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PubMed2026

Sex-specific biomarkers predict bone mineral density loss at the contralateral hip after hip fracture.

OBJECTIVE: To identify inflammatory and hormonal biomarkers that predict bone loss at the contralateral (non-fractured) hip following hip fracture in males and females. METHODS: White participants who were not receiving pre-fracture glucocorticoids, sex-hormone therapy, or bone-active medications (100 males, 76 females) with hip fractures. Data were collected within 22 days of hip fracture and at 2, 6, and 12 months follow-up. Biomarkers were categorized into tertiles: estradiol, 25-hydroxyvitamin D3/D2, intact parathyroid hormone (iPTH), interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), insulin-like growth factor-1 (IGF-1), soluble tumor necrosis factor-α receptor 1, sex hormone-binding globulin, and testosterone. Femoral neck bone mineral density (BMD) at the contralateral hip was assessed, and losses exceeding the mean decline were classified as greater than average. Logistic regression models, stratified by sex, were adjusted for confounders and evaluated selected biomarker associations. RESULTS: Among males, the 2nd (OR = 4.79, P = 0.012) and 3rd (OR = 6.36, P = 0.005) IGF-1 tertiles were associated with greater odds of BMD loss than the 1st tertile. The 3rd iPTH tertile (OR = 3.79, P = 0.037) was similarly associated with increased odds. Among females, the 3rd (OR = 0.20, P = 0.031) IL-1RA tertile was associated with lower odds of BMD loss compared to the 1st tertile, while the 2nd IL-6 tertile (OR = 5.99, P = 0.036) was associated with higher odds. CONCLUSION: These findings suggest that inflammatory and hormonal biomarkers may be sex-specific predictors of accelerated BMD loss following hip fracture.

Biomarkers↗

PubMed2026

No association between alcohol consumption and hip osteoarthritis: a diverse national analysis of 87,585 adults from the "All of Us" research program.

INTRODUCTION: Hip osteoarthritis (OA) is estimated to affect 62.6 million individuals by 2050. A probable link exists between alcohol use and hip OA. However, the results are inconsistent, and the relationship between alcohol and hip OA remains speculative. To address these gaps, this study aimed to utilize the diverse, nationally representative All of Us Research Program dataset to explore the association between alcohol consumption and hip OA. METHODS: This retrospective case-control study utilized data from the All of Us Research Program Controlled Tier Dataset v8. 17,517 hip OA cases and 70,068 controls were identified. A 1:4 case-to-control matching ratio was applied based on age and sex. Alcohol use frequency was categorized into five levels: Never, Monthly or Less, Two to Four Times per Month, Two to Three Times per Week, and Four or More Times per Week. Multivariable logistic regression models evaluated the association between alcohol use frequency and hip OA after adjusting for demographic and clinical variables. RESULTS: Multivariable analysis found that alcohol use frequency was not significantly associated with hip OA. Compared to never users, participants with low (OR 0.98, 95% CI 0.93-1.04, P = 0.583), moderate (OR 0.99-1.01, all P > 0.05), and high (OR 1.02, 95% CI 0.95-1.09, P = 0.599) levels of alcohol consumption had no statistically significant differences in odds of hip OA. Female sex, Asian race, diabetes, hypertension, hyperlipidemia, and nicotine dependence increased the odds of hip OA. CONCLUSION: Any level of alcohol consumption was not significantly associated with the odds of hip OA. This study adds valuable insight to the current body of conflicting evidence. Further prospective studies appear warranted to shed light on the long-term effects of different alcoholic beverages on different joints. Key Points • This study found no significant association between any degree of alcohol consumption and the odds of developing hip osteoarthritis. • Utilizing data from 87,585 adults in the NIH "All of Us" Research Program, this is the first study to analyze this relationship in a large, nationally representative population. • The research provides clarity to previously conflicting literature by demonstrating that alcohol lacks a clear harmful or protective effect on the clinical course of the disease. • The analysis highlights that independent risk factors such as Asian race, nicotine dependence, and components of metabolic syndrome increase the odds of hip osteoarthritis.

Humans↗

PubMed2026

Genetic Ancestry and Colorectal Cancer in the All of Us Dataset.

IMPORTANCE: Genetic ancestry may complement biological, behavioral, and clinical factors in understanding colorectal cancer (CRC) disparities; yet, ancestry-informed analyses in CRC remain limited. OBJECTIVE: To characterize associations of genetic ancestry with CRC burden, age at diagnosis, and age-specific risk, and to develop a multiethnic CRC risk-prediction model. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used All of Us data from July 1986 to October 2023, with follow-up through last visit or death (median [IQR], 133.1 [57.1-186.5] months); analyses were conducted from February to June 2026. All of Us is a US research cohort with linked electronic health record (EHR) and short-read whole-genome sequencing (srWGS) data. All of Us Research Program participants with srWGS and linked EHR data were included, except those with hereditary polyposis or Lynch syndrome. EXPOSURES: Genetically inferred ancestry categories and principal components. MAIN OUTCOMES AND MEASURES: Any CRC was the primary outcome. Associations were evaluated using Fisher exact tests, cumulative incidence functions with Gray tests, cause-specific and Fine-Gray subdistribution hazard models, and pooled multivariable logistic regression. Prediction models used penalized least absolute shrinkage and selection operator and extreme gradient boosting (XGBoost). RESULTS: Among 316 624 participants (median [IQR] age, 56.3 [40.2-68.2] years; 172 327 [54.4%] of European ancestry; 191 705 female [61.2%]; 121 585 male [38.8%]), 2914 (0.9%) developed CRC. European ancestry was associated with higher odds of CRC vs all other ancestries combined (odds ratio, 1.50; 95% CI, 1.39-1.62). The median age at CRC diagnosis was older in European (63.4 [53.9-71.2] years) than in American admixed-Latino, African, East Asian, and Other ancestry groups. In cause-specific hazard models on the attained-age scale, American admixed-Latino (hazard ratio, 1.30; 95% CI, 1.14-1.47) and East Asian (hazard ratio, 1.43; 95% CI, 1.06-1.94) ancestry had higher age-specific CRC hazard than European ancestry, with consistent findings on the subdistribution scale accounting for competing death. The multiethnic XGBoost model performed best (receiver operating characteristic area under the curve, 0.898; 95% CI, 0.882-0.912; precision-recall area under the curve, 0.338; 95% CI, 0.296-0.379) and was well calibrated. CONCLUSIONS AND RELEVANCE: In this cohort study, genetic ancestry was associated with meaningful differences in CRC burden and age-specific risk. These findings suggest that a multiethnic XGBoost model may complement CRC screening as a risk-enrichment tool.

Aged↗

PubMed2026

The presence or absence of standard modifiable cardiovascular risk factors in patients with myocardial infarction impacts long-term but not 30-day mortality: a UK Biobank prospective cohort study.

AIMS: Prior studies reported higher early mortality after acute myocardial infarction (MI) in patients without standard modifiable cardiovascular risk factors (SMuRFs), warranting further validation. We aimed to evaluate whether SMuRF-absence is associated with increased 30-day cardiovascular mortality following MI. METHODS AND RESULTS: We conducted a population-based cohort study using UK Biobank data (n = 487 177). Incident MI cases occurring between 2006 and 2022 were identified through linkage to hospital and death registries. Standard modifiable cardiovascular risk factors (diabetes, hypertension, hypercholesterolaemia, current smoker) were defined at baseline and continuously assessed until MI onset. Thirty-day mortality following MI was estimated using Cox proportional hazards models, adjusted for sociodemographic, clinical, and cardiogenomic variables, were used to estimate 30-day mortality risks. Logistic regression model was used to estimate mortality risk at 10 years post-MI. Among 15 463 patients experiencing an MI (1034 without SMuRFs), SMuRF-absence was not significantly associated with 30-day mortality (HR: 0.82, 95% CI: 0.65-1.04, P = 0.103). Propensity score-matched analyses supported these findings (HR: 0.95, 95% CI: 0.69-1.29, P = 0.729). Further analyses stratified by distinct time intervals (2006-2022) revealed no significant modification of this association by advancements in acute MI management. Interaction analyses indicated no significant effect modification by sex, age, socioeconomic status, or period of MI occurrence. However, extended analysis to 10 years revealed that SMuRF absence was significantly associated with lower long-term mortality (OR: 0.61, 95% CI: 0.49-0.75, P < 0.01). CONCLUSION: In this population-based cohort, SMuRF status significantly impacted long-term but not short-term mortality following MI, indicating early survival is predominantly driven by acute-phase factors rather than baseline cardiovascular risk profiles.

Humans↗

PubMed2026

Oncotype DX-guided vs physician-directed chemotherapy and survival in HR+/HER2- breast cancer.

BACKGROUND: Oncotype DX testing guides adjuvant chemotherapy decisions in early-stage hormone receptor-positive/HER2-negative breast cancer, but testing is not universally performed, and outcomes associated with genomic-informed versus clinicopathologic-based chemotherapy decision pathways remain unclear. METHODS: Using the 2022 National Cancer Database Breast Participant User File, we identified women diagnosed from 2010 to 2022 with pathologic T1b-T2, node-negative, hormone receptor-positive/HER2-negative invasive breast cancer who received adjuvant chemotherapy and endocrine therapy. Patients were classified into an Oncotype-guided group, defined by Oncotype DX testing with a recurrence score of 26 or higher, and a physician-directed group, defined by receipt of chemotherapy without genomic testing. The primary outcome was overall survival. Analyses used multivariable Cox models, logistic-IPTW and MLP-IPTW, restricted mean survival time analysis, and a Bayesian latent confounding survival model. RESULTS: Among 56,625 women, 27,278 were in the Oncotype-guided group and 29,347 in the physician-directed group. Median ages were 59 and 56 years, respectively. The Oncotype-guided group had more favorable overall survival than the physician-directed group in multivariable Cox analysis (HR, 0.906; 95% CI, 0.856-0.959; P < 0.001), with similar findings in IPTW analyses. The association was concentrated among patients aged 56 years or older (HR, 0.866; 95% CI, 0.809-0.927; P < 0.001). The Bayesian model showed no strong residual confounding signal. CONCLUSIONS: Among chemotherapy-treated women, an Oncotype-guided pathway was associated with more favorable overall survival than a physician-directed pathway, particularly among older patients, which indicating prognostic heterogeneity selected using genomic versus conventional clinicopathologic information.

Humans↗

PubMed2026

Unveiling the BMI Risk Threshold for Osteoarthritis: Multi-Database Causal and Nonlinear Evidence.

OBJECTIVE: To characterize the nonlinear relationship between BMI and osteoarthritis (OA), and to identify BMI thresholds that inform precise prevention strategies. METHODS: This multi-database study integrated Global burden of disease 2021, National Health and Nutrition Examination Survey 2007-2018, and Genome-Wide Association Studies. A generalized additive model was performed to visualize the BMI-OA relationship, adjusting for multiple confounders. We applied segmented logistic regression models to identify potential threshold effects and used Mendelian randomization to estimate the causal effects of BMI on OA subtypes. RESULTS: From 1990 to 2021, the age-standardized prevalence and years lived with disability rates for OA were highest in regions with high SDI. OA prevalence rose nonlinearly with BMI, with breakpoints at 24.00 and 41.58 kg/m2. Each unit increase in BMI was associated with higher odds of OA between 24.00 and 41.58 kg/m2 (OR = 1.022, 95% CI: 1.003-1.041) and above 41.58 kg/m2 (OR = 1.055, 95% CI: 1.022-1.090). Women and individuals aged ≥ 45 years exhibited a higher susceptibility to knee osteoarthritis. BMI was causally associated with knee osteoarthritis (OR = 1.63, 95% CI 1.50-1.77) and hip osteoarthritis (OR = 1.54, 95% CI 1.40-1.70). CONCLUSIONS: These findings suggest that OA risk awareness and weight-management strategies should begin before BMI reaches the high range, particularly among individuals with BMI exceeding 24.00 kg/m2.

Humans↗

PubMed2026

Machine learning to differentiate colonization from infection in multidrug-resistant Gram-negative bacteria: implications for further research.

PURPOSE OF REVIEW: Machine learning has emerged as a promising tool to support antimicrobial decision-making in infectious diseases. In colonized patients, distinguishing multidrug-resistant Gram-negative bacteria (MDR-GNB) colonization from true infection remains a major clinical challenge, as both delayed appropriate therapy in severe infections and unnecessary broad-spectrum antimicrobial use may adversely affect patient outcomes and antimicrobial stewardship. This review discusses the current evidence on machine learning models for predicting or detecting MDR-GNB infection in colonized patients, highlights key methodological limitations of the available literature, and outlines future research priorities. RECENT FINDINGS: Current evidence specifically evaluating machine learning models beyond logistic regression in MDR-GNB-colonized patients remains limited. Overall, while machine learning may achieve encouraging discriminatory performance, important methodological limitations persist. Most notably, predictive models are frequently developed in heterogeneous populations that do not reflect the clinically relevant populations of colonized patients in which treatment decisions are made. Furthermore, improvements in predictive performance remain modest, possibly reflecting limited sample sizes and data granularity rather than insufficient algorithmic complexity. In our opinion, future advances could require multicenter datasets enriched with longitudinal clinical, microbiological, and genomic information, together with automated feature extraction from electronic health records. SUMMARY: The main challenge for machine learning in predicting MDR-GNB infection in colonized patients may lie not in developing increasingly sophisticated algorithms, but in generating clinically representative datasets and adopting rigorous methodological standards for model development, validation, calibration, and implementation. Future research should prioritize clinically meaningful target populations and demonstrate improvements in patient outcomes and antimicrobial stewardship beyond conventional measures of predictive performance.

antimicrobial resistance↗

PubMed2025

Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study.

OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classification model that was previously applied to baseline data and a new machine learning model with 195 biopsychosocial features. METHOD: Participants were 7,503 unrelated adolescents (54.5% female, ages 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) Study. An existing baseline STB history classification model was applied to predict longitudinal first-onset STB in adolescents compared with healthy controls and clinical controls (individuals with a mental health disorder but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n = 5,220), and the resulting top 15 features were validated at 7 independent sites (n = 2,283). RESULTS: The previously developed model to classify STB lifetime history also prospectively predicted first-onset STB in adolescents with an area under the curve (AUC) [95% CI] of 0.73 [0.70, 0.75], p < .001, compared with healthy controls and AUC [95% CI] of 0.63 [0.60, 0.66], p < .001, compared with clinical controls. The newly trained model with top 15 features performed similarly with AUC [95% CI] of 0.73 [0.71, 0.76], p < .001, and AUC [95% CI] of 0.64 [0.60, 0.66], p < .001, for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments. CONCLUSION: The models predicted first-onset STB in adolescents with moderate accuracy. This study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate these models in large-scale diverse samples before clinical translation. PLAIN LANGUAGE SUMMARY: This study followed over 7,500 adolescents for 4 years and tested 2 machine learning models using psychological, social, and brain data to identify those at risk of experiencing suicidal thoughts or behaviors. Both models predicted first-time suicidal thoughts or behaviors with moderate accuracy. Key risk factors that were identified included being female, experiencing sleep problems, and negative home and school environments. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Adolescent↗

PubMed2026

Antimicrobial resistance analysis of Klebsiella pneumoniae bloodstream infections based on a random forest algorithm: a longitudinal study based on data from tertiary hospitals in China from 2012 to 2023.

BACKGROUND: Bloodstream infections (BSIs) caused by Klebsiella pneumoniae pose a significant global health burden, complicated by rising antimicrobial resistance (AMR). This study aimed to characterize resistance patterns, identify predictors of carbapenem resistance, and develop a machine learning model to predict patient outcomes. METHODS: In a retrospective analysis of 109 279 K. pneumoniae BSIs from tertiary hospitals in China (2012-2023), 11 000 isolates underwent whole-genome sequencing (WGS) and antimicrobial susceptibility testing. Cox proportional hazards and logistic regression models identified predictors of 30-day mortality and carbapenem-resistant K. pneumoniae (CRKP), respectively. A random forest model predicted AMR trends and outcomes, evaluated by accuracy, precision, recall, and ROC-AUC using R Studio (R Studio, Inc., Boston, MA, USA). RESULTS: Carbapenem resistance occurred in 32.3% of isolates, with rates of 41.9% for third-generation cephalosporins and 41.2% for fluoroquinolones. Among sequenced isolates, ST11 with blaKPC was the dominant CRKP genotype (12.0%). blaKPC (OR 3.97, 95% CI 3.10-5.11) and blaNDM (OR 2.80, 95% CI 2.07-3.71) strongly predicted carbapenem resistance; ICU admission predicted 30-day mortality (HR 2.10, 95% CI 1.80-2.46, p<0.001). Mortality was higher in CRKP (40.2%) vs. susceptible cases (21.5%). The random forest model achieved 89.2% accuracy and 0.92 ROC-AUC, with drug share, age, and CRKP status as top predictors. CONCLUSIONS: CRKP, especially ST11-blaKPC, drives excess mortality. Key predictors highlight the urgency for enhanced AMR surveillance and targeted therapy.

Humans↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

PubMed2026

Association of PCSK9 and CCL22 gene polymorphisms with myocardial infarction in a South Indian population.

Myocardial infarction (MI) remains a major global cause of morbidity and mortality, with a particularly high burden among individuals with type 2 diabetes mellitus (T2DM). Host genetic factors play a significant role in modulating individual susceptibility to MI by influencing lipid metabolism and immune-mediated inflammatory pathways. The proprotein convertase subtilisin/kexin type 9 (PCSK9) gene is a key regulator of cholesterol homeostasis, while C-C motif chemokine ligand 22 (CCL22) is involved in immune cell recruitment and vascular inflammation. In this study, we investigated the association of PCSK9 rs505151 and rs11591147 and CCL22 rs4359426 polymorphisms with MI risk in a South Indian population. This case-control study included 400 participants categorized into controls (n = 100), MI (n = 100), T2DM (n = 100), and MI with T2DM (n = 100). Significant differences in clinical and biochemical parameters, including lipid indices and cardiometabolic risk markers, were observed between groups (p < 0.05). Genetic analysis revealed a significant association between the PCSK9 rs505151 variant and MI susceptibility across allelic and genotypic distributions, with significant effects under dominant and recessive inheritance models. Multivariable logistic regression confirmed that the rs505151 risk genotype was independently associated with MI after adjustment for age, sex, body mass index, and smoking status. In contrast, PCSK9 rs11591147 was rare and showed no significant association. The CCL22 rs4359426 polymorphism showed limited evidence of association with MI, with a significant effect observed only under the dominant inheritance model. Furthermore, combined analysis using a genetic risk score suggested that cumulative genetic burden involving PCSK9 and CCL22 variants was associated with an increased risk of MI. Overall, our findings suggest that genetic variation in lipid-regulatory and immune-related pathways may contribute to MI susceptibility in South Indians. Further studies are warranted to validate these associations and clarify their biological and clinical relevance.

Humans↗

PubMed2024

A methylation risk score for chronic kidney disease: a HyperGEN study.

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Humans↗

PubMed2025

MyESL: A Software for Evolutionary Sparse Learning in Molecular Phylogenetics and Genomics.

Evolutionary sparse learning uses supervised machine learning to build evolutionary models where genomic sites loci are parameters. It uses the Least Absolute Shrinkage and Selection Operator with bi-level sparsity to connect a specific phylogenetic hypothesis with sequence variation across genomic loci. The MyESL software addresses the need for open-source tools to perform evolutionary sparse learning analyses, offering features to preprocess input phylogenomic alignments, post-process output models to generate molecular evolutionary metrics, and make Least Absolute Shrinkage and Selection Operator regression adaptable and efficient for phylogenetic trees and alignments. The core of MyESL, which constructs models with logistic regressions using bi-level sparsity, is written in C++. Its input data preprocessing and result post-processing tools are developed in Python. Compared to other tools, MyESL is more computationally efficient and provides evolution-friendly inputs and outputs. These features have already enabled the use of MyESL in two phylogenomic applications, one to identify outlier sequences and fragile clades in inferred phylogenies and another to build genetic models of convergent traits. In addition to the use in a Python environment, MyESL is available as a standalone executable compatible across multiple platforms, which can be directly integrated into scripts and third-party software. The source code, executable, and documentation for MyESL are openly accessible at https://github.com/kumarlabgit/MyESL.

Phylogeny↗

PubMed2026

The association of 25-hydroxyvitamin D deficiency with neuroinflammation and prognosis in HIV-negative cryptococcal meningitis.

BACKGROUND: Cryptococcal meningitis (CM) in HIV-negative individuals is increasing, yet the role of vitamin D remains unclear. This study investigates serum 25-hydroxyvitamin D [25(OH)D] levels and their clinical implications in HIV-negative CM patients. METHODS: We conducted a retrospective case-control study of 93 HIV-negative CM patients and 191 healthy controls (HCs). Serum 25(OH)D levels, cerebrospinal fluid (CSF) fungal burden, cytokine profiles, the incidence of postinfectious inflammatory response syndrome (PIIRS), and one-year mortality were assessed. Bivariate logistic regression models identified predictors of mortality. RESULTS: CM patients had significantly lower serum 25(OH)D levels than HCs (18.33 vs. 23.69 ng/mL, p < 0.001), with a higher rate of deficiency (<20 ng/mL) in the CM group (59.14% vs. 34.03%, p < 0.001). Lower 25(OH)D levels were associated with elevated CSF levels of IL-6 and IL-8 (p < 0.05). Deficiency was linked to increased PIIRS incidence (43.64% vs. 21.05%, p = 0.028). Bivariate logistic regression showed a protective trend for 25(OH)D levels (OR 0.939, 95% CI 0.877-1.006, p = 0.075), although deficiency was not associated with higher mortality. CONCLUSIONS: Serum 25(OH)D deficiency is prevalent in HIV-negative CM patients and linked to neuroinflammation and increased risk of PIIRS. Serum 25(OH)D levels may serve as a useful prognostic marker, although further research is needed.

Humans↗

PubMed2026

Accelerated Biological Aging Increases the Risk of Head and Neck Cancer: Insights From Genetic Instruments of Epigenetic Clocks.

Epigenetic clocks are robust biomarkers of biological aging and have been associated with cancer susceptibility. However, the relationship between genetically predicted epigenetic age acceleration and head and neck cancer risk remains unclear. Using a large case-control study of 2189 head and neck squamous cell carcinoma (HNSCC) cases and 2189 age- and sex-matched controls, we investigated the associations between polygenic scores (PGSs) for multiple epigenetic clocks and HNSCC risk, and evaluated their potential causal roles using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS)-identified single nucleotide polymorphisms (SNPs) associated with four epigenetic clocks (HannumAge, HorvathAge, GrimAge, and PhenoAge) were used to construct clock-specific PGSs. Logistic regression models were applied to assess associations between PGSs and HNSCC risk, while MR analyses, including inverse-variance weighted (IVW), weighted median, and MR-Egger methods, were used to infer potential causal relationships. Among the 48 epigenetic clock-associated SNPs, 12 showed nominal associations with HNSCC risk, and one variant (rs2275558 in PBX1) remained significant after Bonferroni correction (OR = 0.67, 95% CI: 0.60-0.76). PGSs for all four epigenetic clocks were higher in cases than in controls. In logistic regression analyses, each standard deviation increase in HannumAge PGS was associated with a 25% higher risk of HNSCC (OR = 1.25, 95% CI: 1.10-1.41), whereas HorvathAge, GrimAge, and PhenoAge PGSs showed weaker positive associations (ORs ranging from 1.06 to 1.10). Individuals in the highest PGS quartile for all four epigenetic clocks exhibiting 14%-25% higher risk than those in the lower three quartiles. MR analyses supported potential causal effects of genetically predicted HannumAge (IVW OR = 1.24 per SD increase, 95% CI: 1.09-1.42) and GrimAge (IVW OR = 1.23 per SD increase, 95% CI: 0.98-1.56) on HNSCC risk, with consistent estimates in weighted median analyses. Our results highlight biological aging as a potential etiologic mechanism for HNSCC and suggest that epigenetic clock-related genetic profiles may improve HNSCC risk stratification.

Humans↗

PubMed2026

Genome-wide association analysis reveals specialization to hosts and niches in multiple species of the Lactobacillaceae.

The Lactobacillaceae inhabit diverse environments, but the extent of their habitat adaptation remains unclear and the colonization factors unknown. First, we applied multiple machine learning models to determine if we can distinguish strains of the same species isolated from two different habitats based on their gene content. Surprisingly, we show that no species is differentially adapted to the oral cavity versus the human gut, or food versus the human gut, while only Lactobacillus crispatus showed specialization to the human urogenital system versus human gut. We then asked which species of Lactobacillaceae are habitat-specialized and how they could be identified. Using multiple lifestyle predictors incorporated in logistic regression models, we found that Limosilactobacillus reuteri, Ligilactobacillus ruminis, L. salivarius, L. crispatus, and L. mucosae displayed the highest degrees of host specialization. Applying our microbial genome-wide association study tool, aurora, to these species identified genes encoding adhesins and bacteriocins as the strongest and most common adaptation factors. This work establishes a generalizable framework for identifying novel species-habitat pairs with strong evidence of specialization and for uncovering the genomic features underlying within-species host and habitat adaptation.

Humans↗

PubMed2024

Geospatial Analysis of Multilevel Socioenvironmental Factors Impacting the Campylobacter Burden among Infants in Rural Eastern Ethiopia: A One Health Perspective.

Increasing attention has focused on health outcomes of Campylobacter infections among children younger than 5 years in low-resource settings. Recent evidence suggests that colonization by Campylobacter species contributes to environmental enteric dysfunction, malnutrition, and growth faltering in young children. Campylobacter species are zoonotic, and factors from humans, animals, and the environment are involved in transmission. Few studies have assessed geospatial effects of environmental factors along with human and animal factors on Campylobacter infections. Here, we leveraged Campylobacter Genomics and Environmental Enteric Dysfunction project data to model multiple socioenvironmental factors on Campylobacter burden among infants in eastern Ethiopia. Stool samples from 106 infants were collected monthly from birth through the first year of life (December 2020-June 2022). Genus-specific TaqMan real-time polymerase chain reaction was performed to detect and quantify Campylobacter spp. and calculate cumulative Campylobacter burden for each child as the outcome variable. Thirteen regional environmental covariates describing topography, climate, vegetation, soil, and human population density were combined with household demographics, livelihoods/wealth, livestock ownership, and child-animal interactions as explanatory variables. We dichotomized continuous outcome and explanatory variables and built logistic regression models for the first and second halves of the infant's first year of life. Infants being female, living in households with cattle, reported to have physical contact with animals, or reported to have mouthed soil or animal feces had increased odds of higher cumulative Campylobacter burden. Future interventions should focus on infant-specific transmission pathways and create adequate separation of domestic animals from humans to prevent potential fecal exposures.

Humans↗

PubMed2024

Predictive modeling of gene mutations for the survival outcomes of epithelial ovarian cancer patients.

Epithelial ovarian cancer (EOC) has a low overall survival rate, largely due to frequent recurrence and acquiring resistance to platinum-based chemotherapy. EOC with homologous recombination (HR) deficiency has increased sensitivity to platinum-based chemotherapy because platinum-induced DNA damage cannot be repaired. Mutations in genes involved in the HR pathway are thought to be strongly correlated with favorable response to treatment. Patients with these mutations have better prognosis and an improved survival rate. On the other hand, mutations in non-HR genes in EOC are associated with increased chemoresistance and poorer prognosis. For this reason, accurate predictions in response to treatment and overall survival remain challenging. Thus, analyses of 360 EOC cases on NCI's The Cancer Genome Atlas (TCGA) program were conducted to identify novel gene mutation signatures that were strongly correlated with overall survival. We found that a considerable portion of EOC cases exhibited multiple and overlapping mutations in a panel of 31 genes. Using logistical regression modeling on mutational profiles and patient survival data from TCGA, we determined whether specific sets of deleterious gene mutations in EOC patients had impacts on patient survival. Our results showed that six genes that were strongly correlated with an increased survival time are BRCA1, NBN, BRIP1, RAD50, PTEN, and PMS2. In addition, our analysis shows that six genes that were strongly correlated with a decreased survival time are FANCE, FOXM1, KRAS, FANCD2, TTN, and CSMD3. Furthermore, Kaplan-Meier survival analysis of 360 patients stratified by these positive and negative gene mutation signatures corroborated that our regression model outperformed the conventional HR genes-based classification and prediction of survival outcomes. Collectively, our findings suggest that EOC exhibits unique mutation signatures beyond HR gene mutations. Our approach can identify a novel panel of gene mutations that helps improve the prediction of treatment outcomes and overall survival for EOC patients.

Humans↗