Frailty and in-hospital mortality in older patients with acute exacerbation of COPD: A real-world prospective cohort study.

BACKGROUND: Few evidence exists for the effect of frailty on the patients admitted with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD). OBJECTIVE: We explored the link between frailty and in-hospital death in AECOPD, and whether laboratory indicators mediate this association. METHODS: This was a real-world prospective cohort study including older patients with AECOPD, consisting of two cohorts: a training set (n = 1356) and a validation set (n = 478). The independent prognostic factors, including frail status, were determined by multivariate logistic regression analysis. The relationship between frailty and in-hospital mortality was estimated by multivariable Cox regression. A nomogram was developed to provide clinicians with a quantitative tool to predict the risk of in-hospital death. Mediation analyses for frailty and in-hospital death were conducted. RESULTS: The training set included 1356 patients (aged 86.7 ± 6.6 years), and 25.0 % of them were frail. A nomogram model was created, including ten independent variables: age, sex, frailty, COPD grades, severity of exacerbation, mean arterial pressure (MAP), Charlson Comorbidity Index (CCI), Interleukin-6 (IL-6), albumin, and troponin T (TPN-T). The area under the receiver operating characteristic curve (ROCs) was 0.862 and 0.845 for the training set and validation set, respectively. Patients with frailty had a higher risk of in-hospital death than those without frailty (HR,1.83, 95%CI: 1.14, 2.94; p = 0.013). Furthermore, CRP and albumin mediated the associations between frailty and in-hospital death. CONCLUSIONS: Frailty may be an adverse prognostic factor for older patients admitted with AECOPD. CRP and albumin may be part of the immunoinflammatory mechanism between frailty and in-hospital death.

Worldwide burden attributable to diet high in red meat from 1990 to 2019.

Introduction: Red meat overconsumption is an unhealthy behavior, while its attributed burden and epidemiological pattern remain unclear. This study aimed to describe the status and trend of how the diet high in red meat burdens the world. Material and methods: We accessed the data of summary exposure values (SEVs), deaths, and disability-adjusted life years (DALYs) with their age-standardized rates in each country from the Global Burden of Disease (GBD) Collaborative Network from 1990 to 2019. We calculated estimated annual percentage changes (EAPCs) to evaluate the trends of the disease burden. Results: The age-standardized SEV rates increased in most of the 21 GBD regions, mainly in the low-middle and middle socio-demographic index (SDI) quantiles from 1990 to 2019, while East Asia increased the most rapidly. In 2019, a diet high in red meat was responsible for 0.9 million (95% uncertainty interval (UI) 0.5 to 1.3 million) deaths and 23.9 million (95% UI 15.6 to 32.0 million) DALYs worldwide. From 1990 to 2019, the total deaths and DALYs attributable to a diet high in red meat increased by over 50%. However, the age-standardized death and DALY rates decreased by 30.3% and 23.5%, respectively, during the study period. The age-standardized death and DALY rates in the middle SDI regions surpassed those in the high SDI regions from 2002. Ischemic heart disease, diabetes mellitus, and colorectal cancer were the main causes of diet high in red meat-related deaths and DALYs. Conclusions: Increasing consumption of red meat remains a global challenge, especially in the low-middle and middle SDI countries.

Reduced Skeletal Muscle Mass Is Associated with an Increased Risk of Asthma Control and Exacerbation.

BACKGROUND: Skeletal muscle mass (SMM) has been suggested to be associated with multiple health-related outcomes. However, the potential influence of SMM on asthma has not been largely explored. OBJECTIVE: To study the association between SMM and clinical features of asthma, including asthma control and exacerbation, and to construct a model based on SMM to predict the risk of asthma exacerbation (AEx). METHODS: In this prospective cohort study, we consecutively recruited patients with asthma (n = 334), classified as the SMM Normal group (n = 223), SMM Low group (n = 88), and SMM High group (n = 23). We investigated the association between SMM and clinical asthma characteristics and explored the association between SMM and asthma control and AEx within a 12-month follow-up period. Based on SMM, an exacerbation prediction model was developed, and the overall performance was externally validated in an independent cohort (n = 157). RESULTS: Compared with the SMM Normal group, SMM Low group exhibited more airway obstruction and worse asthma control, while SMM High group had a reduced eosinophil percentage in induced sputum. Furthermore, SMM Low group was at a significantly increased risk of moderate-to-severe exacerbation compared with the SMM Normal group (relative risk adjusted 2.02 [95% confidence interval (CI), 1.35-2.68]; p = 0.002). In addition, a model involving SMM was developed which predicted AEx (area under the curve: 0.750, 95% CI: 0.691-0.810). CONCLUSIONS: Low SMM was an independent risk factor for future AEx. Furthermore, a model involving SMM for predicting the risk of AEx in patients with asthma indicated that assessment of SMM has potential clinical implications for asthma management.

Body Composition-Specific Asthma Phenotypes: Clinical Implications.

BACKGROUND: Previous studies have indicated the limitations of body mass index for defining disease phenotypes. The description of asthma phenotypes based on body composition (BC) has not been largely reported. OBJECTIVE: To identify and characterize phenotypes based on BC parameters in patients with asthma. METHODS: A study with two prospective observational cohorts analyzing adult patients with stable asthma (n = 541 for training and n = 179 for validation) was conducted. A body composition analysis was performed for the included patients. A cluster analysis was conducted by applying a 2-step process with stepwise discriminant analysis. Logistic regression models were used to evaluate the association between identified phenotypes and asthma exacerbations (AEs). The same algorithm for cluster analysis in the independent validation set was used to perform an external validation. RESULTS: Three clusters had significantly different characteristics associated with asthma outcomes. An external validation identified the similarity of the participants in training and the validation set. In the training set, cluster Training (T) 1 (29.4%) was "patients with undernutrition", cluster T2 (18.9%) was "intermediate level of nutrition with psychological dysfunction", and cluster T3 (51.8%) was "patients with good nutrition". Cluster T3 had a decreased risk of moderate-to-severe and severe AEs in the following year compared with the other two clusters. The most important BC-specific factors contributing to being accurately assigned to one of these three clusters were skeletal muscle mass and visceral fat area. CONCLUSION: We defined three distinct clusters of asthma patients, which had distinct clinical features and asthma outcomes. Our data reinforced the importance of evaluating BC to determining nutritional status in clinical practice.

Circulating Tissue Factor-Positive Procoagulant Microparticles in Patients with Type 1 Diabetes.

AIM: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). METHODS: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF+PS+MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF+PS+MPs and PS+MPs (TF+PS+MPs/PS+MPs). RESULTS: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS+MPs, TF+PS+MPs and TF+PS+MPs/PS+MPs were elevated in patients with T1DM (PS+MPs: 1078.5 ± 158.08 vs 686.84 ± 122.04/µL, P <0.001; TF+PS+MPs: 202.10 ± 47.47 vs 108.33 ± 29.42/µL, P <0.001; and TF+PS+MPs/PS+MPs: 0.16 ± 0.04 vs 0.19 ± 0.05, P = 0.004), mostly derived from platelet, lymphocytes and endothelial cells. In the subgroup analysis, the counts of total and platelet TF+PS+MPs were increased in patients with diabetic retinopathy (DR) and with higher HbA1c, respectively. CONCLUSION: Circulating TF+PS+MPs and those derived from platelet, lymphocytes and endothelial cells were elevated in patients with T1DM.

The Metabolic Effects of Oats Intake in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis.

The present study aimed to comprehensively assess if oats intake is beneficial for diabetic patients. The literature search was conducted in PubMed database up to 23 August 2015. Fourteen controlled trials and two uncontrolled observational studies were included. Compared with the controls, oats intake significantly reduced the concentrations of glycosylated hemoglobin A1c (HbA1c) (MD, -0.42%; 95% CI, -0.61% to -0.23%), fasting blood glucose (FBG) (MD, -0.39 mmol/L; 95% CI, -0.58 to -0.19 mmol/L), total cholesterol (TC) (MD, -0.49 mmol/L; 95% CI, -0.86 to -0.12 mmol/L), low-density lipoprotein cholesterol (LDL-C) (MD, -0.29 mmol/L; 95% CI, -0.48 to -0.09 mmol/L). Oatmeal significantly reduced the acute postprandial glucose and insulin responses compared with the control meal. The present study has revealed a beneficial effect of oats intake on glucose control and lipid profiles in type 2 diabetic patients. Further investigations of oats intake in patients with type 1 diabetes and the safety of oats consumption are required.

[Prevalence and related risk factors among residents with diabetes in urban and rural areas in Chengdu].

OBJECTIVE: To determine the prevalence of diabetes mellitus and related risk factors among residents in the urban and rural area of Chengdu. METHODS: A cluster sampling was used to establish a study population of inhabitants aged 30 to 70. Totally, 1 847 participants were enrolled in this study. Questionnaire including general information and dietary information in the past year was used to collect related data. Height, body weight and oral glucose tolerance test (OGTT) were measured. Factor analysis was used to analyze the dietary pattern while multivariate unconditional logistic regression used for risk factors in total population, urban and rural residents, respectively. RESULTS: The population standardized prevalence rates of diabetes in the overall, urban and rural residents were 20.2% , 28.7% , 11.1% , respectively. Among total population, middle-aged (OR = 2.337, 95%CI:1.305-4.185) and the elderly (OR = 5.990, 95%CI:3.389-10.586) residents had higher diabetes risk than the younger ones. Administrators (OR = 1.434, 95% CI:1.000-2.057) and ordinary clerks (OR = 2.870, 95% CI:1.653-4.980) were more vulnerable to diabetes than peasants. Similarly, middle-aged (OR = 2.973, 95% CI:1.101-8.031) and elderly (OR = 5.972, 95% CI: 2.267-15.730) turned out to be more predisposed than young people in the urban area. Compared with peasants, ordinary clerks (OR = 2.196, 95%CI:1.213-3.975) seemed to be more liable to diabetes. In the rural areas, dietary pattern with higher energy and protein (OR = 1.404, 95% CI:1.113-1.772) could be subject to diabetes. CONCLUSION: The prevalence of diabetes in Chengdu was relatively high. Age, career and dietary pattern are mainly risk factors. The factors in different districts are various. Intervention on nutrition should be different, area-wise.

[Energy expenditure at physical activities of young and middle-aged adults in southern China].

OBJECTIVE: To investigate the energy expenditure at physical activities of young and middle-aged adults in southern China. METHODS: 64 healthy young and middle-aged adults living on light physical activity intensity were recruited by questionnaire and physical examination. A Cosmed k4b2 portable metabolic unit was used to measure the oxygen consumption (Vo2) and carbon dioxide production (Vco2) at a status of basic metabolism and during seven physical activities, including slow walking, brisk walking, walking upstairs, walking downstairs, watching TV, jogging and bicycling. The urinary nitrogen excreted in 24 hours was detected by a standard Kjeldahl method. Energy expenditure at physical activities was calculated by Weir equation. RESULTS: The energy expenditure (kJ x h(-1) x kg(-1)) at physical activities were 14.77 +/- 2.47 for slow walking, 22.18 +/- 3.68 for brisk walking, 41.34 +/- 7.32 for jogging, 18.41 +/- 3.89 for bicycling, 26.11 +/- 4.18 for walking upstairs, 13.68 +/- 2.89 for walking downstairs and 5.06 +/- 1.09 for watching TV. The energy expenditure of males at physical activities was higher than those of females (P < 0.05). CONCLUSION: There is a significant difference among energy expenditures at different physical activities; watching TV is a physical activity in light intensity; slow walking, brisk walking, walking upstairs, walking downstairs and bicycling are physical activities in moderate intensity, and jogging is a vigorous physical activity.