Mendelian randomization to evaluate the causal relationship between liver enzymes and the risk of six specific bone and joint-related diseases

Evaluation of the causal relationship between 28 circulating biomarkers and osteoarthritis : a bidirectional Mendelian randomization study

Aims

Circulating biochemistry markers are commonly used to monitor and detect disease-induced dysfunctions including osteoarthritis (OA). However, the causal nature of this relationship is nevertheless largely unknown, due to unmeasured confounding factors from observational studies. We aimed to reveal the causal relationship between 28 circulating biochemistry markers and OA pathogenesis.

Methods

We conducted a comprehensive bidirectional two-sample Mendelian randomization (MR) study between 28 circulating biomarkers and six OA types, using large-scale genome-wide association study (GWAS) summary statistics data from a UK Biobank cohort (n = 450,243) and the latest OA meta-analysis (n = 826,690). We replicated the significant results of low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) in an independent large GWAS dataset obtained from the Global Lipids Genetics Consortium (GLGC) (n > 800,000).

Results

Using 73 to 792 instrumental variables for biomarkers, this large MR analysis identified 11 causal associations at the Bonferroni corrected significance level of 2.98 × 10-4, involving seven biomarkers and five OA types. LDL-C (odds ratio (OR) per SD increase 0.90, 95% CI 0.86 to 0.93), apolipoprotein B (OR 0.86, 95% CI 0.82 to 0.91), TC (OR 0.90, 95% CI 0.86 to 0.94), calcium (OR 0.82, 95% CI 0.75 to 0.90), and glucose (OR 0.81, 95% CI 0.73 to 0.89) are causally associated with a reduced risk of OA, while phosphate (OR 1.18, 95% CI 1.08 to 1.30) and aspartate aminotransferase (OR 1.15, 95% CI 1.07 to 1.24) are causally associated with an increased risk. Analysis of GLGC summary statistics successfully replicated LDL-C (OR 0.93, 95% CI 0.90 to 0.96) and TC (OR 0.92, 95% CI 0.89 to 0.95).

Conclusion

This comprehensive bidirectional MR analysis provides new insights into the prevention and treatment of OA, as well as understanding the biological mechanism underlying OA pathogenesis.

Constructing a nomogram model for patients with cervical spondylotic myelopathy

This study aims to construct a nomogram to predict the prognosis of patients with cervical spondylotic myelopathy (CSM). This study included 358 patients diagnosed with cervical spondylotic myelopathy. We collected serological indicators of patients at admission, including routine blood tests, biochemical tests, liver and kidney function, coagulation function and other laboratory tests. We used t-test, rank-sum test, chi-square test or Fisher test for inter-group comparison. We used univariate and multivariate logistic regression analysis to obtain independent predictors of poor prognosis of patients, and constructed the independent predictors into a nomogram using R language, and verified the predictive performance of the nomogram. The results showed that Platelets (PLT) (1.005 [1.001, 1.009], p = 0.021), Albumin (ALB) (0.891 [0.818, 0.97], p = 0.008), Aspartate aminotransferase (AST) (1.031 [1.002, 1.061], p = 0.035), Alanine aminotransferase (ALT) (0.958 [0.92, 0.998], p = 0.037), and Fibrinogen (FIB) (0.654 [0.464, 0.921], p = 0.015) were independent predictors. Good prediction performance with modest errors was shown by the nomogram in both the training and validation groups. ALB, AST, ALT, FIB, and PLT at admission may be independent predictors of the efficacy of ACDF in CSM patients. The nomogram constructed with these factors has good predictive performance. Serological indicators can be used as a supplement to cervical spine-related imaging indicators, allowing clinicians to better complete the admission diagnosis and treatment process and preoperative evaluation process, so that more postoperative patients can benefit.

Association of Biomarkers Such as CA, TP, TES and ALP With Osteoarthritis Risk: A Mendelian Randomized Study

OBJECTIVE

Osteoarthritis is a common joint disease caused by a variety of risk factors, and it has been found that many biochemical markers are abnormal in peripheral blood and urine of patients with OA. The aim of this study was to elucidate the causal relationship between biomarkers associated with these processes and OA using Mendelian randomization (MR) analysis.

METHOD

The inverse variance weighted (IVW) approach to MR was primarily used to explore causal associations between exposures and outcomes using publicly available genetic variants from large genome-wide association studies (GWAS). That is, single nucleotide polymorphisms (SNPs) associated with 35 human blood and urine markers (363 228 healthy participants) were used as exposure, and osteoarthritis, hip osteoarthritis, and knee osteoarthritis were used as outcome variables, with the aim of exploring the causal relationship between 35 human blood and urine markers and osteoarthritis. MR-Egger, weighted median (WM), and simple and weighted models were used as complementary methods to IVW to assess the reliability of causality. Steiger's test was used to confirm whether the causal relationship between exposure and outcome was biased by reverse causality. Sensitivity analyses used Cochran's Q statistic and funnel plots to detect heterogeneity, and the MR-Egger intercept test and leave-one-out to assess horizontal multidimensionality.

RESULTS

Our MR analysis study identified the protective effects of CA, TP, ALB, SHBG, and VITD on OA and the pathogenic effects of TES, ALP, GGT, CRP, and CHOL on OA. It suggests that the above 10 hematological and urinary markers have the potential to be important indicators for the clinical diagnosis of OA as well as for the assessment of therapeutic efficacy and disease progression.

CONCLUSION

This MR analysis reinforces the importance of biomarkers in the diagnosis and prediction of OA. Future studies should further investigate the mechanisms of these biomarkers and their potential as therapeutic targets for OA.

Causal factors for osteoarthritis risk revealed by mendelian randomization analysis

Osteoarthritis (OA), a prevalent chronic disease among the elderly, presents a complex pathogenesis and currently lacks effective treatment. Traditional observational studies are time-consuming, labor-intensive, susceptible to confounding factors, and cannot establish causal relationships. Mendelian randomization (MR) analysis, leveraging genetic variation to assess causal associations between exposures and outcomes, offers a cost-effective and efficient alternative. Over the past decade, large-scale genome-wide association studies have identified numerous genetic variants linked to OA risk factors, facilitating MR study design. In this review, we systematically identified 52 MR studies meeting specific criteria and evaluated their quality, exploring the impact of lifestyle, nutrition, comorbidities, circulating metabolites, plasma proteins, and other health factors on OA risk. We discuss the results and potential mechanisms of MR findings, addressing conflicting evidence based on existing literature and our prior research. With the ongoing expansion of genome-wide association data, we anticipate MR's role in future OA studies to broaden, particularly in drug development research using targeted MR approaches. We thus aim for this paper to offer valuable insights for researchers and clinicians in related fields.

Circulating 25-hydroxycholecalciferol and calcium levels, and alkaline phosphatase activity among people living with and without human immunodeficiency virus and injecting drugs in kenya

BACKGROUND

People who inject drugs (PWID) and living with the human immunodeficiency virus (PLHIV) are at higher risk of suffering marked derangements in micronutrient levels, leading to poor disease and treatment outcomes. Consequently, this can be monitored by measuring key biomarkers, such as total circulating (serum) 25-hydroxycholecalciferol (25(OH)D3), calcium, and alkaline phosphatase (ALP) for timely intervention. Therefore, circulating levels of 25(OH)D3 and calcium, and ALP activity were determined in PWID and are highly active anti-retroviral treatment (HAART)-experienced or -naive, along with those without HIV infection.

METHODS

This cross-sectional study compared serum concentrations of 25(OH)D3, calcium, and ALP in Kenyan PLHIV and were HAART-naive (n = 30) or -experienced (n = 61), PWID and without HIV (n = 132).

RESULTS

Circulating 25(OH)D3 levels were significantly different amongst the study groups (P < 0.001), and were significantly lower in the HAART-experienced (median, 17.3; IQR, 18.3 ng/ml; P < 0.001) and -naive participants (median, 21.7; IQR, 12.8 ng/ml; P = 0.015) relative to uninfected (median, 25.6; IQR, 6.8 ng/ml) PWID. In addition, the proportions of vitamin D deficiency (55.7%, 40.0%, and 17.4%) and insufficiency (31.1%, 53.3%, and 63.6%) compared to sufficiency (13.1%, 6.7%, and 18.9%; P < 0.001) were greater amongst HAART-experienced, -naive, and uninfected study groups, respectively. Likewise, serum total calcium concentrations were lower in the HAART-experienced relative to HIV-negative (P = 0.019) individuals. Serum ALP activity was also lower in the HAART-experienced in contrast to HIV-negative PWID (P = 0.048). Regression analysis indicated that predictors of circulating 25(OH)D3 were: age (β = 0.287; R2 = 8.0%; P = 0.017) and serum ALP (β = 0.283; R2 = 6.4%; P = 0.033) in the HAART-experienced PWID, and serum ALP (β = 0.386; R2 = 14.5%; P < 0.001) in the HIV-negative PWID.

CONCLUSION

This study suggests that HIV-1 infection and HAART, including injection substance use, decrease circulating 25(OH)D3, calcium and ALP activity. In addition, age and ALP activity are associated with low circulating vitamin D levels in HAART-experienced PWID. The results highlight the importance of incorporating vitamin D and calcium supplementation in treatment and rehabilitation protocols for PLHIV.

Multiplexed bacterial pathogen detection and clinical characteristics of orthopedic infection in hospitalized patients

Introduction

Accurate identification of the etiology of orthopedic infection is very important for correct and timely clinical management, but it has been poorly studied. In the current study we explored the association of multiple bacterial pathogens with orthopedic infection.

Methods

Hospitalized orthopedic patients were enrolled in a rural hospital in Qingdao, China. Wound or exudate swab samples were collected and tested for twelve bacterial pathogens with both culture and multiplex real time PCR.

Results and discussion

A total of 349 hospitalized orthopedic patients were enrolled including 193 cases presenting infection manifestations upon admission and 156 with no sign of infection. Orthopedic infection patients were mainly male (72.5%) with more lengthy hospital stay (median 15 days). At least one pathogen was detected in 42.5% (82/193) of patients with infection while 7.1% (11/156) in the patients without infection (P < 0.001). S. aureus was the most prevalent causative pathogen (15.5%). Quantity dependent pathogen association with infection was observed, particularly for P. aeruginosa and K. pneumoniae, possibly indicating subclinical infection. Most of the patients with detected pathogens had a previous history of orthopedic surgery (odds ratio 2.8, P = 0.038). Pathogen specific clinical manifestations were characterized. Multiplex qPCR, because of its high sensitivity, superior specificity, and powerful quantification could be utilized in combination with culture to guide antimicrobial therapy and track the progression of orthopedic infection during treatment.

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.