Burden of colorectal cancer and its risk factors in the North Africa and Middle East (NAME) region, 1990-2019: a systematic analysis of the global burden of disease study.

OBJECTIVE: The geographical differences in incidence rates of colorectal cancer (CRC) and its burden due to modifiable risk factors warrant investigating the CRC burden and its risk factors in different regions. In the current study, we aimed to estimate the burden of CRC and the share of its risk factors in the North Africa and Middle East (NAME), from 1990 to 2019. STUDY DESIGN: Systematic review. METHODS: The rates of incidence, prevalence, death, years of life lost (YLL), years lived with disability (YLD), and disability adjusted life years (DALYs) of CRC were estimated through the framework of the Global Burden of Diseases (GBD), Injuries and Risk Factors Study 2019 by age, sex, between 1990 and 2019. The CRC-related DALYs attributable to each lifestyle and metabolic risk factor was also estimated through a comparative risk assessment approach. RESULTS: In NAME region, the trends of incidence, prevalence, death, YLL, YLD, and DALYs of CRC were increasing, with higher rates in males than females over this period. High and high-middle socio-demographic index (SDI) countries had greater CRC DALYs rate compared with middle- and low-SDI countries in 2019, except for Palestine [434.66 (95% UI: 368.82, 503.88)]. In NAME region, like the global, dietary risk (33.18%), low whole grain intake (19.79%), and low intake of milk (15.77%) were the major contributing risk factors to DALYs due to CRC in 2019. CONCLUSIONS: Due to increasing trend of CRC burden and the considerable role of lifestyle and metabolic factors in its burden in NAME region, implementing fundamental strategies to minimize CRC burden and its risk factors is imperative.

Targeting Efficient Features of Urate Oxidase to Increase Its Solubility.

With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.

The burden of ischemic heart disease and the epidemiologic transition in the Eastern Mediterranean Region: 1990-2019.

It has been estimated that in the next decade, IHD prevalence, DALYs and deaths will increase more significantly in EMR than in any other region of the world. This study aims to provide a comprehensive description of the trends in the burden of ischemic heart disease (IHD) across the countries of the Eastern Mediterranean Region (EMR) from 1990 to 2019. Data on IHD prevalence, disability-adjusted life years (DALYs), mortality, DALYs attributable to risk factors, healthcare access and quality index (HAQ), and universal health coverage (UHC) were extracted from the Global Burden of Disease (GBD) database for EMR countries. The data were stratified based on the social demographic index (SDI). Information on cardiac rehabilitation was obtained from publications by the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR), and additional country-specific data were obtained through advanced search methods. Age standardization was performed using the direct method, applying the estimated age structure of the global population from 2019. Uncertainty intervals were calculated through 1000 iterations, and the 2.5th and 97.5th percentiles were derived from these calculations. The age-standardized prevalence of IHD in the EMR increased from 5.0% to 5.5% between 1990 and 2019, while it decreased at the global level. In the EMR, the age-standardized rates of IHD mortality and DALYs decreased by 11.4% and 15.4%, respectively, during the study period, although both rates remained higher than the global rates. The burden of IHD was found to be higher in males compared to females. Bahrain exhibited the highest decrease in age-standardized prevalence (-3.7%), mortality (-65.0%), and DALYs (-69.1%) rates among the EMR countries. Conversely, Oman experienced the highest increase in prevalence (14.5%), while Pakistan had the greatest increase in mortality (30.0%) and DALYs (32.0%) rates. The top three risk factors contributing to IHD DALYs in the EMR in 2019 were high systolic blood pressure, high low-density lipoprotein cholesterol, and particulate matter pollution. The trend analysis over the 29-year period (1990-2019) revealed that high fasting plasma glucose (64.0%) and high body mass index (23.4%) exhibited increasing trends as attributed risk factors for IHD DALYs in the EMR. Our findings indicate an increasing trend in the prevalence of IHD and a decrease in mortality and DALYs in the EMR. These results emphasize the need for well-planned prevention and treatment strategies to address the risk factors associated with IHD. It is crucial for the countries in this region to prioritize the development and implementation of programs focused on health promotion, education, prevention, and medical care.

Trends in the burden and determinants of hypertensive heart disease in the Eastern Mediterranean region, 1990-2019: an analysis of the Global Burden of Disease Study 2019.

Background: Hypertensive heart disease (HHD), one of the end-organ damage consequences of hypertension, is an important public health issue worldwide. Data on the HHD burden in the Eastern Mediterranean region (EMR) are scarce. We aimed to investigate the burden of HHD in the EMR, its member countries, and globally from 1990 to 2019. Methods: We used 2019 Global Burden of Disease (GBD) data to report the HHD age-standardised prevalence, disability adjusted life years (DALYs), years of life lost (YLLs), and mortality, as well as HHD risk factors attribution percent with their 95% uncertainty interval (UI). Global data are reported alongside EMR data, and its 22 respective countries. We compared the burden of HHD by socio-demographic index (SDI), sex, age groups, and countries. Findings: The age-standardised prevalence rate (per 100,000 population) of HHD was higher in the EMR (281.7; 95% UI: 204.5-383.4) in 2019, compared with the global prevalence (233.8; 95% UI: 170.5-312.9). The EMR age-standardised DALYs (per 100,000 population) for HHD in 2019 was 561.9 (361.0-704.1), compared with 268.2 (204.6-298.1) at the global level. There was an increase in HHD prevalence, reduction in mortality, and DALYs between 1990 and 2019 (4.01%, -7.6%, and -6.5%, respectively) in EMR. Among EMR countries, the highest versus lowest rates of age-standardised prevalence, mortality, and DALYs in 2019 [estimate (95% UI)] were in Jordan [561.62 (417.9-747.6)] versus Saudi Arabia [94.9 (69.5-129.0)]; Afghanistan [74.5 (23.7-112.3)] versus Saudi Arabia [4.3 (3.3-5.9)]; and Afghanistan [1374.1 (467.2-2020.7)] versus Qatar [87.11 (64.40-114.29)], respectively. Interpretation: HHD remains a significant problem in the EMR, with a higher burden than global levels. Serious efforts toward high-quality management and prevention are strongly recommended. Based on this study, our recommendation for the EMR is to adopt effective preventive strategies. For example, promoting healthy dietary patterns and prompt screening for undiagnosed HTN in public places, promoting regular blood pressure measurements at home, and creating community awareness about early detection of HTN. Funding: None.

Impact of diabesity phenotype on cardiovascular diseases, major cardiovascular events and all-cause mortality.

To investigate the longitudinal association of different phenotypes of diabetes and obesity with the incidence of cardiovascular disease (CVD), CVD- and all-cause mortality. A total of 5432 adults, aged ≥ 35 years and free of CVD were included in this cohort study. Diabesity phenotypes were defined in six categories based on the presence of diabetes (normal (NG), prediabetes and diabetes) and obesity (obese, non-obese). Fasting blood sugar, 2-h post prandial glucose, or using anti-diabetic medicines were used to define diabetes, and body mass index and waist circumference were used to define obesity. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident CVD, CVD- and all-cause mortality across these categories. After a median follow-up of 11.25 years, 819 CVD cases, 181 CVD deaths and 488 all-cause deaths occurred. In multivariable-adjusted models and irrespective of obesity definition, the phenotypes of normal glucose-obese, prediabetes-obese and pre-diabetes-non obese were not associated with CVD incidence in comparison with NG-non obese phenotype, however, the phenotypes of diabesity, either defined by general or abdominal obesity, were associated with increased risk of incident CVD events (HR = 1.42, 95% CI 1.01, 1.99, and HR = 1.46, 95% CI 1.07, 1.98, respectively). These findings were sex-specific and only in men with a phenotype of abdominal obesity-diabetes, a positive link was observed for CVD incidence (HR = 1.60, 95% CI 1.01, 2.52). No significant association was found between diabesity and death from CVD or all causes. Diabesity is a predictor of CVD and stroke incidence, but not CVD or all-cause mortality, among Iranians. This association is more pronounced amongst men than women.

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region.

BACKGROUND: Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machine Learning (ML) and statistical techniques to develop classification models for predicting the future occurrence of CVD events in a large sample of Iranians. METHODS: We used multiple prediction models and ML techniques with different abilities to analyze the large dataset of 5432 healthy people at the beginning of entrance into the Isfahan Cohort Study (ICS) (1990-2017). Bayesian additive regression trees enhanced with "missingness incorporated in attributes" (BARTm) was run on the dataset with 515 variables (336 variables without and the remaining with up to 90% missing values). In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. We used Recursive Feature Elimination (RFE) to select the most contributing variables. Random oversampling technique, recommended cut-point by precision-recall curve, and relevant evaluation metrics were used for handling unbalancing in the binary response variable. RESULTS: This study revealed that age, systolic blood pressure, fasting blood sugar, two-hour postprandial glucose, diabetes mellitus, history of heart disease, history of high blood pressure, and history of diabetes are the most contributing factors for predicting CVD incidence in the future. The main differences between the results of classification algorithms are due to the trade-off between sensitivity and specificity. Quadratic Discriminant Analysis (QDA) algorithm presents the highest accuracy (75.50 ± 0.08) but the minimum sensitivity (49.84 ± 0.25); In contrast, decision trees provide the lowest accuracy (51.95 ± 0.69) but the top sensitivity (82.52 ± 1.22). BARTm.90% resulted in 69.48 ± 0.28 accuracy and 54.00 ± 1.66 sensitivity without any preprocessing step. CONCLUSIONS: This study confirmed that building a prediction model for CVD in each region is valuable for screening and primary prevention strategies in that specific region. Also, results showed that using conventional statistical models alongside ML algorithms makes it possible to take advantage of both techniques. Generally, QDA can accurately predict the future occurrence of CVD events with a fast (inference speed) and stable (confidence values) procedure. The combined ML and statistical algorithm of BARTm provide a flexible approach without any need for technical knowledge about assumptions and preprocessing steps of the prediction procedure.

Changes in the burden and underlying causes of heart failure in the Eastern Mediterranean Region, 1990-2019: An analysis of the Global Burden of Disease Study 2019.

Background: The burden of heart failure (HF) is high globally, but information on its burden in the Eastern Mediterranean Region (EMR) is limited. This study provides a systematic analysis of the burden and underlying causes of HF in the EMR, including at the country level, between 1990 and 2019. Methods: We used the 2019 Global Burden of Disease (GBD) data for estimates of prevalence, years lived with disability (YLDs), and underlying causes of HF in the EMR. Age-standardised prevalence, YLDs, and underlying causes of HF were compared by 5-year age groups (considering 15 years old and more), sex (male and female), and countries. Findings: In contrast with the decreasing trend of HF burden globally, EMR showed an increasing trend. Globally, the HF age-standardised prevalence and YLDs decreased by 7.06% (95% UI: -7.22%, -6.9%) and 6.82% (95% UI: -6.98%, -6.66%) respectively, from 1990 to 2019. The HF age-standardised prevalence and YLDs in the EMR in 2019 were 706.43 (95% UI: 558.22-887.87) and 63.46 (95% UI: 39.82-92.59) per 100,000 persons, representing an increase of 8.07% (95% UI: 7.9%, 8.24%) and 8.79% (95% UI: 8.61%, 8.97%) from 1990, respectively. Amongst EMR countries, the age-standardised prevalence and YLDs were highest in Kuwait, while Pakistan consistently had the lowest HF burden. The dramatic increase of the age-standardised prevalence and YLDs were seen in Oman (28.79%; 95% UI: 28.51%, 29.07% and 29.56%; 95% UI: 29.28%, 29.84%), while Bahrain witnessed a reduction over the period shown (-9.66%; 95% UI: -9.84%, -9.48% and-9.14%; 95% UI: -9.32%, -8.96%). There were significant country-specific differences in trends of HF burden from 1990 to 2019. Males had relatively higher rates than females in all age groups. Among all causes of HF in 2019, ischemic heart disease accounted for the highest age-standardised prevalence and YLDs, followed by hypertensive heart disease. Interpretation: The burden of HF in the EMR was higher than the global, with increasing age-standardised prevalence and YLDs in countries of the region. A more comprehensive approach is needed to prevent underlying causes and improve medical care to control the burden of HF in the region. Funding: None.

A Machine Learning Approach to Assess Differential Item Functioning of the KINDL Quality of Life Questionnaire Across Children with and Without ADHD.

This study aimed to investigate differential item functioning (DIF) of the child and parent reports of the KINDL measure across children with and without Attention-deficit/hyperactivity disorder (ADHD). The sample included 122 children with ADHD and 1086 healthy peers, alongside 127 and 1061 of their parents, respectively. The generalized partial credit model with lasso penalization, as a machine learning method, was used to assess DIF of the KINDL across the two groups. The findings showed that three out of 24 items of the child reports and seven out of 24 items of the parent reports of the KINDL exhibited DIF between children with and without ADHD. Accordingly, Iranian children with and without ADHD along with their parents perceive almost all items in the KINDL similarly. Hence, the observed difference in quality of life scores between children with and without ADHD is a real difference and not a reflection of measurement bias.

Hotspots for mutations in the SARS-CoV-2 spike glycoprotein: a correspondence analysis.

Spike glycoprotein (Sgp) is liable for binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the host receptors. Since Sgp is the main target for vaccine and drug designing, elucidating its mutation pattern could help in this regard. This study is aimed at investigating the correspondence of specific residues to the SgpSARS-CoV-2 functionality by explorative interpretation of sequence alignments. Centrality analysis of the Sgp dissects the importance of these residues in the interaction network of the RBD-ACE2 (receptor-binding domain) complex and furin cleavage site. Correspondence of RBD to threonine500 and asparagine501 and furin cleavage site to glutamine675, glutamine677, threonine678, and alanine684 was observed; all residues are exactly located at the interaction interfaces. The harmonious location of residues dictates the RBD binding property and the flexibility, hydrophobicity, and accessibility of the furin cleavage site. These species-specific residues can be assumed as real targets of evolution, while other substitutions tend to support them. Moreover, all these residues are parts of experimentally identified epitopes. Therefore, their substitution may affect vaccine efficacy. Higher rate of RBD maintenance than furin cleavage site was predicted. The accumulation of substitutions reinforces the probability of the multi-host circulation of the virus and emphasizes the enduring evolutionary events.

An Efficient and Effective Model to Handle Missing Data in Classification.

Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the "Bayesian additive regression trees" (BART) model is improved with the "Missingness Incorporated in Attributes" approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named "BART.m". As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps.