Leptin (LEP) acts as a proinflammatory cytokine and may play an important role in the pathophysiology of cystic fibrosis (CF). This review aimed to assess the quantitative difference in leptin status between CF patients and non-CF controls.In this study, the researchers conducted systematic searches of various databases, such as PubMed, Excerpta Medica Database, Google Scholar, Web of Science, and the China National Knowledge Infrastructure. The data collected from the above databases were assessed using the Stata 11.0 and R 4.1.3 software. The correlation coefficients and the Standardized Mean Differences (SMD) were employed to assess the effect size. A combination analysis was also carried out with the help of either a fixed-effects or random-effects model. In addition, the single-cell sequencing GSE193782 dataset was obtained to determine the mRNA expression levels of LEP and leptin receptor (LEPR) in the bronchoalveolar lavage fluid, to verify the different leptin expression between the CF patients and healthy controls.A total of 919 CF patients and 397 controls from 14 articles were included in this study. CF patients and non-CF controls showed similar serum/plasma leptin levels. Gender, specimen testing, age, and study design were all taken into account for carrying out subgroup analyses. The results revealed no variations in serum/plasma leptin levels between the controls and CF patients in the various subgroups. Female CF patients exhibited higher leptin concentrations compared to male CF patients, and male healthy individuals showed lower leptin levels than female healthy participants. Aside from the fact that serum/plasma leptin appeared to be favorably linked to fat mass and BMI, the findings in this study also indicated that serum/plasma concentrations were not associated with Forced Expiratory Volume in the first second (FEV1). No statistically significant differences were observed in the leptin and leptin receptor mRNA expression levels between the healthy controls and CF patients. The leptin receptor and leptin expression levels in alveolar lavage fluid were low in various cells, without any distinctive distribution patterns.The current meta-analysis indicated the absence of significant differences in leptin levels between CF patients and healthy individuals. Gender, fat mass, and BMI may all be correlated with leptin concentrations.https://www.crd.york.ac.uk/prospero/, identifier CRD42022380118. ThisTR) gene . CFTR isTR) gene . The subTR) gene . AdditioTR) gene . ConsequThe adipocyte-derived protein hormone, i.e., leptin, helps to maintain homeostasis . Leptin,After considering the sporadic research conducted in the past, the meta-analysis and regression analysis were carried out to evaluate the link between CF and leptin concentrations, thus offering new insights into determining the etiology of CF.22.1.The protocol employed in this study was registered on PROSPERO 2.2The studies that determined the plasma and serum leptin concentrations in CF patients and compared them with the non-CF control patients, were included in this review. Case-control studies and intervention trials with a case-control comparison at baseline were both included in the population-based studies. A clinical diagnosis of CF must have been supported by an abnormal sweat test, diagnostic genotyping, and/or the participant being recruited through a CF clinic. The leptin concentrations of all the study participants, who were deemed clinically stable and free of signs of an acute respiratory exacerbation, were tested. The Shwachman score was used to categorize CF severity into three categories: mild disease (71-100 points), moderate disease (41-55 points), and severe disease (<41 points) . Reviews2.3th December 2022.Several databases, including Google Scholar, Web of Science, PubMed, Excerpta Medica database (EMBASE), and China National Knowledge Infrastructure (CNKI), were thoroughly investigated to identify studies conducted up to 122.4The current study applied a 3-part search strategy to find studies that met eligibility requirements, similar to other systematic reviews conducted in this field . The priA different technique determined the references related to specific words in the abstract and/or title (ti/ab) sections. During the search process, the biological molecules and leptin that were determined in the MeSH search were AND-linked to “cystic fibrosis” in the following manner: “cystic fibrosis” [ti/ab] AND “leptin” [ti/ab] AND (blood [ti/ab] OR plasma [ti/ab] OR serum [ti/ab] OR “whole blood” [ti/ab]). To ensure a thorough, targeted, and sensitive search, the MeSH search was repeated at this point. In addition, the authors manually reviewed the conference proceedings, clinical trial registers, and the bibliographies of the studies and reviews that were included.2.5Two authors independently conducted the two steps of the screening and selection process. After eliminating the duplicate articles, it was determined whether the study fulfilled the eligibility requirements by screening the ti/ad sections of all references and excluding any that did not. Subsequently, the full texts of other references were downloaded and scrutinized using eligibility standards. The meta-analysis included all eligible references Table 1.2.6The data were gathered by two independent reviewers. The following data were obtained from each of the included studies using a designed table (1): basic study information  (2); baseline characteristics of patients , number, and sex); and (3) Pearson’s correlation coefficient (COR) or Spearman’s rank COR for Forced Expiratory Volume in the first second (FEV1), fat mass, BMI, and leptin concentrations. The authors that were qualified for meta-analysis were contacted by email at least twice if the study lacked crucial information. The missing information was then obtained from the authors.2.7The Newcastle-Ottawa Scale, which is frequently used to grade the case-control and cross-sectional studies, was employed by both researchers to score the quality of every study in an independent manner. The maximum score for the case-control or cohort studies was 9 points, whereas the highest score for the cross-sectional studies was 6 points . The met2.8The data were extracted, summarized, and examined using R (ver. 4.1.3) and Stata software (11.0). The continuous variables were normalized and reported as Standardized Mean Differences (SMD) with a 95% Confidence interval (95% CI). The links between FEV1/fat mass/BMI and leptin concentrations in the CF patients were investigated in this meta-analysis using Spearman’s CORs. The standard error that is dependent on the importance of rank COR indicates that Spearman’s product-moment COR does not depend on sampling distribution. Consequently, each COR was compared with a Fisher transformation, and after conducting an investigation where transformed values were used as an input, the coefficients of correlation (CORs) were reconverted. Additionally, the relationship between FEV1/fat mass/BMI and leptin concentrations was investigated using Pearson’s COR. Many studies have described a technique for converting Pearson’s to Spearman’s COR, using the below formula:s, respectively . The random- and fixed-effects models were adopted when the studies showed no heterogeneity.where Pearson’s and Spearman’s CORs are represented by r and rectively . Data heThe cause of heterogeneity was assessed using meta-regression, subgroup, and descriptive analyses. To conduct subgroup analysis, the overall population was categorized into groups based on the severity of the condition, sex, the technique used to test the specimen, study design, and age. For sensitivity analysis, one study at a time was eliminated to examine how every study affected the overall effect size. The linear regression, Begg’s, and Egger’s tests were carried out to determine the publication bias.2.9https://www.ncbi.nlm.nih.gov/geo/). Single-cell sequencing data from four healthy controls and three CF patients were included in the GSE193782 dataset (Dataset (GSE193782) was derived from the Gene Expression Omnibus database (GEO) , selection (0-4 points), and outcome (0-3 points); scores of 0-3,4-6 and 7-9 were considered to indicate low- (grade C), medium- (grade B) and high-quality (grade A) studies, respectively. Every study exhibited relatively high quality (grade B and grade A), as shown in 3.33.3.12 was 79.5%. Hence, a random-effects model was selected for validating the reliability of the data. The meta-analysis showed that leptin concentrations between the CF patients and healthy controls did not differ significantly , a random-effects model was selected for further analyses.During the meta-analysis, 15 observational studies were analyzed to determine the relationship between serum leptin levels in CF patients and controls. Serum leptin levels did not show significant variations between CF and healthy control individuals , a random-effects model was employed for further analyses.3.43.4.1Four studies were carried out for comparing the serum leptin concentrations in children. The results indicated no significant variation in the serum leptin levels between the healthy and CF children  was utilized in seven studies to assess serum leptin levels. The results showed no significant variations in serum leptin concentrations between the CF and healthy control individuals  exhibited elevated plasma leptin concentrations compared to the healthy male controls  Figure 43.82 value of 79.5%, indicating a high level of heterogeneity. Therefore, we explored the possible sources of heterogeneity using meta-regression. All included studies had an I3.9vs fat mass; and leptin concentrations vs BMI). The funnel plots showed no publication bias and appeared systematic  nor Egger’s  tests suffered a publication bias Figure 53.104This is the first study to report the results of meta-analysis and meta-regression of the association between leptin concentrations and cystic fibrosis. 14 articles were found to be acceptable for the meta-analysis, which included 919 CF patients and 397 healthy individuals. Two major phenomena were identified in this study. Firstly, in comparison to healthy non-CF individuals, no significant increase or decrease was observed in the serum leptin levels collected from CF patients. No significant variations were observed in serum leptin levels between the healthy participants and CF patients after subgroup analysis by age, research design, sex, and detection technique. Secondly, no significant variations were observed in plasma leptin levels between the CF patients and healthy individuals. The results of subgroup analysis based on age, sex, research design type, detection technique, and glucose tolerance status, indicated that the plasma leptin levels of CF patients that were determined using various study design types, detection methods, and glucose tolerance status, were not statistically different from those exhibited by the control individuals. Furthermore, plasma leptin levels in male CF patients and children with CF were elevated in comparison to those displayed by control participants. However, after analyzing the primary data, the children’s plasma leptin expression data were derived from a single institution . A largeob gene), located on the long arm of human chromosome 7 (7q31), encodes the protein leptin (via the neuropeptide Y transmitter system and α-melanocyte stimulating hormone (α-MSH) system  system . Leptin ) system . Earlier) system , 37. Sev) system , 39. Inc) system . Additio) system . It shou) system .in-vitro adipose tissue cultures revealed that estradiol enhanced the secretion and release of leptin in females, but not in males  was regarded as the most frequent extrapulmonary consequence of cystic fibrosis . CFRD isLEP gene , 48. ZiaLEP gene  observedLEP gene . Many reLEP gene . As a reLEP gene .In a meta-analysis, heterogeneity between studies is typically correlated with demographic characteristics, disease severity, experimental design, the caliber of the included research, and other parameters. The results in this study showed no significant variations across various studies that examined the correlation between CF and leptin levels. Subgroup and meta-analysis were carried out to investigate the underlying cause of heterogeneity. No heterogeneity was observed in the meta-regression analysis related to age, sex, assay techniques, or research design. However, no obvious causes of heterogeneity were discovered. These elements may also enhance heterogeneity and impair the accuracy of this meta-analysis. Additionally, the “leave-one-out” sensitivity analysis found that no individual study was responsible for the high heterogeneity. Thus, it was hypothesized in this study that several factors could lead to heterogeneity, such as variations in blood collection techniques, storage, measurement, and experimental procedures, as well as types of gene mutations, dietary and lifestyle preferences, illness severity, and other underlying confounding variables.This is, to our knowledge, the first meta-analysis study that has examined leptin levels in CF individuals. However, this meta-analysis study has a few limitations. Firstly, it is difficult to establish a causal association since all publications included in this meta-analysis were observational studies and lacked multi-center and large-cohort RCT trials. Numerous gaps were observed in studies that investigated the effect of age, illness severity, and genotype on leptin concentrations. The relationship between leptin levels and CF needs to be verified by longitudinal studies. Secondly, the high-throughput sequencing data related to blood samples or lung tissues were not available for validation in the public databases, and the meta-analysis used only single-cell sequencing datasets for validating the data validated using bioinformatics. Thirdly, innovative therapies that alter the negative consequences of CFTR mutations at the molecular level, such as CFTR modulators, are gaining a lot of acceptance as standard therapies. Further research is needed to determine whether leptin concentrations observed in this meta-analysis study would persist even after CFTR modulator therapy.5The pooled results of different observational studies revealed that leptin concentrations were not significantly different between CF patients and healthy individuals. Furthermore, leptin levels were potentially related to gender, fat mass, and BMI. The association between leptin levels and clinical outcomes in CF patients is still questionable based on the available data; therefore, an in-depth study is required in the future to establish the relationship and explain the underlying mechanisms.The original contributions presented in the study are included in the article/Data curation: JH and HQ. Formal analysis: HL and PZ. Methodology: HL and HQ. Project administration: HL. Resources: JH and PZ. Supervision: HQ. Validation: JH. Writing-original draft: JH. All authors contributed to the article and approved the submitted version.


Atherosclerosis is a key risk factor for developing cardiovascular diseases (CVDs). Flow-mediated dilation (FMD), which reflects vascular reactivity, as well as pulse wave velocity (PWV) and augmentation index (AIx), both markers of arterial stiffness, have emerged as noninvasive, subclinical atherosclerotic markers for the early stages of altered vascular function. In addition to the long-term effects of diet, postprandial processes have been identified as important determinants of CVD risk, and evidence suggests an acute effect of fat quantity and fatty acid (FA) composition on vascular function. However, robust analyses of this association are lacking, especially concerning parameters of arterial stiffness. Therefore, we carried out a systematic literature search in PubMed, Scopus, and the Cochrane Library to investigate the impact of fat quantity and FA composition of meals on postprandial vascular function. Postprandial studies measuring FMD, PWV, and/or AIx in healthy adults and subjects with increased CVD risk  were analyzed. In total, 24 articles were included; 9 studies focused on the effect of high-fat meals compared with control; and 15 studies investigated the effects of different fat sources. We found that consumption of a high-fat meal causes a reduction in FMD (decrease in vasodilation) and AIx . For eicosapentaenoic acid/docosahexaenoic acid (from fish oil), postprandial assessment (FMD and AIx) indicates a beneficial effect on vascular function. There is limited evidence of an influence of CVD risk on the vascular response to meals with varying fat doses or FA composition. However, meaningful conclusions were difficult to draw because of the large heterogeneity of the studies. Inconsistent results regarding both the impact of fat dose and FA composition on postprandial vascular function should be noted. We propose standardized methods for postprandial protocols to improve data quality in future studies.This review was registered in PROSPERO as CRD42022352986. In the past few decades, the number of global, cardiovascular disease (CVD)-related deaths has steadily increased, from 12.1 million in 1990 to 18.6 million in 2019 . During In developed societies, humans spend ∼18 h of the day in a postprandial state  with conThere is convincing evidence that a dose–response relationship exists between total fat intake and postprandial triglyceride (TG) response . In addiIn a recently published meta-analysis, Fewkes et al.  concludeGiven this background, we aimed to systematically review and critically evaluate the existing evidence on the acute effects of fat dose and FA composition on vascular function assessed by FMD, pulse wave velocity (PWV), and augmentation index (AIx). An additional aim was to investigate whether acute changes in vascular function differ between metabolically healthy individuals and participants with increased CVD risk . In addition, to maximize practical relevance, we focused on mixed meals.Our analysis included noninvasive, yet reliable, measures of vascular function  as outcome measures . AlthougThe FMD test was developed in 1992 by Celermajer et al.  and measc–f is calculated by dividing the distance between the common carotid artery and the common femoral artery by the transit time of the pulse wave between these points [c–f on CVD risk was set at 10 m/s [c–f is regarded as the gold standard measurement of arterial stiffness [Likewise, PWV is an independent predictor of CVD risk and cardiovascular events . PWVc–f e points . A highee points . In the t 10 m/s . Accorditiffness .Similar to the PWV, AIx serves as a surrogate parameter of arterial stiffness and these parameters correlate strongly . The AIxhttps://pubmed.ncbi.nlm.nih.gov/), Scopus (https://www.scopus.com), and the Cochrane Library (https://www.cochranelibrary.com) were searched using the search term “postprandial AND fat AND meal AND .” The initial database searches were conducted between July and August 2022, and the last update was made in June 2023. Both authors independently reviewed the identified papers and compared them with the inclusion and exclusion criteria. The main inclusion criteria . Patik et al. [In 7 studies, FMD (%) was measured in metabolically healthy adults  Table 2Table 2. k et al.  observedThe literature search revealed one study in which the effects of an HFM (53.4 g fat) and an LFM (3 g fat) on FMD (%) were measured in participants with enhanced CVD risk . Bae et Two studies investigated the impact of an HFM on AIx (%) ,48 TABL, TABLE 4No studies investigating the effects of fat dose on vascular function measured PWV (m/s).7). Seven were performed in healthy adults [In total, 15 studies were included that analyzed the impact of FA composition on vascular function –7. Seveny adults  and 6 iny adults . The remy adults ,63 incluy adults  measuredy adults  and McMay adults  determiny adults  and Chony adults  only meay adults . In manyy adults ,60 or pay adults . The tesy adults ), an oily adults ,57), or y adults , variousy adults ) TABLE . Seven wThe literature search revealed 7 studies investigating the impact of FA composition on FMD (%) in healthy adults ,62,63 T, TABLE 7In addition to healthy adults, 2 of the above-mentioned studies also included hypercholesterolemic individuals  and subj75  from baseline after a SFA-rich meal  and a MUFA-rich meal  (75 (%) remained significant after adjustment for mean arterial pressure (MAP), whereas the effect on AIx (%) was no longer significant when adjusted for increases in heart rate and MAP. There was no significantly different effect of MUFA-rich meal compared with SFA-rich meal on AIx or AIx75 (%). Chong et al. [75 (%) reduction in response to both meals, the AIx75 (%) increased to a lower extent after a meal enriched with EPA and DHA compared with a control meal   in healthy subjects ,50,55 T. Berry eic acid) . The effean oil) .P = 0.06) (Two publications investigated the effects of FA composition on AIx (%) in adults with CVD risk factors ,57 Tabl. Kendall = 0.06) .c–f (m/s) measured 3 h after ingestion of meals enriched with high-oleic sunflower oil or shea butter blend  (c–f (m/s) from baseline in the postprandial state; however, this effect was no longer significant when adjusted for the increase in MAP. Furthermore, FA composition  had no influence on PWVc–f (m/s)  was measured to analyze the effect of FA composition on arterial stiffness in healthy subjects ,50 Tabl. Compareic acid) . Regardl–f (m/s) .c–f (m/s) [The only study measuring PWV (m/s) in subjects with increased CVD risk factors reported no influence of FA composition  on postprandial PWV–f (m/s)   enabled us to evaluate both vascular reactivity and arterial stiffness. In addition, we increased the practical application of our findings by using a food-based approach, focusing on fat sources and their FA composition from whole foods. Nevertheless, because of our focus on fat dose and FA composition, a possible influence of other meal characteristics  on vascular function that might contribute to the heterogeneity of study results was not considered. The high heterogeneity of the study results was certainly also influenced by the broad range of fat amount administered via the LFMs (0–18.4 g) and HFMs (29–95 g). Because of the high variation of meal compositions , study protocols , and study populations and comparisons, we did not perform a meta-analysis. To obtain convincing results and more consistent evidence on the effects of meal composition on vascular function, standardization of postprandial protocols is required . To provThis review revealed 3 main findings. First, evidence suggests that meal consumption results in decreases in FMD and AIx; specifically, higher fat doses appear to impair vascular reactivity measured by FMD more strongly than lower fat doses. Second, concerning FA composition, most studies indicate no clinically relevant or contradictory effects on subclinical atherosclerosis markers . One exception might be marine n–3 PUFAs (EPA and DHA), as data from 3 studies suggest a beneficial effect on acute vascular function. Third, some studies found differences in the vascular response to meals with varying fat doses or FA composition between metabolically healthy subjects and subjects with CVD risk factors, but based on the analyzed literature with highly heterogenic populations, the specific effects could not be deduced. Our current findings of the impact of meal total fat content and FA composition on postprandial vascular function assessed by FMD, PWV, and AIx are based on meal studies in which a variety of fat sources  was used in different amounts and meal recipes. At this time, it cannot be concluded which fat source in which amount and meal composition has beneficial effects on postprandial vascular function.To enhance the meaningfulness of systematic reviews and to allow valid meta-analyses concerning the effects of meal composition on vascular function, we strongly recommend standardization of postprandial protocols. In The authors’ responsibilities were as follows – HFK, SE: literature search, study selection and evaluation; HFK: preparation of the first draft of the manuscript; HFK, SE: finalization of the manuscript in close collaboration; and both authors: read and approved the final manuscript and declare responsibility for its final content.The authors report no conflicts of interest.Open Access Publication Fund of the University of Bonn.This work was supported by the All data generated or analyzed during this study are included in this published article.


Although some epidemiological studies have shown a positive relationship between high intake of ultra-processed food (UPF) and risk of colorectal cancer (CRC), the results remain inconsistent. Therefore, we conducted this systematic review and meta-analysis to clarify the association between UPF intake and CRC risk.2). Publication bias was assessed by examining the funnel plots, and quantified by Begg’s or Egger’s tests.PubMed/MEDLINE, Web of Science, EMBASE, China National Knowledge Infrastructure (CNKI) and Wan fang databases were used to search the relevant studies published up to February 2023. The summary relative risks (RRs) with the corresponding 95% confidence intervals (CIs) were estimated by comparing the highest category vs. the lowest category of UPF intake, using the random-effects models (DerSimonian-Laird method). Heterogeneity between studies was explored using the Cochran’s Q test and I-square (Ip < 0.0001). Subgroup analyses showed a positive association between UPF intake and CRC risk in case–control studies . When we conducted analyses separately by study area, there was a significant association between UPF intake and CRC risk in developed countries .A total of seven articles (three cohort and four case-control studies), involving 18,673 CRC cases and 462,292 participants, were included in our study. Combining nine effect sizes from seven articles, an increased risk of CRC was shown in the highest compared with the lowest category of UPF intake (RR = 1.26; 95%CI:1.14–1.38, Our results show that high UPF intake is significantly associated with a higher risk of CRC, in the absence, however, of a dose–response association. Further studies in particular of large prospective cohort studies are necessary to confirm these results. Colorectal cancer (CRC) is the third most commonly diagnosed cancer globally and the second leading cause of cancer-related deaths in 2020, with more than 1.9 million new cases and 93,500 deaths , with anDuring the past few decades, some epidemiological studies have examined the relations between diets intake and risk of CRC , but theIn the last decade, with the advent of global interest on UPF, there has been growing evidence that UPF intake is associated with poor health outcomes, including increased risks of obesity, hypertension, diabetes, cardiovascular disease and all-cause mortality . At the The current systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines . A systein vitro studies; (3) did not use the NOVA food classification system ; (4) studies not reported as HRs, RRs or ORs with 95%CIs; (5) unrelated articles.Two authors (Shu L and Huang YQ) independently screened and crosschecked each article from the literature search, and a third author (Zhang XY) was consulted to settle any discrepancies. After screening the titles and abstracts, the full-text versions of the articles were reviewed according to the inclusion and exclusion criteria of the present meta-analysis. Studies were included in our analyses if they met all the following eligibility criteria: (1) were observational studies ; (2) were performed in humans of any age; (3) UPF was recognized as the main exposure (according to the NOVA food classification system); (4) evaluated the association with CRC risk; (5) reported adjusted estimates of the RRs  and 95%CIs for the relationship between UPF intake and CRC risk; (6) If the original published data lacked sufficient detail, the corresponding author of the study is contacted by email for more information. Studies were excluded if they met one of the following criteria: (1) non-observational studies, e.g., reviews, editorials, case reports and conference letters; (2) animal, cell culture, and Data were extracted by two independent authors from all eligible studies, including first author’s last name, publication year, study design, study area, sample size, number of CRC cases, mean age, duration of follow-up, method of UPF assessment, and confounding factors used for adjustments in the multivariable analyses.According to the extent and purpose of industrial food processing, the NOVA food classification system divided foods and food products into four groups: unprocessed or minimally processed food, processed culinary ingredients, processed food and UPFs . UPFs arThe authors (Huang YQ and Si CJ) independently evaluated each included study’s quality using the Newcastle-Ottawa Scale (NOS), which was designed for case-control and cohort studies . In the 2) statistics. p-values of Cochran’s Q test < 0.10 or I2 > 50% were considered to show significant heterogeneity among the included studies, and subsequently the random-effects models (DerSimonnian and Laird method) were used to summary the pooled RRs. Otherwise, the fixed-effects models were performed , moderate (25%~75%) and high (I2 ≥ 75%), respectively. If the results showed significant heterogeneity (I2 > 50%), the potential sources of heterogeneity across studies were explored using subgroup and sensitivity analyses. In our analyses, subgroup analyses were performed based on study design (cohort or case–control studies), outcomes , study quality (≥7 or <7), mean age (≥55 or <55), study area (developed countries or developing countries), sample size , and exposure assessment . Sensitivity analysis was performed, excluding one study removed at one time to confirm whether the results were robust or sensitive to the influence of single study. Publication bias was assessed by examining the funnel plots, and quantified by Begg’s or Egger’s tests  by using the ORs, HRs, RRs and their corresponding 95%CIs. The pooled effect sizes and 95%CIs were estimated comparing the highest vs. the lowest category of UPF intake, using the random-effects models. Heterogeneity among the included studies was examined by the Cochran’s Q test and I-squared (Ierformed . Accordi’s tests . If publ’s tests . All stan = 2), the outcome of interest was pancreatic cancer (n = 1), the main exposure was processed meats (n = 2), conference abstract (n = 1), and reported the same participants (n = 1). Finally, seven articles with 9 effect sizes were included in the present meta-analysis.The main characteristics of included studies are summarized in p < 0.0001). The moderate heterogeneity across studies was found , and data from these studies was evaluated using the random-effects models.Seven articles reporting nine original studies were included in this meta-analysis. 2 = 58.5%; p = 0.013), subgroup analyses were carried out to explain the potential sources of heterogeneity (p < 0.0001). There was no evidence of heterogeneity between studies . Meanwhile, there was a more significant relationship between UPF intake and CRC risk in case–control studies , with less evidence of heterogeneity . For study area (p < 0.0001) in developed countries and the between-studies heterogeneity decreased from 58.5% to 32.3%. However, no statistical association was observed between UPF intake and risk of CRC in developing countries . For study outcome (p < 0.0001). However, the heterogeneity was apparent . In contrast, we found no significant association between UPF intake and CRC risk in the subgroups of colorectal adenomas , and there was more heterogeneity . For study quality (p < 0.0001) and <7 . However, heterogeneity between studies decreased 58.5 to 44.3%. When the results were stratified by mean age (p = 0.007), and there was evidence of significant heterogeneity . Moreover, we also observed the significant positive association between UPF intake and CRC risk in the subgroups of age ≥ 55 . The stratified association between UPF intake and CRC risk according to sample size based on the random-effects model is shown in p = 0.277; I2 = 22.2%), where significant positive association with risk of CRC was observed . In addition, significant positive association was also found between UPF consumption and CRC risk in the studies with sample size < 5,000 , and there was less heterogeneity . For exposure assessment (p < 0.0001) in FFQ, and the heterogeneity was more heterogeneity .Given the moderate heterogeneity of this meta-analysis .As shown in Based on the results of sensitivity analysis , the relThe quality of the included studies using NOS standards is presented in To our knowledge, this systematic review and meta-analysis is the first to comprehensively evaluate the relationship between UPF intake and CRC risk. In this study, results showed a positive relationship between high intake of UPF and CRC risk. Nevertheless, the included studies must be interpreted with caution due to moderate heterogeneity in the results of this meta-analysis. To address the heterogeneity, we conducted subgroup analyses based on study design, study area, exposure assessment, study outcomes, study quality and sample size. In addition, sensitivity analysis also showed that none of included studies could considerably modify the summary effect. Our findings confirm the positive correlation results of previous studies and add to the growing evidence for the impact of high UPF intake on diet-related CRC, despite the lack of a dose–response relationship.2 = 58.5%; p = 0.013). Our results are in agreement with some previous studies reporting that high consumption of UPF was associated with an increase in the risk of CRC (2), a common food additive, is used as a brightening agent or in contact with food or drinks packaging to provide better texture and antimicrobial properties. TiO2 is currently assessed by the WHO and IARC as “probably carcinogenic to humans” in relation to cancer (group 2B)  . Sixth, roup 2B) . A recenroup 2B) . Finallyroup 2B) . Togethe2 = 58.5%, p = 0.013). Hence, we conducted subgroup analyses based on study design (cohort or case-control studies), exposure assessment , outcomes , study quality (≥7 or <7), mean age (≥55 or <55), study area (developed countries or developing countries), and sample size  to explore the sources of heterogeneity. Our results showed that difference in study design, study area and sample size might partially explain the heterogeneity, whereas mean age and study quality had no significant effect. For example, the heterogeneity of cohort studies decreased from 58.5% to 22.2%. There are several possible explanations for the observed heterogeneity. First, 4 of 7 studies were case-control in term of study design. In case-control studies, recall bias due to dietary survey methods  should be considered. Moreover, there were only three prospective cohort studies included, which somewhat limited the significance of the pooled results. Second, although the RRs or ORs were from the highest vs. lowest categories of UPF intake, different studies classified UPF intake based on different standards, such as absolute intake  for publication bias was non-significant. Despite the above-mentioned strengths, several limitations also should be acknowledged in the present study. First, in our meta-analysis, more than half of included studies were case–control in design. We cannot rule out the probability that these findings are susceptible to recall and selection bias. Second, six of the included studies used FFQs that were not specially designed or validated to evaluate UPF consumption, thus misclassification might occur when authors classified food items into the given food groups. Also, misclassification might result in the under- or over-estimation of UPF intake. Third, even though some known risk factors have been adjusted in the analyses, residual confounding from unmeasured factors cannot be totally excluded. Moreover, there were also different adjustment for potential confounding factors in the included studies. Consequently, the data included in our meta-analysis might have varying degrees of completeness and accuracy. Fourth, moderate heterogeneity was found in this meta-analysis. Although subgroup and sensitivity analyses were performed to explore the potential sources of heterogeneity, we could not ascertain and explain the sources of inter-study heterogeneity sufficiently. Finally, the majority of studies in this meta-analysis were performed in developed countries , with only two studies in developing countries, which might compromise the generalizability of our findings.In conclusion, this study indicate that high intake of UPF is associated with a higher risk of CRC. Despite the lack of a dose-response relationship between UPF intake and CRC risk, our findings still suggest that high intake of UPF may be an important public health issue in the prevention and management of CRC, and support the need to emphasize the importance of limiting UPF intake for better health outcomes in national dietary guidelines. Moreover, our findings may also help physicians and dietitians in clinical practice by provide strong evidence on the role of UPF intake in the management of CRC. Future research priorities included well-designed prospective studies exploring the relationship between UPF intake and CRC risk in different populations around the world.The original contributions presented in the study are included in the article/XZ designed the research. LS performed the systematic literature search, identified the studies meeting the inclusion criteria, extracted data from the included studies, and drafted the manuscript. CS acquired the data. YH evaluated the risk of bias of the included studies. QZ performed the statistical analysis. XZ and PZ assisted in the interpretation of the results and the revision of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China (grant number: 82004040), Traditional Chinese Medicine Research Project of Zhejiang , and Medical and Health research fund project of Zhejiang Province (no. 2022KY006).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study aimed to evaluate the association between blood heavy metal (zinc (Zn), copper (Cu), lead (Pb), and cadmium (Cd)) concentrations and spontaneous abortion (SA) and recurrent pregnancy loss (RPL) and explore the possible endocrine dysfunction associated with it.A literature search was performed in the PubMed, Embase, Cochrane Library, and Web of Science databases up to April 2023. The overall effects were expressed as the standard mean difference (SMD). Subgroup analysis was performed according to the type of abortion (SA or RPL). Stata 16.0 was utilized for data analysis.p = 0.003) and Cu concentrations  and higher Pb  and Cd concentrations  than normal pregnant women. Subgroup analysis showed that Zn and Cu deficiency and Cd and Pb exposure were significantly (p <0.05) associated with RPL, whereas Cu deficiency and Cd and Pb exposure were significantly (p <0.05) associated with SA.Based on the integrated findings, abortion women showed significantly lower Zn (SMD = −1.05, 95% CI: −1.74 to −0.36, Zn and Cu deficiencies and Pb and Cd exposure were associated with abortion. Endocrine dysfunction, such as insulin resistance, vitamin D insufficiency, and abnormal thyroid and sex hormone concentrations, is thought to be involved in heavy metal-related abortion. According to the World Health Organization, SA is defined as the involuntary loss of a fetus weighing ≤500 g before the 20th gestational week (GW) , whereasHeavy metals are among the most harmful environmental contaminants because they are not biologically degradable and can accumulate in organisms along the food chain . Heavy mSeveral previous studies have investigated the associations between the concentrations of Cd, Pb, Zn, and Cu in the blood and the risk of abortion , 19; how22.1The systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. PubMed, Embase, Cochrane Library, and Web of Science databases were searched for relevant studies published up to April 2023. The subject terms included ‘Miscarriage,’ ‘Pregnancy loss,’ ‘Abortion, Spontaneous,’ ‘Zinc,’ ‘Copper,’ ‘Lead,’ and ‘Cadmium.’ Random combinations of these subject terms and their synonyms were used for retrieval. The detailed literature search strategy is provided in the 2.2Studies meeting the following criteria were included in the meta-analysis. (a) Study population: Pregnant women without internal and obstetric diseases that impair the normal process of pregnancy, including infectious diseases, gestational hypertension, gestational diabetes mellitus, and infertility. (b) Measurement: Female serum, plasma, or whole blood metal concentrations. (c) Observation group: Women who had experienced abortion, including SA and RPL. SA is defined as the involuntary loss of a fetus before the 28th GW (including the 20th and 24th GW) . (d) ConThe exclusion criteria were as follows: (a) article type: review, meta-analysis, meeting, case report, letter, comment, editorial, note, trial registry record, or protocol, (b) studies that focused on non-human cases , (c) unclear definition of SA or RPL, (d) insufficient data on metal concentration; and (e) unavailable full text.2.3The studies that met our inclusion criteria after the initial search were case–control, nested case–control, and cross-sectional studies. Therefore, the Newcastle-Ottawa Scale (NOS) was used to assess the quality of the studies . Each in2.4Two investigators independently extracted the relevant data from the included studies (MR and LiaW). All data were double-checked by the third author (LiqW). The following information was extracted from the selected studies: first author, publication year, country and continent of the study population, type of detected sample, type of article, type of heavy metal, type of abortion, follow-up endpoint, sample size, concentrations of heavy metals, and analytical method employed.2.52 statistics, and a random effects model was used to estimate the relationship between metal concentrations and abortion, as there was significant heterogeneity . To investigate the impact of metal concentration on the different types of abortions (SA and RPL), a subgroup analysis was performed. To investigate the origin of the heterogeneity, four additional subgroup analyses were performed based on the follow-up endpoints (ongoing pregnancy and live birth) of participants, continent of the study population , type of article , and type of detected sample . An influence analysis  was conducted to improve the reliability of the meta-analysis results. A funnel plot and Begg’s and Egger’s tests were used to detect potential publication bias; p-values <0.05 represented significant statistical publication bias for Begg’s and Egger’s tests.Meta-analysis was performed using Stata 16.0 . The standard mean difference (SMD) was adopted to integrate the data on metal concentration, as it is a continuous variable with different units across various studies. The 95% confidence intervals (CIs) were computed and presented as forest plots. For each study, statistical heterogeneity was assessed using Cochran’s Q-test and I33.13.23.3The quality assessment results for all studies are shown in 3.4p = 0.003, I2 = 96.9%; p <0.001), whereas the Zn concentrations of women with SA and healthy controls were not significantly different . Significant heterogeneity was observed in each subgroup  (p <0.001) for women with SA and −3.92 . However, subgroup analysis based on abortion type showed obvious heterogeneity . Subgroup analyses for the follow-up endpoint, continent, type of article, and type of detected sample showed high heterogeneity . Visual inspection of funnel plots showed asymmetry . Subgroup analysis for SA and RPL showed significantly higher Pb concentrations in women with SA  and RPL  than in healthy pregnant women . Further subgroup analyses based on the follow-up endpoint, continent, type of article, and type of detected sample also showed high heterogeneity .The meta-analysis of the association between Pb concentration and abortion included 15 studies  . However, the heterogeneity was significant in each subgroup   , 58, wheApart from IR, Zn deficiency is also closely related to vitamin D deficiency, as Zn regulates the transcriptional activation of hormone-related genes via a cysteine-rich Zn-finger region in vitamin D receptors (VDRs) –69. VitaZn deficiency appears to interfere with sex hormone synthesis and further causes RPL. Zn can affect the biosynthesis and function of sex hormones, such as progesterone and prolactin, by altering LH and FSH levels and inducing oxidative stress , 76–78. nis, tpo, thrα, dio1, dio2, and ugt1ab) in the hypothalamic–pituitary–thyroid (HPT) axis  axis .4.2We found that women undergoing abortion (both SA and RPL) had lower Cu concentrations, indicating that Cu deficiency may be closely related to the incidence of abortion (SA and RPL). However, the underlying mechanism remains unknown. However, previous studies have reported that Cu deficiency can induce endocrine dysfunction, such as IR, vitamin D insufficiency, and abnormal thyroid and sex hormone concentrations, which may be involved in the pathogenesis of SA and RPL. Insufficient Cu can cause IR by upregulating cytochrome c oxidase 1 (SCO1) and vascular adhesion protein-1 (VAP-1) –90; it c4.32+), and affect various cellular processes, such as apoptosis, cell adhesion, and cell signaling (Our study found that women who experienced abortion (SA and RPL) had higher Pb concentrations, suggesting that Pb exposure could increase the risk of abortion (SA and RPL). Our results are consistent with those of the meta-analysis by Kaur et al. . Pb can ignaling . Howeverignaling , 98. Lowignaling . Additioignaling , 101. Pbignaling , 102. Prignaling , 104. Pbignaling . Pb has ignaling , 108. Fuignaling . As an oignaling , 109.4.4We found that the Cd concentration was significantly higher in women who experienced abortion (SA and RPL) than in normal pregnant women. Our results are in line with those of Kaur et al., who revealed that Cd exposure could increase the risk of abortion . Cd is a4.5The strength of our study is that we comprehensively investigated the relationships between blood Zn, Cu, Pb, and Cd concentrations and abortion rates (SA and RPL). In addition, we systematically reviewed previous publications on the endocrine mechanisms of metal exposure-related abortions. We propose that IR, vitamin D insufficiency, and abnormal thyroid and sex hormone concentrations may be involved in Zn and Cu deficiencies and Pb and Cd exposure-associated abortions. Third, the large sample size of 4,666 pregnant women from 14 countries makes our estimates reliable. Fourth, most included studies were of good quality. In the included studies, the definitions of cases and controls were adequate, and the selection of controls and assessment of exposure were consistent. Fifth, a sensitivity analysis was conducted to verify the associations between the four metals and abortion.4.6This study has some limitations. First, several dated documents that appeared to meet our inclusion criteria were not included because we were unable to reach the authors. Second, despite our best efforts, we were only able to find 28 related papers because of the relatively large number of animal studies and case reports. Third, most meta-analyses included in our study had high heterogeneity. To ascertain its sources, we performed a subgroup analysis based on the type of abortion, follow-up endpoint, continent, type of observational study, and type of detected sample. However, we failed to find sources of heterogeneity by subgroup analysis, as most subgroup analyses showed high heterogeneity. After carefully reviewing the included articles, we found that different diagnoses of SA or RPL may have led to clinical heterogeneity. In addition, regional variations in metal concentrations of the study participants were considered another source of heterogeneity. Local mineral deposits and their exploitation affect the metal concentrations in the environment , and different terrains can impact the diffusion of pollutants . Thus, p4.7The findings of the present study broaden our understanding of the effects of toxic and essential metals on the RPL and SA. Endocrine dysfunction can lead to metal exposure and abortions. It will be helpful to screen blood Zn, Cu, Pb, and Cd concentrations in females. However, well-designed prospective cohort studies are needed to clarify the causal relationship between endocrine dysfunction and heavy-metal-induced abortion.5In the present study, we found that higher blood Pb and Cd concentrations and lower Zn and Cu concentrations in females may be associated with SA and RPL. Exposure to toxic metals, as well as deficiencies in essential metals, may cause SA and RPL through endocrine dysfunction, such as insulin resistance, vitamin D insufficiency, and abnormal thyroid and sex hormone concentrations. However, further prospective cohort and experimental studies are required to provide stronger evidence.The original contributions presented in the study are included in the article/XS and MR proposed the subject and designed the protocol for this systematic review. MR, LTW, LQW and JC conducted literature screening and data extraction. MR, LQW, and LTW assessed the quality of all studies. LTW and MR performed statistical analysis. MR, LTW, and LQW produced the tables, figures. MR, LQW and LTW drafted the manuscript. XS and SQ gave overall supervision, critical revisions, and final approval of the article. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


The relationship between low skeletal muscle mass index (SMI) and all-cause mortality risk in the general adults remains unclear. Our study was conducted to examine and quantify the associations between low SMI and all-cause mortality risks.PubMed, Web of Science, and Cochrane Library for primary data sources and references to relevant publications retrieved until 1 April 2023. A random-effect model, subgroup analyses, meta-regression, sensitivity analysis, and publication bias were conducted using STATA 16.0.Sixteen prospective studies were included in the meta-analysis of low SMI and the risk of all-cause mortality. A total of 11696 deaths were ascertained among 81358 participants during the 3 to 14.4 years follow-up. The pooled RR of all-cause mortality risk was 1.57  across the lowest to the normal muscle mass category. The results of meta-regression showed that BMI (P = 0.086) might be sources of heterogeneity between studies. Subgroup analysis showed that low SMI was significantly associated with an increased risk of all-cause mortality in studies with a body mass index (BMI) between 18.5 to 25 , 25 to 30 , and over 30 .Low SMI was significantly associated with the increased risk of all-cause mortality, and the risk of all-cause mortality associated with low SMI was higher in adults with a higher BMI. Low SMI Prevention and treatment might be significant for reducing mortality risk and promoting healthy longevity. Sarcope disease , and it  disease . It is nIndeed, clinical research has identified low skeletal muscle mass as an independent predictor of mortality in chronic diseases such as chronic liver disease , cardiovTo the best of our knowledge, no available systematic review and meta-analysis were found on the relationship of low skeletal muscle mass index (SMI) with all-cause mortality in the general population. Previous relevant studies often focused on the elderly  or diseaThis study was registered on PROSPERO (ID: CRD42022343325). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were used to conduct this meta-analysis .The PubMed, Web of Science, and Cochrane Library were scoured for relevant publications until 1 April 2023. In the first search, two authors (Yahai Wang and Donglin Luo) independently assessed titles and abstracts before reviewing the full-text of all eligible publications. The third author (Jiahao Liu) mediated the disagreements in order to establish a consensus. We considered prospective cohort studies that assessed the association between skeletal mass index and mortality in the general population. These were the inclusion criteria for this review: (1) The study design was a prospective cohort study; (2) the exposure of interest was skeletal muscle mass index (SMI); (3) the outcome was all-cause mortality; and (4) the researchers reported relative risk (RR), hazard ratio (HR), or odds ratio (OR) of outcome risk along with corresponding 95% confidence intervals (CIs). In the meantime, the exclusion criteria were as follows: (1) participants were not recruited from an overall healthy population; and (2) reviews, randomized controlled trials (RCTs), case-control studies, retrospective cohort studies, nonhuman studies, non-English studies, and letters lacking sufficient data were excluded. In the event of numerous reports from the same study, only those with the longest follow-up and the biggest sample size were considered.The outcome was all-cause mortality.2, or ALM/height2, or SMM/height2) and matching effect size of comparison categories together with 95% confidence intervals (CIs) and variables in the fully adjusted model. If several predictors of muscle mass were reported, the most popular and well-known predictor would be examined. If studies provided data independently by gender, they would be examined as two distinct reports.The process of data extraction was carried out by two trained researchers. The data from each qualifying study were extracted onto a standard form, which included the surname of the first author, publication year, study design, study location, sample size , mean age, follow-up years, body mass index (BMI) of participants, the method used for assessment of SMI , bioimpedance analysis (BIA), anthropometry, computed tomography (CT) and magnetic resonance imaging (MRI)), predictors reported for SMI  in terms of selection (4 stars), comparability (2 stars), and outcomes (3 stars) . Higher 2-statistic [2 greater than 50%.For the purpose of comparing the all-cause mortality risks associated with having the low SMI to those associated with having normal SMI (reference), a random-effects model was utilized to pool risk estimates with 95 percent confidence intervals, to produce more conservative results than a model with a fixed-effect. Version 5.1.0 of the Cochrane Handbook for Systematic Reviews of Interventions, HR was roughly equivalent to RR . In the tatistic . The preSubgroup analyses and meta-regression were performed to explore potential sources of between-study heterogeneities from age at baseline, gender, mean BMI at baseline, location of study (specific country for meta-regression), duration of follow-up, number of participants, method used for assessment of SMI, study quality, and adjustment for confounders (not performed for meta-regression). Additionally, a meta-regression model was also performed with continuous variables, except for method of exposure assessment, and country of study. A P-value of less than 0.1 was considered statistically significant for meta-regression analysis.A leave-one-out meta-analysis (LOOM) was undertaken as a sensitivity analysis, i.e., deleting one piece of research at a time to evaluate the robustness of the primary results and the influence of each report on the effect or heterogeneity. Publication bias was evaluated by funnel plots and Egger’s regression test. The presence of publication bias was indicated by a P-value smaller than 0.1 . In caseTo prevent input mistakes, we ran data analyses using STATA version 16.0  with double data input. Unless otherwise noted, a P-value of less than 0.05 was regarded as statistically significant.The flowchart depicting the study selection procedure is shown in Sixteen articles with twenty-four reports reported skeletal muscle mass index-related mortality risk effect sizes –47. TablNOS was used to evaluate the quality of the study, and the scores are shown in 2 = 96.5%, P < 0.001)  across the lowest to normal muscle mass category, indicating a significant positive association between low SMI and all-cause mortality risk. High heterogeneity was observed among studies (I< 0.001) .The results of subgroup analysis and meta-regression were shown in According to the findings of the subgroup analysis, low SMI was significantly associated with an increase in all-cause mortality in people aged ≤ 45, 45 to 65, and ≥ 65, and the pooled RRs were 1.41 , 1.74 , 1.56 , respectively. low SMI was significantly associated with an increased risk of all-cause mortality in male studies and mixed male and female studies, the pooled RRs were 1.41  and 1.32 . Additionally, low SMI was significantly associated with an increased risk of all-cause mortality in studies with a body mass index (BMI) between 18.5 to 25 , 25 to 30 , and over 30 . Geographically, low SMI was significantly associated with increased risk of all-cause mortality in Asian  and American  countries. Low SMI was significantly associated with an increased risk of all-cause mortality in studies with a follow-up time of 5 to 10 years  and more than 10 years . According to the testing technologies, low SMI was significantly associated with an increased risk of all-cause mortality in studies using BIA , DXA , and anthropometry . Therefore, gender, BMI, geography, duration of follow-up, and testing technologies might be sources of heterogeneity in the study.Concerning the robustness of overall effect sizes, we conducted LOOM analysis for sensitivity. The results of sensitivity analysis showed that after excluding any single study at a time, low skeletal muscle mass index was still significantly associated with the increase in all-cause mortality risk, indicating that our meta-analysis results were robust  S3 Tabl.The Egger’s test and funnel plot indicated no significant publication bias in the primary analysis for all-cause mortality (P = 0.364) .This is a meta-analysis based on 16 prospective cohort studies, and the results showed that the pooled RR of all-cause mortality risk was 1.57  across the lowest to the normal SMI, indicating a significant positive association between low SMI and all-cause mortality risk, and the risk of all-cause mortality associated with low SMI was higher in adults with a higher BMI. In addition, meta-regression and subgroup analysis showed that gender, BMI, country, location, duration of follow-up, and assessment methods might be sources of heterogeneity in the study.Our results were consistent with the results of a previous meta-analysis  based onThis meta-analysis found that low SMI was significantly associated with an increase in all-cause mortality in people aged ≤ 45 years old, 45 to 65 years old, and ≥ 65 years old. Because there were too few samples in the two subgroups (≤ 45 years old and between 45 and 65 years old), the exact correlation between low SMI and all-cause mortality could not be obtained, which needed to be verified by more cohort studies in the later stage. Subgroup analysis of subgroups older than 65 years old showed that low SMI increased the risk of all-cause mortality by 56%, which might be related to the faster decline of muscle mass  in the eMeta-regression results were consistent with subgroup analyses, indicating that the risk of all-cause mortality associated with low SMI was higher in adults with a higher BMI. Interestingly, a cohort study  based onThe results of meta regression and subgroup analysis indicated that studies in different regions might be the source of heterogeneity in this meta-analysis, which might be attributed to diverse ethnic groups in different countries and varied instruments and equipment used to measure SMI.2, or ALM/height2, or SMM/height2). This meta-analysis found that the subgroups using DXA and BIA  to measure SMI showed that low SMI was significantly associated with an increased risk of all-cause mortality. Currently, DXA and BIA are widely used to measure muscle mass [2 = 37.2, P = 0.144) was smaller.As we all know, there were many measurements of muscle mass, including skeletal mass index (SMI), skeletal muscle mass index (SMMI), appendicular lean mass (ALM), appendicular muscle mass (AMM), appendicular skeletal mass index (ASMI), body muscle mass index (BMMI), fat-free mass index (FFMI), and mid-arm muscle circumference (MAMC). Considering that there may be errors between different measurement methods, resulting in great heterogeneity between studies, our study included only prospective cohort studies that reported skeletal muscle quality by SMI , significant heterogeneity was still observed. Through subgroup analysis and meta-regression, we found the possible potential sources of heterogeneity. Nevertheless, the study was still limited by its small sample size. Last, studies reported low SMI diagnosed by different criteria were included in this present meta-analysis, thus the differences in different assessment criteria are inevitable.Low SMI was significantly associated with the increased risk of all-cause mortality, and the risk of all-cause mortality associated with low SMI was higher in adults with a higher BMI. Low SMI prevention and treatment might be significant for reducing mortality risk and promoting healthy longevity.S1 File(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Preterm birth defined as delivery before 37 gestational weeks is a leading cause of neonatal and infant morbidity and mortality. The aim of this study is to summarize the evidence from meta-analyses of observational studies on risk factors associated with PTB, evaluate whether there are indications of biases in this literature, and identify which of the previously reported associations are supported by robust evidence.We searched PubMed and Scopus until February 2021, in order to identify meta-analyses examining associations between risk factors and PTB. For each meta-analysis, we estimated the summary effect size, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. Evidence was graded as robust, highly suggestive, suggestive, and weak.P < 0.05, while 41 (24.7%) were significant at P < 10−6. Ninety-one (54.8%) associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in 37 (22.3%) and 12 (7.2%) associations, respectively. We evaluated all associations according to prespecified criteria. Seven risk factors provided robust evidence: amphetamine exposure, isolated single umbilical artery, maternal personality disorder, sleep-disordered breathing (SDB), prior induced termination of pregnancy with vacuum aspiration (I-TOP with VA), low gestational weight gain (GWG), and interpregnancy interval (IPI) following miscarriage < 6 months.Eighty-five eligible meta-analyses were identified, which included 1480 primary studies providing data on 166 associations, covering a wide range of comorbid diseases, obstetric and medical history, drugs, exposure to environmental agents, infections, and vaccines. Ninety-nine (59.3%) associations were significant at The results from the synthesis of observational studies suggest that seven risk factors for PTB are supported by robust evidence. Routine screening for sleep quality and mental health is currently lacking from prenatal visits and should be introduced. This assessment can promote the development and training of prediction models using robust risk factors that could improve risk stratification and guide cost-effective preventive strategies.PROSPERO 2021 CRD42021227296.The online version contains supplementary material available at 10.1186/s12916-023-03171-4. Preterm birth (PTB) is defined as delivery before 37 gestational weeks and is a leading cause of infant morbidity and mortality –4. It isNumerous systematic reviews and meta-analyses have assessed various, non-genetic risk factors of preterm labor. Several environmental and clinical parameters such as present pregnancy characteristics, previous pregnancy history , infectiTo our knowledge, there is no previous effort to summarize existing evidence of meta-analyses of non-genetic risk factors for PTB. We conducted an umbrella review across published meta-analyses of observational studies, including topics related to a wide range of risk factors including obstetric history, medical history, drugs, socioeconomic status indicators, and environmental and dietary risk factors, with the goal of mapping the existing evidence and critically evaluating the reported associations. We applied stringent criteria to assess potential systematic biases.We conducted an umbrella review which is a comprehensive and systematic approach that collects and critically evaluates all systematic reviews and meta-analyses performed on a specific research topic . We usedA protocol for this umbrella review was registered in the International prospective register of systematic reviews (PROSPERO 2021 CRD42021227296).Two researchers  independently searched PubMed and Scopus databases from inception to February 2021, to identify systematic reviews with meta-analyses of studies that examine the association between risk factors and preterm birth. The search strategy included combinations of the Medical Subject Headings (MESH) terms, keywords, and word variants for terms “preterm birth” AND . Titles and abstracts were screened, and potentially eligible articles were retrieved for full-text evaluation. A detailed description of our search strategy is provided in Additional file We included systematic reviews with meta-analyses investigating the association between various types of exposures as risk factors for PTB. Specifically, we included studies with singleton pregnancies and studies where PTB was evaluated as the primary outcome. Case reports or series and individual participant data meta-analyses were excluded. We also excluded studies that set time limits on time span or were performed on a restricted setting . Studies that assessed PTB as a secondary outcome were also excluded. We excluded meta-analyses that assessed PTB as a secondary outcome for two reasons; first, in any analysis of a secondary outcome, there is a possibility of lack of power to detect an effect, given that studies design their power calculations based on a primary outcome. Therefore, any assessment of effect size, heterogeneity, and other statistics would be meaningless under the lack of power. Second, some components of the grading of evidence, such as publication bias, are assessed based on the primary outcome of the studies and could not be evaluated for secondary outcomes. Furthermore, we excluded studies including multiple pregnancies and studies that assessed genetic or over -omics features as risk factor for PTB. All studies were compared to avoid the possibility of duplicate or overlapping samples. If more than one meta-analysis referring to the same research question was eligible, parameters such as the largest amount of component studies with data on individual studies’ effect sizes, publication year, and in some cases the number of participants on individual studies were considered to retain the appropriate one for the main analysis.Publications whom estimates of the studied associations, such as relative risks (RR) and 95% confidence intervals (CIs) were not reported or could not be retrieved/calculated were excluded from the analysis. For the non-environmental risk factors, we also excluded meta-analyses that did not provide the number of cases in the exposed and non-exposed groups, which is used for the calculation of the excess significance test. For the environmental risk factors, since most commonly they report the results as per unit(s) increase in exposure and the entire population is exposed, we included them even if they did not report the number of cases and total sample size. As most of the included meta-analyses did not report the number of cases or the sample size of the studies included, we were unable to estimate the power of each meta-analysis and the excess significance test.I2 metric and the corresponding χ2P-value for the Q test, and Egger’s regression P-value.Eligible articles were screened by four independent reviewers (AE/IM and EB/TK). Any disagreement between reviewers was resolved by consensus or after the evaluation of a third author (SP or EE). The data of eligible studies were extracted in a predefined data extraction form recording for each study the first author, journal, year of publication, the examined risk factors, and the number of reviewed studies. Either the study-specific relative risk estimates  and the confidence intervals were extracted or the mean and the standard deviation for continuous outcomes were also noted in this form. We also extracted the exposed and control groups used, outcome assessed, study population, exposure characteristics, number of studies in the meta-analysis, meta-analysis metric and method, effect estimate with the corresponding 95% confidence interval, number of cases and total sample size, We re-calculated summary effects and 95% confidence intervals (CIs) for each meta-analysis via fixed and random effects model , 16. 95%The largest study was considered to be the most precise if there was a difference between the point estimate and the upper or lower 95% confidence interval less than 0.20 . If the P-value of the χ2-based Cochran Q test and the I2 metric for inconsistency (reflecting either diversity or bias) was reported, too. I2 metric was used to indicate the ratio of between-study variance over the sum of within- and between-study variances, ranging from 0 to 100%  in all windows of exposure were classified as weak.Regarding the environmental risk factors, higher residential greenness did not technically qualify to be categorized as robust evidence because the random effects In this umbrella review, we evaluated the current evidence, derived from meta-analyses of observational studies on the association between various risk factors and PTB. Overall, from the 166 associations that have been examined, only 4.2% had epidemiologically robust results with no suggestion of bias, as can be inferred by substantial heterogeneity between studies, small-study effects, and excess significance bias. Seven risk factors were supported by robust evidence, including amphetamine exposure , isolateSome of the risk factors identified from our analysis as robust are well-known risk factors and have been incorporated into the screening processes during prenatal visits such as illicit drug use, ultrasonographic markers, and reproductive history . NeverthThis includes maternal psychosocial profile and sleeping quality that are either rarely screened during prenatal visits or not considered by clinicians as risk factors for PTB. Traditionally, emphasis was given to factors such as cervix length and history of PTB and their obstetric management . ScreeniOne association with highly controversial evidence is interpregnancy interval following a miscarriage and the risk of preterm birth. The World Health Organization (WHO) encourages women who experienced a previous miscarriage to wait for a minimum of 6 months before the next conception to achieve optimal outcome and reduce obstetric complications such as preterm birth . ContrarAnother risk factor with robust evidence was sleep-disordered breathing. This meta-analysis clearly demonstrated the increased risk profile of women who experience SDB not only for preterm birth but for other adverse pregnancy outcomes . RegardiIn alignment with this suggestion, women with personality disorders could be identified early through mental health screening, where targeted health interventions and multidisciplinary management can be implemented to reduce adverse outcomes for the baby/child and woman. This early identification and support also have the potential to enable the prevention of maladaptive development trajectories within the mother-infant relationship , 129.The ability to modify those factors, mainly those related to mental health and sleep quality screening, through clinical interventions or public health policy measures remains to be established. Nevertheless, we need to highlight that there is no guarantee that even a convincing observational association for a modifiable risk factor would necessarily translate into large preventive benefits for preterm birth if these risk factors were to be modified .Another association that fulfilled all criteria for a robust association is the prior I-TOP with VA. Concerns have been expressed regarding the validity of the reported association mainly due to the quality of the primary studies . Many ofFurthermore, it is important that clinical examination and medical history includes risk factors which are not well known, identified in meta-analysis with highly suggestive evidence. Regarding highly suggestive evidence, there were a few that are well known and used to classify pregnancies as high risk for PTB such as therapies for cervical intraepithelial neoplasia, advanced maternal age, placental pathology, race, first trimester bleeding, and maternal comorbidities. There were also included factors that are not routinely screened in the obstetric population such as intimate partner violence, cancer survivors, and being unmarried.Obesity is generating an unfavorable metabolic environment from early gestation; therefore, initiation of interventions for weight loss during pregnancy might be belated to prevent or reverse adverse effects, which highlights the need for weight management strategies before conception , 131. MoP-value of the random effect estimate was very close to the stringent threshold of < 10−6. Acknowledging the detrimental projected effect of climate change in greenness and given that it is one of the few protective risk factors for PTB [2.5 and the risk of PTB. This association has been debated in the literature with conflicting results about the timing and magnitude of effect and is less robust than other associations that have been shown to have strong evidence for associations [Regarding environmental risk factors, increased residential greenness was associated with a protective effect on the risk of PTB. Although this finding was categorized as having suggestive evidence, the  for PTB , serious for PTB . There wciations  such as To our knowledge, this umbrella review represents the most comprehensive overview of published literature of observational studies to date investigating associations between a wide array of risk factors and PTB. The epidemiological robustness of meta-analyses of observational studies was assessed against a transparent and replicable set of statistical criteria. In addition, we performed a deeper assessment of these associations and assessed their potential to test causal assumptions. Our assessment has certain limitations. Umbrella reviews focus on existing systematic reviews and meta-analyses and therefore some studies may have not been included either because the original systematic reviews did not identify them, they were too recent to be included, or they did not provide the data to be included. In the current assessment, we used all available data from observational studies; therefore, the meta-analysis estimates may partly reflect the biases in the primary studies. Statistical tests of bias in the body of evidence  offer hints of bias, not definitive proof thereof, while the Egger test is difficult to interpret when the between-study heterogeneity is large. These tests have low power if the meta-analyses include less than 10 studies and they may not identify the exact source of bias , 24, 34.Therefore, it is recommended that future umbrella reviews perform a comprehensive assessment of the associations beyond the classic criteria.The present umbrella review of meta-analyses identified 166 unique risk factors for preterm birth. Our analysis identified seven risk factors with robust evidence and strong epidemiological credibility pertaining to isolated single umbilical artery, amphetamine exposure, maternal personality disorder, sleep-disordered breathing, induced termination of pregnancy with vacuum aspiration, low gestational weight gain, and interpregnancy interval following miscarriage of less than 6 months, but the results should be interpreted with caution. As previously suggested, the use of standardized definitions and protocols for exposures, outcomes, and statistical analyses may diminish the threat of biases, enhance comparability of different studies examining risk factors, and promote the development and training of prediction models that could identify high-risk populations and promote public health.Additional file 1. Search Strategy for Umbrella Review.Additional file 2. Details of the comparisons.Additional file 3. Details of the comparisons with robust evidence.Additional file 4. Details of the protective factors.


Bacteroides and Lachnoclostridium) and type II  gut microbiota enterotypes, with adjusted odds ratios (ORs) of 0.66  and 0.42 (95% CI = 0.29–0.62), respectively. Fruit consumption was the main contributor to this protective effect. No association was found between a healthy dietary pattern and colorectal adenoma risk or between a high-fat diet and colorectal neoplasm risk. Different CRC subtypes associated with gut microbiota enterotypes displayed unique microbial compositions and functions. Our study suggests that specific gut microbiota enterotypes can modulate the effects of diet on CRC risk, offering new perspectives on the relationship between diet, gut microbiota, and colorectal neoplasm risk.Colorectal cancer (CRC) risk is influenced by dietary patterns and gut microbiota enterotypes. However, the interaction between these factors remains unclear. This study examines this relationship, hypothesizing that different diets may affect colorectal tumor risk in individuals with varied gut microbiota enterotypes. We conducted a case-control study involving 410 Han Chinese individuals, using exploratory structural equation modeling to identify two dietary patterns, and a Dirichlet multinomial mixture model to classify 250 colorectal neoplasm cases into three gut microbiota enterotypes. We assessed the association between dietary patterns and the risk of each tumor subtype using logistic regression analysis. We found that a healthy diet, rich in vegetables, fruits, milk, and yogurt, lowers CRC risk, particularly in individuals with type I (dominated by  Colorectal cancer (CRC) is one of the most common and deadly malignancies worldwide, accounting for approximately 10% of all cancer cases and fatalities , the pruBacteroides (enterotype 1), Prevotella (enterotype 2), and Ruminococcus (enterotype 3) ) and diversity indices (Shannon and Simpson). If the Kruskal-Wallis test indicated significant differences, we performed post-hoc pairwise comparisons with Dunn’s test for multiple comparisons. We visualized beta-diversity between subgroups using principal coordinates analysis (PCoA) based on Bray-Curtis distances. Moreover, we performed permutational multivariate analysis of variance (PERMANOVA) with distance matrices to ascertain significant differences in microbial communities while accounting for potential confounding factors.p value < 0.05 and (2) a logarithmic LDA score > 3. We visualized the results of the LEfSe analysis using LDA score plots and cladograms to effectively convey the observed differences in microbial composition between subtypes.We used the linear discriminant analysis (LDA) effect size (LEfSe) method to investigate alterations in gut microbiota composition among subtypes . The sigp value < 0.05 and a logarithmic LDA score > 2 as the significance criteria. To investigate the metabolic pathway variations between the subtypes, we mapped the selected differential metabolites to well-established metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) [To evaluate the functional disparities in gut microbiota among subtypes within both CRC and CRA groups, we performed metabolomic profiling, which included several specific analyses. First, we preprocessed the metabolite peak area data using logarithmic transformation and Z-score standardization. Then, we used PCoA based on Manhattan distances to assess the overall metabolite feature differences between subtypes in the CRC and CRA groups. Next, we applied the LEfSe method to identify significantly distinct metabolites between these subtypes, using a Kruskal-Wallis test s (KEGG) . Moreoves (KEGG) . FinallyWe included three groups in this study: the CRC group with 130 patients, the CRA group with 120 patients, and a control group consisting of 160 participants. Participants with higher healthy dietary scores were predominantly female, older, more educated, and had lower smoking and drinking rates . On the p = 0.001) (p = 0.001 for trend) for participants in the highest tertile of healthy dietary scores compared to those in the lowest tertile  . The adj tertile . However tertile .Bacteroides, Lachnoclostridium, and Escherichia shigella; type II dominated by Bacteroides, Faecalibacterium, and Phascolarctobacterium; and type III characterized by Prevotella 9, Bacteroides, and Faecalibacterium.We applied DMM to analyze 250 colorectal neoplasm cases and identified three unique microbial community profiles (enterotypes or subtypes), designated as type I, type II, and type III . Figure p = 0.006) and type II CRC , compared to lower adherence (p = 0.054 for trend) and a strong negative association with type II CRC  .We further examined the relationship between dietary patterns and colorectal neoplasm risk, stratified by gut microbiota enterotypes. Our results revealed that higher adherence to a healthy dietary pattern was associated with a reduced risk of type I CRC  . Althougr trend) . The assTo investigate the potential role of specific food items in explaining the differential associations between a healthy dietary pattern and the risk of colorectal neoplasm, both overall and subclassified by gut microbiota enterotypes, we analyzed the top four contributing food items to the healthy dietary pattern: vegetables, fruits, milk, and yogurt . Our finp < 0.05 for trend). The association between the healthy dietary pattern score and colorectal neoplasm risk varied significantly by gut microbiota enterotypes . However, no significant heterogeneity was observed between the subgroups in the proximal colon.Furthermore, we investigated the relationship between the healthy dietary pattern and the risk of colorectal neoplasms, classified by lesion site, both overall and by subtype . Our anap < 0.001). Post-hoc Dunn’s test identified both significant and non-significant pairwise comparisons within groups . The results highlight the diverse microbial environments in colorectal tumors and may help to understand the heterogeneity in associations between the healthy dietary pattern and tumor subtypes. Further investigation will focus on comparing the microbial compositions and functions between type I and type III.In both CRC and CRA groups, the median values of Chao, ACE, Shannon, and Simpson indices demonstrated subtype-dependent trends, with significant differences observed among subtypes , and deoxycholic acid.Metabolomic profiling using PCoA revealed significant differences in overall metabolite profiles between type I and type III subtypes in both CRC and CRA groups . Subsequp < 0.05), with their corresponding matched differential metabolites being l-palmitoylcarnitine, tryptamine, and cholic acid. The purine metabolism pathway was significantly enriched in type III CRC (FDR adjusted p < 0.05), with its corresponding matched differential metabolites being adenosine, hypoxanthine, and inosine. Additionally, thiamine metabolism, sphingolipid metabolism, and nicotinate and nicotinamide metabolism pathways were significantly enriched in type III CRC , with their corresponding matched differential metabolites being thiamine, sphinganine, and nicotinic acid. Furthermore, the differential metabolites that matched with the pathways in sphingolipid metabolism, tryptophan metabolism, purine metabolism, and glycerophospholipid metabolism had a significant contribution to these pathways with impact values greater than zero.We conducted a pathway analysis on 96 differentially expressed metabolites between type I and type III CRC groups . The resp < 0.05), with their corresponding matched differential metabolites being tryptamine, tyramine, l-palmitoylcarnitine, 5-methyltetrahydrofolic acid, cholic acid, and LysoPC(16:0/0:0). The pathway of aminoacyl-tRNA biosynthesis was also significantly enriched in type I CRA (FDR adjusted p < 0.05), with its corresponding matched differential metabolites being l-phenylalanine and l-valine. The pathways of valine, leucine, and isoleucine degradation; valine, leucine, and isoleucine biosynthesis; and pantothenate and CoA biosynthesis were also significantly enriched in type I CRA , with their corresponding matched differential metabolites being l-valine. Additionally, the pathways of alpha-linolenic acid metabolism and biosynthesis of unsaturated fatty acids were significantly enriched in type I CRA , with their corresponding matched differential metabolites being linolenic acid. The pathway of purine metabolism was significantly enriched in type III CRA (FDR adjusted p < 0.05), with its corresponding matched differential metabolites being xanthosine and inosine. Furthermore, the pathways of folate biosynthesis, nicotinate and nicotinamide metabolism and pyrimidine metabolism were significantly enriched in type III CRA , with their corresponding matched differential metabolites being 7,8-dihydropteroic acid, nicotinic acid and uridine. Moreover, the differential metabolites that matched with the pathways of phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; alpha-linolenic acid metabolism; nicotinate and nicotinamide metabolism; sphingolipid metabolism; tryptophan metabolism; tyrosine metabolism; glycerophospholipid metabolism; pyrimidine metabolism; and purine metabolism demonstrated a significant contribution to these pathways, with impact values greater than zero.We performed a pathway analysis on 111 differentially expressed metabolites between type I and type III CRA groups . The resBacillus had significant negative correlations with allolithocholic acid, DG(22:6–2OH/0:0/20:0), dodecanedioic acid, glutaric acid, inosine, lithocholic acid, methylglutaric acid, and PA(20:4-OH/i-22:0); Family XIII AD3011 group had significant negative correlations with N,N,N-trimethyl-l-alanyl-l-proline betaine and N1-acetylspermidine; Lachnospiraceae.incertae sedis had a significant negative correlation with deoxycholylproline; and Odoribacter had a significant negative correlation with PA(20:5–3OH/10:0) . Actinomyces had a significant positive correlation with l-valine; Alistipes had a significant positive correlation with methylglutaric acid; Bacteroides had a significant positive correlation with tryptamine; and Eggerthella had a significant positive correlation with asparaginyl-valine . In the type III CRC subgroup, only Christensenellaceae R7 group had a significant negative correlation with ursodeoxycholic acid 3-sulfate (FDR adjusted p = 0.039). No significant correlations were observed between differential metabolites and differential bacterial genera in either subgroup of the CRA group.Bacteroides and Lachnoclostridium or Bacteroides and Faecalibacterium, respectively. Fruit consumption was the main contributor to this protective effect. We did not find any associations between a healthy dietary pattern and the risk of CRA or between a high-fat dietary pattern and the risk of colorectal neoplasms. These findings partially supported our hypothesis that different dietary patterns could differentially affect the risk of developing colorectal tumors in individuals with various gut microbiota enterotypes.In this study, we identified two distinct dietary patterns and three gut microbiota enterotypes among Han Chinese individuals with colorectal neoplasms or controls. We found that a healthy dietary pattern, characterized by high consumption of vegetables, fruits, milk, and yogurt, was associated with a reduced risk of CRC, especially in individuals with type I and type II gut microbiota enterotypes, which were dominated by We first discussed how dietary patterns influence the risk of colorectal neoplasms. We found that a healthy dietary pattern was inversely associated with CRC risk. This is consistent with previous studies that have reported protective effects of a prudent diet , a MeditBacteroides and Lachnoclostridium) and type II CRC , but not type III CRC (characterized by Prevotella 9). This suggests that enterotypes modify the diet-CRC risk association. A possible explanation is that enterotypes metabolize dietary components differently into compounds that affect CRC development. For example, Bacteroides, Lachnoclostridium and Faecalibacterium produce SCFAs from fiber fermentation [Prevotella 9 produces trimethylamine (TMA) from choline and carnitine [N-oxide (TMAO) in the liver and promotes inflammation and oxidative stress in the colon [We then explored whether gut microbiota mediates or modifies the relationship between dietary patterns and colorectal neoplasm risk. Gut microbiota, a complex ecosystem of microorganisms in the human gastrointestinal tract, affects various aspects of human health and disease . Gut micentation ,32, whicentation ,34. Prevarnitine ,36, whiche colon . Therefohe colon . Moreovehe colon . In addiBacteroides and Faecalibacterium, which can produce SCFAs and other metabolites with anticancer effects [Among the food items within a healthy dietary pattern, we found that fruit consumption was the main factor that lowered CRC risk in individuals with type I and type II gut microbiota enterotypes. This may be explained by several mechanisms. First, fruit is rich in antioxidants, such as vitamin C and polyphenols, which can scavenge reactive oxygen species and reduce oxidative stress in the gut . Second, effects .Escherichia shigella [Bifidobacterium [Bacteroides [Prevotella 9 [Alistipes [Odoribacter [To further elucidate the mechanisms underlying the differential associations of a healthy dietary pattern with CRC risk by gut microbiota enterotypes, we compared the diversity, composition, and function of gut microbiota between different tumor subtypes. We found that type I and type III CRC subtypes had distinct microbial community profiles, with significant differences in alpha-diversity and beta-diversity. Alpha-diversity denotes the richness and evenness of microbial species within a sample, whereas beta-diversity reflects the similarity or dissimilarity of microbial communities across samples. Lower alpha-diversity and higher beta-diversity indicate a dysbiosis between beneficial and harmful bacteria that may increase CRC risk by disrupting the balance of host-microbe interactions . We alsoshigella , Bifidobacterium , Bacteroteroides , Prevoteotella 9 , Alistiplistipes , and Odoribacter . These bribacter .Bacteroides and tryptamine, a metabolite derived from tryptophan that can induce apoptosis and inhibit proliferation of CRC cells [Bacillus and allolithocholic acid, a secondary bile acid that can promote inflammation and DNA damage in the colon [To investigate the functional differences of gut microbiota between type I and type III CRC subtypes, we performed metabolomic profiling using UHPLC-QTOFMS. We identified 50 metabolites that differed significantly in abundance between these subtypes. These metabolites were involved in several metabolic pathways that have been implicated in CRC pathogenesis, such as fatty acid degradation , tryptopRC cells . We alsohe colon . These cOur study has several strengths. First, we used ESEM to identify dietary patterns based on eight food items that are commonly consumed in China. ESEM is a novel technique that allows for cross-loadings between factors and indicators, providing a more realistic representation of dietary patterns than traditional methods such as PCA or FA . Second,However, our study also has some limitations. First, we did not perform sample size calculation prior to the study, nor did we calculate the Beta error for each analysis after the study. This was mainly due to the complexity and novelty of our study design and methods, which involved multiple comparisons, data-driven approaches, and high-throughput techniques. Moreover, there were no prior data available in the literature on the relationship between dietary patterns, gut microbiota, and colorectal neoplasms that we could use as references for sample size calculation. The sample size of our study was comparable to or larger than most of the published studies on gut microbiota and colorectal neoplasms before 2015, which rarely exceeded 100 cases. However, we acknowledge that our sample size may not be sufficient to detect small or moderate effects, especially for some subgroups or subtypes with low prevalence. Therefore, our results should be interpreted with caution and validated in larger and more diverse cohorts. Future studies may also consider using simulation methods or Bayesian approaches to estimate sample size or statistical power for complex and exploratory studies similar to ours. Second, we only assessed eight food items in our food frequency questionnaire, which may not capture the full range of dietary intake and diversity. This may limit the accuracy and generalizability of our results. Therefore, our results should be interpreted with caution and validated by future studies with more comprehensive dietary assessments. Moreover, we did not collect information on the types and quantities of fruit consumed by participants, which may affect their impact on gut microbiota and CRC risk. Future studies should also investigate how different types and amounts of fruit may modulate gut microbiota and CRC risk in relation to individual and environmental factors. Third, our study population was limited to Han Chinese and may not be representative of other populations with different genetic and environmental backgrounds. Previous studies have shown that gut microbiota composition and function can vary across different ethnic groups and geographic regions ,60. TherIn conclusion, our study suggests that a healthy dietary pattern, rich in fruits, vegetables, milk, and yogurt, is associated with a decreased risk of CRC, particularly in individuals with type I and type II gut microbiota enterotypes. These enterotypes are characterized by distinct microbial compositions and functions that may modulate the effects of diet on CRC development. Fruit consumption was the main contributor to this protective effect. Our findings provide novel insights into the relationship between dietary patterns, gut microbiota, and colorectal neoplasm risk, and may have implications for future prevention strategies. However, further studies with larger sample sizes, more comprehensive dietary assessments, more advanced gut microbiota and metabolomic analyses, and longitudinal designs are needed to confirm and extend our results.