The prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high‐ and low‐risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research.We searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C‐statistics for each model.n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen . The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta‐analysis of five models included in 15 studies. DECAF  performed well in predicting in‐hospital death  and 90‐day death  and CURB‐65  performed well in predicting 30‐day death .A total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (This study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in‐hospital and 90‐day deaths. Yet, external validation in different populations is still needed to prove this performance. We conducted a meta‐analysis of AECOPD mortality risk prediction models. The results showed that DECAF performs well in predicting in‐hospital death  and 90‐day death  and CURB‐65 performs well in predicting 30‐day death . In many countries, the prevalence of COPD has increased steeply with age, with the highest prevalence amongst those aged >60 years.A clinical risk prediction model is a mathematical equation that relates multiple predictive factors to disease diagnosis or prognosis.2The protocol for this review was registered in the International Prospective Register of Systematic Reviews (PROSPERO), and the registration number is CRD42022328505.2.1PubMed, EMBASE, Cochrane Library, and Web of Science databases were searched from their inception to 10 April 2022. The search terms applied were as follows: (‘acute exacerbation of chronic obstructive pulmonary disease’ OR ‘AECOPD’ OR ‘acute exacerbation of COPD’ OR ‘exacerbation of COPD’ OR ‘COPD exacerbation’) AND  AND . Additionally, we manually searched for references and relevant articles to identify additional studies. The detailed retrieval strategies and steps are presented in Table 2.2We included articles written in English that developed or validated prediction models for mortality risk in patients with AECOPD. Meanwhile, studies with incomplete data, duplicate publications, conference abstracts and study protocols were excluded.2.3Two reviewers (ZLJ and SYL) independently screened the literature and collected data, including author information, year of publication, country, research type, prediction results, sample size, predictors, model discrimination and calibration, modelling method and methods for handling missing data. In cases of disagreement, decisions were made following discussion with a third investigator (YX).2.4The prediction model risk of bias assessment tool (PROBAST)2.5I2 statistic. If I2 was >50%, the studies were considered statistically heterogeneous. The STATA command is listed in Table A descriptive analysis was used to summarize the general findings of the predictive models, and the frequencies of the variables were calculated. In addition, a random‐effects meta‐analysis using STATA software (version 16) was used to synthesize C‐statistics from multiple studies validating the same model.33.1A total of 4376 pieces of literature were obtained through database searching, and 37 pieces of literature3.2n = 15) was the most common predictor of mortality. The prediction models were mainly built in the United States (n = 5), Spain (n = 5) and China (n = 3). The sample sizes ranged from 61 to 150 035 for the development cohort and 284 to 149 646 for the validation cohort. The characteristics of the model building are listed in Table n = 9), random splitting (n = 7) and a combination of methods (n = 2). The two most frequently used modelling methods were logistic regression (n = 20) and classification and regression tree (n = 3). Many studies did not report how to handle missing values, and imputation (n = 6) was used for the few models for which this was performed. Four studiesn = 14) to represent the model, and four studiesn = 17), dyspnoea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen .We found 26 studies3.3As shown in Table 3.4n = 29), insufficient sample size (n = 27), selection of predictors using univariate analysis (n = 17), inappropriate data sources (n = 17), lack of internal validation (n = 8), continuous predictors handled inappropriately (n = 7) and missing data not handled appropriately (n = 2).We evaluated 37 studies3.5We performed a meta‐analysis of the C‐statistics of the five models included in the 15 studies.4This systematic review of risk prediction models for mortality in patients with AECOPD included 37 studies, presenting 26 studies of the model development processes and 11 studies to validate the models. Although a certain number of models can be chosen, there is a high risk of bias for all model developments or validations. Age, dyspnoea grade, altered mental status, pneumonia and urea nitrogen were the most frequently used predictors to develop risk models for mortality from AECOPD. We also performed a meta‐analysis on the external validation of the five models, and the most external validation scales were BAP‐65, CURB‐65 and DECAF.The present study revealed methodological flaws during model building and validation, which were also reflected in the assessment of the risk of bias. The performance of the model was typically demonstrated by discrimination and calibration, with all studies providing C‐statistics; however, only eight studiesThis study found that age, degree of dyspnoea, altered mental status, pneumonia and BUN were important predictors in the models. Consistent with previous reports,The DECAF, BAP‐65 and CODEX indices were specifically developed to predict the risk of death from AECOPD. DECAF used the expectation–maximization algorithm for the imputation of missing data and used the bootstrap method for internal validation. Although its EPV is <20 and screening of predictors using univariate analysis causes some bias to the model, the DECAF score has been consistently shown to be a good predictive model because of the simplicity of the measured variables and its external validation in multiple national populations.One strength of this study was the systematic description of the methodological characteristics during the development and validation of the risk prediction model for AECOPD mortality. A meta‐analysis of the C‐statistics was performed using commonly used external validation models. We also performed a risk of bias assessment of the included studies using the PROBAST. Our limitation is that only English literature was included, and it is possible that high‐quality studies in other languages were missed. Moreover, we did not conduct a meta‐analysis of the calibration of the model because the reports and data were too few to perform that analysis.5This study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. Despite the development of many models, the number of models that have undergone extensive external validation and can be applied clinically is poor. In addition, the safety, clinical effectiveness and cost‐effectiveness of the models should be considered. The meta‐analysis of the present study suggests that the DECAF performs well in predicting in‐hospital and 90‐day mortalities. However, external validation in different populations is still needed to support this.Yang Xie and Xuanlin Li would answer for the design and conception of the article, Zile Ji, Siyuan Lei and Jiaxin Xu would answer for the collection and assembly of materials; Zile Ji, Siyuan Lei and Xuanlin Li would answer for data interpretation and analysis; Zile Ji drafted the manuscript; Xuanlin Li, Siyuan Lei, Jiaxin Xu and Yang Xie revised it. All authors reviewed and approved the final version of the manuscript.The authors declare that they have no competing interests.Not applicable.Table S1: Search strategy(2022.4.10).Table S2: PROBAST: Assessment of Risk of Bias.Table S3: The STATA command.Table S4: Characteristics of excluded studies.Table S5: Basic characteristics of the 26 studies that developed models .Table S6: Methodological characteristics of the 26 studies that developed models .Table S8: Methodological of characteristics of the 11 studies that only validated the models.Table S9: Data from 15 studies used for meta‐analysisClick here for additional data file.


To comprehensively investigate risk factors for proliferative vitreoretinopathy (PVR) after retinal detachment (RD) surgery.PubMed, Embase, Cochrane Library, and Web of Science were systematically searched until May 22, 2023. Risk factors included demographic and disease-related risk factors. Odds ratios (ORs) and weighted mean differences (WMDs) were used as the effect sizes, and shown with 95% confidence intervals (CIs). Sensitivity analysis was conducted. The protocol was registered with PROSPERO (CRD42022378652).P = 0.038). Smokers had a higher risk of postoperative PVR than non-smokers . Presence of preoperative PVR was associated with a greater risk of postoperative PVR . Presence of vitreous hemorrhage was associated with a greater risk of postoperative PVR . Individuals with aphakia or pseudophakia had an increased risk of postoperative PVR in contrast to those without . The risk of postoperative PVR was higher among patients with macula off versus those with macula on . Extent of RD in patients with postoperative PVR was larger than that in patients without . Patients with postoperative PVR had longer duration of RD symptoms than those without .Twenty-two studies of 13,875 subjects were included in this systematic review and meta-analysis. Increased age was associated with a higher risk of postoperative PVR (pooled WMD = 3.98, 95%CI: 0.21, 7.75, Age, smoking, preoperative PVR, vitreous hemorrhage, aphakia or pseudophakia, macula off, extent of RD, and duration of RD symptoms were risk factors for postoperative PVR in patients undergoing RD surgery, which may help better identify high-risk patients, and provide timely interventions. The success of retinal detachment (RD) surgery is dependent on the absence or control of proliferative vitreoretinopathy (PVR) . PVR, onSeveral studies have been performed to explore the risk factors for PVR after RD surgery, but there are still inconsistent results. In the study of Xu et al. , macularThis study intended to comprehensively probe into the risk factors for PVR after RD surgery via a systematic review and meta-analysis, and focused on demographic risk factors and disease-related risk factors.PubMed, Embase, Cochrane Library, and Web of Science were systematically searched until May 22, 2023 by two independent reviews . Disagreements were resolved through discussion. English search terms included “Retinal Detachment” OR “Detachment, Retinal” OR “Detachments, Retinal” OR “Retinal Detachments” OR “Retinal Pigment Epithelial Detachment” AND “Vitreoretinopathy, Proliferative” OR “Proliferative Vitreoretinopathies” OR “Vitreoretinopathies, Proliferative” OR “Vitreoretinopathy Neovascular Inflammatory” OR “Inflammatories, Vitreoretinopathy Neovascular” OR “Inflammatory” OR “Vitreoretinopathy Neovascular” OR “Neovascular Inflammatories, Vitreoretinopathy” OR “Neovascular Inflammatory, Vitreoretinopathy” OR “Vitreoretinopathy Neovascular Inflammatories” OR “Proliferative Vitreoretinopathy”. Review studies were hand-searched to identify additional articles. This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The protocol was registered with PROSPERO (CRD42022378652).Inclusion criteria were: (1) studies on patients undergoing RD surgery; (2) studies reporting whether patients had PVR after RD surgery; (3) studies reporting at least one of the following: demographic risk factors and disease-related risk factors; (4) cohort studies and case-control studies; (5) studies in English; (6) the latest studies when the study population was repeated.Exclusion criteria were: (1) studies with animal experiments; (2) studies unrelated to the research topic, such as studies on drug testing, etc.; (3) studies for which data are incomplete or data cannot be extracted; (4) conference reports, dissertations, case reports, meta-analyses, reviews.Extracted data included first author, year of publication, country, study period, study design, sample size, preoperative PVR, sex ratio , age (years), follow-up time (months), disease type, type of surgery, quality assessment, and risk factor. The quality of the included case-control and cohort studies was evaluated using the modified Newcastle-Ottawa scale (NOS). The scale had a total score of 9, with 0–3 as low quality, 4–6 as medium quality, and 7–9 as high quality .2≥50%, the random-effects model was used for analysis; otherwise, the fixed-effects model was used for analysis. Sensitivity analysis was carried out for all models. P<0.05 was considered to be statistically significant.All studies were statistically analyzed using Stata 15.1 . For enumeration data, odds ratios (ORs) and hazard ratios (HRs) were used as the effect sizes, and for measurement data, weighted mean differences (WMDs) acted as the effect size, with 95% confidence intervals (CIs) calculated. The heterogeneity test was carried out for each index. If the heterogeneity statistic IA total of 6,477 studies were retrieved from the four databases, and 7 additional articles were identified from review studies. After deduplication, 3,495 were left. Subsequently, titles and abstracts and then full texts were subject to screening according to the eligibility criteria. Finally, 22 studies ,12,14–30Sex. Eight studies of 1,875 patients reported the association between sex and the risk of postoperative PVR. No significant difference was observed in the risk of postoperative PVR between male and female patients  ( = 0.0%) .P = 0.234). According to the study of Capeans et al., there is no gender difference between the postoperative PVR group and the postoperative non-PVR group, with a male proportion of 50% vs 42%, respectively.Moussa et al. showed that sex is not a risk factor for postoperative PVR  (= 74.4%) .Ferrara et al. found that elderly patients showed more severe PVR (grade C and above), especially after 60 years old. Kasetty et al. illustrated a higher incidence of PVR in young patients. Gao et al., Loiudice et al. and Moussa et al. reported that age is not a risk factor for postoperative PVR.P<0.001, I2 = 1.0%)  .P = 0.01).Eliott et al. reported that smoking was a risk factor for redetaching due to postoperative PVR   .P = 0.02), Kon et al. (P<0.05), Duquesne et al. , Girard et al. , and Cowley et al.  demonstrated that preoperative PVR was a risk factor for redetaching due to postoperative PVR.The studies of Eliott et al.   .P = 0.044).Girard et al. demonstrated that a history of uveitis was associated with a greater risk of postoperative PVR   .P<0.01), and Bonnet et al. (P<0.05) showed that previous ophthalmic surgery was a risk factor for postoperative PVR.Pastor et al. , Cowley et al.   .P = 0.90). Cowley et al. found that severe vitreous hemorrhage was significantly more frequent in the PVR group.The study of Duquesne et al. showed that 11.3% of patients with preoperative vitreous hemorrhage and 11.8% of patients without preoperative vitreous hemorrhage developed PVR after surgery, respectively   .Pastor et al. illustrated that aphakic or pseudophaki elevated the risk of postoperative PVR . Loiudice et al. found that lens status was not a predictive factor for the development of postoperative PVR . Capeans et al. (10% vs 7%) and Cowley et al. (47.6% vs 42.6%) showed no difference in aphakic or pseudophaki between the postoperative PVR and non-PVR groups.P = 0.002, I2 = 47.2%)  .P = 0.002), indicating that macula off is a risk factor for PVR after RD surgery. Loiudice et al. , and Moussa et al.  showed that macula status was not a risk factor for postoperative PVR. Capeans et al. found no difference in macula off between the postoperative PVR and non-PVR groups (30% vs 41%).According to Cowley et al., patients with macula off accounted for 84.6% in the postoperative PVR group and 64.6% in the non-PVR group   .P = 0.036, I2 = 0.0%)  .P = 0.242).Loiudice et al. showed that extent of RD was not a risk factor for postoperative PVR   .P = 0.643).Loiudice et al. demonstrated that the number of retinal breaks was not associated with PVR after RD surgery   .Sensitivity analysis was conducted by deleting a study at a time and synthetically assessing the remaining studies. It was found that the pooled results were not affected by one-study deletion, suggesting that the findings of this meta-analysis were stable and robust.This review and meta-analysis first comprehensively evaluated the risk factors for PVR after RD surgery from the perspectives of demographic and disease-related risk factors. It was revealed that age, smoking, preoperative PVR, vitreous hemorrhage, aphakia or pseudophakia, macula off, extent of RD, and duration of RD symptoms were risk factors for postoperative PVR in patients undergoing RD surgery. Combined assessment of demographic and disease-related risk factors may aid in better prediction of the PVR risk following RD surgery. Awareness of these factors in the development of PVR may allow for early lifestyle interventions and careful surgical planning to minimize the risk.As regards demographic risk factors, older age was associated with a higher risk of PVR after RD surgery. Apart from the included studies that supported the above statement ,20,26, nWith respect to disease-related risk factors, patients with preoperative PVR, vitreous hemorrhage, aphakia or pseudophakia, macula off, large extent of RD, and long duration of RD symptoms had an elevated risk of postoperative PVR. Scare evidence has been provided on the role of preoperative PVR in such a setting. Bonnet et al.  illustraThis work may act as a reference for vitreoretinal surgeons, and the identified evidence may help the standardization of clinical practice, with more effective management bringing improved outcomes for patients following RD surgery. Given the risk factors, patients should pay attention to and practice smoking cessation and eye care to prevent and lower the risk of postoperative PVR. Targeted screening strategies and interventions could be developed for individuals at a high risk of PVR after RD surgery. Some limitations of this study should be noted. First, the results of combined analysis may be unstable due to the existence of heterogeneity. Second, confounding factors were not taken into account when evaluating these risk factors. Third, the study only included English literature, which may cause language bias. Besides, of 22 included studies, 9 studies ,19,20,22Age, smoking, preoperative PVR, vitreous hemorrhage, aphakia or pseudophakia, macula off, extent of RD, and duration of RD symptoms were risk factors for PVR following RD surgery. Future studies are warranted to support our findings.S1 Checklist(DOCX)Click here for additional data file.


Physical inactivity is an independent risk factor for type 2 diabetes (T2D). Osteoarthritis (OA) is a common joint disease that limits patients' physical activity, which may increase risk of other chronic diseases including T2D. However, studies evaluating the effect of OA on T2D are scarce. This study aimed to investigate the causal effect of knee and hip OA on risk of T2D from a genetic perspective.We performed two‐sample Mendelian randomization (MR) analyses to obtain nonconfounding estimates of the effect of OA on T2D risk. Single nucleotide polymorphisms (SNPs) from genome‐wide association studies were selected as genetic instruments for radiographic knee and hip OA . The associations of these SNPs with T2D were evaluated in participants from the UK Biobank. Sensitivity analyses were conducted to test the robustness of the MR results.p <.001; hip OA: OR 1.04, 95% CI 0.94–1.16, p = .425). Sensitivity analyses showed that the main findings are robust.Genetic predisposition of knee but not hip OA was significantly associated with an increased risk of T2D  1.09–1.27, The current study provides genetic evidence supporting that knee OA is a potential risk factor for T2D. HighlightsWe used data from the genome‐wide association studies database and two‐sample mendelian randomization method, we found that genetic evidence supporting that knee osteoarthritis is a potential risk factor for type 2 diabetes. In 2021, approximately 537 million adults (20–79 years) are living with diabetes, of whom 90% had T2D,Epidemiological studies evaluating the association between OA and diabetes have inconsistent results. In two meta‐analyses of observational studies with different designs , patients with OA at different sites were more likely to have T2D than those without.22.1This genetic association study was conducted according to the Strengthening the Reporting of Observational Studies in epidemiology using Mendelian Randomization (STROBE‐MR).2.1.1https://gwas.mrcieu.ac.uk/).p < 5 × 10−8) were used as IVs for KOA and HOA, respectively. We checked SNPs for independent inheritance (<0.001) without linkage disequilibrium with each other and several bases between two SNPs (kb >10 000). Then, we selected the reference sample formed by the European ancestral individuals from the 1000 genomes project to estimate the allele frequency and linkage disequilibrium level.The summary‐level data for KOA and HOA were obtained from a genome‐wide meta‐analysis conducted by the UK Biobank and the Arthritis Research UK Osteoarthritis Genetics (arcOGEN) resources, which included 24 955 cases with KOA, 15704 cases with HOA, and 378 169 controls of European descent.2.2Publicly available summary‐level data on genetic variations associated with T2D were obtained from a large meta‐analysis of GWAS (62 892 cases and 596 424 controls of European ancestry).2.3p value <.05 represented a statistically significant causal association of KOA/HOA with T2D. The multiplicative random‐effect IVW method was used when the number of IVs is >3, otherwise the fixed‐effect IVW method was used.We conducted two separate two‐sample MR analyses to test the potential causal associations between KOA/HOA with the risk of T2D. Inverse variance weighted (IVW) method was used as the primary analysis for the causal association of KOA/HOA with T2D risk. IVW can provide more precise estimates when all IVs are valid.p value >.05 represented no statistically significant pleiotropy and heterogeneity. Second, the leave‐one‐out sensitivity analysis was used to evaluate the effect of each IV on the risk of T2D. Third, we searched online webtool Phenoscanner to determine pleiotropic SNPs that were also significantly (p = 5E‐08) associated with other risk factors (confounders) of T2D  and MR‐EggerAll statistical analyses were performed using the “TwoSampleMR” package for R version 3.6.1.3A total of 21 independent SNPs associated with T2D were identified (Supplementary file 3.1p <.001)   Tables  and 3. T) Tables . The lea) Tables . No IVs 3.2No statistically significant causal relationships of HOA with risk of T2D were found, based on the IVW, weighted median, MR–Egger regression, weighted mode, and simple mode methods Table . A signi4This study was the first to evaluate the causal relationships of KOA and HOA with risk of T2D from a genetic perspective. Using two‐way Mendelian analyses, we found that radiographic KOA but not HOA was a potential risk factor for T2D. The findings of this study suggest an important role of KOA in developing T2D, and the prevention and treatment of KOA may be beneficial for T2D.Some previous population‐based cohort studies have shown that arthritis, including KOA, was associated with an increased T2D risk.Although the mechanisms underlying the association between OA and T2D are not well understood, a possible explanation is that functional limitations due to OA led to reduced weight‐bearing activities, increased sedentary behaviors, and weight gain, which are important risk factors for T2D.We did not find a causal relationship between HOA and T2D risk, suggesting dissimilar effects of KOA and HOA on the development of T2D. Previous studies have shown that metabolic syndrome plays a more significant role in KOA than in HOA.In this study, the strength for the association between OA and risk of T2D may have been underestimated because the definition of OA was based on the Kellgren–Lawrence grades but not clinical symptoms. Previous studies have indicated that patients with radiographic OA do not necessarily have joint symptoms and vice versa,The strength of this study is that MR analyses provided nonconfounding estimates for the association between OA and T2D and all IVs identified were valid, making our findings more reliable. Our study also has some limitations. First, we cannot exclude the possibility of sample overlap. However, our tests indicated that the impact of sample overlap is small.In conclusion, our two‐sample MR analyses showed a significant causal relationship between radiographic KOA but not HOA and an increased risk of T2D.Guoqi Cai conceived, initiated, and supervised the project. Xing Xing collected and analyzed the data. Yining Wang and Xing Xing contributed to the interpretation of the results. Xing Xing wrote a draft of the manuscript. All authors critically reviewed and revised the manuscript, and agreed to the published version of the manuscript.This work was supported by the National Natural Science Foundation of China (82103933).The authors declare no conflicts of interest.Figure S1. The leave‐one‐out plot of the effects of knee osteoarthritis/hip osteoarthritis (KOA/HOA) in type 2 diabetes (T2D).Figure S2. The funnel plot of the effects of knee osteoarthritis/hip osteoarthritis (KOA/HOA) and in type 2 diabetes (T2D).Click here for additional data file.Table S1. Characteristics of SNPs used in Mendelian randomization analysis of the effects of knee osteoarthritis (KOA) in type 2 diabetes (T2D).Table S2. Characteristics of SNPs used in Mendelian randomization analysis of the effects of hip osteoarthritis (HOA) in type 2 diabetes (T2D).Table S3. Two‐sample Mendelian randomized analyses for the associations of hip osteoarthritis with the risk of type 2 diabetes after adjusted by MR‐PRESSO.Table S4. Statistical power for the Mendelian randomization analysis.Table S5. List of genetic variants associated with more than one phenotype.Click here for additional data file.


The incidence of early-onset colorectal cancer (EOCRC), which means colorectal cancer diagnosed in patients under 50 years, has been increasing around the world. However, the etiology remains unclear. This study aims to identify risk factors for EOCRC.This systematic review was conducted in PubMed, Embase, Scopus, and Cochrane Library databases from inception to November 25, 2022. We examined risk factors for EOCRC, including demographic factors, chronic conditions, and lifestyle behaviors or environmental factors. Random-effects/fixed-effects meta-analysis was adopted to combine effect estimates from published data. Study quality was evaluated with the Newcastle-Ottawa Scale (NOS). Statistical analysis was performed Revman5.3. Studies not suitable for the meta-analysis were analyzed by a systematic review.2>60%).A total of 36 studies were identified for this review, and 30 studies were included in the meta-analysis. Significant risk factors for EOCRC included male , Caucasian , a family history of CRC , inflammatory bowel disease , obesity , overweight , triglycerides , hypertension , metabolic syndrome , smoking , alcohol consumption , a sedentary lifestyle , red meat , processed meat , Western dietary patterns  and sugar-sweetened beverages . However, no statistical differences were found for hyperlipidemia and hyperglycemia. Vitamin D may be a protective factor . There was considerable heterogeneity among studies (IThe study provides an overview of the etiology and risk factors of EOCRC. Current evidence can provide baseline data for risk prediction models specific to EOCRC and risk-tailored screening strategies. Colorectal cancer (CRC) is the third most common cancer worldwide and the second most common cause of cancer-related death . In receMultiple risk factors have been identified, such as a family history of CRC, inflammatory bowel disease (IBD), sedentary behaviors, smoking, elevated body mass index (BMI), diabetes, and poor diet . HoweverTo the best of our knowledge, O’Sullivan et al.  conducteTo improve knowledge based on existing evidence and to address the limitations of previous reviews, we conducted a systematic review and meta-analysis to investigate risk factors for EOCRC. Our goal was to synthesize previous studies, which we hypothesized would be highly heterogeneous, under a uniform framework that can provide insights into development of new, evidence-based identification of risk factors associated with EOCRC, exploration of more effective prevention methods, and improved screening of high-risk populations.The protocol was registered with PROSPERO (CRD42022371340). We conducted this systematic review and meta-analysis according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  and MetaA literature search without any country restriction was performed to identify studies that described risk factors for EOCRC. Two researchers (QP J and HM H) independently searched PubMed (1950-present), Embase (1947-present), Scopus (1970-present), and the Cochrane Library (1995-present) from inception to November 25, 2022. The following keywords or terms were used to search: early-onset colorectal cancer, EOCRC, young colorectal cancer, risk factors, and risk. The complete search strategy is provided in the The inclusion criteria were as follows: (1) study design: case-control or cohort studies, (2) studies of which full-text could be obtained, (3) studies reporting the risk factors related to EOCRC, (4) studies comparing patients with EOCRC and healthy individuals younger than 50 years, and (5) studies on patients with CRC diagnosed for the first time. The exclusion criteria were as follows: (1) studies comparing characteristics between EOCRC cases and late-onset cases, (2) studies on patients with advanced polyps, (3) studies not reporting effect estimates or from which effect estimates could not be obtained, or (4) studies not published in English.According to the inclusion and exclusion criteria, literature screening and data extraction were completed independently by two researchers (QP J and X X) and then cross-checked. Any conflict was resolved by HM H.Study quality was evaluated by two researchers (QP J and P S) using the Newcastle-Ottawa Scale (NOS) . The NOSWe extracted data on characteristics of the included studies, including author, year of publication, country, study type, age at diagnosis of EOCRC, sample size, geographic location, participant sex, and population selection. We extracted information about the types of risk factors , and measurement of risk factors , and extracted the referent category, effect estimates, 95% confidence intervals (CIs) for exposure categories of each risk factor. Risk factors could be obtained through patient self-reported or physician measurements.2 test were used to evaluate heterogeneity. Fixed-effects models or random-effects models were selected according to the heterogeneity of test results. Sensitivity analysis was used to identify the source of heterogeneity. If more than 10 studies were included in the meta-analysis, funnel plots were used to assess the risk of publication bias for each risk factor. Studies not suitable for the meta-analysis were analyzed through a systematic review.For the purposes of this study, the relative ratios and hazard ratios were treated as estimates of odds ratios (ORs). We performed log transformation on the extracted ORs and indirectly estimated their standard errors. As a risk factor was reported in at least 2 studies, we included these studies in meta-analyses. RevMan5.3 was used for statistical analysis. Cochrane Q-test and I2) and normal-weight subjects, and (2) making comparison between overweight (30≥BMI≥24.9 kg/m2) and normal-weight subjects. We also evaluated the effect of abdominal obesity on EOCRC. Glover et al. (15) did not define obesity and Kwak et al.  and never-smokers. For alcohol consumption, we pooled estimates of the effects of drinkers (past and current) and never-drinkers. We included dyslipidemia in the analysis, and a separate meta-analysis was conducted on triglyceride. In terms of diet, due to the limited number of studies, we only included red meat, processed meat, Western dietary patterns, sugar-sweetened beverages, and vitamin D for the meta-analysis. All P values were two-sided, and the significance level was set at 0.05.We used the BMI classification of the World Health Organization. Due to the different classifications of BMI in the included studies, we performed the meta-analysis using two methods: (1) making comparison between obese , and an average score of 7.2 Table 2.2>60%). We did not find heterogeneity through sensitivity analysis. Based on the remaining studies, significant positive associations were found between a low education level    and EOCR45-1.84)  and EOCR2>60%). Three forms of obesity analysis were used in this study: (1) obesity (BMI≥30 kg/m2), (2) overweight (30 kg/m2>BMI>24.9 kg/m2), and (3) abdominal obesity. Obesity  (2 = 97%) and overweight (I2 = 79%), but not in abdominal obesity (I2 = 0%). In the sensitivity analysis for overweight, after excluding the study of Low et al. (2 = 43%), and the results showed that overweight was significantly associated with the development of EOCRC  (2 = 99%), while the studies on triglycerides were homogeneous (I2 = 47%). Six studies (2 = 99%). After excluding the study by Syed et al.  (2 = 100%). Three studies (2 = 55%)  , 44, 46 20-1.91) , 26, 40 20-1.91) , 40, 43.w et al. , the rem12-1.25) , 41, 44,12-1.25) , 26. For studies , 41, 44 12-1.21) , 41, 49  studies  investig2 = 55%) Figure 3ous I2 = %. Six st2 = 55%) , abdomin2 = 55%) , hematoc2 = 55%) , rectal 2 = 55%) . A signi2 = 55%) .vs. never-smoker) or three categories . This study conducted a meta-analysis in the form of two classifications (current or past smoker vs never-smoker). The results of 11 studies (2 = 99%). Each study classified alcohol consumption by the number of drinks per week or the amount of alcohol consumed. In this study, alcohol consumption was analyzed in the form of two categories . Ten studies (2 = 83%). Five studies (2 = 11%). The results showed that aspirin  (2 = 77%), while homogeneity was observed for NSAIDs (I2 = 6%). Nguyen et al. (2 = 59%), but after excluding the study of Archambault et al. (2 = 0%). The results indicated that processed meat was associated with the development of EOCRC   , 24 and 43-1.90) , 36, 38 n et al.  found thn et al. , 38, 49,n et al. , 38, andn et al. , 55, 56 n et al. , 38, 49,n et al. , 38, 49 n et al. Figure 4t et al. , the stu13-2.06) , the emp13-2.06) , and exp13-2.06) . A signi13-2.06) , 49.CRC is a global public health problem that seriously threatens human health. Although the overall incidence and mortality of CRC have tended to stabilize or decline in recent years, the incidence and mortality of EOCRC have shown an increasing trend, and EOCRC is usually diagnosed at later stages with a poor prognosis. Identifying risk factors for EOCRC is essential to reduce the growing burden of this disease.In this systematic review and meta-analysis, we identified risk factors associated with demographics , chronic conditions , and lifestyle or environmental factors . Although some of the factors  were statistically non-significant, they were suggestive and non-negligible risk factors. The study also found that vitamin D was a protective factor for EOCRC. Other potential risk factors included a family history of cancer, a low education level, chronic kidney disease, primary breast tumors, abdominal pain, intestinal function changes, iron-deficiency anemia, hematochezia, weight loss, exposure to dust, and diet-related factors.Male sex, Caucasian race, and a family history of CRC were risk factors for EOCRC. The results were the same as those exhibited by O’Sullivan et al. . Individ2 with the general population, these 2 studies were not included for the analysis in this study. Glover et al.  Most of the included studies used data from databases, with Danial et al. , Syed etSmoking, drinking, sedentary behaviors, red meat, processed meat, sugary drinks, and Western dietary patterns were modifiable risk factors for EOCRC. IBD, obesity, high triglycerides, and hypertension were risk factors that are difficult to be changed by interventions. Although our study identified risk factors for EOCRC, further research is needed for validation and explore other aspects of EOCRC etiology to inform primary and secondary prevention measures. At the same time, our study provides the basis for the future construction of EOCRC risk prediction models to identify high-risk individuals and develop more targeted screening strategies, which in turn can better allocate resources to those most in need to cope with the global growth of EOCRC.All the authors contributed to the study design. QJ and XX screened abstracts and full-text articles for inclusion. QJ and PS appraised study quality. Any disagreements or uncertainties during the screening and quality appraisal process were referred to HH. QJ drafted the manuscript, which was reviewed, edited, and approved by HH. All authors contributed to the article and approved the submitted version.


This meta-analysis was designed to investigate the relationship between genetic polymorphisms  and power athletes’ status. Only case-control studies were included in the meta-analysis. A systematic search of the PubMed and Web of Science databases was performed to identify relevant studies and 23 studies met the inclusion criteria for the meta-analysis. The data from the included studies were pooled and analyzed using a random effects or fix effects model. The effect size was calculated as the odds ratio or a risk ratio with 95% confidence intervals. The results showed that certain genetic polymorphisms, AGT rs699 Thr allele, HIF1A rs11549465 Ser allele and AMPD1 rs17602729 C allele, were significantly more prevalent in power athletes (p < 0.05). When examining the genotype frequency distribution of AGT rs699 and AMPD1 rs17602729, significant differences were found in both the dominant and recessive models (p < 0.05). The results indicate that these gene polymorphisms play a role in power athlete status, however, new and more comprehensive studies are needed to confirm these results. AGT (Angiotensin), AMPD1 (adenosine monophosphate deaminase), HIF1α (Hypoxia-inducible factor 1), and IL-6 (Interleukin-6).With the increase of molecular research in sports, genetic factors related to power and endurance performance have been discovered . Sports AGT) gene encodes a protein that plays a key role in the renin-angiotensin-aldosterone system, which regulates blood pressure and fluid balance in the body. AGT is located on chromosome 1q42 (The angiotensinogen (ome 1q42 . The AGTome 1q42 .Adenosine monophosphate deaminase (AMPD1) plays a crucial role in regulating muscle-energy metabolism during exercise, and is also referred to as myoadenylate deaminase. It is predominantly expressed in all types of skeletal muscle fibers and is encoded by a gene located on chromosome 1 (1p13). In particular, AMPD1 is mostly found in fast-twitch (type II) muscle fibers. It has been suggested that variations in AMPD1 C34T (rs17602729) gene expression may contribute to differences in enzyme activity across different groups of muscle fiber types. During intense exercise, ATP may be depleted by 50%, and it is thought that individuals with low AMPD activity may not be able to effectively perform short-term high-intensity exercise . Low AMPThe HIF1A gene, responsible for encoding the α subunit of HIF-1 protein, was found to exhibit the Pro582Ser polymorphism (rs11549465). This polymorphism entails the substitution of proline (Pro) with serine (Ser) at amino acid position 582 within exon 12 . The hypIL-6) is a cytokine with diverse biological functions that include regulating differentiation, proliferation, survival, and immune acute phase response in target cells. Although primarily produced by immune cells, it is also expressed in muscle cells and its secretion increases during muscle contraction . By expanding the scope of genetic analysis, this study aimed to enhance our understanding of the role of genetics in the athletes’ strength status, acknowledging that the analysis of these four gene polymorphisms alone may provide only a limited perspective.The aim of this meta-analysis was to synthesize the current evidence on the relationship between four genetic polymorphisms  the study examined the association between the AMPD1, AGT, HIF1α, IL-6 gene polymorphism and power athlete status, (2) the study included a case and a control group, and (3) the study provided sufficient data to calculate an odds ratio or a risk ratio with 95% confidence intervals. The exclusion criteria were as follows: (1) the study was a review article, (2) the study was a case report, and (3) the study was not relevant to the research question.A systematic search of the PubMed and Web of Science databases was conducted to identify relevant studies on Two independent reviewers extracted data from the included studies using a standardized form. The following information was collected: study characteristics such as the publication year, the country of origin, and the study design; study population characteristics such as age, sex, and ethnicity; the type of gene polymorphisms under investigation; and outcome measures such as power athlete status. Any discrepancies between the two reviewers were resolved through discussion and consensus.AMPD1, AGT, HIF1-α, and IL-6 gene polymorphisms associated with power sports were included in the study. On the other hand, meta-analyses that contained unclear data, did not include a control group, and those of which full text could not be accessed were excluded from this meta-analysis.Athlete-control articles including The data from the included studies were pooled using random effects or fix effects models due to the significant heterogeneity among the studies. The presence of heterogeneity was assessed using the Cochran Q test and the I2(%) statistic. The presence of publication bias was assessed using funnel plots and the Egger test. The effect size was calculated as the odds ratio or the risk ratio with 95% confidence intervals. Statistical analysis was performed using Jamovi2.3.AGT (rs699), AMPD1 (rs17602729), HIF1α (rs11549465) and IL-6 (rs1800795) single nucleotide polymorphisms of individuals forming the power athletes’ and the control group. Minimum three or more studies per SNP were included in the meta-analysis. When analyzing the overall allele frequency distributions, it was found that the frequency of the major alleles of AGT rs699 ], AMPD1 rs17602729 ], and HIF1α rs11549465 ] polymorphisms were significantly lower in power athletes compared to the control group.AGT rs699 SNP, a significant difference was found in both the dominant model ] and the recessive model ] (p < 0.05). Similarly, significant differences were also found in the dominant ] and recessive models ] of the AMPD1 rs17602729 SNP genotype distribution (p < 0.05). In HIF1α rs11549465 SNP genotypes, only a significant difference was found in the dominant model ] (p < 0.05). No significant difference was found in other overall allele and genotype frequency distributions (p > 0.05).When examining the genotype frequency distribution of p < 0.05).The subgroup analyses of allele frequency distributions and genotype frequency distributions of power athletes and control groups are shown in p < 0.05). When examining the subgroup analysis of HIF1α rs11549465 SNP, it was found that the frequency distribution of the major allele (Pro) in weightlifters was statistically lower than in the controls ] (p < 0.05). In terms of genotype frequency distributions, the dominant model was found to be significantly lower in elite power athletes ] and weightlifters ] (p < 0.05). It was determined that the frequency of the major allele (G) of IL-6 rs1800795 SNP in the subgroup of male power athletes was ] times higher and the frequency of the GG genotype was ] times higher than in the controls (p < 0.05). No other statistically significant differences were found in other analyses (p > 0.05).In the genotype frequency distributions of AGT rs699 in Caucasian power athletes, a significant difference was only found in the recessive model ] (AGT (rs 699), AMPD1 (rs 17602729), HIF1α (rs 11549465), and IL-6 (rs 1800795) polymorphisms in power athletes was evaluated.The purpose of this study was to evaluate the genotype and allele frequency distributions of four popular genes that are believed to be associated with being a power athlete, using a meta-analysis approach. The data obtained from the genotype distributions were included in the analysis according to the ethnic background, gender, athlete’s performance level, and sport discipline. For this meta-analysis, a total of 23 articles were found to be suitable, and according to the results of the analysis, the distribution of AGT and power athlete status. In a meta-analysis study conducted by AGT rs699 polymorphism distribution of a total of 216 power athletes of the Caucasian origin was analyzed. According to the results of that study, the distribution frequency of the Thr allele and the Thr/Thr genotype was significantly higher in power athletes compared to controls. This result is also consistent with our study. It is known that increased levels of angiotensin II enzyme seen in Thr carriers play a significant role in skeletal muscle growth  polymorphism in a total of 3570 individuals, consisting of 907 power athletes and 2663 control individuals. According to the results of the analysis, the Thr allele and the Thr/Thr genotype of the AGT rs699 polymorphism were more commonly observed in power athletes . In the subgroup, the frequency of the Thr allele and the Thr/Thr genotype was found to be higher in power athletes of the Caucasian origin . When the literature was examined, it was seen that there was only one meta-analysis study that examined the relationship between e growth . Based oAMPD1 rs17602729 C/C genotype is higher in power athletes  C allele  and the C/C genotype  in power athletes was found to be higher than in the control group. When sub-dimensions were examined, the AMPD1 rs17602729 polymorphism distributions did not show any significant differences in any model, neither in men nor in elite power athletes. The results of this study help to look at the contradictory results found in the literature from a broader perspective.There are studies indicating that the frequency of the athletes . In anotathletes . Similarathletes . On the HIF1α (rs11549465) Ser allele and the Ser/Ser genotype in power athletes. That meta-analysis included 3795 individuals of the Caucasian origin  and analyzed their genetic polymorphism distributions of HIF1α (rs11549465). According to the results, the frequency of the Pro allele  and the Pro/Pro genotype  in power athletes was found to be less than in the control group. In the recessive model, no statistically significant difference was detected. When the distribution of the HIF1α rs11549465 polymorphism was evaluated according to elite power athlete status, the frequency distribution was lower in the dominant model compared to the control group. Moreover, the dominant model genotype distribution of weightlifters was even lower. When the literature is reviewed, there is no meta-analysis study examining the allele and genotype distribution of the HIF1α rs11549465 polymorphism in power athletes. A study by HIF1α (rs11549465) Ser allele was significantly higher in weightlifters and concluded that it increased the development of athletic performance. Similarly, some studies have also noted that the frequency of the HIF1α (rs11549465) Ser allele is higher in power athletes compared to control individuals . Particularly in sports that require speed and explosiveness, significant relationships have been observed showing higher frequencies of the ividuals . StudiesIL-6 rs1800795 polymorphism in relation to the athletes’ power status. The study found that only male power athletes had a significantly higher frequency of the G allele  and the G/G genotype  compared to controls. However, no significant relationship was found in other variables. The only meta-analysis on the IL-6 rs1800795 polymorphism and gender variable by IL-6 rs1800795 polymorphism and power athlete status.Our study examined the distribution of genotype and allele frequencies of the In conclusion, the findings of this study provide valuable insights into the genetic factors associated with the athletes’ power status. The identified genetic polymorphisms, such as AGT rs699, AMPD1 rs17602729, HIF1α rs11549465, and IL-6 rs1800795, can be considered as potential markers for power athlete status. Specifically, the AGT rs699 Thr allele, HIF1α rs11549465 Ser allele and AMPD1 rs17602729 C allele were found to be more prevalent in power athletes. However, more research is needed to confirm these results and explore the relationships between genetic polymorphisms and the athletes’ power status. Pooling data from multiple studies through collaborative efforts and conducting larger-scale meta-analyses would provide a clearer understanding of the genetic factors determining the athletes’ power status.


In view of the current debate about the relationship between lipids and deep venous thrombosis (DVT) in clinical studies, a two-sample Mendelian randomization (MR) study was conducted to clarify the effects of five circulating lipids  on DVT from the perspective of genetic inheritance. Five lipids (exposure) were analysed by MR with DVT (outcome) from two different data sources. For the analysis, we used inverse variance weighting and a weighted mode, weighted median, simple mode and MR–Egger regression to analyse the effect of circulating lipids on DVT. In addition, we used the MR–Egger intercept test, Cochran’s Q test and “leave-one-out” sensitivity analysis to evaluate horizontal multiplicity, heterogeneity and stability, respectively, in the analysis. In the analysis, the two-sample Mendelian randomization analysis of five common circulating lipids and DVT showed that common circulating lipids had no causal effect on DVT, which is somewhat inconsistent with the findings of many published observational studies. Based on our results, our two-sample MR analysis failed to detect a statistically significant causal relationship between five common circulating lipids and DVT. Thrombosis usually begins in the deep vein of the leg and spreads proximally. The main risk factors for DVT include trauma, cancer and gene mutations that promote blood hypercoagulability2. The formation mechanism of DVT can be explained by Virchow’s triad, i.e., a blood flow disorder, a hypercoagulable state of the blood and a procoagulant state of the vascular wall3.Deep venous thrombosis (DVT) is a common venous thromboembolism that usually affects the lower extremities. The annual incidence rate is close to 1.6 per 1000 people6. However, Schouwenburg et al.7 found no association between apolipoproteins and the risk of venous thromboembolism. Because lipid levels can be regulated by lifestyle and medications, the relationship between lipid levels and VT and its pathophysiology is a clinical issue worth exploring8. A previous study showed that lipid-lowering drugs are associated with a reduced risk of VT, which may indicate the possible role of lipids in the pathophysiology of VT9. The ambiguous relationship between blood lipids and DVT requires further clarification.At present, some studies suggest that lipid metabolism disorders can affect DVT; although there are many clinical studies on the effect of lipids on venous thrombosis (VT), current conclusions are still controversial. Some studies have suggested that the levels of low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides (TGs) are not associated with an increased risk of VT, whereas a decrease in apolipoprotein B (APOB) and apolipoprotein A1 (APOA1) levels is associated with an increased risk of VT, but the specific mechanism has not been clarified10. MR provides a valuable tool, especially when randomized controlled trials to check causality are not feasible and observational studies have biased associations due to confounding or reverse causality11. MR also enables the use of published results from large genome-wide association studies (GWASs) to study risk factors (exposure) and disease (outcome) and to avoid confounding factors and reverse causality deviations in observational studies12. Fernando et al. used MR to analyse the relationship between inflammatory pathways and suicide and found that IL-6 signalling was associated with suicide13. In view of the current debate on the relationship between lipids and DVT in clinical studies and the limitations of clinical studies, a two-sample MR study was conducted to clarify the effects of five kinds of circulating lipids  on DVT from a genetic perspective.Mendelian randomization (MR) is a new strategy based on Mendelian inheritance and uses genetic variation as an instrumental variable (IV) to study the causal relationship between different traits14 two-sample Mendelian randomization study to test the causal relationship between DVT and circulating lipids.We referred to Xu et al.https://gwas.mrcieua.ac.uk/). UKBB details, including geographic areas, recruitment processes, and other features, have been described in previous articles15.The DVT data used in this article all came from Neale lab analysis of UK Biobank phenotypes (excluding pulmonary embolism). The DVT data in the analysis included 6767 DVT patients and 330,392 control cases. Up to 10,894,596 SNPs were included in the analysis. Information on various phenotypes was collected from each participant, and blood samples were collected when the subjects visited the UK Biobank Assessment Centre. DNA extraction and genotyping were carried out in the Affymetrix Research Service Laboratory. Summary GWAS data on DVT can be downloaded from the UK Biobank (UKBB) database and MRC IEU OpenGWAS repositories  included in this analysis can be obtained from previous reports17. Details of all the data and GWAS IDs used are shown in Supplementary File Data on circulating lipids came from two other non-UK biobank studies. APOA1 and APOB data came from the research of Kettunen et al. They conducted an extended genome-wide association study of as many as 24,925 individuals from 10 European studies. Up to 12,133,295 SNPs were included in the meta-analysis after applying quality control filters−8)14. In addition, none of the instrumental SNPs were in linkage disequilibrium (LD). We performed the clumping process  to eliminate the LD between the SNPs14. Third, SNPs with a minor allele frequency (MAF) < 0.01 were removed. By default, if the SNP for a particular request did not exist in the resulting GWAS, the SNP (agent) with the requested SNP (target) in the LD was searched14. The LD agent was defined using 1000 genomes of European sample data. Then, we searched the human gene phenotypic association database (PhenoScanner V2) to evaluate possible pleiotropic associations between instrument variables and other phenotypes and excluded DVT-related IVs (such as weight and whole-body fat mass). In addition, to test whether there was a weak instrumental deviation in the IV, we used the F statistic (F = R2 (n − k − 1)/k (1 − R2), where R2 is the variance of exposure explained by selected instrumental variables , n is the sample size, and k is the total variables. If the F statistic of the IV is much greater than 10, it indicates that the possibility of weak instrument variable bias is very small14.When selecting instrumental variables (IVs), we followed the three basic hypotheses of MR: first, genetic variation should be closely related to the exposure; second, variation should not be affected by confounding factors of the relationship between the exposure and outcome; and third, the exposure should only affect the outcome . Therefore, we extracted genomic single-nucleotide polymorphisms (SNPs) associated with exposure  was regarded as a significant correlation.We first conducted univariable MR analyses for each lipid-related trait. Because there are varying degrees of overlap between SNPs related to different lipid properties, it may not be accurate to test the effect of individual lipids on DVT. Multivariable MR allows associations of SNPs with multiple phenotypes to be included in the analysis so that the direct impact of each phenotype on the outcome can be estimated20. When there is heterogeneity in the analysis, random effects IVW can provide accurate causal estimation. We also used several other MR methods, including the MR–Egger method, weighted median method, simple mode method and weighted mode method, to test the robustness of the results21. We used the MR–Egger method22 to analyse the sensitivity and check the consistency and possible pleiotropy of the correlation. The weighted median method provides consistent causal estimates when the effective tool has more than 50% of the weight23. MR–Egger regression can detect multiplicity by intercept; however, it impairs statistical power22. The effect of horizontal heterogeneity was evaluated and tested by Cochrane’s Q value in the MR–Egger regression and IVW test, with a P value of 0.05 indicating considerable heterogeneity. The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) of lipids were associated with the presence or absence of genetically predicted DVT. We then conducted a further sensitivity analysis using the “leave-one-out” test to evaluate whether the results were affected by individual SNPs. All statistical data analyses were conducted with R (version 4.2.0) software (TwoSampleMR and MendelianRandomization packages).When horizontal pleiotropy does not exist in the analysis, the inverse variance weighted mode can provide the most accurate causal estimationAll data were downloaded from the internet.The details of all independent SNPs associated with exposure are shown in Supplementary File beta = 0.870; weighted median: Beta = − 0.001, Pbeta = 0.480; weighted mode: Beta = − 0.001, Pbeta = 0.447; simple mode: Beta = − 4e−4, Pbeta = 0.847; MR‒Egger: Beta = − 0.006, Pbeta = 0.291)   and DVT (outcome) using the various MR analysis methods (IVW (random effects): Beta = − 3e−4, Pp value = 0.284)  Table . The res4) Table . The forbeta = 0.715; simple mode: Beta = 0.001, Pbeta = 0.669; MR‒Egger: Beta = − 0.008, Pbeta = 0.165). However, the results of the weighted median and weighted mode methods showed that APOB reduced the incidence of DVT     Table , we perf1) Table .The scatter plot shows the estimated impact of SNPs on the exposure (APOB) and outcome (DVT) Fig. . In addibeta = 0.117; weighted median: Beta = − 0.001, Pbeta = 0.366; weighted mode: Beta = − 0.001, Pbeta = 0.637; simple mode: Beta = − 0.001, Pbeta = 0.767; MR‒Egger: Beta = − 3e−4, Pbeta = 0.881)    Table . The MR–9) Table .The scatter plot shows the estimated impact of SNPs on the exposure (LDL) and outcome (DVT) Fig. . The resbeta = 0.670; weighted median: Beta = − 0.001, Pbeta = 0.259; weighted mode: Beta = − 0.001, Pbeta = 0.484; simple mode: Beta = 0.003, Pbeta = 0.384; MR‒Egger: Beta = − 3.54–4, Pbeta = 0.870). The multivariable MR analysis also showed that there was no causal relationship between HDL and DVT  . The MR–Egger intercept in the analysis showed that there was no horizontal multiplicity   Table .The scatter plot shows the estimated impact of SNPs on the exposure (HDL) and outcome (DVT) Fig. . The resbeta = 0.038; weighted median: Beta = − 0.004, Pbeta = 0.017; weighted mode: Beta = − 0.003, Pbeta = 0.038) . The multivariable MR analysis also showed that there was no causal relationship between TGs and DVT  (Table Based on the results of IVW (random effects), weighted median and weighted mode, methods we found a suggested negative causal relationship between TGs and DVT (IVW (random effects): Beta = − 0.004, Pp value > 0.05) (Table The scatter plot shows the estimated impact of SNPs on the exposure (TG) and outcome (DVT) Fig. . The het5) Table .Figure 5The results of the “leave-one-out” method test in the analysis showed that no abnormal IV in the analyses affected the overall results  and VTE (DVT and PE) from a genetic point of view. The GWAS data used in our current Mendelian randomization study are very different from those used in previous studies, and we used new GWAS data to further prove that there is no causal relationship between LDL, HDL, TGs and DVT. In addition, this study found that there was no causal relationship between APOA1 and APOB and DVT, which adds new evidence to the study of the causal relationship between blood lipids and DVT.In this study, we used two-sample Mendelian randomization to analyse summary GWAS data and found that our two-sample MR analysis failed to detect a statistically significant causal relationship between five common circulating lipids and DVT. This result differs from those of previous clinical studies. For example, Morelli et al. found that lower levels of APOA1 and APOB were associated with an increased risk of DVT27. In other clinical studies, these apolipoprotein-DVT associations have been limited to certain subgroups; for example, in a case–control study that included only men, low levels of APOA1 were associated with an increased risk of DVT28. High levels of APOA1 in the Women's Health Study were associated with an increased risk of blood clots in hormone users29. In another small case–control study, high APOB levels appeared to increase the risk of DVT in men30. These contradictory results may be related to the adjustment for potential confounding factors, as the MR method can reduce confounding factors in observational studies. Our study showed that APOA1 and APOB had no significant causal relationship with DVT from a genetic perspective.Compared with major lipids, there are few reports on the relationship between DVT and APOA1 and APOB levels. Apolipoprotein was not associated with the risk of DVT in cohort studies31. The authors did not provide an effective explanation for this result, but our results showed that TGs had no protective effect on DVT. Therefore, well-designed in vitro and in vivo studies are strongly encouraged, and DVT events should be included as a major end point in new clinical studies of fibrates. In recent years, an increasing number of studies have found that statins can effectively reduce the incidence of DVT33; for example, rosuvastatin significantly reduces the incidence of symptomatic venous thromboembolism34, and in vitro and observational studies have found that statins may have beneficial effects on blood vessel walls and antithrombotic formation36. A meta-analysis found that the use of statins and antiplatelet therapy was associated with a significant reduction in the incidence of venous thromboembolism37. It is well known that statins not only have lipid-lowering effects but also have a variety of vascular protective effects that are independent of changes in cholesterol levels. These effects are attributed to the anti-inflammatory and antithrombotic properties of statins, which can alter endothelial dysfunction and regulate angiogenesis38. Therefore, the documentation that statins can effectively reduce the incidence of DVT does not conflict with our results. However, there are also contrary findings about the efficacy of statins in preventing DVT39. Therefore, more clinical and basic studies are needed to explore whether statins can prevent DVT at this stage.Statins and fibrates are commonly used to reduce blood lipids. A meta-analysis also found that statins may reduce the risk of DVT, whereas fibrates may increase this riskIn this study, we selected SNPs with a genome-wide association and independent inheritance without any LD as IVs to detect a causal relationship between circulating lipids  and DVT. To make our conclusions more robust and reliable, we used several analysis methods for comparison and verification. The F value in each analysis was greater than 10, which effectively avoided the weak tool variable bias. Of course, our analysis was somewhat restrictive. First, because our analysis was based on publicly available aggregate data, the data provided did not allow us to conduct other subgroup analyses to address associations with specific factors such as age, sex and other DVT risk factors. Second, this study was limited to participants of European origin. Therefore, our results may not apply to people of other races. Finally, regarding the results that were not statistically significant in this study, we could not completely rule out an association between these circulating lipids and DVT because this may reflect the small sample size and the lack of statistical power of the MR analysis.Based on our results, our two-sample MR analysis failed to detect a statistically significant causal relationship between five common circulating lipids and DVT. Subsequent studies or MR analysis based on more genetic instruments are needed to validate these findings and clarify the potential mechanism of the effect of circulating lipids on the development of DVT.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts.We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA).25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 : 6.51–22.55), 7.76 , 4.65  and 8.23 OR: 8.23, 95 % CI: 4.23–16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %).The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs. As ofDuring the outbreak of the epidemic, with the continuous detection of infected individuals and the large number of close contacts being tracked, enormous work pressure was placed on the transportation and isolation work of close contacts ,14. In pWe can quickly assess the risk level of close contact infection based on fewer risk factors that are easily collected and have a significant impact on infection, and pay attention to high-risk close contacts in the management process, thereby reducing the time and cost of tracking and management, therefore, understanding the SARs of various infection risk factors and identifying important risk factors is crucial for rapid and accurate identification and management of close contacts ,19. PrevThere is an appropriate approach to address this issue. Compared to traditional meta-analysis, network meta-analysis can produce indirect comparisons via a common comparator . Secondly, it can directly and indirectly compare multiple treatments and interventions, more accurately assessing the relative effects of different control groups to draw conclusions . ThirdlyTherefore, the purpose of this study is to comprehensively compare and rank the impact of various risk factors of infection in close contacts with COVID-19, determine the most influential factors of close contact infection, and rank its subgroups by using network meta-analysis. This study provides a provide scientific basis for the rapid and accurate identification and management of close contacts, effectively optimizes epidemic prevention resources, and provides reference for the prevention and control of similar emerging respiratory infectious diseases in the future.22.1The network meta-analysis was performed using Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA). This study was registered with PROSPERO, number CRD 42022336859.2.2The classification of the type of contact was defined according to the Guideline of COVID-19 Control and Prevention-Close Contact Management Procedure (9th ed.) and was adjusted by included studies . The typ2.3For this network meta-analysis, we searched PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), WangFang Database, VIP, China Biology Medicine disc (CBM), medrixv and biorixv from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. We developed and adapted a modified search algorithm for PubMed with combination of subject words and free words (in title and abstract) and then modified for other databases. The literature searching was completed by two reviewers. The search strategies for each database were in 2.4Studies meeting the following criteria were included: (1) Participants of study: close contacts of COVID-19 without medical workers; (2) Exposure: the article involves risk factors or prediction models of COVID-19 close contacts infected with SARS-CoV-2; (3) Outcome: SARS-CoV-2 infection; (4) Two or more risk factors of COVID-19 close contacts infection should be involved. The exclusion criteria were as follows: (1) Duplicate reports (in which case the study with the largest sample size or longest follow-up period was included); (2) Studies about epidemiological model for predicting the epidemic trend of COVID-19 and the case fatality rate; (3) Outcome indicators were not available or could be calculated by manual statistics; (4) Reviews, meeting abstracts, expert consensus and suggestions. The study selection was completed by two reviewers. Conflicts were resolved by a third reviewer.2.5The primary outcome of network meta-analysis was the SARs of COVID-19 close contacts. SARs were defined as the probability that close contacts of index cases become confirmed cases of a disease . The cal2.6The data extracted from each report included title, the first author, publication time, study site, source of data, definition of close contacts, sample size, risk factors, number of contacts infected, number of contacts. Data abstraction was completed by independent pairs of reviewers. Conflicts were resolved by a third reviewer. Risk factors of COVID-19 close contacts infected in each study were classified into four categories based on the three keys to the epidemic of infectious diseases: source of infection, route of transmission and susceptible population. Finally, four categories of risk factors without overlap were included in our study: factors about index cases, factors about contact, factors about close contacts and factors about prevention and control measures. The risk factors were classified by two researchers independently. Disagreement was resolved by discussion to reach a third reviewer.2.7The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of included cohort and case‒control studies . Each st2.82) metrics were used to evaluate the heterogeneity of the studies. Subgroup analysis was based on the study regions, investigation time, study type, literature quality, and sample size to analyze the sources of heterogeneity; For the sensitivity analysis, we remove included studies one by one to observe stability of results. Publication bias was examined by funnel plots and Egger's test. Network meta-analysis was performed by STATA version 14.0.We conducted network meta-analysis based on a random effects model. We used a network plot to show the relationship between the risk factors we included, calculated the relative rankings of risk factors and presented the surface under the cumulative ranking curve (SUCRA) percentages for both groups and subgroups. We selected and analyzed the type of contact based on the result of network plots and SUCRA. A network plot was used to compare the evidence among the type of contact, compared the type of contact by calculating the OR, and indicated which type of contact had the greatest impact on the infection of COVID-19 contacts according to SUCRA. To estimate inconsistency, we calculated the difference between indirect and direct estimates whenever indirect estimates could be constructed with a single common comparator. The Q test and I-squared , and a random effects model was used to merge the SARs . Sensitivity analysis showed robust results after excluding four studies. According to the study region, study data time, study type, literature quality, and sample size, no heterogeneity sources were found in subgroup analysis. The funnel plot and Egger method were used to test publication bias for all included studies, and the results showed the presence of publication bias (P < 0.001). The results of the trim and filling method showed that there is not much difference in the results before and after pruning, and the results are relatively stable. The inconsistency test showed the difference between direct and indirect evidence was not statistically significant (p > 0.05). There was no clear evidence suggesting inconsistency between direct and indirect network effects.The fixed effects model showed differences  and direct contact with contaminated surfaces and fomites . During There are some confounding factors in our study, which may lead to inaccurate result in the overall study analysis. First of all, different countries have different population densities, different criteria for judging COVID-19 close contacts and different national prevention and control policies. These regional factors will influence the infection risk of close contacts and may increase the heterogeneity of our study . Secondl4.2The epidemic prevention and control policies in China were different from other countries, so subgroup analysis was carried out by region. After excluding studies from non-Chinese regions, the result of subgroup analysis suggested that OR for different types of contact changes. There may be several reasons. Most of study sites we included were from China, which was possibly because mainland China had more detailed and unified management and classification of close contacts than many other countries. After excluding studies from non-Chinese regions, estimates became significant and more accurate as the 95 % CIs narrowed, indicating stability and reliability of results increased. These differences may be caused by different standards for the definition and management of close contacts in China and other regions.The types of contact classification varied and often simplified in some other countries and changed depending on the situation. China has conducted strict and detailed epidemiological investigations on close contacts, and divided all types of contact into six categories according to national guidelines. There have not been much changes in types of contact classification thus far. The World Health Organization guidelines for contact tracing classification were similar to Chinese guidelines, including household contacts, closed environment contacts, medical contacts, transportation contacts, and other well-defined contacts, but the latest criteria for close contacts do not have a clear type of contact classification . The US In addition, the Chinese government adopted strict social distancing measures in the early days of the epidemic, people were not allowed to go out unless necessary. Compared with one-off contact and certain social distance between social members in public places, living environment and long-term living conditions provide repeated, frequent, and close contact opportunities with each other, resulting in high recurrence, high infection, and high incidence of family members , which mWe found that daily contact has the highest risk of SARS-CoV-2 infection, this contact is unavoidable. When people want to resume their previous social life, vaccines can help susceptible populations gain immunity, so vaccination may be a great way to reduce the risk of infection . For thoThis network meta-analysis has some limitations. First of all, due to language limitations, our study only included Chinese and English studies, and there was a selection bias in selection of included studies. Secondly, vaccination factors were not analyzed in the included studies. Although the protection of population by COVID-19 vaccine is not complete, it is also an important factor affecting close infection, which may be an important confounding factor. Homologous booster vaccination and sequential vaccination will make the COVID-19 vaccine more effective ,54. Ther5The type of contact had the greatest impact on COVID-19 close contacts infection among risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The ORs of daily contact were significantly higher than other types of contact, supported by more accurate results using studies from China. Therefore, analyzing and ranking the infection risk factors of close contacts will tend to quickly make a decision according to one or several risk factors when tracing or managing, so as to take different management measures, reduce the waste of resources and the cost of tracking and management. In situations of resource scarcity, it is advisable to focus on close contacts with high infection risk levels, monitor their progression and take measures in time. It can also avoid the spread of epidemics due to loose management. This study is the foundation of our ongoing research. We are constructing an infection risk assessment model for close contacts of COVID-19, estimating the probability of close contacts based on factors that have a significant impact on infection, and dividing the risk level. This also has guiding significance for similar emerging respiratory infectious diseases in the future.This article does not contain any studies with human participants or animals performed by any of the authors.This work was supported by Financial Science and Technology Project of the Corps : The XPCC developed and demonstrated the application of a decision support platform for the precise prevention and control of major outbreaks.Data included in article/supp. material/referenced in article.Wei-wen Zhang: Writing – original draft, Methodology, Data curation. Chen-xi Li: Writing – review & editing, Methodology. Shu-jing Cao: Software, Data curation. Yu-yuan Wang: Software, Data curation. Ze-xi Lu: Software, Data curation. Jia-lin Sun: Writing – review & editing, Methodology, Conceptualization. Ming -xia Jing: Writing – review & editing, Conceptualization.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Mingxia Jing reports financial support was provided by Financial Science and Technology Project of the Corps .


The role of regular physical activity in preventing vascular and non-vascular disease is well established. Chronic kidney disease (CKD) is a major cause of global morbidity and mortality and largely preventable, but it is uncertain if regular physical activity can reduce the risk of CKD. Using a systematic review and meta-analysis of published observational cohort studies in the general population, we sought to assess the association between physical activity and CKD risk. Relevant studies with at least one-year of follow-up were sought from inception until 02 May 2022 in MEDLINE, Embase, Web of Science, and manual search of relevant articles. Relative risks (RRs) with 95% confidence intervals (CIs) for the maximum versus the minimal amount of physical activity groups were pooled using random effects meta-analysis. The quality of the evidence was evaluated using the GRADE tool. A total of 12 observational cohort studies comprising 1,281,727 participants and 66,217 CKD events were eligible for the analysis. The pooled multivariable-adjusted RR (95% CI) of CKD comparing the most versus the least physically active groups was 0.91 (0.85–0.97). The association was consistent across several study level subgroups. Exclusion of any single study at a time from the meta-analysis did not change the direction or significance of the association. There was no evidence of small study effects among contributing studies. The GRADE quality of the evidence was low. In the general population, individuals who are most physically active have a lowered risk of CKD compared to those who are not or least physically active. CRD42022327640.The online version contains supplementary material available at 10.1007/s10654-022-00961-7. Chronic kidney disease (CKD), conventionally characterized by the presence of kidney damage and reduced function, is a direct and major cause of global morbidity and mortality . ChronicThe health benefits of physical activity are well documented and include reduction in the risk of several vascular and non-vascular diseases –13. Physhttp://rayyan.qcri.org), an online bibliographic tool that helps to expedite the screening process using a process of semi-automation [We registered the protocol for this systematic review and meta-analysis in the PROSPERO prospective register of systematic reviews CRD42022327640). The conduct and reporting of this review adhered to PRISMA and MOOSE guidelines , 27  studies that had evaluated the relationship between physical activity and risk of incident CKD in adult general populations and reported at least one year follow-up duration for the ascertainment of outcomes. For all CKD outcomes, we accepted the range of definitions as reported by the included studies. The following studies were excluded: (i) case–control and cross-sectional studies because of their lack of temporality; (ii) those involving elite athletes and/or evaluated competitive or endurance sports; and (iii) those evaluating the associations between measures of fitness  and risk of CKD; and (iv) those conducted in people with pre-existing diseases.Using a standardized data collection form which has been used for previous reviews of a similar nature , 13, 15,2 statistic [Relative risks with 95% CIs were used as the summary measures of association. Hazard ratios and ORs were assumed to approximate the same measure of RR based on the assumptions of rare outcomes , consisttatistic , 37. To tatistic , 39. We tatistic . To testtatistic  and perftatistic . We emplWe identified 864 potentially relevant citations following the search of databases, manual screening of relevant articles and Web of Science citation search. After the initial screening of titles and abstracts, 33 articles were selected for full-text evaluation. After detailed evaluation of the full-texts, 21 articles were excluded because of the following reasons: (i) exposure was not relevant (n = 7); (ii) outcome not relevant (n = 7); (iii) duplicate of a cohort already included in the review (n = 3); (iv) population not relevant (n = 2); and (v) study design not relevant (n = 2). Overall, 12 articles based on 12 unique studies comprising 1,281,727 participants and 66,217 CKD events were eligible for the review , 43–49  (Appendix 4).The study design and population characteristics of the eligible observational cohort studies evaluating the associations between physical activity and the risk of CKD are summarized in Table I2 = 71%, 48 to 84%; p < 0.001). Exclusion of any single study at a time from the meta-analysis did not change the direction or significance of the association (Appendix 5).In pooled analysis of 12 studies, the multivariable-adjusted RR (95% CI) of CKD comparing the most physically active versus the least physically active groups was 0.91 (0.85–0.97) . Furthermore, there was no evidence of such selective reporting when studies were grouped by size in meta-regression analysis  the use of only observational cohort studies with at least one year follow-up, hence ensuring temporality in the association; (ii) ability to explore if the association is modified by clinically relevant study level characteristics; (iii) evidence of no significant small study effects (publication bias); (iv) assessment of the risk of bias for each individual study and the certainty of the evidence using well-established tools; and (v) sensitivity analysis to test the robustness of the association. There were several limitations, but these were mostly inherent to the studies and not the methodological approach. First, there was variation in the assessment and categorisation of physical activity exposures across studies, which could have introduced biases in our pooled results. For example, whereas some studies reported risk comparisons as high vs low, others reported it as any vs never. This did not enable transformation into consistent comparisons such as quantiles; hence comparisons could only be made between the most and least physically active. This approach is however, consistent with previous studies , 63, 64; effects  which is effects , given t effects  and Mich effects  reportedIn the general population, individuals who are most physically active have a lowered risk of CKD compared to those who are not or least physically active.Supplementary file1 (DOCX 207 KB)Below is the link to the electronic supplementary material.


Self‐injurious thoughts and behaviours (SITBs) have been associated with dysfunction of the Autonomic Nervous System (ANS) in children and young people, suggesting that objective ANS measures may aid assessment of suicide risk, but a systematic synthesis of this literature is currently lacking.Following a pre‐registered protocol (PROSPERO CRD42022327605), we conducted a systematic search of PubMed, Medline, Embase, PsycINFO, and Web of Science, for empirical studies published until 10th May 2022 that compared indices of ANS functioning in individuals aged 0–25 years with versus without SITBs, or reported continuous associations between ANS measures and SITBs. Study quality was assessed with the Newcastle‐Ottawa Scales. Pooled effect sizes (Hedge's g) were estimated with random‐effects meta‐analytic models.g = −0.328, p < 0.001), which was driven by lower heart rate variability in individuals with SITBs . Overall results for electrodermal activity were not significant , but subgroup analyses showed increased activity in studies of individuals who engaged specifically in non‐suicidal self‐harm  but decreased activity in the remaining studies .Twenty studies (1979 participants) were included in our systematic review, with 16 included in meta‐analyses. Results suggested that SITBs were associated with altered cardiac indices of arousal . Future large longitudinal studies should further clarify how these associations unfold over time, to inform tools for early identification and prevention of SITBs in children and young people. Previous studies have associated self‐injurious thoughts and behaviours (SITBs) with autonomic dysregulation .To further clarify this association, a systematic review and meta‐analysis was conducted; we found that reduced cardiac regulation and altered electrodermal activity characterise children and young people displaying SITBs.Future studies, especially using longitudinal designs and larger and heterogeneous samples, are needed to further clarify the specific associations between autonomic dysregulation and SITBs.This, in turn, could prove helpful for developing tools for early identification of children and young people at highest risk of engaging in self‐harming behaviours, with the ultimate goal of reducing the number of children and young people dying by suicide.Children and young people are at high risk for self‐injurious thoughts and behaviours (SITBs), which include suicidal ideation , suicidal self‐harm  and non‐suicidal self‐harm : Markers of autonomic functioning .We followed the 2020 PRISMA guidelines  to identify those meeting inclusion criteria. Full texts of potentially eligible studies were also assessed by the same authors. Data extraction and assessment of data quality was performed by six authors . Disagreements were settled through discussion. Study quality and risk of bias was assessed with the Newcastle‐Ottawa Scale . For case‐control studies, we calculated Hedge's g as the standardised mean difference, while for cohort studies we converted correlation coefficients in Hedge's g. Random‐effects meta‐analyses estimated the pooled effect size using metafor , followed by trim and fill analyses. Based on the retrieved studies – presented in the ‘Results’ section below – we tested two meta‐analytic models examining the association of SITBs with (1) cardiac measures of autonomic functioning  and heart rate variability (HRV); pre‐ejection period (PEP), respiratory sinus arrhythmia (RSA) and root mean square of successive differences between heartbeats (RMSSD); see Appendix metafor) indicated whether the pooled effect size differed between subgroups. If the analysis reported a p‐value <0.05, it was suggested that subgroups significantly differ in the pooled effect size; when this was the case, we followed‐up the results by investigating the pooled effect size for each subgroup.Studies were included in the meta‐analysis if they provided raw data for effect sizes to be computed   Figure . Out of ) Figure  and S2.g = −0.328, SE = 0.058, 95% CI = , t = −5.667, p < 0.001; Figure Q = 39.638; p = 0.023), while funnel plot asymmetry was not detected  were included in the meta‐analysis. We found a statistically significant negative association between cardiac measures and SITBs , developmental stage , or SITB domain . However, we found a significant effect of the type of cardiac measure , as SITBs were significantly associated with reduced parasympathetically‐mediated HRV , but not with sympathetically‐mediated measures .Subgroup analyses found no differences in results between studies reporting on different experimental conditions , while funnel plot asymmetry was detected  provided sufficient information for the meta‐analysis. We did not find a statistically significant association between SITBs and electrodermal activity , or developmental stage . There was a significant effect of SITB domain . Studies specifically focussing on non‐suicidal self‐harm reported increased electrodermal activity in participants who self‐harmed . In contrast, studies focussing on SITBs more broadly , reported significantly decreased electrodermal activity in participants reporting SITBs compared to those who did not . Note that we excluded one study from this analysis . Overall, we found that SITBs are associated with altered cardiac measures of arousal regulation, with a small‐to‐medium effect size. Results were particularly consistent for indices of heart rate variability (HRV), which were reduced in children and young people with SITBs, whereas we did not find significant effects for other cardiac measures. While findings for cardiac measures appeared consistent across different forms of SITBs, evidence for electrodermal measures appeared weaker and more preliminary, as an association with electrodermal measures did not emerge when considering all SITBs together and varied based on the investigated SITB domain. Specifically, children and young people with non‐suicidal self‐harm showed increased electrodermal activity, with a medium‐to‐large effect size, while reduced electrodermal activity was reported by studies not discriminating between different type of SITBs , with a small‐to‐medium effect size. As most studies did not distinguish between different forms of SITBs, future research is needed to clarify the association between SITBs and electrodermal indices of arousal.There is significant interest in the possible application of objective measures of ANS regulation as biomarkers of clinical conditions, including SITBs and emotional dysregulation. To move towards this direction, further research should investigate the predictive utility of autonomic dysregulation for children and young people. Our meta‐analysis showed no effect of age, suggesting that altered autonomic functioning may characterise individuals displaying SITBs from childhood to young adulthood. Yet, as the majority the reviewed studies reported cross‐sectional group comparisons, primarily during adolescence, future longitudinal research should test whether autonomic dysregulation in those experiencing SITBs persists later in life and how accurately it predicts death by suicide. Such future studies could lead to the development of new tools, based on both self‐ and parent‐report and autonomic measures, showing sufficient specificity and sensitivity for detecting people at high suicide risk. Furthermore, it will be important to identify protective factors promoting improvements in autonomic function and reduction of SITBs, with an eye to suicide prevention, and to understand whether pharmacological  and/or non‐pharmacological interventions  lead – concurrently – to changes in autonomic functioning and reduction of SITBs severity.Our findings partly support previous literature suggesting that SITBs may have a self‐regulatory function , and it was not significant for the meta‐analysis on electrodermal measures. Moreover, publication bias was detected for the meta‐analysis on electrodermal measures but the trim and fill analysis we conducted estimated that no study was missing due to this type of bias; this was probably due to the reduced number of studies existing in the literature, included in our meta‐analysis, highlighting that more empirical studies are needed to clarify the precise associations between electrodermal activity and different SITB domains.The present study has some limitations. Especially in relation to electrodermal measures, we could only include a limited number of studies in the meta‐analysis, even though our search was broad enough to capture the existing literature, limiting the statistical power of our analyses. Several studies were rated as being of poor quality (about 55%) and had methodological flaws, especially in relation to the methods used to ascertain the presence of SITBs (often based on self‐ or parent‐report). However, cross‐study heterogeneity was significant only for the meta‐analysis on cardiac measures, but not when considering the effect of type of cardiac measure , ethical approval was not sought from the authors' institution.Supporting Information S1Click here for additional data file.