PITX2) gene may be one of the risk factors for ischemic stroke (IS) in European populations. However, more recently, studies in Asia have reported that rs2200733 and rs6843082 are only weakly or not associated with increased risk of IS. This difference may be caused by the sample size and genetic heterogeneity of rs2200733 and rs6843082 among different races. For this study, we selected eight articles with nine studies from the PubMed and Embase databases, including five articles from Asian and three articles from non-Asian, to evaluate the risk of IS caused by rs2200733 and rs6843082. Then, we investigated rs2200733 and rs6843082 single-nucleotide polymorphisms (SNPs) by analysis using allele, recessive, dominant, and additive models. We identified that rs2200733 and rs6843082 are weakly significantly associated with IS for the allele model (p = 0.8), recessive model (p = 0.8), dominant model (p = 0.49), and additive model (p = 0.76) in a pooled population. Next, we performed a subgroup analysis of the population, the result of which showed that rs2200733 and rs6843082 covey genetic risk for IS in a non-Asian population, but not in an Asian population. In conclusion, our analysis shows that the effect of PITX2 rs2200733 and rs6843082 SNPs on IS risk in Asia is inconsistent with the effect observed in European IS cohorts.A previous genome-wide association study (GWAS) has reported that variants rs2200733 and rs6843082 in the paired-like homeodomain transcription factor 2 ( PITX2 rs2200733 and rs10033464 variants were identified as significant contributors to IS in a European population (p = 2.18 × 10–10) (Ischemic stroke (IS) is the second leading cause of death worldwide and the main leading cause of intellectual disability in adults . The pat× 10–10) . Howeverversus (vs.) 851 patients without AF and 811 patients with IS vs. 688 patients without IS, all of Chinese. After analysis, rs2200733 was meaningfully correlated with AF (p = 4.1 × 10–12) but not IS in this Chinese population  pulation .vs. 5972 patients without stroke and then replicated their result in 5859 patients with stroke vs. 6281 patients without stroke, all of European ancestry. Their results showed that both rs2200733 and rs1906599 were associated with IS (OR = 1.32)  . Their svs. 501 control individuals, all Chinese (p = 0.448).In 2022, Zhao et al. analyzed 476 patients with IS  Chinese . Their aIn summary, previous studies have reported different results as to whether rs2200733 and rs6843082 increase susceptibility to IS. It is not clear whether the two SNPs (rs2200733 and rs6843082) are related to IS susceptibility. In this study, we further evaluate whether these two SNPs (rs2200733 and rs68430828) increase the risk of IS using nine studies from eight articles.http://www.ncbi.nlm.nih.gov/pubmed) and Embase (https://www.embase.com/) databases. We filtered all relevant studies based on the keywords “Stroke,” “PITX2,” “rs2200733,” and “rs6843082.” The literature search was completed by 10 March 2022. In the following paragraph, we describe the criteria for inclusion.The relevant literature was searched in PubMed  the study used a case–control design, (2) the study evaluated whether the two SNPs (rs2200733 and rs6843082) are risk factors for IS, (3) the study provided a clear and definite number of genotypes or alleles or enough data to calculate these numbers, and (4) the study provided an explicit odds ratio (OR) and 95% confidence interval (CI) or sufficient data to calculate the OR and 95% CI. All studies that did not meet the inclusion criteria were eliminated.For each study that met the inclusion criteria, we extracted the following information: (1) first author, (2) year of publication, (3) race of the study subjects, (4) number of cases and controls, and (5) quantity of rs2200733 and rs6843082 genotypes in cases and controls. The full results are shown in vs. G), recessive model (AA vs. AG+GG), dominant model (AA+AG vs. GG), and additive model (AA vs. GG). These results are helpful to evaluate the susceptibility to IS with the two SNPs (rs2200733 and rs6843082): A allele vs. G allele.We used four common genetic models for this meta-analysis, including the allele model (A vs. G), recessive model (AA vs. AG+GG), dominant model (AA+AG vs. GG), and additive model (AA vs. GG). We performed all relevant Chi-square tests using the R program (http://www.r-project.org/).The HWE of the two SNPs (rs2200733 and rs6843082) in IS cases and the control group were analyzed using the Chi-square test. The relationship between the two SNPs (rs2200733 and rs6843082) and IS was analyzed using four gene models: allele model (A 2 = [Q—(k—1)]/Q × 100% . When the P value from Cochran’s Q statistic <0.1 and the I2 value from Cochran’s Q statistic >50%, the data showed considerable heterogeneity  in the above study. Then, Cochran’s Q test and IQ × 100%  were useogeneity .p < 0.05 or I2 >50%, it indicated that there was heterogeneity between studies, and a random-effect model (DerSimonian–Laird) was used to calculate the pooled OR. If not, we used a fixed-effect model (Mantel–Haenszel). All statistical methods in the meta-analysis were applied by program R (http://www.r-project.org/).In Cochran’s Q statistic, if http://www.r-project.org/).In this analysis, we used funnel plots to assess the possible publication bias. When there was no publication bias, the plot of the funnel was symmetrically inverted. Otherwise, it was an asymmetric inverted funnel . The asy.We retrieved 29 articles from PubMed and 44 articles from the Embase database. Finally, eight articles , includiPITX2 gene (https://snipa.helmholtz-muenchen.de/snipa3/).The rs2200733 and rs6843082 SNPs were located within 10 kb on The study of Bevan et al. was excluded from the dominant, recessive, and additive models. For this analysis, we observed no remarkable heterogeneity in the pooled population when using the four genetic models .p = 0.45), non-Asian (p = 0.74), and pooled populations (p = 0.80)  . Our res = 0.80) .p = 0.70), non-Asian (p = 0.24), and pooled population (p = 0.80)   were not = 0.80) .p = 0.73), non-Asian (p = 0.13), and pooled (p = 0.49) populations  had no significant relationship with IS in Asian (ulations . The resulations .p = 0.67), non-Asian (p = 0.16), and pooled populations (p = 0.76)  in Asian ( = 0.76) . The res = 0.76) .p = 0.943, 0.133, 0.053, and 0.204, respectively  in a pooled population, we can conclude that the G allele has low importance for the risk of IS. In the subgroup analysis, the results showed that the two SNPs had no correlation with the risk of IS in an Asian population, but the results in a non-Asian population showed a significant relevance with the risk of IS. These results suggest that the specific gene expression of that population and/or disease could be affected by genetic variation . Therefory veins . TherefoPITX2 and IS have produced conflicting results. So far, there is still no final and unanimous conclusion. Our analysis includes samples from multiple researchers; therefore, the results of our study may be more reliable than those of single studies. However, some potential limitations in our meta-analysis should be acknowledged. First, there was a small sample of GWAS and candidate gene studies, which may influence the pooled estimated value. Second, environmental factors, such as smoking and alcohol use, may affect the risk of IS, but some studies did not consider these risk factors. Third, different populations have different genetic susceptibilities both to the two SNPs (rs2200733 and rs6843082) and to IS, which can make the primary cause difficult to distinguish. IS is a disease that is effected by the interactions of multiple environmental and genetic factors (Numerous studies on the relationship between  factors , and so


Cadmium is a heavy metal with carcinogenic properties, highly prevalent in industrialized areas worldwide. Prior reviews evaluating whether cadmium influences breast cancer have been inconclusive and not reflected several recent studies.To evaluate the association between cadmium exposure and female breast cancer incidence, with an emphasis on separately estimating dietary vs. airborne vs. biomarker measures of cadmium and studies published until October 2022.We evaluated risk of bias using set criteria and excluded one study judged to have high risk based on self-report of breast cancer and insufficient adjustment. We conducted a random effects meta-analysis of epidemiological studies, including subgroups by exposure route and by menopausal status.I2= 77%). When examining separately by exposure route, dietary cadmium was not linked with an elevated risk – , consistent with prior reviews, but biomarker-based studies showed an elevated but non-significant pooled measure . We did not observe any clear patterns of different risk by menopausal status.A total of 17 studies were eligible for our meta-analysis. Only 2 studies addressed airborne cadmium directly. Breast cancer risk was elevated in women exposed to higher levels of cadmium across all studies − pooled odds ratio: 1.13 , with notable heterogeneity between studies (Findings from our meta-analysis suggest that exposure to higher cadmium increases the risk of breast cancer in women, but with remaining questions about whether non-dietary exposure may be more risky or whether residual confounding by constituents of tobacco smoke may be at play. BC incidence patterns in low- and middle-income countries may be lower than those in high-income countries  . HoweverIn all scenarios, women living in urban and densely populated areas have the highest risk of BC , which sSeveral prior reviews and meta-analyses have been conducted about cadmium and BC incidence . Taken tThe importance of directly examining airborne cadmium exposure distinct from ingested cadmium is supported by several observations. Airborne cadmium may result in higher effective doses; once cadmium is in the lungs, from 10% to 50% of an inhaled dose gets into the bronchoalveolar barrier reaching out to the bloodstream . In contGiven that cadmium is a modifiable toxic exposure with an as-yet unresolved role on BC risk, we performed a systematic review and meta-analyses. We especially focused on subgroup analyses by route of exposure (e.g. airborne vs. diet vs. biomarker) and also examined another subgroup where sufficient numbers of studies allowed this: menopausal status. Furthermore, we included several studies published since the most recent review 2.We followed the Preferred Reporting Items for Systematic Review and Meta-analysis Guidelines − PRISMA− guidelines to prepare this review .2.1.We define our Population, Exposure, Comparator, and Outcomes (PECO) as follows:Population: Female adults (age ≥18 years)Exposure: Cadmium (from any source) measured by biomarker, diet, or air measurement.Comparator: Lower or higher levels of cadmium.Outcomes: Incident cases of breast cancer.Study designs: We included case-control and cohort study designs. We excluded cell models, ecological studies, case reports, and descriptive studies that lacked a measure of association between cadmium and breast cancer.Dates and language: All studies published until October 2022 were included. Searches were conducted in English, but articles in English and Spanish were included in the assessment. In studies that utilized the same population and the same medium for measuring the exposure, we chose the most recent published article in order to avoid duplicating the population in the meta-analysis.2.2.We searched for publications using PubMed (MEDLINE), SCOPUS, and Web of Sciences, using the keywords: heavy metal and breast cancer. The initial search was crafted in PubMed using a combination of Medical Subject Heading (MeSH) terms to guide the vocabulary. MeSH matches a variety of keywords without the use of synonyms in MEDLINE and CENTRAL searches, and provides a comprehensive solid foundation for the search process and transferred well to other platforms. The same search strategy was conducted in the other databases. The search contained the following terms 1) Heavy metal prioritized and related . 2) Breast cancer “breast cancer” OR “breast neoplasms” OR “breast tumor”) and 3) exclusions for irrelevant factors and factors outside the scope of this review (NOT “review” OR “case report” OR “mice” OR “mouse” OR “in vitro”). We broadly searched across all metals to capture papers that reported cadmium results within a suite of metals examined. Only studies in humans were included. To increase the sensitivity of the search, we conducted a PubMed search without the above-mentioned exclusions. Searches were conducted throughout February and March 2021 and then updated in October 2022. (2.3.Two reviewers (VFG & EGR) independently screened retrieved articles for eligibility and extracted the following data from elegible studies using a pilot-tested form: the first author, publication year, design, location, duration of follow-up (cohort studies identified), exposure of interest (cadmium), exposure measurement , subject’s age, menopausal status  recruitment period, relative risk point estimate and 95% CIs from the fully adjusted model, cut-off values for each category of exposure, sample size, and variables adjusted for in the multivariate analysis.2.4.To assess the potential for systematic errors in each study we used the preliminary Risk of Bias (RoB) in Non-randomized Studies of Exposures (ROBINS-E) tool , which i2.5.We conducted a narrative synthesis of data extracted from the studies. Also, we performed a quantitative analysis based on categorical exposure to cadmium. The original measures of association , and the inverse of their variances were explored in a forest chart to compare the results while keeping the original confidence intervals (95%CI).I2 statistic was used to assess the level of heterogeneity of the effect across studies.We pooled the effect estimates from each study, independently of the study design, using a random-effects model. Because we were interested in generalizing beyond these included studies, we were waiting for some heterogeneity and we were conservative in the interpretations, we preferred the random effects model, although we also checked whether results differed for the overall meta-analysis when using a fixed effect model . We condhttp://www.crd.york.ac.uk/PROSPERO/; CRD42022341929).We used meta-regression to evaluate the extent to which study designs (case-control vs. cohort) contributed to the heterogeneity of effects across studies. In addition, we used funnel plots to assess potential publication bias. The analysis was done with RevMan  and Stata 14. The protocol for this study is available in PROSPERO  and a cohort design in 50% (9/18) of the articles . Studies3.1.Among all studies, (17/18) had a moderate risk of bias  and S3. 3.2.I2 = 77%) (I2:54%).Our overall pooled estimate for 17 studies showed that women with higher cadmium exposure were 1.13 times more likely to develop BC , and there was considerable heterogeneity across studies (2 = 77%) . Despite2 = 77%)  contribuI2: 42%) (I2 = 62%). with little difference between fixed and random effects polled estimates (PHet = 0.190).Among premenopausal women (4 studies), there was a slightly elevated and non-statistically significant association between cadmium and BC  . Similarstimates . DespiteI2 = 84%). Similar results were found when limiting to the 4 studies of biomarker exposures  (I2 = 69%). with similar patterns for postmenopausal women and a highly imprecise summary measure for 2 results for premenopausal women. Finally, our findings did not support the hypothesis that airborne cadmium exposure plays a role in BC yielding a summary OR (from 2 studies) of 1.04; 95% CI: 0.93, 1.16), with similar results after stratifying by menopausal status  resulted in a meaningfully-elevated summary measure with BC risk that was not statistically  . In stud2 = 66%) , the risl status .4.When pooling across 17 studies of cadmium exposure (measured in various ways) and BC incidence. We found a positive association reflecting 13% in the risk of BC. The size of this increased risk with higher cadmium is strikingly similar to that of the most recent meta-analysis by Filippini. : 12% incOur study strengths include the evaluation of different sources of exposure and exposure measurements, the inclusion of multicentric studies and studies with different designs. Due to the scarcity of high quality data on the cadmium-BC relationship, these strategies allowed a more comprehensive evaluation of the issue. Further strengths of our review include that it is more comprehensive and updated – including a larger number of studies. We had a clear definition of our outcome – focusing on BC incidence . It is widely accepted that risk factors for mortality may differ from risk factors associated with disease incidence. Indeed, as recognized by the authors of prior reviews, blending BC incidence and mortality endpoints could have been an important source of heterogeneity in their studies. Importantly, we separately examined cadmium by route of exposure/ measurement type, including the first meta-analysis of airborne cadmium and BC. This also helped to shed light on possible sources of bias and helped to integrate with prior reviews, some of which only focused on dietary cadmium.Our subgroup analyses focusing on discrete sources of exposure - dietary and airborne cadmium - did not show an increase in risk, with near-null pooled estimates for both. Previous reviews of the dietary route of cadmium similarly found near-null estimates , constitIn contrast, our summary measure of biomarker studies – which reflect all sources of exposure - revealed an elevated association, a notable 37% increase in BC risk, and while this result did not reach statistical significance, this was influenced by the limited number of studies (8) versus the 17 included in the overall estimate. The magnitude of this summary OR is consistent with results of prior reviews including those by Menopausal status could delineate different responses to cadmium exposure and BC risk. While we separately pooled results for pre-menopausal and post-menopausal status, also within exposure route, the results were impacted by increasingly-smaller numbers of included studies and limited statistical precision. No clear pattern of increased susceptibility by menopausal status was evident.This multi-source meta-analysis included articles with a moderate risk of bias in outcome measurement. Nevertheless, they were conducted in high-income countries, and most were based in cancer registries. Therefore, our findings may not be applicable to population in low and middle-income countries, where cadmium exposure levels may be substantially different.An unavoidable limitation, arising in part from the nature of studying rare endpoints like BC, is the lack of data on exposure accrual time and induction period. BC may develop based on exposures over many previous decades. Each individual study included differed in the latency period reflected based on the timing of questionnaires or whether a biomarker was from urine or blood. For example, cadmium is slowly excreted in urine resulting in accumulation in body tissues, and a biological half-life up to 30 years . We wereIn conclusion, our results add evidence for a role of cadmium exposure in BC with some heterogeneity, but with some caveats based on the possibility of residual confounding by constituents of tobacco smoke, which could be themselves cancer-promoting exposures driving observed associations with cadmium biomarkers.Multimedia component 1


The triglyceride and glucose (TyG) index is associated with the risk of nonalcoholic fatty liver disease (NAFLD), but the dose−response relationship between them is still unknown. We conducted a comprehensive meta-analysis to study the dose−response association between the TyG index and the risk of NAFLD.We systematically searched the Cochrane Library, PubMed, and Embase databases until July 2022 for relevant studies. The robust error meta-regression method was used to investigate the dose−response association between the TyG index and NAFLD. Summary relative risks (ORs) and 95% CIs were estimated by using a random-effects model.subgroup<0.001).A total of 4 cohort and 8 cross-sectional studies were included, with 28,788 NAFLD cases among the 105,365 participants. A positive association for the risk of NAFLD was observed for each additional unit of the TyG index with a linear association (p=0.82), and the summary OR was 2.84 . In the subgroup analyses, a stronger association of the TyG index with NAFLD was shown in females than in males . Nonalcoholic fatty liver disease (NAFLD) is currently the most common chronic liver disease worldwide and not only leads to liver cirrhosis, liver failure or even liver cancer but also increases the risk of atherosclerosis and cardiovascular disease (CVD) . TherefoSince then, multiple new studies regarding the relationship between TyG and NAFLD have been published, but the dose−response association between them is still unclear. Therefore, we aimed to evaluate the dose−response relationship between the TyG index and the risk of NAFLD in this study.International Prospective Register of Systematic Reviews (PROSPERO) (https://www.crd.york.ac.uk/PROSPERO-CRD42022347813). As shown in We have registered our study in the To find all studies on the relationship between the TyG index and NAFLD, we performed an exhaustive literature review through the PubMed and Embase databases and the Cochrane Library until July 18, 2022, and the concrete search strategy is provided in According to the population, intervention, comparison, outcome, and study design (known as the PICOS rules), the included criteria were as follows: (1) participants: adult (age>18 years); (2) comparison: high TyG index versus low TyG index; (3) outcomes: evaluated the relationship between the TyG index and the risk of NAFLD; (4) types of studies: observational studies published as full-length articles; and (5) reported the estimated effect for this association with multivariate analysis and provided useful data for the dose−response analysis. Therefore, studies were excluded for the following reasons: (1) they were reviews, meta-analyses, and congress abstracts; (2) they were abstract-only articles; (3) no relevant data were reported or could not be extracted; and (4) they used languages other than English.Two investigators (Q-L and X-Y) separately completed the entire process of our study from the screening for included studies to data analysis. We identified the final included articles based on the title, abstract, and full text, and disagreements were resolved through coordination or by a third author (X-L) when necessary. An e-mail requesting the article or information was sent to the author when the article was not available or to obtain additional information for the analyses. Duplicated manuscripts were manually identified.The following data were extracted by three independent researchers  and examined for each eligible study by a fourth author (X-L): (1) name of first author; (2) year of publication; (3) country or region; (4) follow-up time; (5) baseline characteristics of the subjects ; (6) study type; (7) outcome assessment; (8) number and percent of NAFLD incidence; (9) variables of adjustments; (10) TyG index value associated with the dose−response analysis; (11) hazard ratio (HR) or relative risk (RR) or odds ratio (OR) with 95% confidence interval (CI) from the most adjusted model.The quality of the cross-sectional studies was judged using Joanna Briggs Institute’s critical appraisal checklist, while the cohort studies were evaluated using the Newcastle−Ottawa Scale (NOS). After an evaluation of selection, comparability, and outcomes, the studies were considered high-quality with an NOS of ≥6 stars.The majority of our included studies reported OR, and the others reported HR, and we uniformly downgraded HR to OR and merged the results. We used the one-stage method of robust error meta-regression (REMR) to fit the dose−response relationship between the TyG index and the risk of NAFLD , 8. In a2 statistics and Cochran Q test (p<0.1); I2>50% was regarded as high heterogeneity. To assess publication bias, a funnel plot, Egger’s test, and Begg’s test were performed. All analyses were performed using Stata 14.0  and Review Manager (RevMan) version 5.3 .Heterogeneity was assessed using the IWe made the process of literature search and screening a flow chart Figure 1The concrete characteristics of these included studies are shown in Among these articles, 8 cross-sectional studies were assessed by the Joanna Briggs Institute critical appraisal checklist  did not 2 = 98%, P<0.001; Ten studies , 45–47 wThe combined results were consistent with the original results when any study was omitted from the sensitivity analysis (Subgroup analyses according to age, study design, sample size, BMI and adjustment for confounders are shown in Our study found that the positive association between TyG and the risk of NAFLD in a linear model was strong, and for additional units of TyG, the risk of NAFLD increased by 2.84 times. To the best of our knowledge, this is the first time that the dose−response relationship between the TyG index and the risk of NAFLD has been presented. Our result is consistent with a previous meta-analysis of large observational studies . In addiWith respect to sex in the subgroup analysis, we found that women had a 1.6 times higher risk of NAFLD than men for each additional unit of TyG. This finding caught our attention because women have more peripheral and subcutaneous fat than visceral and hepatic adipose tissue, and combined with the protective effect of estrogen on NAFLD, women may have a lower risk of NAFLD than men . The conIn the subgroup for confounding factors, there will be a great difference across adjustments stratified by BMI, sedentary lifestyle, and diabetes. These results were not surprising. BMI is an important indicator to assess obesity, and obesity is recognized to be closely associated with NAFLD . MoreoveThe TyG index is calculated by fasting triglycerides and fasting plasma glucose, and it is highly sensitive and specific for the identification of IR and has been widely used as a reliable alternative indicator for IR in recent years . The potBoth NAFLD and a high level of IR are associated with various diseases, such as liver cancer and many extraliver cancers, so the early detection of NAFLD may possibly alleviate or cure the potentially poor prognosis of this disease . The intIn general, most of our included studies were cross-sectional, which cannot prove a causal relationship . AlthougOur dose−response analysis suggested that the TyG index may be a new risk factor for NAFLD independent of traditional risk factors. However, the association may be affected by some confounding factors due to the limitations of our study, so more prospective studies are necessary to confirm this result.The original contributions presented in the study are included in the article/J-L, FW-L and P-Y participated in the whole project and were responsible for revising the draft. QL and JW-C X-L conducted the study selection, data extraction, statistical analysis, and interpretation of the data. Q-L and X-L wrote the first draft of the manuscript. All authors participated in the interpretation of the results and in revising the final version of the manuscript.


Science titled “Epidemiology faces its limits,” which questioned the utility of nonrandomized epidemiologic research and has since been cited more than 1000 times. He highlighted numerous examples of research topics he viewed as having questionable merit. Studies have since accumulated for these associations. We systematically evaluated current evidence of 53 example associations discussed in the article. Approximately one-quarter of those presented as doubtful are now widely viewed as causal based on current evaluations of the public health consensus. They include associations between alcohol consumption and breast cancer, residential radon exposure and lung cancer, and the use of tanning devices and melanoma. This history should inform current debates about the reproducibility of epidemiologic research results.In 1995, journalist Gary Taubes published an article in  Many examples used in Taubes’ influential 1995 article to denigrate epidemiology now have important public health implications. Science titled, “Epidemiology faces its limits.” The main emphasis was that epidemiology was “straying beyond the limits of the possible no matter how carefully the studies are done” because nonrandomized studies are so “plagued with biases, uncertainties, and methodological weaknesses that they may be inherently incapable of accurately discerning… weak associations.” The science of epidemiology, Taubes argued, studied a “mind-numbing array of potential disease-causing agents,” yielding an onslaught of “constitutionally contradictory” results.In July of 1995, journalist Taubes (The article prompted an immediate defense by the epidemiologic community (1) “Identifying major risk factors for disease can lead to misleading results when studying large numbers of variables interacting at multiple social, spatial, and temporal scales” (2) “In [evidence-based medicine], observational studies are taken to suffer from different biases and confounding” (3) “[epidemiology is among the fields] where serious concerns and debates about low reproducibility have been raised” (4) “[…] the vast majority of epidemiological literature has important limitations, which are magnified when assessing low-dose effects”  paper. We excluded 14 duplicate associations and 3 associations that were too vaguely worded for causal evaluation . Thus, 53 associations remained for assessment of causality (table S1). We obtained a recent and representative meta-analysis for 49 of these associations. We did not identify meta-analyses related to the use of phenoxy herbicides on lawns and malignant lymphoma in dogs, having shorter or longer than average menstrual cycles and breast cancer, or the use of the antihypertension medication reserpine and breast cancer. We obtained a credible consensus statement for evaluating causality for 51 associations. No consensus statements from authoritative public health organizations were found for having shorter or longer than average menstrual cycles and breast cancer or the use of the antihypertension medication reserpine and breast cancer. These two associations were automatically deemed indeterminate.Science questioned the utility of epidemiologic research. To bolster his case, Taubes selected numerous examples of research topics that he viewed as unlikely to have public health implications, building an argument for the “inherent incapability” of epidemiologic studies to draw causal inferences for weak associations [risk ratios (RRs) < 3]. Research has since accumulated about these example topics. We evaluated the current evidence regarding Taubes’ selected examples, finding that 11 of the 41 associations (~27%) suggested in his initial paper to be false positives are now widely viewed as causal based on current evaluations of the public health consensus.Twenty-five years ago, Gary Taubes’ widely cited report in With the exception of the association between obesity and esophageal cancer, none of the associations deemed indeterminate in 1995 and subsequently classified as causal by our assessment reached Taubes’ cited threshold (RR ≥ 3) for “serious consideration.” However, each association had sufficient scientific data to support one or more consensus statements from reputable national and international agencies, including the U.S. National Cancer Institute (NCI), U.S. Centers for Disease Control and Prevention (CDC), International Agency for Research on Cancer (IARC), and World Health Organization (WHO)—supporting the value of epidemiologic research in identifying even modest associations.The example of the association between alcohol consumption and breast cancer risk is illustrative. Taubes  for these associations and consistent with Taubes’ previous assessment. Many studies of the proposed associations were too specific for judgment, limiting consensus. For example, IARC concluded that extremely low-frequency magnetic fields are possibly carcinogenic to humans due to evidence of an increased risk of childhood leukemia at high levels of magnetic fields, but associations between electromagnetic fields and other cancers are unclear  per day and bladder cancer. Thus, the 53 identified associations and causal evaluations are based on our own interpretations of Taubes’ 1995 report. For some associations, there was wide variation in meta-analytic estimates, limiting comparability. This was, in part, due to different meta-analyses using different exposure definitions, cut points, or outcomes. Our analysis of the meta-analytic estimates was not intended to be exhaustive. We selected up to three meta-analyses per association to examine patterns in the strength of associations by causal evaluation—not to represent all meta-analyses for these associations or estimate causal effects. Consensus statements by authoritative groups were based on the state of the science and viewpoints of the experts at the time these exposures were reviewed. Thus, they could change as new data become available. Nonetheless, we took a systematic approach to identifying and summarizing the example associations put forth by Taubes, considering both meta-analyses and authoritative consensus statements.Inherent in the title and a common theme throughout Taubes’ 1995 article is that epidemiology is “stretched to its limits or beyond” and “at the edge of what can be done”  or linked to enrich cohort or case-control study data.Technological advances such as wearable devices for health and environmental monitoring and omics data have and will continue to improve exposure assessment—which Taubes .The project was led by two doctorally trained epidemiologists with faculty appointments (T.L.L. and L.E.M.), with most of the work completed by a subset of doctoral students in the epidemiology department at Emory University’s Rollins School of Public Health. This team drafted a protocol, reviewed, revised, and registered it with the Center for Open Science  for use in identifying meta-analyses and consensus statements. A third reviewer submitted terms and noted discrepancies. All three reviewers worked to achieve agreement on final search strings.Each reviewer was assigned to search a single database: PubMed [including term “meta-analysis” (publication type)], Cochrane, PROSPERO , or Embase (review filter). Relevant titles and abstracts on the associations of interest—published after 31 July 1995—were entered into Mendeley reference management software  per meta-analysis that we deemed representative of the exposure-outcome association of interest. If the exposure-outcome association described by Taubes  listed within the first 10 pages of the Google search results. In addition, the following general public health governmental and nongovernmental organizations were searched if they were not included in the first 10 pages of Google search results: IARC, U.S. CDC, WHO, American Public Health Association (APHA), U.S. Environmental Protection Agency, U.S. Agency for Toxic Substances and Disease Registry, U.S. Preventive Services Task Force, and the U.S. National Academy of Medicine. For each identified organization, it was determined whether the organization had a webpage dedicated to scientific evaluations, policy statements, guidelines, or recommendations. If confirmed, then that page was searched for statements relevant to the association of interest. If no such page could be located, then the outcome of interest was searched to identify specific evaluations, policy statements, guidelines, or recommendations. At least two independent reviewers compiled statements—published after 31 July 1995—that addressed the associations of interest and cited evidence from meta-analyses or individual studies. A third independent reviewer noted discrepancies, and all reviewers worked to achieve consensus. For each source, reviewers documented the following information: association of interest, organization and link to statement, title of the statement, author (if applicable), year of publication, and final evaluation or recommendation(s)—citing quotations from the statement.The abstracted information was reviewed by two new randomly assigned evaluators to determine whether the association was viewed as causal. Independently, reviewers classified each association as causal, noncausal, or indeterminate based on the criteria below. A third reviewer identified discrepancies, which were resolved by consensus among all reviewers.Consensus statements were primarily used for determining causality. We prioritized consensus statements that were based on comprehensive reviews conducted by a group of expert scientists for establishing causality . Associations where the meta-analysis was not accompanied by a consensus statement, or accompanied by a vague consensus statement, were assessed for causality based on the following guidelines:1) Consistency of results (number of replicated studies across multiple populations)2) Dose response 3) Demonstration of results in a randomized trial4) Biological plausibility 5) Temporal sequence (association measured using cohort or other study design in which exposure was measured before outcome)Associations without any published meta-analyses or consensus statements were classified as indeterminate. Final determination of whether the association should be considered causal, noncausal, or indeterminate was made taking all guidelines into account.


Observational studies suggested that systemic lupus erythematosus (SLE) might be associated with increased cancer incidence and cancer-related death, however, the results are inconsistent. We aim to comprehensively estimate the causal relationships between SLE and cancer morbidity and mortality using a meta-analysis of cohort studies and Mendelian randomization.A systematic search was conducted using PubMed to identify cohort studies published before January 21, 2021. Meta-analysis was performed to calculate relative risk (RR) and corresponding 95% confidence intervals (CI). In addition, we further evaluated the potentially causal relationships identified by cohort studies using two-sample Mendelian randomization.P<0.001) and cancer-related death  in patients with SLE. Subgroup analysis by site-specific cancer showed that SLE was a risk factor for 17 site-specific cancers, including six digestive cancers , five hematologic cancers , as well as cancer in lung, larynx, cervical, vagina/vulva, renal, bladder, skin, and thyroid. In addition, further mendelian randomization analysis verified a weakly association between genetically predisposed SLE and lymphoma risk .A total of 48 cohort studies involving 247,575 patients were included. We performed 31 main meta-analysis to assess the cancer risk and three meta-analyses to evaluate cancer mortality in SLE patients. Through meta-analyses, we observed an increased risk of overall cancer (RR=1.62, 95%CI, 1.47-1.79, Findings from our study suggest an important role of SLE in carcinogenesis, especially for lymphoma.https://www.crd.york.ac.uk/PROSPERO/, CRD42021243635. Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the presence of nuclear autoantibodies which could cause immune complexes formation, and thus resulting in inflammation of multiple organs . GloballIn past decades, various observational studies have investigated the relationship between SLE and cancers. Patients with SLE generally carry an increased risk of developing cancers. However, the relationship between SLE and cancer is complex. Recent epidemiologic evidence have suggested that there is an increased risk of some malignancies, including lung cancer, liver cancer, cervical cancer, and especially some hematologic cancers, such as non-Hodgkin lymphoma, Hodgkin lymphoma and leukemia among patients with SLE –10. The Meta-analysis of cohort studies is a useful approach to summarize data from multiple studies. This method can increase statistical power, and is a major criterion for determining causality , 21. In In the present study, we systematically investigate the correlation between SLE and overall cancer and site-specific cancers risk by conducting a meta-analysis of published cohort studies. Furthermore, we estimate the cause-specific standard mortality ratio (SMR) for cancer in patients with SLE. In addition, utilizing SLE-related SNPs as instrumental variables identified by the GWAS with the largest sample size, we investigated the correlation between genetically predisposed SLE and site-specific cancer risk using data from MR-Base.Since summary statistics of published studies were used, no additional ethical approval from an institutional review board was required.https://www.crd.york.ac.uk/PROSPERO/.Our methodology for the meta-analysis followed the guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and Meta-analysis Of Observational Studies in Epidemiology (MOOSE) , 27. TheWe searched PubMed to identify cohort studies that investigated the association between SLE and cancer risk published before January 21, 2021 Figure 1We only included studies in our analyses as follows: (1) cohort studies that quantitatively investigated this association; (2) exposure to SLE; (3) the outcome of interest was cancer; (4) providing risk estimates (such as relative risks (RRs), standardized incidence ratios (SIRs), odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) or data to calculate them; (5) written in English language. For multiple publications using the same study population, the latest publication or publication with the largest sample size was included. Abstracts, reviews, letters, case reports and studies that did not provide sufficient data to calculate the risk estimates were excluded.Two authors (MZ and YZW) independently performed literature screening and data extraction, and the disagreement was resolved by the corresponding author (DG). Extract the characteristic data of the study and the effect measure data of the interested results.The Newcastle-Ottawa scale (NOS)  was used2I statistic to quantify heterogeneity across studies. Potential publication bias was assessed using Begg’s and Egger’s approaches. Sensitivity analyses were performed to evaluate the robustness of the results. We considered P values of less than 0.10 in tests of heterogeneity and publication bias, and P values of less than 0.05 in the meta-analyses to be statistically significant. All tests were two-sided, with the exception of tests of heterogeneity and publication bias.Statistical analyses were done using Stata version 15 . We combined relative risks (RRs) of overall cancer and sites-specific cancer in SLE patients using a random-effects meta-analysis. For studies not containing RR, standard incidence ratio (SIR), odds ratio (OR) or hazard ratio (HR) were treated as the substitute of RR in the current study. We also performed subgroup meta-analyses based on region . Furthermore, we estimate the cause-specific standard mortality ratio (SMR) for cancer in patients with SLE. We used the Cochran’s Q statistic to test for heterogeneity and the 2) explained in the risk factor by the SNP(s) and the strength of the instrument (F-statistic). Details of methodology for SNP selection, and calculation of the R2 and F-statistic are presented in the The positive associations identified by the meta-analysis were further confirmed by two-sample Mendelian randomization analysis. The SNPs identified by the largest GWAS in populations of European ancestry were selected as instrumental variables respectively. We calculated the proportion of variance (Rwww.mrbase.org). All tests were two-sided, and P values of less than 0.05 were considered statistically significant unless stated otherwise.We used the inverse variance weighted (IVW) fixed-effect method as the main method to estimate the effect of genetically predicted SLE on sites-specific cancer. Other Mendelian randomization methods including MR-Egger, weighted median, and weighted mode method were used to check the consistency of the direction of effect estimates. All analyses were conducted using the TwoSampleMR and MRInstruments R packages, curated by MR-Base platform , 11.8% relationships (4/34) showed moderate heterogeneity (25%> I2 <50%), and 23.5% relationships (8/34) showed no/little heterogeneity (I2 <25%). However, sensitivity analysis suggested that most associations did not significantly change when a single was excluded (data not shown).As shown in F-statistic was 172.36 (F>100). We utilized the summary-level statistics for colon cancer, pancreatic cancer, lung cancer, cervical cancer, bladder cancer, lymphoma, and non-melanoma skin cancer from the MR-Base. The details of outcomes were shown in The significant associations in European ancestry were further estimated by two-sample Mendelian randomization analysis. The instrumental variable of SLE was constructed using 69 SNPs (P=0.0035), whereas a decreased risk of bladder cancer  in European ancestry as shown in Being consistent with the findings in the meta-analysis, the results of Mendelian randomization analysis indicated a causal association between genetically predisposed SLE and lymphoma , and thyroid cancers, as well as lymphoma, Hodgkin’s lymphoma, non-Hodgkin Lymphoma, leukemia, and multiple myeloma. This comprehensive study will provide epidemiological evidence supporting the associations between SLE and cancer. These evidences could be utilized to drive public policies and to help guide personalized medicine to better manage patients with SLE and reduce associated cancer morbidity and mortality.Furthermore, our study used 69 SNPs identified by the largest GWAS in European ancestry as the final genetic variants, which can explain over 34% of the heritability totally, suggesting a strong instrumental variable for analyzing the causal relation between SLE and cancer risk. Using the Mendelian randomization approach, our study provided weak evidence for a possible causal association between SLE and lymphoma risk in European ancestry. As far as we know, our study provided the most comprehensive and latest evidence for assessing the causality between SLE and site-specific cancers risk through meta-analysis of cohort studies and Mendelian randomization analysis.At present, the exact etiology underlying the attribution of SLE to cancer risk remains unclear. SLE is a chronic systemic autoimmune disease which could induce chronic multiorgan inflammatory lesion. The SLE-induced chronic inflammation may also promote the tissue injury, leading to the cancer development . ChemokiAccording to our study, hematologic cancers, especially non-Hodgkin lymphoma, is a common malignancy in SLE patients. Meta-analysis of cohort studies suggested that SLE patients had a 5.08-fold increased risk of lymphoma, 5.18-fold increased risk of non-Hodgkin lymphoma, and 3.21-fold increased risk of Hodgkin’s lymphoma. Moreover, both meta-analysis and Mendelian randomization analysis results revealed a potential causal relationship between SLE and lymphoma risk in European ancestry. Several potential mechanisms may account for the association between SLE and lymphoma. First, B cell and T cell activation play crucial roles in the pathogenesis of SLE and non-Hodgkin lymphoma, and the dysfunction of immune cells (such as B cell) in patients with SLE may lead to abnormal B cell activation and proliferation, resulting in B cell malignancies . Second,Interestingly, although meta-analysis results suggested an increased risk of bladder cancer, inconsistent results were observed in Mendelian randomization analysis. There are some plausible hypotheses that might explain the elevated risk of bladder cancers in patients with SLE. In particular, the use of immunosuppressive agents has an influence on the risk of bladder cancer in SLE patients. Several case series suggested that immunosuppressive agents increased the risk of bladder cancer in patients with SLE –54. OtheThe limitations of this study include: first, the heterogeneity was high in most of the meta-analyses. Besides, some of the included studies in our meta-analysis were the retrospective studies, and the results might be confounded by recall bias and other factors. However, sensitive analysis was performed which suggested the results were stable. Second, some outcome data were not found in MR-Base, therefore, we did not perform the Mendelian randomization analysis to identify these potentially causal relationships. Third, results from the meta-analysis of cohort studies and Mendelian randomization analysis were inconsistent concerning the relationship between SLE and bladder cancer risk, so future well-designed cohort studies and Mendelian randomization analysis are needed to determine this association.In conclusion, our meta-analysis of cohort studies suggests that patients with SLE are susceptible to overall cancers, particularly among Europeans. In addition, the overall cancer mortality was higher in patients with SLE, especially for lung cancer. Besides, a higher risk of site-specific cancers of esophagus, colon, anal, hepatobiliary, liver, pancreatic, larynx, lung, cervical, vagina/vulva, renal, bladder, skin (non-melanoma), and thyroid, as well as lymphoma, Hodgkin’s lymphoma, non-Hodgkin lymphoma, leukemia and multiple myeloma are observed among patients with SLE. This study highlights the important role of SLE in carcinogenesis.The original contributions presented in the study are included in the article/All authors contributed significantly to this work. DG designed the research study and wrote the first draft of the manuscript; MZ and YZW performed literature screening, data extraction, and quality assessment; YTW and YB analyzed the data and conducted the Mendelian randomization analysis. All authors interpret the results, reviewed, edited and approved the manuscript.This study was supported by the National Natural Science Foundation of China (81903393), and Chongqing Natural Science Foundation Program (cstc2020jcyj-msxmX0021). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.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.


Categorisation leads to loss of information, reduced power, inflation of the type I error rates, and biased results.Modelling treatment effect differences whilst accounting for non‐linear functional shapes may provide the opportunity to accurately make inferences whether a patient should be treated or not. To account for non‐linearities we may estimate the functional shape of the associations and investigate potential treatment effect differences.Splines are being used in single studies, both in intervention and prediction studies, and are also available in IPD‐MA context. In IPD‐MA, models can be estimated in either one or two stages. A generalised additive mixed effects model (GAMM)While the methodology has progressed, the use of splines in IPD‐MA is relatively uncommon. A possible explanation is that the available guidance is limited. Perperoglou et al. provided a review of splines approaches, but limited to single studies.Our goal is to explain and illustrate how to predict a conditional absolute treatment effect, as this measure is most relevant for clinical decision‐making.2In order to illustrate the aforementioned spline approaches in IPD‐MA we generated artificial data to mimic a previously reported non‐linear association between BMI and mortality.To illustrate the performance of splines in IPD‐MA we generated three distinct IPD‐MA scenarios, each consisting of five RCTs comparing two interventions (1:1 randomisation) with 500 participants per study. In the first scenario, which we refer to as the heterogeneous IPD‐set with equal BMI ranges, the association between BMI and mortality is different across studies, see Figure 3“Treatment effect modification”, also called “treatment effect measure modification”A commonly applied approach to investigate treatment effect measure modification is to model the interaction of a potential effect modifier with the treatment. In case of non‐linear associations, a spline‐transformed version of the continuous modifier can be used. Therefore, we model the association between the modifier and the outcome by including a spline‐transformed version of the modifier, both as main effect and in interaction with the treatment. In case of a binary outcome like mortality, a logit link function can be used in the model. In order to calculate the absolute risk difference between the treatment arms, we back‐transform the predicted outcome per treatment arm with the inverse logit function. To calculate the confidence interval of the difference in absolute risk, we use the approach proposed by Newcombe.4In a setting where the association between an outcome and a continuous variable X is non‐linear, one of the options is to use splines. Splines represent a continuous variable as a linear additive combination of (often)‐local parts, which each have a simple mathematical form and are known as basis functions. Numerous basis functions have been developed involving various mathematical forms, such as polynomials, radials and Fourier series. In this paper, we focus on basis functions based on piecewise polynomials. As the term piecewise implies, the range of X is divided into intervals, using cut‐offs called knots. Within each interval, a d‐degree polynomial of X is used to model the association between the outcome Y and X. These polynomials are connected across adjacent intervals. This way, instead of estimating a global non‐linear association over the full range of data, we estimate within intervals the linear association between the outcome and transformations of X.Two important choices have to be made, in addition to the degree of the basis functions: (1) the number and the position of the knots, and (2) whether a penalty should be applied. Splines calculated with the use of pre‐specified knots and without penalties are often called regression splines. The most commonly used regression splines are restricted or natural splines4.1In order to understand the rationale for the use of splines based on piecewise polynomials such as the ones of interest here, we first shortly introduce global and piecewise polynomials. Global transformations of X  include classical methods such as regular polynomials of X. Polynomials with successively higher degrees are able to capture successively more non‐linearity, but at the cost of instability, especially near the boundaries of X. The reason is twofold. First, each non‐linear part of a polynomial is more variable near the boundaries , such that small errors in the estimated coefficients have large impact. Second, each coefficient has an effect along the entire range of X, so the errors accumulate. To avoid this, polynomials fitted on different intervals of X, also called piecewise polynomials, may be preferred to global functions. Forcing the piecewise polynomials to be continuous over the knots leads to “piecewise polynomial” splines, in short “splines” (from a craftsman's tool). To accomplish continuity, several approaches have been proposed. One approach is to fit a global polynomial, whilst including terms that model the deviance from this globally defined shape within truncated parts of X, for example a truncated power series.Smoothing is improved when also the derivatives of the spline function are continuous. Typically continuity up to the second derivative offers sufficient smoothness.4.1.1A restricted spline based on truncated power series is constructed from a linear (first‐degree) global polynomial over the full range of X, where the deviations from this global polynomial are modelled with truncated transformations of X. We refer to truncated power series that are constrained with the “natural” or “restricted” property, as restricted cubic splines, following the terminology of Harrell.In our single study example, we used restricted cubic spline transformations of X both as main effects and as interactions with the treatment. We placed five knots at values corresponding to 5%, 27.5%, 50%, 72.5%, and 95% quantiles of X. In Figure 4.1.2B‐splines are another commonly applied regression spline approach. As opposed to the global nature of restricted cubic splines, the basis functions of B‐splines are generated locally, which improves numerical stability.In Figure 4.1.3κ knots require κ+1 degrees of freedom.The main advantages of regression splines are their relative simplicity and the fact that they can be represented by a formula. As a consequence, the estimated regression coefficients can be reported and used in further analysis, for example, meta‐analysis. Also both restricted splines and natural B‐splines are readily implemented in common models such as generalised linear models (GLMs) and have low computational cost. Natural B‐splines provide greater local support and numerical stability than restricted splines, since their basis functions are generated locally.4.2Jβ) multiplied by a non‐negative λ, often called a tuning parameter. As the term, “tuning” implies, changing the value of λ changes the magnitude of the penalisation.The two commonly applied penalised splines that we discuss, P‐splines and smoothing splines, increase the number of knots to a large set  or even to be equal to the number of observations (smoothing splines). This way they circumvent the problem of choosing the number and positions of the knots. Since estimating one parameter for each observation would clearly lead to a perfect fit and thus generate functional shapes with extreme variability, penalised splines introduce in their optimisation functions a penalty term  one observation is omitted, (2) a model is fitted, (3) using the model, a prediction of the omitted value is generated and (4) the distance between the observed and predicted value is calculated. This procedure is repeated for each observation and for a series of λ values. The λ that minimises the sum of the squared distances, that is, the GCV score, is selected. In Bayesian/mixed effects, modelling approaches the penalty term is estimated in a similar way as a random effects parameter.Penalised splines circumvent the problem of knot selection, but at a cost. By using a penalty in their optimisation function, they introduce bias in their estimate in order to obtain a more stable solution. Furthermore, in both P‐splines and smoothing splines the tuning parameter 4.2.1Jβ=∑Δpβw2. Due to this penalty, the number of basis functions and thereby the flexibility is allowed to be large, but the penalty forces adjacent coefficients to be similar when the data do not support such flexibility. The choice of the number of knots is not as important as with regression splines as long as it is sufficiently large. Note that the degree of the underlying B‐splines may be different from the order of the differences. A common combination is that of a third degree B‐spline with a second order difference. Using a penalty based on a zero‐order difference results in the ridge penalty.A specific type of penalised splines, P‐splines, proposed by Eilers and Marx,λ parameter was selected through a ‘leave one out’ GCV process as described above. In Figure P‐splines are usually based on equidistant knots. While it is possible to use a knot sequence that is not evenly spaced, this would require the introduction of weights,4.2.2fX, where fX is a cubic spline.Smoothing splines are another member of the family of penalised spline methods. Similar to P‐splines, the idea is to increase the number of knots, but this time to be equal or approximately equal to the number of observations. O′ Sullivanλ parameter was selected through a ‘leave one out’ GCV process as described above. In Figure In our single study example, we used smoothing spline transformations of X for both the main effect and its interaction with treatment, while the 4.2.3Penalised splines reflect the belief that the predicted regression lines are more likely to be smooth than not. Therefore, their main advantage is that they are more likely to show smoother functional shapes as compared to unpenalised splines. Another advantage is that they circumvent the need to specify the positions and the number of knots, which in most cases are not known beforehand and may need to be estimated. Penalisation also affects the inference, due to the bias‐variance trade‐off. For instance, the coefficient estimates are subject to a smoothing bias, therefore their interpretation may be problematic. Note that this issue does not necessarily apply to the predicted outcomes. A related issue is that the degrees of freedom have to be modified to account for the penalisation. Wood suggests the use of effective degrees of freedom of a model. Effective degrees of freedom are calculated using the Welch‐Satterthwaite approximation formula and can be used to compare models fitted with different types of splines.5In the previous sections, we focused on estimating non‐linear main effects and interactions with the treatment in a single study. As trials are typically not powered to investigate effect modifiers, exploring non‐linear effects in a single study may often be problematic or yield very wide confidence intervals. Depending on the underlying curvature, splines need a high amount of data and therefore their use is more feasible in the context of an IPD‐MA, where they enable the statistical modelling of complex relationships such as non‐linear associations.5.1In pointwise meta‐analysis, a separate meta‐analysis is conducted per distinct value (point) of X, using the outcomes and standard errors as estimated per study. In the first stage of a two‐stage pointwise meta‐analysis, as proposed by Sauerbrei and Royston in method 3,5.1.1Select a spline approach and fit an appropriate model per study including interaction between X and the treatment. Since pointwise meta‐analysis pools predicted outcomes instead of model coefficients, the study‐specific models need not be the same. Different modelling techniques may be applied across studies, including linear models, fractional polynomials, and splines of different degrees and with different knot specifications. During this stage, we may use several criteria to find the best fitting model per study, for example, Aikaike information criteria, GCV or likelihood ratio tests , we can extrapolate the regression lines to cover the full domain of X. Automatically, the standard errors of the predicted outcomes in the extrapolated regions will be increasing along with the extent of extrapolation, and ensure small weights for the extrapolated outcomes in the meta‐analysis.Using the models from step 1, estimate regression lines g and its inverse g−1:g−1f^TjX and g−1f^CjX for visualisation of the results per study. Pooling in stage 2 will be done on the scale of the link function, for example on the logit scale, per treatment arm, while in case of different ranges across the studies, the outcome is predicted for the full range  of X.If, in stage 2, we aim to show the predicted outcome per treatment arm and conditional on X, we calculate the predicted outcome per treatment arm, g−1f^TjX−g−1f^CjX or the relative treatment effect g−1f^TjX–f^CjX, again over the full range of X, and calculate the corresponding confidence interval , and we estimated per study the absolute risk difference and the confidence interval (step 3.2) over the full range of X. In the second stage, we pooled both the predicted curves per treatment arm  and their risk differences , using random effects meta‐analyses with REML estimators for τ5.2Instead of using pointwise meta‐analysis per distinct value of X, the functional shapes can also be pooled using multivariate meta‐analysis. This approach, as proposed by Gasparrini et al.,The multivariate meta‐analysis algorithm proceeds as follows:5.2.1As a preliminary step, choose the knots corresponding to the optimal locations across the studies along with the degree of the unpenalised spline.Per study j, fit a model including interaction between X and the treatment with the chosen specifications of step 1.∈ , extract per study the estimated coefficients β^qj along with their variance–covariance matrix.With Q the total number of coefficients and q 5.2.2β^qUse either fixed or random effects multivariate meta‐analysis to estimate the pooled To calculate the predicted outcome given X and treatment T, multiply the pooled estimates with the design (or model) matrix containing the values of X along with their spline transformed values.To estimate the treatment effect conditional on X, subtract the pooled‐per‐treatment arm outcomes and calculate the confidence interval as described in section We applied multivariate meta‐analysis in combination with regression splines in all three scenarios. To do so we performed data augmentation as a preliminary step5.3Instead of using a two‐stage meta‐analysis, we may also conduct the analysis in one stage, using a mixed effect model with splines, that is, a generalised additive mixed effect model (GAMM). Hereby, we may include spline transformations of X as main effects and as interactions with the treatment as described in section Xj¯ creating a new variable Z=X−X¯j. Subsequently, include Z, X¯j, and the interaction of Z with the treatment in the one‐stage model.Depending on the estimand of choice and the assumptions researchers wish to make they may use any combination of the above assumptions for their model. On the one side, full stratification provides least bias at the cost of high variance . On the other side, a model with only common effects has most bias and least uncertainty with respect to the parameters. A model utilising random effects will be somewhere in‐between since the variability across studies is forced to follow a certain distribution, which will shrink study‐specific estimates to the overall mean. Note that interaction terms included in one‐stage mixed effect models may be prone to ecological bias and amalgamate the within and across study effects.In our three scenarios, we used the four aforementioned spline transformations both as main effect and in interaction with the treatment. We used a random intercept and random slope for BMI in combination with a fixed spline part. For the restricted cubic splines, we used five knots , for natural B‐splines four equidistant knots (two inner knots plus the boundaries), and for P‐splines we used 17 equidistant knots (15 inner knots plus the boundaries). Note that we positioned the knots over the full domain of BMI and that no manually conducted data‐augmentation nor extrapolation was needed. The pooled mortality risks per treatment arm are presented in Figure 5.42, and prediction intervals per value of X to assess whether it is sensible to pool the associations. Therefore, it is always informative to start with investigating the results per study, similar to the first stage in two‐stage methods. However, two‐stage methods may not always be feasible when some studies are too small with respect to model complexity, that is, when the number of parameters that needs to be estimated is large compared to the number of observed events .We illustrated the association between BMI and mortality risk in three scenarios using four spline methods and two two‐stage approaches  and one one‐stage approach (GAMM). Table 5.52 and the between study heterogeneity τ2. Consequently, a random effects meta‐analysis may give disproportionate weight to the extrapolated outcomes. Hence, attention should be payed to the role of the between‐study heterogeneity in the estimation of the pooled functional form. Also since we are performing a meta‐analysis for each value of X, pointwise meta‐analysis may be more computationally intensive and show wider confidence intervals than multivariate meta‐analysis and GAMMs. Finally, the pooled curves are not represented by a formula, thus there are no pooled regression coefficients to be reported and used in further analysis. This is especially inconvenient when combinations of X are of interest.The main advantages of pointwise meta‐analysis are its flexibility, robustness  and ease of use. In particular, pointwise meta‐analysis is flexible since we are allowed to fit different models across the studies, as we are pooling the predicted outcomes rather than the coefficients. As an extreme example, in one study, we may apply a restricted cubic spline transformation of X, in another a second degree B‐spline and in another we may choose to not transform X. Such differences are more likely to occur when studies greatly differ in sample size and in their baseline characteristics, . Also we are allowed to vary the number and position of knots per study. As we are fitting per study a best fitting model, pointwise meta‐analysis is also robust to model misspecification. When the ranges of X are different across studies, pointwise meta‐analysis uses the whole domain of X, even without data augmentation, by means of extrapolation. The main disadvantage of pointwise meta‐analysis is that the pooled curve may not be smooth. This might be related to non‐smooth estimates of heterogeneity across the range of X  of X across studies, similar to pointwise meta‐analysis, and include all studies regardless of the number of observations. In contrast to pointwise meta‐analysis, GAMMs result in smooth pooled curves and confidence intervals, also in case of differences in measured domains of X across studies. The main disadvantage of GAMMs is that they do not automatically provide insight into the full amount of heterogeneity between studies as provided in two‐stage methods. Therefore, if the data allow, it is advisable to fit study‐level models anyway to inform the one‐stage model. Furthermore, since GAMMs are one‐stage mixed effects models similar to generalised linear mixed models, they require careful modelling while taking into account between‐study heterogeneity. Caution is especially important when aggregation  bias might be present, as discussed by Riley et al., Hua et al., and Belias et al.6All analyses were performed with the statistical software R version 3.6.0. For data manipulation we used the tidyverse7To illustrate the use of splines combined with the aforementioned pooling methods in a real example we consider a previously published IPD‐MA investigating the effect of antibiotics in children with AOM.7.1Data from one study were omitted as information on unilateral or bilateral AOM was not reported. We used data of children till 9 years old, as AOM above 9 years is rare and we had a limited number of children over that age (only 15). From the remaining five studies, one study  had a limited number of events (children with fever/ear pain) and for some age‐bilateral AOM combinations no events at all. Therefore, we followed different strategies across the pooling methods for this study.7.1.12 estimator. To show the pooled risk conditional on children's age, bilateral AOM and treatment group, we back‐transformed the pooled curves into risk curves. To show the treatment effect conditional on children's age and bilateral AOM, we first back‐transformed per study the predicted fever/ear pain risk. To estimate per study the absolute risk difference between the treated and control along with their confidence intervals, we followed the proposal of Newcombe,2 estimator.For pointwise meta‐analysis, for the Appelman study we fitted a logistic regression model including the main effects of bilateral AOM, treatment and age and their two‐by‐two interactions, without any spline transformation for age. For the remaining studies we fitted per study a logistic regression model including the main effects of bilateral AOM, treatment, and age transformed by each of the aforementioned spline approaches, and we included the interactions of the spline transformed age with bilateral AOM (yes/no) and treatment (both two‐way). For restricted cubic splines we followed Harrell's suggestion and used per study three knots at the 10%, 50% and 90% quantiles of age; for natural B‐splines we used third degree basis functions and per study three equidistant knots (1 inner knot plus the boundaries per study), while for P‐splines we used third degree basis functions and per study 17 equidistant knots (15 inner knots plus the boundaries per study). For the penalised splines (P‐splines and smoothing splines) the tuning parameter λ was selected through a ‘leave one out’ GCV process. Subsequently, we extracted the predicted outcomes for fever/ear pain in logit scale and pooled them using a random‐effects meta‐analysis approach with REML τ7.1.22. Finally, to show the risk of developing fever/ear pain conditional on age, treatment and bilateral AOM, we multiplied the pooled coefficients with the corresponding design matrix and back‐transformed the pooled outcomes using the inverse logit function. To calculate the absolute risk differences and their confidence intervals, we followed the proposal of Newcombe,For multivariate meta‐analysis, we omitted the Appelman study mentioned above. Therefore, multivariate meta‐analysis was based on the four remaining studies with a sufficient number of observations to fit splines. Also, since in multivariate meta‐analysis the ranges of age across the studies need to be the same, we performed data‐augmentation as a preliminary step. In the first stage of the multivariate meta‐analysis, we fitted a logistic regression model including the main effects of treatment, bilateral AOM and spline transformed age, and the two‐way interactions of spline transformed age with treatment and bilateral AOM. Since in multivariate meta‐analysis the positions of knots need to be the same across the studies, for restricted cubic splines we used three knots at 10%, 50%, 90% quantiles of age calculated on the four studies combined; for natural B‐splines we used third degree basis functions and three equidistant knots (one inner knot at age 2.5 plus two at the boundaries of age). Subsequently, we extracted the regression coefficients and their variance–covariance matrix and pooled them using a random‐effects meta‐analysis approach with REML estimator for τ7.1.3For GAMMs, we included all five studies. We fitted a logistic regression model including the main effects of treatment, bilateral AOM and spline transformed age and the two‐way interactions of spline transformed age with treatment and bilateral AOM. We used similar definitions for knot positioning and degrees of splines as in pointwise and multivariate meta‐analysis, but using the whole data‐set without data‐augmentation nor extrapolation. We followed Wood's proposal and included random‐effects for the intercept and for the slope of age additively to account for the within study clustering of participants.7.2Figures Since this is an empirical example, the underlying true associations are not known and we cannot draw firm conclusions with respect to the appropriateness of the different approaches. However, we show the pooled curves and compare them with regard to their smoothness and width of confidence intervals, and report convergence issues if any. As we were investigating interactions between treatment, bilaterality of AOM and age, in some studies the combinations of these variables created groups of patients with a limited number of events at certain age ranges. Consequently, pointwise meta‐analysis resulted in wider confidence intervals compared to multivariate meta‐analysis and GAMM. Furthermore, as in the artificial data‐sets, the predicted pooled regression lines were not always smooth due to differences in the age ranges across the studies. Multivariate meta‐analysis resulted in smooth pooled regression curves for both the restricted cubic splines and the natural B splines approach, see Figures 8Our results, in which we illustrated four spline‐based approaches , and three pooling methods  on three scenarios with artificial data, showed that in case of a heterogeneous data‐set with similar ranges of the effect modifier, all approaches performed equally well in modelling the underlying true association. In the two scenarios with different ranges, pointwise meta‐analysis showed less smooth pooled regression lines and confidence intervals, which were wide in subdomains of BMI. Multivariate meta‐analysis failed to converge for restricted cubic splines and showed results only when combined with natural B‐splines, showing smooth regression lines and confidence intervals. GAMM showed results in both scenarios. GAMM's pooled regression lines and confidence intervals were smooth in all cases. In the empirical example, we investigated the association between age and the effect of antibiotics in children from 0 to 9 years with unilateral or bilateral otitis media. Pointwise meta‐analysis resulted in non‐smooth pooled regression lines and confidence intervals, which were also wider than multivariate meta‐analysis and GAMM. Multivariate meta‐analysis showed smooth pooled regression lines and confidence intervals which were wider than GAMM's. In this specific example, GAMM, especially when combined with penalised splines, resulted in smooth pooled regression lines with narrower confidence intervals than the other pooling methods.2 less than 40%. Finally, in the second and third scenarios we generated different boundaries for BMI per study, in order to illustrate the differences between the pooling methods in scenarios where study‐specific domains of X have limited overlap.The major strength of our manuscript is that as far as we are aware, we are the first to provide an introduction on how to apply a variety of spline methods in both single and multiple studies, in order to investigate treatment effect differences when non‐linearities are present. In our illustrative examples based on simulated data we introduced three features into our generating mechanisms. First, the association of mortality risk with BMI was simple and realistic as it was based on previously published papers.Some potential limitations deserve attention. First, we did not illustrate the performance of the aforementioned approaches in a scenario with homogeneous associations and similar ranges of the effect modifier across studies. We considered that this scenario is rarely present in practice and that all approaches would produce similar results. We defined three scenarios, with either none or rather extreme differences in the ranges of the effect modifier across studies, combined with either none or limited between‐study heterogeneity. We chose these settings as they generated data that were appropriate for pooling, and suitable for the purpose of an introductory paper in IPD‐MA, as illustration for the stronger and weaker points of the approaches. We did not investigate the performance of the methods in settings with weaker interaction effects and/or larger between‐study heterogeneity. However, it is reassuring that the findings from our empirical example were similar. Second, we did not illustrate the performance of the pooling methods in scenarios with ecological bias. Modelling choices that avoid ecological bias in presence of non‐linear associations still require further research and were thus outside the scope of this article. Third, we showed the performance of the spline‐based pooling methods in three scenarios and one empirical example. Although results suggest that splines in IPD‐MA provide a helpful tool to evaluate and capture non‐linear treatment effect differences, more research is needed to evaluate their specific strengths and weaknesses. Last, corresponding to our main aim to provide an introduction to splines, we limited our study to spline‐based approaches whereas other techniques might also be able to deal with non‐linear associations, e.g. tree‐based approaches,Other researchers have also drawn the attention to the importance of modelling non‐linear associations in IPD‐MA.In conclusion, taking non‐linear associations into account whilst combining multiple studies requires careful modelling. Across three IPD‐MA scenarios and one empirical example we showed that pointwise meta‐analysis was flexible and robust to model differences, but non‐smoothness could occur in case of different data domains across studies. Multivariate meta‐analysis was efficient if specified “correctly”, but lacked robustness and flexibility when combined with restricted cubic splines from studies with different domains. GAMM could handle different study domains and sample sizes, whilst producing smooth pooled regression curves, but needs careful modelling. Splines provide a helpful tool to capture non‐linear treatment effect differences in IPD‐MA.Appendix S1. Supporting InformationClick here for additional data file.


Results were reported according to PRISMA guidelines. In all cognitive domains, the ASD group showed significantly reduced performance. Importantly, EF subdomains and ToM were not significantly correlated. This finding rules out a significant association between EF subdomains and ToM and questions the relevance of EF dysfunctions for the autism-specific feature of reduced mentalizing.Executive functions (EF) have been shown to be important for the understanding of Autism Spectrum Disorder (ASD), but dysfunctions of EF are not autism-specific. The specific role of EF in ASD, its relationship to core autism characteristics, such as mentalizing, needs to be explored. Medline- and PsychINFO databases were searched for studies published between 1990 and 2020 that included measures of EF in ASD and typically developing control persons (TD) in combination with either Theory of Mind (ToM) or Weak Central Coherence (WCC) tasks. A pre-registered meta-analysis and cross-study regression was performed including a total of 42 studies (ASD  Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that primarily comprises symptoms in the social domain with a focus on interaction and communication, as well as restricted repetitive behaviors . Aside fStudies regarding atypical EF in individuals with ASD present heterogeneous results. On the one hand, it has been found that individuals with ASD show significantly reduced performance in tasks of EF than typically developed control persons (TD) . On the The heterogeneity of EF findings across studies makes general statements about the relevance of EF impairments for the autistic neurocognitive profile difficult, and it is unsatisfactory to attribute this heterogeneity to variability in sampling . SourcesAdditionally, the effects of learning disabilities and ASD on EF are challenging to disentangle. It seems, however, as if a specific profile of executive dysfunction for individuals with ASD exists. In a study investigating EF impairments in two groups of persons with learning disabilities, one with, the other without the comorbidity of ASD, EF impairments were more pronounced in the ASD group . While tFurthermore, EF impairments were reported in Tourette’s syndrome, obsessive-compulsive disorder , and eveFor instance, atypical mentalizing specifically prevails in ASD, as described by the reduced Theory of Mind (ToM) account . LikewisYet, none of the previously mentioned theories can primarily explain ASD. Accordingly, it has been proposed to abandon the idea of a single theory explaining ASD , 26. TheThe proposal of atypical mentalizing being a consequence of EF dysfunctions has been proposed , 27, butThe evidence regarding the relationship between EF and WCC is mixed. For instance, it was considered that difficulties in global processing might be the result of an inability to switch between the local and global aspects of a stimulus , 34. ThiThus, the current pre-registered meta-analysis and cross-study regression aimed to identify whether EF, and if so which particular subdomains, might impact the autistic cognitive profile in terms of autism-specific reduced ToM and WCC.This systematic review and meta-analysis was pre-registered in the Prospero database (registration number: CRD42019139151).Studies were included if published in English or German between January 1990  and September 2020. Eligible studies had to include participants with a diagnosis of ASD according to DSM-III, DSM-IV, DSM- IV-TR, DSM-5, or ICD-10. The studies had to include assessments of EF measures in combination with either ToM or WCC. Further, we only included studies that provided sufficient data to calculate effect sizes and correlations; no minimum sample size was required.We searched Medline (via PubMed) and PsycInfo, applying the following generic literature search:“Autis*” AND “THEORY OF MIND” AND “EXECUTIVE FUNCTION”“Autis*” AND “CENTRAL COHERENCE” AND “EXECUTIVE FUNCTION”Theory of Mind, Executive Function and Central Coherence were replaced in the process by numerous synonyms and tests with respect to their domain . The subdomain that was reported the most was cognitive flexibility (n = 38 articles), followed by inhibition (n = 30), planning (n = 22) and working memory (n = 20). After filtering the initial 71 articles for the inclusion criteria, 59 articles remained; 17 of these articles were excluded because they only offered limited data. Some articles only reported a mean score, without reporting a minimum or maximum, which did not allow us to calculate percentages , demographics of participants (including the specifics of the control groups), exclusion criteria of the respective study, as well as the mean, standard deviation, and other measures of dispersion (SE), out of which we calculated standard deviations for our analysis.We calculated meta-regressions of each of the individual cognitive domains to analyze associations of differences between ASD and TD groups including average age of study samples and IQ difference between ASD and TD groups .Two independent raters evaluated each study’s quality using the Newcastle Ottawa Quality Assessment Scale for Case Control Studies (NOS) .For the analysis of publication bias, we employed funnel plots –3. FunneData analysis was performed using RevMan 5.3, JMP 15 and CMA . All articles included in the final analysis had investigated EF in combination with either ToM or WCC among patients with ASD. Out of the 42 identified studies, nine studies did not have a control group and were subsequently omitted from the calculation of effect sizes, leaving a total of 33 studies.To quantify the magnitude of the different measures of EF (ToM and WCC), we computed effect sizes (Hedges’ g) of performance in the ASD group relative to the control group, based on means, standard deviations and sample sizes for each measure. For the meta-analysis, a random effects model was used . Since tAs conservative approach, we conducted an additional separate meta-analysis without studies reporting effect sizes greater than one.Studies used different tests and scales for measuring the same psychological construct. For example, the concept of inhibition, a subdomain of EF, was measured by the BRIEF (Behavior Rating Inventory of Executive Function), the Luria hand game, the Stroop Inhibition Task, a Card Sorting Task and multiple others . The conTo calculate correlations between different psychological concepts among participants with ASD and to estimate the impact of multiple subdomains of EF on ToM or WCC, we standardized all reported results by transforming them to percentages. In cases where a high test-score indicated bad performance, results were reversed so that, after standardizing, a high percentage indicated a good result.r with 95% confidence intervals between the cognitive constructs of EF subdomains, ToM and WCC, using JMP 15 as a meta-analytical tool. Since the concept of WCC could only be correlated with very few studies, this variable was omitted from further calculations.To choose regression models, normality was analyzed by the Shapiro-Wilk-Test, which showed a normal distribution of almost all measures of cognition. Therefore, we calculated Pearson’s A total of 11 studies had to be excluded for the correlation analysis mainly due to missing data, leaving a total of 31 studies that were eligible for this part of the analysis.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) . For furData came from a total of 42 studies used for the final analysis. The literature search yielded data from 1,546 participants with ASD , that were compared to 1,206 typically developing individuals . Sample sizes ranged from a minimum of 10 participants to a maximum of 181.M = 101.82, SD = 10.76 and that of the TD group was M = 104.03, SD = 21.63. There was no significant effect of IQ between the two groups t(33) = 0.739, p = 0.536.Of the 42 studies observed, 36 had measured IQ. The grand mean IQ for the ASD group was k = 15, g = -0.78, 95% CI , I2 = 72% ASD n = 575 TD n = 578; working memory (EF2), k = 11, g = -0.79, 95% CI , I2 = 89%, ASD n = 286, TD n = 303; flexibility (EF3), k = 19, g = -0.47, 95% CI , I2 = 79%, ASD n = 647, TD n = 657; planning (EF4), k = 13, g = -0.81, 95% CI , I2 = 85%, ASD n = 557, TD n = 549. See results in The different domains of EF that were analyzed showed moderate to large effect sizes: inhibition (EF1), n = 873) performed poorer compared to TD participants (n = 870) in ToM tasks, with a large effect size .Participants with ASD (n = 413), with a large effect size .Regarding WCC, participants with ASD (n = 403) showed lower test scores relative to TD participants , with a total of n = 422 participants with ASD and n = 420 TD participants; working memory ; flexibility  with n = 510 participants with ASD and n = 527 TD participants planning .The different domains of EF that were analyzed showed small to moderate effect sizes: the matter of inhibition (n = 682) compared to TD participants (n = 696) in ToM tasks, with a moderate effect size .Participants with ASD performed poorer (n = 326) showed lower test scores relative to TD participants (n = 342), with a small effect size .Regarding WCC, participants with ASD (p > 0.05); therefore, only the minimum and maximum correlations are reported. Correlations ranged from a maximum of r(6) = 0.143, p = 0.353 for working memory in the TD group to a minimum of r(22) = 0.0040, p = 0.7667 between planning and ToM in the ASD group.In general, the correlations between the EF subdomains  and ToM can be categorized as small for the ASD and the TD group. None of the correlations showed significance . The difference in WCC measures between the ASD and TD group became weaker with increasing age of participants. For ToM, no such effect was observed.In meta-regressions, we found no association between effect sizes and IQ difference between ASD and TD groups. When looking at WCC, a significant effect for age was observed . The studies that qualified for analysis can be drawn from k = 14, g = -0.77, 95% CI , I2 = 74%), with a total of n = 550 participants with ASD and n = 553 TD participants; working memory ; flexibility  with n = 582 participants with ASD and n = 584 TD participants planning .The different domains of EF that were analyzed showed large to moderate effect sizes: for the matter of inhibition (n = 766) compared to TD participants (n = 769) in ToM tasks, with a moderate effect size .Participants with ASD showed reduced performance (n = 338) showed lower test scores relative to TD participants (n = 354), with a moderate effect size .Regarding WCC, participants with ASD . To account for this limitation, future research should include matched control groups to better compare the differences in ToM, EF, and WCC between ASD and control individuals. In addition, given the heterogeneity of ASD, some aspects of cognition are more affected in different age groups. Interestingly, cognitive abilities, such as visual and verbal memory, persist across adulthood in ASD, while other cognitive abilities become less apparent in old age .With respect to EF, we found reduced performance in persons with ASD with large to moderate effect sizes for all studied subdomains in the following descending order: planning, working memory, inhibition, and flexibility. These results are in accordance with previous findings of atypical EF in ASD , 48. AgeConcerning the main question of the current study, the regression analyses showed no significant association between any of the EF subdomains and ToM, neither for the ASD nor the TD group. To our knowledge, no previous studies have looked at the relationship between these two cognitive domains employing meta-analytical tools; although previous studies have suggested that the impairment in ToM, which is regularly observed in ASD, might be understood as a consequence of EF dysfunctions , 27. TheAs a common limitation in ASD research, the included samples were very heterogeneous in multiple aspects, such as sample size and inclusion criteria. For instance, some studies allowed for comorbidities or measured the severity of symptoms whereas others did not. Therefore, all participants that were included showed highly different degrees of impairment, limiting comparability but, at the same time, reflecting the nature of the spectrum. Likewise, studies were heterogeneous in terms of paradigms and methodology. For instance, among the four subdomains of EF that were included, there were at least 16 different tasks reported. Moreover, the tests were often adapted for specific samples , or the tests were shortened due to limited attention of participants. Furthermore, the array of tests was not individually checked for validity in the present study. This suggests that a more critical evaluation of task comparability would be useful for future investigations. The clinical and methodological heterogeneity is reflective of the research activities in the whole field, and while we accounted for this by applying random effects models, it calls for cautious interpretation.Reduced categorization of the ToM tasks remains a further limitation of the data collection. ToM tasks of higher and lower order were not separately assessed but all summarized and considered ToM tasks in the wide sense. Since the cognitive requirements greatly differ depending on the given task in a particular study, the findings may be affected by concentration and cognitive ability. Furthermore, the EF subdomains were not discretely defined, which may also have additionally contributed to the variability.Some included studies reported unusually high effect sizes. A previous study that had looked at subgroups of EFs also noted relatively high effect sizes. They noticed that high effect sizes were present in studies that had used self-report data or questionnaire data . Hence, The independence of EF and ToM renders EF as an unspecific symptom of developmental and neurological conditions , 53, andAn important factor to consider is the ecological validity of the assessments used to measure cognitive ability and executive functioning in ASD. Ultimately, the goal of research is to apply research findings into practice in a real-world setting. While executive tests are widely used as a measure of EF, studies have found that not all neuropsychological tests have strong ecological validity . TherefoThis meta-analysis and regression showed that there is an overall atypical pattern of performance in ASD for all classical cognitive constructs: EF, ToM, and WCC. Although all EF subdomains were significantly different in the ASD group with a moderate effect size, EF did not show any significant association with the autism-specific domain of mentalizing.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.CB supported regarding statistics. KV supported in terms of content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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