Background: Adolescent cannabis use (CU) is associated with adverse health outcomes and may be increasing in response to changing cannabis laws. Recent imaging studies have identified differences in brain activity between adult CU and controls that are more prominent in early onset users. Whether these differences are present in adolescent CU and relate to age/developmental stage, sex, or cannabis exposure is unknown. Methods: A systematic review and subsequent effect-size seed-based d mapping (SDM) meta-analysis were conducted to examine differences in blood-oxygen-level-dependent (BOLD) response during fMRI studies between CU and non-using typically developing (TD) youth. Supplemental analyses investigated differences in BOLD signal in CU and TD youth as a function of sex, psychiatric comorbidity, and the dose and severity of cannabis exposure. Results: From 1371 citations, 45 fMRI studies were identified for inclusion in the SDM meta-analysis. These studies compared BOLD response contrasts in 1216 CU and 1486 non-using TD participants. In primary meta-analyses stratified by cognitive paradigms, CU (compared to TD) youth showed greater activation in the rostral medial prefrontal cortex (rmPFC) and decreased activation in the dorsal mPFC (dmPFC) and dorsal anterior cingulate cortex (dACC) during executive control and social cognition/emotion processing, respectively. In meta-regression analyses and subgroup meta-analyses, sex, cannabis use disorder (CUD) severity, and psychiatric comorbidity were correlated with brain activation differences between CU and TD youth in mPFC and insular cortical regions. Activation differences in the caudate, thalamus, insula, dmPFC/dACC, and precentral and postcentral gyri varied as a function of the length of abstinence. Conclusions: Using an SDM meta-analytic approach, this report identified differences in neuronal response between CU and TD youth during executive control, emotion processing, and reward processing in cortical and subcortical brain regions that varied as a function of sex, CUD severity, psychiatric comorbidity, and length of abstinence. Whether aberrant brain function in CU youth is attributable to common predispositional factors, cannabis-induced neuroadaptive changes, or both warrants further investigation. Cannabis is the most commonly used illicit psychoactive drug by adolescents and consequently is the main drug that youth residing in the United States (U.S.) seek substance use treatment for . CannabiThe clinical relevance of increased cannabis availability and use among youth is related to the purported effects of such use on cognitive and neurobehavioral development . There eIn recent years, there has been a notable increase in reports that examine brain physiology through functional neuroimaging in adolescent cannabis users, examining the relationships between clinical symptoms associated with cannabis use and abnormalities in brain functions implicated in cognitive domains such as decision making, executive control and emotional and motivational processes. For instance, task-based functional magnetic resonance imaging (fMRI) studies showed alterations across distributed brain regions during tasks involving cognitive/executive control, memory and learning, reward processing, cannabis cue-reactivity, and emotional processing ,16. In rSuch quantitative assessments of regional activations that are either task-specific or consistently appear across tasks can be crucial in facilitating the early identification of individuals at risk for poor clinical outcomes and their associated health outcomes, and consequently in promoting the development of effective treatment and prevention strategies. Identifying neural targets of addiction treatment in youth can be crucial in refining existing treatments and informing the future development of diagnostic and prognostic biomarkers and brain circuit-targeted interventions . AccordiA systematic review of peer-reviewed fMRI studies was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and methods . A subseWe searched for studies indexed in the online databases PubMed/Medline, Cochrane, Embase, and Web Science from January 1990 to November 2019 using the following search terms: “Adolescent”[Mesh] OR “adolescent” OR “young adult” OR “youth” OR “teenager” AND “Neuroimaging”[Mesh] OR “Magnetic Resonance Imaging”[Mesh] OR “MRI” OR “functional MRI” OR “fMRI” OR “blood-oxygen-level-dependent” OR “BOLD” OR “brain activation” OR “brain activity” OR “brain function” OR “brain circuit” OR “neural” AND “Cannabis-Related Disorders”[Mesh] OR “cannabis use” OR “marijuana use” OR “cannabis abuse” OR “marijuana abuse” OR “cannabis dependenc*[tiab]” OR “marijuana dependenc*[tiab]” OR “cannabis addiction” OR “marijuana addiction” OR “cannabis use disorder” OR “marijuana use disorder” OR “cannabis*[tiab]” OR “marijuana*[tiab]” OR “marihuana*[tiab]” OR “Δ-9-tetra-hydrocannabidol” OR “THC”. Broad search terms were used to minimize the likelihood of the search not identifying all relevant studies. In addition, we manually scanned the references of included studies and cross-referenced relevant original research, reviews, and meta-analyses to identify studies that may have been missed by the search. Additional articles missed were also identified through subsequent search using parallel search terms but restricting the date window from December 2019 to January 2022.Studies were selected if they met the following criteria: (1) included ≥ 10 participants; (2) participants were between the ages of 12 and 21 years; (3) used diagnostic criteria for cannabis use disorder (CUD) as specified by the DSM (DSM-IV or DSM-5) or described frequency or quantity of cannabis use  in study participants; (4) used whole-brain fMRI and voxel-wise analyses; (5) reported within- or between-subject contrasts in BOLD signal across cannabis use (CU) and non-using typically developing (TD) control youth, or brain-behavior correlations between BOLD response and cannabis-related variables in a combined sample of CU and TD youth; (6) reported coordinates from the above whole-brain analyses in standardized anatomic space  space) and (7) provided information about the inclusion/exclusion (I/E) criteria, clinical characteristics, and demographics of the study sample.Articles that studied adolescent CU within the context of co-occurring psychiatric disorders were included if studies also included active controls that did not use cannabis. Studies with young adult samples were included if they the mean age of participants was <22 years.Articles were extracted, organized, and reviewed using Covidence software (Covidence.org). Initial independent title and abstract evaluations were done to identify potential articles of interest by two authors (A.A. and K.K.). Data extraction accuracy showed high correspondence/agreement (>80%) between reviewers. Abstract evaluation was followed by an independent full-text review of articles. Group discussion was used to resolve uncertainties about inclusion criteria and finalize the list of articles included in the qualitative review and SDM meta-analysis.To facilitate exploration and interpretation of results, relevant design features and sample characteristics from each study were extracted and used to assess study quality and characterize the degree of heterogeneity across studies.To create the final list of studies included in the meta-analysis, we took a three-step approach: Studies identified with the above search that reported coordinates of activation differences between CU and TD control groups from whole-brain analyses in Talairach or MNI space were identified and marked for inclusion in the SDM meta-analysis. For whole-brain fMRI studies that provided insufficient information on coordinates, corresponding authors were contacted via email to determine if unthresholded statistical maps or coordinates and additional study details could be provided. Additionally, we searched NeuroVault (neurovault.org) using select search terms (from above) to try to find unthresholded statistical maps from the relevant studies. These approaches yielded five additional studies, with three providing unthresholded statistical maps. These maps plus peak coordinates from published data and from author correspondence were used for the meta-analysis.http://www.sdmproject.com; accessed on 21 March 2021). SDM meta-analysis is a statistical technique for meta-analyzing neuroimaging data that recreates voxel-level maps of effect sizes and their variance based upon T-maps [SDM meta-analysis procedures: All meta-analyses were carried out using the anisotropic effect-size signed differential mapping permuting subject images (SDM-PSI) software, v.6.21  of their effect was reported as positive or negative. In addition, a table was made with the study identifier (main author), the t-score used to determine significance, and the sample size for the experimental and control groups. If a study reported a statistically significant corrected p-value, but provided insufficient information to transform the corrected p-value into a t-score, a t-score of 3.1 was used, giving a conservative estimate for this effect. Studies that had no significant peaks were also included. Each experimental study was categorized into one of eleven domains based upon the cognitive paradigm(s) administered during the fMRI scan session  and cross-checked for concordance. Coded anatomical foci were then double screened for accuracy. If the studies reported coordinates in either Talairach or MNI coordinates, a text file containing the reported coordinates and the t-score associated with those coordinates was created. If a study reported multiple experiments, the results were still reported in the same text file. sion see . To prepsion see . A numbep-value < 0.005 consistent with other SDM meta-analyses [Meta-analysis procedures: The main analysis was conducted in four steps: (1) Meta-analysis across whole-brain fMRI studies: SDM meta-analyses were conducted on the statistical parametric maps showing group-level effects for each study to examine for unadjusted differences between youth with CU and matched TD youth. This was first done across all whole-brain fMRI studies (45 studies) to characterize whether “general” cannabis use effects could be observed between CU and TD youth across cognitive domains. (2) Cognitive Domain-specific Meta-analyses: Domain-specific subgroup meta-analyses were then conducted. This was done by stratifying each experimental study based upon the main cognitive domain probed and running separate SDM analyses using subgroupings of studies based upon these domains. Our main domain-specific analyses focused on executive control (16 studies), social cognition/emotion processing (9 studies), and reward processing (8 studies) domains. Exploratory analyses investigating other domain/subdomains were also conducted but were underpowered and should be interpreted cautiously. (3) Primary Meta-regression analyses: Next, linear meta-regressions were conducted focusing on age, sex, and cannabis use features, using a priori defined variables from each study as dependent variables to assess whether variation in these variables contributed to variance in the magnitude of BOLD signal differences observed between CU and TD youth across studies. Meta-regression analyses using mean age (years) and proportion of females from each study were conducted with the goal of determining whether BOLD signal differences in CU vs. TD youth were age-related and/or sex-dependent. As some studies have shown differences in health outcomes and brain-behavior relationships in CU as a function of cannabis dose, diagnostic status, and severity, we conducted meta-regressions focusing on cannabis-related variables including duration of cannabis use (years), the proportion of CU participants meeting CUD criteria, and mean CUDIT scores of CU participants. (4) Reliability and sensitivity tests: Lastly, we conducted a series of reliability and sensitivity tests described below. All models were thresholded using an uncorrected analyses .Supplemental Subgroup Meta-analyses and Meta-regressions: Supplemental meta-analyses were conducted to examine the effects of length of abstinence on brain-behavior associations. This was done by stratifying studies into groups based upon the abstinence criteria used  and conducting subgroup meta-analyses. Sensitivity tests examining the impact of design-related factors on results were conducted focusing on different analytic approaches , and population characteristics .Reliability Analysis: A jackknife analysis was performed to establish reliability of our meta-analytic results. This was done for the primary SDM meta-analyses focused on all studies and on specific cognitive domains by removing a single experimental study and repeating the analysis in sequence.Publication Bias Analysis: To tests for publication bias we created and interpreted funnel plots visualizing the effect-by-variance for the results of our primary meta-analysis, and used SDM’s Bias Test tool to quantitatively assess bias.The search identified 1371 citations with 959 records excluded following title and abstract screen. Four hundred and twelve citations underwent full text review. Out of these citations, 45 fMRI studies examining whole-brain BOLD signal differences met all inclusion criteria and were included in the qualitative and quantitative analyses. p = 0.82) and proportion of participants who were of female sex  did not significantly differ between CU and TD groups. One study [Forty-five eligible whole-brain fMRI studies were included in the SDM meta-analysis see . This inne study  examinedne study  comparedne study ,31,38 sop < 0.005, the primary SDM meta-analysis identified no regions showing significant BOLD signal differences between youth with CU compared to TD youth. Rerunning the meta-analysis using a more lenient threshold of p < 0.025, resulted in the identification of a small cluster in the ventral medial  prefrontal cortex  which showed greater activation in CU compared to TD youth. In meta-analyses stratified by cognitive domain, domain-specific differences between CU and TD youth were observed during executive control and social/emotional but not reward processing domains (p = 0.004) when compared to TD youth. Given our a priori interest in executive control we further examined this result by rerunning the executive control meta-analysis at a more lenient threshold of p < 0.025 and conducting sensitivity tests. This meta-analysis identified the rmPFC cluster which was significantly larger  extending to the right ventral mPFC and to the left rostral and ventral mPFC and ACC. It also identified a second cluster centered in the right primary somatosensory cortex extending to the right supramarginal gyrus (SMG) and insula . Sensitivity and reliability tests on the main executive control meta-analysis and results from our executive control meta-regression analysis (see p = 0.00097). This dmPFC/dACC cluster remained significant across reliability tests, and when analyses were restricted to only the emotion processing domain. In the primary reward domain meta-analysis, no differences were observed between CU and TD youth in brain activation during reward processing. Restricting the reward domain meta-analysis to studies reporting reward feedback contrasts (6 studies) also showed no CU vs. TD group differences. Reliability tests of the main reward domain meta-analysis showed little variance in the null result but sensitivity tests related to psychiatric comorbidity and CUD diagnostic status both yielded significant effects (see p = 0.0035) for which CU youth showed greater brain activity compared to TD controls.Primary meta-analytic results are summarized in  domains . During ysis see  showed tects see . Explorap = 0.002).Age-related and Sex-related BOLD Effects: Results from the SDM meta-regression analyses across all studies examining the effect of mean age at the time of scan and proportion of female participants on BOLD differences between CU and TD youth did not yield statistically significant results. In meta-regressions stratified by cognitive domain, domain-specific effects of sex but not age were observed. Regarding age effects, domain-specific meta-regressions focusing on mean age of participants for executive control, social cognition/emotion processing, and reward domains each produced null findings. Regarding sex effects, domain-specific meta-regressions showed activation differences related to sex distribution during executive control but not social cognition/emotion processing or reward domains . Specifip = 0.002) and a relative decrease in BOLD response in CU vs. TD youth in the right insula during reward processing tasks . In meta-regressions focused on CUD severity, increasing CUDIT scores in CU participants were associated with a relative increase in BOLD response in CU vs. TD youth in the right insula during social cognition and emotion processing tasks . None of the other assessed cannabis-related variables were significantly associated with BOLD signal differences between CU and TD youth in meta-regression analyses.Cannabis-related BOLD Effects: Results from cannabis-related meta-regressions showed significant effects from variables indexing cannabis problem severity but not dose . In metaAbstinence-focused subgroup meta-analyses examined BOLD effects related to the length of abstinence at the time of scan assessed across tasks see . Resultsp = 0.0006). In contrast a subgroup meta-analysis focusing on the nine studies that examined brain-behavior associations between BOLD response and cannabis use in combined samples of CU and TD youth showed that CU youth (compared to TD youth) had decreased brain activity in a bilateral dmPFC cluster . To examine the effects of psychiatric comorbidity on brain outcomes, we conducted a subgroup meta-analysis across all paradigms/domains focusing on twenty-three studies that used standardized psychiatric interviews to assess for psychiatric conditions in participants and applied strict I/E criteria related to comorbidity, resulting in a subgroup of studies that had CU and TD samples with limited/very low levels of psychiatric comorbidity. The results of this subgroup meta-analysis paralleled our main meta-analytic results across paradigms/domains yielding no significant findings at the p < 0.005 threshold. Rerunning this low psychiatric comorbidity subgroup meta-analysis at a more lenient threshold of p < 0.025 resulted in the identification of one small cluster in the left orbitofrontal cortex which showed greater activation in CU compared to TD youth. Domain-specific low psychiatric comorbidity subgroup meta-analyses showed variable effects that differed across domains. Restricting the executive control meta-analysis to studies with low levels of psychiatric comorbidity (15 studies) negligibly altered the results, which continued to show a cluster in the right rmPFC. When the reward domain meta-analysis was restricted to studies with low levels of psychiatric comorbidity (4 studies), one significant cluster emerged that was localized to the right insula , and showed greater BOLD response in CU, relative to TD youth. When the social cognition/emotion processing meta-analysis was restricted to studies with low levels of psychiatric comorbidity (4 studies) no significant clusters were identified.Our sensitivity tests investigated variance in brain outcomes related to study design and two relevant sample characteristics—psychiatric comorbidity and CUD diagnostic status. Focusing on study design and specifically different analytic approaches: A subgroup meta-analysis of the thirty-five studies that applied a CU vs. TD group comparison approach showed that CU youth (compared to TD youth) had increased brain activity in a cluster localized to the left IFG and anterior insula  and a second smaller cluster in the left inferior parietal lobule . Results were unchanged when the meta-analysis was rerun using an expanded subgroup that also included studies with samples where >70% of CU participants met CUD criteria.Based upon results from our meta-regression analyses showing significant BOLD differences related to the proportion of CUD, we chose to conduct our planned supplemental subgroup meta-analysis on the five fMRI studies where all CU participants met criteria for CUD, even though this analysis was underpowered. Results of this subgroup meta-analysis  showed tp = 0.0002) that showed greater BOLD response in CU compared to TD youth. This activation cluster directly overlapped with the vmPFC cluster identified in the primary meta-analysis across studies and the rmPFC/vmPFC clusters identified in the main executive control domain meta-analysis and meta-regression analysis, respectively. The subgroup meta-analysis restricted to the twelve other executive control fMRI studies that did not provide diagnostic information on CUD yielded no significant clusters. Similar subgroup meta-analyses related to CUD diagnostic status for social cognition/emotion processing and reward domains were not conducted as zero and one social cognition/emotion processing and reward processing fMRI studies, respectively, had samples in which the majority of CU participants met CUD criteria.Focusing on fMRI studies that used executive control paradigms, three studies had samples in which all CU participants met DSM-IV/5 criteria for CUD. Twelve of the other thirteen executive control fMRI studies did not provide detailed information on CUD diagnostic status. Running executive control domain meta-analyses in these subgroups revealed significant activation differences between CU and TD youth in the CUD subgroup but not the other subgroup. The executive control meta-analysis restricted to the CUD subgroup  identified a large cluster centered in the right rmPFC extending into bilateral ventral and dorsal mPFC regions  when studies were sequentially removed from each analysis. Regarding the reward domain meta-analysis, no significant clusters were identified with sequential removal of each study during the jackknife procedure. Jackknife analyses of the executive control and social cognition/emotion processing domain results were largely preserved through most study combinations  youth, in right rmPFC and vmPFC during executive control tasks and decreased and increased BOLD response in the right insula during reward and social cognition/emotion processing tasks, respectively. Psychiatric comorbidity contributed some variance to brain outcomes with these effects varying across domains, suggesting complicated cannabis use-by-psychiatric comorbidity relationships. Supplemental analyses identified brain activation differences between CU and TD youth in the right caudate, thalamus, insula, precentral and postcentral gyri, and bilateral mPFC regions that varied as a function of the length of abstinence and showed some anatomic overlap and some regional divergence with CUD severity results. Collectively, these findings indicate that differences in brain activity between cannabis using and non-using adolescents in medial prefrontal, insula, and other cortical and subcortical regions implicated in control, social cognition/emotion processing, and motivation are present and vary as a function of sex, CUD severity, psychiatric comorbidity, and length of abstinence. Further, our results provide preliminary evidence that brain activation alterations in adolescent cannabis users with greater cannabis severity or CUD diagnoses are more consistent, more broadly distributed, and may reflect multidomain impairments across executive control, social cognition/emotion processing, and reward processing domains in these youth. Implications of these findings are discussed below.The primary meta-analytic results from our study yielded significant group differences in the activation of regions within the medial PFC  across all tasks, as well as individually in executive control and emotional processing domains. Specifically, across all tasks, higher vmPFC activation were observed in CU, compared to TD. When each cognitive domain was examined separately, in CU compared to TD, higher activations were observed in the right rmPFC during executive control tasks and lower activations were observed in bilateral dmPFC and dACC during social cognition and emotion processing tasks.Our executive control domain meta-analysis identified cannabis-related activation differences in mPFC regions, consistent with prior studies in both adolescent ,18,19,381R) expression and are implicated in endocannabinoid (eCB) signaling [Another possible explanation for both the executive control rmPFC finding and our other findings is that chronic cannabis exposure results in maladaptive neuroplasticity in these brain regions or that constitutional abnormalities in vulnerable youth may be further exacerbated by chronic cannabis exposure during adolescence, a sensitive developmental period. Related to this hypothesis, the spatial distribution of activation findings from the present study appear to correspond with brain regions known to have increased cannabinoid receptor 1 . It is One of the main objectives of this study was to use meta-analysis and meta-regression approaches to investigate the influence of age, sex, psychiatric comorbidity, and cannabis use features on BOLD differences between CU and TD youth. Partially consistent with our hypotheses, our results showed distinct activation differences related to sex, CUD severity, and length of abstinence but no effect of age, and mixed findings with regard to the effects of psychiatric comorbidity on brain function. Null findings for age effects on BOLD response may be due to design and analysis differences and limited variation in mean age of study samples, with early adolescent cannabis users (ages 12–13 years) being underrepresented among studies.p < 0.025. Additionally, domain-specific effects were seen for executive control, social cognition, and reward domains when psychiatric comorbidity was controlled for in the analyses. Specifically, the executive control meta-analysis showed no change in the main rmPFC cluster, and the dmPFC/dACC cluster identified in the main social cognition/emotion processing meta-analysis was no longer present when these analyses were rerun restricting the meta-analytic sample to studies with very low psychiatric comorbidity. Additionally, a difference in right insula activation between CU and TD youth during reward feedback was identified that was not present in the main reward domain meta-analysis when this analysis was rerun in a subgroup of studies with very low psychiatric comorbidity. These findings suggest that the activation differences between CU and TD youth observed in our meta-analytic report cannot be fully explained by the presence of comorbid psychiatric conditions. Rather, they point to psychiatric comorbidity playing a contributory role to variability in BOLD signal in CU and TD youth, in combination with other factors such as cannabis exposure, sex, and unmeasured confounders. Taken together, our results provide early evidence for cannabis use-by-psychiatric comorbidity interaction effects on brain outcomes during executive control, social cognition, and reward processing in CU youth, and suggest that these interaction effects may manifest as different BOLD response patterns in CU youth with and without psychiatric comorbidities. It is important to note that our approach to measuring psychiatric comorbidity effects in this study was non-specific. Thus, the comorbidity results presented here may better reflect the influence of ‘general’ psychopathology as opposed to specific psychiatric conditions on BOLD differences between CU and TD youth. These associations warrant further investigation, and should be examined in relation to both ‘general’ psychopathology and specific psychiatric symptom clusters and disorders in future studies.Regarding our psychiatric comorbidity effects, null results on the main effect of psychiatric comorbidity were not surprising given that our main meta-analytic results across studies (collapsed across domains) were also not significant when conservative thresholding was applied. Notably, the meta-analysis across studies with CU and TD groups matched for low psychiatric comorbidity did show greater activation in CU compared to TD youth in an orbitofrontal cortical region commonly reported in CU studies ,23, when1R in women that correlate with cognitive impairments and, in some cases, are directionally opposite from those found in men [1R expression in the insular cortex, which has been shown across species. These eCB differences emerge during puberty, and may render cannabis using girls more vulnerable to adverse health outcomes related to cannabis exposure [Regarding sex effects, we identified a left insula cluster that varied between CU and TD youth as a function of sex distribution showing decreased BOLD response in CU relative to TD youth in executive control fMRI studies that had a higher proportion of female participants. This suggests that sex may moderate the relationships between cannabis exposure, executive function, and brain activity during adolescence. Previous studies have shown sex differences in neuropsychological functioning in adolescent and young adult cannabis users . Our resd in men ,96,97,98d in men , includiexposure . In suppexposure . Our sexexposure . Additioexposure . These sexposure , which cRegarding our cannabis problem severity effects, we identified rostral and ventral mPFC and right insula clusters that varied between CU and TD youth as a function of CUD severity and affected cognitive domain . During executive control, CU (compared to TD) showed increased activation of the right rmPFC and vmPFC as a function of increasing proportion of CUD diagnoses in the sample. This parallels our main findings and suggests that impaired over-engagement of the rmPFC/vmPFC is associated with greater CUD severity . ActivatIn our abstinence subgroup meta-analyses, we found evidence for distinct patterns of activation differences across domains between CU and TD youth that varied as a function of length of abstinence. Notably, we observed what appeared to be a subcortical-to-cortical gradient in activation differences across abstinence periods with chronic CU (relative to TD) exhibiting a shift from predominantly subcortical circuitry alterations during ad lib use to predominantly cortical circuitry alterations during longer periods of abstinence . These findings converge with previous studies showing state-level differences in cortical and subcortical brain function as a function of ‘sated’ versus ‘abstinent’ states and time since last use in adolescent and adult cannabis users ,78,107. The existing literature on medial PFC regions in adolescents suggests that these are regions that undergo protracted development from childhood to late adolescence, making adolescence a critical period of neuroplasticity ,81,84. AThis meta-analytic report has a number of important limitations. As our main aim was to conduct meta-analyses and meta-regressions, our study was reliant on the study methodology, analytic techniques, and measurements used in each of the included fMRI studies. Many of these studies had small sample sizes and there was significant heterogeneity in design, behavioral measurements, and handling of psychiatric comorbidity and co-use of alcohol and tobacco which likely contributed variance to study outcomes. Consequently, many of our planned subgroup and meta-regression analyses may have been underpowered to detect subtle differences in brain activation with small effect sizes between cannabis users and non-users. Our attempts to address these issues by contacting authors and searching imaging repositories were limitedly successful but did yield an additional five studies that were included in our analysis. Changes in data management, reporting, and data sharing practices  are needed to advance meta-analytic inquiry in the field of neuroimaging . In paraIn our view, this is the most comprehensive quantitative meta-analysis in adolescents with cannabis use completed to date. We conducted a series of comparisons, with our main meta-analytic results showing alterations in the activation of medial PFC regions, suggesting alterations in social cognitions/emotion processing and executive control but not reward processing in adolescent cannabis users: in other words—not the rewarding properties of cannabis per se but rather deficits in navigating social environment might play predisposing and/or perpetuating roles in cannabis use in adolescents. These main findings suggest that interventions providing focal training in social skills and emotion regulation in adolescents may be useful for cannabis prevention, early intervention, and might target neural alterations underlying social processing deficits in cannabis using youth populations. Results from our meta-regression analyses showed differences in insula activation that were sex-dependent and alterations in brain activation of adolescent cannabis users in the rmPFC/vmPFC and insula and in the caudate, thalamus, insula, dmPFC/dACC, and precentral and postcentral gyri that varied as a function of CUD severity and length of abstinence, respectively. Lastly, our sensitivity tests showed evidence of complex cannabis use-by-psychiatric comorbidity interaction effects on brain outcomes across domains in CU youth. These findings help to provide a more coherent picture of brain activation differences within and across domains in cannabis using adolescents and how sex, psychiatric comorbidity, and trait-level and state-level cannabis use features influence the BOLD signal during task-based fMRI in this population. Notably, activation differences were more prominent and distributed across mPFC and other brain regions and cognitive domains in CUD samples. Our meta-regression results lend additional support to the triadic model of addiction vulnerability—specifically showing that greater CUD severity among adolescent cannabis users was associated with a pattern brain activity reflecting impaired “top-down” cognitive control and altered “bottom-up” signaling with decreased reward responsivity and increased emotional reactivity. These findings give a clear path forward for the field and emphasize the need for both large-scale prospective longitudinal studies with standardized substance use measures (such as the ABCD study) and smaller well-controlled studies with serial fMRI scans in specific subgroups of cannabis users  at different stages of abstinence and recovery to characterize different aspects of cannabis-brain function relationships across development.


Studies have suggested that patients with thyroid dysfunction may have an increased risk of developing Parkinson’s disease (PD). However, the results from existing studies are inconsistent. Therefore, we aimed to investigate the association of hypothyroidism and hyperthyroidism with risk of PD using the method of systematic review and meta-analysis.Potentially eligible studies were identified from Medline and EMBASE databases from inception to December 2021 using search strategy that comprised of terms for “Thyroid” and “Parkinson’s Disease”. Eligible cohort study must consist of one cohort of patients with hypothyroidism/hyperthyroidism and another cohort of individuals without hypothyroidism/hyperthyroidism. Then, the study must report effect estimates with 95% confidence intervals (95% CIs) comparing incident PD between the groups. Eligible case-control studies must include cases with PD and controls without PD. Then, the study must explore their history of hypothyroidism/hyperthyroidism. Odds ratio (OR) with 95% CIs of the association between presence of hypothyroidism/hyperthyroidism and PD must be reported. Point estimates with standard errors were retrieved from each study and were combined together using the generic inverse variance method.2 66.9%) and patients with hyperthyroidism . Funnel plots for both meta-analyses were fairly symmetric, which did not indicate presence of publication bias.A total of 3,147 articles were identified. After two rounds of independent review by three investigators, 3 cohort studies and 6 case-control studies met the eligibility criteria and were included into the meta-analysis. Pooled analysis showed an increased likelihood of PD in both patients with hypothyroidism (pooled OR 1.56; 95%CI, 1.38 – 1.77; with moderate heterogeneity, IThis systematic review and meta-analysis found a significant association of both hypothyroidism and hyperthyroidism with an increased risk of PD. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders that causes motor dysfunction including bradykinesia, tremor, rigidity and postural instability as well as neurocognitive impairment and depression . This coThyroid hormone plays a crucial role in not only regulating cellular metabolism and multiple organ systems, but also controlling neurodevelopment as well as modulating neurotransmission –7. It isIt is however still unclear whether the presence of thyroid dysfunction can increase the risk of any specific neurodegenerative disorder . In factThree investigators  independently searched records indexed in Medline and Embase from inception to December 2021. Search terms were obtained from terms related to “Hypothyroidism”, “Hyperthyroidism” and “Parkinson’s disease”. The detailed search strategy is shown in the Eligible study must be either cohort or case-control study. Eligible cohort study must consist of one cohort of patients with hypothyroidism/hyperthyroidism and another cohort of individuals without hypothyroidism/hyperthyroidism. Then, the study must report effect estimates  with 95% confidence intervals (95% CIs) of incident PD between individuals with hypothyroidism/hyperthyroidism versus comparators without hypothyroidism/hyperthyroidism. Eligible case-control studies must consist of cases with PD and controls without PD. Then, the study must explore their history of hypothyroidism/hyperthyroidism. Odds ratio (OR) with 95% CIs of the association between presence of hypothyroidism/hyperthyroidism and PD or percentage of participants with hypothyroidism/hyperthyroidism in each group must be reported.Three investigators  independently reviewed the eligibility of the retrieved articles. Different opinion was resolved by discussion with the senior investigators . Two investigators evaluated the quality of each study (N.C. and T.R.) using the Newcastle-Ottawa quality assessment scale for cohort study and case-control study .A standardized collection form was used for data extraction of the following information: last name of the first author, country of the study, study design, publication year, main findings, number of participants, recruitment of participants, diagnosis of hypothyroidism/hyperthyroidism, diagnosis of PD, follow-up duration (for cohort studies), average age of participants, percentage of female participants, comorbidities of participants and variables adjusted in multivariate analysis.We performed two separate meta-analyses including 1.) the meta-analysis of the association between hypothyroidism and risk of PD and 2.) the meta-analysis of the association between hyperthyroidism and risk of PD. Effect estimates with standard errors were extracted from each eligible study. Extracted effect estimates were combined together using the generic inverse variance method as described by DerSimonian and Laird . Random-2 statistic which determines the proportion of the total variation across studies that is secondary to heterogeneity rather than coincidence. A value of I2 of 0 – 25% represents insignificant heterogeneity, 26–50% low heterogeneity, 51–75% moderate heterogeneity and >75% high heterogeneity  and the two cohort studies , as shown in The meta-analysis found a significant association between hypothyroidism and risk of PD with the pooled odds ratio of 1.56 . This meta-analysis had moderate statistical heterogeneity with I2 of 0.0%. Subgroup analysis by study design revealed significant association between hyperthyroidism and increased risk of PD among the five case control studies  and the two cohort studies , as shown in The meta-analysis found a significant association between hyperthyroidism and risk of PD with the pooled odds ratio of 1.57 . This meta-analysis had insignificant statistical heterogeneity with IFunnel plots were used for assessment for publication bias of the meta-analysis. The funnel plots for both meta-analyses of hypothyroidism and risk of PD Figure 4This study is the first systematic review and meta-analysis that summarizes all availab e data from cohort and case-control studies that investigated the association between thyroid dysfunction and risk of PD. The pooled analysis revealed that both hypothyroidism and hyperthyroidism were associated with approximately 1.6 times increased risk of PD.The underlying mechanism of the observed association between thyroid dysfunction and risk of PD is largely undetermined, but there are some possible explanations. First, thyroid hormone is shown to be a key factor for the induction and function of dopaminergic neurons by inducing the expression of the nuclear receptor-related 1 protein . This prOn the other hand, thyroid hormone excess can lead to increased cellular metabolism resulting in a high burden of oxidative stress from the mitochondria in multiple tissues including the neuron , 28. ThiIn addition, thyroid dysfunction can result in myopathy, decreased muscle strength and impaired physical performance as thyroid hormone signaling is essential for skeletal muscle development and function , 30. RedRASD2, WSB1, MAPT, GIRK2, LRRK2 and genes in the NADPH oxidase/dual oxidase family to affect the risk for both thyroid disease and PD (It is also probable that thyroid disease and PD may share a common genetic predisposition since studies have identified variations of several genes including e and PD .Besides the direct effects of thyroid hormone on dopaminergic neuron and skeletal muscle function and shared genetic risk, it is worth noting that the observed association may be driven by autoimmunity as recent evidence suggests that autoimmune response and neuroinflammation may play a role in the pathogenesis of Parkinson’s disease . This suThe results of this study may have some clinical and research implications as they suggest that thyroid hormone signaling plays an essential role in pathogenesis of PD and that overt thyroid dysfunction could be a modifiable risk factor for PD. Further studies should be conducted to determine how duration and degree of severity of thyroid dysfunction affect the risk and severity of PD and to investigate whether treatment to achieve euthyroid status can reverse the risk and severity of PD. It is also worth investigating if subclinical thyroid dysfunction is associated with PD and whether screening for thyroid dysfunction and treatment for subclinical thyroid dysfunction should be recommended in patients with newly diagnosed PD.This meta-analysis carries some limitations that should be acknowledged. First, in both analyses of hypothyroidism and hyperthyroidism and risk of PD, all except one case-control studies reported no significant association between exposure and outcome. This is likely because most of the case-control studies are of limited quality based on Newcastle-Ottawa score of less than seven , 20, 36,This systematic review and meta-analysis found a significant association of both hypothyroidism and hyperthyroidism with an increased risk of PD.The original contributions presented in the study are included in the article/All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.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.


Meta‐analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations  and variations in study quality due to study design and execution . The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases  in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias‐adjusted random effects model to an already published meta‐analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews. Parameters in statistical models are fixed quantities that we are uncertain about, but want to estimate using statistical inference.The aim of this article is to propose a way to consider uncertainty, arising from ambiguity or ignorance about bias terms, by modeling bias in a bias‐adjusted random effects model with imprecision, and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, resulting in a set of posterior distributions for the quantity of interest. In this case, the set of posteriors are the result of the set of bias terms. Hence, the suggested robust Bayesian bias analysis characterizes uncertainty in the overall effect by bounded probability,We use a bias‐adjusted random effects model with an additive bias  associated with each study specific effect  in study i  and rij denotes the number of patients who have had a positive response respectively.Consider a meta‐analysis for binary outcome of K studies where rij can be modeled with a binomial distribution as follows:pij can be transformed using a link function  as follows:βi is the log‐odds ratio (“log‐OR”) for the control group and δi is the specific treatment effect on the “log‐OR” scale in study i. The specific treatment effect being evaluated in the ith study, δi, can be expressed as the sum of the overall effect, μ, the study specific random effect, θi and study specific error, ϕi:The number of patients θi and ϕi can be described byσθ2 represents between study variability and σϕi2 is the variance of the bias (study specific error). Here, Equation ,ϕi∼Norma2.2qi=σθ2σθ2+σϕi2 is a bias term that can be interpreted as the quality of study iqi=1 represents the situation where there is no bias whereas qi=0.5 implies equal between study variability and variance of the bias term for study i. For example, a high quality study is expected to have a small error in relation to the between study variability and should therefore have a relatively high study quality, for example, q=0.95 . A low quality study should accordingly have a low quality, for example, q=0.1 . The study qualities influence the relative contribution of each study to the likelihood of the model and hence the estimation of the overall effect.To adjust for bias, Equation  is expre2.3μβ, σβ2, μμ, σμ2, α, and λ are hyperparameters for the normal and inverse gamma prior distributions. The study qualities qi can be interpreted as hyperparmeters for the bias adjustment part, or as auxiliary parameters as they are not integrated out in the analysis. The Bayesian bias‐adjusted random effects model is a probabilistic graphical model . This means that for different values qi∈ℳ, there are different distributions for δi and thus, specifying a set of study qualities results in bounds on the probabilities that characterize uncertainty in the quantity of interest. The difference between lower and upper bounds for the probabilities expressing uncertainty in the quantity of interest is therefore a result of ambiguity or ignorance about the bias terms in the quantitative bias analysis.The robust Bayesian bias‐adjusted random effects model allows us to evaluate the impact of uncertainty about bias terms, μ, is summarized by bounds on the expected overall effect, bounds on the 5th percentile, and bounds on P(μ>t), the probability of the overall effect, μ, exceeding a decision relevant threshold, t∈ℝ. For instance, the posterior expected overall effect Eq(μ) is calculated by a multiple integral of μ times the posterior distribution over the domains of the parameters β1,β2,β3,β4,δ1,δ2,δ3,δ4,μ,σθ2 (see Appendix A for details). Then, lower and upper bounds on the expected overall effect are given byUncertainty about the overall effect, Bounds are here estimated using a discretization of the elements of the set of study qualities, in particular, we specify a finite set of study quality using a grid (details are given in Section 4). Then, for each study quality in this set, we estimate the expectation, percentile and exceedance probability of the overall effect using Markov chain Monte Carlo (MCMC) sampling which draws samples from the posterior distribution. Finally, the lower and upper bounds are approximated by the minimum and the maximum values of each estimated quantity.3We describe a way to incorporate qualitative judgments about risk of bias in a bias‐adjusted meta‐analysis. In particular, we show how to transform qualitative judgments about risk of bias into quantitative expressions  and thereby characterizing uncertainty in the specification of bias  by bounded probability.qi across all studies i.The Cochrane's risk of bias table (RoB table) is the recommended tool for assessing the risk of bias in each included study in Cochrane reviews.3.1q(+), q(?) and q(−) denote the study quality of studies with low, unclear and high risk of bias, respectively. The approach is explained using a hypothetical systematic review with 6 studies  which are classified as follows: S1 and S2 with a low risk of bias, S3 and S4 with an unclear risk of bias and S5 and S6 with a high risk of bias. The set of quantitative bias terms are constructed as follows:−).Group the studies according to their risk of bias: low (+), unclear (?) and high (q1=q2=q(+) and q5=q6=q(−).All studies belonging to a group with a clear risk of bias are assumed to have the same study quality. For example: q(−)≤q(?)≤q(+). For example: q(−)≤q3≤q(+) and q(−)≤q4≤q(+).The quality of studies belonging to the group with an unclear risk of bias are not necessarily equal and are somewhere in between Assign a range representing the lower and upper bounds for the bias terms in the low and high risk of bias categories.Let us consider a single domain of the RoB table for establishing a rating between the studies. Let q(+) take values between 0.5 and 0.95, which corresponds to situations where between study variability contributes more to the total variance compared to the variance of study specific errors. We let q(−) take values between 0.1 and 0.5, which corresponds to situations where the variance of study specific errors contributes more than between study variability to the total variance. For the group of studies with unclear risk of bias, we use the most extreme bounds for the high and low risk of bias group, that is, 0.1=q(−)_≤q(?)≤q(+)‾=0.95.In what follows, we let 44.1Data are taken from a systematic review about Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews. For this example, we include four studies investigating the effect of Rituximab plus metrotexato (RT + MTX) vs placebo plus metrotexato (MTX) in patients with rheumatoid arthritis. The effect is assessed by the number of patients who have improved by at least 50% on the American College of Rheumatology scale (ACR50) at week 24.4.2βi,μ, and σθ2 which follow normal and inverse gamma distributions, Equation (9). The following hyperparameter values are assigned: μβ=0, σβ=10, μμ=0, σμ=10, α=0.01, and λ=0.01. Let us note that the estimated overall effect of the Bayesian bias‐unadjusted random effects model  and WA16291 (Study 2) in the category of low risk of bias and DANCER (Study 3) and SERENE (Study 4) in the category of unclear risk of bias. Then, we get:𝒮3: αi represents the weights. We used a grid spacing of 0.1 for each αi  for estimating the bounds. Specifically, the following values are used: q are removed, which results in 736 study quality values, q.Here, we make use of the previously established bounds for studies with low risk of bias and in the absence of studies with high risk of bias, 0.1 is then used as the lower bound for studies with unclear risk of bias. The resulting set isfied asq=∑i=18αBias domain 4‐Studies with unclear risk of bias can have different study qualities and satisfyBased on attrition bias due to incomplete outcome data, the studies are grouped as follows: WA16291 (Study 2), DANCER (Study 3) and SERENE (Study 4) in the category of low risk of bias and REFLEX (Study 1) in the category of unclear risk of bias. Then, we get:similarly, we get𝒮4 are:αi (weights) we used the following valuesq.Following the same procedure as in domain 3, the extreme points of Bias domains 5 and 6‐All the studies in the group of low risk of bias have the same study qualityThe impact of bias due to selective outcome reporting and other potential sources of bias domains are similar because all the studies are rated with a low risk of bias. Thus:and thereforeq1 are considered, which results in 10 study quality values, q.In this case, 10 equally spaced values between 0.5 and 0.95 of Multiple bias domainsSo far, we have focused on single domains of the RoB table. However, the proposed methodology could be extended or adapted, by making use of more than one domain of the risk of bias table. This requires a clear and transparent guidance on how to rate studies considering quality of data from several domains at once, as well as on how to prioritize domains.‐WA16291 (Study 2) should be better than the rest of the studies, (it has four domains with a low risk of bias and two with an unclear risk of bias),‐DANCER (Study 3) and SERENE (Study 4) have the same type of biases,‐It is not possible to relate REFLEX (Study 1) to DANCER (Study 3) or SERENE (Study 4) .An example on how multiple bias domains could be combined for rating the studies is presented. The following considerations are taken into account:which results in:𝒮all are: αi, specifically, q.In this case, it is not possible to arrive to a final category of risk of bias without introducing a scoring rule . Therefore, once again, we specify the most extreme bounds to account for uncertainty, in the specification of bias , resulting in:Bias was accounted for by rating the studies according to each bias domain in the RoB table of the systematic review separately. For example, the effect of selection bias due to random sequence generation was evaluated separately from attrition bias due to incomplete outcome data. We illustrate each possible case according to each different domain listed in Table 4.4rjags and runjags packages, which are R interfaces to JAGS (see https://github.com/Iraices/Robust_bias_adjustment for the code). Forestplots  are used to present the estimated overall effect and specific study effect using the metafor package for R. To express the results from robust Bayesian analysis, the forestplots from metafor were expanded with bounds on the expected overall effect, E(μ), the lower 2.5th percentile and the upper 97.5th percentile  to change from 0.89 to 0.63  whereas the lower bound for the probability of the overall effect exceeding 1 is obtained at q∗=. The values of q∗ and q∗ are not necessarily on the extreme points of the set. Hence, it may be difficult to know in advance the values of q where the bounds are obtained.For a given domain, the values where the bounds are obtained, st Table . For domAdjusting for bias may reveal important aspects to consider when framing a conclusion in evidence synthesis. Here, the evidence in favor of Rituximab plus metrotexato (treatment group) from the published meta‐analysis5Quantitative bias analysisThe proposed framework is not limited to the RoB table. It is still valid and useful, if a different risk of bias table is used. However, modifications may be necessary particularly if there are more than three risk of bias categories. In general, we need to (i) decide the different categories of risk, (ii) assign a category of risk to each study, and (iii) specify the bounds per risk category. All steps are done on a case by case basis. In this paper, we used the RoB table because it is the recommended and therefore, most used tool to assess risk of bias in randomized clinical trials. The proposed framework makes use of the risk of bias table, so either the risk of bias table or expert's bias judgments should be available.The suggested approach estimates quantities of interest using robust Bayesian analysis, which gives more conservative estimates of a quantity of interest compared to standard Bayesian analysis. It is, on the other hand, a more complex model to set up and usually requires the use of numerical algorithms to approximate the bounds. Bounds over the set of study qualities are in this paper approximated using a grid search approach through a discretization of the set, where Bayesian inference is done for each choice of study qualities. Additionally, an increase in the number of studies may affect the computational burden of the discretization of the elements of the set for estimating the bounds. This will be particularly so when all studies have an unclear risk of bias, in which case a larger space must be explored. An alternative approach is to search for bounds and do Bayesian updating in an iterative way using iterative importance sampling.q, is subjective. That is why, our selection, , is quite conservative and includes most of the cases, as well as avoids possible numerical problems when values of q are too close to zero. The mid cut‐off value (0.5) is chosen because it is the median between 0 and 1 and links both categories of risk: high and low risk of bias. In this paper, we grouped the studies based on three different categories of risk of bias and then we specified possible values for each group. In practice, cut‐off values of study quality q can be informed from external information. For instance, future research, through meta‐epidemiological studies , similar case studies in combination with expert knowledge elicitation,q.The choice of cut‐off values of study quality t (for illustrative purposes the threshold t is set to be 1 in the application) is at least 95%. The unadjusted estimate in the application is 0.998  is that it can show that the conclusion is not affected by risk of bias as well as by ignorance or ambiguity regarding how to quantify the bias terms. Robust Bayesian bias analysis can be performed as a first and coarse step motivating a refined bias analysis. When adjusting for bias does (not) have a high impact on the conclusion of the meta‐analysis and therefore, a more detailed analysis may (not) be needed. A more detailed analysis could for instance gather more information regarding risk of bias or more carefully elicit bounds on study qualities. It can also be used to assess the influence of risk of bias on the conclusion of the assessment and then, if necessary, perform a Bayesian bias analysis relying on expert knowledge elicitation to specify a precise value or distribution of study quality. In the application, the bounds from adjustment made on risk of biases categorized as unclear for all studies (such as domain 1‐2) contain the bounds from any other judgment of risk of biases Figure . Hence, 98 Table , and theTo summarize, the proposed approach provides a structured framework to consider ambiguity or ignorance in quantitative bias analysis.The authors declare no potential conflict of interests.Ivette Raices Cruz: Conceptualization and methodology; coding; formal analysis, visualization and writing‐original draft; writing‐review and editing. Matthias C. M. Troffaes: Conceptualization and methodology; writing‐review and editing. Johan Lindström: Conceptualization and methodology; writing‐review and editing. Ullrika Sahlin: Conceptualization and methodology; coding; writing‐review and editing.


Vegetarian diets have been associated with reduced risk of ischemic heart disease (IHD). However, results regarding cardiovascular disease (CVD) overall and stroke are less clear. We conducted a systematic review and meta-analysis of prospective cohort studies on CVD, IHD and stroke risk among vegetarians or vegans versus nonvegetarians to clarify these associations.PubMed and Ovid Embase databases were searched through August 12, 2021. Prospective cohort studies reporting adjusted relative risk (RR) estimates and 95% confidence intervals (CIs) for incidence or mortality from CVD, IHD and stroke, comparing vegetarians and vegans to nonvegetarians were included. Risk of bias (RoB) was assessed using ROBINS-I and the strength of evidence was assessed using World Cancer Research Fund (WCRF) criteria. Summary RRs (95% CIs) were estimated using a random effects model.I2 = 68%, n = 8) for CVD, 0.79  for IHD, 0.90  for total stroke, and for vegans vs. nonvegetarians was 0.82  for IHD. RoB was moderate (n = 8) to serious (n = 5). The associations between vegetarian diets and CVD and IHD were considered probably causal using WCRF criteria.Thirteen cohort studies  were included. The summary RR for vegetarians vs. nonvegetarians was 0.85 (95% CI: 0.79–0.92, Vegetarian diets are associated with reduced risk of CVD and IHD, but not stroke, but further studies are needed on stroke. These findings should be considered in dietary guidelines.No review protocol registered.The online version contains supplementary material available at 10.1007/s00394-022-02942-8. Cardiovascular disease (CVD), which is mainly due to ischemic heart disease (IHD) and stroke , is stilThere is a continued need for public health interventions to further reduce CVD risk through changes in diet and other lifestyle habits like smoking and physical activity . Plant-bVegetarian diets have been consistently shown to reduce the risk of IHD in prospective cohort studies. A pooled analysis of five cohort studies by Key et al. found a 24% reduction in the relative risk of IHD mortality among vegetarians vs. nonvegetarians . SubsequOnline Resource 1 (Supplementary Tables 1 and 2).We conducted a systematic review and meta-analysis of prospective cohort studies on the association between vegetarian or vegan diets and risk of incidence and mortality from CVD, IHD and stroke, both overall and subtypes. The PRISMA  2020 guideline was followed throughout the process . The PRIThe review question was framed with the PECO(S) elements  as recomWe applied the following exclusion criteria: (1) duplicate citations, (2) studies reporting unadjusted risk estimates, (3) studies on patient groups, (4) non-relevant exposures like other plant-based nonvegetarian dietary patterns or diet scores  and (5) non-relevant outcomes  and (6) not relevant study design .Supplementary Table 3 (Online Resource 2). We searched for prospective cohort studies reporting on the association between vegetarian or vegan diets and incidence or mortality from CVD, IHD or stroke. Terms like vegetarian(s) or vegan(s) or vegetarian/vegan diet were searched in combination with cardiovascular disease, ischemic heart disease, coronary heart disease, myocardial infarction, cerebrovascular disease, stroke, cerebral hemorrhage, and subarachnoid hemorrhage. We added terms for ‘Seventh-day Adventists’ as many of the relevant cohort studies have been done in this religious group. ‘Subject headings’ were used for PubMed (MeSHs) and Ovid Embase (Emtrees) databases, and text words were applied to retrieve any articles in press. No restrictions were used for, e.g. age, sex, geographic location, language or date. We also screened the reference lists of relevant cohort studies and reviews to check for papers not contained within our search.The search was conducted by one author (JSD) using PubMed and Ovid Embase databases from their inception in 1958 and 1947 to February 14, 2020, and was later updated on August 12, 2021, using the search strategy shown in Supplementary Table 4 (Online Resource 3).All references were imported into EndNote X9 and initially screened by JSD, while the second part of the screening  was performed in duplicate by JSD and DA. Any disagreements were resolved through discussion. We included prospective cohort studies if they reported adjusted relative risk (RR) estimates (including hazard ratios [HRs] and incidence or death rate ratios) with 95% confidence intervals (95% CIs) for the association between vegetarian or vegan vs. nonvegetarian diets and incidence and/or mortality from CVD, IHD or stroke. When study data had been published on more than one occasion, we included the report with the largest number of cases. Prospective cohort studies were deemed the most relevant study design, as there are to our knowledge no randomized controlled trials available on vegetarian diets and primary prevention of CVD. Retrospective case-control studies were not included since they can be affected by recall bias and selection bias and cross-sectional studies were excluded because of a lack of temporal relation between the exposure and the outcome. A complete list of citations excluded after full-text assessment is shown in Data regarding results and study characteristics were extracted to tables by one author (JSD) and checked for accuracy by a second author (DA). More specifically, the information extracted was as follows: author, year of publication, location by country, study name, study period and years of follow-up, age and sex, sample size, number of cases or deaths from CVD, IHD, total stroke, ischemic stroke and hemorrhagic stroke, and type of diet. We also extracted RRs and 95% CIs and information regarding confounders adjusted for in each study´s statistical analysis.Supplementary Table 5, Online Resource 4). Each domain could be judged as ‘low’, ‘moderate’, ‘serious’ or ‘critical’ RoB or ‘no information’ on which to base a judgement. An overall RoB judgement was then assigned to each study (study-level assessment) using another set of criteria . In agreement with ROBINS, the most severe RoB judgement that was assigned to a given domain was assigned as the overall RoB for a study.All studies were critically appraised by two authors  and discrepancies resolved through consensus with a third author (MS) using the Cochrane ROBINS-I (Risk Of Bias In Non-randomised Studies—of Interventions) tool  as this Supplementary Table 7, Online Resource 4) for grading the overall evidence regarding vegetarian diets and CVD, IHD and stroke, with possible gradings rated as convincing, probable, limited-suggestive, limited-no conclusion, or substantial effect on risk unlikely [The grading of the evidence was done initially by DA and then discussed between all the authors to reach a conclusion. We used the World Cancer Research Fund (WCRF) grading criteria  using the random-effects model by DerSimonian and Laird  to accou2-statistics [p value for Chi-squared statistics significant if < 0.05, and I2-percentage ranged from 0-100%. Subgroup analyses were conducted stratified by sex (men or women), years of follow-up (≥ 10 vs. < 10 years), exclusion of early follow-up years or not, use of incidence or mortality estimates, number of cases or deaths , geographic location , risk of bias , and adjustment for confounding factors  to investigate potential sources of heterogeneity in the main analysis. For CVD, we also conducted a separate subgroup analysis distinguishing between CVD and circulatory disease. For stroke, we added separate subgroup analyses distinguishing between total stroke and cerebrovascular disease, and between ischemic and hemorrhagic stroke. An additional sensitivity analysis was conducted only on studies reporting both BMI adjusted and BMI unadjusted risk estimates to assess more directly the impact of adjustment for BMI on the results, since BMI could be an intermediary variable through which vegetarian or vegan diets can influence cardiovascular risk. We added a similar sensitivity analysis restricted to studies reporting results with both inclusion and exclusion of early follow-up to assess more directly the impact of excluding early follow-up, since reverse causality could potentially bias the results. Meta-regression analyses were conducted to test for heterogeneity between subgroups. We conducted a sensitivity analysis using more adjusted estimates from a pooled analysis by Key et al. [Heterogeneity was assessed using Cochran’s Q-statistics, Chi-squared-statistics  and I2-satistics . We consy et al.   –27 to asAge ranged from 10 to 90 years at baseline across studies. In the studies that included non-adult participants, the participants below < 18 years are likely to have made up a small percentage of the total population. No studies were performed exclusively in pregnant or breastfeeding women.Follow-up ranged from 5.14 to 28.3 years. The number of incident cases or deaths ranged from 219 to 1117 for circulatory disease, 375 to 106,690 for CVD, 60 to 24,794 for IHD, 31 to 909 for cerebrovascular disease, 54 to 5946 for total stroke, 31 to 519 for ischemic stroke and 28 to 300 for hemorrhagic stroke. The sample size ranged from 1724 to 422,791 participants across all outcomes. All studies used record linkage for outcome ascertainment except the Heidelberg Study , which rSupplementary Tables 5, 6 and 8 (Online Resource 4), and results of RoB judgements for the domains and for each study overall are shown for CVD, IHD and stroke separately in Supplementary Tables 9 and 10 (Online Resource 4), and Supplementary Figs. 1, 2, 3 (Online Resource 4). In the overall (study-level) RoB assessment for CVD and IHD, we judged three studies (three publications) [The basis for RoB judgements are shown in cations) –23 to becations) , 26, 27 cations) –25 to becations) , 26 to bMost studies included a statement on funding  , 47–49, I2 = 68%, pheterogeneity = 0.003) , Begg's test (p = 0.39), or by inspection of the funnel plot . The summary RRs (95% CI) ranged from 0.84  when excluding the Health Food Shoppers Study to 0.88  when excluding the Adventist Health Study 1 .Eight prospective cohort studies (six publications) , 26, 27 I2 = 0%, pheterogeneity = 0.52)  , 21, 23 I2 = 67%, pheterogeneity = 0.003) , Begg's test (p = 0.90), or by inspection of the funnel plot . The summary RR (95% CI) ranged from 0.76  when excluding UK Biobank to 0.83  when excluding Adventist Health Study 1 .Eight prospective cohort studies (six publications) , 26, 27 03) Fig. , showingI2 = 0%, pheterogeneity = 0.83)  , 21, 23 I2 = 61%, pheterogeneity = 0.003) , Begg's test (p = 0.63), or by inspection of the funnel plot . The summary RR (95% CI) ranged from 0.86  when excluding EPIC-Oxford to 0.94  when excluding Tzu Chi Vegetarian Study .Twelve prospective cohort studies (six publications) , 22–26 i03) Fig. , suggestI2 = 82%, pheterogeneity = 0.004, n = 3) for ischemic stroke  for hemorrhagic stroke   , 23 inclSupplementary Tables 11 and 12 show results from all subgroup analyses. Although there was high heterogeneity in our main analysis as measured by I2, this was for CVD and IHD mainly driven by differences in the strength of the association, rather than due to differences in the direction of the association, as all studies reported risk estimates in the direction of an inverse association. For stroke, the results were less consistent as there was heterogeneity also with regard to the direction of the association.For CVD and IHD, the inverse associations persisted in subgroup analyses stratified by sex, duration of follow-up, exclusion of early follow-up, outcome subtype, exclusion of prevalent disease at baseline, geographic location, number of cases, risk of bias and adjustment for confounding factors. In the analysis of CVD, there was little evidence of heterogeneity between subgroups, with the exception of the subgroup analysis stratified by geographic location, which showed a stronger association in North American studies than in European studies (Supplementary Table 11). In the analysis of IHD, there was also little evidence of heterogeneity between subgroups, with the exception of the subgroup analysis stratified by whether early follow-up was included or not, which showed a weaker association when early follow-up was excluded compared to when it was included (Supplementary Table 12). When comparing the associations for vegetarian diets and IHD among studies that provided risk estimates both adjusted and not adjusted for BMI, the association was slightly stronger when not adjusted for BMI (18% vs. 22% reduction in risk), suggesting that approximately 1/5 of the association potentially could be mediated by BMI (Supplementary Table 12). The null results for stroke persisted in most subgroup analyses; however, there was heterogeneity between subgroups when analyses were stratified by duration of follow-up and geographic location with inverse associations among studies with shorter vs. longer follow-up and among two Asian (Taiwanese) studies, but not among European or American studies (Supplementary Table 12).I2 = 71%, pheterogeneity = 0.009) while the summary estimate using the less adjusted model was 0.79 , suggesting little difference in the overall findings.To assess the impact of more rigorous adjustment on the summary estimate we conducted a sensitivity analysis using the more adjusted risk estimates  from the pooled analysis of Key et al. , and repE-values for the association between vegetarian diets and CVD and IHD were 1.64 (lower CI: 1.40) and 1.86 (lower CI: 1.49), and e-value for vegan diets and IHD was 1.74 (lower CI: 1.00).Supplementary Table 13 and 14, Online Resource 4) [Using WCRF criteria for judging the evidence, we considered the overall evidence to indicate a probable protective causal association between vegetarian diets and reduced risk of CVD and IHD, and for stroke, the evidence was deemed limited-no conclusion (ource 4) , 37. We ource 4) , 26, 27 ource 4) , systoliource 4)  and weigource 4) , and theource 4) –55 (see ource 4) –63 in a The present systematic review and meta-analysis showed a 15% and a 21% reduction in the relative risk of CVD and IHD, respectively, for vegetarians compared to nonvegetarians, but no clear association was observed for total stroke or subtypes of stroke. In addition, an 18% reduction in the relative risk of IHD was observed among vegans when compared to nonvegetarians, although this association was imprecise. No clear association was observed between vegan diets and CVD or stroke; however, the number of studies was limited. Although there was high heterogeneity in the main analyses for CVD and IHD, this was largely explained by differences in the strength of the associations as all studies reported risk estimates in the direction of an inverse association. For CVD, the association was stronger among North American studies than among the European studies. For stroke, studies were less consistent, and an inverse association was observed only among Asian studies and not in European or American studies. There was no indication of publication bias in the three main analyses. The findings regarding IHD are consistent with previous meta-analyses , 64–67; Our analysis has several limitations as well as strengths. Although the meta-analysis was not registered with a pre-defined analysis plan or study protocol, the analysis used a similar format to previous meta-analyses –63 and eHeterogeneity was an apparent issue across all outcomes. However, heterogeneity is expected for a number of reasons, including differences in the (1) detail of the dietary assessment methods used, (2) geographic location and background food choices and dietary patterns , (3) conVegetarians are often more health-conscious than nonvegetarians, and given the observational design of the included studies, confounding from other lifestyle factors could be an issue. However, the results persisted across multiple subgroup analyses of studies that adjusted for age, education, alcohol, smoking, BMI and physical activity, and there was little indication of heterogeneity between these subgroups. In a pooled analysis, further adjustment for alcohol, education, exercise and BMI in addition to adjustment for age, sex, and smoking status made little impact on the association between vegetarian diets and IHD , and wheThe definitions of vegetarian and nonvegetarian diets were not entirely uniform across studies, and it is possible that this could have affected the results; however, we do not expect a substantial impact of this on the overall results. In most studies, vegetarian status was defined based on meat and fish consumption reported on food frequency questionnaires, while in the Health Food Shoppers Study  participAs exposure assessment in most cohorts was only conducted at baseline , paThe current analysis was not able to assess the association between quality of vegetarian (or vegan) diets and CVD, IHD or stroke risk, as there were no studies that have investigated this directly to date. Other studies that have assessed the association between plant-based dietary indices and CVD risk have reported inverse associations between plant-based dietary indices overall as well as for healthy plant-based dietary indices (characterized by high intake of whole plant foods) and CVD risk, while unhealthy plant-based dietary indices  have been associated with increased CVD risk , suggestStrengths of our meta-analysis include the following: (1) the detailed search strategy; (2) rigorous and comprehensive risk of bias assessments that more adequately assess internal validity of the included studies (ROBINS-I) , 33, 34,The current findings are consistent with other epidemiological and experimental studies which have found that vegetarians have a lower BMI  and reduOur results are also consistent with studies on food groups and risk of CVD and IHD. Several cohort studies have shown an increased risk not only of both CVD and IHD –60, but The present meta-analysis suggests a vegetarian diet offers important health benefits by reducing the risk of both CVD and IHD, although not stroke. These are findings with important public health implications given that CVDs still are the leading causes of death and disease globally and suggest that adoption of plant-based dietary patterns such as vegetarian diets can be useful for reducing the CVD burden. These findings support a stronger emphasis on vegetarian dietary patterns in public health recommendations as a measure for CVD prevention.Future research should focus on additional large-scale and high-quality studies as they are needed to clarify results for stroke and stroke subtypes, as well as to provide results stratified by other risk factors and results with adequate adjustment for confounding factors to better rule out potential residual confounding. Further studies from other geographic regions are also needed. Detailed and repeated dietary assessments may be important to take into account dietary changes during follow-up. Future studies should focus on recruiting more vegans as there were few studies with sufficient numbers to detect a clear association among the vegans.In conclusion, a 15% reduction in the relative risk of CVD and a 21% reduction in the relative risk of IHD was observed for vegetarians compared to nonvegetarians, but no clear association was observed for total stroke or stroke subtypes. There was an 18% reduction in the relative risk of IHD among vegans, but the association lacked precision and no clear association was observed for CVD or stroke; however, there were few studies in the analyses of vegans. These findings are consistent with existing guidelines recommending plant-based dietary patterns for CVD prevention but suggest more emphasis may be put on vegetarian diets. Further studies are needed to clarify the association between vegetarian diets and stroke risk, as well as the association between vegan diets and CVD, IHD and stroke.Supplementary file1 (PDF 59 KB)Supplementary file2 (PDF 16 KB)Supplementary file3 (PDF 255 KB)Supplementary file4 (PDF 1269 KB)Below is the link to the electronic supplementary material.


The role of biomarkers in the early diagnosis and prognosis prediction of tumors has been paid more and more attention by researchers. Mucins are markers that have been found to have an abnormal expression in many tumors in recent years, which have been proved to have a predictive effect on the prognosis of tumors such as cholangiocarcinoma and colon cancer. However, whether it can predict the prognosis of pancreatic cancer remains unknown. The purpose of our study is to investigate whether the mucins and their subtypes are related to the prognosis of patients with pancreatic cancer.We systematically searched the Pubmed, Embase, and Cochrane Library for all eligible studies on the relationship between mucin and the prognosis of patients with pancreatic cancer up to November 2021. We used R 4.12 to calculate the combined risk ratio (HR) and 95% confidence interval (CI). For studies that did not provide HR values, we used scientific methods to calculate their values as accurately as possible. We used fixed effect model due to low heterogeneity. Subgroup analysis and sensitivity analysis were used to study heterogeneity. The funnel plot and Egger test were used to test whether the publication bias existed. The trim and filling method were used to evaluate the impact of publication bias on the results of the study.A total of 18 studies were included in this meta-analysis, including 4 subtypes of mucin family members and 1643 patients. There was a slight heterogeneity between studies . Meta-analysis showed that MUC4 , MUC16 , and whole mucin . The expression level was negatively correlated with the prognosis of pancreatic cancer patients, MUC1 , MUC5  The expression level was not related to the prognosis of pancreatic cancer patients.The meta-analysis demonstrated that the overall expression level of mucin and the expression levels of MUC4 and MUC16 were important prognostic predictors for pancreatic cancer patients. MUC1 and MUC5 had no predictive value for the prognosis of pancreatic cancer patients. Future studies should validate these and other promising biomarkers.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021291962.PROSPERO registration number is CRD42021291962.  Pancreatic cancer (PC) is a malignant tumor of the alimentary system with concealed pathogenesis, a high degree of malignancy, rapid progression, and poor prognosis. According to the location of pancreatic cancer, it is mainly divided into two clinical types: pancreatic head carcinoma and carcinoma of the pancreatic body and tail. Most pancreatic cancers originate from the pancreatic ductal glandular epithelium and form pancreatic ductal adenocarcinoma (PDAC). A few are from other sources such as mucinous cystadenocarcinoma, acinar cell carcinoma, and so on. Smoking, obesity, diabetes, and alcohol intake are recognized as the most important risk factors for pancreatic cancer . In addiMucins are glycoproteins synthesized by epithelial cells. Dozens of mucin families have been found, including MUC1, MUC2, MUC4, MUC5, MUC6, MUC16, MUC20, and so on . These pUsing the words “MUC,” “Mucin,” “pancreatic cancer,” and “prognosis” as keywords. A comprehensive and detailed search of PubMed, Embase, Cochrane and other medical databases was conducted to collect all the literature about the relationship between the members of the mucin family and the prognosis of pancreatic cancer. The deadline was November 2021. In order to make this study more reliable and rigorous, we set the inclusion and exclusion criteria of the literature according to the research needs. The inclusion criteria were as followed: (1) Explain the detection method of mucin expression level clearly, and this method could reflect the mucin expression level of pancreatic cancer correctly. (2) The diagnosis of pancreatic cancer was confirmed by imaging and pathological examination. (3) The study’s groups were divided into high expression groups and low expression groups according to the expression level of mucin. (4) The study must provide at least one of the following three items: explicit HR and 95%CI, survival curve from which HR can be extracted, or explicit HR and P value. (5) The study had high literature quality to ensure reliability, and the Newcastle-OttawaScale score was not less than 6. (6) In order to ensure the comparability of the members of the mucin family and facilitate the subgroup analysis in the future, only when the number of studies on a member of the mucin family was not less than 3, the research on that member will be included in the final analysis—each final included study needed to meet the above six criteria simultaneously. In addition, when the study meets any of the following exclusion criteria, it will be excluded. The exclusion criteria were as followed:(1) Animal experiments, abstracts, reviews, case reports, and studies that have nothing to do with the contents of this study. (2) The study of mucin combined with other mucins rather than alone. (3) The study only provided the survival curve. According to the standard method, the P value of the risk ratio extracted from the survival curve was quite different from that in the original study. (4) The study of other pancreatic tumors without pancreatic cancer or combining pancreatic cancer with other pancreatic tumors. The only study that met all inclusion criteria and did not meet any of the exclusion criteria would be included in the meta-analysis.The primary extracted data of the included study were as follows: title, first author name, publication time, mucin member type, number of control group and case group, region, the detection method of Mucin expression level, HR value, and 95%CI, the source of the sample population. Wei Xu and Man Zhang extracted and summarized independently from the research finally included in this study and compared after the extraction to prevent errors in the extraction process. For the divergent study, whether or not to be included was decided through discussion or consultation with the third author. For the lack of data in the study, contact the original author as much as possible to supplement. If the author could not be reached, then according to the impact on the study to decide whether to exclude. The Newcastle-Ottawa Scale scoring standard was used to evaluate the quality of the included research.2 statistics were used to evaluate the heterogeneity among the studies. It is statistically significant as P < 0.1. When I 2< 50%, it was considered that there did not have significant heterogeneity among studies, and the fixed effect model could be used. When I 2> 50%, it was considered significant heterogeneity among studies, and the random effect model could be used. Subgroup analysis would be carried out to search for the possible sources of heterogeneity. Sensitivity analysis was performed by omitting studies included one by one to estimate the possible sources of heterogeneity. Funnel plot and Egger’s test were conducted to evaluate whether this meta-analysis had publication bias. If P < 0.05, it represented that there might exist publication bias. All the statistical tests in this study were bilateral.All data were analyzed with the R 4.1.2 software meta-package (version 4.9). If the study only provided a survival curve, we referred to Jayne F Tierney’s method , used thThrough extensive review and strict compliance with the inclusion and exclusion criteria, a total of 18 studies –33 were 2 = 24.4%, P = 0.14). Therefore, the result indicated that the overall expression level of the mucin family was negatively correlated with the prognosis of patients with pancreatic cancer, which means that we can determine the prognosis and survival of patients by detecting the expression of mucin in clinical work . The fixed effect model was used because of the slight heterogeneity in our study and MUC16  were significantly correlated with the prognosis of patients with pancreatic cancer, suggesting that patients with high expression of mucin 4 and 16 often have a poor prognosis. There was no significant correlation between the expression of MUC1  and MUC5 and the prognosis of patients with pancreatic cancer .Since our study included several studies that did not provide HR and 95%CI directly, the inclusion of the effect values calculated by the estimated method in the study may reduce the study’s credibility. In order to observe the impact of the inclusion of estimates on this study, we conducted a subgroup analysis according to whether the effect value and 95%CI were provided directly. The result of subgroup analysis showed that pancreatic cancer patients with high/positive mucin expression still had a poor prognosis after removing the estimated study . Therefore, it could be considered that the study of the estimated effect value we included did not significantly impact the results, and the original results were still credible .The changes of pooled HR and 95%CI were observed by deleting the literature one by one. If there was no significant change in HR and 95%CI after the deletion of a study (HR and 95%CI were still greater than 1), it indicates that the study has no significant effect on the study. A significant change in the overall results after the deletion of a study  showed that the study had a great impact on this study. The sensitivity analysis results suggested that excluding any study would not significantly change the pooled HR value, and 95% CI. Pooled HR ranged from 1.47 to 1.57, and the 95% CI was always greater than 1, suggesting that this study’s results were highly reliable .The funnel plot was used to detect the publication bias. We observed that the funnel plot was asymmetric , suggestResearchers have devoted themselves to studying the relationship between the expression level of mucin and human tumors since the discovery of mucin. In 2017, Ryan J. King et al. conducted a detailed and comprehensive study on the relationship between mucin and human tumors. In their studies, they found that various types of mucins increased expression in tumors through various forms , indicating that there has a link between mucin family and various types of cancer . Mucin iThe mechanism of the role of mucin in the occurrence and development of pancreatic cancer has not been fully elucidated. However, some of the mechanisms have been revealed after in-depth study. In normal cells, the promoter of the mucin gene is usually silenced because of methylation, and the mucin gene is also in a state of low expression. In the process of tumorigenesis, the promoter of the mucin gene will be demethylated under the action of methyltransferase DNMT1, which leads to the high expression of the mucin gene . The higParadoxically, our study did not seem to confirm this correlation though many studies suggested that MUC1 was associated with pancreatic cancer progression. MUC1 is a heterodimeric type I transmembrane protein that is normally expressed on the luminal surfaces of ductal epithelia. MUC1 is significantly overexpressed in tumor tissues. Although the receptor-like function of MUC1 provides mobility and environment-dependent adhesion / anti-adhesion function for cells, most of the carcinogenesis induced by MUC1 is due to its abnormal signal transduction interaction. In the case of tissue damage and loss of apical-basal polarity in tumor cells, MUC1 loses its apical localization and interacts with other receptor tyrosine kinases located on the base, thereby completely changing the signal transduction function of MUC1, thereby significantly regulating transcriptional regulation and other carcinogenic functions . In PankIn addition to being a biomarker, more and more studies have shown that mucins play an important role in tumorigenesis, immune escape, metastasis, and chemotherapy resistance. Therefore, they are the targets of developing new therapies and enhancing cytotoxic drugs . As mentThis study proved that the expression of mucin family and its subtypes were correlated with the prognosis of pancreatic cancer patients, and heterogeneity was not significant. There might exist publication bias, but we prove that it could have no significant effect on the results. Although we used estimated values to replace some included studies’ effect values, we thought it had no significant impact on the overall results after subgroup analysis, so the overall results were reliable. However, the shortcomings of our study could not be ignored. All the studies included were case-control studies, and the number of cases was small, so it was difficult to avoid selection bias and recall bias. Some of the studies used the expression effect of mucin fragments instead of the whole mucin expression effect, which might lead to the opposite outcome once the two have a different biological effect. Due to the lack of research data, other mucin subtypes such as MUC2, MUC6, and MUC7and so on could not be included in the meta-analysis. Most studies used the immunohistochemical score to evaluate the expression level of mucin, which was somewhat subjective. Some studies reflected the expression level of mucin by mRNA level and degree of promoter methylation, so the evaluation methods are quite different. In addition, the localization of mucin in pancreatic cancer cells varies from study to study. Some studies evaluated the expression level of mucin on the cell membrane, while others evaluated it in the cytoplasm and nucleus. Therefore, the different localization of mucin in pancreatic cancer cells might also lead to inaccurate results.Our study is consistent with the previous study on the relationship between mucin and the prognosis of pancreatic cancer, affirming the study on the relationship between the two and supporting further research to explore the predictive ability of mucin of various subtypes to the prognosis of patients with pancreatic cancer. In addition, future research should focus on making mucin detection simpler and making mucin detection more widely used in predicting the prognosis of pancreatic cancer patients. More studies are needed to further clarify the mechanism of mucin in the progression of pancreatic cancer. In addition to being a biomarker to predict the prognosis of patients with pancreatic cancer, mucin may become a new target in treating pancreatic cancer in the future.S1 Checklist(DOCX)Click here for additional data file.