Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects.

BACKGROUND: The development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples. RESULTS: We develop a supervised model for time to event data (e.g., death, biochemical recurrence) that simultaneously accounts for redundant information within Omics profiles and leverages prior biological associations between them through a multi-block PLS framework. The interactions between data from different molecular compartments (e.g., epigenome, transcriptome, methylome, etc.) were captured by using cis-regulatory quantitative effects in the proposed model. The model, coined Cox-sMBPLS, exhibits superior prediction performance and improved feature selection based on both simulation studies and analysis of data from heart failure patients. CONCLUSION: The proposed supervised Cox-sMBPLS model can effectively incorporate prior biological information in the survival prediction system, leading to improved prediction performance and feature selection. It also enables the identification of multi-Omics modules of biomolecules that impact the patients' survival probability and also provides insights into potential relevant risk factors that merit further investigation.

Trend analysis and longitudinal clustering of tuberculosis mortality in Asian and North African countries: Results from the global burden of disease 2017 study.

Background: Tuberculosis (TB) is still a serious health problem with a remarkable global burden. In this study, we aimed to assess the trend of TB mortality in Asian and North African countries in the period 1990-2017 and provide a new classification according to TB mortality trend. Methods: TB mortality rates from 1990 to 2017 were extracted from the Global Burden of Disease website for 55 Asian and North African countries. Trend analysis of TB mortality rates for males, females, and the total population was performed using the marginal modeling approach. Moreover, the latent growth mixture modeling (LGMM) framework was applied to classify these 55 countries based on their trend of TB mortality rate. Results: In the period between 1990 and 2017, South Asia and High Income Asia-Pacific regions had the highest and lowest death rates due to TB, respectively. The marginal modeling results showed that the Asian and North African countries had experienced a downward trend with an intercept of 28.79 (95%CI: 19.64, 37.94) and a slope (mean annual reduction) of -0.67 (95%CI: -0.91, -0.43) per 100,000 the study period. Finally, the LGMM analysis classified these 55 countries into four distinct classes. Conclusion: In general, our findings revealed that although the countries in Asia and North Africa super region experienced a descending TB mortality trend in the past decades, the slope of this reduction is quite small. Also, our new classification may be better suited for combating TB through future healthcare planning in lieu of the commonly used geographic classifications.

Trend analysis of disability adjusted life years due to cardiovascular diseases: results from the global burden of disease study 2019.

BACKGROUND: Cardiovascular diseases (CVDs) are the number one cause of global mortality representing about one third of all deaths across the world. The objective of the present study was to model the global trend in disability-adjusted life years (DALY) and its components due to CVD over the past three decades. We also aimed to evaluate the longitudinal relationship between CVD DALY and Human Development Index (HDI) in this period of time. METHODS: The age-standardized rates of years lost due to disability (YLD), years of life lost (YLL) and DALY were extracted for cardiovascular diseases from the Global Burden of Disease (GBD) Study 2019 in years 1990 to 2019. Additionally, the United Nations Development Programme (UNDP) database was used to retrieve HDI values for all world countries at the same period time. The trend analysis was performed using the joinpoint regression model. RESULTS: The obtained revealed a significant downward trend for DALY and its components with the average annual percent change of - 1.0, - 0.3 and - 1.1 per 100,000 population, respectively for DALY, YLD and YLL. We also found that countries with high/very high levels of HDI have remarkably experienced steeper declining slope of trend than those in lower levels of HDI over the study period. CONCLUSIONS: Although the observed decreasing trend of CVD burden is a hopeful message for all world countries, the considerable gap in slope of trend between richer and poorer parts of the world is a serious alarm for health policy makers. Regarding this, there is an urgent need to put more efforts on implementing preventive programs, improving the level of patients' care and providing efficient treatment, especially in regions with lower levels of HDI.

Spatial statistical analysis of pre-existing mortalities of 20 diseases with COVID-19 mortalities in the continental United States.

BACKGROUND: Although the United States is among the countries with the highest mortalities of COVID-19, inadequate geospatial studies have analyzed the disease mortalities across the nation. METHODS: In this county-level study, we investigated age-adjusted co-mortalities of 20 diseases, including cardiovascular, cancer, drug and alcohol disorder, respiratory and infectious diseases with COVID-19 over the first ten months of epidemic. One-way analysis of variance was applied to the Local Moran's I classes (High-High and Low-Low clusters, and non-significant counties of COVID-19) to examine whether the mean mortality measures of covariates that fall into the classes are significantly different. Moreover, a mixed-effects multinomial logistic regression model was employed to estimate the effects of mortalities on COVID-19 classes. RESULTS: Results showed that the distribution of COVID-19 case fatality ratio (CFR) and mortality rate co-occurrence of High-High clusters were mainly concentrated in Louisiana, Connecticut, and New Jersey. Also, positive associations were observed between High-High cluster of COVID-19 CFR and Asthma (OR = 4.584, 95 % Confidence Interval (CI): 2.583-8.137), Hepatitis (OR = 5.602, CI: 1.265-24.814) and Leukemia (OR = 2.172, CI: 1.518-3.106) mortality rates compared to the non-significant counties, respectively. CONCLUSIONS: Our results indicated that counties with higher mortality of some cancers and respiratory diseases are more vulnerable to fall into clusters of HH COVID-19 CFR. Future vaccine allocation and more medical professionals and treatment equipment should be a priority to those High-High clusters.

County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States.

As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify "vulnerable" clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25-Jun3, 2020), followed by similar data for 1344 counties (in the "sunbelt" region of the country) during the 2nd wave (Jun4-Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3-Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies "more vulnerable" clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3-2.1-3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08-0.52% MIR↑). We identified "more vulnerable" county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.

Transcriptome-wide analysis associates ID2 expression with combined pre- and post-capillary pulmonary hypertension.

Heart failure with preserved ejection fraction (HFpEF) patients who develop pulmonary hypertension (PH) have an increased risk of death, with combined pre- and post-capillary PH (CpcPH) having the highest risk. However, the mechanism behind PH development in HFpEF is poorly understood. We aimed to identify transcriptomic associations with PH development in HFpEF. Blood was collected from 30 HFpEF patients: 10 without PH, 10 with isolated post-capillary PH, and 10 with CpcPH. Gene expression measurements were completed using transcriptome-wide RNA sequencing. Gene expression differences were compared using a quasi-likelihood method adjusting for age, sex, race, and smoking-status. Biological pathways were compared using global gene expression differences. A replication in 34 additional heart failure patients and a validation in lung tissue from a representative mouse model were completed using quantitative PCR. Six differentially expressed genes were identified when comparing transcriptomics between subjects with CpcPH and those without PH. When tested in additional subjects, only the association with ID2 replicated. Consistent with clinical findings, Id2 expression was also upregulated in mice with HFpEF and PH. Pathway analysis identified proliferative and mitochondrial pathways associated with CpcPH. Thus, these patients may possess systemic pathophysiological differences similar to those observed in pulmonary arterial hypertension patients.

A joint overdispersed marginalized random-effects model for analyzing two or more longitudinal ordinal responses.

Disease severity is a latent concept which should be observed using a measurement tool; it can be useful in assessing disease status both cross-sectionally and longitudinally. Medsger scale is a valid instrument for assessing the systemic sclerosis severity in which the items are categorized from 0 (normal) to 4 (endstage) for each organ system. We simultaneously analyzed two of the Medsger scale items, namely, general system and skin system as two correlated ordinal responses using an overdispersed marginalized random-effects model for longitudinal ordinal data exhibiting an overdispersion pattern. In general, a random-effects approach is implemented to account for the correlation between these two stochastic processes and to make simultaneous inference; our model also accounts for temporal correlations amongst observations taken on the same subject. Another important aspect of our model is its capacity to handle data overdispersion in order to make reliable inference. Last but not least, it is proved that certain parameters in our joint model have marginal interpretations. We investigate the statistical properties of our estimators through extensive simulation study. Finally, the methodology is applied to a data of systemic sclerosis patients.

Growth velocity of children and its affective factors in northwestern Iran: A longitudinal study using marginal models.

Background: Child growth is an important health indicator in human's lifespan. Evaluating growth variations in infants and children is highly important. This study was conducted to evaluate the risk factors of child growth retardation and determine childhood growth velocity in Zanjan province, Iran. Methods: This longitudinal study was conducted on 7892 children aged 0 to 2 years in Zanjan province in 2013. The sampling method was multi-stage cluster sampling. To determine the growth velocity, 2- pointed model was used. We applied the marginal models of generalized equation estimators (GEE). The related curves were obtained using LOWESS method, and data were analyzed using R Version 2.14.1. Results: In this study, 3879 (49%) of the participants were female, and the mean age of mothers was 27.1±5.4 years. Growth velocity had a significant association with gender, mother's residency, multiple pregnancies, gestational age, and exclusive breastfeeding (p<0.001). A significant association was observed between gender and weight growth velocity (p<0.001). Moreover, exclusive breastfeeding had a significant association with weight growth velocity (p<0.001). Conclusion: Growth velocity in girls was slightly higher than in boys, however, no considerable difference was found between height growth velocity of girls and boys. The findings of this study also indicated that each month increase in exclusive breastfeeding leads to less growth velocity in children. So, exclusive breastfeeding is strongly recommended to prevent childhood obesity.

Empirical Bayesian Geographical Mapping of Occupational Accidents among Iranian Workers.

BACKGROUND: Work-related accidents are believed to be a serious preventable cause of mortality and disability worldwide. This study aimed to provide Bayesian geographical maps of occupational injury rates among workers insured by the Iranian Social Security Organization. METHODS: The participants included all insured workers in the Iranian Social Security Organization database in 2012. One of the applications of the Bayesian approach called the Poisson-Gamma model was applied to estimate the relative risk of occupational accidents. Data analysis and mapping were performed using R 3.0.3, Open-Bugs 3.2.3 rev 1012 and ArcMap9.3. RESULTS: The majority of all 21,484 investigated occupational injury victims were male (98.3%) including 16,443 (76.5%) single workers aged 20 - 29 years. The accidents were more frequent in basic metal, electric, and non-electric machining jobs. About 0.4% (96) of work-related accidents led to death, 2.2% (457) led to disability (partial and total), 4.6% (980) led to fixed compensation, and 92.8% (19,951) of the injured victims recovered completely. The geographical maps of estimated relative risk of occupational accidents were also provided. The results showed that the highest estimations pertained to provinces which were mostly located along mountain chains, some of which are categorized as deprived provinces in Iran. CONCLUSIONS: The study revealed the need for further investigation of the role of economic and climatic factors in high risk areas. The application of geographical mapping together with statistical approaches can provide more accurate tools for policy makers to make better decisions in order to prevent and reduce the risks and adverse outcomes of work-related accidents.

Assessment of length of stay in a general surgical unit using a zero-inflated generalized Poisson regression.

Background: The effective use of limited health care resources is of prime importance. Assessing the length of stay (LOS) is especially important in organizing hospital services and health system. This study was conducted to identify predictors of LOS among patients who were admitted to a general surgical unit. Methods: In this cross-sectional study, the sample included all patients who were admitted to the general surgical unit of Shariati hospital in 2013 (n= 334). To determine the factors affecting LOS, Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated generalized Poisson (ZIGP) regression models were fitted using R software, and then the best model was selected. Results: Among all 334 patients, the mean (±SD) age of the patients was 45.2 (±16.47) years and 220 (65.9%) of them were male. The results revealed that based on ZIGP model, type of surgery (appendicitis, abdomen and its contents, hemorrhoids, lung, and skin), type of insurance, comorbid diseases (hypertension, heart disease, and hyperlipidemia), place of residence (local and non-local), age, and number of tests had significant effects on the LOS of GS patients. Conclusion: According to the Akaike information criterion (AIC) in each fitted model, it was found that ZIGP regression model is more appropriate than ZIP and ZINB regression models in assessing LOS in GS patients, especially due to the presence of excess zeros and overdispersion in count data.

Length and weight growth trends for children less than two years old in Zanjan, Iran: Longitudinal modeling.

BACKGROUND: Growth failure in children less than five years old can lead to the serious complications such as increased mortality, learning difficulties or physical disability. The aim of this study was to investigate the nonorganic factors affecting the growth trend in less than two years children living in Zanjan, Iran. METHODS: This longitudinal study was conducted on a sample of 3566 children less than two years old in Zanjan. Weight and length growth trends were recorded as ordinal variables and analyzed by longitudinal marginal model. RESULTS: About 12% (n=289) and 8% (n=212) of children had at least one decline/stagnation in the weight and length growth curve, respectively. Based on the marginal model, the effect of the child's age and residence area on the weight and length growth trends were statistically significant (p<0.05). CONCLUSION: Given the relatively high prevalence of growth failure among studied children less than two years old in rural areas of Zanjan, raising the awareness of parents in rural areas about feeding and nutritional behaviors of children seems an important issue. Additionally, healthcare providers should mostly focus on monitoring the growth of children older than 12 months.

Work-related accidents among the Iranian population: a time series analysis, 2000-2011.

BACKGROUND: Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. OBJECTIVES: To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. METHODS: In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents. RESULTS: There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). CONCLUSIONS: The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

BACKGROUND: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. METHODS: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. RESULTS: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. CONCLUSION: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Application of Rasch model for evaluating the quality of life in blind war veterans.

UNLABELLED: Background Quality of life evaluates the general well-being of individuals and it can be considered as one of the important aspects in programming and giving service to disabled people. Blindness is one of the most important kinds of physical disability that has a direct effect on quality of life, so this study aimed to explore how war blindness influences the quality of life. METHODS: In this cross-sectional study, data from 71 blind war (Iran-Iraq) veterans in 2010 were collected using the Short Form Health Survey instrument (SF36). Rasch model was fitted by running WINSTEPS software and then item parameter (ß), i.e. difficulty of items, and person parameter ([Formula: see text] ), i.e. the ability or attainment level of respondents, were estimated. RESULTS: In a total of 71 cases, 69 cases (97 %) were male with a mean (±SD ) age and blindness duration range of 48.97 (±10.655) yrs and 25.74 (±3.692) yrs, respectively. Item difficulty ranged from 2.962 to 4.441. Comparison of the SF36 scores and Rasch measurements showed that standard error of Rasch model estimates in physical and total scores are less than SF36 scores. CONCLUSION: Due to the advantages and higher accuracy of the Rasch model, using this model can be a good alternative for the traditional models. With due regard to the relative low quality of life of blind war veterans in this study and other similar study, further investigation are recommended to be carried out to this group of society.