PRAD patients. The risk score of the MPM significantly predicted the progression of PRAD patients (p < 0.001, area under the curve (AUC) = 0.745). Furthermore, myo-inositol oxygenase (MIOX), the most prominently upregulated metabolic enzyme and hub gene in the protein-protein interaction network of the MPM, showed significant prognostic implications. Next, MIOX expression in normal prostate tissues was lower than in PRAD tissues, and high MIOX expression was significantly associated with disease progression (p = 0.005, HR = 2.274) in 81 PRAD patients undergoing first-line androgen receptor signaling inhibitor treatment from the Renji cohort. Additionally, MIOX was significantly involved in the abnormal immune infiltration of the tumor microenvironment and associated with the DNA damage repair process of PRAD. In conclusion, this study provides the first opportunity to comprehensively elucidate the landscape of prognostic MDEGs, establish novel prognostic modeling of MPM using large-scale PRAD transcriptomic data, and identify MIOX as a potential prognostic target in PRAD patients from multiple cohorts. These findings help manage risk assessment and provide valuable insights into treatment strategies for PRAD.

Causal relationships between sex hormone traits, lifestyle factors, and osteoporosis in men: A Mendelian randomization study

Although observational studies have explored factors that may be associated with osteoporosis, it is not clear whether they are causal. Osteoporosis in men is often underestimated. This study aimed to identify the causal risk factors associated with bone mineral density(BMD) in men. Single nucleotide polymorphisms (SNPs) associated with the exposures at the genome-wide significance (p < 5x10-8) level were obtained from corresponding genome-wide association studies (GWASs) and were utilized as instrumental variables. Summary-level statistical data for BMD were obtained from two large-scale UK Biobank GWASs. A Mendelian randomization (MR) analysis was performed to identify causal risk factors for BMD. Regarding the BMD of the heel bone, the odds of BMD increased per 1-SD increase of free testosterone (FT) (OR = 1.13, P = 9.4 × 10-17), together with estradiol (E2) (OR = 2.51, P = 2.3 × 10-4). The odds of BMD also increased with the lowering of sex-hormone binding globulin (SHBG) (OR = 0.87, P = 7.4 × 10-8) and total testosterone (TT) (OR = 0.96, P = 3.2 × 10-2) levels. Regarding the BMD of the lumbar spine, the odds of BMD increased per 1-SD increase in FT (OR = 1.18, P = 4.0 × 10-3). Regarding the BMD of the forearm bone, the odds of BMD increased with lowering SHBG (OR = 0.75, P = 3.0 × 10-3) and TT (OR = 0.85, P = 3.0 × 10-3) levels. Our MR study corroborated certain causal relationships and provided genetic evidence among sex hormone traits, lifestyle factors and BMD. Furthermore, it is a novel insight that TT was defined as a disadvantage for osteoporosis in male European populations.