A non-linear partial least squares based on monotonic inner relation.

A novel regression model, monotonic inner relation-based non-linear partial least squares (MIR-PLS), is proposed to address complex issues like limited observations, multicollinearity, and nonlinearity in Chinese Medicine (CM) dose-effect relationship experimental data. MIR-PLS uses a piecewise mapping function based on monotonic cubic splines to model the non-linear inner relations between input and output score vectors. Additionally, a new weight updating strategy (WUS) is developed by leveraging the properties of monotonic functions. The proposed MIR-PLS method was compared with five well-known PLS variants: standard PLS, quadratic PLS (QPLS), error-based QPLS (EB-QPLS), neural network PLS (NNPLS), and spline PLS (SPL-PLS), using CM dose-effect relationship datasets and near-infrared (NIR) spectroscopy datasets. Experimental results demonstrate that MIR-PLS exhibits general applicability, achieving excellent predictive performances in the presence or absence of significant non-linear relationships. Furthermore, the model is not limited to CM dose-effect relationship research and can be applied to other regression tasks.

Analysing pharmacodynamic interactions of traditional Chinese medicine in treating acute pancreatitis based on OPLS method.

Acute pancreatitis (AP) is a surgical abdominal disease for which the Dachengqi Decoction (DCQD) of traditional Chinese medicine (TCM) is widely used in China. This study aims to analyse the pharmacodynamic interactions and quantitative relationship of DCQD in the treatment of AP based on orthogonal partial least squares (OPLS) analysis. The experimental data show organic chemical components as candidate pharmacodynamic substances (PS) in the blood and include pharmacodynamic indicators (PIs). Taking each PI as the target and using OPLS method to construct three types of mathematical equations, including the mathematical relationship between the pharmacodynamic substances and each target pharmacodynamic indicator (PS-TPI); the mathematical relationship between the pharmacodynamic substances, the pharmacodynamics indicators and each target pharmacodynamic indicator (PS, PI-TPI); and the mathematical relationship between the pharmacodynamic indicators and each target pharmacodynamic indicator (PI-TPI). Through analysis, we find that the R2Y(cum) values and VIP values indicate that PS and PI are the follow-up factors of TPI; the coefficient value indicates that there is a quantitative relationship between the PS and the TPI; and there also is a quantitative relationship between PI and TPI. The results demonstrated that PS and other PIs are the important influencing factors of TPI, and that there are interactions and quantitative relationships among the PIs.

New research for detecting complex associations between variables with randomness.

Many correlation analysis methods can capture a wide range of functional types of variables. However, the influence of uncertainty and distribution status in data is not considered, which leads to the neglect of the regularity information between variables, so that the correlation of variables that contain functional relationship but subject to specific distributions cannot be well identified. Therefore, a novel correlation analysis framework for detecting associations between variables with randomness (RVCR-CA) is proposed. The new method calculates the normalized RMSE to evaluate the degree of functional relationship between variables, calculates entropy difference to measure the degree of uncertainty in variables and constructs the copula function to evaluate the degree of dependence on random variables with distributions. Then, the weighted sum method is performed to the above three indicators to obtain the final correlation coefficient R. In the study, which considers the degree of functional relationship between variables, the uncertainty in variables and the degree of dependence on the variables containing distributions, cannot only measure the correlation of functional relationship variables with specific distributions, but also can better evaluate the correlation of variables without clear functional relationships. In experiments on the data with functional relationship between variables that contain specific distributions, UCI data and synthetic data, the results show that the proposed method has more comprehensive evaluation ability and better evaluation effect than the traditional method of correlation analysis.

Classification method of traditional Chinese medicine compound decoction duration based on multi-dimensional feature weighted fusion.

This paper extends a text classification method utilizing natural language processing (NLP) into the field of traditional Chinese medicine (TCM) compound decoction to effectively and scientifically extend the TCM compound decoction duration. Specifically, a TCM compound decoction duration classification named TCM-TextCNN is proposed to fuse multi-dimensional herb features and improve TextCNN. Indeed, first, we utilize word vector technology to construct feature vectors of herb names and medicinal parts, aiming to describe the herb characteristics comprehensively. Second, considering the impact of different herb features on the decoction duration, we use an improved Term Frequency-Inverse Word Frequency (TF-IWF) algorithm to weigh the feature vectors of herb names and medicinal parts. These weighted feature vectors are then concatenated to obtain a multi-dimensional herb feature vector, allowing for a more comprehensive representation. Finally, the feature vector is input into the improved TextCNN, which uses k-max pooling to reduce information loss rather than max pooling. Three fully connected layers are added to generate higher-level feature representations, followed by softmax to obtain the final results. Experimental results on a dataset of TCM compound decoction duration demonstrate that TCM-TextCNN improves accuracy, recall, and F1 score by 5.31%, 5.63%, and 5.22%, respectively, compared to methods solely rely on herb name features, thereby confirming our method's effectiveness in classifying TCM compound decoction duration.

Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model.

In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.

Trends and future projections of liver cancer incidence in Hong Kong: a population-based study.

BACKGROUND: Liver cancer remains a significant burden in Hong Kong. We sought to examine trends in liver cancer incidence using 30 years of cancer registry data in Hong Kong. Additionally, we aimed to assess the impact of age, period and birth cohort on liver cancer incidence, provided projections for liver cancer incidence until 2030, and examined the drivers of liver cancer incidence. METHODS: Data on liver cancer incidence were collected from the Hong Kong Cancer Registry (HKCaR). We assessed age, period, and birth cohort effects using age-period-cohort (APC) models. We employed Bayesian APC analysis with integrated nested Laplace approximations to project the future burden of liver cancer in Hong Kong. Furthermore, we attributed the changes in new liver cancer cases to population growth, population ageing, and epidemiological changes. RESULTS: The study included a total of 51,333 individuals, of whom 39,287 (76.53%) were male. From 1991 to 2020, the age-standardized liver cancer incidence rate in Hong Kong continued declining, while the number of new cases increased significantly, especially among males. The net drift, representing the overall annual percentage change of the age-adjusted rate, was - 3.06% (95% confidence interval [CI]: -3.31% to -2.80%) for males and - 3.85% (95% CI: -4.61% to -3.09%) for females. Local drift, which estimates the annual percentage change over time specific to age group, decreased in all age groups for both sexes, with a more pronounced decrease in younger age groups. The period and cohort risk of developing liver cancer also showed decreasing trends for both sexes. The study projected a decline in liver cancer cases for males but an increase for females in Hong Kong, with an estimated 1,083 cases in males and 710 cases in females by 2030. Demographic decomposition analysis revealed that while population growth and ageing were the main drivers of increased liver cancer cases, epidemiologic shifts mostly offset these factors. CONCLUSION: The period and cohort risk of developing liver cancer in Hong Kong declined due to epidemiological changes. Although the age-standardized incidence rates of liver cancer have also declined, demographic and epidemiological factors have led to lower case expectations in males but a likely increase in females. Further research and epidemiological assessment of the disease are needed.

Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares.

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods.

Topical streptomycin irrigation of lesions to prevent postoperative site infections in spinal tuberculosis: a retrospective analysis.

PURPOSE: In spinal tuberculosis surgery, topical administration of drugs to the lesion is a preventive treatment measure. The aim is to achieve better bacterial inhibition and to prevent complications. As one of the most common complications after spinal tuberculosis surgery, many factors can lead to surgical site infection (SSI). No definitive reports of local streptomycin irrigation of the lesion and SSI of spinal tuberculosis have been seen. This study analyzed data related to surgical site infections (SSI) after the treatment of spinal tuberculosis using this regimen. METHODS: In this study, 31 were in the observation group (streptomycin flush) and 34 in the control group (no streptomycin flush). All patients received the same standard of perioperative care procedures. General information, operative time, intraoperative bleeding, ESR and CRP at one week postoperatively, time on antibiotics, total drainage, days in hospital, incision infection rate and secondary debridement rate were compared between the two groups. RESULTS: Patients in both groups completed the surgery successfully. The ESR and CRP levels in the observation group were lower than those in the control group one week after surgery (p < 0.05); the duration of postoperative antibiotics and hospital stay were lower than those in the control group (p < 0.05); the incidence of SSI in the two groups was 5.88% and 6.45% respectively, with no significant difference (p > 0.05). CONCLUSION: The use of topical streptomycin irrigation of the lesion during surgical procedures for spinal tuberculosis had no significant effect on the incidence of SSI, however, it helped to control the level of infection in the postoperative period and reduced the length of time patients had to use postoperative antibiotics and the number of days they stayed in hospital. Future prospective randomised controlled trials in more centres and larger samples are recommended.

Changing patterns of nasopharyngeal carcinoma incidence in Hong Kong: a 30-year analysis and future projections.

BACKGROUND: This study aims to evaluate the relationship between age, period, and birth cohort with the incidence trends of Nasopharyngeal Carcinoma (NPC) in Hong Kong, make projections through 2030 and parse the drivers of the incidence. METHODS: Using data from the Hong Kong Cancer Registry, we used an age-period-cohort model to uniquely estimate age, period, and cohort effects on NPC incidence trends and make projections. We further assessed the drivers of NPC incidence using a validated decomposition algorithm. RESULTS: From 1991 to 2020, crude and age-standardized incidence rates of NPC decreased significantly. The net drifts showed significant downward trends for both sexes, and local drift declined in all age groups. Period and cohort rate ratios revealed monotonic declining patterns for both sexes. Projections suggested that NPC incidence will continue to decline. Population decomposition showed that while population growth and ageing have led to an increase in NPC cases, epidemiologic changes offset these increases, resulting in an encouraging downward trend in the incidence and new NPC cases in Hong Kong. CONCLUSIONS: The period and cohort risk of NPC in Hong Kong decreased, and epidemiologic changes offset the contribution of demographic factors, resulting in a continued decline in NPC incidence and cases.

Trends and projections of ovarian cancer incidence in Hong Kong: A population-based study.

INTRODUCTION: This study analyzed the incidence of ovarian cancer in Hong Kong and its association with age, calendar period and birth cohort, made projections through 2030, and attributed differences in new cancer cases to demographic and epidemiologic changes. MATERIAL AND METHODS: Incidence data for ovarian cancer were obtained from the Hong Kong Cancer Registry. We employed the age-period-cohort modeling approach to investigate the association between ovarian cancer incidence and age in Hong Kong women, with particular emphasis on examining the changing trends of period and cohort effects on incidence. We projected the incidence of ovarian cancer in Hong Kong between 2018 and 2030 and attributed the rise in new cancer cases to epidemiologic and demographic shifts. RESULTS: Between 1990 and 2017, a total of 11 182 women were diagnosed with ovarian cancer in Hong Kong. Crude and age-standardized rates increased from 8.2 and 7.8 per 100 000 person-years to 16.3 and 11.5 per 100 000 person-years, respectively. New cases of ovarian cancer rose from 225 in 1990 to 645 in 2017. We observed an increased risk of ovarian cancer throughout the study period and in the post-1940 birth cohort. The projected incidence rate and new cases of ovarian cancer are expected to continue growing due to demographic and epidemiologic changes such as fertility patterns and lifestyle factors, with an estimated 981 cases in 2030. CONCLUSIONS: The period risk and cohort risk of ovarian cancer among Hong Kong women is increasing. Demographic and epidemiologic changes may continue to increase ovarian cancer incidence and new cases in Hong Kong.

Trends of non-melanoma skin cancer incidence in Hong Kong and projection up to 2030 based on changing demographics.

OBJECTIVE: To assess the trends in non-melanoma skin cancer (NMSC) incidence in Hong Kong from 1990 to 2019 and the associations of age, calendar period, and birth cohort, to make projections to 2030, and to examine the drivers of NMSC incidence. METHODS: We assessed the age, calendar period, and birth cohort effects of NMSC incidence in Hong Kong between 1990 and 2019 using an age-period-cohort model. Using Bayesian age-period-cohort analysis with integrated nested Laplace approximations, we projected the incidence of NMSC in Hong Kong to 2030. RESULTS: From 1990 to 2019, the age-standardized incidence rate of NMSC increased from 6.7 per 100,000 population to 8.6 per 100,000 population in men and from 5.4 per 100,000 to 5.9 per 100,000 population in women, among the 19,568 patients in the study (9812 male patients [50.14%]). The annual net drift was 2.00% (95% confidence interval [CI]: 1.50-2.50%) for men and 1.53% (95% CI: 0.95-2.11%) for women. Local drifts increased for both sexes above the 35-39-year age group. The period and cohort risk of developing NMSC tended to rise but slowed gradually in the most recent period and post-1975 birth cohort. From 2019 to 2030, it is projected that the number of newly diagnosed NMSC cases in Hong Kong will increase from 564 to 829 in men and from 517 to 863 in women. Population aging, population growth, and epidemiologic changes contributed to the increase in incident NMSCs, with population aging being the most significant contributor. CONCLUSION: The slowing of the period and cohort effects suggests that the rising incidence of NMSC is partly attributable to increased awareness and diagnosis. The increasing prevalence of NMSC among the elderly and an aging population will significantly impact the clinical workload associated with NMSC for the foreseeable future.

Trends and Projections of Stomach Cancer Incidence in Hong Kong: A Population-Based Study.

OBJECTIVES: The burden of stomach cancer remains high in Hong Kong. We sought to evaluate the associations of age, period, and birth cohort with the changing trend in the incidence of stomach cancer and to provide projections through 2030. MATERIALS AND METHODS: We performed an age-period-cohort analysis and projections up to 2030 using data from the Hong Kong Cancer Registry. Additionally, we used a population decomposition algorithm to assess the drivers in the number of incident cases of stomach cancer in Hong Kong. RESULTS: Among the 26,813 stomach cancer patients, from 1994 to 2018, the age-standardized incidence rate of stomach cancer decreased for both sexes. The incidence increased with age and was highest for those aged 85 years or older. Period relative risk (RR) showed a monotonic decreasing pattern throughout the study period for both sexes before 2010. Cohort RR for males was monotonically decreasing but changed little after the 1967-1971 birth cohort. In contrast, cohort RR for females declined in the pre-1927-1931 birth cohort but slowed down since. It is projected that there will be 906 male patients and 954 female patients in 2030. Decomposition analysis suggested that population growth and aging were associated with substantial changes in the number of incident cases of stomach cancer in Hong Kong. CONCLUSIONS: Both period and cohort risk of developing stomach cancer in Hong Kong have slowed down or plateaued. Our study demonstrates that population aging and growth are the main drivers of the increased number of incident cases of stomach cancer in Hong Kong.

Feature selection based on neighborhood rough sets and Gini index.

Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection. The Gini index is an indicator used to evaluate the impurity of a dataset and is also commonly employed to measure the importance of features in feature selection. This article proposes a novel feature selection methodology based on these two concepts. In this methodology, we present the neighborhood Gini index and the neighborhood class Gini index and then extensively discuss their properties and relationships with attributes. Subsequently, two forward greedy feature selection algorithms are developed using these two metrics as a foundation. Finally, to comprehensively evaluate the performance of the algorithm proposed in this article, comparative experiments were conducted on 16 UCI datasets from various domains, including industry, food, medicine, and pharmacology, against four classical neighborhood rough set-based feature selection algorithms. The experimental results indicate that the proposed algorithm improves the average classification accuracy on the 16 datasets by over 6%, with improvements exceeding 10% in five. Furthermore, statistical tests reveal no significant differences between the proposed algorithm and the four classical neighborhood rough set-based feature selection algorithms. However, the proposed algorithm demonstrates high stability, eliminating most redundant or irrelevant features effectively while enhancing classification accuracy. In summary, the algorithm proposed in this article outperforms classical neighborhood rough set-based feature selection algorithms.

A nationwide study of maternal exposure to ambient ozone and term birth weight in the United States.

BACKGROUND: Maternal exposure to ozone (O3) may cause systemic inflammation and oxidative stress and contribute to fetal growth restriction. We sought to estimate the association between maternal exposure to O3 and term birth weight and term small for gestational age (SGA) in the United States (US). METHODS: We conducted a nationwide study including 2,179,040 live term singleton births that occurred across 453 populous counties in the contiguous US in 2002. Daily county-level concentrations of O3 data were estimated using a Bayesian fusion model. We used linear regression to estimate the association between O3 exposure and term birth weight and logistic regression to estimate the association between O3 exposure and term SGA during each trimester of the pregnancy and the entire pregnancy after adjusting for maternal characteristics, infant sex, season of conception, ambient temperature, county poverty rate, and census region. We additionally used distributed lag models to identify the critical exposure windows by estimating the monthly and weekly associations. RESULTS: A 10 parts per billion (ppb) increase in O3 over the entire pregnancy was associated with a lower term birth weight (-7.6 g; 95 % CI: -8.8 g, -6.4 g) and increased risk of SGA (odds ratio = 1.030; 95 % CI: 1.020, 1.040). The identified critical exposure windows were the 13th- 25th and 32nd -37th gestational weeks for term birth weight and 13th- 25th for term SGA. We found the association was more pronounced among mothers who were non-Hispanic Black, unmarried, or had lower education level. CONCLUSIONS: Among US singleton term births, maternal exposure to O3 was associated with lower rates of fetal growth, and the 13th- 25th gestational weeks were the identified critical exposure windows.

APAview: A web-based platform for alternative polyadenylation analyses in hematological cancers.

Background: Hematologic malignancies, such as acute promyelocytic leukemia (APL) and acute myeloid leukemia (AML), are cancers that start in blood-forming tissues and can affect the blood, bone marrow, and lymph nodes. They are often caused by genetic and molecular alterations such as mutations and gene expression changes. Alternative polyadenylation (APA) is a post-transcriptional process that regulates gene expression, and dysregulation of APA contributes to hematological malignancies. RNA-sequencing-based bioinformatic methods can identify APA sites and quantify APA usages as molecular indexes to study APA roles in disease development, diagnosis, and treatment. Unfortunately, APA data pre-processing, analysis, and visualization are time-consuming, inconsistent, and laborious. A comprehensive, user-friendly tool will greatly simplify processes for APA feature screening and mining. Results: Here, we present APAview, a web-based platform to explore APA features in hematological cancers and perform APA statistical analysis. APAview server runs on Python3 with a Flask framework and a Jinja2 templating engine. For visualization, APAview client is built on Bootstrap and Plotly. Multimodal data, such as APA quantified by QAPA/DaPars, gene expression data, and clinical information, can be uploaded to APAview and analyzed interactively. Correlation, survival, and differential analyses among user-defined groups can be performed via the web interface. Using APAview, we explored APA features in two hematological cancers, APL and AML. APAview can also be applied to other diseases by uploading different experimental data.

Global, regional and national burden of non-Hodgkin lymphoma from 1990 to 2017: estimates from global burden of disease study in 2017.

BACKGROUD: Non-Hodgkin lymphoma (NHL) is a common B/NK/T cell lymphoma. We collected detailed data about the incidence and mortality of NHL from Global Burden of Disease (GBD) Study in 2017 and extensively assessed the disease burden of NHL at the global level and also analysed its current trends according to sex, age, socio-demographic index (SDI), country and region. METHODS: By obtaining relevant data from Global Burden of Disease Study in 2017, estimated annual percentage changes (EAPCs) of age-standardized rate (ASR) were calculated to assess the current trends of the rate of incidence and mortality. RESULTS: Globally, ASR of incidence in NHL was increased while ASR of mortality and its annual percentage change was relatively stable. EAPCs in the incidence of NHL decreased in the low SDI regions but increased in the high SDI regions. The ratio of male to female mortalities was the highest in the 50-69-year-old age group, especially in the middle and middle-high SDI regions. CONCLUSION: The incidence of NHL was increased globally, whereas the deaths and its annual percentage change were relatively stable from 1990 to 2017.Key messagesAge-standardized rate (ASR) of incidence in NHL was increased globally from 1990 to 2017.ASR of mortality and its annual percentage change in NHL were relatively stable globally from 1990 to 2017.Estimated annual percentage changes (EAPCs) in the incidence of NHL decreased in the low socio-demographic index (SDI) regions but increased in the high SDI regions.

Traditional Chinese Medicine Text Similarity Calculation Model Based on the Bidirectional Temporal Siamese Network.

The text similarity calculation plays a crucial role as the core work of artificial intelligence commercial applications such as traditional Chinese medicine (TCM) auxiliary diagnosis, intelligent question and answer, and prescription recommendation. However, TCM texts have problems such as short sentence expression, inaccurate word segmentation, strong semantic relevance, high feature dimension, and sparseness. This study comprehensively considers the temporal information of sentence context and proposes a TCM text similarity calculation model based on the bidirectional temporal Siamese network (BTSN). We used the enhanced representation through knowledge integration (ERNIE) pretrained language model to train character vectors instead of word vectors and solved the problem of inaccurate word segmentation in TCM. In the Siamese network, the traditional fully connected neural network was replaced by a deep bidirectional long short-term memory (BLSTM) to capture the contextual semantics of the current word information. The improved similarity BLSTM was used to map the sentence that is to be tested into two sets of low-dimensional numerical vectors. Then, we performed similarity calculation training. Experiments on the two datasets of financial and TCM show that the performance of the BTSN model in this study was better than that of other similarity calculation models. When the number of layers of the BLSTM reached 6 layers, the accuracy of the model was the highest. This verifies that the text similarity calculation model proposed in this study has high engineering value.

Bayesian Age-Period-Cohort Prediction of Mortality of Type 2 Diabetic Kidney Disease in China: A Modeling Study.

Background: The burden of type 2 diabetic kidney disease (DKD) continues to rise in China. We analyzed time trends in DKD mortality and associations with age, period, and birth cohort from 1990 to 2019, made projections up to 2030, and examined the drivers of deaths from DKD. Methods and Findings: The number of DKD deaths in China from 1990 to 2019 was obtained from the GBD 2019. We used age-period-cohort modeling to estimate age, period, and cohort effects in DKD mortality between 1990 and 2019. We calculated net drift (overall annual percentage change), local drift (annual percentage change in each age group), longitudinal age curves (expected longitudinal age-specific rates), period, and cohort relative risks. We used Bayesian age-period-cohort analysis with integrated nested Laplace approximations to project future age-specific DKD death cases from 2020 to 2030. We used a validated decomposition algorithm to attribute changes in DKD deaths to population growth, population aging, and epidemiologic changes from 1990 to 2030. From 1990 to 2019, the age-standardized mortality rate of DKD in China was relatively stable, but the absolute number of DKD deaths showed a noticeable increasing trend. The overall annual percentage change (net drift) was -0.75% (95% confidence interval, CI: -0.93 to -0.57) for males and -1.90% (95% CI, -2.19 to -1.62) for females. The age-specific annual percentage changes (local drifts) were below zero in all age groups from 1990 to 2019 except for males aged above 65 to 69 years, and for females aged above 70 to 74 years. The risk of DKD deaths increased exponentially with age for both sexes after controlling for period deviations. The Bayesian age-period-cohort analysis projects that there would be 88,803 deaths from DKD in 2030, increased by 224.2% from 1990. Despite a decrease in age-specific DKD death rates, the reduction would be entirely offset by population aging. Conclusions: Although China has made progress in reducing DKD deaths, demographic changes have entirely offset the progress. The burden of DKD deaths is likely to continue increasing. Our findings suggest that large-scale screening is imperative for DKD control and prevention, particularly for high-risk groups.

Global burden attributable to high sodium intake from 1990 to 2019.

BACKGROUND AND AIMS: High sodium intake is associated with a higher risk of a wide range of diseases. We aimed to estimate the pattern and trend of the global disease burden associated with high sodium intake from 1990 to 2019. METHODS AND RESULTS: We obtained numbers and rates of death and disability-adjusted life year (DALY) attributable to high sodium intake by sex, socio-demographic index, and country from the Global Burden of Disease Study 2019. We calculated the estimated annual percentage change to evaluate the age-standardized rate (ASR) of the burden attributable to high sodium intake between 1990 and 2019. We further calculated the contribution of population growth, population aging, and age-specific rates of death and DALY to the net change in the total number of deaths and DALYs attributable to high sodium intake. From 1990 to 2019, global age-standardized rates of death and DALY attributable to high sodium intake substantially decreased for both sexes. However, there were significant increases in the total numbers of deaths and DALYs attributable to high sodium intake, which were driven by population growth and population aging. The attribution of population growth and population aging varied widely across countries, with a higher contribution of population growth in most developing countries and a higher contribution of population aging in countries with slow population growth. CONCLUSIONS: Although the global burden attributable to high sodium intake in terms of age-standardized rate declined from 1990 to 2019, the absolute burden increased significantly, which was driven by population growth and population aging.