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Objective:To analyze the effects of chronic obstructive pulmonary disease (COPD) combined with obstructive sleep apnea hypopnea syndrome (OSAHS) on hypercapnia and its related factors.Methods:In this cross-sectional study, patients with stable COPD were continuously recruited from July 2016 to December 2018 in the Respiratory Department of Peking University Third Hospital. General clinical data of patients were collected, and lung function test, arterial blood gas analysis and portable sleep monitoring were also conducted. Patients with COPD complicated with apnea hypopnea index (AHI)≥10 times/h and apnea events being mainly blockage-type events, accompanied by snoring, sleep apnea, daytime sleepiness and other symptoms were defined as overlapping group, patients with COPD complicated with AHI<10 times/h were defined as simple COPD group. Correlation analysis and logistic regression model were used to explore the determinants of daytime hypercapnia in patients with COPD.Results:Compared with simple COPD group, the median arterial partial pressure of carbon dioxide (PaCO 2) was significantly higher in the overlapping group (42.00 vs 38.95 mmHg (1 mmHg=0.133 kPa), P<0.001), and the rate of daytime hypercapnia was significantly higher (23.3% vs 3.3%, P=0.002). PaCO 2 was correlated with forced vital capacity (FVC), percent predicted forced expiratory volume in one second (FEV 1%pred), the ratio of residual volume (RV) to total lung capacity (TLC), AHI, nocturnal average transcutaneous oxygen saturation (SpO 2), nocturnal minimum SpO 2 and the total sleep time spent with SpO 2≤90% (T90) (all P<0.05). In logistic regression analysis, after adjusting for age, sex, and body mass index (BMI), only severe OSAHS, GOLD Ⅲ-Ⅳ grade (FEV 1%pred<50%), and T90>1% were independent risk factors for hypercapnia. Conclusions:OSAHS can increase the risk of hypercapnia in patients with COPD. AHI, lung function injury and T90 are closely related to hypercapnia.
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Objective:To construct a chronic obstructive pulmonary disease (COPD) assessment test (CAT) score prediction model based on a deep network fused with air data, and to explore its significance.Methods:From February 2015 to December 2017, the outdoor environmental monitoring air data near the residential area of the patients with COPD from the Respiratory Outpatient Clinics of Peking University Third Hospital, Peking University People′s Hospital and Beijing Jishuitan Hospital were collected and the daily air pollution exposure of patients was calculated. The daily CAT scores were recorded continuously. The CAT score of the patients in the next week was predicted by fusing the time series algorithm and neural network to establish a model, and the prediction accuracy of the model was compared with that of the long short-term memory model (LSTM), the LSTM-attention model and the autoregressive integrated moving average model (ARIMA).Results:A total of 47 patients with COPD were enrolled and followed up for an average of 381.60 days. The LSTM-convolutional neural networks (CNN)-autoregression (AR) model was constructed by using the collected air data and CAT score, and the root mean square error of the model was 0.85, and the mean absolute error was 0.71. Compared with LSTM, LSTM-attention and ARIMA, the average prediction accuracy was improved by 21.69%.Conclusion:Based on the air data in the environment of COPD patients, the fusion deep network model can predict the CAT score of COPD patients more accurately.
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Objective@#To investigate the association between hepatic controlled attenuation parameter (CAP) and metabolic syndrome (MetS) and the correlation of CAP and its changes with the incidence of MetS.@*Methods@#A total of 2461 subjects who underwent physical examination from July 2013 to September 2015 were enrolled. Spearman correlation analysis was used to investigate the correlation of CAP with the number of MetS components and each MetS component, and the chi-square test was used to investigate the prevalence rates of MetS and each component under different CAP levels. Logistic regression analysis was used to analyze the odds ratio (95% confidence interval (CI)) of MetS under different CAP levels. A total of 230 subjects without baseline MetS were selected; in a prospective cohort study, these subjects were divided into groups according to the baseline CAP, change in CAP, and percent change in CAP, and the chi-square test was performed to compare the incidence of MetS. The Cox regression analysis was used to analyze the values of baseline CAP, change in CAP, and percent change in CAP in predicting MetS.@*Results@#CAP was positively correlated with the number of MetS components (r = 0.309, P < 0.01) and significantly correlated with all components. There were significant differences in the prevalence rates of MetS and its components under different CAP levels (< 238 dB/m, 238-258 dB/m, 259-291 dB/m, and ≥292 dB/m) (P < 0.05). After the adjustment for sex and age, with < 238 dB/m as a reference, the odds ratios (95% CI) of MetS in patients with CAP levels of 238-258 dB/m, 259-291 dB/m, and ≥292 dB/m were 1.784 (1.369-2.325), 2.936 (2.292-3.760), and 4.363 (3.435-5.543), respectively (all P < 0.05). Follow-up data showed that 28 patients (12.2%) developed MetS. After the adjustment for related factors, the hazard ratios (95% CI) of MetS in patients with baseline CAP > 238 dB/m, change in CAP > 30 dB/m, and percent change in CAP > 25.0% were 3.337 (1.163-9.569), 7.732 (2.453-24.366), and 11.656 (3.329-40.813), respectively (all P < 0.05).@*Conclusion@#CAP is closely associated with MetS and its components. CAP and its change can be used to predict the risk of MetS.
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Objective@#To investigate the serum lipidomic profile in patients with nonalcoholic fatty liver disease (NAFLD), and to analyze the lipid metabolism characteristics of NAFLD.@*Methods@#The subjects were divided into control group (23 patients) and pathologically confirmed NAFLD group (42 patients), and ultra-high-performance liquid chromatography-tandem mass spectrometry was used to measure serum lipidomic metabolites. The partial least squares-discriminant analysis (PLS-DA) model was established to analyze the differences in lipid metabolism with reference to the univariate analysis. The t-test and Mann-Whitney U test were used for data analysis.@*Results@#A total of 239 lipids were identified and qualitative and quantitative analyses were performed. The PLS-DA model (R2 = 0.753, Q2 = 0.456) and the univariate analysis showed that 77 lipids were metabolized differentially between the NAFLD group and the control group (VIP > 1, P < 0.05), including free fatty acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, lysophosphatidylcholine, lysophosphatidylinositol (LPI), choline plasmalogen (PlsCho), ethanolamine plasmalogen (PlsEtn), ceramide (Cer), sphingomyelin, and triglyceride (TG). Compared with the control group, the NAFLD group had significant increases in monounsaturated fatty acids (increased by 39%, t = -3.954, P < 0.05) and TGs (increased by 36%, Z = -2.662, P < 0.05), mainly TGs with low numbers of carbon atoms and unsaturated bonds, while there were reductions in TGs with high numbers of carbon atoms and unsaturated bonds. In addition, compared with the control group, the NAFLD group had significant increases in the levels of LPI (increased by 223%, t = -3.858, P < 0.05) and Cer (increased by 21%, t = -2.481, P < 0.05) and significant reductions in PlsCho (reduced by 18%, t = 3.184, P < 0.05) and PlsEtn (reduced by 20%, t = 2.363, P < 0.05).@*Conclusion@#There is a significant difference in lipid metabolism profile between NAFLD patients and healthy people, and a serum lipidomic analysis of NAFLD helps to further clarify the characteristics of lipid metabolism in patients with NAFLD.