Associations of fasting plasma glucose with all-cause mortality and cardiovascular events in older Chinese diabetes patients: A population-based cohort study.

AIMS/INTRODUCTION: Our aim was to investigate the optimal fasting glucose (FPG) range in Chinese older adults with type 2 diabetes, and to clarify whether the optimal range varies according to the control of risk factors. MATERIALS AND METHODS: The baseline survey for the cohort study began in 2018, with follow up ending in 2022. Our study enrolled 59,030 older diabetes patients with no history of cardiovascular disease (CVD). Participants were divided into nine groups based on their baseline glycemic status. The association between FPG and the risk of adverse outcomes was mainly estimated by multivariate Cox proportional risk models and restricted spline analysis. RESULTS: During the 4-year follow-up period, a total of 5,637 deaths and 4,904 CVD events occurred. The associations of FPG with mortality and CVD events showed J-shaped curves. Among all-cause deaths, the hazard ratios for FPG ≤4.50 and >11.50 mmol/L were 1.50 (95% confidence interval [CI] 1.31-1.71) and 1.84 (95% CI 1.67-2.02). Among CVD, the hazard ratios for FPG ≤4.50 and >11.50 mmol/L were 1.31 (95% CI 1.13-1.53) and 1.71 (95% CI 1.54-1.89), respectively. The optimal FPG ranges of all-cause mortality and CVD were 5.50-7.50 and 4.50-7.50 mmol/L, respectively. For participants with at least two risk factors, the optimal FPG levels were higher than those with fewer risk factors. CONCLUSIONS: In older Chinese diabetes patients, the FPG ranges related to the minimum death and CVD event rates were 5.50-7.50 and 4.50-7.50 mmol/L, respectively. Patients with more cardiovascular risk factors had higher optimal blood glucose ranges than those with fewer risk factors.

Association of breakfast, total diet quality, and mental health in adolescents: a cross-sectional study of HBSC in Greece.

The emerging field of nutritional psychiatry offers proof that diet quality can be changed to reduce one's risk of developing mental illness. What you eat has a big impact on teenage mental health, and the quality and frequency of breakfast, as well as the different food groups, can affect adolescent mental health. In this study, regression models were employed to analyze four indicators (self-rated health, body satisfaction, life satisfaction, and eight symptoms) of mental health problems as well as demographic factors (gender, age, body mass index, affluence class, physical activity), with forest plots displaying the regression connections. This study, a descriptive cross-sectional survey of 3480 adolescents aged 11 to 15 years, chosen data from the 2018 Greek Health Behaviour in School-aged Children (HBSC) International Study for secondary analysis. The data revealed that high levels of four indicators of mental health (self-rated health, body satisfaction, life satisfaction, and eight symptoms) were significantly related to breakfast quality, total dietary patterns. Students reporting poor total food quality, for example, were more predictive of self-assessment when unhealthy (2.286 95% CI 1.851-2.824), and the largest connections with eating a "bad" breakfast were discovered when the eight symptoms of mental status were the worst (- 0.869 95% CI: - 1.300, - 0.439); physical activity may enhance the positive relationship of diet quality on mental health, while obesity weakens this positive relationship; and age patterns varied by gender, with girls and older individuals showing a higher risk of diet quality and mental health.   Conclusions: The findings revealed that each of the four indices of general mental health was correlated with teenage breakfast and overall diet quality. Physical activity and weight were shown to be moderating factors, and obesity had the biggest impact on psychological risk. Gender, age, and family economic inequality were potential explanations for the rise in psychological symptoms. Researchers are urged to focus more on nutritional psychiatry research and look into psychological perception disparities in the future. What is Known: • There is a potential correlation between adolescent dietary patterns or quality and mental health. • Factors such as age, gender, socio-economic status, body weight and physical activity all have an impact on diet and mental health outcomes. What is New: • There have been no studies of the ability to simultaneously predict breakfast quality and overall diet quality for broadly defined mental health complaints [positive (life satisfaction), neutral (body satisfaction, self-rated health) and negative (eight symptoms: depressed mood, irritability, nervousness, difficulty sleeping, dizziness, headache, stomachache and backache)]. • Differences in overall diet quality trends between demographic subgroups remain unclear.

The relationship between mushroom consumption and cognitive performance among middle-aged and older adults: a cross-sectional study.

Objectives: Bioactive compounds in mushrooms may protect the brain from neurodegeneration by inhibiting the production of amyloid-ß and playing an antioxidant role. This study aimed at examining the associations of mushroom consumption with cognitive function and mild cognitive impairment (MCI) among middle-aged and older adults in China. Design: A cross-sectional study. Setting and participants: This study was conducted in seven cities in China and included 2203 middle-aged and older adults. Methods: Data on mushroom consumption were collected using a semi-quantitative food frequency questionnaire. Cognitive function was evaluated by the Auditory Verbal Learning Test (AVLT), Verbal Fluency Test (VFT), Digit Symbol Substitution Test (DSST), and Trail Making Test-B (TMT-B). The composite z score was used to reflect global cognition. MCI was determined according to the Petersen criteria. Multiple linear regression and logistic regression were used to examine the relationship between mushroom consumption and cognitive performance. Results: This study included 2203 participants aged 55 years and above (mean age = 63.43 years). After controlling demographic characteristics, lifestyle factors, other dietary factors, and history of chronic disease, higher mushroom consumption was associated with better global cognition. Compared to the lowest quartile (Q1, 0-4.00 g day-1), the ßs (95% confidence intervals, 95% CIs) were 0.10 (0.03, 0.18) for Q2 (4.01-10.42 g day-1), 0.13 (0.06, 0.20) for Q3 (10.43-20.84 g day-1), and 0.13 (0.06, 0.20) for Q4 (>20.84 g day-1). The higher mushroom consumption was positively related to better performance in DSST and TMT-B (P-values < 0.05). A 10 g day-1 increment in mushroom consumption was related to 12% lower odds of MCI (odds ratio = 0.88, 95% CI: 0.80-0.97). Conclusions: Higher mushroom consumption was positively related to better cognitive function and associated with lower odds of MCI. Further studies are needed to replicate our findings in other populations and determine the underlying mechanisms.

Exposures to ambient air pollutants increase prevalence of sleep disorder in adults: Evidence from Wuhan Chronic Disease Cohort Study (WCDCS).

BACKGROUND: Sleep disorder contributes to memory dysfunction and chronic diseases. Clear evidence of environment disturbance, such as residential noise, are associated with an increased risk of sleep disorder. However, not enough studies have been conducted on association between residential air pollutants and sleep disorder. We sought to determine whether exposures to residential air pollutants associated with risk of sleep disorder among adults. METHODS: Using the dataset of the Wuhan Chronic Disease Cohort Study (WCDCS), we investigated the prevalence of sleep disorder and five sleep disorder symptoms in the study. The data of air pollutants (including PM10, PM2.5, NO2, SO2 and O3) were obtained from 10 air quality monitoring stations in Wuhan. We utilized logistic regression model to evaluate the associations of five types of air pollutants with odds ratio (OR) of sleep disorder and symptoms. The potential moderating effects of socio-demographic factors in the associations were explored using the interaction effects model. RESULTS: Of the study participants, 52.1 % had sleep disorder. Exposures to higher concentrations of air pollutants were associated with increased prevalence of sleep disorder. For example, per interquartile range (IQR) increases in concentrations of PM10, PM2.5 or SO2 corresponded to the increase of sleep disorder increased prevalence at 14.7 % (adjusted odds ratio (aOR) = 1.147, 95 %CI:1.062, 1.240), 8.9 % (aOR = 1.089, 95 %CI: 1.003, 1.182) and 15.8 % (aOR = 1.158, 95 %CI: 1.065, 1.260). For symptoms specific analyses, significant linkages of PM10, PM2.5, SO2 with difficulty in falling asleep, wake up after falling asleep and early awaken were observed. Moderating effects of age and place of residence on the linkages of PM10 with increased prevalence of sleep disorder were identified. CONCLUSION: Higher level of air pollution exposure could increase the prevalence of sleep disorder. Middle-aged and elderly population, as well as the rural residents are more likely to suffer from sleep disorder.

Serum uric acid is associated with chronic kidney disease in elderly Chinese patients with diabetes.

BACKGROUND: The relationship between hyperuricemia and chronic kidney disease (CKD) has been investigated extensively. However, studies on elderly individuals are still limited. Moreover, there is no consensus on whether hyperuricemia or elevated serum uric acid (SUA) within the normal range is correlated with the new onset of CKD and whether there are differences between males and females. METHODS: We included 39039 elderly diabetic patients without CKD at baseline from a community-based cohort in Wuhan, China. The outcome event was the new onset of CKD (defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2). Multivariate Cox models were used to assess the adjusted hazard ratio (HR). RESULTS: During the 2-year follow-up period, 3162 (8.10%) patients with diabetes developed new-onset CKD. The optimal cutoff value of SUA for incident CKD was 347.4 µmol/L. The adjusted HRs of hyperuricemia for new-onset CKD were 1.925 (1.724-2.150) and 1.676 (1.520-1.848) for males and females, respectively. The risk of developing CKD increased across the Q4 group up to 2.242 times for their counterparts in the lowest SUA quartile, independent of age, sex, diabetes duration, obesity, hypertension, systolic blood pressure, diastolic blood pressure, smoking, drinking, dyslipidemia, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and fasting plasma glucose. CONCLUSIONS: Hyperuricemia is an independent predictor of incident CKD. Elevated SUA was linearly correlated with CKD in elderly patients with diabetes, showing a relatively higher intensity among males compared with that among females. The optimal cutoff value of SUA for the risk of new-onset CKD in elderly patients with diabetes was 347.4 µmol/L.

Disability weight measurement for the severity of different diseases in Wuhan, China.

BACKGROUND: Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. METHODS: We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). RESULTS: A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state "mild anemia" (DW = 0.005, 95% UI 0.000-0.027) or "allergic rhinitis (hay fever)" (0.005, 95% UI 0.000-0.029) had the lowest DW and "heroin and other opioid dependence, severe" had the highest DW (0.699, 95% UI 0.579-0.827). A high correlation coefficient (Pearson's r = 0.876; P < 0.001) for DWs of same health states was observed between Wuhan's survey and GBD 2013 DW survey. Health states referred to mental symptom, fatigue, and the residual category of other physical symptoms were statistically significantly associated with a lower Wuhan's DWs than the GBD's DWs. Health states with disfigurement and substance use symptom had a higher DW in Wuhan population than the GBD 2013 study. CONCLUSIONS: This set of DWs could be used to calculate local diseases burden for health policy-decision in Wuhan population. The DW differences between the GBD's survey and Wuhan's survey suggest that there might be some contextual or culture factors influencing assessment on the severity of diseases.

A multicenter prospective study of next-generation sequencing-based newborn screening for monogenic genetic diseases in China.

BACKGROUND: Newborn screening (NBS) is an important and successful public health program that helps improve the long-term clinical outcomes of newborns by providing early diagnosis and treatment of certain inborn diseases. The development of next-generation sequencing (NGS) technology provides new opportunities to expand current newborn screening methodologies. METHODS: We designed a a newborn genetic screening (NBGS) panel targeting 135 genes associated with 75 inborn disorders by multiplex PCR combined with NGS. With this panel, a large-scale, multicenter, prospective multidisease analysis was conducted on dried blood spot (DBS) profiles from 21,442 neonates nationwide. RESULTS: We presented the positive detection rate and carrier frequency of diseases and related variants in different regions; and 168 (0.78%) positive cases were detected. Glucose-6-Phosphate Dehydrogenase deficiency (G6PDD) and phenylketonuria (PKU) had higher prevalence rates, which were significantly different in different regions. The positive detection of G6PD variants was quite common in south China, whereas PAH variants were most commonly identified in north China. In addition, NBGS identified 3 cases with DUOX2 variants and one with SLC25A13 variants, which were normal in conventional NBS, but were confirmed later as abnormal in repeated biochemical testing after recall. Eighty percent of high-frequency gene carriers and 60% of high-frequency variant carriers had obvious regional differences. On the premise that there was no significant difference in birth weight and gestational age, the biochemical indicators of SLC22A5 c.1400C > G and ACADSB c.1165A > G carriers were significantly different from those of non-carriers. CONCLUSIONS: We demonstrated that NBGS is an effective strategy to identify neonates affected with treatable diseases as a supplement to current NBS methods. Our data also showed that the prevalence of diseases has significant regional characteristics, which provides a theoretical basis for screening diseases in different regions.

Mortality and Years of Life Lost Due to Brain and Other Central Nervous System Cancer in Wuhan, China, from 2010 to 2019.

BACKGROUND: Brain cancer is one of the worst types of cancer worldwide. Understanding the epidemiology of CNS cancer is critical for properly allocating healthcare resources. METHODS: We collected data on CNS cancer deaths in Wuhan, China, during 2010-2019. We constructed the cause-eliminated life tables to calculate life expectancy (LE), mortality, and years of life lost (YLLs) by age and sex. The BAPC model was used to forecast the future trends of age-standardized mortality rate (ASMR). Decomposition analysis was adopted to explore the contribution of population growth, population aging, and age-specific mortality to the change in total CNS cancer deaths. RESULTS: In 2019, the ASMR of CNS cancer was 3.75, and the ASYR was 135.70 in Wuhan, China. ASMR was expected to decrease to 3.43 in 2024. The age distribution of deaths due to CNS cancer was concentrated in the middle-aged and older population, with a peak in the 65-69 age group. Caidian, Jianghan, and Qingshan had the greatest ASMRs in 2019 in Wuhan, with ASMRs of 6.32, 4.78, and 4.75, respectively. Population aging is critical to the change in total CNS cancer deaths. CONCLUSION: We analyzed the current status, temporal trends, and gender and age distributions of the burden of CNS cancer in Wuhan, during 2010-2019, providing a valuable reference for better lessening the CNS cancer burden.

Localized Instillation Enables In Vivo Screening of Targeting Peptides Using One-Bead One-Compound Technology.

The One-Bead One-Compound (OBOC) library screening is an efficient technique for identifying targeting peptides. However, due to the relatively large bead size, it is challenging for the OBOC method to be applied for in vivo screening. Herein, we report an in vivo Localized Instillation Beads library (LIB) screening method to discover targeting peptides with the OBOC technique. Inspired by localized instillation, we constructed a cavity inside of a transplanted tumor of a mouse. Then, the OBOC heptapeptide library was injected and incubated inside the tumor cavity. After an efficient elution process, the retained beads were gathered, from which three MDA-MB-231 tumor-targeting heptapeptides were discovered. It was verified that the best peptide had 1.9-fold higher tumor accumulation than the commonly used targeting peptide RGD in vivo. Finally, two targeting proteins were discovered as potential targets of our targeting peptide to the MDA-MB-231 tumor. The in vivo LIB screening method expands the scope of OBOC peptide screening applications to discover targeting peptides in vivo feasibly and reliably.

Secular Trends of Liver Cancer Mortality and Years of Life Lost in Wuhan, China 2010-2019.

BACKGROUND: Liver cancer has caused a heavy burden worldwide. This study aimed to estimate the trends in the mortality and years of life lost (YLL) due to liver cancer and decompose the total deaths into three contributors: population growth, population aging, and mortality change. METHODS: Our study used data from the cause-of-death surveillance system in Wuhan. The mortality and YLL rates were standardized according to the sixth national population census in China. This study calculated the estimated annual percentage change (EAPC) to estimate the trends in the age-standardized mortality rate (ASMR) and age-standardized YLL rate (ASYR). Meanwhile, a decomposition analysis was used to explore the effect of population growth, population aging, and age-specific mortality change on the change in liver cancer deaths. RESULTS: The ASMR of liver cancer declined at an annual rate of 4.6% from 30.87 per 100,000 people in 2010 to 20.29 per 100,000 people in 2019, while the ASYR was at an annual rate of 5.6% from 969.35 per 100,000 people in 2010 to 581.82 per 100,000 people in 2019. Similar downward trends were seen in men and women. The decomposition analysis found that total deaths number changed by -12.42% from 2010 to 2019, of which population growth and population aging caused the total death numbers to increase by 9.75% and 21.15%, while the age-specific mortality change caused the total death numbers to decrease by 43.32%. CONCLUSION: Although the ASMR of liver cancer has declined in recent years in Wuhan, it still causes a heavy burden with the increasing population and rapid population aging and remains an essential public health issue. The government should take measures to reduce the burden of liver cancer, especially among men.

Temporal trends in lung cancer mortality and years of life lost in Wuhan, China, 2010-2019.

Objective: Lung cancer is responsible for millions of deaths yearly, and its burden is severe worldwide. This study aimed to investigate the burden of lung cancer in the population of Wuhan based on the surveillance data from 2010 to 2019. Methods: Data of this study was obtained from the Mortality Register System established by the Wuhan Center for Disease Control and Prevention. The study systematically analyzed the burden of lung cancer deaths in the population of Wuhan and its 13 administrative regions from 2010 to 2019 via the Joinpoint regression models, Age-Period-Cohort (APC) models, and decomposition analysis. Results: This study found the upward and downward trends in the age-standardized mortality rates (ASMRs) and age-standardized years of life lost rates (ASYLLRs) of lung cancer from 2010 to 2019. In Joinpoint regression models, the corresponding estimated annual percentage change (EAPC) were 1.00% and -1.90%, 0.60%, and -3.00%, respectively. In APC models, lung cancer mortality tended to increase with age for both sexes in Wuhan, peaking at the 85-89 age group; The period effects for different populations have started to gradually decline in recent years. In addition, the cohort effects indicated that the risk of lung cancer death was highest among those born in the 1950s-1955s, at 1.08 (males) and 1.01 (females). Among all administrative districts in Wuhan, the ASMR of lung cancer in the Xinzhou District has remained the highest over the study period. In decomposition analysis, both population aging (P<0.01) and population growth (P<0.01) aggravated (Z>0) lung cancer deaths in the Wuhan population. Conclusions: The burden of lung cancer death in the Wuhan population has shown a gradual decline in recent years, but the impact of aging and population growth on lung cancer mortality should not be ignored. Therefore, lung cancer surveillance must be strengthened to reduce the burden of lung cancer in Wuhan.

Association of social support with cognition among older adults in China: A cross-sectional study.

Objective: This study aimed to examine the relationship between social support and its sub-domains and cognitive performance, and the association with cognitive impairment among older adults in China. Design: A cross-sectional study. Setting and participants: We included 865 community-based individuals aged 65 and above from Hubei province, China. Methods: The level of social support was evaluated using the social support rating scale (SSRC). The Mini-Mental State Examination was adopted to assess cognitive function, and its cut-offs were used to determine cognitive impairment among the participants. Multiple linear regression models and logistic regression models were used to estimate the ß and odds ratios (ORs) and their 95% CIs, respectively. Results: The participants were divided into quartiles 1-4 (Q1-Q4), according to the total scores of SSRC. After adjusting for sociodemographic characteristics, lifestyle factors, and history of diseases, for MMSE scores, compared to these in Q1, the ß of Q2-Q4 were -0.22 (-0.88, 0.43), 0.29 (-0.35, 0.94), and 0.86 (0.19, 1.53), respectively; For cognitive impairment, the ORs of Q2-Q4 were 1.21 (0.80, 1.82), 0.62 (0.40, 0.94), and 0.50 (0.32, 0.80), respectively. Considering SSRC scores as the continuous variable, per 1-unit increase, the ß was 0.05 (0.02, 0.09) for the cognitive score, and the OR was 0.95 (0.92, 0.98) for cognitive impairment. In addition, higher levels of both subjective support and support utilization were related to better MMSE performance and lower risks of cognitive impairment. Conclusion and implications: Among the older adults in China, as expected, there is a positive relationship between social support and cognitive performance, and high levels of social support, particularly in support utilization, were related to low risks of cognitive impairment. More social support should be provided in this population to improve cognitive function and reduce the risks of cognitive impairment.

Association of Sleep Duration With All-Cause and Cardiovascular Mortality: A Prospective Cohort Study.

To clarify the association of sleep duration with all-cause and cardiovascular mortality, and further estimate the population attributable fraction (PAF) for the 10-year risk of cardiovascular disease (CVD) due to inappropriate sleep duration among US adults, we included data of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2014 by linkage to the National Death Index until December 31, 2015 in a prospective design. Cox proportional hazards models were used for multivariate longitudinal analyses. The Pooled Cohort Equations methods was adopted to calculate the predicted 10-year CVD risk. In the current study, sleep <5 h or longer than 9 h per day were significantly associated with elevated risks of all-cause mortality, and the multivariable-adjusted HRs across categories were 1.40 (95% CI, 1.14-1.71), 1.12 (95% CI, 0.91-1.38), 1 (reference), 1.35 (95% CI, 1.12-1.63), and 1.74 (95% CI, 1.42-2.12). Similarly, the HRs of cardiovascular mortality across categories were 1.66 (95% CI, 1.02-2.72), 1.15 (95% CI, 0.77-1.73), 1 (reference), 1.55 (95% CI, 1.05-2.29), and 1.81 (95% CI, 1.09-3.02). Under a causal-effect assumption, we estimated that 187 000 CVD events (PAF 1.8%, 0.9% to 2.3%) were attributable to short sleep duration and 947 000 CVD events (PAF 9.2%, 6.4% to 11.6%) were attributable to long sleep duration from 2018 to 2028. This study informed the potential benefit of optimizing the sleep duration for the primary prevention of CVD in a contemporary population.

Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults.

Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field.

Prevalence, awareness and control of type 2 diabetes mellitus and risk factors in Chinese elderly population.

BACKGROUND: Type 2 diabetes mellitus is an expanding global public health issue, especially in developing countries. This study aimed to investigate the prevalence, awareness and control rate of type 2 diabetes mellitus, and assess its risk factors in elderly Chinese individuals. METHODS: The health screening data of 376,702 individuals aged ≥ 65 years in Wuhan, China, were collected to analyse the prevalence, awareness, and control rates of diabetes. Indices, including fasting plasma glucose and other biochemical indicators, were measured for all participants using standard methods at the central laboratory. Multilevel logistic regression analysis was performed to assess the key determinants of the prevalence, awareness, and control rates of diabetes. RESULTS: The prevalence, awareness, and control rates of diabetes in the Chinese individuals aged ≥ 65 years were 18.80%, 77.14%, and 41.33%, respectively. There were statistically significant differences in the prevalence, awareness, and control rates by gender. Factors associated with diabetes prevalence were age, body mass index (BMI), and central obesity; while those associated with awareness and control were gender, education level, marital status, physical activity, alcohol consumption, BMI, and central obesity. CONCLUSIONS: Diabetes is an important public health problem in the elderly in China. The awareness and control rates have improved, but overall remained poor. Therefore, effective measures to raise awareness and control the rates of diabetes should be undertaken to circumvent the growing disease burden in elderly Chinese people.

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques.

Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019-2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.

How long-term air pollution and its metal constituents affect type 2 diabetes mellitus prevalence? Results from Wuhan Chronic Disease Cohort.

BACKGROUND: Epidemiological evidence linking type 2 diabetes mellitus (T2DM) with air pollution is discrepant and most are restricted to the influences of air-pollutant mass concentration. This study aims to explore the effects of long-term exposure to air pollution and its metal constituents on T2DM prevalence in China. METHODS: We used data on 10,253 adult residents from the baseline survey of Wuhan Chronic Disease Cohort in 2019. Ambient PM2.5, PM10 and NO2 exposure were estimated at residences based on Chinese Air Quality Reanalysis Dataset. Concentrations of 10 metal constituents were measured by 976 PM2.5 filter samples collected from four monitoring stations. Logistic regression models were employed to examine associations of T2DM prevalence with 3-year mean concentrations of each air pollutant and PM2.5 metal constituents prior to the baseline investigation. RESULTS: A total of 673 T2DM cases (6.6%) were identified. The 3-year mean exposures to PM2.5, PM10 and NO2 were 50.89 µg/m3, 82.86 µg/m3, and 39.79 µg/m3, respectively. And interquartile range (IQR) of 10 metals in PM2.5 varied from 0.03 ng/m3 to 78.30 ng/m3. For 1 µg/m3 increment in PM2.5, PM10 and NO2, the odds of T2DM increased by 7.2% (95%CI: 1.026, 1.136), 3.1% (95%CI: 1.013, 1.050), and 2.1% (95%CI: 1.005, 1.038) after adjusting for potential confounders. Cd and Sb in PM2.5 were significant risk factors to T2DM with odds ratios of 1.350 (95%CI: 1.089, 1.673) and 1.389 (95%CI: 1.164, 1.658) for per IQR increase, respectively. Stratification analyses indicated that males and those aged ≥45 years were more susceptive to long-term air pollution. CONCLUSIONS: Long-term exposure to PM2.5, PM10 and NO2 increased T2DM prevalence in a Wuhan population, especially for men and middle-aged and elderly people. Moreover, T2DM was significantly associated with Cd and Sb in PM2.5. Further research to validate these results and to clarify the underlying mechanisms is warranted.

Associations of long-term exposure to ambient air pollutants with metabolic syndrome: The Wuhan Chronic Disease Cohort Study (WCDCS).

BACKGROUND: Evidence on the associations between long-term exposure to ambient air pollutants (including particle with aerodynamic diameter ≤10 µm (PM10), particle with aerodynamic diameter ≤2.5 µm (PM2.5), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2)) and prevalence of metabolic syndrome (MetS) remains inconclusive. This study aimed to determine the associations based on a case-control study nested in the Wuhan Chronic Disease Cohort study (WCDCS), a population-based study with baseline survey in 2019. METHODS: A total of 10,253 residents living in Wuhan were recruited. The 3-year average concentrations of main pollutants (PM10, PM2.5, O3, NO2, and SO2) at residences prior to the survey date were estimated to evaluate the long-term exposures. The generalized linear mixed models were used to investigate the changes in MetS prevalence by an IQR increases in each air pollutant exposure concentrations. Interaction effects between air pollutants and demographic, lifestyle, and dietary factors on MetS were evaluated by including an interactive item in the main model. RESULTS: The prevalence of MetS in Wuhan was 9.8%, and the 3-year exposure concentrations of PM10, PM2.5, O3, NO2, and SO2 were 84.1 µg/m3, 50.5 µg/m3, 55.7 µg/m3, 46.0 µg/m3, and 9.4 µg/m3, respectively. Higher PM10, PM2.5 and O3 exposure concentrations were associated with an elevated MetS prevalence (e.g. an IQR increase in PM2.5, OR = 1.193, 95% confidence intervals (95%CIs): 1.028, 1.385; for O3, OR = 1.074, 95%CIs: 1.025, 1.124), whereas NO2, and SO2 were negatively or insignificant correlated with odds of Mets (e.g. an IQR increase in NO2, OR = 0.865, 95%CIs: 0.795, 0.941). Males, smokers, alcohol drinkers and individuals who intake fruits occasionally exposure to PM10 and PM2.5 were found had a higher risk of developing MetS. CONCLUSIONS: Long-term exposure to higher concentrations of ambient air pollutants may elevate the prevalence of MetS in populations in Central China. Susceptible individuals especially those with unhealthy lifestyles had a higher risk for MetS.

Transmission of COVID-19 from community to healthcare agencies and back to community: a retrospective study of data from Wuhan, China.

BACKGROUND: The early spatiotemporal transmission of COVID-19 remains unclear. The community to healthcare agencies and back to community (CHC) model was tested in our study to simulate the early phase of COVID-19 transmission in Wuhan, China. METHODS: We conducted a retrospective study. COVID-19 case series reported to the Municipal Notifiable Disease Report System of Wuhan from December 2019 to March 2020 from 17 communities were collected. Cases from healthcare workers (HW) and from community members (CM) were distinguished by documented occupations. Overall spatial and temporal relationships between HW and CM COVID-19 cases were visualised. The CHC model was then simulated. The turning point separating phase 1 and phase 2 was determined using a quadratic model. For phases 1 and 2, linear regression was used to quantify the relationship between HW and CM COVID-19 cases. RESULTS: The spatial and temporal distributions of COVID-19 cases between HWs and CMs were closely correlated. The turning point was 36.85±18.37 (range 15-70). The linear model fitted well for phase 1 (mean R2=0.98) and phase 2 (mean R2=0.93). In phase 1, the estimated [Formula: see text]s were positive (from 18.03 to 94.99), with smaller [Formula: see text]s (from 2.98 to 15.14); in phase 2, the estimated [Formula: see text]s were negative (from -4.22 to -81.87), with larger [Formula: see text]s (from 5.37 to 78.12). CONCLUSION: Transmission of COVID-19 from the community to healthcare agencies and back to the community was confirmed in Wuhan. Prevention and control measures for COVID-19 in hospitals and among HWs are crucial and warrant further attention.

Spatial and temporal characteristics of drought and its correlation with climate indices in Northeast China.

The spatial and temporal characteristics of drought in Northeast China are investigated, using monthly meteorological data from 140 stations over the period 1970-2014. The study area was divided into three regions using hierarchical cluster analysis based on the precipitation and potential evapotranspiration data. The standardized precipitation evapotranspiration index (SPEI) was calculated for each station on 3-month and 12-month time scales. The Mann-Kendall (MK) trend test and Sen's slope method were applied to determine the trends for annual and seasonal SPEI time series. Periodic features of drought conditions in each sub-region and possible relationship with large-scale climate patterns were respectively identified using the continuous wavelet transform (CWT) and cross wavelet transform. The results show mitigations in spring and winter droughts and a significant increasing trend in autumn drought. On the annual scale, droughts became more severe and more intense in the western regions but were mitigated in the eastern region. CWT analysis showed that droughts in Northeast China occur predominantly in 14- to 42-month or 15- to 60-month cycles. Annual and seasonal droughts have 2- to 6-year cycles over the three defined regions. Cross wavelet analysis also shows that the statistically significant influence of large-scale climate patterns (the Southern Oscillation, the Atlantic Multidecadal Oscillation, the Arctic Oscillation, and the Polar-Eurasian Pattern) on drought in Northeast China is concentrated in a 16- to 50-month period, possibly causing drought variability in the different regions. The Southern Oscillation, Polar-Eurasia pattern, and Arctic Oscillation are significantly correlated with drought on decadal scales (around 120-month period). The findings of this study will provide valuable reference for regional drought mitigation and drought prediction.