Sepsis is a major public health problem that cannot be ignored in China and even in the world. However, the prevalence of sepsis in Chinese adults varies among different studies.To evaluate the prevalence of hospital-wide sepsis and intensive care unit (ICU) sepsis in Chinese adults.PubMed, EMBASE, Cochrane Library, Web of science, China National Knowledge Infrastructure, Chinese biomedical literature service system, Wanfang Database, and VIP databases were systematically searched for studies on sepsis in China published before March 2, 2022. Random effects model was used to calculate pooled prevalence estimates with 95% confidence interval. Subgroup and sensitivity analyses were performed to address heterogeneity. Funnel plots and Egger's test were used to assess the publication bias.I2 = 99.9%) and 26% , respectively. Five studies of ICU sepsis involving 22,748 patients presented pooled prevalence and mortality of 25.5%  and 40% , respectively. Subgroup analysis of sepsis in ICUs revealed that the pooled prevalence was higher among males [17% ], in lung infections [66% ], and Gram-negative bacteria infections [37% ]. The pooled prevalence of sepsis, severe sepsis and septic shock was 25.5% , 19% , and 13% , respectively.Overall, nine observational studies involving 324,020 Chinese patients  were analyzed. Four hospital-wide studies involving 301,272 patients showed pooled prevalence and mortality of 3.8% (95% CI: 2.9–4.7%, Sepsis is prevalent in 25.5% of ICU patients in China, and sex, sepsis severity, infection site, causative microorganism, and infection type are significant influencing factors. Larger trials are needed to evaluate the prevalence of sepsis in China, which may help the development of global strategies for sepsis management.PROSPERO, identifier: CRD42022314274. Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection , which het al pooled data on sepsis from 1979 to 2015 across seven high-income countries and reported a population incidence rate of 288 hospital-treated sepsis cases and 148 severe sepsis cases per 100000 person-years , and the protocol was submitted to the Prospective Register for Systematic Reviews (PROSPERO) (CRD42022314274).*” OR “septic shock*” OR “severe sepsis*” OR “systemic inflammatory response syndrome” OR “SIRS” OR “septic” OR “septicaemic shock”) AND  AND (“China” OR “Chinese”). In addition, we screened the references of the identified articles and existing systematic reviews to further identify relevant studies. The complete search strategy is detailed in Two researchers independently and systematically searched eight different databases, including PubMed, EMBASE, Cochrane Library, Web of science, China National Knowledge Infrastructure, Chinese biomedical literature service system, Wanfang Database, and VIP database from their inception to March 2, 2022. The Medical Subject Headings (MESH) terms and keywords used in the search were as follows: observational design, including cohort, case-control, or cross-sectional studies; (2) study population aged ≥18 years; (3)sepsis diagnosis in accordance with appropriate criteria (such as the sepsis-1 or sepsis-2 or sepsis-3) or the current relevant guidelines; (4) report on the incidence or prevalence of sepsis and available data related to the defined primary outcomes of the current systematic review; and (5) population-based design and conducted in hospital-wide or ICUs in China. The exclusion criteria were as follows: (1) conference abstracts, study protocols, case studies, reviews; (2) duplicate publications; and (3) incomplete data or no relevant outcome.Two reviewers independently screened the titles and abstracts to identify relevant articles. The full text of each potentially eligible article was then read to identify studies for analysis. Any disagreement was resolved through a discussion with a third investigator.Two reviewers independently extracted data on participant and study characteristics according to the guideline for data extraction for systematic reviews and meta-analysis , such asThe quality of included studies was evaluated using the 10-item disease prevalence quality tool modified by Hoy et al. . Each stI2 value were implemented to assess heterogeneity, and P < 0.1 or I2≥ 75% was defined as significant heterogeneity. Subgroup analysis was performed to determine whether the prevalence was influenced by sex and severity of sepsis. Sensitivity analyses was conducted by plotting the pooled prevalence and excluding one study each time to judge the robustness of our results. Funnel plots and Egger's test were used to assess publication bias. All statistical analyses were performed using Stata software (version 15.1), and P < 0.05 was considered statistically significant.We extracted sepsis events and the total sample size to calculate the prevalence of sepsis, number of deaths and sepsis events for each included study to calculate mortality. The random-effects model was used to separately estimate the ICU and hospital-wide prevalence of sepsis. The Chi-square test and A total of 12,006 articles were found, after removing 1,772 duplicates, the titles and abstracts of 10,201 articles were screened, and 33 articles were subjected to a full-text review. Of these, 24 articles were excluded: 7 articles were Master PhD thesis, 5 articles were conference abstracts, and 12 articles were neonatal studies . FinallyThe characteristics of the included studies are presented in The five , 22–24 sStaphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, and Acinetobacter baumannii. The characteristics of infection and organisms in ICUs are shown in All five , 22–24 sThe quality of the included studies is presented in I2 = 99.9%, P = 0.000) for hospital-wide sepsis and 25.5%  for ICU sepsis  for hospital-wide and 40%  for ICU sepsis  among males, and 9%  for females , 19% , and 13% , respectively , followed by that in abdominal infections at 37% , hematologic infections at 10% , urinary tract infections at 7% , skin and soft tissue infections at 7% , and the lowest in neurology infections at 3%   .I2 = 98.3%, P = 0.000) of Gram–negative infections, 20%  of Gram–positive infections, and 12%  of fungi infections  of community–acquired infections and in 36%  of hospital–acquired infections . The result of Egger's test was P = 0.017. Meanwhile, the asymmetric funnel plot showed that publication bias existed among the studies on the prevalence for hospital–wide sepsis  for hospital–wide sepsis and 25.5% (95%CI: 13.9–37.0%) for ICU sepsis. The estimated pooled of hospital mortality for hospital–wide sepsis and ICU sepsis was 26% (95%CI: 16–36%) and 40% (95%CI: 34–47%), respectively. In addition, sex, sepsis severity, infection site, causative microorganisms and infection type were identified as significant factors of prevalence of ICU sepsis. To the best of our knowledge, this is the first meta–analysis to examine the prevalence of sepsis in China. Our findings could provide strong evidence about high prevalence of sepsis in China.Sepsis could develop from healthcare–associated infections, and the WHO has provided strong evidence and recommendations on the effectiveness of infection prevention and control measures to reduce the incidence of hospital–wide sepsis . A 2011 Sepsis also has a more crucial impact in ICUs, and there have been several studies on the prevalence of ICU sepsis. A study analyzed more than 17,000 sepsis cases in the USA and found that 55% of these cases required ICU admission . In a BrSubgroup analysis showed that the prevalence of ICU sepsis was higher in males than in females, consistent with the results of a German study . This maAs in other epidemiologic studies of sepsis –39, the The strengths of this study are that it provided a comprehensive review of the prevalence of sepsis both hospital–wide and in ICUs in China and included a large number of studies, either in English or in Chinese. Second, we performed a pooled analysis of the characteristics of ICU sepsis according to sex, severity, microorganism, and site of infection. However, we also acknowledge several limitations in our study. Firstly, the definitions of sepsis were heterogeneous, and thus, the estimation of the prevalence might be biased. Second, our study did not cover all provinces and regions in China, restricting the generalizability of the findings. Differences in the prevalence of sepsis across regions could not be explored because of limited information in the included studies. Third, significant heterogeneity exists in our review. Despite our effort, the source of heterogeneity could not be identified because of the limited information in the primary studies. Lastly, important factors related to sepsis mortality, such as age, comorbidities, microbial distribution and use of medicine, were not analyzed due to insufficient data. More high–quality studies are required to report on the epidemiology of sepsis and resolve the many unknown factors related to sepsis.This systematic review shows that the pooled prevalence of hospital–wide and ICU sepsis remains high in China. These findings provide evidence–based data that can help promote further sepsis research and prevention efforts throughout the nation, and contribute to develop global strategies for sepsis.The original contributions presented in the study are included in the article/JL and XL would answer for design and conception of the article. SL, XL, and HZ would answer for the collection, assembly of materials and would answer for data interpretation, analysis. SL and XL drafted the manuscript and contributed equally to the manuscript. XL, HZ, YX, and JL revised it. All authors reviewed and approved the final version of the manuscript.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: the Chinese Medicine Inheritance and Innovation Hundred and Ten Million Talent Project—Chief Scientist of Qi–Huang Project ([2020] No. 219); Zhong–yuan Scholars and Scientists Project (No.2018204); and Characteristic backbone discipline construction project of Henan Province (STG–ZYX03–202123).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The joint effect of leukocyte telomere length (LTL) and type 2 diabetes (T2D) on the risk of all-cause death has been sparsely explored. The study designed to examine the joint effect of T2D and LTL on the probability of death in American adults.A cohort of 6862 adults with LTL measurements and with or without T2D from the NHANES 1999-2002 with follow-up information until 2015 was studied. Quantitative PCR was used to measure the length of telomeres relative to standard reference DNA (T/S ratio). Individuals were grouped into three tertiles according to the LTL levels, with the first tertile demonstrating the lowest one and used as the reference group. The effects of LTL and T2D status on death were evaluated using Kaplan–Meier curves along with log-rank test. Three Cox proportional hazards models with adjustment for various confounders were used to examine the links between TL and all-cause death possibility using adjusted hazard ratios (HRs).P = 0.11). Conversely, the individuals with T2D had a higher probability of death than individuals without . When LTL and T2D status were investigated jointly, subjects in the highest TLT tertile and with T2D had the highest probability of mortality compared with their counterparts . However, there was no independent effect of low TLT on mortality as demonstrated among individuals with diabetes .Adults in the sample averaged 45.54 years of age, with 49.51% being male. After a median follow-up period of 14.4 years, 1543 (22.5%) individuals died from all cause. The probability of all-cause mortality was higher among individuals with LTL in the highest tertile than individuals in the lowest tertile ; however, the difference did not reach the level of statistical significance (The joint effect of TLT and T2D was larger than the sum of the independent effects on the risk of all-cause death. Participants with high TLT and diabetes showed the highest possibility of death compared with other groups. The worldwide prevalence of diabetes in adults has increased dramatically over recent decades, which has become as a significant cause of morbidity and mortality globally . Type 2 The telomeres at the ends of chromosomes serve as protective structures for the ends of eukaryotic chromosomes . TelomerThis study designed to explore whether LTL can, independently and jointly with T2D, influence the possibility of all-cause death using data from the NHANES. Understanding this association could contribute to prevent T2D and develop health promotion programs.Information of individuals enrolled in the NHANES 1999–2002, a nationally representative survey sample of civilian noninstitutionalized US population was utilized. In brief, as an ongoing cross-sectional survey, the NHANES surveys using complex, multi-stage and probability sampling approaches to estimate the health and nutritional status of the civilians in US and to provide vital and health statistics for the nation. NHANES organizes the family face-to-face interviews, medical examination, laboratory examination, and further gathers the details about the demographics, lifestyles factors, dietary intake, health status and medical history.A total of 21,004 individuals were recorded in the initial analysis. Individuals with missing records for telomere test were excluded . We further ruled out 751 subjects without reporting diabetes status. Because NHANES recorded who were ≥85 years old as 85 years, subjects younger than 18 years and older than 85 years were excluded (N = 211). Finally, missing data regarding follow-up were excluded (N = 3), thus yielding the sample size to 6,862 eligible adults Figure 1https://wwwn.cdc.gov/Nchs/Nhanes/2001-2002/TELO_B.htm. The CDC Institutional Review Board provided human subject approval for the study.LTL measures are available for two cycles of NHANES from 1999 to 2002. Blood samples containing their DNA were collected from all adults and stored at −80°C at the Centers for Disease Control and Prevention (CDC). For DNA analysis, qPCR assays were carried out at a laboratory in San Francisco, California to measure the telomere length relative to standard reference DNA, as described in detail elsewhere previously , which was computed as weight in kg divided by height in m, squared. Information on age (years), sex , family poverty income ratio, race/ethnicity , education , marital status , smoking , history of antidiabetic drug, and alcohol drinking was based on self-report during the questionnaire portion of the survey. For simplicity, there were two categories of marital status, namely, unmarried (widowed/divorced/separated/never married) and married (living as married). Alcohol drinking was defined as an individual who had alcohol abuse at least 12 times per year. Furthermore, individuals’ history of cardiovascular disease (CVD), cancer, asthma, and hypertension was obtained. Metabolic Syndrome (MetS) was defined as individuals with the evidence of three or more of five components  . Particihttps://www.cdc.gov/nchs/datalinkage/mortality-public.htm. Adult individuals were followed for mortality through December 31, 2015 to ascertain survival status. The primary endpoint event was all-cause death. The cause of death in the present study was based on ICD-10 system codes.Survival status data was downloaded from the National Death Index (NDI). The NHANES data was merged with the NDI using a unique study identifier. The detailed mortality records are available online at P-value was less than 0.05. A variety of analyses were performed with Empower software  and R version 3.6.3 .According to the NHANES recommendation and guidelines, an appropriate sampling weight for the variable of interest that was collected on the smallest number of respondents was calculated and accounted for complex multistage survey design strategies in the analysis. In the survey, continuous variables were expressed as a survey-weighted mean with 95% confidence intervals (CI), whereas categorical variables were expressed as survey-weighted percentages with 95% confidence intervals (CI). The LTL levels were divided into three tertiles, with the first tertile representing the lowest level and being considered the reference group. In comparison with those in tertile 1, the hazard ratios (HRs) and 95% CIs for subjects in tertile 2 and 3 were computed. A potential confounder was selected if it altered LTL estimates by more than 10% or was notably associated with mortality . The effIn total, 6862 adults participated in the study, which included 954 individuals with T2D and 5908 without. An overview of the demographic baseline characteristics of the included individuals can be found in Compared with individuals with LTL in the lower tertile (T1–T2), individuals in the highest tertile (T3) had a lower mean age, BMI, waist circumference, C-reactive protein, creatinine, total cholesterol, high school diploma, married status, history of cancer, hypertension, CVD, and MetS at baseline, and had a higher albumin, eGFR, and now smoking. Compared with individuals without T2D, individuals with T2D had higher mean age, BMI, waist circumference, C-reactive protein, creatinine, total cholesterol, uric acid, and more male, Mexican American, non-Hispanic Black, former smoking, history of cancer, hypertension, CVD, and MetS at baseline. Participants with T2D had lower family poverty income ratio, albumin, HDL-cholesterol, eGFR, alcohol drinking, never smoking, and now smoking.P < 0.0001), and model I . However, in the full adjusted model, LTL was not associated with a lower risk of death . The results remained when LTL was used as categorical variable. LTL in the higher tertiles  was not associated with decreased risk of death in the full adjusted model . When individuals without T2D were used as reference, it was found a significant association between LTL and all-cause mortality in the crude model , model I , and the full adjusted model .During a mean of 160 months of follow-up, 1543 deaths from all causes were identified. In P < 0.001; comparing LTL in the T1-T2 and T3 among those individuals with T2D, the P < 0.0001; Pfor trend < 0.001). However, the all-cause death risk was higher among the individuals with T2D.When LTL and T2D were investigated together, the Kaplan-Meyer curves demonstrated that the cumulative hazard of death notably differed among the four groups defined by LTL and T2D categories  after adjusting for age, smoking, cancer, sex, CVD, MetS, hypertension, eGFR, albumin, and antidiabetic drug. However, for individuals in lower LTL tertile (T1-T2), the all-cause mortality was comparable regardless of the T2D status . Nevertheless, in individuals without T2D, higher LTL tertile was associated with a decreased risk of all-cause death when compared with those individuals with lower LTL tertile .The Cox proportional hazards regression analyses in T2D is characterized by increased blood glucose values, while the LTL is used as a biological biomarker of cell ageing. Previous studies have demonstrated that shortened LTL was significantly associated with incident T2D , 25. ThuTelomere shortening is confirmed as a key molecular mechanism of vascular aging , and shoIn our study, we demonstrated a joint effect of T2D and LTL on the risk of mortality in American adults. The highest probability of all-cause death was noted in the group with highest LTL tertile and T2D. Furthermore, we also found that in individuals without T2D, higher LTL tertile was associated with a decreased risk of all-cause death when compared with those individuals with lower LTL tertile. A recent meta-analysis contained 6,991 individuals and 2,011 incident T2D events concluded that the combined relative risk for T2D incidence was 1.31 when comparing the lowest with the highest LTL at baseline . HoweverIn this prospective cohort study, a nationally representative survey of US adults is used to examine the joint effect of T2D and long-term care on mortality from all causes. However, there are several shortcomings in this study that need to be addressed. Firstly, it is possible that the measurement of LTL at baseline does not reveal all behavioral changes during follow-up. Furthermore, although multiple factors were adjusted in different models, unmeasured confounders associated with T2D and LTL may influence the findings.In a large survey conducted among US adults, it was found that the joint effect of TLT and T2D was larger than the sum of the independent effects on the risk of all-cause death. Participants with high TLT and diabetes had the highest risk of death compared with other groups.https://www.cdc.gov/nchs/nhanes/index.Publicly available datasets were analyzed in this study. This data can be found here: Publicly National Health and Nutrition Examination Survey dataset was analyzed in this study. They are publicly available at Ethical review and approval was not required for the study of human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients OR patients legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.BL and XZ contributed to the conception or design of the work. BL and YB contributed to the acquisition, analysis, or interpretation of data for the work. BL, YB, and XC drafted the manuscript. XZ critically revised the manuscript. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


PCSK9 with low levels of LDL-cholesterol, can lead to the discovery of new drug targets and treatments. Rare loss-of-function variants are more likely to be found in the homozygous state in consanguineous populations, and deep molecular phenotyping of blood samples from homozygous carriers can help to discriminate between silent and functional variants. Here, we combined whole-genome sequencing with proteomics and metabolomics for 2,935 individuals from the Qatar Biobank (QBB) to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. As proof-of-concept, we identified a homozygous carrier of a very rare PCSK9 variant with extremely low circulating PCSK9 levels and low LDL. Our study demonstrates that the chances of finding such variants are about 168 times higher in QBB compared with GnomAD and emphasizes the potential of consanguineous populations for drug discovery.Natural human knockouts of genes associated with desirable outcomes, such as   •Studied 3,000 individuals from a highly consanguineous Arab population•Found homozygous protein-changing variants associated with extreme protein/metabolite levels•Compared with non-consanguineous populations, 168 times more likely to identify human knockouts•Identified Qatar exclusive rare homozygous PCSK9 variants with low LDL-C Belkadi et al. combined whole-genome sequencing with proteomics and metabolomics in a highly consanguineous Arab population to discover rare homozygous protein-changing variants coinciding with extreme protein and metabolite levels. We found that the chance of identifying such variants is 168 times higher than in a non-consanguineous population. We manually curated the identified associations and selected 12 cases in which the mutated genes affected the corresponding protein and metabolite levels for further in-depth investigation as potential drug targets . The remaining PCV-extreme phenotype associations are presented in the supplementary tables.Here we used deep (30x) whole-genome sequencing data from 2,935 subjects included in the Qatar Biobank (QBB)PCSK9 variants associated with low PCSK9 protein and LDL-C levels: the first was the original rs11591147 variant that led to the development of PCSK9 inhibitors,ACY1 variants affecting both protein (ACY1) and related metabolite (acetylated amino acids) levels and one PLG variant possibly associated with excessive coagulation of the blood, which was identified in an individual already on warfarin treatment according to the responses given on medical questionnaires. The PCV-extreme phenotype associations reported here, thus, shed new light on the functions of the proteins affected and may support further drug target development.Interestingly, we identified two homozygous TTN and MUC4, carried 111 and 493 variants, respectively.We consider homozygous gene variants that alter the encoded protein and are rare in the population to be candidate human knockouts. After quality control filtering, we identified 32,868 exonic and 5′ UTR variants (PCVs) of 12,466 different genes that (1) had a moderate or high impact on the encoded protein, (2) were present in the homozygous state in at least one, but no more than five individuals from the 2,935 subjects included in QBB, and (3) had a minor allele frequency in the GnomAD populations <0.05 A. These Almost half  of the PCVs identified in QBB were not found in the homozygous state in 125,748 subjects from the different populations of the GnomAD project D. In totWe estimated the excess homozygosity in QBB by comparing the inbreeding coefficient F for QBB cohort with that for the Europeans of the 1000 Genomes Project A. The F ,Long runs of homozygosity (ROH) generated by recent consanguinity enable rare deleterious variants to exist in the homozygous form.The expected homozygote frequency (EHF) in QBB was estimated at seven per million for the median gene, a value 168 times higher than that for the non-consanguineous populations of GnomAD D. Based We characterized the 32,868 PCVs that were homozygous in one to five individuals in QBB further, by determining the levels of 1,305 blood-circulating proteins using the SomaLogic  affinity proteomics platform, and of 1,159 metabolites in plasma using the Metabolon  non-targeted metabolomics platform. We also included data from 71 clinical biochemistry assays  and the We estimated the false discovery rate (FDR) of our approach by sampling, by repeating the analysis 100 times with randomized sample ids. The mean number of trait-variant pairs found by chance was 370,384 for proteins and 296,643 for metabolites, suggesting that more than 95% of such associations (for either proteins or metabolites) would be expected to be found on average by chance A and S2Bin-cis protein associations (pPCVs), that is, variants affecting the protein determined. We identified 95 pPCVs for 63 proteins associated with 72 variants , by retaining only gene-metabolite pairs reported as metabolite quantitative trait loci (mQTLs) in genome-wide association studies (GWASs). We assumed that mQTLs indicate a functional link between the gene and the metabolite.in-cis protein associations) and mPCVs (metabolite association reported in the mGWAS) reported in each dataset were similar, regardless of the threshold used  that were reported in the Human Metabolite database HMDB, . These PAs per comparison, we ran two types of rare variant association with metabolites and laboratory data analyses: (1) a burden test using the combined multivariate and collapsing method (CMC),For two of the 12 cases—PCSK9 and PLG—extreme associations for proteomics and laboratory data were identified. For the ACY1 case, both protein (ACY1) and metabolite (acetylated amino acids) levels were extreme. For the betaine case, five homozygotes for PCVs of various genes  were associated with high betaine levels and different levels of dimethylglycine: high in individuals homozygous for the BHMT and SLC6A12 variants and low in individuals homozygous for the CBS and SLC6A5 variants. The eight remaining cases involved various homozygotes for a single gene that were found to be associated with one or several metabolites, such as ACSM2A with indolepropionic acid and phenylpropanoic acid; ABCC2 with the glycolic acid sulfate; PAOX with polyamine and polyamine metabolites, acetylspermidine, acisoga, and acetylisoputreanine; AFMID with formylanthranilic acid; UPB1 with beta-ureidopropionic acid and beta-aminoisobutyric acid; AOX1 with pyridoxic acid, methylnicotinamide, and N1-methyl-2-pyridone-5-carboxamide; and ALOX15 with linoleic acid and arachidonic acid.We describe below three cases that are well-established in the literature . We discuss the nine other cases in detail in the Supplementary Text.We identified one individual homozygous for the extensively studied PCSK9 missense variant rs11591147 in QBB. This variant has been reported to lower the levels of LDL in the blood and to reduce the risk of coronary heart disease. Confirming this finding, QBB homozygote for rs11591147 had a low level of LDL-C A. Our prACY1 on blood circulating protein and metabolite levels: the levels of ACY1 protein in the rs121912698 and rs2229152 homozygotes were the lowest of the 2,935 QBB participants  and angiostatin (the plasmin cleavage product) but normal level of active plasmin B. IndiviA detailed description of the three cases discussed here  and of the other nine cases is provided in the form of vignettes in the supplementary text.,,We identified homozygous PCVs causing extreme protein and metabolite levels in QBB. Our data show that the population included in QBB is highly consanguineous.In our extreme metabolite associations, we excluded individuals with undetected metabolite levels from the analysis. However, a PCV can disrupt the gene function related to the production of a specific metabolite resulting in undetected metabolite levels.,We identified PCVs causing extreme levels of proteins and metabolites, and extreme biochemical data, reflecting a disruption of gene function. The PCVs identified were PCSK9, BHMT, ACY1, PLG, ACSM2A, ABCG5, ABCC2, PAOX, AFMID, UPB1, AOX1, and ALOX15. PCSK9 provides proof-of-concept for our strategy for identifying true associations.In summary, we report here evidence for both new and previously reported associations between genetic variants and extreme protein and metabolite levels in a consanguineous population. The protein  and meta,,,Our study also has some limitations. First, genetic variation affecting the protein sequence may lead to changes in the higher order structure of the protein and, thus, its aptamer binding affinity.The members of the Qatar Genome Program Research Consortium are Said I. Ismail, Wadha Al-Muftah, Radja Badji, Hamdi Mbarek, Dima Darwish, Tasnim Fadl, Heba Yasin, Maryem Ennaifar, Rania Abdellatif, Fatima Alkuwari, Muhammad Alvi, Yasser Al-Sarraj, Chadi Saad, Asmaa Althani, Eleni Fethnou, Fatima Qafoud, Eiman Alkhayat, Nahla Afifi, Sara Tomei, Wei Liu, Stephan Lorenz, Najeeb Syed, Hakeem Almabrazi, Fazulur Rehaman Vempalli, Ramzi Temanni, Tariq Abu Saqri, Mohammedhusen Khatib, Mehshad Hamza, Tariq Abu Zaid, Ahmed El Khouly, Tushar Pathare, Shafeeq Poolat, Rashid Al-Ali, Omar Albagha, Souhaila Al-Khodor, Mashael Alshafai, Ramin Badii, Lotfi Chouchane, Xavier Estivill, Khalid Fakhro, Hamdi Mbarek, Younes Mokrab, Jithesh V. Puthen, Karsten Suhre, and Zohreh Tatari.kas2049@qatar-med.cornell.edu).Further information and requests may be directed to the lead contact Karsten Suhre . Reads were then aligned with the GRCh37 (hs37d53) reference genome, with bwa.kit (v0.7.12) (https://github.com/lh3/bwa/tree/master/bwakit). Quality control was performed on the mapped reads with Picard (v1.117) [CollectWgsMetrics] (https://gatk.broadinstitute.org/hc/en-us). Variant calling was performed in accordance with GATK 3.4 best practices (https://software.broadinstitute.org/gatk/documentation/article?id=3238): indel realignment and base recalibration were performed on the initial bam file and HaplotypeCaller was then run on each sample to generate an intermediate genomic variant call file (gVCF). Joint variant calling was performed simultaneously on all the gVCF files generated. We first ran GenomicsDB8 to combine the different samples by region, and we then ran GenotypeGVCFs for each region. We applied the variant quality score recalibrator (VQSR) for SNVs and indels separately. In total, 105 million variants passed the VQSR filter for 2,935 QBB individuals.DNA was extracted from peripheral blood with the automated QIASymphony SP instrument, according to the manufacturer’s instructions . DNA was quantified in the Quant-iT dsDNA Assay  on a FlexStation 3 machine . Whole-genome sequencing was performed with 2 × 150 bp paired-end reads on a HiSeq X Ten sequencer  at the Sidra Clinical Genomics Laboratory Sequencing Facility. The mean depth of coverage was 30x. Quality control was performed on the reads (Fastq files) with FastQC (v0.11.2) (The Variant Effect Predictor (VEP) was used to annotate the 100 million variants identified.Common functional alleles are less likely to exert strong functional effects as they are less constrained by purifying selection. We, thus, retained 35,567 protein-changing variants (PCVs) for which one to five homozygotes were present in QBB for analysis. To identify rare PCV that were not present in the non-consanguineous population, we excluded 2,699 PCVs with a minor allele frequency (MAF) ≥ 0.05 in the GnomAD populations. We finally retained 32,868 PCVs in the analysis and we annotated the identified PCVs with both the Combined Annotation Dependent Depletion (CADD)The coefficient of inbreeding distributions of 2,935 QBB individuals were compared with those of 503 Europeans from the 1,000 genomes project.We estimated the mean proportion of the genome covered by ROH in QBB participants, as previously described for the East London Genes & Health (ELGH) study2 + ap(1 – a)p, where p is the cumulative allele frequency (CAF) estimated for all GnomAD individuals and a is the mean proportion of the genome covered by ROH in individuals self-reporting having second-cousin or more closely related parents in the ELGH study 5419. In GnomAD, the CAF was calculated as p = 1 – sqrt(q), where q is the fraction of GnomAD participants without loss of function. This calculation therefore requires the individual genotyping of GnomAD participants and is time-consuming. Here, we calculated the ‘classic’ CAF, as described by Minikel et al.The EHF in the GnomAD consanguineous population was estimated as EHF = We applied the method used on the GnomAD consanguineous population to estimate the EHF in QBB. The EHF for the GnomAD non-consanguineous population was estimated at four per hundred million for the median gene, a value different from the EHF reported by Minikel et al.The levels of 1,305 proteins were measured with the Somascan aptamer-based proteomics platform for 2,935 QBB participants. Six aptamers targeting human virus proteins were excluded. The Uniprot identifier for each protein was used in the BiomaRt package to identify the associated protein-coding genes.For the identification of variants associated with extreme protein levels in QBB, we first browsed the 32,868 PCVs and identified homozygotes. For each PCV, there were one to five homozygotes. We restricted analyses to proteins for which all homozygotes ranked in the top 20 or the bottom 20 for protein levels. In total, 378,572 associations of 20,909 PCVs from 10,025 protein-coding genes with 1,301 proteins were identified.The levels of 1,159 metabolites were determined for 2,935 QBB participants with the non-targeted HD4 metabolomics platform from Metabolon. For the identification of PCVs associated with extreme metabolite levels, we excluded participants with missing metabolite determinations. As for proteins, analyses of metabolite associations were restricted to situations in which all homozygotes ranked in the top 20 or the bottom 20 for metabolite levels. Only metabolites with non-missing measures for more than 1,000 QBB participants were kept. In total, we identified 312,899 associations of 29,999 PCVs from 11,967 protein-coding genes with 989 metabolites.A knowledge of the distribution of a test statistic under the null hypothesis is important for FDR estimation. Permutation methods have become popular for estimating null distributions, due to their flexibility and generalizability. For both proteins and metabolites, we performed 100 permutations to estimate the null distribution. We randomly permuted the participant identifiers 100 times and determined the extreme associations for each permuted dataset, as for the unpermuted data. The FDR was estimated by dividing the mean number of associations in the permuted data by the number of associations identified in the unpermuted data.in cis associations (pPCVs).We corrected for multiple testing and decreased the FDR for protein associations, by retaining associations for which the PCVs causing extreme protein level were variants of the gene encoding the protein concerned, that is, ,For metabolites, we decreased the FDR by extracting pairs of genes and metabolites from metabolite associations already reported in the available mGWAS (mQTLs) (mPCVs). We extracted the mQTLs from the mGWAS reported in the GWAS catalog.https://hmdb.ca/) to identify metabolite associations reported in the HMDB. An in-house python script was used to extract metabolite-enzyme and metabolite-transporter associations. We linked the metabolites with a known HMDB identifier to enzymes, transporters and carriers as reported in the HMDB. The complete list of all associations is also provided in The XML version of the HMDBMissing data can be classified in three patterns: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). The missingness is MCAR, if the probability of being missing is the same for all cases; MAR, if the probability of being missing is the same only within groups defined by the observed data; MNAR, if the probability of being missing depends on both observed and non-observed quantities. The missingness in most of metabolites is due to the limits of detection (LOD).p values indicate a low probability of these values being missing by chance.We used Fisher’s exact test to determine whether the missing metabolite or laboratory data values for homozygotes were missing by chance, in analyses of the effects of PCVs on the metabolite level and laboratory data. The two inputs for Fisher’s exact test were: (a) the number of missing values for each genotype group , and (b) the total number of samples in each genotype group. We used fisher.test method in R for this calculation. In case of missingness due to LOD, low In gene-based association analyses, the effects of various variants on a gene unit are aggregated in burden and SKAT tests. We used the ‘FamCMC’ and ‘FamSKAT’ functions implemented in rvtestsp value was calculated by dividing the product of extreme ranks for homozygotes by the total number of QBB participants to the power of the number of homozygotes:k is the number of homozygotes, N is the number of non-missing data points divided by 2 (division by 2 because we looked at both ends of the extreme) and Ri is the extreme rank of homozygote i.We collected additional evidence for pPCVs and mPCVs, by identifying homozygous participants. The extreme ranks for homozygotes were obtained for all phenotypes: 71 for laboratory data, 1,159 for metabolites and 1,299 for proteins. An approximate After correction for multi-testing, a significant p-value will be:M is the total number of phenotypes: M = 2,529 . A quantile-quantile plot /2,9353 = 2.37e-10. A low p value for a phenotype indicates a statistically significant association.Note that the same extreme ranks for high and low levels give the same 2 > 0.8) were extracted with Phenoscanner.Genome-wide association study hits, expression quantitative trait loci (QTL), protein QTL, metabolite QTL, and methylation QTL displaying strong linkage disequilibrium with pPCVs and mPCVs in the European population (rWe used the R package VarfromPDBhttps://go.drugbank.com) and used the lxmx toolkit implemented in Python (v.2.7) to identify genes carrying pPCVs and mPCVs associated with drugs. We included only gene-drug combinations with a known action of the drugs on the genes.We downloaded the DrugBank database (The quantitative and statistical analyses are described in the relevant sections of the


These results suggest that genotyping genes in these pathways may help predict the response to antiresorptive drugs and hence make personalized therapeutic choices.Osteoporosis is a skeletal disorder defined by a decreased bone mineral density (BMD) and an increased susceptibility to fractures. Bisphosphonates and selective oestrogen receptor modulators (SERM) are among the most widely used drugs. They inhibit bone resorption by targeting the mevalonate and oestrogen pathways, respectively. The aim of this study was to determine if common variants of genes in those pathways influence drug responses. We studied 192 women treated with oral aminobisphosphonates and 51 with SERMs. Genotypes at 154 SNPs of the mevalonate pathway and 806 in the oestrogen pathway were analyzed. Several SNPs located in genes FDPS and FNTA were associated with the bisphosphonate-induced changes in hip bone mineral density (BMD), whereas polymorphisms of the PDSS1, CYP19A1, CYP1A1, and CYP1A2 genes were associated with SERM-induced changes in spine BMD. After multivariate analyses, genotypes combining genes FDPS and FNTA showed a stronger association with bisphosphonate response (r = 0.34;  Osteoporosis is a highly prevalent disorder, particularly among postmenopausal women and elderly men. It is characterized by a decrease in bone mass and alterations of bone microarchitecture that impair the ability of the skeleton to resist mechanical loads . FragiliOsteoporosis, and specifically osteoporosis-related fractures, represent an important burden both for individual patients and the health systems. Fortunately, several drugs effectively impact BMD and decrease fracture risk. The most widely used drugs belong to the antiresorptive class because they inhibit bone resorption. Among them, selective oestrogen receptor modulators (SERMs) increase BMD at the spine and lower the risk of vertebral fractures but do not show consistent effects at the hip . AminobiBone turnover is a major determinant of BMD, which is the consequence of the balance between bone formation and bone resorption. These processes take several months at each remodelling site. Therefore, therapy-induced changes in BMD can provide useful information about drug effectiveness, but usually take at least 2 years to develop. Hence, for clinicians and patients making therapeutic choices, it could be very interesting to have tools helping to predict the probability of responses to a certain drug.Bone mass has an important hereditary component . GeneticWe included postmenopausal osteoporotic women who were treated with oral aminobisphosphonates  or with SERMs . Subjects diagnosed with secondary osteoporosis were excluded. BMD was measured twice by dual X-ray densitometry (DXA) at the spine and the hip, both at baseline and after 1 to 4 years of treatment, using a Hologic QDR 4500 densitometer . In vivo precision was 0.5% at the lumbar spine and 0.4% at total hip. The mean annual change in BMD was computed for each patient and region and expressed as the percentage of baseline BMD. Patients with annual BMD changes >6% or <−6% were regarded as unreliable, and thus, they were excluded from the analysis.The study protocol was approved by the institutional review board (Comité de Ética en Investigación Clínica de Cantabria). All patients gave informed written consent.DNA was isolated from 200 µL aliquots of peripheral blood using commercially available column-based kits, following manufacturer instructions. DNA was quantitated by both Nanodrop and Qubit dsDNA BR Assay Kit .DNA samples were genotyped at the Spanish National Genotyping Center , using the Axiom™ Spain Biobank array and strictly following the manufacturer’s instructions . Briefly, total genomic DNA (200 ng) was amplified and randomly fragmented into 25 to 125 base pair fragments, which were then purified and resuspended in a hybridization cocktail. The hyb-ready targets were then transferred to the GeneTitan Multichannel Instrument for automated, hands-free processing . CEL files were automatically processed for allele calling using the Axiom GT1 algorithm available through the Axiom Analysis Suite v4.0.3.3 and following the Axiom™ Genotyping Solution Data Analysis User Guide .p < 1 × 10−6), were also excluded from further analyses.The Axiom Analysis Suite and PLINK software were applied to conduct the quality control of genotype data. Thresholds were DQC: ≥0.82 and call rate: ≥97%. The percent of passing samples was ≥95, and the average call rate for passing samples was ≥98.5. In addition, SNPs with minor allele frequency less than 0.02, or departure from the Hardy-Weinberg equilibrium -HWE- . These analyses as well as haplotype-based analysis were carried out using PLINK software . The sigAnalyses combining alleles from several genes were carried out by multiple linear regression using the R package “olsrr”. Gene-based analysis that incorporates the association data of all SNPs in the gene was carried out with MAGMA .We included 243 patients in the analysis. As stated, those with BMD changes >6% were excluded because of potential errors in DXA analysis. Thus, the final study sample included 205 patients with valid densitometric data for the lumbar spine and 230 for the total hip. p = 2.2 × 10−16) and 0.8% for the total hip (p = 9.5 × 10−9). In those on SERMs, the mean annual changes were 0.8% and 0.4%, at the spine (p = 0.0065) and the hip (p = 0.1225), respectively.The mean annual change in BMD in BP-treated patients was 2.2% at the lumbar spine  with the BP-induced change in spine BMD, whereas 15 SNPs  were associated with hip BMD changes  with spine BMD changes and 31 with hip BMD changes. None of them reached the overall-corrected significance threshold (0.05/806 = 0.000062).The association of the 806 SNPs related to the 36 genes in the oestrogen pathway with BMD changes in SERM-treated patients is shown in The results were also explored using Venn diagrams , showingSince no significant changes in hip BMD were found in the SERM-treated group, these data were not further analysed. The number of SNPs included varied across the genes studied, and we used a significance threshold adjusted by the number of SNPs in each gene. Thus, genes with at least one SNP significantly associated with BMD changes are shown in p = 0.0008; p = 0.0009, and p = 0.0065, respectively). The three pairs were in strong LD, with D’ score values equal to 1 , an omnibus haplotype-based association analysis was performed. In all three genes, a significant association with drug-induced change in BMD was detected , with a window of 10 kb upstream and 10 kb downstream of each gene. The results are shown in Performing multivariate linear regression analyses, by selecting the most significant SNP of each gene and adjusting the results by anthropometric variables and duration of therapy , did not significantly modify the results. There was no interaction between the variables.p = 0.25). The mean changes in each genotype combination are shown in the We also explored the combined effect of several genetic polymorphisms using stepwise linear regression. The combination of FDPS and FNTA gene polymorphisms increased the association value of each separate gene with the hip BMD changes in the BP group . There wp = 0.65). The average changes in each combined genotype are shown in The combination of PDSS1, CYP19A1, CYP1A1, and CYP1A2 genes also increased the association value (in comparison with each gene separately) with spine BMD changes in the SERMs group. In the stepwise analysis, CYP19A1 and PDSS1 were the genes with a significant independent association with spine BMD change . There wBone mass and strength are maintained by the balance between bone resorption and bone formation, carried out by osteoclasts and osteoblasts, respectively. Thus, when bone resorption exceeds bone formation, bone mass decreases, and the strength of bone is impaired, leading to a high risk of fragility fractures, the main consequence of osteoporosis. Several factors determine the strength of the skeleton, including absolute bone mass, geometry, bone turnover, microarchitecture, and some other parameters of bone quality. Nevertheless, in clinical practice, BMD assessed by DXA is the main determinant of fracture risk and an important surrogate marker of the antifracture effect of antiosteoporotic drugs .Antiresorptive agents, including BPs, SERMs, and denosumab, are the most widely used therapy for osteoporosis. The oral BPs, alendronate and risedronate, are the preferred therapy for many patients. These drugs inhibit bone resorption, reduce bone turnover, increase BMD, and decrease the risk of vertebral and nonvertebral fractures, including hip fractures .AminoBPs, such as risedronate and alendronate, act by targeting the mevalonate pathway, which is involved in the biosynthesis of cholesterol and isoprenoid lipids . The latMany regulatory proteins are subjected to prenylation, including small GTPases of the Ras, Rho, Rac, Cdc42, and Rab families. They play a critical role in the regulation of osteoclast activity, and subsequently bone resorption, such as cytoskeletal arrangement, membrane ruffling, trafficking of intracellular vesicles, and apoptosis .Type I geranylgeranyltransferase and farnesyltransferase are heterodimers that share the same alpha subunit (encoded by the FNTA gene) but have different beta subunits . Type II geranylgeranyl transferase  chains are encoded by the RabGGTA and RabGGTB genes . FarnesyFarnesyl diphosphate synthase (FDPS) is the main target of BPs, but they may also inhibit other enzymes, including isopentenyl diphosphate (IPP) isomerase, geranylgeranyl diphosphate synthase (GGPP), geranylgeranyl diphosphate synthase (GGDPS), geranylgeranyl transferase, and squalene synthase ,18. Intehttps://gtexportal.org (accessed on 14 February 2022)), with normalized effect sizes of about 0.3. Hence, it is unclear if any of these SNPs or some others else in linkage disequilibrium is leading the pharmacogenetic effect.In this study, we found an association between the changes in hip BMD induced by BP and allelic variants of the FDPS and FNTA genes, encoding FDPS and the alpha chain of farnesyl transferase, respectively see . The assOur results also indicate that FNTA genetic variants are related to the BP effect. In particular, rs35601968, a noncoding variant located in the first intron of the FNTA gene. The minor allele of rs35601968 was also associated with less positive changes in BMD following BP therapy. This SNP is located about 27 kb from the TSS, and no evidence supports its role as an eQTL. Therefore, the mechanisms explaining the association are unknown. It is unclear if prenyltransferases are direct targets of BPs. However, the relative abundance of farnesyltransferases and geranylgeranyltransferases impact the balance between farnesylation and geranylgeranylation of proteins, which in turn may influence cellular activities . TherefoBone remodelling and bone turnover are under the control of several local and systemic factors. Among them, oestrogens play a major role in bone mass acquisition and maintenance. Thus, oestrogens decrease bone resorption and reduce the risk of fractures. Unfortunately, the possibility of adverse effects precludes widespread use of these agents for preventing or treating osteoporosis. SERMs were developed to overcome some of these drawbacks, particularly the protumorigenic effect. Thus, SERMs, such as raloxifene or bazedoxifene, have a neutral or inhibitory effect on the proliferation of endometrial and breast tissues, while they increase BMD at the spine and lower the risk of vertebral fractures. However, they have a smaller antiresorptive effect than oestrogens, and they do not have clear effects on bone mass at the hip . In lineOvaries are the main source of oestrogens in young women. However, in postmenopausal women and men, a certain level of oestrogens is maintained by the activity of aromatase . This enp < 0.05) with lumbar spine BMD changes after SERM therapy. Two of them, rs3889391 and rs1062033, showed p-values below the adjusted threshold, considering the number of SNPs analysed in the CYP19A1 gene. Both intronic variants were in strong LD, and their minor alleles were associated with a smaller BMD response induced by SERMs treatment. These SNPs are also in strong LD with other SNPs located from the 5’-region down to the coding region of CYP19A1  gene, required for oestrogen synthesis, particularly after menopause, and in the PDSS1 gene, which is part of the mitochondrial respiratory chain, are associated with the response to SERMs. These results may help build polygenic scores that help predict the response to drug therapy and therefore, to personalize therapy of osteoporosis and other bone fragility conditions. On the other hand, in view of these results, it may be worthwhile to explore the skeletal effect of drugs targeting mevalonate-related enzymes other than bisphosphonates.


Physical activity is an essential need of the human body that helps improve the physical fitness of an individual and creates a positive impact on overall wellbeing. Smartphone applications play an essential role in providing several benefits to consumers by offering various capabilities in terms of health and fitness.COVID-19 preventive measures shut down public places, and people cannot go to the gym and parks for physical activity. Smart applications for physical activity are an effective way to keep active while staying at home.The objective of the present study was to assess the mediating role of the e-platforms physical activity among the Chinese people in China during the COVID-19 lockdown.The participants in this study were Chinese citizens living in home isolation during the early stages of the epidemic in China. The primary data was collected via an online survey using a convenience sample strategy in accordance with the study purpose. The collected data were cleaned by using the SPSS-25 statistical software. SmartPLS 3.0 software was used to investigate the suggested study framework utilizing the structural equation modeling technique.n = 2,626) male and 51% females in the entire sample. SEM results show that all hypotheses  were statistically confirmed.Descriptive statistics shows that the ratio of gender includes 49% (In COVID-19, when there are limited physical activity resources, smart applications play an essential role as an alternative to gyms and change people's perspective regarding the adoption of health and fitness. Smart applications have made exercise and physical activity accessible and convenient to adopt. The outbreak of COVID-19 emerged in China in December 2019 and spread worldwide. It has affected more than 179 M people and resulted in 3.08 M deaths worldwide. Due to COVID-19, there were lockdown conditions due to which people had to stay at home. In addition, everyone was under quarantine which was applied to alleviate the additional risk of contracting this virus among individuals. Physical activity is an essential need of the human body that helps improve the physical fitness of an individual and creates a positive impact on their mental health. It helps individuals combat various diseases and reduces stress levels .H2: The live streaming workout classes (LSWc) are positively influenced by the COVID19 preventive measures (CPM).H3: The use of virtual reality fitness (VRF) for physical activity is positively influenced by the COVID19 preventive measures (CPM).H4: COVID19 preventive measures (CPM) positively influence physical activities (PA's).H5: Fitness apps (FA's) positively affect physical activities (PA's).H6: Physical activities (PA's) positively impacted by live-streaming workout classes (LSWc).H7: Physical activities (PA's) are positively impacted by virtual reality fitness (VRF).H8: The relationship between COVID19 preventive measures (CPM) and physical activities (PA's) is positively mediated by fitness apps (FA's).H9: The relationship between COVID19 preventive measures (CPM) and physical activities (PA's) is positively mediated by live-streaming workout classes (LSWc).H10: The relationship between COVID19 preventive measures (CPM) and physical activities (PA's) is positively mediated by virtual reality fitness (VRF).The method section comprises four parts; study locale, participants, instrument and data collection, measures, and analysis.The current study was conducted in China. The study upheld the standards of the World Medical Helsinki Policy. Before the final data collection, the study was approved by the Ethical Committee of Soochow University, Suzhou, Jiangsu. The study participants gave their informed consent prior to the start of the final data collection.The participants in this study were Chinese citizens living in home isolation during the early stages of the epidemic in China. In order to obtain primary data for the study, a convenience sampling strategy was adopted. Convenience sampling is the most common type of non-probability sampling, and it focuses on getting responses from persons who are 'convenient' for the investigator to contact. The current study used convenience sampling due to time limitations, cheap sampling method, easy to use, and getting results quickly. A pre-testing of 75 respondents was conducted before the final survey to determine the reliability. Between January and March of 2020, an online survey was done. A total of 5,600 questionnaires were sent to the targeted population. About 5,450 (97.32%) questionnaires were responded to by the eligible individuals for participation in the study. After a quality check of the data, a total of 5,351 respondents' response was analyzed see .A well-designed questionnaire was used to obtain primary data. Demographic questions were related to gender, age, education, occupation, and marital status. Closed-ended, open-ended, and Likert scale items were used in the questionnaire see .Regular exercise is a natural remedy for a variety of ailments and strengthens the immune system of the human body. The World Health Organization issued specific instructions for remaining active at home throughout the lockdown time . PhysicaCOVID-19 is a newly discovered infectious disease affecting society globally in every aspect of life. Health experts, especially the WHO, announced various measures to prevent the COVID-19 transmission for health and wellbeing. Preventive measures related to the COVID-19 include washing hands, keeping social distance, wearing masks, staying at home, darning lockdown, quarantine, doing physical activity while staying at home, and monitoring health . COVID-1To display the demographic data and clean and prepare the data for testing all hypotheses, SPSS-25 statistical software was utilized. For this work, the proposed research framework was explored utilizing the structural equation modeling (SEM) technique, which was implemented using SmartPLS 3.0 software. In terms of constructs and indicators, the partial least squares (PLS) SEM approach was employed . The SEMn = 2,626) male and 51% females of the full sample. For age, 2,848  respondents belonged to the 20–29 age group; 30–39 had 25.70%  respondents. The remaining 20% of respondents belong to three groups (the 40–49 age group have 527 respondents) (50–59 age group have 444 respondents), and (60+ age group have 156 respondents). The education profile of respondents was included with college graduation 31.34% , university graduate 42.46% , and others 26.20% . Further, most of total respondents were earning privately with 42.7% of 5,351 , 19.8% of 5,351 were students, 16.8% of 5,351 (n = 897) were self-employed, government employees were 11.7 % of 5,351 (n = 628), the retired respondents were less with 0.7% (n = 39), 1% (n = 48) have no occupation, and 7.3% of 5,351 (n = 390) have nameless occupations. More, 62.5% of 5,351  respondents were married, 35% of 5,351  were single/unmarried, and 133 respondents were belonging to nameless marital option.According to To examine the construct reliability and validity, the factor loadings, average variance extracted (AVE), scale composite reliability (SCR), and convergent and discriminant validity are used. The factor loadings of all items were found between 0.651 and 0.942, and loading values were significant at a 5% level see . Several2), normed fit index (NFI), unweighted least squares discrepancy (d_ULS), and geodesic discrepancy (d_G) values  value, Chi-Square (χ) values . The staP-values, and T-values. Surprisingly, SEM results show that all assumptions were statistically significant at P = <0.001. In other words, overall Covid-19 preventive measurement (CPM) has a substantial effect on human physical activities PA. According to Hypothesis H1, H2, and H3, the impact of CPM on the use of advanced technologies such as fitness apps (FA), live streaming workout classes (LSWc), and virtual reality fitness (VRF) is proposed as positive. H1 indicates that CPM positively influences FA with  values, which confirmed the acceptance. The estimated values  of H2 indicate that the effect of CPM on LSWc is positive and statistically confirmed. According to H3, VRF is positively affected by CPM , and H3 is accepted. Further, the direct effect of CPM on PA as H4 is also included. The results for H4 indicate that CPM has a positive impact on PA with , which means the proposed H4 is supported. The effect of using technologies such as FA, LSWc, and VRF toward PA is presented as H5, H6, and H7, respectively. Final results indicate that H5, H6, and H7 are supportive, and the effect of FA, LSWc, and VRF on PA are positive with , , and , respectively. To examine the indirect relationships among CPM and PA, hypotheses H8, H9, and H10 are proposed as the positive mediating role of FA, LSWc, and VRF. SEM results show that the indirect effect of FA, LSWc, and VRF is positive and supportive with  for H8,  for H9, and  for H10.The structural model is examined by achieving the validity of the measurement model, and the structural model is confirmed (Reference). The SEM approach tests the proposed hypothesis in the conceptual framework. SEM approach accepts or rejects the proposed hypothesis based on the significance level (5% level) of relationships13. Six direct and four indirect assumptions are presented in the structural model, and the structural model was examined by employing the bootstrapping method See . The resPhysical exercise plays a vital role in the life of a person. It helps to improve an individual's mental and physical health and other benefits in the long term. It also helps reduce many disease risks like those who include physical exercise in their routine face fewer risks of cardiovascular, cancer, and type 2 diabetes diseases. Those who do physical activity regularly can improve their lives and reduce the risk of chronic illness. It helps enhance sleep quality, manage weight, improve memory and brain function in people of all ages, improves joint stiffness, and maintain balance strength muscles . The fouT = 43.068, and P = <0.001) confirm that smart applications such as fitness apps are positively influenced by preventive measures against the COVID-19. People highly acknowledge the importance of physical activity for health and wellbeing during the COVID-19 preventive measures and adopt various smart and digital platforms for active living through physical activity. Similar results were reported by Lynch et al. , of youngsters have a smartphone . Smart ah et al. , and theh et al. . Health h et al. . A health et al. .T = 41.078, and P = <0.001). The LSWc has a positive role in promoting physical activity and achieving the recommended level of physical activity during the CPM. Parker et al.  show that VRF has a positive impact on PA during the CPM implementations. Virtual reality fitness apps are based upon virtual environment exergames. Exercise with video games that rely on technology refers to an exergame that measures body movements during physical activity  who were in home isolation throughout the epidemic and engaged in physical exercise via internet platforms under the “Internet plus Exercise” intervention. The participant's participation in the study was voluntary. Under the non-probability sampling (NPS) strategy, the convenience sampling method was utilized to acquire data. The original data were collected by a self-administered survey, and the findings of convenience sampling cannot be applied to the entire population.A future study can be conducted jointly keeping in view the WHO guidelines about the use of the app for fitness and the Chinese government intervention “Internet plus Exercise” for physical activity in the context of the kind of exercise; mobility, strengthening, aerobic, anaerobic, the average time people used the products; per session, per week, overall.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethical Committee of Soochow University, Suzhou, Jiangsu, approved the study before the final data collection. The patients/participants provided their written informed consent to participate in this study.RM is the principal investigator, designed the study developed the hypothesis, conducted the survey, and wrote the final draft. ZS performed the statistical analysis. RM, RL, JD, and XP review and edit the final draft. All authors approved the final draft for publication.Hunan Province Educational Science Planning Project Lost and Redemption: Research on School Sports Justice in the Background of Sports Middle School Entrance Examination (XJK22QTW001).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Japan has witnessed an unusual increase in the number of suicides among women during the coronavirus disease 2019 pandemic. An analysis is required to identify the influencing factors during the pandemic and develop new measures for preventing suicides.Data on the number of monthly suicides were collected from the National Police Agency of Japan. The expected number of suicides among women during the pandemic was estimated using a time-series model based on pre-pandemic data, considering year-to-year trends. The observed-to-expected (O/E) ratio of suicides was estimated from March 2020 to October 2021 using job status, suicide motive, and age.The number of suicides among women in Japan increased beyond the expected number until October 2021. The O/E ratio based on job status, suicide motive, and age  was significantly above 1.0 from March–December 2020, and the increase in suicides continued in almost all categories in 2021.Although several reasons were reported for increased suicides among women in Japan during the pandemic , suicides increased irrespective of job status, suicide motive, or age. Comprehensive measures to prevent suicide might have been important during the pandemic, instead of limiting interventions to the reported specific population. The increase was 37% higher than the pre-pandemic period, particularly between July and October 2020.2–4 Surprisingly, such an increase has not been observed in other developing countries.5,6 This unusual increase in women’s suicides during the pandemic is a serious concern, particularly as the COVID-19-related mortality rate is declining with widespread vaccination.7Despite substantial mitigation efforts, the suicide rate in Japan has been alarming among both men and women.8 The stress from personal responsibilities, such as family obligations,9,10 was considered a key cause impacting this high prevalence. During the pandemic, the economic downturn, worsening unemployment, and exacerbation of psychiatric disorders—more frequent among women than men—could have contributed to the increased numbers in psychologically vulnerable Japanese women.11,12 We need to identify the factors influencing suicide among women during the pandemic to prevent suicide as the pandemic continues. Previous studies have reported increased suicide rates for multiple women’s categories.13,14Before the COVID-19 pandemic, Japan had the second highest suicide rate among women globally, with 8.6 suicides per 100,000 women.15,16 However, previous studies investigating categories13,14 have ignored these trends. This may have led to the underestimation of their impact on suicides during this pandemic.These numbers of suicides have been associated with annual changes in the unemployment rate; the trends change depending on such factors.To address this research gap, we evaluated the observed-to-expected (O/E) ratio of female suicides in Japan from March 2020 to October 2021, which were categorized using job status, suicide motive, and age. A time-series trend analysis was considered to assess the suicide and unemployment rate before the pandemic. The O/E subgroup ratios based on the COVID-19 infection was compared to evaluate regional differences.13 March–December 2020 was defined as Term I and January–October 2021 as Term II; emergency declarations and steps to prevent infection spread continued intermittently in Term II.We downloaded the monthly number of female suicides from January 2013 to October 2021 collected by the National Police Agency from the website of the Ministry of Health, Labour and Welfare.The monthly female suicides were categorized by job status, suicide motive, and age. The job status categories were as follows: self-employed, employee, student, housewife, unemployed, pension/unemployment insurance liver, other non-worker, and unknown. Suicide motives included family, health, economic, work-related, relationship, school-related, other problems, and unknown reasons; each suicide had one to three motives. The ages of the women who attempted suicide were categorized as: <20, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, and ≥80 years. Eleven suicides in the “unknown” age category were excluded.17 and unemployment rates18 were obtained from the Statistics Bureau of Japan. The prefecture population was allocated using each year’s composition ratio. The cumulative number of COVID-19 cases in the prefectures for January 2021 were collected using a web-based system.19Monthly provisional female populationA formal ethical review was not required as public aggregate data were used.tO and tE represented the observed and expected number of suicides during the month t  in Terms I and II, respectively.The O/E ratio was defined as the total number of observed women’s suicides in Japan divided by the total number of expected suicides during a non-pandemic period. The O/E ratio for each category was derived as:tE in terms of the number of suicides (ty) based on female suicide trends in each category from January 2013 to February 20203:tmonth represented the month during t and I(·) is the indicator function. tu and tu−1 represented the unemployment rates during t, and t − 1, and tpop represented the population during t.We used the following time-series model to estimate β0, Mβ , Uβ1 and Uβ2—were estimated in the model where Mβ at t is set to zero for M = 1. Although in a previous study, the time-series model included the year,16 we excluded it from our model due to the strong correlation between the unemployment rate and time.The coefficients—tE for Terms I and II. We substituted tu with the mean unemployment rate at t in 2018 and 2019 to evaluate the pandemic’s impact, as the unemployment rate had been stable in the beginning of 2018 until the COVID-19 outbreak.18Based on our estimated model, we calculated 16 that included the year effect. We also plotted the number of men’s suicides.First, we plotted the number of women’s suicides between January 2013 and October 2021 using a 95% prediction interval (PI). Our analysis focused on data before February 2020, and we applied a model proposed in our studyNext, we estimated the O/E ratios for the categories of job status, suicide motive, and age for Terms I and II, provided the profile-likelihood-based 95% confidence interval (CI) of the O/E ratio and its forest plots, and conducted sensitivity analyses on the unemployment rates in 2018 and 2019.To evaluate regional difference due to COVID-19 infection, we estimated O/E ratios from March 2020 to October 2021 in subgroups defined as cumulative number of cased <1,000, 1,000 to 2,000 and ≥2,000 per million population.P < 0.05 for all statistical analyses, which were conducted using the R software .The significance level was set at The number of women’s suicides during the pandemic period after March 2020 was 11,786. Since July 2020, the number of women’s suicides across all months have increased beyond the interval predicted by the steadily decreasing suicide trend since 2013, the non-pandemic period  were relatively higher than those in other categories. A recent report showed that young women are susceptible to COVID-19 fears and the stigma associated with it.5 Numerous common factors might be affecting the suicide rates in Japan during the pandemic. For example, due to shutting down of schools, daycare centers, and inaccessible formal medical care, Japanese women have to personally care for family members, owing to gender stereotypes in the Japanese culture,23,24 thus leading to poor mental health and suicide.25,26 Including common factors—such as official financial support by the government27 and specific aspects based on the investigated categories—is important to prevent suicide among Japanese women during the pandemic.Suicidal behaviors are highly complex and based on multiple dimensions.15 This factor only negatively influenced the numbers of unknown job status. Although there are several suicide-related factors, most observations were included in the PI throughout the non-pandemic period. Deviations of this model were smaller for all categories compared to the model without the unemployment rate. Thus, we could provide a reasonable expected number under “normal” conditions (16The unemployment rate should be considered as an indicator reflecting social conditions that positively correlates with suicide in Japanese women. Thus, the unemployment rate, specifically that of the previous month, influences suicides in women—irrespective of job status, suicide motive, and age category.nditions .1613,14 Consequently, the results revealed that the unusual increase in female suicides has yet to decline in Term II for most categories. Although the increase is not limited to certain prefectures based on infected cases’ frequency, the increase might be related to restricted activity or anxiety due to widespread infection. Note that the results for the unknown category of job status might be affected by changes in survey method and other factors. Future work to investigate the factors influencing such regional differences might be important.The expected values were calculated based on the unemployment rates of 2018 to 2019, continuing to the pandemic period, along with the O/E ratios. Our method could remove the bias of ignoring the unemployment rate while comparing suicide numbers, and the bias from using predicted data-based numbers that include the pandemic impact.This study has some limitations. First, data were collected on three suicide motives; however, a combination of the three could not be evaluated. There is potential bias, since no scientific measures can accurately determine motives. Second, we could not determine how COVID-19 affected suicides among women in Japan. However, as the data were collected over an extended period, a meaningful analysis of the difference between the observed and expected numbers was possible. Third, although we only analyzed the suicidal trends among women until October 2021, both the pandemic and trend of increased suicides are ongoing. While further long-term research important, we should consider the changed relationship between suicide and unemployment rate in the post-pandemic period. Another future direction could be studying the timing of the suicide attempts during the pandemic using questionnaires, possibly highlighting unforeseen critical issues.


Intrahepatic cholangiocarcinoma (iCCA) patients had no obvious symptoms at early stage and poor postoperative survival. Therefore, the establishment of an iCCA prognostic prediction model to carry out refined management of iCCA patients is expected to improve the survival of the iCCA patient population. In this paper, we analyzed the expression profiling data of patients from 32 iCCA tissues and eight paracancerous tissues in The Cancer Genome Atlas (TCGA) database. Perl software was used to separate M6A-related genes and lncRNAs from expression matrix files obtained from the TCGA database. The differentially expressed lncRNAs in the iCCA samples and the normal samples were screened out by differential analysis using the R package limma, and the m6A-related lncRNAs were further screened by Pearson correlation analysis. WGCNA clustering analysis constructs a random network to extract the module genes most related to iCCA, and take the intersection of differentially expressed lncRNAs related to m6A. Univariate Cox screening was performed for the intersection lncRNAs that had significant influence on the prognosis of iCCA patients, and further screening was performed by LASSO method and multivariate Cox regression analysis. Risk model was constructed and prognostic ability was evaluated according to risk score. In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA, and established a novel m6A-related lncRNA-based prognostic model, which can be used as an independent prognostic factor to predict the prognosis of clinical patients. Cholangiocarcinoma (CCA) is a malignant tumor that occurs in the lining epithelium of the bile duct system. It is the most common biliary tract malignant tumor and the second most common primary liver malignant tumor, accounting for about 2% of all cancer patients worldwide. Cholangiocarcinoma can be further divided into intrahepatic Cholangiocarcinoma (iCCA), perihilar Cholangiocarcinoma (pCCA) and distal Cholangiocarcinoma (dCCA), the latter two can be collectively referred to as extrahepatic Cholangiocarcinoma (ECC) . RelevanMETTL3), methyltransferase-like 14 (METTL14), Wilms’ tumor 1-associating protein (WTAP) and other proteins  or regulates the structure of local RNAs, thereby inducing the binding of RNA-binding proteins (RBPs) and regulating the function of lncRNAs.Weighted gene co-expression network analysis (WGCNA) is a comprehensive analysis technology based on biological network, which can identify a class of genes (or proteins) that are co-expressed, associate clustering modules with phenotypes through algorithms, and explore the core of the modules gene (or protein) . The proThe LncSEA database integrates the collection of lncRNA functional information from more than 20 published databases, including lncRNA functional collections including downstream regulation, as well as TF ChIP-seq, DNase-seq, ATAC-seq, and H3K27ac ChIP-seq. This database focuses on the published information of various human lncRNAs, and can perform annotation and enrichment analysis on the lncRNA collections submitted by users, providing more than 40,000 reference lncRNA collections, including 18 types and 66 subtypes, including more than 50,000 lncRNAs.The Cox proportional hazards model is essentially a regression model commonly used in statistics in medical research to study the association between a patient’s survival time and one or more predictor variables. It is suitable for both quantitative predictors and categorical variables . UnivariMETTL3, METTL14, METTL16, WTAPI, VIRMA, ZC3H13, RBM15, RBM15B, YTHDC1, YTHDC2, YTHDF1, YTHD F2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, RBMX, FTO, ALKBH5) expression matrix which was updated on 29 March2022, we obtained transcriptome-sequencing (RNA-seq) information from 32 iCCA samples and eight adjacent non-tumor samples with corresponding clinical data. Perl (Strawberry Perl v5.30.2) was used to extract the gene expression information of lncRNAs, and Python (Python 3.6) was used to match 23 m6A-related genes (n matrix .p < 0.025) , and these up-regulated or down-regulated genes were plotted by plotting volcano plot and heatmaps shows up. Pearson correlation analysis was used to identify m6A-related lncRNAs .The gene co-expression network analysis was constructed using the WGCNA package of the R. In order to ensure that it conforms to the scale-free network distribution, the “pickSoftTreshold” function in the WGCNA package calculates the correlation coefficient of the β value and the mean value of gene connectivity, and selects an appropriate soft threshold β to make the network more flexible. Then, a one-step method is used to construct the related gene network, the adjacency matrix is converted into a topological overlap matrix TOM, and a hierarchical clustering tree of a gene is generated by hierarchical clustering. Calculate gene significance (GS) and module significance (MS) to measure the significance of genes and clinical traits, and analyze significant associations between modules and models.http://bio.liclab.net/LncSEA/index.php) for Annotation Analysis.The most significant associations with cancer traits in the WGCNA modules were selected for study and compared with the association results using Python. Retaining the part of the most significant modules of WGCNA that coincides with the correlation results, and finally obtaining the correlation network of m6A-related lncRNAs. Use Gephi (v0.9.2) software to draw the network, export the network nodes, modules and in- and out-degree information, and import the lncRNAs with higher weights into the lncSEA (p < 0.05), and then the LASSO regression method was used to further screen m6A-related prognostic lncRNAs. Perform multivariate Cox regression analysis for prognostic modeling and draw forest plots. Using the Kaplan-Meier method of the “survival” package to perform survival analysis on the screened genes, and draw survival curves. According to the median prognostic risk value, the samples of patients with intrahepatic cholangiocarcinoma were divided into low-risk group and high-risk group, and the risk level between the low-risk group and the high-risk group, as well as the survival status and survival time of the patients were drawn as a scatterplot, and then use the Kaplan-Meier method to draw survival curves for the risk model. Compare risk values to various clinical traits and perform independent prognostic analyses to test the prognostic power of risk models. The Roc curve was drawn to represent the accuracy of predicting patient survival through the model, and the accuracy was judged by the area under the curve.Univariate Cox regression analysis was performed for all lncRNAs in the network  and RStudio software were utilized to carry out the statistical analysis and figure preparation. The quantitative data from the control and the experimental groups were compared by using the student t test. Pearson correlation was used to calculate the correlation coefficients. 2 FC|>1, p< 0.05). Differentially expressed genes are plotted on a volcano plot . The limma was used to analyze the differentially expressed lncRNAs in normal and iCCA samples, and finally 3,099 differential lncRNAs were obtained (|logano plot , where rano plot  is drawnβ = 4, the scale-free topology fitting index R2 reaches 0.9. At this time, the average network connection corresponding to this threshold is close to the scale-free network (WGCNA analysis was performed using m6A regulators and expression matrix of lncRNAs. After setting the height to 2000, one outlier sample was removed, and 32 iCCA samples and seven normal samples were retained for subsequent analysis .The pick network . Obtain  network . Accordi network .Comparing the node-node information in the blue module with the node-node information of the correlation, and taking the intersection, a total of 723 nodes are obtained, corresponding to 1,560 edges, that is, the correlation co-expression network. By mapping the network using the Force Atlas layout of Gephi software, the network was divided into five modules, and the labels of the m6A regulators were abbreviated and displayed, resulting in nine m6A regulators in the WGCNA highly correlated modules . The netAC015917.2, AC010735.2, LINC02160, and AC009063.3, may have a significant impact on the prognosis of patients with iCCA (The lncRNAs related to the above nine m6A regulators were screened by the single-factor Cox method , and theith iCCA . Construith iCCA . AccordiDraw a forest diagram . Accordip = 0.0017 for AC015917.2, p = 0.0195, p = 0.0324 for LINC02160, and p = 0.0088 for AC009063.3, indicating that these four lncRNAs may have an impact on the prognosis of iCCA patients. The median of the patient’s risk value was used as the threshold, and the patients were divided into high-risk group and low-risk group (p = 0.006 (The four long-chain non-coding expression matrices obtained in the model were combined with the survival time and survival status of the clinical data, and the survival curve was drawn (sk group . Status sk group . After d = 0.006 . It show = 0.006 , indicat = 0.006 .Cholangiocarcinoma is a common primary liver malignancy worldwide, and its incidence is increasing year by year, seriously endangering the life and health of patients . BecauseH19 is overexpressed in prostate cancer, glioblastoma, and breast cancer and is associated with cancer progression (HIF2PUT is a marker of aggressive osteosarcoma, and overexpression of HIF2PUT is associated with an extremely poor prognosis in patients (With the in-depth study of lncRNAs, we have gained new understanding of cancer. In cancer, abnormal expression of lncRNAs determines disease progression, explains patient resistance, and can predict patient prognosis . Currentgression . High expatients . At the patients . And it patients . TherefoAC015917.2, AC010735.2, LINC02160, AC009063.3. Based on this, a prognostic model was established, and then the prognosis was predicted according to the risk score. The risk score calculated by these four m6A-related lncRNAs as risk signals can relatively accurately predict the prognosis of patients with intrahepatic cholangiocarcinoma, and is an independent prognosis factor for patients with iCCA.In this study, the gene expression map data of iCCA in the TCGA database was used to separate the mRNA and lncRNAs in the gene matrix file by using Perl software, and the lncRNAs gene expression matrix file of iCCA was obtained, and aggregated by WGCNA. Class analysis to further screen out differentially expressed lncRNAs in normal and iCCA patients. After correlation analysis of m6A-related genes, lncRNAs with differential expression in m6A-related genes were further obtained. By drawing a Venn diagram to take the intersection of the two, taking the lncRNAs with higher weights in the network, and analyzing them in the LncSEA database, it is found that the overexpression of lncRNAs co-expressed with m6A is likely to cause the deterioration of cholangiocarcinoma. The above lncRNAs were screened by univariate Cox regression, LASSO method screening and multivariate Cox regression method, and obtained four lncRNAs related to the prognosis of patients with intrahepatic cholangiocarcinoma, namely In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA and developed a prognostic. They may be applied in the determination of the treatment efficacy in patients with iCCA. However, this study also has certain limitations. The data analyzed are all from the TCGA database, and the data analysis results may be biased. A large amount of relevant data information of iCCA patients is still needed to verify the predictive power of this prognostic model. Therefore, in the follow-up study, we will continue to collect the relevant data of iCCA patients and their survival information, and combine the conclusions of this paper to conduct further clinical validation of the screened related genes and the constructed prognostic model.