Ambient fine particulate matter and allergic symptoms in the middle-aged and elderly population: results from the PIFCOPD study.

BACKGROUND: The associations between short- and long-term exposure to ambient fine particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5) and allergic symptoms in middle-aged and elderly populations remain unclear, particularly in China, where most cities have severe air pollution. METHODS: Participants (n = 10,142; age = 40-75 years) were recruited from ten regions in China from 2018 to 2021 for the Predictive Value of Inflammatory Biomarkers and Forced Expiratory Volume in 1 s (FEV1) for Chronic Obstructive Pulmonary Disease (PIFCOPD) study. Short-term (lag0 and lag0-7 day) and long-term (1-, 3- and 5-year) PM2.5 concentrations at residences were extracted from the air pollutant database known as Tracking Air Pollution (TAP) in China. Multivariate logistic regression models were used to estimate associations for short- and long-term PM2.5 exposure concentrations and long-term exposure models were additionally adjusted for short-term deviations. RESULTS: A 10 µg/m3 increase in PM2.5 on the day the allergic symptoms questionnaire was administered (lag0 day) was associated with higher odds of allergic nasal (1.09, 95% CI 1.05, 1.12) and eye symptoms (1.08, 95% CI 1.05, 1.11), worsening dyspnea caused by allergens (1.06, 95% CI 1.02, 1.10), and ≥ 2 allergic symptoms (1.07, 95% CI 1.03, 1.11), which was similar in the lag0-7 day concentrations. A 10 µg/m3 increase in the 1-year average PM2.5 concentration was associated with an increase of 23% for allergic nasal symptoms, 22% for eye symptoms, 20% for worsening dyspnea caused by allergens, and 21% for ≥ 2 allergic symptoms, similar to the 3- and 5-year average PM2.5 concentrations. These associations between long-term PM2.5 concentration and allergic symptoms were generally unchanged after adjustment for short-term deviations. CONCLUSIONS: Short- and long-term exposure to ambient PM2.5 was associated with an increased risk of allergic nasal and eye symptoms, worsening dyspnea caused by allergens, and ≥ 2 allergic symptoms. TRIAL REGISTRATION: Clinical trial ID: NCT03532893 (29 Mar 2018).

Age, Pulse, Urea, and Albumin Score: A Tool for Predicting the Short-Term and Long-Term Outcomes of Community-Acquired Pneumonia Patients With Diabetes.

Objective: The predictive performances of CURB-65 and pneumonia severity index (PSI) were poor in patients with diabetes. This study aimed to develop a tool for predicting the short-term and long-term outcomes of CAP in patients with diabetes. Methods: A retrospective study was conducted on 531 CAP patients with type 2 diabetes. The short-term outcome was in-hospital mortality. The long-term outcome was 24-month all-cause death. The APUA score was calculated according to the levels of Age (0-2 points), Pulse (0-2 points), Urea (0-2 points), and Albumin (0-4 points). The area under curves (AUCs) were used to evaluate the abilities of the APUA score for predicting short-term outcomes. Cox regression models were used for modeling relationships between the APUA score and 24-month mortality. Results: The AUC of the APUA score for predicting in-hospital mortality was 0.807 in patients with type 2 diabetes (P<0.001). The AUC of the APUA score was higher than the AUCs of CURB-65 and PSI class (P<0.05). The long-term mortality increased with the risk stratification of the APUA score (low-risk group (0-1 points) 11.5%, intermediate risk group (2-4 points) 16.9%, high risk group (≥5 points) 28.8%, P<0.05). Compared with patients in the low-risk group, patients in the high-risk group had significantly increased risk of long-term death, HR (95%CI) was 2.093 (1.041~4.208, P=0.038). Conclusion: The APUA score is a simple and accurate tool for predicting short-term and long-term outcomes of CAP patients with diabetes.

The relationship between age-related kidney dysfunction and Framingham risk score in healthy people in China.

BACKGROUND: Aging process reduces kidney function, glomerular filtration rate (GFR) and/or creatinine clearance rate (Ccr), and also significantly increases the risk level for cardiovascular diseases. The classical Framingham risk equation provides a method for predicting cardiovascular risk, but it does not include the kidney function indexes. In this study, we investigated the relationship between age-related kidney function and Framingham risk score (FRS) in healthy Chinese people, and validated to assess the risk factor by GFR/Ccr. METHODS: This community-based cross-sectional study recruited 505 healthy subjects (age from 35 to 93 yr.) with both gender is during September 2007 to June 2008 in Shenyang. Framingham risk equation was used to evaluate cardiovascular risk factors and generate FRS. GFR and Ccr were calculated with Cockcroft-Gault (CG) equation (GFR(CG)), abbreviated Modification of Diet in Renal Disease Study (MDRD) equation (GFR(MDRD1)) and modified MDRD equation (GFR(MDRD2)). All data were sorted according to FRS (low, moderate and high) risk levels, and five different age groups (≤44 yr; 45-54 yr; 55-64 yr; 65-74 yr and ≥75 yr). The ANOVA, correlation, partial correlation between GFR/Ccr and FRS, as well as other risk factors were analyzed with SPSS16.0 statistical package. RESULTS: As the FRS level increased, GFR(CG), GFR(MDRD1), GFR(MDRD2) and Ccr decreased about 10 to 30% (low>moderate> high risk group, p<0.01). While the subjects were getting older, GFR(CG), GFR(MDRD1), GFR(MDRD2) and Ccr showed significant reduction (P<0.001). Ccr decreased about 50% from the young to oldest group (p<0.001). There was a significantly inverse correlation between FRS and GFR with Ccr having the Pearson correlation coefficient -0.586 (GFR(CG), P<0.001), - 0.449 (GFR(MDRD1) and GFR(MDRD2), P<0.001), -0.459 (Ccr, P<0.001). However, the relationship between FRS and Ccr was lost after controlling for age and other confounding variables. CONCLUSION: In healthy population, we found inverse correlations between Framingham risk score and GFR, Ccr and GFR were independently related to the FRS with similar correlation coefficient among three equations. With the increase of FRS, the GFR and Ccr decrease. Aging is the major factor of GFR and Ccr reduction in the healthy population. We suggest that GFR/Ccr could be used as risk indexes for cardiovascular diseases.

[Correlation of cardiovascular risk factors versus glomerular filtration rate in healthy population].

OBJECTIVE: To evaluate the relationship between cardiovascular risk factors and glomerular filtration rate in healthy population. METHODS: A community-based cross-sectional study was conducted in Shenyang. The Framingham sex-specific risk equation was employed to evaluate the cardiovascular risk factors of 501 healthy study objects, calculate Framingham risk score and estimate the risk of 10-year coronary heart disease (CHD). A total of 501 study subjects were then divided into 3 groups according to 10-year CHD risk: low risk group (< 10%), moderate risk group (10% -20%) and high risk group ( > 20%). Study subjects were also divided into 5 groups according to age: < or = 44 years old; 45 - 54 years old; 55 - 64 years old; 65 - 74 years old and > or = 75 years old. The Cockcroft-Gault equation (GFR(CG)), abbreviated MDRD equation (GFR(MDRD1)) and modified MDRD equation (GFR(MDRD2)) were used to estimate glomerular filtration rate (GFR). Glomerular filtration rate (GFR) were compared among different risk groups and correlation coefficients between Framingham risk score and glomerular filtration rate calculated. RESULTS: GFR(CC), GFR(MDRD1) and GFR(MDRD2) in the low risk group was [(103 +/- 27) ml x min(-1) GFR(MDRD2) in moderate risk group all decreased [(84 +/- 24) ml x min(-1) x (1.73 m2) (-1), (101 +/- 27) ml x min(-1) x (1.73 m2) (-1), (124 +/- 33)ml x min(-1) (1.73 m2) (-1), all P < 0.01]. GFR(CG), GFR(MDRD1) and GFR(MR(MDRD2) in the high risk group all decreased [(71 +/- 15) ml x min(-1) (1.73 m2) (-1), (88 +/- 15) ml x min(-1) x (1.73 m2)(-1), (109 +/-18)ml x min(-1) x (1.73 m2) (-1), all P < 0.01]. The GFR(CG), GFR(MDRD1) and GFR(MDRD2) in the high risk group all decreased compared with the moderate risk group (P < 0.05). There was a significantly inverse correlation between Framingham risk score and GFR with the Pearson correlation coefficient -0.586 (GFR(CG), P < 0.01) and -0.449 (GFR(MDRD1) and GFR(MDRD2), P < 0.01). CONCLUSION: There is an inverse correlation between cardiovascular risk factors and GFR in healthy population. With the increasing of risk factors and their severity, Framingham risk score increases and GFR decreases.