Communications medicine最新文献

Background: Long COVID is an infection-associated chronic condition with uncertain evolution, leading to ambiguity in case definitions and various hypotheses about its pathophysiology. Despite this diversity, causal models may offer a unified understanding of post-acute COVID-19 mechanisms. This study aimed to examine whether dynamic Bayesian networks could facilitate inferences on long COVID.

Methods: Using a causal engineering approach, we developed directed acyclic graphs and qualitatively parametrised them as Bayesian networks to depict the hypothesised mechanisms of long COVID in a theory-agnostic manner. Based on the literature and expert knowledge, we created a general modelling framework summarising biological pathways from mild or severe COVID-19 to the development of respiratory symptoms and fatigue over four key periods (t₁ to t₄). We used qualitative parametrisation for design and validation, and tested the framework against four scenarios: A) mild COVID-19 at t₁ (start of acute infection); B) severe acute COVID-19 at t₁; C) symptoms reported at t₁ (acute COVID-19 disease); and D) symptoms reported at t₁ and t₃ (e.g., 3-to-6 months post-acute infection), indicating long COVID.

Results: Here we show that, in scenario A, the probability of progressing to severe disease and developing persistent organ dysfunction 1-to-2 years post-acute COVID-19 was lower than in scenario C. Those reporting symptoms at t₁ and t₃ have the highest probability of developing persistent organ dysfunction beyond the acute infection period.

Conclusions: Our findings lay the foundations for a better understanding of the progression of long COVID syndromes. Illustrative simulations support the use of causal models to help address both diagnostic and prognostic questions in long COVID research.

Background: Continuous positive airway pressure (CPAP) remains the cornerstone of therapy for obstructive sleep apnea, yet its impact on preventing cardiovascular disease remains uncertain. Despite widespread clinical use, randomized controlled trials have not shown cardiovascular benefits with CPAP. Emerging evidence suggests that obstructive sleep apnea is a heterogeneous disease, and a uniform approach to treatment may obscure potential benefits or harm for individuals.

Methods: To address this, we applied causal survival forest analysis to data from the SAVE trial (n = 2,687), the largest clinical trial evaluating CPAP for cardiovascular disease prevention, to estimate individualized treatment effect scores for each participant.

Results: Our model reveals significant heterogeneity in treatment response across the cohort (area under the target operator characteristic curve 2.6; 95% confidence interval 2.03-4.55; p < 0.001). Survival analysis demonstrates that participants in the tertile predicted to benefit from CPAP experienced a 100-fold improvement in event-free survival when randomized to CPAP (p < 0.001), whereas those in the tertile predicted to be harmed experienced a > 100-fold increase in major adverse cardiovascular outcomes (p < 0.001).

Conclusions: To our knowledge, these findings provide the first evidence of individualized treatment effect estimates for CPAP therapy in obstructive sleep apnea. These results also highlight the potential for precision medicine approaches to guide treatment decisions, reduce cardiovascular disease risk, and avoid harm in susceptible individuals.

Background: Genetic aberrations are among the critical driving factors of lung cancer. Importantly, the impact of genetic variations on proteomic dysregulations with the goal of characterizing potential diagnostic biomarkers at the population-level requires additional investigation. Modeling such proteogenomic interactions is crucial in understanding early-stage biological disruptions to inform biomarker discovery, successful clinical trials, and developing effective therapeutics.

Methods: We investigated two complementary aspects of lung cancer risk. First, we performed a genome-wide association study of lung cancer using population-scale datasets, then examined whether lung cancer risk-associated variants influence plasma protein levels using the UK Biobank Pharma Proteomics Project data. Second, we identified plasma proteomic dysregulations in presymptomatic and symptomatic patients with the objective of pinpointing diagnostic biomarkers through leveraging machine learning methods.

Results: Using the identified proteins, machine learning models achieved median cross-validated AUCs of 0.85-0.88 (0-4 years before diagnosis [YBD]), 0.81-0.84 (5-9 YBD), and 0.80-0.86 (0-9 YBD). Performing survival analyses within the 5-9 YBD group, elevated levels of eight proteins, such as CALCB, PLAUR, and CD74, were found to significantly associate with lower survival. We identified 22 disease-associated proteins, of which 14 have been previously implicated in lung cancer, including CEACAM5, CXCL17, GDF15, WFDC2 along with 8 novel proteins. These proteins were enriched in pathways related to cytokine signaling, interleukin regulation, neutrophil degranulation, and lung fibrosis.

Conclusions: While these findings do not establish mechanistic causality, they highlight proteomic alterations reflecting systemic changes preceding the diagnosis. Our study contributes to understanding genome-proteome relationships in lung cancer and identifies circulating proteins warranting further investigation as potential early biomarkers for screening and risk stratification.

Background: Moderate-to-vigorous physical activity (MVPA) is inversely associated with risks of cancer, cardiovascular diseases (CVD), type 2 diabetes (T2D), and their co-occurrence, defined as multimorbidity; however, the underlying biological pathways remain unclear.

Methods: In 33,806 UK Biobank participants with 2911 measured blood proteins, a proteomic signature of MVPA was derived with linear and LASSO regressions. Multivariable Cox models, adjusted for MVPA, estimated prospective associations with cancer, CVD, T2D, and multimorbidity.

Results: We show that after multiple testing corrections, 220 proteins are retained in the MVPA signature. Proteins related to food intake, metabolism, and cell growth (e.g., LEP, MSTN) are inversely associated, while those involved in immune cell migration and musculoskeletal integrity (e.g., integrins, COMP) are positively associated with MVPA. Several proteins positively associated with MVPA are inversely associated with disease risk (e.g., integrins, CLEC4A for cancer; LPL, LEP for T2D), while proteins negatively associated with MVPA are positively associated with disease risk (e.g., CD38, TGFA for CVD). The proteomic signature score is inversely associated with cancer risk (hazard ratio per interquartile range: 0.87; 95% confidence interval: 0.78, 0.96) and T2D (0.66; 0.60, 0.72). For multimorbidity, proteins inversely related to MVPA align with expected risk patterns (e.g., GGT1, HR: 1.32; 95% CI: 1.12, 1.57), but the proteomic signature score is not associated.

Conclusions: This study identifies several proteins associated with MVPA that are also associated with cancer, CVD, T2D, and the multimorbidity of these conditions. Further studies investigating the causal nature of these associations are welcome.

Background: Proteins and RNA circulate in plasma and can offer insights into human physiology. Yet, despite their clinical importance, direct comparisons between these analytes remain unexplored.

Methods: Here, we measure and compare plasma cell-free RNA (cfRNA) and protein levels for 263 children diagnosed with inflammatory diseases, specifically either Kawasaki disease (KD) or Multisystem Inflammatory Syndrome in Children (MIS-C), by RNA sequencing (n = 108 KD and n = 47 MIS-C, mean age=4.2 years) and SomaScan proteomics (n = 70 KD and n = 101 MIS-C, mean age=6.8 years).

Results: Here we show that cell-free RNA and protein levels are largely uncorrelated across samples (feature-by-sample correlation coefficient 0.052; median feature-level correlation coefficient 0.009). Nonetheless, machine learning models based on either modality distinguish KD from MIS-C with similar high accuracy (median area under the curve greater than 0.93). Analysis of KD subtypes reveals distinct cell-free RNA and protein signatures, with one group showing molecular similarity to MIS-C.

Conclusions: These findings underscore the complementary nature of cell-free RNA and protein profiling and highlight the utility of integrating multiple plasma analytes to improve disease classification and deepen our understanding of complex inflammatory conditions.

Background: Alzheimer's disease (AD) carries a high societal burden inequitably distributed across demographic groups. Using real-world electronic health record (EHR) data with accurate population identification, we examine demographic differences and potentially modifiable drivers of AD decline.

Methods: Leveraging EHR data (1994-2022) from two large independent healthcare systems, we applied an unsupervised phenotyping algorithm to predict AD diagnosis and validated using gold-standard chart-reviewed and registry-derived diagnosis labels. Among patients with ≥24 months of EHR data not living in nursing homes pre-AD diagnosis, we estimated the time-to-decline (nursing home admission, death) in healthcare system-specific covariate-adjusted competing risk survival analyses stratified by demographic groups. We then performed covariate-adjusted fixed-effects meta-analyses using inverse variance weighting.

Results: The algorithm demonstrates robust performance in identifying AD populations across healthcare systems and demographic groups (AUROC score range: 0.835-0.923). Of the 29,262 AD patients in both healthcare systems (61% women, 90% non-Hispanic White, 79.52 ± 9.39 years of age at AD diagnosis), 49% transition to nursing homes and 52% die during follow-up. In covariate-adjusted fixed-effects meta-analysis, women have higher nursing home admission risk (HR [95% CI] = 1.061 [1.024-1.100], p = 1.203×10^-3) but lower death risk (HR [95% CI] = 0.856 [0.811-0.904], p = 2.434×10^-8) than men. Non-Hispanic White individuals have similar nursing home risk (HR [95% CI] = 1.006 [0.952-1.063], p = 8.306×10^-1) but higher death risk (HR [95% CI] = 1.376 [1.245-1.521], p = 4.084×10^-10) than racial and ethnic minorities. Older age at AD diagnosis and greater comorbidity burden increase both nursing home admission and death risk.

Conclusions: We provide real-world evidence of drivers of demographic differences in AD decline that could inform individual clinical management and public health policies.

Background: Crush syndrome (consisting of hyperkalemia, acidosis, hypocalcemia, and acute kidney injury), is the second-most common cause of death in earthquakes, and a frequent cause of critical illness after burn, blast, and prolonged immobility. No specific treatment exists; supportive treatment is burdensome, contributing to deaths in austere environments, especially disasters and conflicts. There is urgent need for specific treatment which reduces burden of care. Crush syndrome is dependent on the renal megalin-dependent endocytic system. We investigated whether cilastatin sodium, a megalin inhibitor which is US Food and Drug Administration-approved for another purpose, has efficacy as a crush syndrome treatment in a highly translational large animal trauma model.

Methods: Anesthetized 40 kg female pigs received blunt muscle injury and 48 h protocolized critical care management. Cilastatin sodium or vehicle was administered 30 minutes after injury in randomized, blinded fashion. Renal function and injury were assessed by repeated quantification of iohexol clearance, serial plasma assessment, and histopathologic analysis. Linear mixed models and Kaplan-Meier analysis were used to assess differences. A power estimate for a clinical trial was performed.

Results: Here we show that cilastatin has efficacy to reduce kidney impairment from crush syndrome, resulting in increased measured glomerular filtration rate and reduced creatinine, histopathologic kidney damage, and need for treatment of hyperkalemia. Animals receiving cilastatin excrete more myoglobin in the urine and are more likely to recover from acute kidney injury. The effect size suggests feasibility of future clinical trials.

Conclusions: Cilastatin sodium has efficacy to ameliorate crush syndrome in a translational large animal model These results support further efforts to translate this potential therapy.

Background: DNA methylation analysis provides a promising triage strategy for cervical intraepithelial neoplasia (CIN) and cancer detection following Human Papillomavirus (HPV) testing on self-collected samples, including urine.

Methods: This study aimed to develop an entirely molecular cervical screening approach based on HPV and DNA methylation analysis in at-home collected first-void urine from healthy females (n = 69) and a referral population (n = 385; CIN-cancer). CIN3+ detection was analyzed by multivariate logistic regression.

Results: Here we show that urinary ASCL1/LHX8 methylation levels increase significantly in relation to disease severity, with AUC-values for CIN3+ of 0.81 (95% CI: 0.74-0.88) and 0.83 (95% CI: 0.74-0.92) in the training (n = 285) and validation cohort (n = 160), respectively. This corresponds to a validated CIN3+ sensitivity of 73.0% (95% CI: 57.0-84.6%) at 81.9% specificity (95% CI: 73.5-88.1%;

Conclusions: The ASCL1/LHX8 methylation test detects nearly all cancers and a majority of CIN3 in first-void urine, supporting the potential of full molecular screening in urine by primary HPV testing and methylation triage.

Background: The combination of immune checkpoint inhibitors (ICIs) with radiotherapy (RT) has improved survival in patients with advanced lung cancer, yet treatment-related pneumonitis (TRP) is highly toxic. Current limitations include the absence of robust risk stratification models for TRP and unclear safety criteria for ICI rechallenge, hindering the management of recurrent TRP (rTRP).

Methods: In this multicenter retrospective study, we analyzed 262 lung cancer patients receiving radioimmunotherapy. The data included baseline demographics, immunotherapy regimens, and radiotherapy parameters. TRP was graded per CTCAE v5.0. Univariate and multivariate logistic regression identified predictors of TRP and rTRP severity.

Results: The incidence of TRP is 57.6% (151/262), with grade 1, 2, and ≥ 3 TRP occurring in 24.4%, 25.6%, and 7.6% of patients, respectively. The median time to TRP onset is 113 days (IQR, 73-155). Multivariate analysis reveals PD-1 inhibitors and ICI cycle number as predictors of G ≥ 1 TRP, whereas Dmean predicts all grades. Dose‒response modeling reveals Dmean thresholds for 50% TRP risk: 8.7 Gy (G ≥ 1), 15.3 Gy (G ≥ 2), and 23.4 Gy (G ≥ 3). Among the 53 patients receiving ICI rechallenge, 25.4% (13/53) developed rTRP. Rechallenge within 4 weeks significantly increases rTRP risk (OR, 0.061; 95% CI, 0.010-0.368; P = 0.002).

Conclusions: Dmean serves as a continuous risk modifier for TRP severity, with Dmean > 15.3 Gy associated with grade ≥ 2 toxicity. An interval of ≥ 4 weeks after TRP is proposed as a safety window for ICI rechallenge, addressing critical gaps in radioimmunotherapy toxicity management.