Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.  •SIMON is an open-source software for analysis of high-dimensional biomedical data•SIMON facilitates application of 180+ machine learning algorithms•Easy-to-use graphical use interface and no programming expertise required•SIMON empowers biomedical researchers to identify patterns in biomedical data Over the past years, technological advances have enabled the generation of large amounts of data at multiple scales. The integration of high-dimensional data is particularly important in biomedical sciences, as they can be used to identify biological mechanisms and predict clinical outcomes well in advance of their occurrence. Because of the lack of powerful analytical tools that can be used by the average biomedical researcher, translation of such knowledge has been extremely slow. We have developed an open-source software, SIMON, to facilitate the application of machine learning to high-dimensional biomedical data. In SIMON, analysis is performed using an intuitive graphical user interface and standardized, automated machine learning approach allowing non-technical researchers to identify patterns and extract knowledge from high-dimensional data and build high-quality predictive models. Tomic et al. developed SIMON, an open-source software for application of machine learning algorithms to high-dimensional biomedical data ranging from the transcriptome to flow cytometry to the microbiome. Using a graphical user interface, standardized pipelines for predictive modeling, and automated machine learning, SIMON empowers non-technical biomedical researchers to identify patterns in their data and build high-quality predictive models. Many of the packages offer different modeling functions and have different syntaxes for model training, predictions, and determination of variable importance. Due to the lack of a unified method for proper application of ML processes, even experienced bioinformaticians struggle with these time-consuming ML tasks. To provide a uniform interface and standardize the process of building predictive models, ML libraries were developed, for example, mlr3https://mlr3.mlr-org.com), classification and regression training (caret),https://rdrr.io/cran/caret), scikit-learnhttps://scikit-learn.org), mlPyhttps://mlpy.fbk.eu), and SciPy (https://www.scipy.org/), including also ones for deep learning, such as TensorFlow (https://www.tensorflow.org/), PyTorch (https://pytorch.org/), and Keras (https://keras.io/). Since those libraries do not have a graphical user interface, usage requires extensive programming experience and general knowledge of R or Python, making them inaccessible for many life science researchers. Therefore, there is an increased effort to harmonize those libraries and develop a software that will facilitate application of ML in life sciences.The biggest challenge is the proper application of ML methods and the translation of the results into meaningful insights. The analysis of massive datasets and extraction of knowledge using ML require knowledge of many different computational libraries for data pre-processing and cleaning, data partitioning, model building and tuning, evaluation of the performance of the model, and minimizing overfitting.The software should provide a standardized ML method for data pre-processing, data partitioning, building predictive models, evaluation of model performance, and selection of features. Moreover, such software should be adapted for biological datasets that have a high percentage of missing values,https://cran.r-project.org/web/packages/mulset/index.html), a method for identifying differentially expressed genes using significance analysis in microarrays,https://www.docker.com/), ensuring code reproducibility and solving installation compatibility issues across major operating systems . In both versions parallel computing is supported, which is essential for more efficient ML analysis by distributing the workload across several processors. To promote collaboration and data sharing and support distributed cloud processing, SIMON is also available as a server version. The server version can be installed on a private or public Linux cloud service. Distributed cloud processing (multiNode) is implemented utilizing OpenStack, a free and open-source cloud computing platform (https://www.openstack.org/). The advantage of the server version is that it has multiNode capability, which allows users to distribute workload on multiple computers simultaneously to optimize SIMON performance. The multiNode process can be used to horizontally scale analysis to large infrastructures, such as high-performance computing clusters to meet the computational needs and accommodate parallel processing of large amounts of data. In addition, in the server version, users can configure data storage either on a local server or in a cloud-using service that is interoperable with the Amazon Web Services S3 application programming interface.http://www.genular.org/.To address these challenges, we developed SIMON , a free and open-source software for application of ML in life sciences that facilitates production of high-performing ML models and allows researchers to focus on the knowledge discovery process. SIMON provides a user-friendly, uniform interface for building and evaluating predictive models using a variety of ML algorithms. Currently, there are 182 different ML algorithms available . The entSalmonella Typhi infection (VAST),,We demonstrate the accuracy, ease of use, and power of SIMON on five different biomedical datasets and build predictive models for arboviral infection severity (SISA),To demonstrate SIMON's capabilities for analyzing biomedical datasets with missing data, we applied SIMON to (1) the Cyclists dataset studying the impact of physical activity on the immune system in adulthood based on immunophenotyping using flow cytometryWe also applied SIMON to (1) a dataset with a large number of features measured using whole-metagenome shotgun sequencing of fecal samples   . In compT cells) F and S1.T cells) . Naive aT cells) G, as expT cells) , while rT cells) , indicatT cells) . MoreoveT cells) . For thestomatis . The feaWe have developed SIMON, a powerful software platform for data mining that facilitates pattern recognition and knowledge extraction from high-quality, heterogeneous biological and clinical data, especially where there are missing data, an unbalanced distribution, and/or high dimensionality. It can be used for the identification of genetic, microbial, and immunological correlates of protection and it can help in guiding the further analysis of the biomedical data.https://cloud.google.com/automl), DataRobot (https://www.datarobot.com/), BigML (https://bigml.com/), MLjar (https://mljar.com), and RapidMiner (https://rapidminer.com/), they come at a high price and have hidden ML methods and algorithms, and thus have not been adopted by the biomedical community. In academia, open-source ML software is being developed, for example, Waikato Environment for Knowledge Analysis (WEKA)https://www.cs.waikato.ac.nz/∼ml/weka/), Orangehttps://orange.biolab.si/), the Konstanz Information Miner (KNIME)https://www.knime.com/), and ELKIhttps://elki-project.github.io/). For the development of SIMON, our aim was to integrate the capabilities of commercial software openly and freely for everyone. The currently available open-source software offers only a limited number of the most commonly used ML algorithms using a user-friendly graphical interface, while focusing on the manual configuration to achieve optimal model predictive performance. Therefore, usage of these softwares requires extensive knowledge of the ML process, so the primary users are data scientists, statisticians, and ML experts. In contrast, commercial software versions are implementing an automated ML (autoML) process that rapidly builds high-performance models by identifying the optimal ML method, including the selection of an appropriate algorithm, optimization of model hyperparameters, and evaluation of the best-performing models.,Over the past years, technological advances have enabled the generation of large amounts of data at multiple scales. Monitoring and analyzing these complex datasets is particularly important in the biomedical sciences, as they serve to advance knowledge about health and disease, as well as predicting clinical outcomes in advance of their occurrence. Despite major clinical and economic consequences of these approaches, due to the lack of powerful analytical tools that can be used by the average biomedical researcher, the translation of such knowledge can be extremely slow. Although several commercial softwares are available, for instance, Google's cloud-based AutoML .The lead contact for this article is Adriana Tomic ,http://doi.org/10.5281/zenodo.4121831),http://doi.org/10.5281/zenodo.4115626),http://doi.org/10.5281/zenodo.4121516),http://doi.org/10.5281/zenodo.4121594).The source code of SIMON is available at https://github.com/genular/simon-frontend) or a pre-built version can be installed from DockerHub (https://www.docker.com/). Users need to install Docker (version 17.05 or later required) following instructions available on the Docker website (https://docs.docker.com/). Installation instructions for Windows , MacOS , and Linux  are provided. After Docker installation, users must download and run a SIMON image from DockerHub. To do that users must run Terminal on Linux and MacOS or Windows Power Shell if using Windows OS and type following command:SIMON can be installed directly from the GitHub (docker run --rm --detach --name genular --tty --interactive --env IS_DOCKER='true' --env TZ=Europe/London --volume genular_data:/mnt/usrdata --publish 3010:3010 --publish 3011:3011 --publish 3012:3012 --publish 3013:3013 genular/simon:latesthttps://www.mozilla.org/firefox/) and type http://localhost:3010. Create an administrator account. SIMON will run until you shut down/restart your computer or stop it manually using the following command: docker stop genular. Advance instructions for installing a server version are provided on our GitHub page.Variable ‘TZ=’ stands for time zone and can be replaced with appropriate time zone. Once the command is executed, SIMON will be downloaded and started. To access SIMON, open a web browser (Firefox recommended, available at


Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability and the clinical utility of genetic discoveries to other ethnically diverse populations are unknown. The Indian population has been under-represented in PD research. The Genetic Architecture of PD in India (GAP-India) project aims to develop one of the largest clinical/genomic bio-bank for PD in India. Specifically, GAP-India project aims to: (1) develop a pan-Indian deeply phenotyped clinical repository of Indian PD patients; (2) perform whole-genome sequencing in 500 PD samples to catalog Indian genetic variability and to develop an Indian PD map for the scientific community; (3) perform a genome-wide association study to identify novel loci for PD and (4) develop a user-friendly web-portal to disseminate results for the scientific community. Our “hub-spoke” model follows an integrative approach to develop a pan-Indian outreach to develop a comprehensive cohort for PD research in India. The alignment of standard operating procedures for recruiting patients and collecting biospecimens with international standards ensures harmonization of data/bio-specimen collection at the beginning and also ensures stringent quality control parameters for sample processing. Data sharing and protection policies follow the guidelines established by local and national authorities.We are currently in the recruitment phase targeting recruitment of 10,200 PD patients and 10,200 healthy volunteers by the end of 2020. GAP-India project after its completion will fill a critical gap that exists in PD research and will contribute a comprehensive genetic catalog of the Indian PD population to identify novel targets for PD. Parkinson's disease (PD) is the second most common neurodegenerative disorder in adults over the age of 60 years . AccordiLRRK2), glucocerebrosidase (GBA), and alpha-synuclein (SNCA) genes identified in the western population have been shown to pose negligible risk to the Indian patients  project to provide for the first time, a large-scale genetic catalog of the Indian PD population. This paper describes the design of the GAP-India project including the study sites, subject recruitment, clinical assessments, biospecimens processing, plan for data analysis and sharing, capacity building, and the ethical and regulatory frameworks within which we operate.GAP-Indiastudy aims to understand the genetic architecture of PD in the Indian population through large scale sample collection and federated data analysis models. The study aims to collect pan-Indian genetic and phenotypic data and will develop one of the largest clinico-genomic PD resources for the scientific community from India. To achieve our objectives, we have formed a trilateral consortium, the Luxembourg-German-Indian Alliance on Neurodegenerative diseases and Therapeutics (Lux-GIANT) . The “knThe GAP-India study revolves around a network of clinical sites in India organized in a “hub and spoke” manner. Patient recruitment at each nodal or sub-center will be supervised by a neurologist with specific expertise in Movement Disorders. Subjects will be enrolled at all the sub-centers and the four nodal centers . The nodal centers are all high-volume academic centers with established movement disorders programs. The sub-centers include additional public sector teaching hospitals, larger multispecialty hospitals, and neurology clinics in the private sector.Genetic evidence indicates that most Indians descended from a mixture of two divergent populations: Ancestral North Indians  and Ancestral South Indians (not closely related to other genetic groups) and almost all the current inhabitants are admixtures of these two broad groups to varying extents . Within Geographical locations of the enrolling sites in India were chosen to consider this unique population structure and to enable a pan-Indian representation . The stuSubjects will be recruited from the Movement Disorder clinics or Neurology clinics run by the PIs of nodal and sub-centers. A detailed history and systemic and general neurological examination will be performed in all subjects. Research staff at all recruiting centers will be trained in the standard operating procedures including clinical assessments and familiarized with online data entry systems before site initiation. Subjects who meet all the following inclusion criteria will be recruited in the patient group: (1) clinical diagnosis of PD as per United Kingdom Parkinson's Disease Society Brain Bank (UKPDSBB) diagnostic criteria , (2) ageTrained personnel at each clinical site will collect clinical and demographic data. The demographic data collected includes information on the geographical origin within India. Structured questions will capture information related to environmental exposures known to be associated with PD including pesticides, fungicides, insecticides, and other chemicals, smoking, caffeine, and head injury. Patients will be asked to report if they ever held a job requiring exposure to pesticides, herbicides, fungicides, insecticides, rodenticides, and fumigants and whether they were exposed to these chemicals at their place of residence through self-use or via another person. Lifetime smoking history of 100 or more cigarettes will be documented. History of head injury or concussion including falls, sporting activities, violence, and car or other accidents in childhood or adulthood will be queried. Patients will also be asked about exposure to caffeinated coffee in quantities more than once per week for 6 months or longer. Years of education will be documented. Furthermore, our environmental exposure data collection will align with the environmental questionnaire from the Genetic-Epidemiology of Parkinson disease (GEoPD) consortium to harmonize the dataset across the ongoing studies. A structured history and clinical examination will be conducted to collect data regarding onset symptoms, motor fluctuations, and dyskinesias, medications, and non-motor symptoms. Non-motor symptoms included in the interview are cognitive impairment, psychosis, depression, sweating abnormalities, seborrhea, sleep disorders including REM behavioral disorder, restless legs, hyposmia, orthostatic hypotension, constipation, dysphagia, and urinary/fecal incontinence. Data from Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), Dopamine Active Transporter- Single Photon Emission Computed Tomography (DaT SPECT) will be collected if available. The family history will be probed for consanguinity and to identify any known relations with PD, dementia, tremor, or other neurological disorders. For patients who have undergone functional neurosurgery, the target, time since surgery and other details will be collected. The motor symptoms at the time of recruitment will be assessed by the Unified Parkinson's Disease Rating Scale (UPDRS Parts I- IV) . Subjectsalting-out method. The quantity and quality of DNA will be analyzed using a micro-volume UV/visible spectrophotometer . Additionally, the quality will also be checked by agarose gel electrophoresis. Those samples with an A260/280 ratio of 1.8–2.0 and A260/230 ratio of >2.0 will be stored in 1.2 ml screwcap cryovials barcoded with a unique sample ID at SCTIMST Biobank (−80°C), in aliquots. The samples received from the nodal centers and sub-centers will be again checked (using Nanodrop and agarose gel) at SCTIMST to ensure the quality and quantity of DNA that is required for the genotyping. For sequencing, 50 μl of 50–100 ng/μl DNA will be transported in 96-well microtiter plates sealed with peelable heat seals in a waterproof container and dry ice. At the time of collection, specimens will be de-identified by avoiding any personal identifiers on the label. Specimen labels and data collection instruments will be labeled by center-specific serial numbering. No direct personal identifiers will be stored in the online data capture system and quasi-identifiers like date of birth are flagged as such and de-identified by the system before export. Only the site PIs hold identifying information if required for re-identification at a later stage. The central clinical node and other investigators with access to the online database will not have access to direct personal identifiers. Biospecimens collected at the sub-centers will be transported to the nodal centers for DNA isolation. All biospecimens are finally routed to the clinical core at SCTIMST, Trivandrum for storage and in consenting subjects, longer-term bio-banking. In keeping with the existing regulatory framework in India, to promote capacity development, array processing and genotyping will be done at the Center for Cellular and Molecular Biology, Hyderabad. The genetic core at Tübingen will perform the bioinformatics analysis and a subset of specimens will be processed at the Lux-GIANT genotyping core facility in Munich for quality control purposes. All clinical and genetic data will be stored on a shared electronic platform with access restrictions and security protocols in place. Functional validation of putative pathogenic variants including patient-specific induced pluripotent stem cell modeling will be done at the National Brain Research Center, Gurgaon, India. In this way, the study is designed to comprehensively capture clinical and genetic information from a large Indian cohort in a manner that enables integration with existing international cohorts. DNA isolation from whole blood will be done at the four nodal centers and centralized quality control monitoring at SCTIMST, Trivandrum. DNA specimens from consenting subjects will be maintained in a biorepository at SCTIMST for potential future research.The whole blood samples will be collected at each recruiting center 10–15 ml of blood samples collected in EDTA tubes will be processed for DNA extraction using the For genetic analysis, a two-stage design will be followed. Currently, the arrays available for genotyping lack in-depth genetic variability information from the Indian population. GAP-India aims to address this issue by performing whole-genome sequencing (WGS) of around 500 subjects covering the north, south, east, and west of India.https://github.com/imgag/megSAP) developed at the Institute of Medical Genetics and Applied Genomics, University Hospital of Tübingen . In brief, SeqPurge (v. 2020_03) will be used for adapter and quality trimming (https://github.com/imgag/ngs-bits) will be used for data cleaning.The data generated from whole-genome sequencing will be analyzed using the megSAP pipeline  containing neurodegenerative specific content.http://pngu.mgh.harvard.edu/_purcell/plink) and R software packages (http://www.r-project.org/), installed on a Linux based computation resource  coefficient>0.1 . PrincipImputation will be carried out using the Haplotype reference consortium (HRC) as a reference panel consisting of individuals from more than 26 worldwide populations . The SNP2-statistics. Lastly, Manhattan and QQ plots will be constructed to visualize the results. All the summary statistics will be made available publicly. Heterogeneity in allelic effect sizes between different Indian sub-populations contributing to the meta-analysis will be assessed using Cochran's Q statistic.Post-QC and imputation, association analysis will be conducted for each sub-population using binary logistic regression analysis assuming an additive genetic model adjusting for age, sex, and relevant principal components. A conventional genome-wide significance threshold of 5 × 10–8 will be used to identify the significant SNPs. The fixed meta-analyses inverse-variance weighting of log-ORs will be implemented in METAL to combine summary statistics across all the Indian sub-populations . Genome We will further use genome-wide complex trait analysis (GCTA) to perform conditional and joint analysis to identify the top variants that account for heritable variation among different loci . PolygenWe will further integrate our GWAS summary statistics with expression and network data using Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) to perform the tissue specificity and pathway enrichment analysis .The genomics/bioinformatics core in Tubingen will be responsible for data integration and analysis.https://www.project-redcap.org) widely used in various clinical and translational projects and is hosted at the “clinical core” site, SCTIMST. The Lux-GIANT REDCap instance is aligned with the Genetic Epidemiology of Parkinson disease (GEoPD) consortium minimal dataset Case Report Forms (CRFs). This will ensure uniform clinical data collection across various participating countries in GEoPD that are spread across five continents. This will facilitate cross-study data pooling and analysis for future multinational projects. All Indian nodal centers and sub-centers taking part in this study are collecting pseudonymized data into this secure and access-controlled instance centrally. All the identifiable information of each study participant stored separately at each site in a corresponding hospital system and only authorized clinical people from that site to have access to it. This setup is aligned with European general data protection regulations (GDPR) as well as Personal Data Protection Bill (PDP) 2019. This pseudonymized clinical data, as well as the corresponding molecular data, will be served to all Lux-GAINT partners via the “data core” site established at Tubingen by leveraging the infrastructure established by the German Network for Bioinformatics and Infrastructure . Through the dashboard of the de.NBI Cloud Site Tubingen the allocation of the desired resources , amount of storage and RAM) will be covered. Furthermore, to provide secure access to the VMs to researchers, so-called security groups will be created, which can be seen as a VM specific firewall to control incoming and outgoing network traffic connections. To provide an additional layer of security to Lux-GIANT genomics data, a private network will be added to the Lux-GIANT cloud project to protect the network traffic from and to the volume where the genomics data reside. In addition to the network traffic protection, a certificate-based approach will be used to grant specific permissions  on a per-user base. The certificates will be distributed to the particular person using a state-of-the-art asymmetric encryption technique. The whole process starting with the project application is illustrated in https://datacatalog.elixir-luxembourg.org) that facilitates the Findability of the data. Both clinical and associated molecular data will be curated, harmonized, and integrated into a discovery analytics system—Ada (https://ada-discovery.github.io). It will be hosted in the de.NBI cloud Tubingen and will facilitate the data exploration and analysis through intuitive web interface rich with dynamic visual analytics and advanced machine learning (Deep Learning).To ensure seamless data exchange among Lux-GIANT partners, we have established a secure data management and analysis platform. At this moment this platform is equipped with REDCap, a state-of-the-art EDC (Electronic Data Capturing) system  controlled access, and (ii) open access.The controlled access via the Lux-GIANT portal hosted on the de.NBI Cloud will be given to researchers/institutes who will comply with the data protection and ethical regulations, as described in the GDPR, and PDP 2019. The open-access data which does not require prior ethical clearance will be made available to the scientific community either via the Lux-GIANT Portal or PDgene database.GAP-India project aims to address two main issues: (1) To generate the most comprehensive PD genome-phenome catalog, including iPSC biobank of the Indian PD population, and (2) to develop scientific and infrastructure capacities in India which have so far lagged in PD genomics research.One of the major reasons that hinder the collaboration in the genomic era between various research consortia which are primarily led by institutions either in the USA and/or Europe and under-represented population such as India were concerns that data generated from the under-represented population will not be properly represented by the local stakeholders. GAP-India aims to dispel this notion of “scientific imperialism” by developing the “knowledge-sharing” model and also establishing the guidelines which adequately protect the interests of local investigators as well.The data generated from the GAP-India project follows the strict ethical guidelines, as stipulated by the Indian Medical Council of Research (ICMR)- HMSC for international collaborative research and follows the provisions of ICMR guidelines for biomedical research in India . All theThe GAP-India project involves multi-centers across India. There exists a considerable disparity in access to and protocols for regular health care among patients. Therefore, various ethical considerations have arisen during the development stage of the GAP-India project. Specifically, the following issues have been considered. (1) return of genetic results generated from the study, and how they will be received; (2) providing information about genetic findings to patients and care-providers; (3) concerns about stigmatization; and (4) ensuring equity and fairness in collaboration.As per the Indian guidelines, we are mandated to return actionable results, with the potential to improve the health outcome of the participants. For this, a re-identification process will be followed through the PI of the recruiting center. Genetic counseling and guidance will be offered in case of such a return of results. Incidental findings that are not actionable will not be returned .One of the major spin-offs from this study will create a core network of clinicians and researchers dedicated to PD genetics in India. A long-term biorepository and capacity building in terms of infrastructure and skill upgradation are additional advantages. Taken together, GAP-India aims to develop a dedicated pool of researchers and health care professionals to raise PD awareness in India.The GAP-India study and the LUX-GIANT network aim to address a critical gap in knowledge regarding the genetic origins of PD, by leveraging the population diversity afforded by as a yet unaddressed population. We expect to generate novel data that may drive targeted therapies and make them applicable on a global scale.The studies involving human participants were reviewed and approved by Institutional Ethics Committees of all participating clinical centers. The patients/participants provided their written informed consent to participate in this study.RR conceived, designed, organized and wrote the first draft, reviewed, and critically revised the manuscript. KD, RMK, and RY conceived the project, reviewed, and critically revised the manuscript. VS, UM, PA, NK, TF, HK, AS, KS, SM, SD, SK, LP, MB, PW, SR, MH, GW, and SP reviewed and critically revised the manuscript. FB, MH, JK, AK-S, SG, PL, MSt, and JR reviewed, and critically revised the manuscript. VB, GC, JS, PS, TG, OR, and VG reviewed and critically revised the manuscript. PP, RK, and RB organized the research project, and reviewed and critically revised the manuscript. KB, NC, RC, CT, MD, CG, HM, NK, SK, PM, CS, AKS, and DW reviewed and critically revised the manuscript. AK and MSh conceived, organized, and executed the research project; designed, executed, reviewed, and wrote the first draft; and reviewed and critically revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Overweight and obesity are defined as an unnecessary accumulation of fat, which poses a risk to health. It is a well-identified risk factor for increased mortality due to heightened rates of heart disease, certain cancers, musculoskeletal disorders, and bacterial, protozoan and viral infections. The increasing prevalence of obesity is of concern, as conventional pathogenesis may indeed be increased in obese hosts rather than healthy hosts, especially during this COVID-19 pandemic. COVID-19 is a new disease and we do not have the luxury of cumulative data. Obesity activates the development of gene induced hypoxia and adipogenesis in obese animals. Several factors can influence obesity, for example, stress can increase the body weight by allowing people to consume high amounts of food with a higher propensity to consume palatable food. Obesity is a risk factor for the development of immune-mediated and some inflammatory-mediated diseases, including atherosclerosis and psoriasis, leading to a dampened immune response to infectious agents, leading to weaker post-infection impacts. Moreover, the obese host creates a special microenvironment for disease pathogenesis, marked by persistent low-grade inflammation. Therefore, it is advisable to sustain healthy eating habits by increasing the consumption of various plant-based and low-fat foods to protect our bodies and decrease the risk of infectious diseases, especially COVID-19. The World Health Organization (WHO) reports that over 50% of the world’s existing population is obese and overweight, with a growing annual incidence . An estiThis systematic review was carried out on the basis of the Statement of Preferred Reporting Items for Systemic Review and Meta-Analysis (PRISMA).In this review article, a comprehensive search was conducted on May 2020 using the following databases: Research gate, PubMed, Web of Science, and Google scholar along with a manual hand for studies published from 2002 to 2020 using keywords as described in Data from each study were obtained in a standardized form, updating citations from the study, baseline features of the subjects included, appropriate intervention, and findings from the study. Study citations included the first author’s name, study title, and year of publication. Meanwhile, each study’s characteristics related to study design characteristics of the patients and location of the study. We specified the extracted comorbidities of cardiovascular disease, hypertension, and dyslipidemia by other systematic reviews and meta-analysis according to the recognized co-morbidities for obesity and COVID-19 ,16. The abstracts of the articles acquired during the initial search were read by four independent reviewers. All reviewers approved relevant articles and full-text versions of the articles were revised to identify studies that examine diet, the risk of overweight/obesity, and factors associated with increased morbidity and mortality of obese and overweight COVID-19 patients. Data were synthesized with consistent findings, based on at least three different and high-quality studies.It is worth noting that obese patients have consistently higher leptin (pro-inflammatory adipokine) levels and lower adiponectin (anti-inflammatory adipokine) levels. This undesirable hormone environment also induces immune response dysregulation and may lead to the pathogenesis of obesity-related complications . In basaObesity-associated gene expression has been modified more in the adenovirus 36 (Ad36)-infected group than in the control one . Kim et Oxygen, which limits adipocyte hypertrophy, is not enough in swollen adipocytes . ConsequThe coronavirus disease (COVID-19) pandemic is triggered by the influenza-like strain of the virus (SARS-CoV-2)  that cauSARS-CoV-2 enters the human host through binding to angiotensin-converting enzyme 2 (ACE2) found in the lower respiratory tract of humans, the same receptor as with SARS-CoV . VirusesRegrettably, obesity is an unfavorable factor for COVID-19 patients. In this scenario, obesity may lead to more critical signs and complications. The intubation of obese patients is extremely challenging. Diagnostic imaging can be harder to obtain. It is more difficult for the nursing staff to move or place obese individuals and hence the need for special beds and equipment . Currentp = 0.021) times higher risk of in-hospital mechanical ventilation compared to COVID-19 normoweight using a multivariate analysis. This is associated with higher mortality rates in the obese population infected with COVID-19. In addition, Lighter et al. [p < 0.0001) and 2.2  times, compared with norm-and overweight groups, respectively. The hospitalization rates increased 2.0  and 2.2  times in the younger group of patients with obesity grades I and II, respectively compared with normo-and overweight groups. Additionally, younger group patients with obesity grades I and II were more likely to receive critical care 1.8  and 3.6  times, respectively, compared to normo-and overweight groups. Although Wu et al. [p = 0.003) times in COVID-19 patients with BMI above 25 kg/m2 to develop extreme COVID-19 relative to normo-and overweight patients, the multivariate analysis has attenuated this danger.Simonnet et al.  found thr et al.  classifiu et al.  recorded2) as a significant clinical risk factor for worse prognosis and overall death in COVID-19 patients [2) have been closely related to poor prognosis in COVID-19 patients [Early high-risk indications for developing serious SARS-CoV-2 symptoms involve hypertension, cardiovascular disease, cancers, and chronic respiratory diseases . For thepatients . In addipatients . Obesitypatients . Excessipatients . Simonnepatients  identifipatients  indicatepatients .Obesity demonstrates insulin resistance and an overactivity of the renin angiotensin-aldosterone system (RAAS) related with bad outcomes in COVID-19 . As a reHâncu and Mihălţan  hypothesWe review the peer-reviewed and pre-printed literature on COVID-19-related obesity considerations and highlight key information gaps that need further research specific to patients, health care workers, and health systems. Obesity was rarely reported in early clinical studies assessing the clinical risk factors for COVID-19 disease. Few data from a single-center retrospective research stated that obesity seems to have a high prevalence between COVID-19 patients admitted to intensive care needing intrusive mechanical ventilation and the disease severity increased with BMI . For insSome individuals may become depressed, stressed, and unwilling to exercise, while others will consume more food without any action as a result of COVID-19 spreading negative news, which can lead to weight gain and become obese . Yet, itGastrointestinal symptoms identified in SARS-CoV-2 infection, such as abdominal distension, diarrhea, and bloating, may be dramatically improved by restoring microbiota equilibrium. A micro-ecological treatment can minimize the translocation of bacteria, increase positive bacteria, and decrease toxin production. Infection due to intestinal bacterial dysbiosis might be prevented. Gut biota analysis seems not to be affordable and thus not available as a routine test, but dysbiosis could be defined on the basis of symptomatology. Probiotics can be effective in the prevention or recovery of dysbiosis . ObesityThe frequency of COVID-19 cases is closely related to hypercoagulable condition and thrombosis . AnomaloObesity itself is an independent and causal risk factor for the development of immune-mediated disease, e.g., psoriasis, suggesting that such adipose condition may have a systemic immune consequence upon additional environmental provocation. In fact, investigations suggest that obesity interferes with the various pathways of immune system. Some strategies include reduced cytokine production, modified lymphocyte and monocyte function, decreased dendritic cell and macrophage function, natural killer (NK) cell dysfunction, and lowered response to antigen/mitogen stimulation . ObesityObesity leads to a dampened immune response to infectious agents, leading to weaker post-infection impacts . SystemiArticulating the obesity impact on the immune response to IAV has been achieved through the use of genetically obese (OB) mice missing leptin signaling receptor and diet-induced obese (DIO) models. In DIO mice, IAV-infected mice had a 7-fold higher death rate and pulmonary pathology, and slow wound healing compared to lean controls ,86. LikeDendritic cells (DCs) promote the immune system’s T-cell response by displaying phagocytosis antigens . Among tA lot of information about the pathophysiological mechanisms of obesity and atherosclerosis have been discovered over the past three decades. These conditions have historically been considered as lipid storage disorders with accumulation of triglycerides in the fat tissue and cholesterol esters in atherosclerotic plaques . There aPsoriasis is a widespread inflammatory chronic skin disease with a complex pathogenesis composed of an immune dysfunction environmental factor and genetic aspect. Several studies produced evidence that obesity predisposes patients to psoriasis and enhances psoriasis inflammation . Setty eEffective preventive strategies and control measures for influenza are important, in particular in the overweight and obese community. This may be problematic due to weak vaccine responses among obese or overweight individuals . ClinicaTorres et al.  conclude2) and, in specific, obesity function (BMI ≥ 30 kg/m2) are commonly discussed in relation to RTI vulnerability [Frequent and more intense respiratory tract infections (RTIs) have also been a major cause of morbidity in our community and a substantial cost burden in terms of hospital care and working time reduction . Overweirability . Many inrability ,115. Macrability  has showrability .Since the 1918 “Spanish” flu pandemic, it is understood that malnourishment (both under-and over-nutrition) is associated with a poorer viral infection diagnosis . InfectiAvian adenovirus (Ad) , a variety of human adenoviruses , and canine distemper virus have been tested for their capacity to induce animal and human adiposity . These vThe 1957–1960 “Asian” and the 1968 “Hong Kong” influenzas indicated that diabetes and obesity led to higher death rates and longer periods of disease, as subjects did not have any other medical disorders that raise the likelihood of influenza-related complications . In addiStreptococcus pneumoniae at day 7 post-influenza infection increased mortality when compared to controls [Numerous research findings on IAV infection in OB mice indicate a high incidence of disease, elevated secondary bacterial infection, and decreased vaccine effectiveness . In addicontrols . Even thcontrols . In a vicontrols . Obese mcontrols . Excisedcontrols . Remarkably, this evolution phenotypic expression was preserved using human primary respiratory cells. IAV reproduced to higher titers of normal human bronchial epithelial cells (NHBE) from obese donors than those of lean individuals. Both obese mice and obese-donor NHBEs had reduced interferon and interferon-stimulated genes (ISGs) and developed post-infection, potentially decreasing viral shed control and restricting viral replication . Ritter Obese individuals are more susceptible to infection than lean ones because of the following factors. First, obese individuals with influenza spread the virus for a longer period of time (up to 104% prolonged) than slim subjects and significantly raise the risk of transmitting the virus to others . Secondl2), as these patients are more difficult to image, ventilate, nurse, and rehabilitate [In the current pandemic of COVID-19, physicians should understand that obese, and much more extremely obese, are at greater risk of health decline with COVID-19. Consequently, these patients need to be closely observed and more effectively treated to minimize morbidity and mortality . It is abilitate . Williambilitate  revealedbilitate . This isbilitate . Recentlbilitate . Expert bilitate ,148. Redbilitate . CDC idebilitate . The eviObesity is described by increasing the number and size of adipocytes that promote lipid aggregation and induce a low inflammation level . ExercisObesity is a medical disorder with complex pathophysiology, containing multiple pathways that now appear as major risk factors for COVID-19. Obese patients have higher leptin (pro-inflammatory adipokine) levels and lower adiponectin (anti-inflammatory adipokine) levels that induce immune response dysregulation and may lead to the pathogenesis of obesity-linked complications . Additio


Teachers' psychological job resources  mediated relationships between emotional personal work-identity and cognitive collective work-identity, respectively, and self-determined work motivation. Thus, teachers might be disadvantaged by stronger personal work-related thinking and collective work-related feeling when related to exhaustion, to some extent accounted for by psychological job demands, and they might find advantage in stronger personal work-related feeling and collective work-related thinking when related to work motivation, to some extent accounted for by psychological job resources.The aim of this study was to investigate the mediating roles of teachers' psychological job demands and resources regarding personal and collective work-identity, respectively, and exhaustion and self-determined work motivation, respectively. A total of 2,905 members of a Swedish teacher's trade union received an online questionnaire by e-mail; 768 individuals answered the questionnaire and so participated in this study. The data were obtained by self-reported measures  and analyzed by mediation regression analyses. The results showed that teachers' psychological job  Prevalence of burnout syndrome among teachers is higher than in other professional groups. However, teachers may also have access to strong work motivation and engagement in their daily educational work. These two different phenomena might be predicted by teachers' work-identity and might be explained to some extent, by different aspects of teachers' psychosocial work environment.Thus, the aim of the present study was to investigate the mediating roles of teacher's psychological job demands and resources in relations between personal and collective work-identity, respectively, and exhaustion and self-determined work motivation, respectively.According to Hallsten et al.  9.6% of Teachers, however, have also access to job resources  model describes how work-related environmental characteristics, in terms of job demands and resources, are related to each other and how they are related to adverse mental health, foremost burnout and exhaustion as well as work engagement  is suggested to precede the emotional component  when collective work-identity, is accountable for psychological wellness at work  personal  memory perspective and (2) social identity perspective. By this one may gain some more empirical and theoretical knowledge of job demands and resources as mediating factors between the individual teacher (work-identity) and adverse mental health (exhaustion) and work motivation (self-determined motivation at work). To our knowledge, similar modifications have only been carried out to a lesser extent in previous research on the JD-R Model  and emotional collective work-identity (H1b), respectively, and exhaustion is mediated by teachers' psychological job demands.The second aim was to investigate if teachers' psychological job resources mediated a positive relationship between their emotional personal work-identity and cognitive collective work-identity, respectively, and self-determined motivation at work  and cognitive collective work-identity (H2b), respectively, and self-determined motivation at work is mediated by teachers' psychological job resources.Since the present study is a part of a larger research project, resulting in several publications on work-identity and health, the method section in general terms complies with previous publications within this project . Mean age of the participants was M = 46.3 years  and mean employment time within the organization (school) 14 years (SD = 10.2). Of the n = 768 respondents, 99% worked as teachers or similar, i.e., had an educational function, 95.2% were in permanent employment, 92.1% of the participants worked within the municipal sector, 80.5% had full-time jobs, 75.5% were female and 68% had university studies as their highest level of education.Two thousand nine hundred and five members of the Swedish trade union, “The National Union of Teachers” (in Swedish “Lärarnas Riksförbund”), originating from 11 different local union branches and working in the south and middle part of Sweden, received an online questionnaire by e-mail. The response rate was 26% , type of employment, permanent vs. non-permanent employment, private vs. non-private sector, full-time vs. non-full-time job and level of education were measured as demographical background variables.Exhaustion was measured using the “Karolinska Exhaustion Scale 26” (KES26), developed by Saboonchi et al.  which meTo measure work motivation, we used the “Work Extrinsic and Intrinsic Motivation Scale,” WEIMS , CFI = 0.95 and RMSEA = 0.08 , CFI = 0.97, and RMSEA = 0.10 for the collective work-identity concept/measure , on a five-point Likert-scale ranging from 1 (completely disagree) to 5 (completely agree). Cronbach alpha (α) for this scale was 0.64, indicating moderate consistency  on a five-point Likert-scale ranging from 1 (completely disagree) to 5 (completely agree). Cronbach alpha (α) for this scale was 0.73, indicating acceptable consistency , four types of mediation analyses were performed by Hayes  PROCESS not to be affected by: monthly income; school sector (public vs. private); years of employment; and permanent employment (no vs. yes)  were calculated.N, mean and standard deviation statistics for all variables included in the regression analyses  and emotional collective work-identity (H1b), respectively, and exhaustion, were mediated by psychological job demands ; see All this suggests that when the cognitive processes of personal, and emotional processes of collective, work bonding increases, teachers may experience greater psychological job demands, which in turn may increase their exhaustion.As predicted, it was also shown that the positive relationships between teachers' emotional personal work-identity (H2a) and cognitive collective work-identity (H2b), respectively, and self-determined motivation at work were mediated by psychological job resources ; see This suggests that when the emotional processes of personal and the cognitive processes of collective work bonding increase, teachers may experience greater psychological job resources, which in turn may increase their self-determined motivation at work.The objectives of the present study were to investigate the mediating roles of teachers' psychological job demands and resources in the relationships between personal and collective work-identity, respectively, and exhaustion and self-determined work motivation.only through the mediation of teachers' psychological job demands. (2) Teachers' psychological job resources positively mediated the positive relationship between emotional personal work-identity and cognitive collective work-identity, respectively, and self-determined motivation at work.The results obtained were in line with our predictions see . That isAccordingly, when (H1a) cognitive processes of coherence, correspondence, mental time, reflection and agency of personal work bonding increase, teachers may experience greater psychological job demands in terms of prosocial extra-role performance, such as “extra-role support given to the pupils; colleagues; students' parents”  emotional processes of pride, esteem, and affective commitment of collective work bonding increase, teachers may also experience greater psychological job demands in terms of prosocial extra-role performance  feeling, respectively, and their experiences of exhaustion. Thus, and in contrast to some results showing prosocial extra-role performances to be positively related to mental health  the emotional processes of attachment/belongingness/closeness of personal work bonding increase, teachers may experience stronger psychological job resources in terms of educational inspiration in their interplay with students and the overall educational work  cognitive processes of incorporation, identification and assimilation of collective work bonding increase, teachers may experience stronger psychological job resources in terms of educational inspiration in their interplay with students and the overall educational work  work-related thinking, respectively, and their self-determined motivation at work. These results indicate that educational inspiration may constitute a psychological resource for teachers' self-determined motivation at work  may play a positive mediating role in the relationships between teachers' cognitive personal and emotional collective work-related bonding, respectively, and exhaustion.In sum, from a theoretical and an applied perspective, our results suggest that teachers might be advantage in stronger personal work-related feelings, organizational/school-related thinking and educational inspiration since these factors, to some extent, might enhance their self-determined motivation at work. Consequently, teachers' educational inspiration  may play a positive mediating role in the relationships between teachers' emotional personal and cognitive collective work-related bonding, respectively, and self-determined motivation at work.Furthermore, teachers might find By focusing on personal and social identity perspectives, the present study showed that work conditions as defined by the Job Demand-Resource model  and organizational/school-related thinking, respectively, and self-determined motivation at work. Teachers' prosocial extra-role performances should be reduced since they may mediate positively the relationships between teachers' cognitive personal and emotional collective work-related bonding, respectively, and their exhaustion problems.Promotion of teachers' educational inspiration and reduction of their prosocial extra-role performances might be implemented for example, by clearer cut and explicit work descriptions and expectations of in-role vs. extra-role performances within the national educational system programs. Here, increased job autonomy of teachers should also be promoted since it might buffer the negative effects of prosocial extra-role performances  thinking by mental training programs may reduce their exhaustion problems and increase work motivation  were calculated. However, we estimate that our sample includes about 300 to 500 different schools. Given the large number of schools the number of participants per school was so low that no meaningful ICC-analysis on school level was possible to calculate.thinking and collective work-related feeling associates positively with their exhaustion problems indirectly via psychological job demands ; (2) teachers' personal work-related feeling and collective work-related thinking associates positively with their work motivation indirectly via psychological job resources : (3) teacher's psychological job demands and resources might to some extent constitute explaining factors between the individual teacher (work-identity) and exhaustion problems and work motivation, respectively.The present study contributes to the understanding of teachers' work-identity and psychosocial working conditions in relation to their exhaustion problems and motivation at work in that: (1) teachers' personal work-related All datasets generated for this study are included in the article/supplementary material.The studies involving human participants were reviewed and approved by Swedish Regional Ethical Committee of Uppsala University (Dnr 2015/423). The patients/participants provided their written informed consent to participate in this study.ON: substantial contributions to the conception and design of the work, data collection, manuscript writing, analysis and interpretation of data for the work, and drafting the work critically for important intellectual content. IK: substantial contributions to the conception and design of the work, manuscript writing, interpretation of data for the work, and drafting the work critically for important intellectual content. FS and JW: manuscript writing, interpretation of data for the work, and drafting the work critically for important intellectual content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background: Problematic smartphone use (PSU) has been associated with screen time in general, but little is known about the effect of different screen-based activities. We examined the associations of self-reported time spent on overall and specific screen-based activities with PSU and its addictive symptoms in Hong Kong Chinese adults.Methods: We analyzed data from 562 smartphone owners  in a population-based telephone survey in 2017. PSU was measured using Smartphone Addiction Scale-Short Version (range 10–60) which includes symptoms of daily-life disturbance, withdrawal, cyberspace-oriented relationship, overuse, and tolerance. Screen time was self-reported as average hours per day spent on the internet, online book/newspaper/magazine, online video, and social networking sites (SNS). Multivariable linear regression analyzed the associations of self-reported screen time with PSU severity and symptoms. Interaction effects of sex, age group, educational attainment, and monthly household income were examined.Results: Self-reported time spent on overall screen-based activities was associated with PSU severity  and withdrawal and overuse symptoms, after adjusting for sociodemographic and health-related variables. Independent association was observed for self-reported SNS time with PSU severity  and symptoms of withdrawal and cyberspace-oriented relationship, after mutually adjusting for time on other activities. The strongest association between self-reported SNS time and PSU severity was observed in younger than older adults .Conclusions: The independent association of self-reported SNS time with PSU and core addictive symptoms highlighted the addiction potential of SNS use, particularly in younger users. Excessive use of the internet has raised concerns about its behavioral addiction potential , among which online gaming disorder has been added in ICD-11  showing PSU is found to be associated with self-reported overall screen time , 16. TheResults are mixed on associations between different screen-based activities and PSU. Social-oriented activity was associated with PSU , particuWe used the Uses and Gratifications Theory (UGT)  and the Hong Kong, the most developed city in China, has one of the highest smartphone penetration rates worldwide (91.5% in 2019) . Our preThe Hong Kong Family and Health Information Trends Survey (FHInTS) was a periodic territory-wide telephone survey on the general public's behaviors and views regarding information use, individual health, and family well-being, under the project “FAMILY: A Jockey Club Initiative for a Harmonious Society.” We conducted five waves of FHInTS since 2009. The present landline telephone survey was part of the fifth wave of FHInTS from February to August 2017. Details of the study design have been reported elsewhere , 35. ThePSU was measured using the ten-item Smartphone Addiction Scale-Short Version (SAS-SV), with each item scoring on a six-point Likert scale (1 = strongly disagree to 6 = strongly agree) . A higheRespondents were asked that “In the past month, how many hours did you spend on…per day? .” Process-oriented screen-based activities included surfing the internet; reading online book/newspaper/magazine; watching online video ; and social-oriented activity included using SNS . We categorized the amount of time as “0,” “>0 to <1 hours per day,” “≥1 to <2 hours per day,” “≥2 to <3 hours per day,” and “≥3 hours per day.” The similar categorization was used in large-scale longitudinal studies on self-reported screen time ]Sociodemographic characteristics included sex, age group, marital status, employment status, educational attainment , and monthly household income  . Cigarette smoking  and alcohol drinking  were examined, given the co-occurrence of substance use and addictive behaviors . HistoryP for interactor was calculated using adjusted Wald tests. Missing data were handled by available case analyses as missing values for all variables were minimal (<2.0%). All analyses were conducted using STATA version/MP 15.1 . A two-side P < 0.05 was considered statistically significant.All data were weighted according to sex, age, and educational attainment distributions of the Hong Kong general population to increase the sample representativeness. We examined the associations of time spent on overall and each of four specific screen-based activities with PSU severity and symptoms using bivariate and multivariable linear regression analyses adjusting for sociodemographic and health-related characteristics (Model 1). In Model 2, we repeated Model 1 with mutually adjusting time spent on other types of screen-based activities. The variance inflation factors  suggested the minimal multicollinearity among time spent on different types of screen-based activities in Model 2 . DichotoN = 562; 56.5% female; 82.1% aged 25–64 years) had a mean SAS-SV score of 27.8 [standard deviation (SD) 10.2], with the highest symptom score was observed for cyberspace-oriented relationship  (The weighted sample ( SD 1.6) .Nearly three-quarters of respondents (71.5%) reported spending over 3 h per day on overall screen-based activities . The mosMultivariable analyses showed that each hour of increase in time spent on overall screen-based activities was associated with higher symptom severity of withdrawal  and overuse  and higher PSU severity , after adjusting for sociodemographic and health-related characteristics (Model 1) . AssociaP for interaction = 0.004). Sex appeared to have no interaction effect on the association of SNS time with PSU severity . Respondents with higher educational attainment tended to have the stronger association of SNS time with PSU severity , but the interaction effect did not reach significance level (P for interaction = 0.06).We also examined the interaction effects of sex, age group, educational attainment, and monthly household income on the association of SNS time with PSU severity in Model 2 . YoungerIn a random sample of Hong Kong Chinese adults, self-reported time spent on overall screen-based activities was positively associated with PSU severity. This finding was consistent with studies showing that longer smartphone usage time was associated with higher PSU severity using the same SAS-SV in Swiss, Spanish, and Belgian populations , 16.Our study added to the literature by exploring the associations of self-reported time spent on overall and specific types of screen-based activities with PSU and its addictive symptom. Specifically, we found that overall screen time was associated with overuse . The self-awareness of excessive use was also reported in a qualitative interview in users with higher PSU severity . HoweverSelf-reported time spent on using SNS was independently associated with PSU and its core addictive symptoms, including withdrawal and cyberspace-oriented relationship. This association was observed after adjusting for time spent on reading online book/newspaper/magazine, watching online video, and surfing the internet. The findings showed that social- and process-oriented activity were not equally associated with PSU, which supported the UGT  and I-PAYounger adults appeared to be the most susceptible to the association of self-reported SNS time with PSU in our subgroup analyses. A similar finding was reported in a cross-generation study showing the predictive effect of SNS time on PSU only in the younger group . YoungerSelf-reported time spent on surfing the internet was associated with daily-life disturbance in our study. Internet has an array of contents for information seeking, shopping, gaming, pornography viewing, gambling, or aimless browsing, and some of which have been identified with addiction potential  and were associated with impairments of school/work performance , 55. We Our results have theoretical and practical implications. The different associations between time spent on social- and process-oriented activities and PSU informed future research to distinguish between using motives. This fits with the UGT  and I-PAOur study has several limitations. The cross-sectional data restricted the inference of the temporal sequence of self-reported screen time and PSU. Reverse direction of the association is possible as people with PSU tend to have longer screen time to deflect negative emotions induced by PSU . LongituSelf-reported screen time was positively associated with PSU severity in Chinese adults in Hong Kong, but the associations varied by types of screen-based activities. The independent associations of self-reported SNS time with PSU severity and core addictive symptoms of withdrawal and cyberspace-oriented relationship highlighted recent warnings about excessive SNS use. Younger adults were the most susceptible to the association of SNS time with PSU severity. Our study provided reminders to smartphone users of the addiction potential of screen time in particular on using SNS. The study could help SNS providers and policymakers to develop regulations to prevent excessive SNS use and PSU.https://www.family.org.hk/en/about-us/contact-us-en/.The data analyzed in this study is subject to the following licenses/restrictions: The data that support the findings of this study are available from the FAMILY project but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Hong Kong Jockey Club Charities Trust. Requests to access these datasets should be directed to FAMILY project, The studies involving human participants were reviewed and approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster. All respondents provided verbal informed consent. Telephone interviews were audio-recorded for quality checking with respondents' consent. Interview records were then erased 6 months after completing the survey. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.NG: formal analysis, data curation, writing—original draft, and writing—review and editing. TTL: writing—review and editing. MPW: conceptualization, methodology, and writing—review and editing, supervision. SYH: conceptualization, methodology, writing—review and editing, and supervision. DYTF: writing—review and editing and supervision. AW: project administration. SSC: conceptualization and methodology. THL: conceptualization, methodology, writing—review and editing, and funding acquisition. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gut microbiota composition and function are major areas of research for functional gastrointestinal disorders. There is a connection between gastrointestinal tract and central nervous system and this is mediated by neurotransmitters, inflammatory cytokines, the vagus nerve and the hypothalamic-pituitary-adrenal axis. Functional gastrointestinal disorders are prevalent diseases affecting more than one third of the population. The etiology of these disorders is not clarified. Visceral hyperalgesia is the main hypothesis for explaining clinical symptoms, however gut-brain axis disorder is a new terminology for functional disorders. In this review, microbiota-gut-brain axis connection pathways and related disorders are discussed. Antibiotics are widely used in developed countries and recent evidence indicates antibiotic-induced dysbiosis as an important factor for functional disorders. Antibiotics exert negative effects on gut microbiota composition and functions. Antibiotic-induced dysbiosis is a major factor for occurrence of post-infectious irritable bowel syndrome. Cognitive and mood disorders are also frequent in functional gastrointestinal disorders. Animal and human trials show strong evidence for the causal relationship between gut microbiota and brain functions. Therapeutic implications of these newly defined pathogenic pathways are also discussed. The human microbiota is a collection of microorganisms that live in and on us. Over the past two decades, we have had an enormous amount of publications about gut microbiota in health and disease. Improvement in sequencing technologies coupled with bioinformatics science are making this research more feasible and cheaper. For instance, the estimated ratio of human:microbiota cells was recently revised from 10 times to 1.3 times more than human cells . Even thFunctional gastrointestinal disorders (FGID) are prevalent diseases affecting more than one third of the population . EmerginA search for peer-reviewed articles published before September 2020 was performed in PubMed, Google, Web of Science, and MEDLINE to identify studies observing the effects of antibiotics in gut-brain-microbiota axis. Search terms used were (“gut” or “gut-brain” or “antibiotic”) or and (“microbiota”) and . All titles/abstracts of the occasioning articles were appraised manually. For relevant abstracts, full articles were achieved and reviewed.The brain-gut-microbiota axis is a bidirectional system enabling gut microorganisms to communicate with the central nervous system (CNS), and the CNS with the gut . The mecBifidobacteria for instance, can increase the amount of tryptophan, the precursor of serotonin [Lactobacilli species alter gamma-aminobutyric acid (GABA) metabolism and change brain GABA receptor expression and behavior [Lactobacillus rhamnosus has a central effect in animals and this was ameliorated by vagotomy [Lactobacillus and Bifidobacterium species can produce GABA; Escherichia, Bacillus and Saccharomyces spp. can produce noradrenaline; Candida, Streptococcus, Escherichia and Enterococcus spp. can produce serotonin; Bacillus can produce dopamine; Lactobacillus can produce acetylcholine [Gut microbes are capable of producing most neurotransmitters found in the human brain. While these neurotransmitters primarily act locally in the gut, modulating the enteric nervous system, there is also undeniable evidence indicating that gut microbes can influence CNS through multiple mechanisms. The treatment with probiotic erotonin . Some Labehavior . Preclinvagotomy . In factvagotomy . Syntheslcholine ,34,35. Alcholine ,45,46,47lcholine .E.coli infection later developed IBS-like symptoms including co-morbid depression, anxiety disorder [Post-infectious IBS is a prototype for gut-brain axis disorders. Water-born gastroenteritis outbreak occurred in United States and people affected from this disorder .There are diverse ways of communication between gut microbiota and brain such as autonomic nervous system, vagus nerve, enteric nervous system, neurotransmitters, and immune system .The autonomic nervous system (ANS) comprises the sympathetic and parasympathetic branches. Combined with activity from the enteric nervous system (ENS) and influenced by the CNS, the ANS is responsible for physiological homeostasis, as well as responding to endocrine, motor, autonomic, and behavioral areas. Gut microbiota communicate with ANS bidirectional both antagonistic and synergistically ,50. AffeIt is the tenth cranial nerve and the longest in the body with extensive connections and networks with peripheral organs. The vagus exerts anti-inflammatory actions via medullary dorsal motor nucleus. The vagal modulation of macrophage action an important factor for the inflammation in inflammatory bowel disease (IBD) .Bacteroides fragilis and the capsular exopolysaccharide, are good examples, which can influence ENS function [L. rhamnosus strain (JB-1) performs this action via a G protein-coupled receptor-mediated pathway [At the interface of microbiota and host, there is a network of gut neurons called ENS. Anatomically divided as submucosal and myenteric plexus, ENS regulates gut motility and secretions . Factorsfunction . L. rham pathway . Recent  pathway . This pr pathway . Moreove pathway .Gastrointestinal tract has the highest number of immune cells in the body and there is a delicate, complex communication with the gut microbiota . Mucus pEnvironmental factors affect immune function. The release of cytokines promotes the activation and recruitment of eosinophils, B cells, and mast cells. In addition to the traditional T-helper 2 pathway, secretion of IL-23 from antigen-presenting cells, such as dendritic cells, B cells, and macrophages, promotes T helper-cell 17 differentiation. Degranulation of these cells disrupt intestinal barrier and enteric nerves. This results in visceral hypersensitivity and dysmotility . α4β7 guThe bi-directional communication between gut microbiota and brain ensures the maintenance of intestinal homeostasis and likely affects CNS functions in different time points of life, from early life neurodevelopment to aging and neurodegeneration . StudiesInitial colonization of the gut begins in early life and mainly shaped by gestational age , mode ofThe majority of the studies providing evidence for the role of the gut microbiota in neurodevelopment come from germ-free animal experiments including altered hippocampal neurogenesis and decreased expression of brain-derived neurotrophic factors in germ-free mice ,85,86. UClostridium spp., resulted in improved eye contact behavior and less constipation in autistic children [In addition to evidence from experimental studies, a recent longitudinal population-based study reported that early-life antibiotic exposure is associated with an increased risk for psychiatric disorders . Anotherchildren .Although not specifically focused in this review, neurodegenerative disorders such as Alzheimer’s disease (AD) are another consequence of gut microbiota-brain axis impairment. Minter et al.  reportedIn addition to early life being of critical importance in terms of disruptive antibiotic effect on gut microbiota, antibiotic studies on adult animals also showed impaired behavior and brain function ,93,94,95Experimental reports summarizing the effect of antibiotics by considering the type of antibiotic, duration, route and age of exposure on gut-brain axis are depicted in While majority of the experimental studies in Gut microbiota plays a vital role in the pathogenesis of functional gastrointestinal disorder. Gut microbiota and brain interactions are important factors for prevention and therapy. Further clinical studies are required for understanding the true effect of gut microbiota on disease progression in humans. While antibiotics are essential treatment strategies in most conditions, the long-lasting effects on host microbiome and immune functions, especially during early life should be interpreted with caution. We need prospective randomized trials for the therapeutic potential of gut microbiota modulation in functional gastrointestinal disorders.


Myokines, classified as proteins secreted from skeletal muscle, are representative exercise factors. The roles of myokines have been demonstrated in a variety of exercise-related functions linked to health benefits. In addition to myokines, metabolites are also exercise factors. Exercise changes the levels of various metabolites via metabolic reactions. Several studies have identified exercise-induced metabolites that positively influence organ functions. Here, we provide an overview of selected metabolites secreted into the circulation upon exercise.Regular exercise has a myriad of health benefits. An increase in circulating exercise factors following exercise is a critical physiological response. Numerous studies have shown that exercise factors released from tissues during physical activity may contribute to health benefits  Elucidation of the mechanism underlying interorgan crosstalk and biological networking involving exercise factors will help to identify potential therapeutic targets. In this review, we selected six metabolites that have been extensively characterized and have therapeutic potential in metabolic disorders, neurodegenerative diseases, osteosarcoma, or sarcopenia. We summarize our current knowledge of these metabolites, focusing on their biological functions  is a crucial intermediate in the TCA cycle required for a rate limiting step . AKG is in blood . Yuan etin blood . AKG eliin blood . Severalin blood . AKG inhin blood . In addiin blood . AKG suppromoter . These bpromoter . These svia catabolic reactions of L-valine, while D-BAIBA is generated in the cytosol as an intermediate product of thymine degradation (via its action on AMP-activated protein kinase (AMPK) and PPARδ in skeletal muscle (via Mas-related G protein-coupled receptor type D (MRGPRD), and reduced MRGPRD expression with age results in loss of the protective effect of L-BAIBA (β-Aminoisobutyric acid (BAIBA) is a non-proteinogenic amino acid. It is a natural catabolite of thymine and valine metabolism in mammals. There are two enantiomers of BAIBA in biological systems: D-BAIBA (R-BAIBA) and L-BAIBA (S-BAIBA). L-BAIBA is produced radation . Howeverradation . Consistradation . In typeradation . In addil muscle . In addi L-BAIBA . BAIBA a L-BAIBA . These rTryptophan is not only an α-amino acid used to biosynthesize proteins, but is also a precursor of many biologically active compounds such as serotonin, melatonin, and indole. The main route for tryptophan catabolism is through the kynurenine (KYN) pathway. In this pathway, a product of one branch is kynurenic acid (KYNA) and the final product is nicotinamide adenine nucleotide (NAD+). KYNA is generated in diverse types of peripheral tissues and cells, and is also present in various products consumed by humans. The KYN pathway is responsible for over 90% of peripheral tryptophan metabolism . KYN andvia the circulation to peripheral tissues including skeletal muscle and the brain, where they can be converted to acetyl-CoA , inducing cell quiescence. Intraperitoneal injection of BHB alleviates vascular aging in mice  pathway to generate 12,13-diHOME. Lipidomics analysis demonstrated that the plasma 12,13-diHOME level increases in response to an acute bout of exercise in humans and mice (Oxylipins are oxidized metabolites of long-chain polyunsaturated fatty acids (PUFAs). PUFAs can be obtained directly from the diet or from metabolism of linoleic acid and α-linolenic acid. Oxylipins are detected in all tissues, urine, and blood . Imbalanand mice . Exercisand mice . SimilarExercise induces release of many regulatory factors into the circulation and these factors influence body changes. Studies have found many exercise factors that link exercise to beneficial effects. Myokines are representative exercise factors secreted by skeletal muscle and affect diverse peripheral tissues as mediators of interorgan crosstalk. Exercise induces great metabolic changes and release of biologically active metabolites into blood. Studies using diverse approaches have identified novel metabolites that link exercise to beneficial effects. Many exercise-induced factors including metabolites and myokines have been reviewed elsewhere .Investigations have mainly focused on upregulated metabolites that influence tissue functions. Similar to exercise-induced metabolites, plasma metabolites whose levels are reduced during exercise may play important roles in regulating the beneficial effects of exercise. Indeed, the levels of many plasma metabolites increase with age and some of these metabolites might be associated with aging and age-related diseases . It willYY and K-SK conceptualized, designed, and wrote the review. K-SK approved the final version of the review. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The diagnostic criteria defined by the International Diabetes Federation for adolescents (IDF-adoMetS) and the Joint Interim Statement for adults (JIS-AdMetS) were employed to evaluate MetS. In the training dataset, principal component analysis converted 12 interrelated obesity indices into bodyfat-, lipid-, and body-shape-enhanced groups, with the first two characteristic-groups having a higher discriminatory capability in identifying IDF-adoMetS and JIS-AdMetS. In the validation dataset, abdominal volume index (AVI) among girls and waist circumference (WC) among boys were respectively validated to have a higher Youden’s index (0.740–0.816 and 0.798–0.884) in identifying the two MetS. Every 7.4 and 4.3 positive tests of AVI (cutoff = 13.96) had an accurate IDF-adoMetS and JIS-AdMetS, respectively, and every 32.4 total tests of WC (cutoff = 90.5 cm) had a correct identification for the two MetS. This study stresses the discriminatory capability of bodyfat- and lipid-enhanced adiposity indicators for identifying MetS. AVI and WC were, respectively, supported as a risk screening tool for identifying female and male MetS as adolescents transition to adulthood.A simple, robust, and characterized adiposity indicator may be appropriate to be used as a risk screening tool for identifying metabolic syndrome (MetS) in adolescents. This study used dual adolescent populations to develop and validate efficient adiposity indicators from 12 characterized candidates for identifying MetS that may occur during the transition from adolescence to young adulthood. Data from the adolescent Nutrition and Health Survey in Taiwan ( Metabolic syndrome (MetS) is a health hazard condition that reflects a constellation of several cardiometabolic risk factors, including excessive abdominal adiposity, high blood pressure and fasting plasma glucose, and abnormal triglyceride and high-density lipoprotein-cholesterol . LongituAmong 5 components of MetS, 3 cardiometabolic dysfunctions must be determined via blood biochemical examination. Because blood assessment is an invasive and more costly test approach, using it as a universal screening tool for MetS may be less feasible among younger children. Therefore, the development and validation of a simple and robust screening instrument for identifying adolescent MetS is a vital work for pediatric public health.Anthropometric studies have reported that, compared with entire body fatness measured by body mass index (BMI), regional fat distribution is more associated with metabolic disturbances and cardiovascular health risks . AdiposiAdiposity indices created to measure body adiposity, abdominal obesity, body shape, and visceral fat accumulation are a group of interrelated variables that may have a characteristic aggregation. Principal component (PC) analysis is a statistical method that can convert interrelated variables into reduced independent and interpretable PCs with a specific characteristic through multilinear subspace algorithms, and produce a characteristic-weighted combined score for each retained PC . This teIn one school-based longitudinal investigation of the alteration of MetS typology, 16.4%, 5.7%, and 1.1% of adolescents were observed to have new incident, unstable/remitted, or persistent MetS, respectively, as adolescents aged into young adulthood [The development and validation of adiposity indicators for this study were carried out using 2 sample series of participants. The first sample, obtained from the ado-NAHSIT, was used as a training dataset to derive optimal adiposity indicators for the identification of MetS using the criteria for adolescents and young adults. Two commonly applied diagnosis criteria for the 2 types of MetS were used. The second sample, obtained from the mRP-aMS study, was used as a validation dataset to verify the accuracy and efficiency of determining each MetS for the chosen obesity indicators. All participants and their guardians in the two studies provided a written informed assent and consent. The research protocol for this study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital .The ado-NAHSIT as a nationwide adolescent survey in Taiwan that used a multistage, geographic area and population density stratified random sampling design to recruit nationally representative samples for investigating the health and nutritional status of adolescents aged 12–18 years during 2010–2011 . AdolescThe mRP-aMS investigation was a large-scale cross-sectional study that employed a multistage, geographically stratified cluster sampling scheme to recruit representative adolescents for monitoring multilevel risk profiles of MetS in southern Taiwan during 2007–2009 ,29,30,31The International Diabetes Federation definition of MetS for adolescents aged 10–18 years (IDF-adoMetS) and the Joint Interim Statement for adult MetS (JIS-AdMetS) were respectively employed to determine MetS ,32. The Body weight, height, WC, and hip circumference was measured according to World Heatlh Organization standards . AdiposiTo avoid the occurrence of non-positive values in LAP, we assigned the WC values of 59 cm and 66 cm to female and male participants, respectively, who had a negative LAP value . This prThe indicators studied are adiposity-based variables that correlate with each other and may have a characteristic clustering. A combined score that additionally weights characterized adiposity indicators may offer a better discriminatory capability in identifying MetS. To investigate this issue, we transformed 12 adiposity indicators into 3 uncorrelated PCs that explained the majority of overall variance using PC analysis, as has been done in previous studies ,23,35. TR-square (pR2), and logistic regression-derived odds ratio (OR) were used to measure the adjusted correlation, contribution, and risk of each adiposity indicator for IDF-adoMetS and JIS-AdMetS, respectively, after controlling for possible confounding effects. Adjusted covariates included study area, age, daily energy intake, physical activity, puberty status, cigarette smoking, and alcohol drinking.Fourth, area under receiver operating characteristic curve (AUC) and the sensitivity and specificity of each cutoff point were calculated for each adiposity indicator, and were used to evaluate the discriminatory ability in identifying IDF-adoMetS and JIS-AdMetS. The best cutoff points for each MetS identification were determined by maximizing the Youden’s index . Fifth, the adiposity indicators that have the greatest AUC and/or YI in each characteristic-group and in the PC score group were selected to verify their discriminatory capability in identifying each MetS. We employed DeLong et al.’s non-parametric approach to evaluate the difference of AUCs across the selected adiposity indicators . Lastly,In the training dataset, the principal component analysis (PCA) procedure converted 12 obesity indicators into a comparable PC structure in each sex, with the first 3 PCs explaining 92.4% and 93.8% of the overall variance for girls and boys, respectively . Among gR2 of single and combined adiposity indicators associated with the number of abnormal IDF-adoMetS and JIS-AdMetS components in ado-NAHSIT participants. For the IDF-adoMetS, AVI, WHR, LAP, and PC1 respectively had the highest pCorr  in bodyfat-enhanced, body-shape-enhanced, lipid-enhanced, and combined adiposity index groups, after adjusting for the covariates. These obesity indicators separately explained the greatest variability in the number of abnormal IDF-adoMetS components in each group . Comparable pCorr and pR2 patterns for these adiposity indicators were observed for the JIS-AdMetS.p-values ≤ 0.028).Except for the ABSI for girls, the single and combined adiposity indicators investigated were all qualified as a risk marker for IDF-adoMetS and JIS-AdMetS after taking multiple confounding effects into account. In the training dataset, AVI/WC, WHR, LAP, and PC1 were, respectively, the principal adiposity indicators for identifying the 2 MetS among bodyfat-enhanced, body-shape-enhanced, lipid-enhanced, and combined indicator groups for both sexes. In the validation dataset, AVI/WC for IDF-adoMetS and PC1/AVI for JIS-AdMetS among girls, and WC/AVI for IDF-adoMetS and WC/PC1 for JIS-AdMetS among boys were validated to have an excellent discrimination for the identification of MetS.For the diagnosis of MetS, the IDF-adoMetS criteria  are stricter than that for JIS-AdMetS  ,32; thusThe assessed obesity indicators were derived from mathematical models for abdominal fat and adipose tissue volumes ,13,14,15This study revealed that all adiposity indicators (ABSI aside) were positively associated with IDF-adoMetS and JIS-AdMetS risks and their abnormal component numbers among girls and boys, and the associations were statistically independent of potential confounders. Obesity is a vital contributor of adolescent MetS ,32. In aIn the training dataset, we found that bodyfat- and lipid-enhanced adiposity indicators generally had a better discriminating ability for identifying IDF-adoMetS and JIS-AdMetS than did body-shape-enhanced indicators among both sexes , respectively, for the two MetS. AVI also was recommended as a risk-screening instrument for identifying female IDF-adoMetS in Spanish adolescents; however, the YI of this indicator with cutoff of 10.89 was only 0.70  . In our In a clinically established adolescent database, if the blood samples of the participants had not been collected, or blood samples had not been examined for MetS-related variables, an adiposity indicator with very high sensitivity may be used as a tool for discovering adolescent MetS. Furthermore, an adiposity indicator with very high specificity may be employed as an instrument for confirming adolescent MetS. This framework can be applied to clinical settings. However, the appropriate adiposity indicators and their associated cutoff points have to be developed for the study population.This study had several strengths. First, a large-scale nationally representative sample was used to develop efficient adiposity indicators and the accuracy and efficiency of the selected indicators were validated in another large-scale representative sample. Second, the clustering characteristics of obesity indicators and characterized functions were evaluated and validated. Third, multiple adiposity indicators were concurrently assessed and were used to investigate their discriminating capability for identifying MetS in the transition from adolescence to young adulthood. Fourth, although appropriate adiposity indicators and their cutoff points might vary by study populations, our research methodology and network can be applied to other countries that want to develop their specific obesity indicators. Alternatively, we recognize the limitations of our analyses. First, the recommended adiposity indicators had no causally predictive capability in determining MetS since they were all developed in a cross-sectional nature. Second, the participants used to develop indicators were Taiwanese adolescents, thus our findings may not be directly generalizable to other populations.In cardiometabology, this study uncovered the characteristic cluster of adiposity indicators. Bodyfat- and lipid-enhanced adiposity indicators revealed a higher risk and discriminatory ability for MetS than did body-shape-enhanced indicators in adolescents. This highlights the consideration of indicator characteristics when evaluating the association between pediatric obesity and cardiometabolic diseases. In public health, the findings from the development and validation procedures support AVI as a female, and WC as a male, risk-screening tool for MetS that can be applied during the transition from adolescence to young adulthood in Taiwan.