Xiaodiao Zhang

Background: Chronic obstructive pulmonary disease (COPD) and non-alcoholic fatty liver disease share common metabolic and inflammatory pathways. This study aims to elucidate the dual association of the fatty liver index (FLI) with COPD. Methods: A dual-cohort study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2017–2020 ( n = 15,560) and a clinical cohort from the Wenzhou Third Affiliated Hospital, China ( n = 296). Feature selection was performed using three machine learning algorithms. Multivariate logistic regression, restricted cubic splines were used to examine associations. The receiver operating characteristic curves, confusion matrix evaluated model accuracy. A nomogram was developed to visualize the contribution of key predictive factors for COPD. Results: After adjustment for machine learning-selected covariates, FLI was positively associated with COPD prevalence in the NHANES cohort (OR = 1.013, 95% CI: 1.005–1.021, p = 0.002) but inversely associated with COPD severity in the Wenzhou cohort (OR = 0.567, 95% CI: 0.336–0.955, p = 0.033). A non-linear threshold relationship was identified in the Wenzhou cohort ( p -non-linear = 0.002), with an inflection point at FLI = 1.045. Machine learning-optimized models demonstrated good predictive accuracy, with the NHANES model achieving an AUC of 0.819. Conclusion: FLI exhibits a dual association with COPD, serving as a risk factor for its prevalence in the general population yet being inversely associated with disease severity among diagnosed patients. These findings support FLI as a clinically relevant, non-invasive biomarker for stratifying COPD risk and assessing severity.

Purpose: This study aimed to investigate the associations of the Geriatric Nutritional Risk Index (GNRI) and the simplified Geriatric Nutritional Risk Index (sGNRI) with Chronic Obstructive Pulmonary Disease (COPD). Methods: We assessed 161 patients with COPD and 947 healthy controls. The sGNRI was derived using the formula: serum albumin (g/dL) + 0.1 × body mass index (BMI, kg/m ² ). Primary outcomes included the BODE (body mass index, airflow obstruction, dyspnea, exercise capacity) index and hospital length of stay (LOS). The secondary outcome was the prevalence of COPD. Results: Malnourished patients (GNRI p < 0.05). GNRI (Odds Ratio [OR] = 0.945, 95% Confidence Interval [CI]: 0.903-0.989, p = 0.015) and sGNRI (OR = 0.413, 95% CI: 0.191-0.890, p = 0.024) were significantly associated with the severity of COPD in a multivariable-adjusted model. Subgroup analysis revealed stronger predictive performance of GNRI and sGNRI among males, non-hypertensive patients, and smokers (for sGNRI). Conclusions: Compared with GNRI, sGNRI may serve as a more practical tool for identifying malnutrition-related risk among patients with COPD.

Background: Chronic obstructive pulmonary disease (COPD) poses a major global health burden with high morbidity. Relative fat mass (RFM), as a novel body fat measurement indicator, can reflect the distribution of body fat. This study aims to elucidate its associations with COPD prevalence and severity in two cohorts to enhance prevention and treatment strategies. Methods: We retrospectively investigated the medical records of 166 patients with COPD and the data of 2654 subjects from two cohorts. To explore the relative importance of factors in COPD prevalence and severity, we built an extreme gradient boosting (XGBoost) machine-learning model. Logistic regression models were used to assess the relationship between COPD and RFM, with subgroup analysis to clarify the difference across diverse subgroups. Furthermore, restricted cubic spline (RCS) curves were used to explore the exposure-response relationship. Results: Multivariate logistic regression analysis revealed a significant positive association between RFM and COPD prevalence (OR = 1.043, 95% CI: 1.004– 1.083, p = 0.030) and a negative association with COPD severity (OR = 0.892, 95% CI: 0.813– 0.978, p = 0.015). According to the RCS curves, there was no nonlinear association between RFM and COPD prevalence or severity (p for nonlinear = 0.703, p for nonlinear = 0.348). Conclusion: RFM was positively associated with the prevalence of COPD but inversely associated with its severity. Specifically, RFM predicted COPD prevalence more accurately in individuals aged 40– 60 and smokers, while it predicted COPD severity more effectively in those aged ≥ 60.

Malnutrition plays a considerable role in the deterioration of chronic obstructive pulmonary disease (COPD). Nevertheless, current research indicates body mass index may not fully capture alterations in nutritional status. Trunk-to-peripheral fat ratio, measured by dual-energy x-ray absorptiometry (DXA) offers a more precise approach. This research aims to investigate the association between trunk-to-peripheral fat ratio and the severity of COPD as measured by COPD Assessment Test (CAT) scale, along with its related intermediary factors. We incorporated 168 patients admitted for COPD and obtained body composition data through DXA. The main outcome was 1-year CAT scale score, with 6-month CAT scale score serving as the secondary outcome. Significant disparities were observed between trunk-to-appendicular fat ratio (TAR) and trunk-to-leg fat ratio (TLR) across different groups with a 6-month CAT (all p < 0.05) and a 1-year CAT (all p < 0.05). After adjusting for covariates, TAR (OR = 0.330, p = 0.013) and TLR (OR = 0.525, p = 0.026) were independently correlated with one-year CAT. Upon inclusion of TAR in the initial model, the area under the curve (AUC) was 0.739 (95% CI 0.663–0.815, p < 0.001), along with an elevated Net Reclassification Improvement (NRI) of 37.81 (95% CI 7.85–67.77, p = 0.013) and Integrated Discrimination Improvement (IDI) of 3.39 (95% CI 0.63–6.14, p = 0.016). Similarly, the previous model’s AUC and IDI climbed with the addition of TLR. Mediation analysis indicated that triglyceride levels partly influenced the correlation. Trunk-to-peripheral fat ratio was associated with the severity of COPD, which was partly mediated by triglycerides.

Total protein (TP), comprising albumin and globulin, is a common and readily available indicator. This study aimed to assess the correlation between total protein and disease severity as well as exercise tolerance in patients with stable chronic obstructive pulmonary disease (COPD). A total of 84 patients with COPD were recruited in this study. Plasma total protein levels, body mass index, air-flow obstruction, dyspnea, exercise capacity (BODE) index, the six-minute walking distance (6MWD), lung function and other relevant data were recorded. Patients were divided into three groups according to TP tertiles. Receiver operating characteristic (ROC) curves were used to evaluate the performance of TP in predicting severe COPD (BODE ≥ 5). Besides, logistic regression analyses were conducted to assess whether TP was an independent factor associated with BODE grades. COPD subjects with TP levels below 61.2 exhibited a significantly higher proportion of severe COPD. Logistic regression analyses substantiated that TP was associated with COPD severity. Compared with albumin and globulin levels, TP levels showed the highest accuracy in predicting a high BODE index in COPD (BODE ≥ 5) (the area under the ROC curve (AUC) = 0.789). Moreover, TP was positively related to 6WMD during the first 30 days of admission. A lower level of TP was associated with a higher BODE index, indicating more severe COPD.

This research aims to elucidate the association between red cell distribution width (RDW) and the severity of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). This retrospective study enrolled patients with AECOPD and healthy controls. A comprehensive meta-analysis was further conducted by systematically searching PubMed, Embase, and the Cochrane Library up to 27 April 2023. Univariate logistic regression analysis indicated that elevated RDW levels were significantly associated with AECOPD (p = 0.016). The pooled results yielded a diagnostic sensitivity of 0.76 (95% CI: 0.70-0.81) and specificity of 0.53 (95% CI: 0.38-0.68) for RDW in predicting AECOPD severity. Elevated RDW levels exhibit a linear positive correlation with AECOPD severity, further supported by meta-analysis.

Background: Chronic Obstructive Pulmonary Disease (COPD) has been a concern all over the world because of its high prevalence and mortality. The ratio of low-density-lipoprotein to lymphocyte (LLR) has been widely used to predict the prognosis of cerebral infarction, but its association with COPD is less known. We aim to explore the relationship between LLR and COPD and to investigate its indicative role in the severity and prognosis of COPD. Methods: In this study, 279 participants (n = 138 with COPD and n = 138 age- and sex-matched health control) were recruited. COPD patients were divided into two groups according to the optimal cut-off value of LLR determined by the receiver operating characteristic curve (ROC). We collected the clinical characteristics, pulmonary function, LLR, neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR) and other data of all subjects. t-test, Pearson correlation test, logistic regression analysis and other statistical analysis were carried out. Results: Compared with the healthy control group, COPD patients had a significantly higher LLR level (p < 0.001). The disease was more serious in the high LLR group, which was reflected by Global Initiative for Chronic Obstructive Lung Disease (GOLD) and BMI, airway obstruction, dyspnoea, severe exacerbations (BODE) index and St. George’s Respiratory Questionnaire (SGRQ) index (p = 0.001, p = 0.013, p = 0.011, respectively). The forced expiration volume in 1 second (FEV₁) (p = 0.033) and forced expiratory volume in 1 second in percent of the predicted value (FEV₁%) (p = 0.009) in high LLR group were lower. Univariate and multivariate logistic regression analysis showed that LLR was an independent factor affecting the severity of COPD patients (odds ratio [OR] = 2.599, 95% CI: 1.266-5.337, p = 0.009). Conclusion: We found that LLR is a novel biomarker in predicting the severity of patients with COPD. Further studies with larger database were recommended to verify our findings.

Objective: To assess the relationship between chronic obstructive pulmonary disease (COPD) severity and bone mineral density (BMD) in the whole body and different body areas. Methods: This retrospective, cross-sectional study included patients with COPD. Demographic and lung function data, COPD severity scales, BMD, and T scores were collected. Patients were grouped by high (≥–1) and low (<–1) T scores, and stratified by body mass index, airway obstruction, dyspnoea, and exercise capacity (BODE) index. The relationship between whole-body BMD and BODE was evaluated by Kendall’s tau-b correlation coefficient. Risk factors associated with COPD severity were identified by univariate analyses. BMD as an independent predictor of severe COPD (BODE ≥5) was verified by multivariate logistic regression. BMD values in different body areas for predicting severe COPD were assessed by receiver operating characteristic curves. Results: Of 88 patients with COPD, lung-function indicators and COPD severity were significantly different between those with high and low T scores. Whole-body BMD was inversely related to COPD severity scales, including BODE. Multivariate logistic regression revealed that BMD was independently associated with COPD severity. The area under the curve for pelvic BMD in predicting severe COPD was 0.728. Conclusion: BMD may be a novel marker in predicting COPD severity, and pelvic BMD may have the strongest relative predictive power.

Background We tend to find out whether BMI and weight have difference in the connection with the disease severity of AECOPD patients. Besides, we aim to explore the relationships between body fat (BF) and lean body mass (LBM) with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods Information like weight and body mass index (BMI) were recorded at hospital admission. BF and LBM were assessed using dual energy X-ray absorptiometry (DEXA). Logistic regression analysis, receiver operating characteristic (ROC) curves and other statistical analyses were performed. Results Weight and BF + LBM showed significant linkages with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) in multivariate logistic regression analysis (Model 3: OR = 0.932, p = 0.016; OR = 0.915, p = 0.006, respectively). And BMI did not show correlation with GOLD. In Stratification analysis, BF showed the predictive value for GOLD (AUC = 0.709, p = 0.004) while LBM showed the predictive ability for modified Medical Research Council scale (mMRC) (AUC = 0.761, p < 0.001). Conclusion BF + LBM and weight were better than BMI in the connection with the disease severity of AECOPD patients represtented by GOLD. BF and LBM may have different or even opposite effects on patients with AECOPD.