Somatotypes trajectories during adulthood and their association with COPD phenotypes

Implications of Mean Pulmonary Arterial Wedge Pressure Trajectories in Pulmonary Arterial Hypertension

Rationale: The mean pulmonary arterial wedge pressure (mPAWP) is the critical hemodynamic factor differentiating group 1 pulmonary arterial hypertension (PAH) from group 2 pulmonary hypertension associated with left heart disease. Despite the discrepancy between the mPAWP upper physiologic normal and current PAH definitions, the implications of the initial mPAWP for PAH clinical trajectory are poorly understood. Objectives: To model longitudinal mPAWP trajectories in PAH over 10 years and examine the clinical and hemodynamic factors associated with trajectory membership. Methods: Adult patients with PAH with two or more right heart catheterizations were identified from a multiinstitution healthcare system in eastern Massachusetts. mPAWP trajectories were constructed via group-based trajectory modeling. Feature selection was performed in least absolute shrinkage and selection operator regression. Logistic regression was used to assess associations between trajectory membership, baseline characteristics, and transplant-free survival. Measurements and Main Results: Among 301 patients with PAH, there were two distinct mPAWP trajectories, termed "mPAWP-high" (n = 71; 23.6%) and "mPAWP-low" (n = 230; 76.4%), based on the ultimate mPAWP value. Initial mPAWP clustered around median 12 mm Hg (interquartile range [IQR], 8-14 mm Hg) in the mPAWP-high and 9 mm Hg (IQR, 6-11 mm Hg) in the mPAWP-low trajectories (P < 0.001). After feature selection, initial mPAWP ⩾12 mm Hg predicted an mPAWP-high trajectory (odds ratio, 3.2; 95% confidence interval, 1.4-6.1; P = 0.0006). An mPAWP-high trajectory was associated with shorter transplant-free survival (vs. mPAWP-low, median, 7.8 vs. 11.3 yr; log-rank P = 0.017; age-adjusted P = 0.217). Conclusions: Over 10 years, the mPAWP followed two distinct trajectories, with 25% evolving into group 2 pulmonary hypertension physiology. Using routine baseline data, longitudinal mPAWP trajectory could be predicted accurately, with initial mPAWP ⩾12 mm Hg as one of the strongest predictors.

From pre-COPD to COPD: a Simple, Low cost and easy to IMplement (SLIM) risk calculator

BACKGROUND

The lifetime risk of developing clinical COPD among smokers ranges from 13% to 22%. Identifying at-risk individuals who will develop overt disease in a reasonable timeframe may allow for early intervention. We hypothesised that readily available clinical and physiological variables could help identify ever-smokers at higher risk of developing chronic airflow limitation (CAL).

METHODS

Among 2273 Lovelace Smokers' Cohort (LSC) participants, we included 677 (mean age 54 years) with normal spirometry at baseline and a minimum of three spirometries, each 1 year apart. Repeated spirometric measurements were used to determine incident CAL. Using logistic regression, demographics, anthropometrics, smoking history, modified Medical Research Council dyspnoea scale, St George's Respiratory Questionnaire, comorbidities and spirometry, we related variables obtained at baseline to incident CAL as defined by the Global Initiative for Chronic Obstructive Lung Disease and lower limit of normal criteria. The predictive model derived from the LSC was validated in subjects from the COPDGene study.

RESULTS

Over 6.3 years, the incidence of CAL was 26 cases per 1000 person-years. The strongest independent predictors were forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) <0.75, having smoked ≥30 pack-years, body mass index (BMI) ≤25 kg·m2 and symptoms of chronic bronchitis. Having all four predictors increased the risk of developing CAL over 6 years to 85% (area under the receiver operating characteristic curve (AUC ROC) 0.84, 95% CI 0.81-0.89). The prediction model showed similar results when applied to subjects in the COPDGene study with a follow-up period of 10 years (AUC ROC 0.77, 95% CI 0.72-0.81).

CONCLUSION

In middle-aged ever-smokers, a simple predictive model with FEV1/FVC, smoking history, BMI and chronic bronchitis helps identify subjects at high risk of developing CAL.

Reduced frequencies of Foxp3+GARP+ regulatory T cells in COPD patients are associated with multi-organ loss of tissue phenotype

Background

Expression of glycoprotein A dominant repeat (GARP) has been reported to occur only in activated human naturally occurring regulatory T cells (Tregs) and their clones, and not in activated effector T cells, indicating that GARP is a marker for bona fide Tregs. A different phenotype of chronic obstructive pulmonary disease (COPD) may have a different immunologic mechanism.

Objective

To investigate whether the distribution of Tregs defined by GARP is related to the multi-organ loss of tissue phenotype in COPD.

Methods

GARP expression on T cells from peripheral blood and bronchoalveolar lavage (BAL) collected from patients with COPD was examined by flow cytometry. The correlation of GARP expression to clinical outcomes and clinical phenotype, including the body mass index, lung function and quantitative computed tomography (CT) scoring of emphysema, was analyzed.

Results

Patients with more baseline emphysema had lower forced expiratory volume, body mass index (BMI), worse functional capacity, and more osteoporosis, thus, resembling the multiple organ loss of tissue (MOLT) phenotype. Peripheral Foxp3⁺GARP⁺ Tregs are reduced in COPD patients, and this reduction reversely correlates with quartiles of CT emphysema severity in COPD. Meanwhile, the frequencies of Foxp3⁺GARP⁻ Tregs, which are characteristic of pro-inflammatory cytokine production, are significantly increased in COPD patients, and correlated with increasing quartiles of CT emphysema severity in COPD. Tregs in BAL show a similar pattern of variation in peripheral blood.

Conclusion

Decreased GARP expression reflects more advanced disease in MOLT phenotype of COPD. Our results have potential implications for better understanding of the immunological nature of COPD and the pathogenic events leading to lung damage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02099-2.