Maladaptive Immune Activation in Age-Related Decline of Muscle Function

Analysis of COPD: Distinguishing Characteristics and Management of Smoking vs Never Smoking Patients

Background

Chronic obstructive pulmonary disease (COPD) is a significant public health issue characterized by gradually worsening airflow limitation. It is a leading cause of mortality and morbidity worldwide, yet research on COPD patients who have never smoked is limited. This study aims to document the demographic, symptomatic, and therapeutic characteristics of COPD patients receiving outpatient pulmonary care in Hungary, focusing on smoking history, and evaluate their distribution according to the GOLD A/B/E classification.

Methods

The study recorded demographic data, symptom severity, occurrence of severe and moderate exacerbations, treatment, comorbidities, quality of life, and COVID-19 vaccination status among COPD patients from November 2021 to January 2023. A total of 6974 patients were categorized into current smokers, former smokers, and never smokers.

Results

Patients had an average age of 67.2±8.9 years, with 48.2% male and 51.8% female. Of participants, 86.1% had a smoking history, while 13.9% had never smoked. COPD patients who had never smoked showed significantly better quality of life (CAT: 15.2±7.6 vs 15.8±6.9; p=0.006), oxygen saturation (SpO2%: 96.7±2.3 vs 95.8±2.4; p<0.001), higher body mass index (BMI: 29.4±5.9 vs 27.1±6.3; p<0.001), and better lung function (FEV1ref%: 67.9±20.7 vs 58.9±18.1; p<0.001) compared to smokers. However, non-smoking COPD patients had a higher frequency of comorbidities (3.5±2.2 vs 2.9±2.1; p<0.05). These differences may arise from complex genetic and environmental interactions.

Conclusion

COPD patients who have never smoked exhibited better quality of life, nutritional status, and lung function compared to smokers, indicating the need for tailored treatment approaches. Further long-term studies are essential to validate these differences in quality of life and lung function between smoking and non-smoking COPD patients.

Prediction of Osteoporotic Hip Fracture Outcome: Comparative Accuracy of 27 Immune-Inflammatory-Metabolic Markers and Related Conceptual Issues

Objectives: This study, based on the concept of immuno-inflammatory-metabolic (IIM) dysregulation, investigated and compared the prognostic impact of 27 indices at admission for prediction of postoperative myocardial injury (PMI) and/or hospital death in hip fracture (HF) patients. Methods: In consecutive HF patient (n = 1273, mean age 82.9 ± 8.7 years, 73.5% females) demographics, medical history, laboratory parameters, and outcomes were recorded prospectively. Multiple logistic regression and receiver-operating characteristic analyses (the area under the curve, AUC) were used to establish the predictive role for each biomarker. Results: Among 27 IIM biomarkers, 10 indices were significantly associated with development of PMI and 16 were indicative of a fatal outcome; in the subset of patients aged >80 years with ischaemic heart disease (IHD, the highest risk group: 90.2% of all deaths), the corresponding figures were 26 and 20. In the latter group, the five strongest preoperative predictors for PMI were anaemia (AUC 0.7879), monocyte/eosinophil ratio > 13.0 (AUC 0.7814), neutrophil/lymphocyte ratio > 7.5 (AUC 0.7784), eosinophil count < 1.1 × 109/L (AUC 0.7780), and neutrophil/albumin × 10 > 2.4 (AUC 0.7732); additionally, sensitivity was 83.1-75.4% and specificity was 82.1-75.0%. The highest predictors of in-hospital death were platelet/lymphocyte ratio > 280.0 (AUC 0.8390), lymphocyte/monocyte ratio < 1.1 (AUC 0.8375), albumin < 33 g/L (AUC 0.7889), red cell distribution width > 14.5% (AUC 0.7739), and anaemia (AUC 0.7604), sensitivity 88.2% and above, and specificity 85.1-79.3%. Internal validation confirmed the predictive value of the models. Conclusions: Comparison of 27 IIM indices in HF patients identified several simple, widely available, and inexpensive parameters highly predictive for PMI and/or in-hospital death. The applicability of IIM biomarkers to diagnose and predict risks for chronic diseases, including OP/OF, in the preclinical stages is discussed.