Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Goodness-of-fit testing for meta-analysis of rare binary events.

Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.

Bayesian estimation and testing in random-effects meta-analysis of rare binary events allowing for flexible group variability.

Rare binary events data arise frequently in medical research. Due to lack of statistical power in individual studies involving such data, meta-analysis has become an increasingly important tool for combining results from multiple independent studies. However, traditional meta-analysis methods often report severely biased estimates in such rare-event settings. Moreover, many rely on models assuming a pre-specified direction for variability between control and treatment groups for mathematical convenience, which may be violated in practice. Based on a flexible random-effects model that removes the assumption about the direction, we propose new Bayesian procedures for estimating and testing the overall treatment effect and inter-study heterogeneity. Our Markov chain Monte Carlo algorithm employs Pólya-Gamma augmentation so that all conditionals are known distributions, greatly facilitating computational efficiency. Our simulation shows that the proposed approach generally reports less biased and more stable estimates compared to existing methods. We further illustrate our approach using two real examples, one using rosiglitazone data from 56 studies and the other using stomach ulcers data from 41 studies.

Multimodality Screening For (Peri)Myocarditis In Newly Diagnosed Idiopathic Inflammatory Myopathies: A Cross-Sectional Study.

BACKGROUND: Cardiac involvement in idiopathic inflammatory myopathy (IIM or "myositis") is associated with an approximate 4% mortality, but standardised screening strategies are lacking. OBJECTIVE: We explored a multimodality screening on potentially reversible cardiac involvement -i.e. active (peri)myocarditis -in newly diagnosed IIM. METHODS: We included adult IIM patients from 2017 to 2020. At time of diagnosis, patients underwent cardiac evaluation including laboratory biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging (CMR). Based on 2019 consensus criteria for myocarditis, an adjudication committee made diagnoses of definite, probable, possible or no (peri)myocarditis. We explored diagnostic values of sequentially added diagnostic modalities by Constructing Classification and Regression Tree (CART) analysis in patients with definite/probable versus no (peri)myocarditis. RESULTS: We included 34 IIM patients, in whom diagnoses of definite (six, 18%), probable (two, 6%), possible (11, 32%), or no (peri)myocarditis (15, 44%) were adjudicated. CART-analysis showed high-sensitivity cardiac troponin T (cut-off value < 2.3 times the upper limit of normal (xULN)) ruled out (peri)myocarditis with a sensitivity of 88%, while high-sensitivity troponin I (cut-off value > 2.9 xULN for females and > 1.8 xULN for males) ruled in (peri)myocarditis with a specificity of 100%. Applying high-sensitivity cardiac troponins with these cut-off values in a diagnostic algorithm without and with a CMR to the total population of 34 patients demonstrated a diagnostic accuracy for a clear diagnosis of probable/definite or no (peri)myocarditis of 59% and 68%, respectively. CONCLUSIONS: A diagnostic algorithm for detection of (peri)myocarditis in adult IIM may consist of sequential testing with high-sensitivity cardiac troponins and CMR.

B-cell receptor profiling before and after IVIG monotherapy in newly diagnosed idiopathic inflammatory myopathies.

OBJECTIVE: To unravel B-cell receptor (BcR) characteristics in muscle tissues and peripheral blood and gain more insight into BcR repertoire changes in peripheral blood in idiopathic inflammatory myopathies (IIMs), and study how this correlates to the clinical response to IVIG. METHODS: Nineteen treatment-naive patients with newly diagnosed IIM were prospectively treated with IVIG monotherapy. RNA-based BcR repertoire sequencing was performed in muscle biopsies collected before, and in peripheral blood (PB) collected before and nine weeks after IVIG treatment. Results were correlated to patients' clinical improvement based on the total improvement score (TIS). RESULTS: Prior to IVIG treatment, BcR clones found in muscle tissue could be retrieved in peripheral blood. Nine weeks after IVIG treatment, new patient-specific dominant BcR clones appeared in peripheral blood while pre-treatment dominant BcR clones disappeared. The cumulative frequency of all dominant BcR clones before treatment was significantly higher in individuals who responded to IVIG compared with those who did not respond to IVIG, and correlated with a higher CK. During follow-up, a decrease in the cumulative frequency of all dominant clones correlated with a higher TIS. CONCLUSION: In treatment-naive patients with newly diagnosed IIM, muscle tissue and peripheral blood share expanded BcR clones. In our study a higher cumulative frequency of dominant BcR clones in blood before treatment was associated with a higher CK and better treatment response, suggesting that response to IVIG may depend on the composition of the pre-treatment BcR repertoire.

TCRß clones in muscle tissue share structural features in patients with idiopathic inflammatory myopathy and are associated with disease activity.

Objectives: To characterize the T cell receptor (TCRß) repertoire in peripheral blood and muscle tissues of treatment naïve patients with newly diagnosed idiopathic inflammatory myopathies (IIMs). Methods: High throughput RNA sequencing of the TCRß chain was performed in peripheral blood and muscle tissue in twenty newly-diagnosed treatment-naïve IIM patients (9 DM, 5 NM/OM, 5 IMNM and 1 ASyS) and healthy controls. Results thereof were correlated with markers of disease activity. Results: Muscle tissue of IIM patients shows more expansion of TCRß clones and decreased diversity when compared to peripheral blood of IIM as well as healthy controls (both p=0.0001). Several expanded TCRß clones in muscle are tissue restricted and cannot be retrieved in peripheral blood. These clones have significantly longer CDR3 regions when compared to clones (also) found in circulation (p=0.0002), while their CDR3 region is more hydrophobic (p<0.01). Network analysis shows that clonal TCRß signatures are shared between patients. Increased clonal expansion in muscle tissue is significantly correlated with increased CK levels (p=0.03), while it tends to correlate with decreased muscle strength (p=0.08). Conclusion: Network analysis of clones in muscle of IIM patients shows shared clusters of sequences across patients. Muscle-restricted CDR3 TCRß clones show specific structural features in their T cell receptor. Our results indicate that clonal TCRß expansion in muscle tissue might be associated with disease activity. Collectively, these findings support a role for specific clonal T cell responses in muscle tissue in the pathogenesis of the IIM subtypes studied.

An overview of heavy-tail extensions of multivariate Gaussian distribution and their relations.

Many extensions of the multivariate normal distribution to heavy-tailed distributions are proposed in the literature, which includes scale Gaussian mixture distribution, elliptical distribution, generalized elliptical distribution and transelliptical distribution. The inferences for each family of distributions are well studied. However, extensions are overlapped or similar to each other, and it is hard to differentiate one extension from the other. For this reason, in practice, researchers simply pick one of many extensions and apply it to the analysis. In this paper, to enlighten practitioners who should conduct statistical procedures not based on their preferences but based on how data look like, we comparatively review various extensions and their estimators. Also, we fully investigate the inclusion and exclusion relations of different extensions by Venn diagrams and examples. Moreover, in the numerical study, we illustrate visual differences of the extensions by bivariate plots and analyze different scatter matrix estimators based on the microarray data.

How many people participated in candlelight protests? Counting the size of a dynamic crowd.

The recent controversy about the size of crowds at candlelight protests in Korea raises an interesting question regarding the methods used to estimate crowd size. Protest organizers tend to count all participants in the event from its start to finish, while the police usually report the crowd size at its peak. While several counting methods are available to estimate the size of a crowd at a given time, counting the total number of the participants at a protest is not straightforward. In this paper, we propose a new estimator to count the total number of participants that we call the size of a dynamic crowd. We assume that the arrival and departure times of the crowd are randomly observed and that the number of the attendees in the crowd at a specific time is estimable. We estimate the number of total attendees during the entire gathering based on the capture-recapture model. We also propose a bootstrap procedure to construct a confidence interval for the crowd size. We demonstrate the performance of the proposed method with simulation studies and the data from Korea's March for Science, a global event across the world on Earth Day, April 22, 2017.

Ultrasound and MR muscle imaging in new onset idiopathic inflammatory myopathies at diagnosis and after treatment: a comparative pilot study.

OBJECTIVES: To prospectively compare ultrasound (US) and whole-body MRI for detection of muscle abnormalities compatible with idiopathic inflammatory myopathies (IIM). METHODS: Newly diagnosed IIM patients underwent US (14 muscles) and MRI (36 muscles) at diagnosis and after nine weeks monotherapy with intravenous immunoglobulin. Muscles were compatible with IIM when quantitative US echo-intensity (EI) z scores was ≥1.5, semi-quantitative US Heckmatt score was ≥2, qualitative US was abnormal, or when MRI showed oedema on T2-weighted images. At patient level, findings were classified as abnormal when quantitative US EI z scores was >1.5 (n = 3 muscles), >2.5 (n = 2 muscles) or >3.5 (n = 1 muscle), or if ≥3 muscles showed abnormalities as described above for the other diagnostic methods. RESULTS: At diagnosis, in 18 patients US of 252 muscles revealed abnormalities in 36 muscles (14%) with quantitative, in 153 (61%) with semi-quantitative and in 168 (67%) with qualitative analysis. MRI showed oedema in 476 out of 623 muscles (76%). Five patients (28%) reached abnormal classification with quantitative US, 16 (89%) with semi-quantitative and qualitative US, and all patients (100%) with MRI. Nine-week follow-up of 12 patients showed no change over time with quantitative US or MRI, and a decrease in abnormalities with semi-quantitative US (P <0.01), and qualitative US (P <0.01). CONCLUSION: At diagnosis, MRI was more sensitive than US to detect muscle abnormalities compatible with IIM. Semi-quantitative US and qualitative US detected abnormalities in the majority of the patients while evaluating fewer muscles than MRI and showed change over time after nine weeks of treatment.

Empirical likelihood inference for area under the receiver operating characteristic curve using ranked set samples.

The area under a receiver operating characteristic curve (AUC) is a useful tool to assess the performance of continuous-scale diagnostic tests on binary classification. In this article, we propose an empirical likelihood (EL) method to construct confidence intervals for the AUC from data collected by ranked set sampling (RSS). The proposed EL-based method enables inferences without assumptions required in existing nonparametric methods and takes advantage of the sampling efficiency of RSS. We show that for both balanced and unbalanced RSS, the EL-based point estimate is the Mann-Whitney statistic, and confidence intervals can be obtained from a scaled chi-square distribution. Simulation studies and two case studies on diabetes and chronic kidney disease data suggest that using the proposed method and RSS enables more efficient inference on the AUC.

Metabolic and Cardiovascular Benefits of Apple and Apple-Derived Products: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Background: Quantitative evidence of the metabolic and cardiovascular effects of apples (Malus domestica) is lacking in interventional studies. This study aimed to summarize the available evidence of the beneficial effects of apples and apple-derived products (ADPs) on metabolic and cardiovascular markers. Methods: Peer-reviewed randomized controlled trials (RCTs) were identified from four databases on May 3, 2021 and regularly updated until the end of May 2021. Demographic characteristics, intervention types, and evaluation parameters were extracted. A meta-analysis on the mean difference of change scores was conducted on commonly presented outcomes in the RCTs. Results: The metabolic and cardiovascular effects of diverse regimens, including whole apple, apple extract, and apple juice, were examined in 18 eligible RCTs. Nine common evaluation outcomes were eventually introduced to the meta-analysis, including total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride, glucose, insulin, C-reactive protein, and systolic/diastolic blood pressures. The levels of TC (-2.69 mg/dL; 95% CI: -5.43, 0.04 mg/dL) and LDL (-2.80 mg/dL; 95% CI: -5.78, 0.17 mg/dL) showed a non-significant decreasing tendency after at least a week of apple consumption. Further subgroup analysis, particularly, a comparison with placebo as a control, showed a significant reduction in TC and LDL levels. When stratified by the baseline level, subjects with high TC and LDL level were shown to have more benefits from the apple intake. Intriguingly, apple and ADPs significantly reduced HDL levels to a small extent (-1.04 mg/dL; 95% CI: -1.79, -0.29 mg/dL). The other markers were mostly unaffected by the intervention. Conclusion: Our investigation revealed that apples could improve blood cholesterol levels. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42020215977].

Lipidome profile predictive of disease evolution and activity in rheumatoid arthritis.

Lipid mediators are crucial for the pathogenesis of rheumatoid arthritis (RA); however, global analyses have not been undertaken to systematically define the lipidome underlying the dynamics of disease evolution, activation, and resolution. Here, we performed untargeted lipidomics analysis of synovial fluid and serum from RA patients at different disease activities and clinical phases (preclinical phase to active phase to sustained remission). We found that the lipidome profile in RA joint fluid was severely perturbed and that this correlated with the extent of inflammation and severity of synovitis on ultrasonography. The serum lipidome profile of active RA, albeit less prominent than the synovial lipidome, was also distinguishable from that of RA in the sustained remission phase and from that of noninflammatory osteoarthritis. Of note, the serum lipidome profile at the preclinical phase of RA closely mimicked that of active RA. Specifically, alterations in a set of lysophosphatidylcholine, phosphatidylcholine, ether-linked phosphatidylethanolamine, and sphingomyelin subclasses correlated with RA activity, reflecting treatment responses to anti-rheumatic drugs when monitored serially. Collectively, these results suggest that analysis of lipidome profiles is useful for identifying biomarker candidates that predict the evolution of preclinical to definitive RA and could facilitate the assessment of disease activity and treatment outcomes.

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data.

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

Efficacy and safety of intravenous and subcutaneous immunoglobulin therapy in idiopathic inflammatory myopathy: A systematic review and meta-analysis.

OBJECTIVE: To perform a systematic review and meta-analysis on the efficacy and safety of intravenous (IVIg) and subcutaneous (SCIg) immunoglobulin (Ig) therapy in the treatment of idiopathic inflammatory myopathy (IIM) and juvenile dermatomyositis (JDM). METHODS: PubMed, Embase and SCOPUS were searched to identify studies on Ig therapy in patients with IIM and/or JDM (2010-2020). Outcome measures were complete response (CR) or partial response (PR) in terms of muscle power and extramuscular disease activity measures on the International Myositis Assessment and Clinical Studies Group (IMACS) core set domains. RESULTS: Twenty-nine studies were included (n = 576, 544 IIM, 32 JDM). Muscle power PR with pooled Ig therapy was 88.5% (95% confidence interval (CI): 80.6-93.5, n = 499) and PR with SCIg treatment was 96.61% (95% CI: 87.43-99.15, n = 59). Pooled PR with first-line use of IVIg was 77.07% (95% CI: 61.25-92.89, n = 80). Overall, mean time to response was 2.9 months (95% CI: 1.9-4.1). Relapse was seen in 22.76% (95% CI: 14.9-33). Studies on cutaneous disease activity and dysphagia showed significant treatment responses. Glucocorticoid and immunosuppressant sparing effect was seen in 40.9% (95% CI: 20-61.7) and 42.2% (95% CI: 20.4-64.1) respectively. Ig therapy was generally safe with low risk of infection (1.37%, 95% CI: 0.1-2.6). CONCLUSIONS: Add-on Ig therapy improves muscle strength in patients with refractory IIM, but evidence on Ig therapy in new-onset disease and extramuscular disease activity is uncertain.

Assessment of disability in idiopathic inflammatory myopathy: a call for linearity.

OBJECTIVES: To evaluate the clinimetric properties of the Academic Medical Centre Disability Score (ALDS) in patients with idiopathic inflammatory myopathy (IIM). METHODS: We used prospectively collected data of IIM patients who completed a phase-2 study with first-line IVIG monotherapy. The ALDS is a patient-reported questionnaire which contains 25 items relevant for disability in myositis. ALDS and all core set measures (CSMs) for myositis [including HAQ-Disability Index (HAQ-DI)] were evaluated at baseline and 9 weeks follow-up. In addition, the 2016 ACR/EULAR myositis response criteria outcome called Total Improvement Score (TIS) was evaluated at 9 weeks. We examined floor/ceiling effects, reliability and construct validity of the ALDS. To examine known-group validity, ALDS change scores over time were compared with TIS and physician impression of clinical response. RESULTS: Nineteen patients with IIM [median age 59 years, 12 (63%) female] were enrolled. At baseline, ALDS showed a median score of 65.4 (IQR 58.2-73.5), good Cronbach's alpha (α = 0.84) and a small ceiling effect (11%). Construct validity was confirmed by moderate to strong correlations between ALDS and HAQ-DI [rs = -0.57 (baseline); -0.86 (follow-up)]. ALDS change score correlated with TIS (rs = 0.70), discriminated between responders and non-responders (TIS ≥ 40; P = 0.001), between groups based on physician impression of clinical response (P = 0.03), and detected deterioration. CONCLUSION: The ALDS showed promising clinimetric properties and detected relevant changes in disability in patients with myositis. These results warrant further investigations.

Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea.

The fused lasso signal approximator (FLSA) is a smoothing procedure for noisy observations that uses fused lasso penalty on unobserved mean levels to find sparse signal blocks. Several path algorithms have been developed to obtain the whole solution path of the FLSA. However, it is known that the FLSA has model selection inconsistency when the underlying signals have a stair-case block, where three consecutive signal blocks are either strictly increasing or decreasing. Modified path algorithms for the FLSA have been proposed to guarantee model selection consistency regardless of the stair-case block. In this paper, we provide a comprehensive review of the path algorithms for the FLSA and prove the properties of the recently modified path algorithms' hitting times. Specifically, we reinterpret the modified path algorithm as the path algorithm for local FLSA problems and reveal the condition that the hitting time for the fusion of the modified path algorithm is not monotone in a tuning parameter. To recover the monotonicity of the solution path, we propose a pathwise adaptive FLSA having monotonicity with similar performance as the modified solution path algorithm. Finally, we apply the proposed method to the number of daily-confirmed cases of COVID-19 in Korea to identify the change points of its spread.

Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants.

Chromatin dynamics are central to the regulation of gene expression and genome stability. In order to improve understanding of the factors regulating chromatin dynamics, the genes encoding these factors are deleted and the differential gene expression profiles are determined using approaches such as RNA sequencing. Here, we analyzed a gene expression dataset aimed at uncovering the function of the relatively uncharacterized chromatin regulator, Set4, in the model system Saccharomyces cerevisiae (budding yeast). The main theme of this paper focuses on identifying the highly differentially expressed genes in cells deleted for Set4 (referred to as Set4 Δ mutant dataset) compared to the wild-type yeast cells. The Set4 Δ mutant data produce a spiky distribution on the log-fold changes of their expressions, and it is reasonably assumed that genes which are not highly differentially expressed come from a mixture of two normal distributions. We propose an adaptive local false discovery rate (FDR) procedure, which estimates the null distribution of the log-fold changes empirically. We numerically show that, unlike existing approaches, our proposed method controls FDR at the aimed level (0.05) and also has competitive power in finding differentially expressed genes. Finally, we apply our procedure to analyzing the Set4 Δ mutant dataset.

Borrelia burgdorferi sensu lato seroconversion after intravenous immunoglobulin treatment: A cohort study.

OBJECTIVE: Intravenous immunoglobulin (IVIg) consists of pooled donor immunoglobulins (IgG), possibly including anti-Borrelia burgdorferi (Bbsl) antibodies. Apparent IVIg-related Bbsl seroconversion could lead to incorrect diagnosis of Lyme borreliosis. This cohort study was designed to determine how often IVIg treatment leads to apparent Bbsl seroconversion and whether antibodies disappear post-treatment. METHODS: Sera from chronic inflammatory demyelinating polyneuropathy (CIDP) and myositis patients were analyzed, drawn pre-treatment and 6-12 weeks after the start of IVIg. In patients with apparent seroconversion, follow-up samples after treatment withdrawal were analyzed, if available. Patients treated with corticosteroids were included as controls. A two-tier protocol was used for serological testing consisting of the C6 Lyme ELISA (Oxford Immunotec) and confirmation by immunoglobulin M (IgM) and immunoglobulin G (IgG) immunoblot (Mikrogen® ). RESULTS: We included 61 patients: 51 patients were treated with IVIg and 10 with dexamethasone. Of the patients treated with IVIg, 42 had CIDP (82%) and were treated with Nanogam® (Sanquin Plasma Products). Nine patients had myositis (18%) and were treated with Privigen® (CSL Behring). Anti-Bbsl IgG seroprevalence pre-treatment was 3% (2/61). Apparent seroconversion during IVIg treatment occurred in 39% (20/51) of patients, all treated with Nanogam. Post-treatment seroreversion occurred in 92% (12/13) of patients with available follow-up samples; in 78% (7/9) seroreversion was observed within 3 months. CONCLUSIONS: Transient presence of anti-Bbsl IgG antibodies after IVIg is regularly observed. This effect appears to be dependent on the IVIg brand, probably reflecting variation in Bbsl exposure of plasma donors. Lyme borreliosis serological testing during, and weeks to months after, IVIg is therefore of limited utility.

Effects of ß-Cryptoxanthin on Improvement in Osteoporosis Risk: A Systematic Review and Meta-Analysis of Observational Studies.

Many studies have analyzed the effects of ß-cryptoxanthin (BCX) on osteoporosis and bone health. This systematic review and meta-analysis aimed at providing quantitative evidence for the effects of BCX on osteoporosis. Publications were selected and retrieved from three databases and carefully screened to evaluate their eligibility. Data from the final 15 eligible studies were extracted and uniformly summarized. Among the 15 studies, seven including 100,496 individuals provided information for the meta-analysis. A random effects model was applied to integrate the odds ratio (OR) to compare the risk of osteoporosis and osteoporosis-related complications between the groups with high and low intake of BCX. A high intake of BCX was significantly correlated with a reduced risk of osteoporosis (OR = 0.79, 95% confidence interval (CI) 0.70-0.90, p = 0.0002). The results remained significant when patients were stratified into male and female subgroups as well as Western and Asian cohorts. A high intake of BCX was also negatively associated with the incidence of hip fracture (OR = 0.71, 95% CI 0.54-0.94, p = 0.02). The results indicate that BCX intake potentially reduces the risk of osteoporosis and hip fracture. Further longitudinal studies are needed to validate the causality of current findings.