[A prospective study on the relationship between exposure to solid fuels for heating and its duration and the risk of morbidity of respiratory diseases among residents aged 30-79 years]

Objective: To research the association between exposure to solid fuels for heating and its duration and the risk of respiratory diseases morbidity. Methods: Data from the China Kadoorie Biobank project sited in Pengzhou City, Sichuan Province. Cox proportional hazard regression model was used to analyze the association between exposure to solid fuels for heating and its duration and the risk of total respiratory diseases and the association between exposure to solid fuels for heating and the risk of chronic obstructive pulmonary disease (COPD) and pneumonia among respiratory diseases. Results: A total of 46 082 participants aged 30-79 years were enrolled, with 11 634 (25.25%) heating during the winter, of whom 8 885 (19.28%) used clean fuels and 2 749 (5.97%) used solid fuels, of whom 34 448 (74.75%) did not heat. After controlling for multiple confounding factors, Cox proportional hazard regression model was used, which revealed that compared with clean fuels, unheating could reduce the risk of total respiratory disease (HR=0.81,95%CI:0.77-0.86), COPD (HR=0.86,95%CI:0.78-0.95) and pneumonia (HR=0.80,95%CI:0.74-0.86), respectively. Exposure to solid fuels increased the risk of total respiratory disease (HR=1.10, 95%CI:1.01-1.20) and were not associated with COPD and pneumonia. Compared with no solid fuel exposure, the risk of total respiratory disease (1-19 years:HR=1.23, 95%CI:1.10-1.37; 20-39 years:HR=1.25, 95%CI:1.16-1.35; ≥40 years:HR=1.26, 95%CI:1.15-1.39) and COPD (1-19 years: HR=1.21, 95%CI:1.03-1.42; 20-39 years: HR=1.30, 95%CI:1.16-1.46; ≥40 years:HR=1.35, 95%CI:1.18-1.54) increased with the length of exposure of solid fuels (trend test P<0.001). Solid fuels exposure for 1-19 years and 20-39 years increased the risk of COPD by 23% (HR=1.23,95%CI:1.02-1.49) and 16% (HR=1.16, 95%CI:1.00-1.35). Conclusion: Heating solid fuels exposure increases the risk of total respiratory disease, COPD, and pneumonia.

[A prospective study of association between physical activity and ischemic stroke in adults]

Objective: To explore the prospective associations between physical activity and incident ischemic stroke in adults. Methods: Data of China Kadoorie Biobank study in Tongxiang of Zhejiang were used. After excluding participants with cancers, strokes, heart diseases and diabetes at baseline study, a total of 53 916 participants aged 30-79 years were included in the final analysis. The participants were divided into 5 groups according to the quintiles of their physical activity level. Cox proportional hazard regression models was used to calculate the hazard ratios (HR) for the analysis on the association between baseline physical activity level and risk for ischemic stroke. Results: The total physical activity level in the participants was (30.63±15.25) metabolic equivalent (MET)-h/d, and it was higher in men [(31.04±15.48) MET-h/d] than that in women [(30.33±15.07) MET-h/d] (P<0.001). In 595 526 person-years of the follow-up (average 11.4 years), a total of 1 138 men and 1 082 women were newly diagnosed with ischemic stroke. Compared to participants with the lowest physical activity level (<16.17 MET-h/d), after adjusting for socio-demographic factors, lifestyle, BMI, waist circumference, and SBP, the HRs for the risk for ischemic stroke in those with moderate low physical activity level (16.17-24.94 MET-h/d), moderate physical activity level (24.95-35.63 MET-h/d), moderate high physical activity level (35.64-43.86 MET-h/d) and the highest physical activity level (≥43.87 MET-h/d) were 0.93 (95%CI: 0.83-1.04), 0.87 (95%CI: 0.76-0.98), 0.82 (95%CI: 0.71-0.95) and 0.76 (95%CI: 0.64-0.89), respectively. Conclusion: Improving physical activity level has an effect on reducing the risk for ischemic stroke.

[A prospective study on association between sleep duration and the risk of chronic obstructive pulmonary disease in adults in Suzhou]

Objective: To investigate the prospective association of sleep duration with the development of chronic obstructive pulmonary disease (COPD) in adults in Suzhou. Methods: The study used the data of 53 269 participants aged 30-79 years recruited in the baseline survey from 2004 to 2008 and the follow-up until December 31, 2017 of China Kadoorie Biobank (CKB) conducted in Wuzhong District, Suzhou. After excluding participants with airflow limitation, self-reported chronic bronchitis/emphysema/coronary heart disease history at the baseline survey and abnormal or incomplete data, a total of 45 336 participants were included in the final analysis. The association between daily sleep duration and the risk for developing COPD was analyzed by using a Cox proportional hazard regression model, and the hazard ratio (HR) values and their 95%CI were calculated. The analysis was stratified by age, gender and lifestyle factors, and cross-analysis was conducted according to smoking status and daily sleep duration. Results: The median follow-up time was 11.12 years, with a total of 515 COPD diagnoses in the follow-up. After adjusting for potential confounders, multifactorial Cox proportional hazard regression analysis showed that daily sleep duration ≥10 hours was associated with higher risk for developing COPD (HR=1.42, 95%CI: 1.03-1.97). The cross analysis showed that excessive daily sleep duration increased the risk for COPD in smokers (HR=2.49, 95%CI: 1.35-4.59, interaction P<0.001). Conclusion: Longer daily sleep duration (≥10 hours) might increase the risk for COPD in adults in Suzhou, especially in smokers.

[Physical activity and its influencing factors in patients with diabetes mellitus: a comparative study between China and the United Kingdom]

Objective: To compare the differences in low-level physical activity (PA) and related influencing factors in patients with diabetes mellitus in China and the United Kingdom (UK). Methods: Using baseline survey data from the China Kadoorie Biobank and the UK Biobank, we analyzed the association between diabetes mellitus and low-level PA using logistic regression, with the participants' self-reported whether they had diabetes mellitus as the independent variable, and low-level PA as the dependent variable. Results: We included 509 254 Chinese adults and 359 763 British adults in the analysis. After adjusting for multiple factors, we found that both Chinese and British patients with diabetes mellitus were at elevated risk for low-level PA, with corresponding ORs (95%CIs) of 1.15 (1.12-1.19) and 1.37 (1.32-1.41), respectively. Patients with diabetes mellitus with longer disease duration and poorer glycemic control were at greater risk of having low-level of PA. Female, rural-distributed, employed, never-smoking Chinese diabetics, and male, urban-distributed, retired/unemployed, quit-smoking British diabetics were more likely to have low-level PA. Conclusions: Chinese and British patients with diabetes mellitus were more likely to have low-level PA compared with the general population, but the risk of low-level PA for patients in both countries varied by population characteristics. Therefore, PA guidelines and intervention measures should be based on the characteristics of individuals in the target countries and regions, which could improve PA levels among patients with diabetes mellitus.

[Association between waist circumference and ischemic stroke: a prospective study in adults from Qingdao]

Objective: To analyze the association between waist circumference (WC) and ischemic stroke (IS). Methods: The data for the present study were from the prospective cohort study of China Kadoorie Biobank in Qingdao. Using baseline information and IS events of the participants, the Cox proportional hazard regression model and restricted cubic spline (RCS) were used to analyze the association between WC and IS. Results: A total of 33 355 participants were included in the study, with 302 008.88 person-years of follow-up. A total of 1 093 new cases of IS were observed. Multivariate Cox proportional hazard regression model analysis showed that compared to the respondents with normal WC (male <85.0 cm, female <80.0 cm), respondents with excessive WC (male ≥85.0 cm, female ≥80.0 cm) had a 78% higher risk of IS incidence [hazard ratio(HR)=1.78, 95%CI: 1.51-2.10], and the risk increased by 72% (HR=1.72, 95%CI: 1.40-2.12) and 83% (HR=1.83, 95%CI: 1.40-2.39) in men and women. According to the RCS, the increase in WC and the risk of IS showed an "S" trend of nonlinear dose-response relationship. Conclusions: The risk of IS would increase with the WC. Keeping a normal WC is important for preventing IS.

Genetically Encoded Lizard Color Divergence for Camouflage and Thermoregulation

Local adaptation is critical in speciation and evolution, yet comprehensive studies on proximate and ultimate causes of local adaptation are generally scarce. Here, we integrated field ecological experiments, genome sequencing, and genetic verification to demonstrate both driving forces and molecular mechanisms governing local adaptation of body coloration in a lizard from the Qinghai-Tibet Plateau. We found dark lizards from the cold meadow population had lower spectrum reflectance but higher melanin contents than light counterparts from the warm dune population. Additionally, the colorations of both dark and light lizards facilitated the camouflage and thermoregulation in their respective microhabitat simultaneously. More importantly, by genome resequencing analysis, we detected a novel mutation in Tyrp1 that underpinned this color adaptation. The allele frequencies at the site of SNP 459# in the gene of Tyrp1 are 22.22% G/C and 77.78% C/C in dark lizards and 100% G/G in light lizards. Model-predicted structure and catalytic activity showed that this mutation increased structure flexibility and catalytic activity in enzyme TYRP1, and thereby facilitated the generation of eumelanin in dark lizards. The function of the mutation in Tyrp1 was further verified by more melanin contents and darker coloration detected in the zebrafish injected with the genotype of Tyrp1 from dark lizards. Therefore, our study demonstrates that a novel mutation of a major melanin-generating gene underpins skin color variation co-selected by camouflage and thermoregulation in a lizard. The resulting strong selection may reinforce adaptive genetic divergence and enable the persistence of adjacent populations with distinct body coloration.

Biolinguistic graph fusion model for circRNA-miRNA association prediction

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

[Prevalence of urinary incontinence in middle-aged and elderly adults in 10 areas in China]

Objective: To describe the population and area distribution differences in the prevalence of urinary incontinence in middle-aged and elderly adults in 10 areas in China. Methods: A total of 24 913 participants aged 45-95 years who completed the third resurvey of China Kadoorie Biobank during 2020-2021 were included. The prevalence of urinary incontinence was assessed by an interviewer-administered questionnaire, and urinary incontinence was classified as only stress urinary incontinence, only urgency urinary incontinence and mixed urinary incontinence. The prevalence of urinary incontinence and its subtypes were reported by sex, age and area, and the severity of urinary incontinence and treatment were described. Results: The average age of the participants was (65.4±9.1) years. According to the seventh national census data in 2020, the age-standardized prevalence rates of urinary incontinence was 25.4% in women and 7.0% in men. The age-standardized prevalence rates of only stress, only urgency and mixed incontinence were 1.7%, 4.2% and 1.2% in men and 13.5%, 5.8% and 6.1% in women, respectively. The prevalence rates of urinary incontinence and all subtypes in men and the prevalence of urinary incontinence and all subtypes except only stress urinary incontinence in women all increased with age (P<0.001). After adjusting for age, the prevalence of urinary incontinence in both men and women were higher in rural area than in urban area (P<0.001). The treatment rates in men and women with urinary incontinence were 15.4% and 8.5%, respectively. Conclusions: The prevalence of urinary incontinence was high in middle-aged and elderly adults in China, and the prevalence rate was higher in women than in men, but the treatment rate of urinary incontinence was low.

[Epidemiological characteristics of preserved vegetable intake in adults in 10 areas of China]

Objective: To describe the epidemiological characteristics of intakes of different types of preserved vegetables in participants from the China Kadoorie Biobank (CKB). Methods: The CKB project conducted baseline survey, the first resurvey, and the second resurvey during 2004-2008, 2008, and 2013-2014, respectively. According to the average intake levels of salted and sour pickled vegetables in the second resurvey, the 10 survey areas were classified as the area where people mainly consumed salted vegetables, the area where people mainly consumed sour pickled vegetables, and the area where people rarely consumed preserved vegetables. For the first two areas, logistic regression model was used to describe the temporal trends and population distribution of preserved vegetable intake and analyze the distribution of other dietary factors. Results: The area where people mainly consumed salted vegetables included Qingdao, Harbin, Suzhou, and Zhejiang (baseline participant number: 204 036), while the area where people mainly consumed sour pickled vegetables included Gansu and Sichuan (baseline participant number: 105 573). In the area where people mainly consumed salted vegetables, the average intake frequencies of preserved vegetables was 3.1, 3.3, and 1.8 days/week in the baseline survey, the first resurvey, and the second resurvey, respectively, showing a declining trend (P<0.001). Similarly, the average intake frequencies of preserved vegetables were 2.8, 2.7, and 1.6 days/week in the baseline survey, the first resurvey and the second resurvey in the area where people mainly consumed sour pickled vegetables (P<0.001). At baseline survey, the married and those had lower education level tended to have more preserved vegetable intakes in both areas (P<0.001). In the area where people mainly consumed salted vegetables, the elderly had higher frequency of preserved vegetable intake (P<0.001), which was converse in the area where people mainly consumed sour pickled vegetables. In the participants with higher frequency of preserved vegetable intake, more people consumed spicy food daily and preferred salty food (P<0.05). Conclusions: The area and population specific differences in the type and frequency of preserved vegetable intake were observed in adults in the CKB project in China. Besides, the average level of preserved vegetable intake showed a declining trend. Preserved vegetable intake might be associated with other dietary habits.

[A prospective cohort study of factors associated with longevity in older adults in 10 areas of China]

Objective: To evaluate the associations of sociodemographic characteristics and lifestyle factors with longevity status in older adults in China. Methods: After excluding those born after 31st December 1938, a total of 51 870 older adults from the China Kadoorie Biobank (CKB) were included. The attained age was defined according to the survival age or age on 31st December 2018. According to the attained age, the old persons were categorized into non-longevity (died before age 80 years) and longevity (attained age ≥80 years). The longevity group was further divided into two groups: longevity with death occurring before 2019, and longevity and survival to 2019. The information about socio-demographic characteristics and lifestyles was collected at the 2004-2008 baseline survey. Multinomial logistic regression models were used to analyze the associations between exposure factors and outcomes by taking the non-longevity group as the reference group. Results: A total of 51 870 older adults aged 65-79 years in the baseline survey were included for analysis. During a follow-up for (10.2±3.5) years, 38 841 participants were longevity, and 30 354 participants still survived at the end of 2018. Compared to men, rural populations, non-married individuals, those with an annual household income of less than 10 000 yuan, and those with education levels of primary school or below, the adjusted ORs(95%CI) for longevity and survival to 2019 in women, urban residents, married individuals, those with annual household incomes ≥20 000 yuan, and those with education levels of college or university were 1.68 (1.58-1.78), 1.69 (1.61-1.78), 1.15 (1.10-1.21), 1.44 (1.36-1.53), and 1.32 (1.19-1.48), respectively. The OR (95%CI) for longevity and survival to 2019 was 1.09 (1.08-1.10) for those with an increase of 4 MET-hour/day in total physical activity level. With those who never or almost never smoked, had no alcohol drinking every week, had normal weight (BMI: 18.5-23.9 kg/m2), and WC <85 cm (man)/<80 cm (woman) as the reference groups, the ORs(95%CI) of longevity and survival to 2019 were 0.64 (0.60-0.69) for those smoking ≥20 cigarettes per day, 1.29 (1.14-1.46) for those with alcohol drinking every week, 1.13 (1.01-1.26) for those with pure alcohol drinking <30 g per day, 0.56 (0.52-0.61) for those being underweight, 1.27 (1.19-1.36) for those being overweight, 1.23 (1.11-1.36) for those with obesity, and 0.86 (0.79-0.93) for those with central obesity. Further stratified analysis by WC was performed. In the older adults with WC <85 cm (man)/<80 cm (woman), the ORs (95%CI) of longevity and survival was 1.80 (1.69-1.92) for those with each 5 kg/m2 increase in BMI and 1.02 (0.96-1.08) for those with WC ≥85 cm (man)/≥80 cm (woman). There was a statistically significant difference in the association between BMI and longevity between the two WC groups (interaction test P<0.001). Conclusion: This study showed that women, the married, those with higher socioeconomic status and education level, and those with healthy lifestyles were more likely to achieve longevity.

[Epidemiological distribution of mosaic loss of chromosome Y in adult men in 10 areas in China and its prospective association with lung cancer]

Objective: To detect the prevalence of mosaic loss of chromosome Y in adult men in ten study areas in China, describe the epidemiological distribution of mosaic loss of chromosome Y (mLOY) carriers and assess its prospective association with lung cancer. Methods: Based on the data from baseline survey, genetic analysis and follow-up (as of December 31, 2018) from China Kadoorie Biobank, we used Mosaic Chromosomal Alterations pipeline to detect mLOY carriers in 10 areas in China and described the epidemiological characteristics of mLOY carriers in adult men, including age, area distribution, lifestyle and disease history. We used multivariate logistic regression model to identify the potential relevant factor of mLOY. Cox proportional hazard regression model was fitted to assess the prospective association of mLOY with lung cancer. Stratification analysis were conducted to evaluate the potential modification effects of smoking and age. We also conducted mediation analysis to assess the mediating effect of mLOY in the association between smoking and lung cancer. Results: A total of 42 859 adult men were included in our analysis, in whom 2 458 mLOY carriers were detected (5.7%). The detection rate increased with age (P<0.05). The detection rate was higher in urban area (7.3%±0.2%) than that in rural area (4.7%±0.1%). The results of logistic regression analysis indicated that smoking might be a risk factor for the detection of mLOY (OR=1.49, 95%CI:1.36-1.64). After follow-up for average 11.1 years, 1 041 lung cancer cases were observed. The prospective analysis showed that mLOY carriers had an increased risk for lung cancer by 24% compared with non-mLOY carriers (HR=1.24, 95%CI:1.01-1.52) and expanded mLOY carriers (mLOY cell proportion ≥10%) had an increased risk for lung cancer by 50% (HR=1.50, 95%CI:1.13-2.00). Stratification analysis showed no modification effects of smoking and age in the association between mLOY and lung cancer (interaction P>0.05). Mediation analysis showed that mLOY could be a mediating factor in the association between smoking and lung cancer, the estimated effect was 0.09 (0.01-0.17). Conclusions: There were significant differences in the detection rate of mLOY in adult men with different social-economic characteristics and lifestyles in ten areas in China. Besides, mLOY carriers, especially expanded mLOY carriers, had increased risk for lung cancer and mLOY might be a mediating factor in the association between smoking and lung cancer.

[Progress and practice of objective measurement of physical behaviors in large-scale cohort research]

Due to the limited reliability of traditional self-completed questionnaire, the accuracy of measurement of physical behaviors (physical activity, sedentary behavior and sleep) is not high. With the development of technology, wearable devices (e.g. accelerometer) can be used for more accurate measurement of physical behaviors and have great application potential in large-scale research. However, the data of objective measurement of physical behaviors from large-scale cohort research in Asian populations is still limited. Between August 2020 and December 2021, the 3rd resurvey of China Kadoorie Biobank (CKB) project used Axivity AX3 wrist triaxial accelerometer to collect the data of participants' daily activity and sleep status. A total of 20 370 participants from 10 study areas were included in the study, in whom 65.2% were women, and the age was (65.4±9.1) years. The participants' physical activity level varied greatly in different study areas. The objective measurement of participants' physical behaviors in CKB project has provided valuable resources for the description of 24-hour patterns of physical behaviors and evaluation of the health effect of physical activity, sedentary behavior and sleep as well as their association with diseases in the elderly in China.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

[Comparative study on physical activity and its influencing factors in patients with chronic pulmonary obstructive disease between China and the United Kingdom]

Objective: To compare physical activity and its influencing factors in patients with chronic obstructive pulmonary disease (COPD) between China and the United Kingdom. Methods: We analyzed baseline data from China Kadoorie Biobank and the United Kingdom Biobank among COPD patients who were diagnosed with a one-second rate (FEV1/FVC) less than 70%. Physical activity level was calculated as metabolic equivalent (MET) and divided into three levels: low, medium, and high, according to tertiles stratified by gender and age. Multiple logistic regression was used to estimate ORs and 95%CIs for COPD and Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade about physical activity level, and subgroup analysis was conducted. Results: A total of 506 073 Chinese adults and 231 884 British adults were included. After adjusting for potential confounders, COPD was associated with lower physical activity levels in both Chinese and British COPD patients, with OR (95%CI) of 1.07(1.03-1.10) and 1.03(1.01-1.06) compared with non COPD patients, respectively. The GOLD grade was inversely correlated with physical activity level, particularly in a dose-response manner in the CKB population (trend test P<0.001). The negative relationship was stronger among the elderly, people with less education and lower economic status, and those with a smoking or chronic disease history. Chinese rural COPD patients were at high risk of decline of physical activity. Conclusions: Physical activity is inversely related to COPD, with a dose-response connection to GOLD grade. Therefore, physical activity maintenance and improvement should be encouraged and promoted in COPD patients, especially in high-risk groups.

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding

MOTIVATION

Accumulating clinical evidence shows that circular RNA (circRNA) plays an important regulatory role in the occurrence and development of human diseases, which is expected to provide a new perspective for the diagnosis and treatment of related diseases. Using computational methods can provide high probability preselection for wet experiments to save resources. However, due to the lack of neighborhood structure in sparse biological networks, the model based on network embedding and graph embedding is difficult to achieve ideal results.

RESULTS

In this paper, we propose BioDGW-CMI, which combines biological text mining and wavelet diffusion-based sparse network structure embedding to predict circRNA-miRNA interaction (CMI). In detail, BioDGW-CMI first uses the Bidirectional Encoder Representations from Transformers (BERT) for biological text mining to mine hidden features in RNA sequences, then constructs a CMI network, obtains the topological structure embedding of nodes in the network through heat wavelet diffusion patterns. Next, the Denoising autoencoder organically combines the structural features and Gaussian kernel similarity, finally, the feature is sent to lightGBM for training and prediction. BioDGW-CMI achieves the highest prediction performance in all three datasets in the field of CMI prediction. In the case study, all the 8 pairs of CMI based on circ-ITCH were successfully predicted.

AVAILABILITY

The data and source code can be found at https://github.com/1axin/BioDGW-CMI-model.

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder

Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the 'behavior relationships' of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.

[Study on the associations of meeting intensive systolic blood pressure control goals with risk for incident cardiovascular and cerebrovascular diseases among the adult hypertensive patients in China]

Objective: To evaluate the associations of meeting intensive systolic blood pressure (SBP) control goals with risk for incident cardiovascular and cerebrovascular diseases among the adult hypertensive patients in China. Methods: We used data from adult hypertensive patients from the China Kadoorie Biobank. logistic regression models evaluated the influencing factors of meeting intensive and standard SBP control goals. Cox proportional hazard models evaluated the associations between meeting intensive vs. standard SBP control goals and risk for incident cardiovascular and cerebrovascular diseases. Results: A total of 3 628 hypertensive patients who reported continuous medication use were included in this study, of which 5.0% of the participants met the goals of intensive SBP control (≤130 mmHg). Participants with higher educational attainment (OR=2.36,95%CI: 1.32-4.04), healthier diet (OR=2.09,95%CI: 1.45-2.96), daily intake of fresh fruit (OR=1.67,95%CI: 1.17-2.36) and combination treatment (OR=1.82,95%CI: 1.03-3.09) were more likely to meet intensive SBP control goal after adjustment of age, sex and urban/rural areas. During an average follow-up of (10.0±3.7) years, 1 278 cases of composite cardiovascular outcome were recorded. This study did not find a statistical correlation between achieving the goal of enhanced SBP control and the occurrence of composite cardiovascular and cerebrovascular outcomes (HR=0.89, 95%CI: 0.63-1.25). For major adverse cardiovascular events (MACE), cerebrovascular diseases, stroke, and ischemic stroke, we observed a trend of decrease in risk of outcomes with more intensive SBP control (trend test P<0.05). Conclusions: We observed decreased risk for MACE and cerebrovascular diseases with more intensive SBP control. However, there was no significant risk reduction for cardiovascular and cerebrovascular diseases when meeting the intensive SBP control goal, compared to the standard SBP control goal.

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

[Epidemiological distribution characteristics of peripheral blood mosaic chromosomal alteration in adults from 10 regions of China]

Objective: To describe the epidemiological distribution characteristics of peripheral blood mosaic chromosomal alteration (mCA) in community adults aged 30-79 years in 10 regions of China. Methods: A total of 100 297 participants with complete baseline information (demographic characteristics, lifestyle, physical examination, etc.) and genotyping data of blood-derived DNA in ten regions of the China Kadoorie Biobank study were included. The mCAs were detected with the Mosaic Chromosomal Alterations pipeline, and logistic regression models were used to compare the differences in the detection rate of mCAs in different regions and populations. Results: A total of 5 810 mCA carriers were detected, with the detection rate of 5.8%. The standardized detection rate was 5.1%. The baseline detection rate of mCA increased with age, which were 3.4%, 5.0%, and 9.4% in those aged 30-, 51-, and >60 years, respectively (trend test P<0.001). A more significant proportion of mCAs were found in men (8.0%) than women (4.0%), as well as in urban areas (6.4%) than in rural areas (5.3%), the difference was significant (P<0.001). After adjusting for age and gender, the detection rate of mCA was higher in current smokers or people quitting smoking due to illness and people with low physical activity level, and the mCA detection rate was lower in obesy people (5.3%) than that in people with normal body weight (5.9%) (P=0.006). Conclusions: The detection rate of mCAs varied with region and population in community adults aged 30-79 years in 10 regions of China. The study results might contribute to the molecular identification of aging populations and guide precision prevention of age-related diseases such as cancers.

[Tea consumption and cancer: a Mendelian randomization study]

Objective: A Mendelian randomization (MR) analysis was performed to assess the relationship between tea consumption and cancer. Methods: There were 100 639 participants with the information of gene sequencing of whole genome in the China Kadoorie Biobank. After excluding those with cancer at baseline survey, a total of 100 218 participants were included in this study. The baseline information about tea consumption were analyzed, including daily tea consumption or not, cups of daily tea consumption, and grams of daily tea consumption. We used the two-stage least square method to evaluate the associations between three tea consumption variables and incidence of cancer and some subtypes, including stomach cancer, liver and intrahepatic bile ducts cancer, colorectal cancer, tracheobronchial and lung cancer, and female breast cancer. Multivariable MR and analysis only among nondrinkers were used to control the impact of alcohol consumption. Sensitivity analyses were also performed, including inverse variance weighting, weighted median, and MR-Egger. Results: We used 54, 42, and 28 SNPs to construct non-weighted genetic risk scores as instrumental variables for daily tea consumption or not, cups of daily tea consumption, and grams of daily tea consumption, respectively. During an average of (11.4±3.0) years of follow-up, 6 886 cases of cancer were recorded. After adjusting for age, age², sex, region, array type, and the first 12 genetic principal components, there were no significant associations of three tea consumption variables with the incidence of cancer and cancer subtypes. Compared with non-daily tea drinkers, the HR (95%CI) of daily tea drinkers for cancer and some subtypes, including stomach cancer, liver and intrahepatic bile ducts cancer, colorectal cancer, tracheobronchial and lung cancer, and female breast cancer, are respectively 0.99 (0.78-1.26), 1.17 (0.58-2.36), 0.86 (0.40-1.84), 0.85 (0.42-1.73), 1.39 (0.85-2.26) and 0.63 (0.28-1.38). After controlling the impact of alcohol consumption and performing multiple sensitivity analyses, the results were similar. Conclusion: There is no causal relationship between tea consumption and risk of cancer in population in China.

[Progress on genome-wide association studies on mosaic chromosomal alterations]

Mosaic chromosomal alteration (mCA) is referred to as large-scale somatic mutations on chromosomes, which results in diverse karyotypes in body. The mCA is regarded as one of the phenotypes of aging. Studies have revealed its associations with many chronic diseases such as hematopoietic cancers and cardiovascular diseases, but its genetic basis (e.g. genetic susceptibility variants) is still under-investigated. This paper reviews GWAS studies for mCA on autosomal chromosomes and sex chromosomes [mosaic loss of the Y chromosome (mLOY) and mosaic loss of the X chromosome (mLOX)] based on large population, respectively. Most of the genetic susceptibility loci found in studies for autosomal mCA were associated with copy-neutral loss of heterozygosity. The study of sex chromosome mCA focused on mosaic loss mutations. The number of genetic susceptibility loci for mLOY was high (up to 156), but it was relatively less for mLOX.

[Prospective association between physical activity and mortality in patients with chronic kidney disease]

Objective: To investigate the prospective association of physical activity with all-cause, cardiovascular disease (CVD), and chronic kidney disease (CKD) mortality in CKD patients in China. Methods: Cox proportional hazard models were used to evaluate the association of total, domain-specific, and intensity-specific physical activity with the risk of all-cause, CVD, and CKD mortality based on data from the baseline survey of China Kadoorie Biobank. Results: During a median follow-up of 11.99 (11.13, 13.03) years, there were 698 deaths in 6 676 CKD patients. Compared with the bottom tertile of total physical activity, participants in the top tertile had a lower risk of all-cause, CVD, and CKD mortality, with hazard ratios (HRs) (95%CIs) of 0.61 (0.47-0.80), 0.40 (0.25-0.65), and 0.25 (0.07-0.85), respectively. Occupational, commuting, and household physical activity were negatively associated with the risk of all-cause and CVD mortality to varying degrees. Participants in the top tertile of occupational physical activity had a lower risk of all-cause (HR=0.56, 95%CI: 0.38-0.82) and CVD (HR=0.39, 95%CI: 0.20-0.74) mortality, those in the top tertile of commuting physical activity had a lower risk of CVD mortality (HR=0.43, 95%CI: 0.22-0.84), and those in the top tertile of household physical activity had a lower risk of all-cause (HR=0.61, 95%CI: 0.45-0.82), CVD (HR=0.44, 95%CI: 0.26-0.76) and CKD (HR=0.03, 95%CI: 0.01-0.17) mortality, compared with the bottom tertile of corresponding physical activity. No association of leisure-time physical activity with mortality was observed. Both low and moderate-vigorous intensity physical activity were negatively associated with the risk of all-cause, CVD and CKD mortality. The corresponding HRs (95%CIs) were 0.64 (0.50-0.82), 0.42 (0.26-0.66) and 0.29 (0.10-0.83) in the top tertile of low intensity physical activity, and the corresponding HRs (95%CIs) were 0.63 (0.48-0.82), 0.39 (0.24-0.64) and 0.23 (0.07-0.73) in the top tertile of moderate-vigorous intensity physical activity. Conclusion: Physical activity can reduce the risk of all-cause, CVD, and CKD mortality in CKD patients.

[A descriptive analysis of hyperlipidemia in adult twins in China]

Objective: To describe the distribution characteristics of hyperlipidemia in adult twins in the Chinese National Twin Registry (CNTR) and explore the effect of genetic and environmental factors on hyperlipidemia. Methods: Twins recruited from the CNTR in 11 project areas across China were included in the study. A total of 69 130 (34 565 pairs) of adult twins with complete information on hyperlipidemia were selected for analysis. The random effect model was used to characterize the population and regional distribution of hyperlipidemia among twins. The concordance rates of hyperlipidemia were calculated in monozygotic twins (MZ) and dizygotic twins (DZ), respectively, to estimate the heritability. Results: The age of all participants was (34.2±12.4) years. This study's prevalence of hyperlipidemia was 1.3% (895/69 130). Twin pairs who were men, older, living in urban areas, married,had junior college degree or above, overweight, obese, insufficient physical activity, current smokers, ex-smokers, current drinkers, and ex-drinkers had a higher prevalence of hyperlipidemia (P<0.05). In within-pair analysis, the concordance rate of hyperlipidemia was 29.1% (118/405) in MZ and 18.1% (57/315) in DZ, and the difference was statistically significant (P<0.05). Stratified by gender, age, and region, the concordance rate of hyperlipidemia in MZ was still higher than that in DZ. Further, in within-same-sex twin pair analyses, the heritability of hyperlipidemia was 13.04% (95%CI: 2.61%-23.47%) in the northern group and 18.59% (95%CI: 4.43%-32.74%) in the female group, respectively. Conclusions: Adult twins were included in this study and were found to have a lower prevalence of hyperlipidemia than in the general population study, with population and regional differences. Genetic factors influence hyperlipidemia, but the genetic effect may vary with gender and area.

[A descriptive analysis on hypertension in adult twins in China]

Objective: To describe the distribution characteristics of hypertension among adult twins in the Chinese National Twin Registry (CNTR) and to provide clues for exploring the role of genetic and environmental factors on hypertension. Methods: A total of 69 220 (34 610 pairs) of twins aged 18 and above with hypertension information were selected from CNTR registered from 2010 to 2018. Random effect models were used to describe the population and regional distribution of hypertension in twins. To estimate the heritability, the concordance rates of hypertension were calculated and compared between monozygotic twins (MZ) and dizygotic twins (DZ). Results: The age of all participants was (34.1±12.4) years. The overall self-reported prevalence of hypertension was 3.8%(2 610/69 220). Twin pairs who were older, living in urban areas, married, overweight or obese, current smokers or ex-smokers, and current drinkers or abstainers had a higher self-reported prevalence of hypertension (P<0.05). Analysis within the same-sex twin pairs found that the concordance rate of hypertension was 43.2% in MZ and 27.0% in DZ, and the difference was statistically significant (P<0.001). The heritability of hypertension was 22.1% (95%CI: 16.3%- 28.0%). Stratified by gender, age, and region, the concordance rate of hypertension in MZ was still higher than that in DZ. The heritability of hypertension was higher in female participants. Conclusions: There were differences in the distribution of hypertension among twins with different demographic and regional characteristics. It is indicated that genetic factors play a crucial role in hypertension in different genders, ages, and regions, while the magnitude of genetic effects may vary.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks

Motivation

A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data.

Results

In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed.

Availability

The data and source code can be found at https://github.com/1axin/JSNDCMI.

[Progress in research of risk prediction model for chronic kidney disease]

Chronic kidney disease (CKD) is an important global public health problem that greatly threatens population health. Application of risk prediction model is a crucial way for the primary prevention of CKD, which can stratify the risk for developing CKD and identify high-risk individuals for more intensive interventions. By now, more than twenty risk prediction models for CKD have been developed worldwide. There are also four domestic risk prediction models developed for Chinese population. However, none of these models have been recommended in clinical guidelines yet. The existing risk prediction models have some limitations in terms of outcome definition, predictors, strategies for handling missing data, and model derivation. In the future, the applications of emerging biomarkers and polygenic risk scores as well as advances in machine learning methods will provide more possibilities for the further improvement of the model.

[Prevalence of chronic kidney disease and its association with lifestyle factors in adults from 10 regions of China]

Objective: To investigate the distribution of chronic kidney disease (CKD) in participants from the China Kadoorie Biobank (CKB) study and evaluate the association between lifestyle risk factors and CKD. Methods: Based on the baseline survey data and follow-up data (as of December 31, 2018) of the CKB study, the differences in CKD cases' area and population distributions were described. Cox proportional hazards regression model was used to estimate the association between lifestyle risk factors and the risk of CKD. Results: A total of 505 147 participants, 4 920 cases of CKD were recorded in 11.26 year follow up with a incidence rate of 83.43/100 000 person-years. Glomerulonephropathy was the most common type. The incidence of CKD was higher in the urban area, men, and the elderly aged 60 years and above (87.83/100 000 person-years, 86.37/100 000 person-years, and 132.06/100 000 person-years). Current male smokers had an increased risk for CKD compared with non-smokers or occasional smokers (HR=1.18, 95%CI: 1.05-1.31). The non-obese population was used as a control group, both general obesity determined by BMI (HR=1.19, 95%CI: 1.10-1.29) and central obesity determined by waist circumference (HR=1.27, 95%CI: 1.19-1.35) were associated with higher risk for CKD. Conclusion: The risks for CKD varied with area and population in the CKB cohort study, and the risk was influenced by multiple lifestyle factors.

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model

LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis

BACKGROUND

Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS

We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS

To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS

All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

[Characteristics of daytime napping and its correlation with chronic diseases in Chinese adults]

Objective: To describe the prevalence of daytime nap habit in participants of the China Kadoorie Biobank (CKB) study, across 10 study regions and explore its correlation with prevalence of major chronic diseases. Methods: Participants with a self-reported pre-diagnosis of any cancer at baseline survey were excluded. Logistic regression models were used to analyze the differences in study regions and age distribution of the prevalence daytime nap habit, and its correlation with the prevalence of diabetes, hypertension, coronary heart disease (CHD), stroke, chronic obstructive pulmonary disease (COPD), and chronic liver diseases. Results: Among 510 145 participants, 39.9% had daytime nap habit in summer and 20.8% had daytime nap habit all the year round. Urban-rural differences were observed in the prevalence of summer nap habit and perennial nap habit. Daytime nap in summer was common in rural areas and Suzhou, with prevalence ranged from 32.9% to 73.3%. Haikou and Liuzhou had higher prevalence of perennial nap (60.4% and 63.3%). The proportion of people with daytime nap habit all the year round increased with age (P for trend <0.001), the proportion was highest in those aged 70- years (31.9%). Daytime nap habit in summer was positively correlated with the prevalence of diabetes, hypertension, CHD and chronic liver disease with OR of 1.10 (95%CI: 1.07-1.14), 1.03 (95%CI:1.02-1.05), 1.07 (95%CI: 1.02-1.12) and 1.07 (95%CI:1.00-1.14), respectively. Daytime nap habit all the year round was positively correlated with the prevalence of diabetes, hypertension, CHD, stroke, COPD and chronic liver disease with OR of 1.33 (95%CI: 1.29-1.37), 1.11 (95%CI: 1.09-1.13), 1.39 (95%CI: 1.33-1.45), 1.33 (95%CI: 1.26-1.41), 1.12 (95%CI: 1.08-1.16) and 1.27 (95%CI:1.18-1.37) respectively. Conclusion: There were regional and age differences in prevalence of daytime nap habit among CKB participants. Daytime nap habit, especially daytime nap habit all the year round, was positively correlated with the prevalence of major chronic diseases.

[Research progress on the epidemiological distribution and influencing factors of autosomal mosaic chromosomal alteration]

Somatic mosaicism is defined as the occurrence and accumulation of somatic mutations in humans, and mosaic chromosomal alterations (mCA) are recognized as one of the aging phenotypes due to their impact on genome integrity. With the coming acceleration of global population aging, understanding the prevalence and influencing factors of mCA will help to explore the "genomic instability" of human aging and its biological mechanisms and provide the scientific basis for the primary prevention of age-related diseases. This review aims to summarize the epidemiological distribution and influencing factors of autosomal mCA in peripheral blood based on previous large-scale population-based studies.

SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes

Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA–miRNA interactions but also to predict circRNA–cancer and circRNA–gene associations. The AUCs of circRNA—miRNA, circRNA–disease, and circRNA–gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.

[Correlation between sleep status and frailty in adults aged 30-79 years in China]

Objective: To explore the correlation between sleep status and frailty in adults aged 30-79 years in China, and explore the potential effect modification of general and central obesity. Methods: Based on the baseline data of the China Kadoorie Biobank, we used multinomial logistic regression to analyze the correlation between long and short sleep duration, insomnia disorder, snoring, and unhealthy sleep score with risks of pre-frailty and frailty. Both overall and obesity-stratified analyses were performed. Result: Among the 512 724 participants, 2.3% had frailty and 40.1% had pre-frailty. There was a U-shaped relationship between sleep duration and frailty score. Short (OR=1.21, 95%CI: 1.19-1.23) or long sleep duration (OR=1.19, 95%CI: 1.17-1.21), insomnia disorder (OR=2.09, 95%CI: 2.02-2.17), and snoring (OR=1.61, 95%CI: 1.59-1.63) were all positively correlated with pre-frailty, and dose-response relationships were observed between unhealthy sleep score and pre-frailty (P for trend<0.001), with OR values of 1.46 (1.44-1.48), 1.97 (1.93-2.00) and 3.43 (3.21-3.67) respectively for those having unhealthy sleep score of 1 to 3. These sleep problems were also positively correlated with frailty. Compared with the overweight or obesity group, stronger relationships were observed between short sleep duration and frailty or pre-frailty and between insomnia disorder and pre-frailty, while the relationships between snoring and frailty and pre-frailty were weaker in the participants with normal weight (P for interaction <0.007 for all). We also observed similar effect modification by central obesity. Conclusion: Long or short sleep duration, insomnia disorder, snoring and higher unhealthy sleep scores were positively correlated with pre-frailty or frailty, general and central obesity status could modify the relationships.

A novel circRNA-miRNA association prediction model based on structural deep neural network embedding

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning

Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing the circRNA–miRNA interaction provides a new perspective for the detection and treatment of human complex diseases. Compared with the traditional biological experimental methods used to predict the association of molecules, which are limited to the small-scale and are time-consuming and laborious, computing models can provide a basis for biological experiments at low cost. Considering that the proposed calculation model is limited, it is necessary to develop an effective computational method to predict the circRNA–miRNA interaction. This study thus proposed a novel computing method, named KGDCMI, to predict the interactions between circRNA and miRNA based on multi-source information extraction and fusion. The KGDCMI obtains RNA attribute information from sequence and similarity, capturing the behavior information in RNA association through a graph-embedding algorithm. Then, the obtained feature vector is extracted further by principal component analysis and sent to the deep neural network for information fusion and prediction. At last, KGDCMI obtains the prediction accuracy (area under the curve [AUC] = 89.30% and area under the precision–recall curve [AUPR] = 87.67%). Meanwhile, with the same dataset, KGDCMI is 2.37% and 3.08%, respectively, higher than the only existing model, and we conducted three groups of comparative experiments, obtaining the best classification strategy, feature extraction parameters, and dimensions. In addition, in the performed case study, 7 of the top 10 interaction pairs were confirmed in PubMed. These results suggest that KGDCMI is a feasible and useful method to predict the circRNA–miRNA interaction and can act as a reliable candidate for related RNA biological experiments.

[Review of genome-wide association research of aging phenotypes]

"Active health" has been emphasized in "Healthy China 2030" in dealing with the challenges of population aging, so the anti-aging strategies are requires to be more precise and effective at both individual and population levels. Aging is influenced by both genetic and environmental factors. In the recent 20 years, the research of genetics of human ageing has been greatly facilitated owning to the development of high-throughput sequencing techniques, statistical methodology for multi-omics data, as well as the growing qualified evidence of large-scale population-based genomic research. This paper provides a review of genome-wide association research of aging.

BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network

As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecular level. As we all know that conventional wet experiments suffer from many problems, including time-consuming, labor-intensity, and high cost. Thus, there is an urgent need to develop a novel computational model to facilitate the identification of miRNA–drug interactions (MDIs). In this work, we propose a novel bipartite network embedding-based method called BNEMDI to predict MDIs. First, the Bipartite Network Embedding (BiNE) algorithm is employed to learn the topological features from the network. Then, the inherent attributes of drugs and miRNAs are expressed as attribute features by MACCS fingerprints and k-mers. Finally, we feed these features into deep neural network (DNN) for training the prediction model. To validate the prediction ability of the BNEMDI model, we apply it to five different benchmark datasets under five-fold cross-validation, and the proposed model obtained excellent AUC values of 0.9568, 0.9420, 0.8489, 0.8774, and 0.9005 in ncDR, RNAInter, SM2miR1, SM2miR2, and SM2miR MDI datasets, respectively. To further verify the prediction performance of the BNEMDI model, we compare it with some existing powerful methods. We also compare the BiNE algorithm with several different network embedding methods. Furthermore, we carry out a case study on a common drug named 5-fluorouracil. Among the top 50 miRNAs predicted by the proposed model, there were 38 verified by the experimental literature. The comprehensive experiment results demonstrated that our method is effective and robust for predicting MDIs. In the future work, we hope that the BNEMDI model can be a reliable supplement method for the development of pharmacology and miRNA therapeutics.

[Association of lifestyle and cardiometabolic risk factors with epigenetic age acceleration in adults in China]

Objective: To explore the association of lifestyle and cardiometabolic risk factors with five epigenetic age acceleration (AA) indices. Methods: This study included 980 participants of China Kadoorie Biobank, for whom genome-wide DNA methylation of peripheral blood cells had been detected in baseline survey. Five indices of DNA methylation age (DNAm age) were calculated, i.e. Horvath clock, Hannum clock, DNAm PhenoAge, GrimAge and Li clock. Epigenetic AA was defined as the residual of regressing DNAm age on chronological age. Lifestyle factors studied included smoking status, alcohol consumption, eating habits, physical activity level and body shape defined by a combination of BMI and waist circumference. Cardiometabolic risk factors included blood pressure, blood glucose level and total cholesterol level. Linear regression model was used to analyze the association of lifestyle and cardiometabolic risk factors with AA (β). Results: GrimAge_AA was associated with smoking status, alcohol consumption, physical activity level and BMI. Compared with non-smokers, non-drinkers, or participants with BMI of 18.5- 23.9 kg/m2, the smokers who smoked 1-14 cigarettes/day (β=0.71, 95%CI: 0.57-0.86), 15-24 cigarettes/day (β=0.88, 95%CI: 0.73-1.03), and ≥25 cigarettes/day (β=0.99, 95%CI: 0.81-1.18), respectively, heavy drinkers with daily pure alcohol consumption ≥60 g (β=0.33, 95%CI: 0.11-0.55) and participants with BMI<18.5 kg/m2 (β=0.23, 95%CI: 0.03-0.43) showed accelerated aging. Compared with those in the lowest quintile of physical activity level, participants in the top three quintile of physical activity level showed decelerated aging (β=-0.13, 95%CI: -0.26-0.01, β=-0.12, 95%CI: -0.26-0.02, and β=-0.14, 95%CI: -0.27- -0.00, respectively). GrimAge_AA decreased with the increase of the number of healthy lifestyle factors (P<0.001). Compared with the participants with 0 to 1 healthy lifestyle factor, the β of those with 2, 3, or 4 to 5 healthy lifestyle factors were -0.30 (95%CI: -0.47- -0.12), -0.47 (95%CI: -0.65- -0.30) and -0.72 (95%CI: -0.90- -0.53), respectively. The other four indices were not statistically significantly associated with most lifestyle factors. None of the five indices of AA was associated with blood pressure, blood glucose level or total cholesterol level. Conclusion: People with unhealthy lifestyle showed accelerated epigenetic aging, that is, the predicted DNAm age is older than their own chronological age.

[Associations between sleep status and risk for kidney stones in Chinese adults: a prospective cohort study]

Objective: To analyze the associations between sleep status and the risk for kidney stone in Chinese adults. Methods: This study used baseline and long-term follow-up data of China Kadoorie Biobank. After excluding those with self-reporting of diagnosed chronic kidney disease and cancer and those with extreme values of sleep duration at baseline survey, 501 701 participants were included in this study. The information about their sleep status were collected, including insomnia symptoms, daytime sleepiness, nap habit, snoring and sleep duration. The sleep score was constructed based on insomnia symptoms, daytime sleepiness, and sleep duration, ranging from 0 to 3. Cox proportional hazards regression models were used to evaluate the association of sleep status with the risk for kidney stone, including individual sleep factors and combined sleep score. Results: During the follow-up for average (10.7±2.2) years, 12 381 cases of kidney stone were recorded for the first time. Compared with participants without insomnia symptoms, the multivariable-adjusted HR of kidney stone in those with difficulty falling asleep and waking up early were 1.12 (95%CI: 1.06-1.18) and 1.06 (95%CI: 1.00-1.12), respectively. There was no statistically significant association of kidney stone risk with sleeping pill use, daytime sleepiness, nap habit, or snoring. Compared with participants with sleep duration ≥7 hours per day, the HR of kidney stone in those with sleep duration <7 hours per day was 1.13 (95%CI: 1.08-1.18). Compared with participants with sleep score of 3 (highest sleep quality), the HR of kidney stone in those with sleep score of 2, 1, and 0 were 1.08 (95%CI: 1.03-1.13), 1.16 (95%CI: 1.10-1.23), and 1.19 (95%CI: 1.03-1.37), respectively. Conclusion: In China, adults with insomnia symptoms or short sleep duration have increased risk for kidney stone.

[A prospective cohort study of premature death and influencing factors in adults aged 56-69 years from 10 regions of China]

Objective: To describe and analyze the epidemiological characteristics of premature death (death before age of 70 years) and related risk factors in approximate 100 000 adults recruited from 10 regions of China during a 10-year follow-up. Methods: Data, including demographic characteristics, lifestyle and physical indicators as well as health outcomes as of December 31, 2017, were obtained from baseline survey and long-term follow-up of the China Kadoorie Biobank (CKB) study. All-cause and cause-specific premature death in different areas, in men and women and in people with different lifestyles were analyzed. Cox proportional risk model was used to analyze the associations between baseline factors and premature death. Results: A total of 99 993 participants aged 56-69 years were included in the study. During 10 years of follow-up, 7 530 premature deaths were recorded and the premature death rate was 7.15 per 1 000 person-years. The main causes of premature death were cancer and cardiovascular and cerebrovascular diseases. The premature mortality rate was higher in rural areas, in northern region and in men, and decreased with age (P<0.05). Premature death was more likely to occur in smokers, and a dose-response relationship was observed. Compared with non-drinkers, the risk for premature death was higher in ex-drinkers (HR: 1.25 [95%CI:1.16-1.36]) and heavy drinkers (average alcohol consumption ≥60 g/d) (HR: 1.20 [95%CI:1.08-1.34]). The risk for premature death decreased with the increase of physical activity. Low body weight and central obesity were independently associated with increased risk for premature death (HR: 1.67 [95%CI:1.55-1.81] and 1.13 [95%CI:1.05-1.21], respectively). Conclusions: The main causes of premature death in adults aged 56-69 years in China during 10-year follow-up were cancer and cardiovascular and cerebrovascular diseases. The premature mortality rate varied with socioeconomic and demographic characteristics. The risk for premature death was influenced by multi factors, such as lifestyle and physical conditions.

[Heritability and genetic correlation of body mass index and coronary heart disease in Chinese adult twins]

Objective: To examine the heritability of body mass index (BMI) and coronary heart disease (CHD), and to explore whether genetic factors can explain their correlation. Methods: Participants were from 11 provinces/municipalities reqistered in the Chinese National Twin Registry (CNTR) from 2010 to 2018. Participants data were collected from face-to-face questionnaire survey. Bivariate structure equation model was used to estimate the heritability and the genetic correlation of BMI and CHD. Results: A total of 20 340 pairs of same-sex twins aged ≥25 years were included in this study. After adjusting for age and gender, the heritability of BMI and CHD was 0.52 (95%CI: 0.49-0.55) and 0.76 (95%CI: 0.69-0.81), respectively. Further, a genetic correlation was identified between BMI and CHD (r_A=0.10, 95%CI:0.02-0.17). Conclusion: In Chinese adult twin population, BMI and CHD are affected by genetic factors, and their correlation can be attributed to the common genetic basis.

[Development and applications of digital public health]

Rapidly upgraded digital technology has impacted all walks of life, and public health field is also undergoing a digital transformation. The COVID-19 pandemic has accelerated the wide use of digital technology in the prevention and control of infectious diseases, greatly enhancing the capacity of public health system in emergency response and routine disease prevention and control. This article summarizes the definition of digital public health, applications of digital technology in the prevention and control of infectious diseases and chronic non-communicable diseases, as well as in public health surveillance, discusses the challenges in the development of digital public health and introduces the eight principles for digital transformation of public health proposed by the Pan American Health Organization.

BioChemDDI: Predicting Drug-Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism

Simple Summary

Throughout history, combining drugs has been a common method in the fight against complex diseases. However, potential drug–drug interactions could give rise to unknown toxicity issues, which requires the urgent proposal of efficient methods to identify potential interactions.We use computer technology and machine learning techniques to propose a novel computational framework to calculate scores of drug–drug interaction probability for simplifying the screening process. Additionally, we built an online prescreening tool for biological researchers to further verify possible interactions in the fields of biomedicine and pharmacology. Overall, our study can provide new insights and approaches for rapidly identifying potential drug–drug interactions.

Abstract

During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.

[Gene-lifestyle interaction on coronary heart disease in adult twins of China]

Objective: To explore the gene-lifestyle interaction on coronary heart disease (CHD) in adult twins of China. Methods: Participants were selected from twin pairs registered in the Chinese National Twin Registry (CNTR). Univariate interaction model was used to estimate the interaction, via exploring the moderation effect of lifestyle on the genetic variance of CHD. Results: A total of 20 477 same-sex twin pairs aged ≥25 years were recruited, including 395 CHD cases, and 66 twin pairs both had CHD. After adjustment for age and sex, no moderation effects of lifestyles, including current smoking, current drinking, physical activity, intake of vegetable and fruit, on the genetic variance of CHD were found (P>0.05), suggesting no significant interactions. Conclusion: There was no evidence suggesting statistically significant gene-lifestyle interaction on CHD in adult twins of China.

[Association between genetic predisposition to childhood obesity and the risk of adult ischemic heart disease in China]

Objective: To examine the associations of childhood obesity, assessed by genetic variations of childhood body mass index (BMI), with the risk of adult ischemic heart disease (IHD) and major coronary event (MCE). Methods: More than 69 000 participants from the China Kadoorie Biobank were genotyped. After excluding those with coronary heart disease, stroke, or cancer at baseline, a total of 64 454 participants were included in this study. Based on genome-wide significant single nucleotide polymorphisms (SNPs), childhood BMI genetic risk score were constructed for every participant and divided into quintiles, with the lowest quintile as the low genetic risk group and the highest quintile as the high genetic risk group. Cox proportional hazards regression models were used to estimate the association between genetic predisposition to childhood obesity and the risk of ischemic heart disease. Results: During a median of 10.7 years of follow-up, 7 073 incident cases of IHD and 1 845 cases of MCE were documented. After adjusting for sex, age, region, and the first ten genetic principal components, the HRs (95%CIs) for IHD and MCE in the high genetic risk group were 1.10 (1.02-1.18) and 1.10 (0.95-1.27), compared with the low genetic risk group. IHD risk increased by 4% (2%-6%) for each one standard deviation increase in genetic risk score (trend P=0.001). After further adjustment for baseline BMI, the differences between genetic risk groups were not statistically significant, but there was still a linear trend between genetic risk score and IHD risk (trend P=0.019). Conclusions: IHD risk increased with genetic predisposition to childhood obesity, suggesting that childhood obesity is an important risk factor for the development of IHD in China. As an easily identifiable feature, changes of childhood BMI should be monitored regularly to realize early intervention of IHD in adults.

DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network

The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.