Spondyloarthritis (SpA) is a group of multifactorial bone diseases influenced by genetic factors, the environment and lifestyle. However, current studies have found a limited number of SpA-related genes, and the genetic and pathogenic mechanisms of SpA are still unclear.A tissue-specific transcriptome-wide association study (TWAS) of SpA was performed using GWAS  summary data and gene expression weights of whole blood and skeletal muscle. The SpA-associated genes identified by TWAS were further compared with the differentially expressed genes (DEGs) identified in the SpA gene expression profile acquired from the Gene Expression Omnibus database . Finally, functional enrichment and annotation analyses of the identified genes were performed.CTNNAL1 . The gene expression profile of SpA identified 20 candidate genes that overlapped in the TWAS data, such as MCM4  and KIAA1109 . Enrichment analysis of the genes identified by TWAS identified 93 significant GO terms and 33 KEGG pathways, such as mitochondrion organization (GO: 0007005) and axon guidance (hsa04360).The TWAS detected 499 suggestive genes associated with SpA in whole blood and skeletal muscle, such as We identified multiple candidate genes that were genetically related to SpA. Our study may provide novel clues regarding the genetic mechanism, diagnosis, and treatment of SpA.The online version contains supplementary material available at 10.1186/s13018-023-04029-4. This is the first study to identify susceptibility genes associated with spondyloarthritis (SpA), revealing 499 suggestive genes by TWAS analysis.The TWAS results were further verified by comparing the gene expression profiles.This study provides novel clues for understanding the genetic mechanism of SpA, especially focusing on aberrant gene expression in the pathogenesis of SpA.The online version contains supplementary material available at 10.1186/s13018-023-04029-4. Spondyloarthritis (SpA) is a group of related but phenotypically distinct disorders, including psoriatic arthritis, arthritis related to inflammatory bowel disease, reactive arthritis, juvenile idiopathic arthritis, and ankylosing spondylitis , 2. The IL23/IL17 pathways. However, the GWAS is only recommended for evaluating the risk of disease. Because most GWAS-identified single nucleotide polymorphisms (SNPs) are located in the noncoding regions of the genome, so the interpretation of those variants at the gene expression level is limited [Genome-wide association studies (GWASs) have been considered to be one of the primary approaches for determining genetic links to diseases , 9. By a limited . Express limited . Therefo limited . Differe limited .In recent years, TWAS has been widely used to identify genetic loci associated with target diseases. For example, Liao et al.  identifihttps://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=20002. Accessed 19 March 2021) . Detailed information on the subjects, genotyping, imputation, and quality control can be found in a published study . FUSION is a new approach to identify genes whose expression is significantly associated with complex traits in individuals without directly measuring the expression level by integrating GWAS summary data and precalculated gene expression weights of different tissues [http://gusevlab.org/projects/fusion/). For TWAS results, a significant association threshold of P < 5.38 × 10−6 (0.05/9300) after strict Bonferroni correction was adopted. P values between P < 5.38 × 10−6 and 0.05 were considered suggestive of significance [The TWAS of SpA was carried out by functional summary-based imputation software . Briefly, DNA microarray-based gene expression levels were examined in peripheral blood from 11 patients with juvenile SpA (jSpA) patients and 4 healthy controls from Croatia, along with a bioinformatics analysis of the retrieved data, and the carefully selected differentially expressed genes (DEGs) of all participants in the study were analyzed by qRT-PCR [P value < 0.05 by the moderated t statistic and |log2FC|> 1 [The gene expression profile of SpA was acquired from the Gene Expression Omnibus (GEO) database ( qRT-PCR . The GEO qRT-PCR . Genes wg2FC|> 1 .https://metascape.org/gp/index.html) tool to perform Gene Ontology (GO) and pathway enrichment analyses of candidate genes identified by the TWAS and gene expression profiles [https://fuma.ctglab.nl/) tool [In this study, we used the Metascape (profiles . The sigl/) tool .P values between 5.38 × 10–6 and 0.05  , AC000078.5 , RP11-165J3.6  and ZNF100 . Table TWAS analysis identified 390 suggestive genes in skeletal muscle and 137 suggestive genes in whole blood, with MCM4 , KIAA1109  and SFMBT2  , generation of precursor metabolites and energy (GO:0006091), and histone H4-R3 methylation (GO:0043985). We also identified 33 KEGG pathways in SpA, such as axon guidance (hsa04360) and purine metabolism (hsa00230)  of proteins. Hussain et al. [SFMBT2 interference altered the expression of key metabolic genes in chondrocytes; SOX9 and COL2A1 were decreased, whereas MMP13 and ADAMTS4 were significantly increased. Some studies have shown that upregulation or downregulation of these genes, which are altered by the SFMBT2 gene, can lead to cartilage degeneration and further cause SpA [ATRNL1 gene, which was identified in this study, can regulate the expression of SOX9 and is significantly highly expressed in the cartilage tissues of patients with osteoarthritis [SFMBT2 and ATRNL1 may be associated with the genetic mechanisms of SpA pathogenesis. SFMBT2 and ATRNL1 may be candidate genes related to SpA, and the genetic mechanisms linking these genes and SpA need further research.The TWAS identified several genes associated with SpA, such as n et al.  found thause SpA , 28. Therthritis . In summMCM4. The MCM4 protein is a DNA replication licensing factor that is essential for DNA replication initiation and elongation in eukaryotic cells. In other words, the MCM4 gene acts as an essential regulator of the cell cycle. MCM4 can cause many diseases by regulating the cell cycle and inducing apoptosis [Another important candidate gene identified in this study was poptosis , 31. In KIAA1109 was identified in both the TWAS and the gene expression profile of SpA. A study showed that a 480 kb block on chromosome 4q27 encompassing the KIAA1109/Tenr/IL-2/IL-21 gene cluster is associated with rheumatoid arthritis [KIAA1109/Tenr/IL-2/IL-21 gene cluster is involved in susceptibility to multiple autoimmune diseases, suggesting that this locus is a general risk factor for diseases such as rheumatoid arthritis and celiac disease. SpA is a subset of seronegative rheumatic-related autoimmune diseases, and Bowes et al. found significant evidence associating SpA susceptibility with the IL-2 and IL-21 genes [KIAA1109 has a direct effect on SpA. Therefore, this is the first study exploring the genetic correlation between KIAA1109 and SpA.In addition, rthritis . Zhernakrthritis  found th21 genes , 35. To CTNNAL1 is ubiquitously expressed in many tissues, including skeletal muscle, the pancreas, and the heart [RUNX1, FUT8, and CTNNAL1. The proteins in Wnt/β-catenin signaling play essential roles in the development of SpA. Xie et al. [CTNNAL1.he heart . By comphe heart  identifie et al.  found the et al. . In summIn our study, GO enrichment analysis and KEGG pathway analysis were also conducted to explore the functions of candidate genes and how they are distributed in SpA. For example, mitochondrion organization (GO: 0007005) was identified by both TWAS analysis and gene expression profiling. Cytochrome c is primarily known for its role in the mitochondria as a key participant in the life-supporting function of ATP synthesis . RecentlTNF-α/ADAMTS-4, and blocking semaphorins can decrease cartilage and bone destruction, cell infiltration into the synovium, and the production of TNF-α and IL-6 [Axon guidance (KEGG: hsa04360) was also identified as enriched in SpA. Recently, the semaphorin family was originally identified as axonal guidance molecules, and semaphorins affect the pathogenesis of multiple types of arthritis by regulating immunity, angiogenesis, bone remodeling, apoptosis, cell migration and invasion , 43. In and IL-6 . The TWAGGTA1P in skeletal muscle and whole blood were opposite each other. This difference potentially can be explained in two aspects. First, they may be attributed to the biological variations in gene expression across different tissue types. For instance, studies have reported that in patients with type 2 diabetes, GLUT4 is selectively downregulated only in adipose tissue, while its expression level remains relatively high in skeletal muscle [In addition, we found that some common genes identified in skeletal muscle and whole blood tissue showed different risk directions. For example, the Z values of l muscle . This all muscle . TherefoP < 5.38 × 10−6. P values between P < 5.38 × 10−6 and 0.05 were considered suggestive of significance. Unfortunately, according to our results, the TWAS results showed only suggestive associations with SpA. Because reference panel size affects the P value. We should focus more on effective size (Z score) instead of P value. Although P < 5.38 × 10−6 and 0.05 were considered to be suggestive of significance, these genes are still valuable.After strict Bonferroni correction, the significance threshold of the TWAS results was One of the strengths of TWAS lies in its ability to accurately prioritize genes likely to be causal while excluding non-causal genes . Our stuThis study also has some limitations. First, the GWAS summary data are based on those with European ancestry and may not apply to other ancestry studies. Therefore, caution should be taken when applying our results to other populations. Further TWAS analysis of other populations is needed to verify our results. Second, to validate the TWAS results, we compared the significant genes identified by TWAS analysis of SpA with the gene expression profile of jSpA, but jSpA is one subtype of SpA. Our results should be interpreted with caution. Further biological studies should be conducted to confirm our findings.In summary, based on GWAS summary data, a TWAS analysis identified novel and common susceptibility genes for SPA. This study not only provides novel clues for understanding the genetic mechanism of SpA but also provides a basis for further experiments. In addition, beyond specific mechanistic findings for SpA, this work outlines a systematic approach for identifying functional mediators of complex-trait diseases.Additional file 1: Risk Loci in GWAS Dataset of SpA.Additional file 2: Quality Control Steps.Additional file 3: Table S1 Top genes selected by transcriptome-wide association study (TWAS) analysisAdditional file 4: Table S2 GO terms identified by Metascape enriched for SpA


P = 0.004 for baseline anemia, 0.18; 0.08–0.42; P < 0.0001 for prophylactic G-CSF administration). The primary cause of the reduction was febrile neutropenia, and the same factors were identified. Our study revealed that patients with baseline anemia and prophylactic G-CSF administration have less risk for treatment reduction in DOC + RAM for NSCLC treatment.Treatment using docetaxel (DOC) and ramucirumab (RAM) is an effective regimen in second or later line advanced non-small cell lung carcinoma (NSCLC) treatment. However, it induces severe adverse effects, resulting in treatment reduction such as dose reduction and/or discontinuation. This study aimed to reveal the factor(s) associated with treatment reduction in DOC + RAM. We retrospectively evaluated patients with advanced NSCLC (n = 155). Treatment reduction of the second course due to severe adverse effects was conducted in 25.8% of the participants, and relative dose intensity at the second course was 95.7 ± 8.4% for DOC and 91.9 ± 24.8% for RAM. Multivariate logistic regression analyses identified that baseline anemia and prophylactic granulocyte colony-stimulating factor (G-CSF) administration are preventive factors for the reduction (adjusted odds ratio, 0.29; 95% confidence interval, 0.12–0.66;  Non-small cell lung carcinoma (NSCLC) accounts for approximately 85% of all lung cancer2, and chemotherapy is the main strategy for advanced NSCLC treatment3.Lung cancer is a leading cause of cancer-related death worldwide, with an increasing number of patients5. DOC monotherapy used to be the representative second-line regimen for NSCLC treatment, and its dose limiting toxicity (DLT) is known to be neutropenia6. RAM binds to the extracellular domain of the vascular endothelial growth factor (VEGF) receptor-2 with high affinity, leading to inhibition of responses resulting from the activation of VEGFR-2 by VEGF-A, C and D7. In contrast, a combination of these medicines induces stronger adverse effects, such as hematotoxicities, febrile neutropenia (FN), anorexia, oral mucositis, and peripheral edema, compared to DOC monotherapy, and its DLT is reportedly hematotoxicity5. Moreover, as this treatment is normally conducted in outpatient settings, these adverse effects can be difficult to manage. Therefore, this treatment is recommended for patients younger than 75 years old and with a performance status (PS) of 0–1 in Japan, considering adverse effects3. Severe symptoms significantly reduce patients’ quality of life (QOL) and can cause dose reduction and/or discontinuation of these medicines. Risk factors of DOC-induced neutropenia are reportedly advanced aging, leucopenia, neutropenia, serum creatinine elevation, and serum albumin decrease at baseline, and a history of localized radiation therapy10. In contrast, the risk factors for the development of severe adverse effects causing treatment reduction such as dose reduction and/or discontinuation in this regimen are still unclear.Docetaxel (DOC) and ramucirumab (RAM) therapy is one of the effective regimens in second or later line NSCLC treatmentThis study aimed to reveal the risk factor(s) for treatment reduction due to severe adverse effects in DOC + RAM therapy to improve treatment management.In total, 155 out of 183 patients were enrolled in this study, according to the eligibility criteria .Table P = 0.0003), and 15.1% and 49.0% of those with and without prophylactic G-CSF administration (P < 0.0001), respectively. The median RDI of DOC at the second course in patients with and without baseline anemia was 97.8% and 91.8%, and that in prophylactic G-CSF administration was 97.7% and 91.2%, respectively, with significant differences (P < 0.0001 for both). In contrast, median RAM RDI was 93.3% and 89.5% in baseline anemia, which was not significant (P = 0.18), although a significant difference was identified in prophylactic G-CSF use .We also assessed the influence of baseline anemia and prophylactic G-CSF administration on the RDI of DOC and RAM at the second course Table . TreatmeP = 0.004 for dose reduction, and 29.6% and 63.6%, P = 0.02, for FN incidence).Table 11. In addition, some adverse effects such as hematotoxicities, FN, anorexia, oral mucositis, and peripheral edema appeared in higher rates and increased in severity in Japanese patients compared to non-Japanese patients registered in the REVEL study including approximately 10% Asians, although DOC dosage in Japanese patients was lower (60 mg/m2)5. Similar results were confirmed in gemcitabine + nanoparticle albumin-bound paclitaxel treatment for pancreatic cancer12. Kenmotsu et al. suggested that Japanese patients seemed to be more susceptible to DOC-induced toxicities6. They speculated that this could be caused by unknown genetic factors, higher sensitivity to adverse effects, differences in unbound docetaxel concentrations, or baseline counts of white blood cells6. In addition, Choi et al. suggested that ABCB1 (2677G/T) and SLCO1B3 (rs11055585) might be major genetic predictors of DOC-related toxicities in DOC-containing chemotherapy13. We consider that multiple factors, such as differences in single nucleotide polymorphism in transporters and cytochrome P450 (CYP) and higher sensitivity to the adverse effects in the Japanese population, might have caused the differences. Therefore, the results of the present study should be interpreted considering this possibility.DOC + RAM therapy is one of the most effective regimens in the second or later line NSCLC treatment. However, its administration induces severe adverse effects. In the REVEL study, adverse effects such as FN, neutropenia, thrombocytopenia, anorexia, oral mucositis, and peripheral edema occurred more strongly in treatments with DOC + RAM compared to DOC + placebo, leading to higher dose delays and reduction Treatment reduction was performed in 25.8% of the patients who received at least two courses of the treatment, and the most problematic adverse effect inducing the reduction was FN. Logistic regression analyses reveled that baseline anemia and prophylactic G-CSF use were preventive factors for the treatment reduction and FN development.16. On the other hand, pegfilgrastim administration reportedly worsens taxane-associated acute pain syndrome (T-APS), although it can be controlled by analgesics such as non-steroidal anti-inflammatory drugs or acetaminophen17. However, T-APS is taxane-dose-dependent symptoms18, and its incidence in 60 mg/m2 DOC is not that problematic. Consequently, prophylactic G-CSF administration with careful attention to T-APS could be one of the best strategies for safer and more efficient DOC + RAM provision.Several reports have also shown that prophylactic pegfilgrastim administration significantly reduces FN incidence to 0–5% in this regimen22. Additionally, we compared patient backgrounds between those with and without baseline anemia and found that males and those with hypoalbuminemia were more likely to be included as patients with anemia 23. In contrast, Hirasawa et al. and Kwon et al. reported that baseline hemoglobin levels are not associated in patients with castration-resistant prostate cancer9. However, the patient populations for NSCLC and prostate cancer are notably distinct, which can explain the disparity between the observations. Consequently, further evaluation is required to confirm the results.Interestingly, baseline anemia was detected as a preventive factor, although previous reports suggested it as a risk factor for severe neutropenia development in other chemotherapeutic treatments30. Dunst et al. reported that tissue hypoxia is a major stimulus for the upregulation of VEGF via impairment of tissue oxygenation, suggesting that anemia may promote tumor angiogenesis via hypoxia27. Krzystek-Korpacka et al. also reported that mild pre-treatment anemia is associated with cancers metastasizing, especially to regional lymph nodes, which seems to be mediated by angiogenic factors29. Furthermore, Lim et al. reported that vascular dilation through VEGFR2 signaling is the mechanism underlying VEGF-induced bone marrow mobilization and anemia30. In addition, vascular normalization by VEGF blockade induces a pressure gradient across the vasculature and improves drug penetration in tumors32. Consequently, it is possible that anemia progressed tumor angiogenesis, leading to the elevation of DOC delivery to tumors by RAM efficacy, reducing the delivery to other organs, which resulted in a decrease in severe adverse effects. However, further studies are needed to elucidate the mechanisms.Several studies have suggested that anemia and the effect of VEGF or VEGF inhibitors are related, but this remains controversial23. However, all previously reported factors were not identified as factors in DOC + RAM treatment; RAM addition significantly induces more severe adverse effects, such as FN and/or neutropenia5, which has significantly affected the non-identification of these factors.As previously mentioned, some baseline factors are reportedly associated with DOC-induced neutropenia and FNConsequently, prophylactic pegfilgrastim should be administered and adverse effects should be cautiously monitored, particularly in the first course of treatment in patients with normal baseline hemoglobin levels.6. Moreover, there may be unknown factors that enhance the RAM-intensifying effect of DOC-induced adverse effects. Thus, our preliminary findings should be validated in future research.The present study had some limitations. First, this study was a retrospective evaluation with a relatively small patient population. Particularly, we consider that evaluation of factors for FN incidence was insufficient owing to small number of the events. Second, we assessed risk factors during the first course, as the treatment dosage and supportive care would generally be structured by referring to the degree of adverse effects in the first course. In contrast, multiple courses of the treatment can result in more severe adverse effects, such as peripheral edema, neuropathy, and proteinuria. Therefore, evaluation of all treatment courses is needed, and the influence of dose reduction due to cumulative toxicities must be considered. Third, we did not evaluate the treatment efficacy. Finally, we did not assess polymorphisms of drug-metabolizing enzymes or transporters such as CYP3A4, CYP3A5, ABCB1, ABCC2, and SLCO1B3, which can impact the pharmacokinetics of DOCIn conclusion, our study revealed that patients with baseline anemia and prophylactic G-CSF administration have less risk for treatment reduction in DOC + RAM for NSCLC treatment. This treatment highly induces severe adverse effects, leading to treatment reduction. Therefore, further evaluation for the prophylaxis is needed.3 cells/µL and platelet count ≥ 1.0 × 105 cells/µL; and (5) sufficient renal or liver function for treatment induction. Patients who discontinued the treatment in the first course due to disease progression or transferred hospitals during chemotherapy were excluded.In this retrospective multicenter observational study, we evaluated 183 patients with advanced NSCLC who received the DOC + RAM regimen from September 2016 to October 2021. All patients met the following baseline criteria: (1) age ≥ 20 years old; (2) detailed patient information available from medical records; (3) 0–2 Eastern Cooperative Oncology Group PS (ECOG-PS); (4) absolute neutrophil count ≥ 1.0 × 105. We tried to include approximately four covariates in the multivariate logistic regression analysis. The present study was approved by the institutional review board of each participating institution  and was conducted in accordance with the Declaration of Helsinki and STROBE statement.We calculated the number of final patients to be approximately 150, based on the assumption that approximately 30% of the patients received dose reduction at the second course by reference to the previous reports and our clinical experience2) and RAM (10 mg/kg) were intravenously administered every 3 weeks5. Dexamethasone 6.6 mg was administered before the chemotherapeutic agents for premedication. Treatment was reduced or discontinued by the physician’s decision according to the criteria in the previous reports5.DOC  for the frequency of treatment reduction, such as dose reduction and/or discontinuation at the second course owing to severe adverse effects at the first course. The secondary endpoints were the elucidation of the risk factor(s) associated with the development of FN during the first course of treatment and comparison of the rate of treatment reduction and FN incidence, and RDI between the specific populations. Adverse effects were evaluated in accordance with the Common Terminology Criteria for Adverse Events version 5.0.P < 0.20), were considered when building the multivariable model. The RDI and treatment reduction frequency between the specific patient groups were compared using Student’s t-test and Fisher’s exact probability test, respectively. All analyses were performed using JMP version 16.1 statistical software (SAS Institute Inc.). Differences were considered statistically significant when P-values were < 0.05.Univariate and multivariate logistic regression analyses were conducted to determine the independent factors associated with the frequency of treatment reduction and FN, using the following baseline covariates: sex, age, ECOG-PS, clinical staging, histology, liver metastasis existence, body surface area (BSA), neutropenia, anemia, thrombopenia, liver dysfunction , renal dysfunction , hypoalbuminemia, smoking history, regular alcohol intake (≥ 5 days in a week), treatment line, number of prior cytotoxic regimens, immune checkpoint inhibitor treatment history, dose reduction from treatment initiation, and prophylactic G-CSF administration. Variables that had potential associations, as suggested by univariate logistic regression analysis . The requirement for formal consent for this type of study was waived by the Ethical Review Board for Life Science and Medical Research of Hokkaido University Hospital, Institutional Review Board of KKR Sapporo Medical Center, Teine Keijinkai Hospital, Hokkaido Cancer Center, and NTT Medical Center Sapporo.Supplementary Table 1.


Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory infections (ALRIs) in young children. To design preventive efforts in sub-Saharan Africa, a better knowledge of the true role of RSV in pediatric ALRIs is required. Therefore we conducted a systematic review and meta-analysis of case–control studies to estimate the etiological role of RSV to ALRIs in under 5 years children in sub-Saharan Africa.This study was done according to PRISMA guidelines. PubMed, EMBASE, SCOPUS, Web of Sciences databases, and Google Scholar were used to retrieve articles. STATA software version 17 was used for data analysis. The results of all the included studies were standardized to odds ratios (ORs) with accompanying 95 % confidence intervals (95 % CIs) and the pooled estimates of ORs, attributable fraction among the exposed (AFE), and population attributable fraction (PAF) were reported. The heterogeneity was assessed using Cochrane chi-square (I 2) statistics.A total of 6200 cases and 4986 controls from 14 articles that fulfilled the inclusion criteria were included. The pooled prevalence of RSV among cases and controls was 23.52 % [95 % CI (20.68–26.47)] and 4.33 % [95 % CI (3.11–5.73)], respectively. The pooled OR is 7.04 [95 % CI (4.41–11.24)], which indicated a significant association between RSV and ALRI. Among ALRIs cases positive for RSV, the proportion of disease that was not attributable to the background rate (AFE) was 85.8 % [95 % CI (77.3–91.1)]. The fraction of ALRIs children that can be attributed to RSV (PAF) was 20.2 % [95 % CI (16–24.1)].This study showed clear associations between RSV and ALRI hospitalization in young children in sub-Saharan Africa indicating the need for prophylactic measures against RSV in this age group. RespiraRSV has been a priority for vaccine and anti-viral development for close to 6 decades ,6 but yeIn addition, RSV diagnostic testing is challenging in resource limited sub-Saharan African countries. The sensitivity of antigen-based testing is low and the more sensitive polymerase chain reaction (PCR)-based testing is almost nonexistent due to its relatively high costs. Detection of RSV infections is further complicated by the lack of uniform clinical case definition and the non-specificity of its symptoms and the obvious challenge to distinguish colonization from infection. Thus, estimating the true burden of acute lower respiratory infections (ALRIs) due to RSV is very challenging particularly in resource limited settings. To resolve the latter, several studies use case-control study design and compare the infection status of people with ALRIs (cases) to people without ALRIs (controls) . ConsecuThus, a better understanding of the real contribution of RSV in childhood ALRIs is needed to guide clinical management and preventive measures in sub-Saharan Africa particularly when promising RSV candidate vaccines or therapy are currently under evaluation. Therefore, we conducted a systematic review and meta-analysis of case–control studies aiming at estimating the role of RSV in the etiology of ALRIs in under 5-year-old children in sub-Saharan Africa.22.1The study protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) with registration code CRD42022361757 .2.2We conducted and reported a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram  16]. Th. Th16]. Potentially relevant studies were retrieved in full text from February 7–14, 2023 and eligible studies were assessed in detail against the inclusion criteria by two reviewers (FisehaWadilo and Tamrayehu Seyoum). Reasons for the exclusion of studies during full text critical appraisal were recorded and reported. Discrepancies between reviewers during screening at each stage were resolved through discussion.Inclusion criteria: We included studies that fulfilled our strict eligibility criteria: studies in children younger than five years of age; studies investigating etiological role of RSV using RT PCR; studies conducted in sub-Saharan African countries, studies where respiratory specimens were collected and diagnostic test conducted using qRT PCR; case–control/prospective cohort studies that reported data in both case and control groups; studies of under 5-year-old children with clinical pneumonia (or lower respiratory infection); studies that have conducted on all age groups and disaggregated by age category and data on under 5 is available, and case control studies reporting virus–specific proportions separately in both groups. We only included studies where the case definition for ALRI  was clearly defined and consistently applied.Exclusion criteria: We excluded studies that were conducted outside sub-Saharan Africa. We also excluded studies conducted in children >5 years of age, those using cross-sectional study design, those not using valid laboratory diagnostic tests, and those without clear case and control definition.2.3Data was extracted to an excel spreadsheet. A data extraction tool was prepared that included author (s) name, publication year, study design, sample size, and study area, case and control definition, type of respiratory specimens used, RNA extraction techniques, diagnostic assay, total number of pathogens isolated in cases and controls. Data extraction was conducted by FW and TS. In addition, there were two rounds of meetings for further data cross-check and validation.2.4FW and TS did an assessment of the methodological quality of eligible studies using the Joanna Briggs Institute's critical appraisal instrument for prevalence studies . The res2.52) statistics, while Egger intercept was used to assess publication bias. The p-value of <0.05 for I2 statistics was used to determine the presence of heterogeneity. The findings were reported using the pooled ORs with a 95 % confidence interval (CI) and forest plot.Data synthesis and statistical analysis were conducted using STATA version 17 software. The results of all the included studies were standardized to odds ratios (ORs) with accompanying 95 % confidence intervals (95 % CIs) to facilitate interpretation and analysis in the subsequent meta–analyses. Whenever possible, we extracted adjusted odds ratios (aOR). We performed the meta–analysis of case-control studies and reported pooled estimates of ORs with corresponding 95 % CIs using the random effects model (DerSimonian–Laird method) because these studies do not share common effect size due to methodological heterogeneity  and 4.33 % [95 % CI (3.11–5.73)], respectively , Fig. 3.3.5Meta-analyses of RSV OR were reported as well as the corresponding attributable fraction among the exposed (AFE) and population attributable fraction (PAF) . The poo3.6Acute lower respiratory infections were also significantly associated with other respiratoryviruses, including Influenza A virus, Parainfluenza virus, and Metapneumovirus. .Table 4T4This systematic review and meta-analysis reinforces RSV as an important cause of ALRI in young children in sub-Saharan Africa, and provides quantitative estimates of the absolute proportion of RSV–associated ALRI cases to which a viral cause can be attributed . A previous global systematic review and meta–analysis in children under five years also supported the causal attribution of RSV  . Older aThere is considerable global attention on RSV associated ALRI in young children and efforts to develop a RSV vaccine remain highly active. The susceptible populations for RSV infections are varied and include neonates, young children, pregnant women, and older adults. Induction of protective immune responses in each of these groups may require different vaccine types, adding a further challenge for vaccine development . GeneralIn sub-Saharan Africa region, where there are very limited viral infections diagnostic capacity, it is a common practice to empirically prescribe antibiotics to treat ALRIs s . RSV is Considering the huge health and economic burden of RSV disease in sub-Saharan Africa, the potential interventions against RSV among children under 5 years are likely to be cost-effective. The RSV-associated disease burden among children in the 72 GAVI  countries (most of them are sub-Saharan Africa countries) is estimated to be an average of 20.8 million cases, 1.8 million hospital admissions, 40 thousand deaths, 1.2 million discounted DALYs, and US$611 million discounted direct costs . Out of This study was limited by the inclusion of only 7 countries  out of 49 sub-Saharan Africa countries. We found a limited number of articles that fulfilled our inclusion criteria. More accurate estimates of the RSV burden in sub-Saharan Africa will require more active RSV surveillance and research programs that includes both ALRI and healthy children.5This systematic review and meta-analysis provides accurate and timely RSV disease burden estimates in young children of sub-Saharan Africa to inform future policies and interventions. In this systematic review and meta-analysis, we showed a high magnitude of RSV in <5 years children with a clear associations between RSV and ALRI hospitalization in young children in sub-Saharan Africa. Given the possibility of an RSV vaccine, this review provides useful baseline data for future studies assessing the interventions in Sub-Saharan Africa and early introduction of RSV vaccine once licensed.Data included in article/supp. material/referenced in article.This research received no external funding.Not applicable.Not applicable.Fiseha Wadilo: Writing – original draft, Software, Methodology, Formal analysis, Data curation, Conceptualization. Minyahil Tadesse Boltena: Formal analysis, Data curation. Rawliegh Howe: Writing – review & editing, Supervision. Fithamlak Bistegen Solomon: Writing – original draft. Adey Feleke: Writing – review & editing, Supervision. Tamrayehu Seyoum: Data curation. Andargachew Mulu: Writing – review & editing, Conceptualization. Adane Mihret: Writing – review & editing, Conceptualization.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Patients with nonalcoholic steatohepatitis (NASH) are at risk for developing cirrhosis and hepatic cancer. Currently, the definitive gold-standard method of diagnosing NASH is a liver biopsy, an invasive and costly method. Our objective was to compare three non-invasive methods of identifying NASH by using data on 10,007 subjects from NHANES III (1988–1994) to determine the prevalence and variables associated with NASH, as defined by each non-invasive method.We used ultrasound data to identify subjects with moderate-to-severe hepatic steatosis, of whom we identified the NASH population using either the HAIR score, the NASH liver fat score, or the Gholam score, each of which had been validated with liver biopsy. We performed multinomial logistic regression to compare each NASH population to the normal population (those with no-to-mild hepatic steatosis).We identified 1136 (9.5%) subjects as having NASH by at least one method and 219 (1.8%) were identified by all 3 methods. Independent of the non-invasive method used, Mexican-Americans (MA) had the highest prevalence of NASH. All three methods identified significant risk factors for NASH (p<0.05), including: elevated waist-to-hip ratio, elevated levels of C-peptide, total cholesterol, or C-reactive protein (CRP).We conclude that the combined non-invasive methods can help identify candidates with a high likelihood of being diagnosed with NASH. Health care providers can screen people with the combined non-invasive methods for the risk factors and identify candidates for interventions, including exercise and/or referral to biopsy. Nonalcoholic steatohepatitis (NASH) is a serious liver condition marked by hepatic steatosis, steatohepatitis, cell injury and apoptosis. Hepatic steatosis is a characteristic of several liver diseases, including alcoholic fatty liver disease and non-alcoholic fatty liver (NAFLD). A subset of individuals with NAFLD develops NASH, which can progress to liver cirrhosis and cancer –3.Several studies have suggested possible risk factors for NASH, such as insulin resistance  and HispThere have been several recent reviews of non-invasive methods for identifying NASH –9. HowevWe analyzed data from the National Health and Nutrition Examination Survey III (NHANES III) 1988–1994 . NHANES Hepatic ultrasound data identified subjects with no, mild, moderate, and severe hepatic steatosis. To limit the population to NAFLD and no other causes of liver damage, subjects with elevated serum transferrin level >50% (an indicator of a predisposition for iron overload), chronic hepatitis B, chronic hepatitis C, excessive alcohol use, or taking prescription medications that known to cause hepatic steatosis were excluded. Chronic hepatitis B was defined as positive results for both the hepatitis B surface antigen (HBsAg: SAP) and hepatitis B core antibody (anti-HBc: HBP) tests. Chronic hepatitis C was defined as positive results for both the hepatitis C antibody (HCV: HCP) and RNA tests (HCPRNA). Average alcohol use was determined by multiplying the response to each of the two questions: “number of days drank alcohol in past 12 months” and “average drinks on drinking day” and dividing by 365 to get a daily average . ExcessiFrom the NAFLD population , we used three different non-invasive methods to identify the NASH population. These three methods are: the HAIR score , the GhoHOMA insulin resistance (HOMA-IR), hypertension, and alanine aminotransferase (ALT). Subjects received 1 point for each pathological value, for a possible total of 3. The cutoffs used for each variable were HOMA-IR>5, ALT>40, and blood pressure ≥140/90 or prior diagnosis of hypertension or prescription of hypertension medication. Subjects were considered to have NASH if they have NAFLD and a HAIR score of ≥ 2. The HAIR score had an Area Under the Receiver Operating Curve (AUROC) of 0.9, a sensitivity of 0.8, and a specificity of 0.89 , respectIn addition to evaluating each method individually (method 1), we also identified NASH in two additional ways, by combining the three methods and by at least one method. For the former, we estimated the net sensitivity as follows: if method 1 and method 2 are positive, then sensitivity [SE]=SE method 1* SE method 2 and Specificity [SP]=SP method 1+SP method 2-(SP method 1 * SP method 2). The resulting net sensitivity (range=36%–56%) is less than that of any one method, but the net specificity (range= 97–99) is higher than any single method .To evaluate NASH by at least one method, we estimated the net sensitivity using the following: if method 1 or method 2 is positive, then Sensitivity [SE]=SE method 1+SE method 2-(SE method 1*SE method 2) and Specificity [SP]=SP method 1* SP method 2. The resulting net sensitivity (range=97%–98%) is higher than that of any one method but the net specificity (range=19%–47%) will be lower than any single method.The following variables were included in the analyses: demographics , physical activity status, smoking status, body composition (waist circumference and waist-hip ratio), laboratory values , and healthy-eating index (HEI). However, variables that were part of a NASH definition were not included in the regression analysis.Physical activity was classified into 3 categories based on the frequency of activity per week , based on recommendations from the American College of Sports Medicine at the time of the survey –17. Age Race/ethnicity was categorized as White, non-Hispanic Black (Black), Mexican-American (MA) or others. Federal poverty level (FPL) was classified as <1, 1–2, and >2 FPL. Urbanization classification was based on USDA Rural/Urban continuum codes. .Alcohol status was classified as current, former, and did not drink alcohol based on the response to the following questions: “In your entire life, have you had at least 12 drinks of any kind of alcoholic beverage and “In the past 12 months did you have at least 12 drinks of any kind of alcoholic beverage” If the response is “Yes” to the first question, then a subject is classified as current drinker. If the response is “Yes” to the first question and “No” to the second question, then subject is classified as former drinker. If the response is “No” to the first question, then the subject is classified as did not drink alcohol.Smoking status was categorized into a non-smoker, former and current smokers. Waist-to-hip ratio was categorized as normal , or high risk (>0.85 as for women and >0.9 for men).C-peptide levels were classified as low (<0.26 nmol/L), normal (0.26–1.03 nmol/L), and high (>1.03 nmol/L). Total cholesterol was categorized as normal ( ≤ 200 mg/dL), elevated (200–239 mg/dL), and high (≥240 mg/dL). CRP was categorized as normal (0.1–0.3 mg/dL), mild (mild inflammation) (0.3–1 mg/dL), and high (significant inflammation) (>1 mg/dL). Based on the HEI total score, subjects were classified into having poor diet (HEI score <50), diet needed improvement (HEI score=50–80) and good diet (HEI score=80–100).Descriptive statistics were used to characterize the study population. We report un-weighted number and weighted percent for the categorical variables.Age-adjusted prevalence estimates of NASH were calculated by using direct standardization with age group proportions based on 2000 US Census data. We used these age-group proportions to weight the estimates of the NASH prevalence identified by each individual method, identified by at least one method, and identified by all methods combined .Bivariate analysis using the chi-Squared test for categorical variables was used to determine the statistical difference between the prevalence of NASH in racial/ethnic groups and in the other population characteristics.Multinomial logistic regression was used to compare the NASH population to the normal population (those with no or mild hepatic steatosis) in the predictors of NASH. We developed a model for each non-invasive method of diagnosis separately. In addition, we developed a model for the prevalence of NASH by the three combined methods and by at least one method. Results are presented as unadjusted and adjusted odds ratio with 95% confidence interval. A p-value of <0.05 was considered statistically significant. Data were analyzed using SAS  and the survey module of STATA version 14. Sample weights, provided by the NCHS, were used to correct differential selection probabilities and adjust for non-coverage and non-response . All estOf the 10,007 subjects in our sample from NHANES 1988–1994, 37.1% were 20–34 years old and 10.8% were 65 years and older. 10.2% were Black and 4.3% were Mexican-American (MA). About half of the population were male (47.9%), 53.6% lived in rural areas, 20.8% had less than high-school education and 11.6% were poor . 30.2% were current smokers, 14.0% were physically inactive (did no exercise), 41.6% did not meet the exercise guidelines, 65.1% had high waist-to-hip ratio  and 16.2% had poor diet quality based on HEI score. About nineteen percent (18.6%) had high total cholesterol ( ≥ 240 mg/dL), 6.3% had significant inflammation as indicated by >1 mg/dL CRP level, and 17.4% had high C-peptide level .The overall prevalence of NASH was 3.3% using the HAIR score, 8.5% using the Gholam score, and 3.2% using the NASH Liver Fat score separately. When subjects were positive by all of the methods combined, NASH prevalence was 1.9%, and when subjects were positive by at least one method, the prevalence of NASH was 9.5%.The age adjusted prevalence of NASH using the direct adjustment method (1990 census standard population) was 4.2% by the HAIR score, 10.0% by the Gholam score, and 3.7% by the NASH Liver Fat score. Using the three methods combined, the age-adjusted prevalence was 2.2% and for diagnosing NASH using at least one positive method, the age-adjusted prevalence was 11.2%.The prevalence of NASH was highest among subjects of 50–64 years of age by all three methods (p<0.05): for HAIR Score (6.1%), Gholam Score (12.2%), NASH Liver Fat Score (3.9%), (3.1%) when three methods were combined and 13.5% when at least one method was used.Independent of the non-invasive method used, Mexican-Americans had the highest prevalence of NASH and, except when using the HAIR score, non-Hispanic Blacks had the lowest prevalence of NASH. Using the HAIR Score, NASH had a prevalence of 5.3% among Mexican-Americans, 3.2% among Whites and 3.3% among Blacks. Using the Gholam Score, NASH had a prevalence of 14.7% among Mexican-Americans and 7.2% among Blacks. Using NASH liver fat score, NASH had a prevalence of 5.6% among Mexican-Americans, and 2.6% among Blacks. Combining the 3 methods, NASH had a prevalence of 3.5% among Mexican-Americans and 1.4% among Blacks, and using at least 1 method, NASH had a prevalence of 15.7% among Mexican-Americans and 8.5% among Blacks. The differences in prevalence were statistically significant (p<0.05), irrespective of the non-invasive method used .NASH prevalence was the highest among subjects with high inflammation (as indicated by CRP level >1 mg/dL) based on all methods except for when Gholam was used individually, NASH appeared to be highest among those with mild inflammation (0.3–1 mg/dL) (p<0.05). Males, subjects who lived in rural areas, those with less than high-school education, ex-smokers, physically inactive and those with high waist-to-hip ratio, C-peptide and total cholesterol levels had the highest prevalence of NASH using all the non-invasive methods (p<0.05) .Multivariable regressions adjusting for other covariates showed independent statistical associations between NASH  and race/ethnicity [Mexican-Americans versus Whites], waist-to-hip ratio [high versus healthy], smoking status [current versus never], C-peptide [high versus normal], total cholesterol [high versus normal], and CRP levels [mild or significant versus normal] (p<0.05) . Gender Independent significant risk factors that were associated with NASH diagnosis were race/ethnicity (other groups relative to Whites) , high waist-to-hip ratio versus normal , former alcohol drinkers versus those who did not drink alcohol  , higher than normal level of C-peptide  and of cholesterol , mild inflammation [CRP of 0.3–1.0 mg/dL]  and significantly higher than normal inflammation [CRP>1 mg/dL] . There was no association between current alcohol drinking status versus those who did not drink alcohol and NASH.Independent significant risk factors for NASH identified by at least one method of non-invasive diagnosis were race/ethnicity , high versus normal waist-to-hip ratio  , physical activity where those who did not meet exercise guidelines had higher chance to develop NASH compared to those who met guidelines , and elevated levels of C-peptide [>1.03 nmol/L]  and cholesterol [200–239 mg/dL]  relative to normal, higher than normal cholesterol [≥240 mg/dL] , as well as mild [CRP of 0.3–1.0 mg/dL] , and significant inflammation [CRP>1 mg/dL]  compared to normal .et al., [et al., [et al., [~3000 Finnish subjects by the NASH Score and NASH Liver Fat Score as 6.0% and 4.2%, respectively. The novelty of the current study was that we tested these three methods in a large population sample and estimated a high net sensitivity.Our study used three non-invasive methods  to identify the prevalence and risk factors independently associated with the likelihood of NASH diagnosis in the representative sample of the non-institutionalized US population who had hepatic ultrasound data in NHANES III. Our results were based on each non-invasive method separately  and in combination . Several other non-invasive methods have been used to identify NASH. Most of these used clinical or biochemical markers, and have accuracy similar to the methods we tested, but lower sensitivity compared to the combined methods we analyzed with high net sensitivity. For example, Campos et al.,  develope[et al.,  develope[et al., . The NAS[et al.,  combined[et al.,  calculatet al., [et al., [In our study, we estimated the age-adjusted prevalence of NASH using HAIR Score as 4.2% and by NASH Liver Fat Score as 3.7%, by Gholam Score as 10.0%, and by at least one score as 11.2%. Estimates report 3–5% of adults in the US had NASH in 1988–1994 through a modeled approximation that assumes that 20% of NAFLD cases would be classified as NASH . This eset al.,  and Will[et al.,  reported[et al., , 26. Mos[et al., .In the multivariable analysis, we found that independent of the method of diagnosis, people more likely to be diagnosed with NASH were Mexican-Americans , male 55, and thSurprisingly, although the unadjusted prevalence of NASH in our study was high among those with diet quality of needed improvement, as indicated by the HEI, the adjusted odds ratio of the association between diet quality and NASH was not statistically significant. This is a finding that needs further explanation as lifestyle modifications constitute primary therapy for the management of NAFLD and NASH . This dial., indicated that higher adherence to healthy diets and physical activity, as well as smoking were associated with a lower risk of NAFLD [In addition, the diagnosis of NASH by all of the methods was independently associated with smoking status, where smokers were significantly less likely to be diagnosed with NASH relative to non-smokers, which warrants further investigation. A recent prospective cohort study by Ma et of NAFLD . Lazo anof NAFLD , consistWe found higher odds for NASH diagnosis  in the former alcohol drinkers versus those who did not drink alcohol. There was no association between current alcohol drinking status versus those who did not drink alcohol and diagnosis of NASH.et al., [Previous research regarding the relationship between alcohol consumption and NASH is controversial. Dunn et al.,  found inet al., studied a population of 285 participants with NAFLD not receiving pharmacologic therapy of whom 168 were modest alcohol users  and 117 were abstinent [Ajmera bstinent . Compariet al., [et al., [et al., [In our study, not meeting the physical activity guidelines was associated with an increased likelihood of NASH using at least one method which is consistent with previous research indicating that physical inactivity and the associated lower cardiorespiratory fitness have been related to an increase in NASH and its severity . This iset al.,  found th[et al.,  identifi[et al.,  found sl[et al., . We wereOur current study has the strength of testing three non-invasive methods in a large population sample and estimation of net sensitivity and net specificity of the combined methods. Our study is limited, however, by the analysis of secondary data where some variables thought to be related to NAFLD/NASH were not available. We used data from NHANES III (1988–1994) that were collected around 30 years ago and it is likely that the demographics of NASH has changed in the years since these data were obtained; however, NHANES III data is the only representative sample of the US population with ultrasonography data, demographic data, behavioral and laboratory data. Some variables were measured by self-report  which could be associated with recall bias. Since NHANES data is a cross-sectional study, we cannot infer a causal relationship or temporality, but we can measure association. Although we controlled for major confounders and robust associations, it is possible that other unknown confounders could account for the associations found. In addition, our study is limited by the small sample size in the subgroup analysis. We used equations that were developed using liver biopsies in other populations, but liver biopsies were not performed in our sample.We conclude that non-invasive methods can help identify the high-risk population and risk factors associated with NASH. Several common risk factors were identified by the combined three methods in the NHANES population. Care must be taken in classifying people with NASH based on one non-invasive methods because results differ depending on the method used, but using the combined methods improves the ability to identify the population at high likelihood of developing NASH. Health care providers can screen people with the combined non-invasive methods for the risk factors and identify candidates for referral to biopsy and/or could benefit from interventions including exercise. We plan to use the recently released 2017–2018 NHANES database that provides liver ultrasound elasticity measures to update the ability of the three non-invasive method scores to predict NASH diagnosis and its associated risk factors.


The relationship between metabolically healthy obese individuals (MHO) and cardiovascular disease (CVD) risk is disputed. This study investigated the association of metabolically unhealthy non-obese(MUNO) individuals and MHO with arterial stiffness and 10-year CVD risk.A total of 13,435 participants were enrolled and further divided into the metabolically healthy non-obese (MHNO) phenotype (n = 4927), MUNO phenotype (n = 1971), MHO phenotype (n = 2537) and metabolically unhealthy obese (MUO) phenotype (n = 4000) according to body mass index (BMI) and metabolic status. We used brachial ankle pulse wave velocity (baPWV) to measure arterial stiffness and the Framingham risk score (FRS) to evaluate the 10-year CVD risk.P < 0.001). Logistic regression analysis indicated that the MUNO and MUO phenotypes were independently correlated with elevated baPWV and 10-year CVD risk, while the MHO phenotype was independently associated with only the 10-year CVD risk. In metabolically healthy subjects, BMI showed a dose-dependent increase in the risk of elevated baPWV, with an adjusted OR of 1.007 . However, in metabolically unhealthy participants, the estimate for the relationship between elevated baPWV and BMI was nonsignificant.The MUO and MUNO phenotypes had higher mean baPWV values than the MHO and MHNO phenotypes, regardless of age (1446.19 ± 233.65 vs. 1423.29 ± 240.72 vs. 1283.57 ± 213.77 vs. 1234.08 ± 215.99 cm/s, The MUNO phenotype exhibits increased arterial stiffness and 10-year CVD risk. However, BMI is positively and dose-dependently correlated with arterial stiffness only in metabolically healthy subjects. We speculate that metabolic status may be a strong confounder in the obesity–elevated baPWV association. The prevalence of metabolic syndrome (MetS) has rapidly and substantially increased in recent decades, including in developing and developed countries, representing a considerable clinical burden and a public health concern. Its components, such as obesity, hyperglycemia, hypertension and hyperlipidemia, are all significantly correlated with cardiovascular disease (CVD) , 2. A beAn increasing number of studies have shown that being overweight and obesity are the strongest risk factors for MetS . It is oThe sample comprised 13,435 adult individuals who underwent baPWV measurements at the Health Promotion Center of the First Affiliated Hospital of Nanjing Medical University from September 2017 to January 2020. Each participant underwent a face-to-face interview by physicians. We excluded participants with histories of cardiovascular and cerebrovascular events, taking medications that affect lipid levels. Some conditions such as inflammatory conditions, malignancy, pregnancy, renal or hepatic dysfunction that affect the study results were also excluded. All eligible participants agreed to participate in this study and provided written informed consent. The Ethics Committee of the First Affiliated Hospital of Nanjing Medical University approved the study protocol used in this work.Anthropometric measurements, including height, weight, and blood pressure, were taken by well-trained nurses according to standard protocols. Body mass index (BMI) was then calculated by dividing body weight (in kilograms) by the square of the body height (in meters). Blood samples were collected after an overnight fast of 12 h. All eligible participants received blood tests (including white blood cell count (WBCC), neutrophil count, lipid profile, fasting plasma glucose (FPG), glycated hemoglobin A1c (HbA1c), and uric acid levels). An automated blood cell counter  and a biochemical autoanalyzer  were used in this study.Before examination, participants had a minimum resting time of 5–10 min in a supine position. We used a VP-1000 automated PWV/ABI analyzer  to measure bilateral baPWV. Briefly, baPWV was measured from the ascending point of the right brachial pulse volume recorder to the ascending point of each ankle pulse volume recorder. We adopted the mean value of the right and left baPWV for analysis. Due to the lack of a definite cutoff value of baPWV for arterial stiffness, we considered values higher than the cutoff level between the third and fourth quartiles (> 75th percentile) of baPWV of this study population as arterial stiffness, according to a previous study .The Framingham risk score (FRS) is the best known and most common tool to estimate CVD risk. It is calculated based on traditional risk factors, including sex, age, tobacco smoking, systolic blood pressure (SBP) (treated or untreated), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) . The est2. A metabolically unhealthy individual was defined as an individual having two or more of the following components according to the National Cholesterol Education Program Adult Treatment Panel III: (1) elevated triglyceride (TG): serum TG level ≥ 150 mg/dL (1.70 mmol/L); (2) reduced high density lipoprotein-cholesterol (HDL-C): HDL-C level < 40 mg/dL (1.04 mmol/L) in men or 50 mg/dl (1.29 mmol/L) in women; (3) elevated blood pressure: systolic blood pressure (SBP) ≥ 130 mmHg and/or diastolic blood pressure (DBP) ≥ 85 mmHg or antihypertensive drug treatment; and (4) increased fasting blood glucose (FBG): serum glucose level ≥ 100 mg/dl (5.6 mmol/L) and/or the use of drug treatment for increased glucose. The waist circumference criterion was excluded due to its collinearity with BMI. By combining BMI and metabolic components, all participants were further divided into four phenotypes: ) and were compared using the one-way ANOVA or KruskaleWallis H test. Categorical variables were presented as numbers and proportions and were compared using the chi-square test. Multivariate logistic regression analyses were used to analyze the significance of variables in arterial stiffness and the 10-year CVD risk after adjusting for other confounding risk factors. The metabolic status interaction between BMI and baPWV was assessed using generalized linear models (GLMs). Significance tests were two-tailed, and a Overall, 44.44% n = 5971) of the participants were metabolically unhealthy, and 48.66% (n = 6537) were obese. The MUNO phenotype (n = 1971) and MHO phenotype (n = 2537) accounted for 14.67% and 18.88% of the total population, respectively. Participants with the MUO and MUNO phenotypes were older and had higher SBP, diastolic blood pressure (DBP), WBCC, neutrophil count, FPG, HbA1c, TC, triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) and lower HDL-C than those with the MHNO and MHO phenotypes. The percentage of males, smoking rates, and serum uric acid levels were higher in participants with the MHO phenotype than in those with the MHNO phenotype. Detailed results are listed in Table  of the pP < 0.001). This phenomenon was also observed in men  and women . In addition, the percentages of individuals with intermediate and high CVD risk were significantly higher in the MUO and MUNO phenotypes than in the MHNO and MHO phenotypes  and MUO  phenotypes were independently associated with elevated baPWV after adjusting for multiple risk factors  for the MHO group, 2.198  for the MUNO group, and 3.244  for the MUO group, after adjusting for confounding variables.Table 95% CI 1.041–1.092, P < 0.001) was independently correlated with elevated baPWV after adjusting for MetS components, including SBP, TG, HDL-C and FPG. However, the association between BMI  and elevated baPWV was nonsignificant in metabolically unhealthy participants.In metabolically healthy participants, BMI  for the metabolically healthy participants and 1.047  for the metabolically unhealthy participants.In both metabolically healthy and metabolically unhealthy participants, BMI was significantly correlated with intermediate or high CVD risk, even after adjustment for MetS components, including SBP, FPG, TG and HDL-C. The OR value was 1.049 (P < 0.001). In metabolically healthy participants, BMI indicated a dose-dependent increase in the risk of elevated baPWV, with an adjusted OR of 1.007   Fig. . HoweverThe present study showed that MUNO individuals exhibited significantly higher baPWV and 10-year CVD risk than MHO or MHNO individuals. Furthermore, the ORs for elevated baPWV and intermediate or high CVD risk in MUNO individuals were significantly higher than those in the MHO or MHNO groups, even after adjusting for confounding factors. However, there was no association between MHO and elevated baPWV after adjusting for classical CVD risk factors. Moreover, BMI was independently associated with elevated baPWV in metabolically healthy participants. The relationship was not present in metabolically unhealthy participants, for whom MetS itself was the dominant risk factor for arterial stiffness.MetS is a worldwide epidemic that consists of abdominal obesity, elevated blood pressure, dyslipidemia and hyperglycemia. All components are correlated with an increased CVD risk. Interestingly, there is a unique condition of being a metabolically abnormal but not obese individual, referred to as MUNO. Compelling evidence has demonstrated that MUNO individuals have a high risk of CVD. The Nurses’ Health Study demonstrated that women with metabolically unhealthy normal weight and those who are overweight had a considerably higher CVD risk . The FraRecently, whether MHO increases the risk of CVD has been a debated topic in the field of medicine and public health. However, there is considerable controversy surrounding this issue. Evidence arising from longitudinal prospective studies has revealed that MHO is not a stable state and that some MHO individuals will transition to the metabolically unhealthy phenotype , 28. A pAnother major finding of the present study was that the percentages of individuals with intermediate and high CVD risk were significantly higher in the MHO phenotype than in the MHNO phenotype. We also found that the MHO phenotype was positively associated with increased intermediate or high CVD risk, even after adjustment for confounding factors. Moreover, this association existed in both metabolically healthy and metabolically unhealthy participants. The Framingham risk score was calculated based on sex, age, SBP, tobacco smoking, TC and HDL-C. Obesity was significantly correlated with most components of the Framingham risk score. However, our findings were not consistent with the study of Adair KE et al. . The difThis study has several limitations. First, this study was a cross-sectional study to investigate the association of MUNO and MHO individuals with arterial stiffness and 10-year CVD risk; however, a prospective study is more convincing than a cross-sectional study. Second, we could not collect data that affected obesity and metabolic status, such as the nutritional diet and exercise habits of the participants. Third, measured variables such as BMI are also limited because they do not take into account true body composition.In conclusion, the present study has shown an increased arterial stiffness and 10-year CVD risk in individuals with the MUNO phenotype. However, after adjusting for other CVD risk factors, MHO individuals only had an increased 10-year CVD risk. In particular, a mediating effect of metabolic profile has been detected between arterial stiffness and BMI. These findings provide new evidence that decisions on the initiation of lifestyle interventions should not be based solely on BMI; rather, metabolic status seems to be even more important.


Bovine respiratory disease (BRD) is one of the most common diseases in intensively managed cattle, often resulting in high morbidity and mortality. Although several pathogens have been isolated and extensively studied, the complete infectome of the respiratory complex consists of a more extensive range unrecognised species. Here, we used total RNA sequencing  of nasal and nasopharyngeal swabs collected from animals with and without BRD from two cattle feedlots in Australia.Mollicutes in BRD cases and unaffected control animals. Of the non-Mollicutes species, Histophilus somni was detected, whereas there was a low abundance of Mannheimia haemolytica.A high abundance of bovine nidovirus, influenza D, bovine rhinitis A and bovine coronavirus was found in the samples. Additionally, we obtained the complete or near-complete genome of bovine rhinitis B, enterovirus E1, bovine viral diarrhea virus (sub-genotypes 1a and 1c) and bovine respiratory syncytial virus, and partial sequences of other viruses. A new species of paramyxovirus was also identified. Overall, the most abundant RNA virus, was the bovine nidovirus. Characterisation of bacterial species from the transcriptome revealed a high abundance and diversity of This study highlights the use of untargeted sequencing approaches to study the unrecognised range of microorganisms present in healthy or diseased animals and the need to study previously uncultured viral species that may have an important role in cattle respiratory disease.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-023-01591-1. Respiratory disease is one of the most common causes of disease and death in intensively managed cattle. While the severity of bovine respiratory disease (BRD) varies, it can account for up to 70% of the mortality observed in intensive beef production systems  . Mild orWhile some viruses and bacteria have been widely studied and are known to be an important component of the respiratory complex, novel, emerging or unrecognised pathogens are not easily detected because they are not routinely considered in the differential diagnosis or laboratory diagnostic assays are not available. Metagenomics approaches have facilitated the detection of complete microbiomes and viromes, including emerging pathogens or pathogens that cannot be easily cultured and isolated. Untargeted RNA sequencing  can help detect the complete taxonomic profile of eukaryotic organisms, bacteria, or DNA viruses, as well as the genomic RNA of RNA viruses, enabling the identification of viral species in a single assay –6. A fewHistophilus somni, Mycoplasma bovis, Pasteurella multocida and Mannheimia haemolytica are often associated with disease . Th. ThNarmoPorphyromonadaceae family (n = 3) and Moraxella bovoculi (n = 1) were filtered out due to low relative abundance. A total of 18 species from 10 genera and 7 families were retained. Mycoplasmataceae form the majority across all libraries, with library L10 having the lowest relative abundance  and Mycoplasma arginini in 5 case libraries and no libraries from unaffected ‘control’ libraries. Of the non-Mycoplasmataceae, Fusobacterium necrophorum 4/10 cases and 1/5 unaffected controls, Histophilus somni 8/10 cases and 4/5 unaffected controls, Pasteurella multocida 3/10 cases, 1/5 unaffected controls, Mannheimia haemolytica only found in 1/10 cases.The relative abundance of bacteria was inferred across taxonomic ranks for all libraries. The p = 0.289); Shannon (p = 0.540); Simpson (p = 0.713); Fisher (p = 0.270)) , so it was considered a contaminant. The contig in which erm(B) gene found in seven libraries was also identified as part of a cloning vector (Cloning vector pZJ23).Six AMR genes were identified in the transcriptome, conferring resistance against tetracycline: tetQ tetracycline resistance gene, which was also present in unaffected “control” samples. A variable abundance of tetH, mefA and vatE was identified in all libraries, while the abundances of mphE and msrE were consistently low in samples from unaffected control animals  had a higher abundance of als Fig. .Fig. 9HeAspergillaceae, Ascobolaceae, Nectriaceae, Trichosporonaceae, Wallemiaceae, Rhabditidae, Strongyloididae, Ancylostomatidae, Chabertiidae, Cooperiidae, Babesiidae, Theileriidae, Sarcocystidae, Entamoebidae, Hypotrichomonadidae, Trichomonadidae and Simplicimonadidae. Families present with higher frequency in the libraries were Strongyloididae (n = 12), Entamoebidae (n = 12) and Trichomonadidae (n = 11). Entamoeba was present in 5 libraries from animals without BRD and only 2/10 libraries from affected animals; p = 0.003). Similarly, the presence of Simplicimonas similis was associated with “control” libraries (p = 0.005). None of the other species or genus found was associated with cases.We identified RNA that was taxonomically classified within the families Paramyxovirus and the wide diversity of Mollicutes.We have identified a variety of viruses infecting the upper respiratory tract in Australian feedlot cattle. While some of the viruses identified are known to cause respiratory disease in cattle, this study highlights that there is a variety of other viruses that may have an impact on the occurrence of BRD. Of most note was the high abundance of BoNV in all samples, the presence of IDV, the identification of a new There are no previous reports of BoNV in cattle in Australia. Globally, the previously published genome of BoNV was assembled from sequences obtained from lung samples of cattle during a severe bovine BRD outbreak in a US feedlot in 2013, but the significance of BoNV in disease was not conclusive . AlthougInfluenza D virus has been previously detected in North America, Europe and Asia. Most detections have been in cattle and swine populations, although it can also affect sheep, goats, horses and camels –41. It iBCoV was present in most animals sampled in this study, and RNA load was higher in nasal swabs compared to nasopharyngeal swabs. Although BCoV is most often known to cause gastroenteric disease, it has been associated with respiratory disease outbreaks in the past in Australia and Italy , 45. WhiBoHV-1 is the etiologic agent of infectious bovine rhinotracheitis and a known pathogen within the BRD complex (39). Although we identified BoHV-1 in 7/10 case libraries and 2/5 control libraries, the abundance estimation is not directly comparable to the abundance estimated for RNA viruses as no genomic DNA was sequenced. However, this virus was detected by qPCR in almost all samples and the viral DNA concentration was high for several animals (Ct < 25). It is not possible to interpret the relevance of these data in the context the BRD complex because a live BoHV-1 vaccine virus had been administered intranasally to these cattle at induction to the feedlot .Picornaviruses such as BRAV, BRBV and EV-E have been infrequently associated with disease outbreaks in livestock. Recent metagenomics studies have revealed that BRAV and BRBV are often found in intensively managed cattle , 50. HowBovine respirovirus 3). While a previous study found that seroprevalence at feedlot induction was for 89% BRSV and 91% for BPI3, we detected BRSV in a low number of samples and PI3 was not detected in this study. The lack of detection may be due to animals exposed at an earlier age, or that seasonal waves of infection affect feedlots, but it is unlikely that this virus is completely absent from the feedlots sampled. BRSV is a known pathogenic virus and plays a role in the BRD complex in many countries. BRSV seroprevalence is high in Australian feedlot cattle [Two respiratory paramyxoviruses are known to circulate in Australian cattle, namely BRSV and bovine parainfluenza 3 . The current lack of comprehensive reference sequences for all viral species and genotypes in non-human hosts impedes the automation of taxonomic classification of viromes and requires specialised and labour-intensive assembly of divergent viral genomes. The automation of viral metagenomics data analyses tools depends on the creation of a thorough database with livestock viruses collected from different geographic regions to complete a species and genotype database.There are several challenges and limitations when interpreting microbial genetic data and its association with disease in cross-sectional studies. The microbial diversity observed may vary across different seasons or feedlot operations; therefore, it is only representative of the animals present at the feedlot at the time of sampling. Causation must be carefully assessed by the isolation of the pathogen candidate and followed up with reproducible clinical outcome with experimental studies. Even in cases where this can be achieved, different experimental studies may yield different results based on the specific genotype or variant used as the inoculum. Cross-sectional studies may therefore guide a deeper understanding of disease association, which may be followed up with observational longitudinal as well as experimental studies. The final outcome depends on complex interactions that are influenced by the timing of infection, the virulence of different virus strains, interactions with other viruses and microorganisms and a wide range of animal hosts, livestock management and environmental factors. For example, the failure to detect viruses such as BVDV and BoHV-1 at a single time point from a modest number of samples should not be misinterpreted and reduce their proven capacity as respiratory pathogens. Instead, the detection of novel agents should be used as a signal to investigate whether these additional viruses could also play a role in the BRD complex.With respect to the consensus genomes obtained in this study, the quality of the genomes is subject to assembly errors inherent in metagenome-assembled genomes. This becomes particularly relevant in libraries where we identified different genotypes and variants . For example, if more than one closely related virus was present in a sample, the assembly process could fail or could produce a chimeric consensus sequence . DespiteWe have found a variety of viral and bacterial species in the respiratory tract of feedlot cattle from two feedlots in Australia. From an Australian perspective, we described three previously unrecognised viral species in addition to providing sequence data for known ones. More extensive sample collections, experimental work with recently recognised viruses, automated sequencing and efficient bioinformatic analyses can each contribute to securing information that will provide a better understanding of the role of the infectome in BRD.Additional file 1. Samples collected and pooled for qPCR analysis and sequencing.Additional file 2:Table. Primers and probes used to determine viral abundance through qRT-PCR and qPCR.Additional file 3: Table. Contigs taxonomically classified as Bovine and Porcine Rotavirus and their amino acid identity to published reference. Additional file 4. Phylogenetic tree of BoNV depicting bootstrap values. Additional file 5. Overview of Influenza D virus’ segments phylogenies. 5.1. Maximum likelihood phylogenies of the seven segments of Influenza D virus. 5.2. Influenza D virus- segment 1. 5.3. Influenza D virus- segment 2. 5.4. Influenza D virus- segment 3. 5.5. Influenza D virus- segment 4. 5.6. Influenza D virus - segment 5. 5.7. Influenza D virus- segment 6. 5.8- Influenza D virus – segment 7.Additional file 6. Phylogenies with bootstrap values of BRAV, BRBV, EV-E capsid  and 3D polymerase. 6.1. Bovine rhinitis A virus – capsid . 6.2. Bovine rhinitis A virus– 3D polymerase. 6.3. Bovine rhinitis B virus – capsid . 6.4. Bovine rhinitis B virus– 3D polymerase. 6.5. Enterovirus E– capsid . 6.6. Enterovirus – 3D polymerase. Additional file 7. Overview of Bovine narmovirus-1 (Bulang virus) phylogenies. Fig 7.1. Maximum likelihood phylogenies of Bovine narmovirus-1 (Bulang virus). 7.2. Bovine narmovirus-1 (Bulang virus)– L protein. 7.3. Bovine narmovirus-1 (Bulang virus)– Nucleoprotein. 7.4. Bovine narmovirus-1 (Bulang virus)– Matrix. 7.5. Bovine narmovirus-1 (Bulang virus)– Phosphoprotein. 7.6. Bovine narmovirus-1 (Bulang virus)– Fusion protein. Additional file 8:Fig. Alpha and beta diversity of the metatranscriptomic sequence libraries obtained from animals with clinical bovine respiratory disease (cases) and without clinical disease (controls). Fig(a): The alpha diversity computed using different methods. Fig(b). NMDS plot using Bray-Curtis dissimilarities between samples in cases and controls.


Mucosal healing has become the primary treatment target for patients with Crohn’s disease (CD). We aimed to develop a noninvasive and convenient tool to evaluate the endoscopic activity in patients with ileocolic CD.A retrospective multicenter study including 300 CD patients  was conducted at two tertiary referral centers. Independent risk factors associated with endoscopic activity were explored, which were then combined into a comprehensive index. The predictive performance was evaluated with the area under receiver operating characteristic curve (ROC). Cohen’s Kappa was adopted to examine the consistency between each indicator and endoscopic activity.P < 0.05). This was further well demonstrated by the external test cohort, which showed good discrimination . Intra-individual comparison revealed the comprehensive index to be superior in the prediction of endoscopic activity. In the subgroup analysis, the AUC of comprehensive index was significantly higher than CDAI especially in inflammatory phenotype .A total of 210 CD patients were recruited in the training cohort. We found that Crohn’s Disease Activity Index (CDAI), C-reactive protein (CRP) and platelet-to-lymphocyte percentage ratio (PLpR) were independently associated with endoscopic activity. Additionally, the comprehensive index generated from the above three indices achieved good discrimination and performed better than CDAI in AUC (0.849 vs. 0.769, Combining CDAI, CRP and PLpR significantly improved the accuracy for predicting endoscopic activity in ileocolic CD, which can help better monitor an endoscopic flare.The online version contains supplementary material available at 10.1186/s12876-023-02968-0. Crohn’s disease (CD) is a chronic inflammatory disorder which affects the whole gastrointestinal tract with symptoms involving abdominal pain, chronic diarrhea and weight loss . As a woThe Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) consensus defined clinical remission as a short- or intermediate-term target and endoscopic healing as a long-term therapeutic target of CD , 8. A ‘tCrohn’s Disease Activity Index (CDAI) has been most widely used in clinical practice and trials to assess the symptomatic response or remission. However, its subjective nature and poor correlation with endoscopic activity limited its clinical application. The CALM trial is an open-label multicenter phase 3 randomized controlled trial which enrolled 244 patients with active endoscopic CD who were randomized to tight control or clinical management groups. In clinical management group, patients had a therapy escalation if CDAI decrease of < 100 points or CDAI ≥ 200, while in tight control group, a therapy escalation was conducted when fecal calprotectin ≥ 250 µg/g, C-reactive protein (CRP) ≥ 5 mg/L and CDAI ≥ 150. It turned out that compared with clinical management group, patients in tight control group presented better mucosal healing with absence of deep ulcers, deep remission, biological remission and steroid-free remission. These results suggested that the optimization of therapy based on combination of clinical symptoms and laboratory parameters was more likely to achieve better endoscopic and clinical outcomes than conventional symptoms-based decision . ConsistVarious markers for CD have been identified in blood, stool, urine and colonic tissue over the past decades. Despite the fecal indicators, such as calprotectin and lactoferrin have been considered as efficient tools for discriminating endoscopic activity, there were still several limitations in clinical application. Previous studies showed that compliance of about two-thirds of the patients with calprotectin was poor , and theThe present study was designed to identity objective and noninvasive serum indicators on the basis of CDAI to assess endoscopic disease activity in CD patients. We believe that combined use of clinical symptoms and laboratory serum indices has great potential to develop as surrogate markers of endoscopic activity.This retrospective multicenter cohort study included 300 patients who were diagnosed with CD and underwent hematology and endoscopy examination from 2 tertiary referral centers from January 2015 to January 2023. The training cohort was recruited from the Affiliated Hospital of Yangzhou University and the external test cohort enrolled patients from Northern Jiangsu People’s Hospital Affiliated to Yangzhou University. This study was approved by the Research Ethics Committee of the Affiliated Hospital of Yangzhou University [No. (2021-YKL06-09-006)].The inclusion criteria were as follows: (1) the diagnosis CD on the basis of clinical symptoms, laboratory examinations, endoscopic findings, histological results and imaging data; (2) endoscopy and laboratory examination data must be available concurrently, and the results of laboratory examination were obtained within 7 days before colonoscopy. The exclusion criteria were: (1) limited to upper gastrointestinal or small intestinal CD; (2) history of intestinal resection; (3) coexistence of other autoimmune diseases ; (4) hematologic diseases or other diseases which influence the results of the complete blood counts ; (5) pregnancy or lactation.Data including age, gender, age at diagnosis, disease duration, body mass index (BMI), Montreal classification, CDAI score, endoscopic and radiological results, and therapeutic methods were achieved from the electronic medical record system. The result relating to hematological indicators including whole blood count, CRP and albumin were collected from the clinical laboratory system. Furthermore, the indirect indicators were calculated .Endoscopic disease activity was evaluated by the Simple Endoscopic Score for CD (SES-CD) which divides the intestinal segment into five parts: terminal ileum , right colon, transverse colon, left colon (including sigmoid colon) and rectum . The sevA sample size of at least 34 endoscopic procedures (17 endoscopic disease remission and 17 endoscopic disease activity) was required in the training and test cohorts according to the following hypothesis: power, 90%; two-sided significance level, 0.05; alternative hypothesis of the AUC, 0.8 compared with the null hypothesis of the AUC, 0.5, and the allocation of the positive group was equal to that of negative group. Therefore, sample sizes of 210 (54 endoscopic disease remission and 156 endoscopic disease activity) in the training cohort and 90 (21 endoscopic disease remission and 69 endoscopic disease activity) in the test cohort were sufficient to detect an AUC difference of 0.5 with 90% power if the true AUC was > 0.8. Statistical analyses were performed using PASS (version 2021).P-value < 0.05 was considered as significant. Statistical analysis was performed in SPSS version 25.0  and R version 4.1.1 .Non-normally distributed data were expressed as median  and compared with Mann-Whitney U test between different groups of endoscopic activity. Categorical data were described as frequencies and compared using Chi-square test and fisher exact test. The Spearman correlation coefficient was used to express the correlation between indices and SEC-CD. Logistic regression by stepwise regression was performed to explore the independent risk factors associated with endoscopic activity which were then combined into a new composite index. The receiver operating characteristic (ROC) curve was used to describe the ability of each index to predict endoscopic activity and to determine the cut-off value (according to Youden index). The calculation of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy was based on the cut-off values and corresponding computational formulas. The prediction efficacies were evaluated by calibration. Cohen’s Kappa were adopted to examine the consistency between each indicator and endoscopic activity, and Delong’s test was used for comparison of prediction ability among indicators. A A total of 210 CD patients  were enrolled in training cohort. The demographic and clinical characteristics are shown in Table 9/L) and ALB (41.4 vs. 37.4 g/L) (P < 0.05). For patients with endoscopic activity, neutrophils (3.57 vs. 3.17 × 109/L), PLT (247.5 vs. 205 × 109/L), CRP (13.9 vs. 0.77 mg/L), NLR (3.01 vs. 2.10), PLpR (11.5 vs. 6.27), PLT/ALB (6.38 vs. 4.88), CRP/ALB (0.36 vs. 0.02) were significantly higher than those in endoscopic remission (P < 0.05). Concerning the CDAI score, the median score in the remission group and activity group in the training cohort was 77 and 130, respectively, which were moderately correlated with SES-CD  . Table . We founP values were determined by Mann-Whitney U test for continuous variables.Data are presented as median (IQR). We detected endoscopic activity by drawing ROC curves based on clinical and laboratory indicators which were significant in factor analysis Table . When weP < 0.05 in factor analysis were enrolled in the binary multivariable logistic regression analysis. Considering the collinearity, we adopted the backward stepwise regression method. Ultimately when including CRP, CDAI and PLpR, the fit of the model is optimal. As shown in Table P = 0.02), higher CRP  and higher CDAI  were positively related to the endoscopic activity.The indices with P < 0.05). The predictive performance of the comprehensive index in the training and test cohort was presented in Fig. P = 0.87). Furthermore, the sensitivity (80.77% vs. 81.16%), specificity (79.63% vs. 76.19%), positive predictive value (PPV) (0.808 vs. 0.918), negative predictive value (NPV) (0.796 vs. 0.552) and accuracy (0.805 vs. 0.8) of the model for evaluating endoscopic activity in the training and test cohort were shown in Table κ = 0.542), followed by CRP (0.446), then CDAI (0.380), and finally PLpR (0.299).Subsequently, we combined three indicators to form a new comprehensive index. As shown in Fig. P < 0.05). Whereas it showed no significant difference to CDAI in patients with stricturing and penetrating phenotype .In order to evaluate the role of comprehensive index in endoscopically disease severity, we performed a subgroup analysis and revealed that no statistically significant difference was found only between the endoscopic mild activity group and moderate group Fig. . RegardiThe absence of endoscopic disease activity in patients with CD is associated with better clinical outcomes and is considered as the long-term targets . Howeverr = 0.503) between CDAI and SES-CD  in inflammatory phenotype (B1), while they were no statistical significance in stricturing (B2) and penetrating (B3) phenotype. One possible explanation for this phenomenon might be the difference of CRP levels and clinical severity in each phenotype. CRP is an acute-phase protein and rises frequently at the early stage in CD [Considering that there was statistical difference between disease behaviours and endoscopic disease activity in our cohort, we performed a subgroup analysis on the basis of the phenotypes and found that the AUC of the composite index was higher than CDAI significantly (0.824 vs. 0.751, ge in CD , and mange in CD , 38. Bo ge in CD . AlthougThe main limitations of the present study include its retrospective design and relatively small number of participants. To reduce the bias occurred in data collection and analysis, doctors in charge of endoscopic scoring were not aware of other data. Although external validation was performed, the sample size of the validation group was relatively small. Further studies involving a large-scale sample in multicenter validation awaits to be conducted. Considering the fact that SES-CD scored by colonoscopy is the most widely used endoscopic scoring system in evaluating disease activity, we chose it as the gold standard. However, it could not assess the condition of small bowel CD accurately , which lThe combination of CDAI, CRP and PLpR provides physicians with a novel noninvasive, convenient and economical measurement which can accurately evaluate the disease activity and highly correlated with SES-CD, especially in patients with inflammatory phenotype. Our findings suggest the potential of applying the surrogate indicators to discriminating disease activity in ileocolic CD patients. It could be used as a clinical decision support tool for the management of patients with CD.Below is the link to the electronic supplementary material.Supplementary Material 1