Distinguishing Smoking-Related Lung Disease Phenotypes Via Imaging and Molecular Features

Interplay between microglia and environmental risk factors in Alzheimer's disease

Alzheimer's disease, among the most common neurodegenerative disorders, is characterized by progressive cognitive impairment. At present, the Alzheimer's disease main risk remains genetic risks, but major environmental factors are increasingly shown to impact Alzheimer's disease development and progression. Microglia, the most important brain immune cells, play a central role in Alzheimer's disease pathogenesis and are considered environmental and lifestyle "sensors." Factors like environmental pollution and modern lifestyles (e.g., chronic stress, poor dietary habits, sleep, and circadian rhythm disorders) can cause neuroinflammatory responses that lead to cognitive impairment via microglial functioning and phenotypic regulation. However, the specific mechanisms underlying interactions among these factors and microglia in Alzheimer's disease are unclear. Herein, we: discuss the biological effects of air pollution, chronic stress, gut microbiota, sleep patterns, physical exercise, cigarette smoking, and caffeine consumption on microglia; consider how unhealthy lifestyle factors influence individual susceptibility to Alzheimer's disease; and present the neuroprotective effects of a healthy lifestyle. Toward intervening and controlling these environmental risk factors at an early Alzheimer's disease stage, understanding the role of microglia in Alzheimer's disease development, and targeting strategies to target microglia, could be essential to future Alzheimer's disease treatments.

Cluster Analysis to Identify Long COVID Phenotypes Using 129Xe Magnetic Resonance Imaging: A Multi-centre Evaluation

BACKGROUND

Long COVID impacts ∼10% of people diagnosed with COVID-19, yet the pathophysiology driving ongoing symptoms is poorly understood. We hypothesised that 129Xe magnetic resonance imaging (MRI) could identify unique pulmonary phenotypic subgroups of long COVID, therefore we evaluated ventilation and gas exchange measurements with cluster analysis to generate imaging-based phenotypes.

METHODS

COVID-negative controls and participants who previously tested positive for COVID-19 underwent 129XeMRI ∼14-months post-acute infection across three centres. Long COVID was defined as persistent dyspnea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully-recovered. 129XeMRI ventilation defect percent (VDP) and membrane (Mem)/Gas, red blood cell (RBC)/Mem and RBC/Gas ratios were used in k-means clustering for long COVID, and measurements were compared using ANOVA with post-hoc Bonferroni correction.

RESULTS

We evaluated 135 participants across three centres: 28 COVID-negative (40±16yrs), 34 fully-recovered (42±14yrs) and 73 long COVID (49±13yrs). RBC/Mem (p=0.03) and FEV1 (p=0.04) were different between long- and COVID-negative; FEV1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster1 was the youngest with normal MRI and mild gas-trapping; Cluster2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster3 had mildly increased Mem/Gas with normal PFTs; and Cluster4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern.

CONCLUSION

We identified four 129XeMRI long COVID phenotypes with distinct characteristics. 129XeMRI can dissect pathophysiologic heterogeneity of long COVID to enable personalised patient care.

COPD Phenotyping

COPD is a heterogeneous condition, the onset and trajectory of which is influenced not only by tobacco exposure but also an individual's genetics and the exposures they accumulate over their life course. In such a complex chronic disease, phenotyping individuals based on similar clinical or molecular characteristics can aid in guiding appropriate therapeutic management. Treatable traits, characteristics for which evidence exists for a specific favorable treatment response, are increasingly incorporated into COPD clinical guidelines. But the COPD phenotyping literature is evolving. Innovations in lung imaging and physiologic metrics, as well as omics technologies and biomarker science, are contributing to a better understanding of COPD heterogeneity. This review summarizes the evolution of COPD phenotyping, the current use of phenotyping to direct clinical care, and how innovations in clinical and molecular approaches to unraveling disease heterogeneity are refining our understanding of COPD phenotypes.

Bronchial gene expression alterations associated with radiological bronchiectasis

OBJECTIVES

Discovering airway gene expression alterations associated with radiological bronchiectasis may improve the understanding of the pathobiology of early-stage bronchiectasis.

METHODS

Presence of radiological bronchiectasis in 173 individuals without a clinical diagnosis of bronchiectasis was evaluated. Bronchial brushings from these individuals were transcriptomically profiled and analysed. Single-cell deconvolution was performed to estimate changes in cellular landscape that may be associated with early disease progression.

RESULTS

20 participants have widespread radiological bronchiectasis (three or more lobes). Transcriptomic analysis reflects biological processes associated with bronchiectasis including decreased expression of genes involved in cell adhesion and increased expression of genes involved in inflammatory pathways (655 genes, false discovery rate <0.1, log2 fold-change >0.25). Deconvolution analysis suggests that radiological bronchiectasis is associated with an increased proportion of ciliated and deuterosomal cells, and a decreased proportion of basal cells. Gene expression patterns separated participants into three clusters: normal, intermediate and bronchiectatic. The bronchiectatic cluster was enriched by participants with more lobes of radiological bronchiectasis (p<0.0001), more symptoms (p=0.002), higher SERPINA1 mutation rates (p=0.03) and higher computed tomography derived bronchiectasis scores (p<0.0001).

CONCLUSIONS

Genes involved in cell adhesion, Wnt signalling, ciliogenesis and interferon-γ pathways had altered expression in the bronchus of participants with widespread radiological bronchiectasis, possibly associated with decreased basal and increased ciliated cells. This gene expression pattern is not only highly enriched among individuals with radiological bronchiectasis, but also associated with airway-related symptoms in those without discernible radiological bronchiectasis, suggesting that it reflects a bronchiectasis-associated, but non-bronchiectasis-specific lung pathophysiological process.

Pulmonary MRI and Cluster Analysis Help Identify Novel Asthma Phenotypes

BACKGROUND

Outside eosinophilia, current clinical asthma phenotypes do not show strong relationships with disease pathogenesis or treatment responses. While chest x-ray computed tomography (CT) phenotypes have previously been explored, functional MRI measurements provide complementary phenotypic information.

PURPOSE

To derive novel data-driven asthma phenotypic clusters using functional MRI airway biomarkers that better describe airway pathologies in patients.

STUDY TYPE

Retrospective.

POPULATION

A total of 45 patients with asthma who underwent post-bronchodilator ¹²⁹ Xe MRI, volume-matched CT, spirometry and plethysmography within a 90-minute visit.

FIELD STRENGTH/SEQUENCE

Three-dimensional gradient-recalled echo ¹²⁹ Xe ventilation sequence at 3 T.

ASSESSMENT

We measured MRI ventilation defect percent (VDP), CT airway wall-area percent (WA%), wall-thickness (WT, WT* [*normalized for age/sex/height]), lumen-area (LA), lumen-diameter (D, D*) and total airway count (TAC). Univariate relationships were utilized to select variables for k-means cluster analysis and phenotypic subgroup generation. Spirometry and plethysmography measurements were compared across imaging-based clusters.

STATISTICAL TESTS

Spearman correlation (ρ), one-way analysis of variance (ANOVA) or Kruskal-Wallis tests with post hoc Bonferroni correction for multiple comparisons, significance level 0.05.

RESULTS

Based on limited common variance (Kaiser-Meyer-Olkin-measure = 0.44), four unique clusters were generated using MRI VDP, TAC, WT* and D* (52 ± 14 years, 27 female). Imaging measurements were significantly different across clusters as was the forced expiratory volume in 1-second (FEV₁ %_pred ), residual volume/total lung capacity and airways resistance. Asthma-control (P = 0.9), quality-of-life scores (P = 0.7) and the proportions of severe-asthma (P = 0.4) were not significantly different. Cluster1 (n = 15/8 female) reflected mildly abnormal CT airway measurements and FEV₁ with moderately abnormal VDP. Cluster2 (n = 12/12 female) reflected moderately abnormal TAC, WT and FEV₁ . In Cluster3 and Cluster4 (n = 14/6 female, n = 4/1 female, respectively), there was severely reduced TAC, D and FEV₁ , but Cluster4 also had significantly worse, severely abnormal VDP (7 ± 5% vs. 41 ± 12%).

DATA CONCLUSION

We generated four proof-of-concept MRI-derived clusters of asthma with distinct structure-function pathologies. Cluster analysis of asthma using ¹²⁹ Xe MRI in combination with CT biomarkers is feasible and may challenge currently used paradigms for asthma phenotyping and treatment decisions.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage.

Chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity, mortality, and health-care use worldwide. COPD is caused by exposure to inhaled noxious particles, notably tobacco smoke and pollutants. However, the broad range of factors that increase the risk of development and progression of COPD throughout the life course are increasingly being recognised. Innovations in omics and imaging techniques have provided greater insight into disease pathobiology, which might result in advances in COPD prevention, diagnosis, and treatment. Although few novel treatments have been approved for COPD in the past 5 years, advances have been made in targeting existing therapies to specific subpopulations using new biomarker-based strategies. Additionally, COVID-19 has undeniably affected individuals with COPD, who are not only at higher risk for severe disease manifestations than healthy individuals but also negatively affected by interruptions in health-care delivery and social isolation. This Seminar reviews COPD with an emphasis on recent advances in epidemiology, pathophysiology, imaging, diagnosis, and treatment.

Molecular approaches to lung cancer prevention

Lung cancer is generally diagnosed at advanced stages when surgical resection is not possible. Late diagnosis, along with development of chemoresistance, results in high mortality. Preventive approaches, including smoking cessation, chemoprevention and early detection are needed to improve survival. Smoking cessation combined with low-dose computed tomography screening has modestly improved survival. Chemoprevention has also shown some promise. Despite these successes, most lung cancer cases remain undetected until advanced stages. Additional early detection strategies may further improve survival and treatment outcome. Molecular alterations taking place during lung carcinogenesis have the potential to be used in early detection via noninvasive methods and may also serve as biomarkers for success of chemopreventive approaches. This review focuses on the utilization of molecular biomarkers to increase the efficacy of various preventive approaches.