[Association between exposure to PM_(2.5) and chronic kidney disease in a population with metabolic syndrome].

OBJECTIVE: To investigate the association between long-term fine particulate matter(PM_(2.5)) exposure and the risk of chronic kidney disease(CKD) in people with abnormal metabolism syndrome(MS) components. METHODS: Based on health checkup data from a hospital in Beijing, a retrospective cohort study was used to collect annual checkup data from 2013-2019. A questionnaire was used to obtain information on demographic characteristics and lifestyle habits. We measured blood pressure, height, weight, waist circumference, concentrations of triglycerides(TG), fasting glucose, and high-density lipoprotein cholesterol(HDL-C). Longitude and latitude were also extracted from the addresses of the study subjects for pollutant exposure data estimation. Logistic regression models were used to explore the estimated effect of long-term PM_(2.5) exposure on the risk of CKD prevalence in people with abnormal MS components. Two-pollutant and multi-pollutant models were developed to test the stability of these result. Subgroup analysis was conducted based on age, the presence of MS, individual MS component abnormalities, and dual-component MS abnormalities. RESULTS: The study included 1540 study subjects with abnormal MS components at baseline, 206 with CKD during the study period. The association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions was statistically significant, with a 2.26-fold increase in risk of CKD for every 10 µg/m~3 increase in PM_(2.5) exposure(OR=3.26, 95% CI 2.72-3.90). The result in the dual-pollutant models and multi-pollutant models suggested that the association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions remained stable after controlling for contemporaneous confounding by other air pollutants. The result of subgroup analysis revealed that individuals aged 45 or older, without MS, with TG<1.7 mmol/L, HDL-C≥1.04 mmol/L, without hypertension, and with central obesity and high blood sugar had a stronger association between PM_(2.5) exposure and CKD-related health effects. CONCLUSION: Long-term exposure to PM_(2.5) may increase the risk of CKD in people with abnormal MS components. More attention should be paid to middle-aged and elderly people aged ≥45 years, people with central obesity and hyperglycemia.

Long-term effect of submicronic particulate matter (PM1) and intermodal particulate matter (PM1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study.

BACKGROUND: There is insufficient evidence of associations between incident dyslipidemia with PM1 (submicronic particulate matter) and PM1-2.5 (intermodal particulate matter) in the middle-aged and elderly. We aimed to determine the long-term effects of PM1 and PM1-2.5 on incident dyslipidemia respectively. METHODS: We studied 6976 individuals aged ≥45 from the China Health and Retirement Longitudinal Study from 2013 to 2018. The concentrations of particular matter (PM) for every individual's address were evaluated using a satellite-based spatiotemporal model. Dyslipidemia was evaluated by self-reported. The generalized linear mixed model was applied to quantify the correlations between PM and incident dyslipidemia. RESULTS: After a 5-year follow-up, 333 (4.77%) participants developed dyslipidemia. Per 10 µg/m³ uptick in four-year average concentrations of PMs (PM1 and PM1-2.5) corresponded to 1.11 [95% confidence interval (CI): 1.01-1.23)] and 1.23 (95% CI: 1.06-1.43) fold risks of incident dyslipidemia. Nonlinear exposure-response curves were observed between PM and incident dyslipidemia. The effect size of PM1 on incident dyslipidemia was slightly higher in males [1.14 (95% CI: 0.98-1.32) vs. 1.04 (95% CI: 0.89-1.21)], the elderly [1.23 (95% CI: 1.04-1.45) vs. 1.03 (95% CI: 0.91-1.17)], people with less than primary school education [1.12 (95% CI: 0.94-1.33) vs. 1.08 (95% CI: 0.94-1.23)], and solid cooking fuel users [1.17 (95% CI: 1.00-1.36) vs. 1.06 (95% CI: 0.93-1.21)], however, the difference was not statistically significant (Z = -0.82, P = 0.413; Z = -1.66, P = 0.097; Z = 0.32, P = 0.752; Z = -0.89, P = 0.372). CONCLUSIONS: Long-term exposure to PM1 and PM1-2.5 were linked with an increased morbidity of dyslipidemia in the middle-aged and elderly population. Males, the elderly, and solid cooking fuel users had higher risk. Further studies would be warranted to establish an accurate reference value of PM to mitigate growing dyslipidemia.

Causal effect of PM1 on morbidity of cause-specific respiratory diseases based on a negative control exposure.

BACKGROUND: Extensive studies have linked PM2.5 and PM10 with respiratory diseases (RD). However, few is known about causal association between PM1 and morbidity of RD. We aimed to assess the causal effects of PM1 on cause-specific RD. METHODS: Hospital admission data were obtained for RD during 2014 and 2019 in Beijing, China. Negative control exposure and extreme gradient boosting with SHapley Additive exPlanation was used to explore the causality and contribution between PM1 and RD. Stratified analysis by gender, age, and season was conducted. RESULTS: A total of 1,183,591 admissions for RD were recorded. Per interquartile range (28 µg/m3) uptick in concentration of PM1 corresponded to a 3.08% [95% confidence interval (CI): 1.66%-4.52%] increment in morbidity of total RD. And that was 4.47% (95% CI: 2.46%-6.52%) and 0.15% (95% CI: 1.44%-1.78%), for COPD and asthma, respectively. Significantly positive causal associations were observed for PM1 with total RD and COPD. Females and the elderly had higher effects on total RD, COPD, and asthma only in the warm months (Z = 3.03, P = 0.002; Z = 4.01, P < 0.001; Z = 3.92, P < 0.001; Z = 2.11, P = 0.035; Z = 2.44, P = 0.015). Contribution of PM1 ranked first, second and second for total RD, COPD, and asthma among air pollutants. CONCLUSION: PM1 was causally associated with increased morbidity of total RD and COPD, but not causally associated with asthma. Females and the elderly were more vulnerable to PM1-associated effects on RD.

Long-term effects of PM2.5 components on hypertension: A national analysis in China.

BACKGROUND: Evidence is less about the associations between fine particulate matter (PM2.5) components and hypertension. We aimed to examine the long-term effects of PM2.5 components on prevalence of hypertension, diastolic blood pressure (DBP) and systolic blood pressure (SBP). METHODS: We included participants between March 1, and July 31, 2021, from 13 provinces in China. Geocoded residential address was used for exposure assignment. Mixed-effect regression was used to assess 3-year average concentrations of PM2.5 and its components (black carbon, organic matter, nitrate, ammonium, and sulfate) on prevalence of hypertension, DBP and SBP with covariate-adjusted. SHapley Additive exPlanation was used to compare the contribution of PM2.5 components to hypertension, DBP, and SBP. Sex and age subgroup were also analyzed. RESULTS: We enrolled a total of 113,159 participants aged ≥18 years. Long-term exposure to PM2.5 and its components (black carbon, organic matter, nitrate, ammonium, and sulfate) had associations with prevalence of hypertension, with the Odds Ratios and 95% confidence interval (CI) of 1.06 (95%CI: 1.03-1.09), 1.07 (95%CI: 1.04-1.09), 1.07 (95%CI: 1.04-1.10), 1.05 (95%CI: 1.01-1.08), 1.03 (95%CI: 1.00-1.06), and 1.03 (95%CI: 1.00-1.04), respectively. Effects of that except for black carbon on DBP with per interquartile upticks of concentration were 0.23 (95%CI: 0.11-0.35), 0.17 (95%CI: 0.04-0.29), 0.35 (95%CI: 0.21-0.48), 0.40 (95%CI: 0.28-0.52), and 0.25 (95%CI: 0.13-0.26), respectively. Ammonium was associated with SBP, corresponding to an increase of 0.18 (95%CI: 0.01-0.35). Males had higher risks of DBP (Z = 2.54-6.08, P < 0.001). Older people were substantially more affected by PM2.5 and its components. Nitrate showed the highest contribution to hypertension, DBP and SBP compared with other components. CONCLUSIONS: Long-term exposure to PM2.5 and its components had adverse consequences on prevalence of hypertension, DBP and SBP, especially for males and older people. Nitrate contributed the highest to hypertension, DBP and SBP. Findings may have implications for pollution and hypertension control.

Long-term effects of particulate matter on incident cardiovascular diseases in middle-aged and elder adults: The CHARLS cohort study.

BACKGROUND: Although there is evidence of long-term effects of particulate matter (PM) on cardiovascular diseases (CVD), researches about long-term effects of PM1 on CVD are limited. We aimed to examine the long-term effects and magnitude of PM, especially PM1, on incident CVD in China. METHODS: We included 6016 participants aged ≥ 45 years without CVD at baseline in 2011 from the China Health and Retirement Longitudinal Study. Personal PM (PM1, PM2.5, and PM10) concentrations were estimated using geocoded residential address. Generalized linear mixed models and SHapley Additive exPlanation were utilized to calculate the impacts and contributions of PM on CVD. Sensitivity analyses were used to check the robustness. RESULTS: After a follow up of 4-year, 481 (7.99 %) participants developed CVD. Per 10 µg/m3 uptick in 1-year average concentrations of PM1, PM2.5 and PM10 was associated with a 1.20 [95 % confidence interval (CI): 1.05-1.37], 1.13 (95 % CI: 1.11-1.15), and 1.10 (95 % CI: 1.06-1.13) fold risk of incident CVD, respectively. The 2-year average concentrations of PM1, PM2.5 and PM10 were associated with incident CVD, corresponding to a 1.03 (95 % CI: 0.96-1.10), 1.11 (95 % CI: 1.02-1.21), and 1.09 (95 % CI: 1.03-1.15) fold risk, respectively. The SHapley Additive exPlanation values of PM1, PM2.5, and PM10 were 0.170, 0.153, and 0.053, respectively, corresponding to the first, second, and fifth among all air pollutants. Effects of PM1, PM2.5 and PM10 on CVD remained statistically significant in two-pollutant models. The elderly, males, smokers and alcohol drinkers tended to have slightly higher effects, while the differences were not statistically significant (all P-values > 0.05) between subgroups. CONCLUSION: Long-term exposure to PM1, PM2.5, and PM10 was associated with an increased incidence of CVD. The smaller the particle size, the more important it was for incident CVD indicating that emphasis should be placed on small size of PM.

Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.

Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.

Combined evaluation of arterial stiffness, glycemic control and hypertension for macrovascular complications in type 2 diabetes.

BACKGROUND: Arterial stiffness, glycemic control and blood pressure are risk factors of macrovascular complications in type 2 diabetes. This study aimed to investigate the combined association of arterial stiffness, glycemic control and hypertension status with the occurrence of diabetic macrovascular complication. METHODS: A total of 1870 patients of diabetes were enrolled from Beijing Health Management Cohort between 2008 and 2018 as baseline, and then followed for macrovascular complication onset. We proposed a composite risk score (0-4) by arterial stiffness severity, pool glycemic control and hypertension status. Cox model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI). RESULTS: The mean age (SD) of this population was 59.90 (12.29) years. During a median follow-up of 4.0 years, 359 (19.2%) patients developed macrovascular complication. Compared to the normal arterial stiffness and good glycemic control group, patients with severe arterial stiffness and pool glycemic control had the highest risk of macrovascular complications (HR: 2.73; 95% CI: 1.42-5.25). Similarly, those of severe arterial stiffness and hypertension had the highest risk (HR: 2.69; 95% CI: 1.61-4.50). Patients of the composite score > 2 had a significantly increased risk of macrovascular complication. CONCLUSION: This study suggested the clinical importance of combined evaluation of arterial stiffness, glycemic control and hypertension status for the risk stratification and management of macrovascular complication of type 2 diabetes.

Association of visceral adiposity index with incident nephropathy and retinopathy: a cohort study in the diabetic population.

BACKGROUND: The association between visceral adiposity index (VAI) and diabetic complications has been reported in cross-sectional studies, while the effect of VAI on complication development remains unclear. This study aims to evaluate the longitudinal association of VAI and Chinese VAI (CVAI) with the incidence of diabetic nephropathy and retinopathy using a Chinese cohort. METHODS: A total of 8 948 participants with type 2 diabetes from Beijing Health Management Cohort were enrolled during 2013-2014, and followed until December 31, 2019. Nephropathy was confirmed by urine albumin/creatinine ratio and estimated glomerular filtration rate; retinopathy was diagnosed using fundus photograph. RESULTS: The mean (SD) age was 53.35 (14.66) years, and 6 154 (68.8%) were men. During a median follow-up of 4.82 years, 467 participants developed nephropathy and 90 participants developed retinopathy. One-SD increase in VAI and CVAI levels were significantly associated with an increased risk of nephropathy, and the adjusted hazard ratios (HR) were 1.127 (95% CI 1.050-1.210) and 1.165 (95% CI 1.003-1.353), respectively. On contrary, VAI and CVAI level were not associated with retinopathy after adjusting confounding factors. CONCLUSION: VAI and CVAI are independently associated with the development of nephropathy, but not retinopathy in Chinese adults with diabetes.

Association of household solid fuel use and long-term exposure to PM2.5 with arthritis in middle-aged and older population in China: A cohort study.

Air pollutants are common modifiable risk factors for arthritis. To explore the longitudinal effects of air pollution on arthritis based on a cohort study in middle-aged and elder people of China. Data was obtained from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018. A total of 7449 participants aged 45 years and older were involved in our study. The generalized linear mixed models were conducted to examine the separate and joint effects of household air pollution and outdoor air pollution on arthritis, respectively. We found a strong significant association between air pollution and arthritis incidence. Individuals cooking primarily with solid fuel were more likely in higher risk of arthritis compared with cleaner fuel (OR= 1.15; 95% CI: 1.08-1.23). The group-based trajectory model identified four trajectory groups, compared with group "High-Decreasing rapidly", adjusted ORs of incident arthritis for group "Middle-Decreasing moderately", "Low-Decreasing slowly" and "Low-Stably" were 1.36 (95% CI, 1.03-1.79), 1.36 (95% CI, 1.01-1.83) and 1.81 (95% CI, 1.30-2.52), respectively. These associations were generally higher in participants younger than 65 years. In addition, solid fuel use and PM2.5 exposure had additive and multiplicative effects on arthritis. The results suggested that solid fuel use and long-term PM2.5 exposure were associated with a higher incidence of arthritis. Therefore, it is necessary to restrict solid fuel use to reduce household air pollution and make stronger environmental protection policies to reduce PM2.5 concentration.

Research on prediction of daily admissions of respiratory diseases with comorbid diabetes in Beijing based on long short-term memory recurrent neural network.

To compare the performance of generalized additive model (GAM) and long short-term memory recurrent neural network (LSTM-RNN) on the prediction of daily admissions of respiratory diseases with comorbid diabetes. Daily data on air pollutants, meteorological factors and hospital admissions for respiratory diseases from Jan 1st, 2014 to Dec 31st, 2019 in Beijing were collected. LSTM-RNN was used to predict the daily admissions of respiratory diseases with comorbid diabetes, and the results were compared with those of GAM. The evaluation indexes were calculated by five-fold cross validation. Compared with the GAM, the prediction errors of LSTM-RNN were significantly lower [root mean squared error (RMSE): 21.21±3.30 vs. 46.13±7.60, <0.01; mean absolute error (MAE): 14.64±1.99 vs. 36.08±6.20, <0.01], and the value was significantly higher (0.79±0.06 vs. 0.57±0.12, <0.01). In gender stratification, RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting female admission (all <0.05), but there were no significant difference in predicting male admission between two models (all >0.05). In seasonal stratification, RMSE and MAE of LSTM-RNN were lower than those of GAM in predicting warm season admission (all <0.05), but there was no significant difference in value (>0.05). There were no significant difference in RMSE, MAE and between the two models in predicting cold season admission (all >0.05). In the stratification of functional areas, the RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting core area admission (all <0.05). has lower prediction errors and better fitting than the GAM, which can provide scientific basis for precise allocation of medical resources in polluted weather in advance.

Association of TyG index and TG/HDL-C ratio with arterial stiffness progression in a non-normotensive population.

BACKGROUND: Cross-sectional studies have reported that insulin resistance (IR) is associated with arterial stiffness. However, the relationship between IR and arterial stiffness progression remains unclear. This study aims to evaluate the association of triglyceride glucose (TyG) index and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio with arterial stiffness progression in a non-normotensive population. METHODS: A total of 1895 prehypertensive (systolic pressure 120-139 mmHg or diastolic pressure 80-90 mmHg) or hypertensive (systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg or using antihypertensive medication) participants were enrolled in 2013 and 2014, and followed until December 31, 2019. Arterial stiffness progression was measured by brachial-ankle pulse wave velocity (baPWV) change (absolute difference between baseline and last follow-up), baPWV change rate (change divided by following years), and baPWV slope (regression slope between examination year and baPWV). RESULTS: During a median follow-up of 4.71 years, we observed an increasing trend of baPWV in the population. There were linear and positive associations of the TyG index and TG/HDL-C ratio with the three baPWV parameters. The difference (95% CI) in baPWV change (cm/s) comparing participants in the highest quartile versus the lowest of TyG index and TG/HDL-C ratio were 129.5 (58.7-200.0) and 133.4 (52.0-214.9), respectively. Similarly, the evaluated baPWV change rates (cm/s/year) were 37.6 (15.3-60.0) and 43.5 (17.8-69.2), while the slopes of baPWV were 30.6 (9.3-51.8) and 33.5 (9.0-58.0). The observed association was stronger in the hypertensive population. CONCLUSION: Our study indicates that the TyG index and TG/HDL-C ratio are significantly associated with arterial stiffness progression in hypertensive population, not in prehypertensive population.

Histologic subtype classification of non-small cell lung cancer using PET/CT images.

PURPOSES: To evaluate the capability of PET/CT images for differentiating the histologic subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from radiomics-based machine learning/deep learning algorithms. METHODS: In this study, 867 patients with adenocarcinoma (ADC) and 552 patients with squamous cell carcinoma (SCC) were retrospectively analysed. A stratified random sample of 283 patients (20%) was used as the testing set (173 ADC and 110 SCC); the remaining data were used as the training set. A total of 688 features were extracted from each outlined tumour region. Ten feature selection techniques, ten machine learning (ML) models and the VGG16 deep learning (DL) algorithm were evaluated to construct an optimal classification model for the differential diagnosis of ADC and SCC. Tenfold cross-validation and grid search technique were employed to evaluate and optimize the model hyperparameters on the training dataset. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity and specificity was used to evaluate the performance of the models on the test dataset. RESULTS: Fifty top-ranked subset features were selected by each feature selection technique for classification. The linear discriminant analysis (LDA) (AUROC, 0.863; accuracy, 0.794) and support vector machine (SVM) (AUROC, 0.863; accuracy, 0.792) classifiers, both of which coupled with the ℓ2,1NR feature selection method, achieved optimal performance. The random forest (RF) classifier (AUROC, 0.824; accuracy, 0.775) and ℓ2,1NR feature selection method (AUROC, 0.815; accuracy, 0.764) showed excellent average performance among the classifiers and feature selection methods employed in our study, respectively. Furthermore, the VGG16 DL algorithm (AUROC, 0.903; accuracy, 0.841) outperformed all conventional machine learning methods in combination with radiomics. CONCLUSION: Employing radiomic machine learning/deep learning algorithms could help radiologists to differentiate the histologic subtypes of NSCLC via PET/CT images.

Triglyceride glucose index and carotid atherosclerosis incidence in the Chinese population: A prospective cohort study.

BACKGROUND AND AIMS: The association of the triglyceride glucose (TyG) index with carotid atherosclerosis has not been reported in longitudinal studies. The present study aimed to investigate whether the TyG index increases the risk of carotid atherosclerosis incidence. METHODS AND RESULTS: This study included data from the Beijing Health Management Cohort (BHMC; n = 6955) and the Beijing Physical Examination Cohort (BPEC; n = 8473). Participants without a history of carotid atherosclerosis who underwent health examination in 2011 or 2012 were annually followed until 2019. The TyG index was denoted as ln [triglycerides (mmol/L)∗fasting glucose (mmol/L)/2]. During a median follow-up of 5.02 years and 5.36 years, 1441 individuals in the BHMC group and 2181 individuals in the BPEC group developed carotid plaque, respectively. The adjusted hazard ratios (HRs) of the continuous TyG index were 1.253 (95% CI, 1.044 to 1.505) and 1.252 (95% CI, 1.091 to 1.437) for the BHMC and BPEC groups, respectively. Individuals in the highest quartile of the TyG index were associated with an increased risk of carotid plaque compared with those in the lowest quartile (BHMC: HR, 1.366; 95% CI, 1.101 to 1.695, P for trend = 0.010; BPEC: HR, 1.379; 95% CI, 1.196 to 1.591, P for trend = 0.013). CONCLUSION: These findings suggested that a higher TyG index increases the risk of carotid atherosclerosis incidence in the general population.

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study.

BACKGROUND: The supervised deep learning approach provides state-of-the-art performance in a variety of fundus image classification tasks, but it is not applicable for screening tasks with numerous or unknown disease types. The unsupervised anomaly detection (AD) approach, which needs only normal samples to develop a model, may be a workable and cost-saving method of screening for ocular diseases. OBJECTIVE: This study aimed to develop and evaluate an AD model for detecting ocular diseases on the basis of color fundus images. METHODS: A generative adversarial network-based AD method for detecting possible ocular diseases was developed and evaluated using 90,499 retinal fundus images derived from 4 large-scale real-world data sets. Four other independent external test sets were used for external testing and further analysis of the model's performance in detecting 6 common ocular diseases (diabetic retinopathy [DR], glaucoma, cataract, age-related macular degeneration, hypertensive retinopathy [HR], and myopia), DR of different severity levels, and 36 categories of abnormal fundus images. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the model's performance were calculated and presented. RESULTS: Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in 1 external proprietary data set. In the detection of 6 common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR, and myopia were 0.891, 0.916, 0.912, 0.867, 0.895, and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR, and 0.926 for detecting vision-threatening DR. CONCLUSIONS: The AD approach achieved high sensitivity and specificity in detecting ocular diseases on the basis of fundus images, which implies that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. Future studies are required to evaluate the practical applicability of the AD approach in ocular disease screening.

Acute effect of particulate matter pollution on hospital admissions for stroke among patients with type 2 diabetes in Beijing, China, from 2014 to 2018.

BACKGROUND: The health effect of particulate matter pollution on stroke has been widely examined; however, the effect among patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown. METHODS: A time-series study was conducted to investigate the short-term effect of fine particulate matter (PM2.5) and inhalable particulate matter (PM10) on hospital admissions for stroke among patients with T2D in Beijing, China, from 2014 to 2018. An over-dispersed Poisson generalized additive model was employed to adjust for important covariates, such as weather conditions and long-term and seasonal trends. RESULTS: A total of 159,298 hospital admissions for stroke comorbid with T2D were reported. Approximately linear exposure-response curves were observed for PM2.5 and PM10 in relation to stroke admissions among T2D patients. A 10 µg/m3 increase in the four-day moving average of PM2.5 and PM10 was associated with 0.14% (95% confidence interval [CI]: 0.05-0.23%) and 0.14% (95% CI: 0.06-0.22%) incremental increases in stroke admissions among T2D patients, respectively. A 10 µg/m3 increase in PM2.5 in the two-day moving average corresponded to a 0.72% (95% CI: 0.02-1.42%) incremental increase in hemorrhagic stroke, and a 10 µg/m3 increase in PM10 in the four-day moving average corresponded to a 0.14% (95% CI: 0.06-0.22%) incremental increase in ischemic stroke. CONCLUSIONS: High particulate matter might be a risk factor for stroke among patients with T2D. PM2.5 and PM10 have a linear exposure-response relationship with stroke among T2D patients. The study provided evidence of the risk of stroke due to particulate matter pollution among patients with comorbid T2D.

Acute effect of particulate matter pollution on hospital admissions for cause-specific respiratory diseases among patients with and without type 2 diabetes in Beijing, China, from 2014 to 2020.

BACKGROUND: Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. METHODS: A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM2.5 and PM10 were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. RESULTS: A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM2.5 corresponded to 1.91% (95% CI: 1.33-2.49%), 2.16% (95% CI: 1.08-3.25%), and 1.92% (95% CI: 1.46-2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0-8 days, lag 8 days, and lag 8 days. The effect size of PM2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM10 were 3.86% (95% CI: 2.48-5.27%), 3.73% (95% CI: 1.72-5.79%), and 3.92% (95% CI: 2.65-5.21%), respectively, at lag 0-13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM10 on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. CONCLUSIONS: Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM2.5 was higher in patients with T2D than those without T2D.

Spatial-temporal analysis of cause-specific cardiovascular hospital admission in Beijing, China.

BACKGROUND: The main aim of this study was to explore the spatial-temporal patterns of cause-specific CVD admission in Beijing using retrospective SaTScan analysis. METHODS: A spatial-temporal analysis was conducted at the district level based on the rates of total and cause-specific CVD admissions, including coronary heart disease (CHD), atrial fibrillation (AF), and heart failure (HF) from 2013 to 2017. We used joint point regression, Global Moran's I and Anselin's local Moran's I, together with Kulldorff's scan statistic. RESULTS: Hospital admission trend decreased during the study period. Admission rates followed a spatially clustered pattern with differences occurring between cause-specific CVDs. Clusters were mainly identified in ecological preservation areas, with a more likely cluster found in Daxing, Fangshan, Xicheng district for total CVD, CHD, AF and HF, respectively. CONCLUSIONS: Hospital admission of cause-specific CVD showed spatial clustered pattern, especially in ecological preservation areas.

Acute effects of ambient particulate matter on blood pressure in office workers.

Exposure to ambient particulate matter with a diameter of <2.5 µm (PM2.5) has been linked to increases in blood pressure. The aim of this study was to assess the effects of short-term exposure to PM2.5 on blood pressure in office workers in Beijing, China. A total of 4801 individuals aged 18-60 years underwent an annual medical examination between 2013 and 2017. Levels of air pollutants were obtained from 35 fixed monitoring stations and correlated with the employment location of each participant to predict personal exposure via kriging interpolation. Linear mixed-effects models were used to estimate the changes in blood pressure associated with PM2.5 exposure at various lag times. After adjusting for personal characteristics and other potential confounders, each interquartile range increase in PM2.5 was associated with a 0.413-mmHg (95% confidence interval [CI]: 0.252-0.573), 0.171-mmHg (95% CI: 0.053-0.288), 0.278-mmHg (95% CI: 0.152-0.404), and 0.241-mmHg (95% CI: 0.120-0.362) increase in systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure, respectively (p < 0.05). Men, individuals previously diagnosed with hypertension, and subjects working in the northern districts of Beijing had larger changes in blood pressure, and the effect sizes were 0.477-mmHg (95% CI: 0.286-0.669), 0.851-mmHg (95% CI: 0.306-1.397, and 0.672-mmHg (95% CI: 0.405-0.940). The findings suggested that exposure to PM2.5 had adverse effects on blood pressure, especially among males and hypertensive patients.

Short-term effects of extreme temperatures on cause specific cardiovascular admissions in Beijing, China.

Extreme temperature-related cardiovascular diseases (CVDs) have become a growing public health concern. However, the impact of temperature on the cause of specific CVDs has not been well studied in the study area. The objective of this study was to assess the impact of temperature on cause-specific cardiovascular hospital admissions in Beijing, China. We obtained data from 172 large general hospitals from the Beijing Public Health Information Center Cardiovascular Case Database and China. Meteorological Administration covering 16 districts in Beijing from 2013 to 2017. We used a time-stratified case crossover design with a distributed lag nonlinear model (DLNM) to derive the impact of temperature on CVD in hospitals back to 27 days on CVD admissions. The temperature data were stratified as cold (extreme and moderate ) and hot (moderate and extreme ). Within five years (January 2013-December 2017), a total of 460,938 (male 54.9% and female 45.1%) CVD admission cases were reported. The exposure-response relationship for hospitalization was described by a "J" shape for the total and cause-specific. An increase in the six-day moving average temperature from moderate hot (30.2 °C) to extreme hot (36.9 °C) resulted in a significant increase in CVD admissions of 16.1%(95% CI = 12.8%-28.9%). However, the effect of cold temperature exposure on CVD admissions over a lag time of 0-27 days was found to be non significant, with a relative risk of 0.45 (95% CI = 0.378-0.55) for extreme cold (-8.5 °C)and 0.53 (95% CI = 0.47-0.60) for moderate cold (-5.6 °C). The results of this study indicate that exposure to extremely high temperatures is highly associated with an increase in cause-specific CVD admissions. These finding may guide to create and raise awareness of the general population, government and private sectors regarding on the effects of current weather conditions on CVD.

Three-Dimensional Texture Feature Analysis of Pulmonary Nodules in CT Images: Lung Cancer Predictive Models Based on Support Vector Machine Classifier.

To extract texture features of pulmonary nodules from three-dimensional views and to assess if predictive models of lung CT images from a three-dimensional texture feature could improve assessments conducted by radiologists. Clinical and CT imaging data for three dimensions (axial, coronal, and sagittal) in pulmonary nodules in 285 patients were collected from multiple centers and the Cancer Imaging Archive after ethics committee approval. Three-dimensional texture feature values (contourlets), and clinical and computed tomography (CT) imaging data were built into support vector machine (SVM) models to predict lung cancer, using four evaluation methods (disjunctive, conjunctive, voting, and synthetic); sensitivity, specificity, the Youden index, discriminant power (DP), and F value were calculated to assess model effectiveness. Additionally, diagnostic accuracy (three-dimensional model, axial model, and radiologist assessment) was assessed using the area under the curves for receiver operating characteristic (ROC) curves. Cross-sectional data from 285 patients (median age, 62 [range, 45-83] years; 115 males [40.4%]) were evaluated. Integrating three-dimensional assessments, the voting method had relatively high effectiveness based on both sensitivity (0.98) and specificity (0.79), which could improve radiologist diagnosis (maximum sensitivity, 0.75; maximum specificity, 0.51) for 23% and 28% respectively. Furthermore, the three-dimensional texture feature model of the voting method has the best diagnosis of precision rate (95.4%). Of all three-dimensional texture feature methods, the result of the voting method was the best, maintaining both high sensitivity and specificity scores. Additionally, the three-dimensional texture feature models were superior to two-dimensional models and radiologist-based assessments.