Pulmonary artery denervation versus conventional therapies for PAH: a systematic review and updated network meta-analysis.

AIMS: A recent guideline presented by the ESC Congress in 2022 had indicated a novel therapy targeted at pulmonary artery hypertension, known as pulmonary artery denervation (PADN), which get inspired from a laboratorial trial that could lowering the pulmonary artery pressure through the intervention on the animals. Our aim is to conduct a network meta-analysis to compare the efficacy and safety of PADN from six aspects with the current conventional therapies. METHODS AND RESULTS: According to the PRISMA guidance, databases including Ovid, ClinicalTrials.gov, Medline, Embase, and PubMed were searched from inception to 22 August 2023, along with a full assessment of the previous five meta-analyses. Data were extracted and curated for Bayesian network meta-analysis. The primary outcome was the change in the 6-min walking distance (6MWD) from baseline with a secondary outcome called change in mean pulmonary artery pressure (mPAP) from baseline. The four safety outcomes included risk of clinical worsening, hospitalization, mortality and severe adverse events (SAEs). The comparison is structured on a contrast model based on 65 randomized controlled trials (RCTs) on PADN and the other conventional mainstream drugs. PADN had a better effect in improving 6MWD than Placebo (-77.76 m, 95% CI: -102.04 to -54.34 m), Macitentan (-65.32 m, 95% CI: -95.34 to -36.1 m), Bosentan (-64.5 m, 95% CI: -94.7 to -35.07 m), Iloprost (-62.66 m, 95% CI: -99.48 to -27.13 m), Oxygen (-62.42 m, 95% CI: -100.01 to -25.78 m), Treprostinil (-62.01 m, 95% CI: -89.04 to -35.61 m), Riociguat (-60.59 m, 95% CI: -86.11 to -35.98 m), Selexipag (-47.2 m, 95% CI: -85.61 to -10.19 m), Sildenafil (-44.92 m, 95% CI: -74.43 to -16.15 m), or Sitaxsentan (-39.53 m, 95% CI: -78.99 to -0.76 m). PADN had a better antihypertensive effect than placebo and showed statistical significant lower risks to induce clinical worsening and re-hospitalization than treprostinil, riociguat, and placebo groups. No statistically significant difference in risk of mortality and severe adverse events was observed between PADN versus the other interventions. CONCLUSIONS: Compared with 16 types of conventional therapies and Placebo, PADN has advantage over nine single therapies and Placebo in improving 6MWD and appears to be better than two types of dual-drug combined therapies while with no statistical significance. PADN shows a favourable antihypertensive effect on mPAP and has a lower risk to trigger clinical worsening or hospitalization, while its risk on mortality and severe adverse events is still inconclusive.

Metabolic dysfunction-associated steatotic liver disease-related hepatic fibrosis increases risk of insulin resistance, type 2 diabetes, and chronic kidney disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) (previously called nonalcoholic fatty liver disease, NAFLD) is associated with cardiometabolic risk factors and chronic kidney disease (CKD). However, evidence is lacking regarding whether the severity of fibrosis is affected by these risk factors and diseases and to what degree. We aimed to determine the correlation between these factors and vibration-controlled transient elastography-determined liver stiffness measurements (LSMs) and controlled attenuation parameter (CAP) values in a sample of the US population. Data from the 2017-2018 cycle of the National Health and Nutrition Examination Survey were pooled. The association between LSM and cardiometabolic risk factors and CKD was assessed using generalized linear or logistic regression analyses. In multivariate regression analyses, CAP and BMI were adjusted as confounders. Of 3647 participants, 2079 (57.1%) had NAFLD/MASLD [weighted prevalence 54.8%; 95% confidence interval (CI) 51.8-57.9%]; the weighted prevalence of significant fibrosis (LSM ≥ 7.9 kPa) was 9.7% (95% CI 8.2-11.3%). Log LSM was associated with higher levels of homeostatic model assessment of insulin resistance ( ß â€…= 2.19; P  = 0.017), hepatic steatosis (CAP > 248 dB/m) [odds ratio (OR) 3.66; 95% CI 2.22-6.02], type 2 diabetes (OR 2.69; 95% CI 1.72-4.20), and CKD (OR 1.70; 95% CI 1.24-2.34). These correlations did not change notably after adjustments were made for waist circumference, CAP, and BMI. LSM and CAP, although influenced by waist circumference and BMI, are good indicators of hepatic fibrosis and steatosis. LSM is associated with insulin resistance, diabetes, and CKD independent of hepatic steatosis and obesity.

OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations.

BACKGROUND: A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model. METHODS: We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCOm2012), PLCO modified 2014 (PLCOall2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RIEO), and illustrated the clinical utility by the decision curve analysis. FINDINGS: For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCOall2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCOall2014 (AUCPLCOall2014-AUCOWL < 1%). For ever-smokers, OWL outperformed PLCOm2012 and PLCOall2014 among ever-smokers in validation set 1 (AUCOWL = 0.842, 95% CI: 0.814-0.871; AUCPLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUCPLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCOm2012 and PLCOall2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCOm2012, PLCOall2014 (RIEO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RIEO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCOall2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCOm2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCOm2012 and PLCOall2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations. INTERPRETATION: OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer. FUNDING: National Natural Science Foundation of China, the US NIH.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians.

INTRODUCTION: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). METHODS: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. RESULTS: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. CONCLUSIONS: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.