Ambient air pollution exposure and the risk of probable sarcopenia: A prospective cohort study.

BACKGROUND: Sarcopenia is characterized by decreased muscle mass and strength, posing threat to quality of life. Air pollutants are increasingly recognized as risk factors for diseases, while the relationship between the two remains to be elucidated. This study investigated whether exposure to ambient air pollution contributes to the development of sarcopenia. METHODS: We employed the data from the UK Biobank with 303,031 eligible participants. Concentrations of PM2·5, NO2, and NOx were estimated. Cox proportional hazard regression models were applied to investigate the associations between pollutants and sarcopenia. RESULTS: 30,766 probable sarcopenia cases was identified during the follow-up. We observed that exposure to PM2.5 (HR, 1.232; 95% CI, 1.053-1.440), NO2 (HR, 1.055; 95% CI, 1.032-1.078) and NOx (HR, 1.016; 95% CI, 1.007-1.026) were all significantly associated with increased risk for probable sarcopenia for each 10 µg/m3 increase in pollutant concentration. In comparison with individuals in the lowest quartiles of exposure, those in the upper quartiles had significantly increased risk of probable sarcopenia. Sarcopenia-related factors, e.g., reduced lean muscle mass, diminished walking pace, and elevated muscle fat infiltration ratio, also exhibited positive associations with exposure to ambient air pollution. On the contrary, high level physical activity significantly mitigated the influence of air pollutants on the development of probable sarcopenia. CONCLUSIONS: Air pollution exposure elevated the risk of developing sarcopenia and related manifestations in a dose-dependent manner, while physical activity maintained protective under this circumstance. Efforts should be made to control air pollution and emphasize the importance of physical activity for skeletal muscle health under this circumstance.

[FARSB stratifies prognosis and cold tumor microenvironment across different cancer types: an integrated single cell and bulk RNA sequencing analysis].

OBJECTIVE: Immunotherapy has brought significant clinical benefits to a subset of patients, but has thus far been disappointing in the treatment of immunologically "cold" tumors. Existing biomarkers that can precisely identify these populations are insufficient. In this context, a potential cold tumor microenvironment (TME) marker FARSB was investigated to reveal its impact on TME and patients' response to immunotherapy across pan-cancer. METHODS: The expression levels and mutational landscape of FARSB in pan-cancer were investigated. Kaplan-Meier and univariate Cox regression analyses were applied to analyze the prognostic significance of FARSB. Pathways affected by FARSB were investigated by gene set enrichment and variation analysis. The relationship between FARSB expression and immune infiltration was examined using the TIMER2 and R packages. Single-cell RNA sequencing (scRNA-seq) data of several cancer types from GSE72056, GSE131907, GSE132465, GSE125449 and PMID32561858 were analyzed to validate the impact of FARSB on the TME. The predictive effect of FARSB on immunotherapy efficacy was explored in 3 immune checkpoint inhibitors (ICIs)- treated cohorts (PMID32472114, GSE176307, and Riaz2017). RESULTS: FARSB expression was significantly higher in 25 tumor tissues than in normal tissues and was associated with poor prognosis in almost all tumor types. FARSB expression exhibited a strong association with several DNA damage repair pathways and was significantly associated with TP53 mutation in lung adenocarcinoma (P < 0.0001, OR=2.25). FARSB characterized a typical immune desert TME and correlated with impaired expression of chemokines and chemokines receptors. Large-scale scRNA-seq analysis confirmed the immunosuppressive role of FARSB and revealed that FARSB potentially shapes the cold TME by impeding intercellular interactions. In 3 ICI-treated cohorts, FARSB demonstrated predictive value for immunotherapy. CONCLUSION: This study provides a pan-cancer landscape of the FARSB gene by integrated single-cell and bulk DNA sequencing analysis and elucidates its biological function to promote DNA damage repair and construct the immune desert TME, suggesting the potential value of FARSB as a novel marker for stratifying patients with poor immunotherapeutic benefits and "cold" TME.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS: In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS: As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS: Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS: Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.

PARP Inhibition Induces Synthetic Lethality and Adaptive Immunity in LKB1-Mutant Lung Cancer.

Contradictory characteristics of elevated mutational burden and a "cold" tumor microenvironment (TME) coexist in liver kinase B1 (LKB1)-mutant non-small cell lung cancers (NSCLC). The molecular basis underlying this paradox and strategies tailored to these historically difficult to treat cancers are lacking. Here, by mapping the single-cell transcriptomic landscape of genetically engineered mouse models with Kras versus Kras/Lkb1-driven lung tumors, we detected impaired tumor-intrinsic IFNγ signaling in Kras/Lkb1-driven tumors that explains the inert immune context. Mechanistic analysis showed that mutant LKB1 led to deficiency in the DNA damage repair process and abnormally activated PARP1. Hyperactivated PARP1 attenuated the IFNγ pathway by physically interacting with and enhancing the poly(ADP-ribosyl)ation of STAT1, compromising its phosphorylation and activation. Abrogation of the PARP1-driven program triggered synthetic lethality in NSCLC on the basis of the LKB1 mutation-mediated DNA repair defect, while also restoring phosphorylated STAT1 to favor an immunologically "hot" TME. Accordingly, PARP1 inhibition restored the disrupted IFNγ signaling and thus mounted an adaptive immune response to synergize with PD-1 blockade in multiple LKB1-deficient murine tumor models. Overall, this study reveals an unexplored interplay between the DNA repair process and adaptive immune response, providing a molecular basis for dual PARP1 and PD-1 inhibition in treating LKB1-mutant NSCLC. SIGNIFICANCE: Targeting PARP exerts dual effects to overcome LKB1 loss-driven immunotherapy resistance through triggering DNA damage and adaptive immunity, providing a rationale for dual PARP and PD-1 inhibition in treating LKB1-mutant lung cancers.

Organ-specific metastatic landscape dissects PD-(L)1 blockade efficacy in advanced non-small cell lung cancer: applicability from clinical trials to real-world practice.

BACKGROUND: Organ-specific metastatic context has not been incorporated into the clinical practice of guiding programmed death-(ligand) 1 [PD-(L)1] blockade, due to a lack of understanding of its predictive versus prognostic value. We aim at delineating and then incorporating both the predictive and prognostic effects of the metastatic-organ landscape to dissect PD-(L)1 blockade efficacy in non-small cell lung cancer (NSCLC). METHODS: A total of 2062 NSCLC patients from a double-arm randomized trial (OAK), two immunotherapy trials (FIR, BIRCH), and a real-world cohort (NFyy) were included. The metastatic organs were stratified into two categories based on their treatment-dependent predictive significance versus treatment-independent prognosis. A metastasis-based scoring system (METscore) was developed and validated for guiding PD-(L)1 blockade in clinical trials and real-world practice. RESULTS: Patients harboring various organ-specific metastases presented significantly different responses to immunotherapy, and those with brain and adrenal gland metastases survived longer than others [overall survival (OS), p = 0.0105; progression-free survival (PFS), p = 0.0167]. In contrast, survival outcomes were similar in chemotherapy-treated patients regardless of metastatic sites (OS, p = 0.3742; PFS, p = 0.8242). Intriguingly, the immunotherapeutic predictive significance of the metastatic-organ landscape was specifically presented in PD-L1-positive populations (PD-L1 > 1%). Among them, a paradoxical coexistence of a favorable predictive effect coupled with an unfavorable prognostic effect was observed in metastases to adrenal glands, brain, and liver (category I organs), whereas metastases to bone, pleura, pleural effusion, and mediastinum yielded consistent unfavorable predictive and prognostic effects (category II organs). METscore was capable of integrating both predictive and prognostic effects of the entire landscape and dissected OS outcome of NSCLC patients received PD-(L)1 blockade (p < 0.0001) but not chemotherapy (p = 0.0805) in the OAK training cohort. Meanwhile, general performance of METscore was first validated in FIR (p = 0.0350) and BIRCH (p < 0.0001), and then in the real-world NFyy cohort (p = 0.0181). Notably, METscore was also applicable to patients received PD-(L)1 blockade as first-line treatment both in the clinical trials (OS, p = 0.0087; PFS, p = 0.0290) and in the real-world practice (OS, p = 0.0182; PFS, p = 0.0045). CONCLUSIONS: Organ-specific metastatic landscape served as a potential predictor of immunotherapy, and METscore might enable noninvasive forecast of PD-(L)1 blockade efficacy using baseline radiologic assessments in advanced NSCLC.

Adversarial Incomplete Multiview Subspace Clustering Networks.

Multiview clustering aims to leverage information from multiple views to improve the clustering performance. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the problem of incomplete multiview clustering (IMC). Previous approaches to this problem have at least one of the following drawbacks: 1) employing shallow models, which cannot well handle the dependence and discrepancy among different views; 2) ignoring the hidden information of the missing data; and 3) being dedicated to the two-view case. To eliminate all these drawbacks, in this work, we present the adversarial IMC (AIMC) framework. In particular, AIMC seeks the common latent representation of multiview data for reconstructing raw data and inferring missing data. The elementwise reconstruction and the generative adversarial network are integrated to evaluate the reconstruction. They aim to capture the overall structure and get a deeper semantic understanding, respectively. Moreover, the clustering loss is designed to obtain a better clustering structure. We explore two variants of AIMC, namely: 1) autoencoder-based AIMC (AAIMC) and 2) generalized AIMC (GAIMC), with different strategies to obtain the multiview common representation. Experiments conducted on six real-world datasets show that AAIMC and GAIMC perform well and outperform the baseline methods.

Inferring Homologous Recombination Deficiency of Ovarian Cancer From the Landscape of Copy Number Variation at Subchromosomal and Genetic Resolutions.

BACKGROUND: Homologous recombination deficiency (HRD) is characterized by overall genomic instability and has emerged as an indispensable therapeutic target across various tumor types, particularly in ovarian cancer (OV). Unfortunately, current detection assays are far from perfect for identifying every HRD patient. The purpose of this study was to infer HRD from the landscape of copy number variation (CNV). METHODS: Genome-wide CNV landscape was measured in OV patients from the Australian Ovarian Cancer Study (AOCS) clinical cohort and >10,000 patients across 33 tumor types from The Cancer Genome Atlas (TCGA). HRD-predictive CNVs at subchromosomal resolution were identified through exploratory analysis depicting the CNV landscape of HRD versus non-HRD OV patients and independently validated using TCGA and AOCS cohorts. Gene-level CNVs were further analyzed to explore their potential predictive significance for HRD across tumor types at genetic resolution. RESULTS: At subchromosomal resolution, 8q24.2 amplification and 5q13.2 deletion were predominantly witnessed in HRD patients (both p < 0.0001), whereas 19q12 amplification occurred mainly in non-HRD patients (p < 0.0001), compared with their corresponding counterparts within TCGA-OV. The predictive significance of 8q24.2 amplification (p < 0.0001), 5q13.2 deletion (p = 0.0056), and 19q12 amplification (p = 0.0034) was externally validated within AOCS. Remarkably, pan-cancer analysis confirmed a cross-tumor predictive role of 8q24.2 amplification for HRD (p < 0.0001). Further analysis of CNV in 8q24.2 at genetic resolution revealed that amplifications of the oncogenes, MYC (p = 0.0001) and NDRG1 (p = 0.0004), located on this fragment were also associated with HRD in a pan-cancer manner. CONCLUSIONS: The CNV landscape serves as a generalized predictor of HRD in cancer patients not limited to OV. The detection of CNV at subchromosomal or genetic resolution could aid in the personalized treatment of HRD patients.

Effect of expanding opioid agonist therapies on the HIV epidemic and mortality in Ukraine: a modelling study.

BACKGROUND: As HIV incidence and mortality continue to increase in eastern Europe and central Asia, particularly among people who inject drugs (PWID), it is crucial to effectively scale-up opioid agonist therapy (OAT), such as methadone or buprenorphine maintenance therapy, to optimise HIV outcomes. With low OAT coverage among PWID, we did an optimisation assessment using current OAT procurement and allocation, then modelled the effect of increased OAT scale-up on HIV incidence and mortality for 23 administrative regions of Ukraine. METHODS: We developed a linear optimisation model to estimate efficiency gains that could be achieved based on current procurement of OAT. We also developed a dynamic, compartmental population model of HIV transmission that included both injection and sexual risk to estimate the effect of OAT scale-up on HIV infections and mortality over a 10-year horizon. The compartmental population model was calibrated to HIV prevalence and incidence among PWID for 23 administrative regions of Ukraine. Sources for regional data included the SyrEx database, the Integrated Biological and Behavioral Survey, the Ukrainian Center for Socially Dangerous Disease Control of the Ministry of Health of Ukraine, the Public Health Center of the Ministry of Health of Ukraine, and the Ukrainian Census. FINDINGS: Under a status-quo scenario (OAT coverage of 2·7% among PWID), the number of new HIV infections among PWID in Ukraine over the next 10 years was projected to increase to 58 820 (95% CI 47 968-65 535), with striking regional differences. With optimum allocation of OAT without additional increases in procurement, OAT coverage could increase from 2·7% to 3·3% by increasing OAT doses to ensure higher retention levels. OAT scale-up to 10% and 20% over 10 years would, respectively, prevent 4368 (95% CI 3134-5243) and 10 864 (7787-13 038) new HIV infections and reduce deaths by 7096 (95% CI 5078-9160) and 17 863 (12 828-23 062), relative to the status quo. OAT expansion to 20% in five regions of Ukraine with the highest HIV burden would account for 56% of new HIV infections and 49% of deaths prevented over 10 years. INTERPRETATION: To optimise HIV prevention and treatment goals in Ukraine, OAT must be substantially scaled up in all regions. Increased medication procurement is needed, combined with optimisation of OAT dosing. Restricting OAT scale-up to some regions of Ukraine could benefit many PWID, but the regions most affected are not necessarily those with the highest HIV burden. FUNDING: National Institute on Drug Abuse.