Likewise, higher absolute retinol levels suggestively reduced the pancreatic cancer risk in FinnGen—the OR per 10% unit increase in ln-transformed retinol was 0.705 , which was consistent with the results in UK Biobank and the combined OR was 0.747 . Sensitivity analyses verified the above suggestive evidence. Our findings suggest that higher levels of antioxidants are unlikely to be a causal protective factor for most digestive system cancers, except for the suggestive protective effects of ascorbate on colon cancer and of retinol on pancreatic cancer.Molecular mechanisms and observational studies have found that diet-derived antioxidants are associated with digestive system cancers, whereas there is a lack of causal evidence from randomized clinical trials. In this study, we aimed to assess the causality of these associations through a Mendelian randomization (MR) study. Single nucleotide polymorphisms of diet-derived circulating antioxidants , accessed by absolute levels and relative metabolite concentrations, were used as genetic instruments. Summary statistics for digestive system cancers were obtained from the UK Biobank and FinnGen studies. Two-sample MR analyses were performed in each of the two outcome databases, followed by a meta-analysis. The inverse-variance weighted MR was adopted as the primary analysis. Five additional MR methods  and replicate MR analyses for outcomes from different sources were used as sensitivity analyses. Genetically determined antioxidants were not significantly associated with five digestive system cancers, after correcting for multiple tests. However, we found suggestive evidence that absolute ascorbate levels were negatively associated with colon cancer in UK Biobank—the odds ratio (OR) per unit increase in ascorbate was 0.774 (95% confidence interval [CI] 0.608–0.985,  Digestive system cancers, most commonly including esophageal, stomach, colon, pancreatic, and liver cancers, are the leading cause of morbidity and mortality worldwide, accounting for some 4.2 million new cases and 3.1 million deaths each year . ConsequAccording to the possible protective effect mechanisms of antioxidants on the prevention of digestive system cancers, a large number of epidemiological studies have explored the relationship between antioxidants and digestive system cancers. Some observational studies have found that higher levels of vitamin E, vitamin C, and carotenoids in dietary components, supplements, or blood concentration are associated with a lower risk of esophageal cancer ,12,13, sMendelian randomization (MR) uses genetic variants that are randomly allocated during conception, generally single-nucleotide polymorphisms (SNPs), as instrumental variables for exposure, which is similar to the random assignment in RCTs ,23. TherIn this study, we used two-sample MR analyses to assess the causal associations between genetically predicted diet-derived absolute circulating antioxidants and their metabolites, with the risk of five digestive system cancers.This study used two-sample MR analyses of summary-level genetic data to investigate whether diet-derived antioxidants, including α-and γ-tocopherol , ascorbate , retinol , β-carotene, lycopene, and urate, were causally associated with the risk of five digestive system cancers, including esophageal cancer, stomach cancer, colorectal cancer, pancreatic cancer, and liver cancer. For these diet-derived antioxidants, we examined the following two phenotypes: (1) absolute circulating antioxidants, measured as authentic absolute levels in the blood; and (2) circulating antioxidant metabolites, quantified as relative concentrations in plasma or serum. The principle of MR analysis and three assumptions of the instrumental variables are as follows: the relevance assumption, the independence assumption, and the exclusion-restriction assumption are presented in p < 5 × 10−8, linkage disequilibrium [LD]: r2 = 0.001 and clump distance = 10,000 kb). Three independent, single nucleotide polymorphisms (SNPs) associated with α-tocopherol were obtained from the latest GWAS on 4014 individuals of European ancestry . Beta indicates the estimated genetic effect of SNP on exposure, EAF is effect allele frequency, SE is standard error of the estimated effect, and N is sample size. The F-statistic for each SNP was calculated by the following formula: BETA2/SE2. To avoid the risk of bias caused by weak instrumental variables, SNPs with F-statistics greater than 10 were retained as the final genetic instrument.Based on the PhennoScanner database  was used in the present study , p = 0.024; and 0.699 , p = 0.026). For most outcomes, the effect size and direction of the MR-Egger method were consistent with the IVW method, though with low precision. The MR-PRESSO method identified outlier SNPs for absolute urate on colon cancer and for γ-Tocopherol metabolites on pancreatic cancer in the UK Biobank study. MR analyses after removing outliers showed that the OR estimates did also not change significantly. No outlier SNPs were identified in the MR-PRESSO analysis for the other outcomes.p = 0.020).For the MR analyses with two SNPs, the results of the likelihood ratio method were basically consistent with the main analysis results. Notably, we found that a genetically determined higher level of absolute retinol was associated with a reduced risk of pancreatic cancer in the FinnGen study : 0.83, 95% CI: 0.70, 0.97] . AnotherRetinol is one of the most biologically active forms of vitamin A and is a biochemical index of vitamin A status in the human body . Twenty The above observational studies, RCT, and molecular mechanism studies can support our findings. However, the effects of genetic susceptibility are lifelong, whereas the effects of antioxidant supplementation may only last up to the trial period. Considering that short-term supplementation therapy may not alter long-term risk—similar to the results of the nutritional intervention studies conducted by Wang et al. —slight eOur study has several advantages. First, the MR design of two independent samples, based on genetic instrumental variables, reduced the possibility of subjects being exposed to unnecessary risks and hazards in the RCT study and supplemented the genetic theoretical basis of dietary antioxidants and digestive system cancers. Second, we used two independent sets of instrumental variables, including the absolute blood levels of antioxidants and the corresponding circulating metabolite concentrations. The effect direction of the antioxidants on the cancers was generally consistent in both sets, suggesting that the measurements were reliable. Third, this study examined the associations in two independent populations and performed a meta-analysis. The consistency of the results demonstrates the reliability of our findings. The combination of two large databases increased the sample size of this study and, thus, enhanced our ability to detect associations. Finally, our study performed a series of sensitivity analyses, which showed that the results obtained by other methods were consistent with the main IVW method. This, again, verifies the robustness of our results. The pleiotropic assessment confirmed that our findings were not biased by pleiotropic effects.Inevitably, there were several limitations in our study. First, our analyses were based on published summary data rather than individual data, therefore, we were unable to test nonlinear causal relationships between antioxidant levels and the risk of developing the selected digestive system cancers. Similarly, due to the lack of individual cancer histological subtype data, the associations between the different cancer histological subtypes and the antioxidants could not be explored. Second, although an inherent limitation of MR analysis is that there may be potential polymorphic effects, the MR-Egger intercept test in our study showed no significant pleiotropy, and no SNPs significantly associated with confounding were detected in the PhennoScanner database , indicatIn conclusion, the present study showed that diet-derived antioxidants were not significantly associated with the five digestive system cancers. However, we found suggestive evidence that genetically predicted, dietary-derived higher absolute ascorbate and retinol concentrations were associated with a marginally reduced risk of colon cancer and pancreatic cancer, respectively. Future MR studies with larger replication samples of genetic data on the corresponding cancers and larger GWASs of circulating antioxidants are needed to validate our findings.


Frailty as a common geriatric syndrome can affect the clinical outcomes in patients with gastric cancer. However, the impact of frailty on survival and readmission patients with gastric cancer has not been well-characterised.To investigate the impact of frailty on survival and readmission in patients with gastric cancer undergoing gastrectomy by conducting a meta-analysis.Eligible studies were identified by searching the PubMed, Web of Science, Cochrane Library, and Embase databases until 2 September 2022. Observational studies that evaluated the value of frailty in predicting adverse outcomes in gastric cancer patients undergoing gastrectomy were included. The outcomes of interest were overall survival, disease-specific survival (death from gastric cancer), and readmission. Adjusted hazard ratios (HR) with 95% confidence intervals (CI) were pooled to calculate the association of frailty with adverse outcomes.Eight studies reported on nine articles with 2,792 patients with gastric cancer were included. A fixed-effect meta-analysis indicated that frailty was associated with a reduced in-hospital overall survival , long-term overall survival , and disease-specific survival . In addition, frailty was associated with increased risk of readmission within 1 year .Frailty was associated with a reduced overall survival and disease-specific survival and an increased risk of readmission in patients with gastric cancer undergoing gastrectomy. Frail status may play an important role in the risk stratification of gastric cancer after gastrectomy. Gastric cancer is the fifth most common malignancy that is responsible for more than 1 million new cases annually worldwide . DespiteFrailty is a geriatric syndrome characterised by a decline in physiological reserve and functioning across multiple-organ systems , 6. ThisTo address these knowledge gaps, we performed this more focused meta-analysis to examine the impact of frailty on survival and readmission in patients with gastric cancer undergoing gastrectomy.This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines . Two indStudies that met the following criteria were included: 1) population: patients with gastric cancer undergoing gastrectomy; 2) predictor: frailty assessed using a valid tool before gastric surgery; 3) comparison: individuals with frailty versus those without; 4) outcome measures: overall survival, disease-specific survival (defined by death from the gastric cancer), or readmission; 5) study design: retrospective or prospective cohort studies published in peer-reviewed journals; and 6) reported the risk estimate associated frailty in multivariate regression analyses. For multiple articles from the same population, only articles with the most comprehensive data were included. The exclusion criteria were as follows: 1) not restricted in patients with gastric cancer; 2) outcome measures were not of interest; and 3) conference abstract, letter, or unpublished studies.Two authors independently extracted the last name of the first author, year of publication, country, study design, sample size, age of patients, gender distribution, surgical type, assessment of frail tool, outcome measures, follow-up duration, fully adjusted risk summary associated with frailty, and variable adjustment from eligible studies. The Newcastle–Ottawa Scale (NOS) for cohort studies was used to assess the risk of bias of the included studies . A totalStata software 12.0  was used for all meta-analyses.non-exposed group) + ]. The impact of frailty on adverse outcomes was expressed by pooling the fully adjusted HR with 95% confidence intervals (CI) from individual studies. Between-study heterogeneity was investigated using the I2 statistics and Cochrane Q test. A fixed-effect model was selected in the absence of significant heterogeneity . Sensitivity analysis was performed by sequentially removing an individual study for each turn. Begg’s test (If the relative risk of an outcome is reported as odds ratio (OR), we calculated the hazard ratio (HR) from the OR by using the following formula: HR = OR/[. A fixed-effect model meta-analysis showed that frailty was associated with a reduced overall survival . Subgroup analysis on the duration of follow-up showed that frailty was associated with a reduced in-hospital overall survival . Begg’s test (n = 0.452) and Egger’s test (n = 0.142) suggested no evidence of publication bias.Six studies –18, 25 r2 = 17.0%, p = 0.300). A fixed-effect model meta-analysis showed that frailty was associated with a reduced disease-specific survival . After removing the most influential study (Lu 2017), the pooled HR was 3.08 (95% CI 1.54–6.17) for disease-specific survival. The results of the leave-out one study sensitivity analysis suggested that the pooled HR of disease-specific survival ranged from 1.80 to 3.08 .Two studies  was observed between studies. A fixed-effect model meta-analysis showed that frailty was associated with an increased risk of readmission .Three studies , 15, 23  studies , 24 repoThe overall certainty of evidence for overall survival was high, and that for disease-specific survival/readmission was low , or gastrectomy  because of insufficient such data. Future studies should further investigate whether the prognostic value of frailty is affected by these factors. Only two studies were included in the analysis of readmission. Therefore, this outcome should be interpreted with caution because meta-analysis that involves two studies may result in biased information. Fourth, the result of the publication bias for overall survival may be potentially unreliable because the number of studies was less than the recommended arbitrary minimum number of 10 . MoreoveFrailty was associated with a reduced overall survival and disease-specific survival and increased risk of readmission in patients with gastric cancer undergoing gastrectomy. Frail status may play an important role in risk stratification of gastric cancer after gastrectomy.The original contributions presented in the study are included in the article/YF and DG contributed to study design and guaranteed the integrity of study. XW, YS, and PW searched the literature, extracted the data, assessed the study quality, and conducted the statistical analysis. GL drafted the manuscript. XW revised/edited the manuscript. All the authors have read and approved the final version of manuscript.This work is supported by the Suqian Science and Technology Support Project Fund (K202014), the Zhenjiang Key Research and Development Fund (SH2021038), and the Jiangsu 333 Talent Fund .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.


Statin intolerance (SI) represents a significant public health problem for which precise estimates of prevalence are needed. Statin intolerance remains an important clinical challenge, and it is associated with an increased risk of cardiovascular events. This meta-analysis estimates the overall prevalence of SI, the prevalence according to different diagnostic criteria and in different disease settings, and identifies possible risk factors/conditions that might increase the risk of SI.P = 0.04], female gender , Asian and Black race (P < 0.05 for both), obesity , diabetes mellitus , hypothyroidism , chronic liver, and renal failure (P < 0.05 for both) were significantly associated with SI in the meta-regression model. Antiarrhythmic agents, calcium channel blockers, alcohol use, and increased statin dose were also associated with a higher risk of SI.We searched several databases up to 31 May 2021, for studies that reported the prevalence of SI. The primary endpoint was overall prevalence and prevalence according to a range of diagnostic criteria  and in different disease settings. The secondary endpoint was to identify possible risk factors for SI. A random-effects model was applied to estimate the overall pooled prevalence. A total of 176 studies  with 4 143 517 patients were ultimately included in the analysis. The overall prevalence of SI was 9.1% . The prevalence was similar when defined using NLA, ILEP, and EAS criteria . The prevalence of SI in RCTs was significantly lower compared with cohort studies [4.9% (4.0–6.0%) vs. 17% (14–19%)]. The prevalence of SI in studies including both primary and secondary prevention patients was much higher than when primary or secondary prevention patients were analysed separately . Statin lipid solubility did not affect the prevalence of SI [4.0% (2.0–5.0%) vs. 5.0% (4.0–6.0%)]. Age [odds ratio (OR) 1.33, Based on the present analysis of >4 million patients, the prevalence of SI is low when diagnosed according to international definitions. These results support the concept that the prevalence of complete SI might often be overestimated and highlight the need for the careful assessment of patients with potential symptoms related to SI.  Cannon, 1 It has been clearly demonstrated that statin therapy confers significant mortality and morbidity benefits in both the primary and secondary prevention of CVD.2 Although statins are among the most commonly prescribed drugs, non-adherence and discontinuation of statin therapy is an ongoing problem worldwide.3 The most common cause of discontinuation of statin therapy is statin-associated muscle symptoms (SAMS).5 Other possible statin-related adverse effects include neurocognitive disorders, hepatotoxicity, haemorrhagic stroke, and renal toxicity.7 These conditions may lead to discontinuation, but causality has been confirmed only for SAMS, temporary elevation of aminotransferase alanine, and newly diagnosed diabetes.6 According to the International Lipid Expert Panel (ILEP), statin intolerance (SI) is an inability to tolerate a dose of statin required to sufficiently reduce an individual’s CV risk, limiting the effective treatment of patients at risk of, or with, CVD.7 The National Lipid Association (NLA) has a wider definition, including any adverse effects relating to the quality of life and leading to the decision to decrease or stop the use of an otherwise beneficial drug.8 The Luso-Latin American Consortium (LLAC) definition of SI is similar to that of the Canadian Consensus Working Group (CCWG). It refers to an inability to tolerate ≥2 statins at any dose or an inability to tolerate increasing doses. The symptoms must not be attributable to drug–drug interactions or conditions known to increase SI.10 They indicate that symptomatic criteria include intolerable muscle symptoms  or severe myopathy, and they must appear in the first 12 weeks after initiating treatment or following an increase in dose.10Cardiovascular (CV) disease (CVD) is the leading cause of morbidity and mortality worldwide, despite continuous improvement of medical treatment, diagnosis, and risk factor control.11 In contrast with randomized controlled trials (RCTs) , cohort studies suggest that SI occurs in as many as 30% of treated patients.12 However, this is likely to be an overestimate or underestimate and in many cases, the symptoms are likely to be attributable to the nocebo/drucebo effect.11The prevalence of SI is widely debated, in part because of difficulties in identification and diagnosis, possible interaction of different risk factors, different diseases, drugs, and other clinical and demographic indices.Because of these inconsistent findings, the present meta-analysis aimed to estimate the overall prevalence of SI, its prevalence according to various diagnostic criteria, in different disease settings, and to identify possible risk factors for SI.13 A PECOS  model was used to shape the clinical question and to design the search strategy (see Table S1). The following databases were searched from inception through 31 May 2021: PubMed-Medline, EMBASE, Scopus, Google Scholar, the Cochrane Central Registry of Controlled Trials, and ClinicalTrial.gov. The following keywords were used: statin intolerance, statin toxicity, statin adverse effects, statin side effects, statin-associated muscle symptoms, SAMS, statin-related myopathy, statin-related side effects, statin-related myalgia, statin discontinuation, statin withdrawal, prevalence, occurrence rate, and frequency rate (see Table S2). In addition, the references from the selected articles and relevant review articles, and the abstracts from selected congresses: scientific sessions of the European Society of Cardiology, the American Heart Association (AHA), American College of Cardiology (ACC), NLA, and European Atherosclerosis Society (EAS) were screened for additional relevant articles. The wild-card term ‘*’ was used to increase the sensitivity of the search strategy.We followed the methods recommended by the Cochrane Collaboration and complied with the reporting standards of the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guideline of 2020.Articles were eligible if they reported the prevalence of SI either in primary or secondary prevention and met the following inclusion criteria: (i) trials or cohorts reporting SI, (ii) at least 100 participants included in the analysis, and (iii) available criteria for SI diagnosis. Exclusion criteria were as follows: (i) studies with unclear methodologies to obtain the estimates of SI frequency, (ii) studies that investigated a statin that has been withdrawn from the market, (iii) ongoing trials (unless they reported relevant interim results), (iv) studies only investigating statin discontinuation without specifying intolerance, and (v) short follow-up (<1.5 month/6 weeks).14 For the assessment of the risk of bias in cohort studies, the Newcastle-Ottawa Scale (NOS) was used. Three domains were evaluated with the following items: (i) selection, (ii) comparability, and (iii) exposure. The risk of bias in each study was judged to be ‘good’, ‘fair’, or ‘poor’.15The search, screening, and data extraction were performed independently by two reviewers (I.B. and J.R.); any disagreements were resolved through discussion with senior investigators (M.B. and P.E.P.). Non-relevant articles were excluded on the basis of title and abstract screening. For each trial, the risk of bias was independently assessed by the same investigators using the revised Cochrane RoB2 tool involving five domains . The risk of bias in each study was judged to be ‘low’, ‘high’, or ‘unclear’.8 The ILEP definition stated that SI is an inability to tolerate a dose of statin required to reduce a person’s CV risk sufficiently from their baseline risk and could result from different statin-related side effects.7 The EAS definition focused only on SAMS: the assessment of the probability of SAMS being due to a statin considering the nature of the muscle symptoms, the elevation in CK levels, and their temporal association with statin initiation, discontinuation, and re-challenge.16 As stated by the CCWG and LLAC, SI was defined as a clinical syndrome characterized by significant symptoms and biomarker abnormalities that is documented by challenge/dechallenge/re-challenge using ≥2 statins that is not due to drug interactions or untreated risk factors for intolerance10 . Because the main outcome was not limited by the type of statin, the CCWG and LLAC criteria were not used in further analyses.The primary endpoint was the overall prevalence and the prevalence based on each of the international diagnostic criteria: NLA, EAS, and ILEP. The secondary endpoint was the prevalence of SI in groups of patients with different diseases and the analysis of the association between possible risk factors/conditions and the risk of SI. According to the NLA, SI is defined as adverse effects relating to the quality of life, leading to decisions to decrease or stop the use of an otherwise beneficial drug.Table S1) were estimated from the proportion of cases of SI and sample size using the binomial exact method (Clopper–Pearson method). An inverse variance method was used for weighting each study in the meta-analysis. For the difference of subgroup analysis, we employed post hoc analysis. To investigate the differences between groups, we used the significance test. An I2 statistic was also computed for subgroup differences.14 With the inverse variance method, when the estimated probability of the condition of a single study approaches 0 or 1, the variance of the study approaches zero, which in turn causes the inverse variance to approach infinity; subsequently, the inflated inverse variance substantially increases the adjusted weight of the study in the pooled mean, resulting in an over-contribution of the study in the final pooled estimation of the meta-analysis. Therefore, to avoid the overestimated results, we conducted the Freeman–Tukey double arcsine. The final pooled result and 95% CIs were then back-transformed and expressed as percentages for ease of interpretation. The baseline characteristics are reported as the median and range. The mean and standard deviation values were estimated using the method described by Hozo et al.16 Heterogeneity between studies was assessed using Cochrane’s Q-test and the I2 index. As a guide, I2 < 25% indicated low, 25–50% moderate, and >50% high heterogeneity.17The meta-analysis was conducted using R Statistical Software , using the packages ‘meta’ and ‘metafor’ for meta-analysis. A random-effects model (DerSimonian and Laird method) was applied to estimate the pooled prevalence across the studies. The 95% confidence intervals (CIs) for the prevalence reported in the individual studies  was considered statistically significant.18Potential demographic, clinical, and drugs as modifiers of SI were further explored by meta-regression. Meta-regression coefficients and corresponding 19–194 Out of 176 articles, 112 were RCTs (195 575 patients) and the remaining 64 were cohort studies with 3 947 942 patients. The PRISMA flow diagram is shown in Figure 1 and the key characteristics of the included studies are presented in Table S3. The mean age of patients was 60.5 ± 8.9 and 40.9% were females. The White or Caucasian race made up a greater proportion of participants than Afro-American, Asian, Hispanic, or others .A total of 3569 articles were retrieved from the search after duplicates from the different databases were discarded. These articles were first screened by title and abstract, leading to 271 articles that underwent full-text review. After a stringent selection process, a total of 176 studies with 4 143 517 patients and a mean follow-up of 19 ± 7.3 months were included in the analysis.Figure S2). The prevalence based on NLA criteria was similar compared with using the ILEP or EAS definitions . The prevalence of SI in RCTs was significantly lower compared with cohort studies .The pooled prevalence of SI was 9.1% , 9.1% , respectively; 4].In an analysis stratified by the type of disease prevention, SI was more common in pooled analyses of studies which included both primary and secondary prevention . A summary of SI prevalence is shown in Figure 5. Between-study heterogeneity was large (I2 ≥ 93%). Tests assessing bias were non-significant (P > 0.28).We also compared the prevalence of SI in patients treated with lipophilic  and hydrophilic statins (pravastatin and rosuvastatin). The pooled prevalence was similar in these two types [4.0%  and female sex were associated with a higher risk of SI  . Analysis of demographic indices revealed that the prevalence of SI was associated with the percentage of participants of Asian and African-American race . However, no association was observed with White, Caucasian, and Hispanic races with SI . A summary of the meta-regression of demographic indices on SI is shown in In meta-regression analyses, age (as a continuous variable) was found to be significantly associated with the higher risk for SI [odds ratio (OR) 1.33, 95% CI 1.25–1.41; P = 0.02), diabetes mellitus , hypothyroidism , chronic liver disease , and chronic renal failure , whereas the percentage of individuals with depression was found to have a negative association with SI . Conversely, arterial hypertension was not associated with the prevalence of SI .A range of potential factors was tested for possible interaction with SI. Positive associations were found for obesity , whereas the percentage of alcohol users used showed a significant association with the prevalence of SI . Moreover, exercise , calcium channel blockers (CCB) , and antiarrhythmic agents  were associated with higher risk of SI, whereas warfarin use was not . In addition, increased statin dose was associated with a higher prevalence of SI , whereas the duration of study follow-up was not associated with the occurrence of SI . A summary of the results of meta-regression with respect to associations between risk factors and drugs on SI is shown in The percentage of smokers was not significantly associated with the prevalence of SI .The assessment of the risk of bias in the included studies using RoB2 for RCTs and NOS for cohort studies showed that most studies had moderate to high-quality level in defining objectives and the main outcomes .To the best of our knowledge, the present meta-analysis is the first to evaluate the overall prevalence of SI worldwide, the prevalence based on different diagnostic criteria and in different disease settings. The results of our meta-analysis of 176 studies with 4 143 517 patients and a mean follow-up of 19 ± 7.3 months showed that the worldwide prevalence of SI is 9.1%, irrespective of the definition applied. Older age, female gender, Asian and African-American races, obesity, T2DM, alcohol use, hypothyroidism, chronic liver, and renal diseases were associated with a higher risk of SI, as were increased statin doses and the concomitant administration of antiarrhythmic agents . There is debate on the definition of SI. We compared the prevalence of SI according to all major definitions. Despite the fact that the EAS definition of SI is focused solely on SAMS, the pooled prevalence in our analysis did not show significant differences between the EAS, NLA, and ILEP definitions.Although a wide range of values for the prevalence of SI has been reported in the literature (from 2 to 3% to as high as 50%),197–199 This also suggests that the prevalence of SI is overestimated in real-life data. It is also possible that RCTs underestimate the prevalence by excluding older patients and those with comorbidities such as chronic liver and kidney diseases and abnormal laboratory values that may increase the risk of SI. Some previous studies have reported substantially lower adherence rates in primary prevention compared with patients with CVD or after MI.200 In contrast, our sub-analysis of the pooled prevalence of SI in primary prevention (93 papers with 1 762 384 participants) and secondary prevention (54 papers with 1 166 745 participants) did not find a significant difference (8.2 vs. 9.1%). However, in observational cohort studies which included mixed patients (both primary and secondary prevention), the pooled prevalence of SI was twice as high (18%). This finding suggests that such studies overestimate the prevalence of SI. Similarly, in the subgroup analysis based on different diseases in the primary prevention cohorts  and secondary prevention , the mean overall SI prevalence was not significantly different. Likewise, regarding the safety of different classes of statins, we found no difference between lipophilic and hydrophilic statins.The prevalence of SI in cohort studies was significantly higher than that reported in RCTs. This is associated with large difficulties of correct SI diagnosis in clinical practice and lack of possibility of using of new one-of-trial approach or even cross-over design as it was applied, e.g. in PCSK9 inhibitors trials.4 It is now 20 years since the ACC/AHA/National Heart Lung and Blood Institute first identified risk factors in their recommendations for statin safety; however, there has been no attempt to validate their suggested risk factors using data from clinical trials or observational studies.201 In this meta-analysis, we have attempted to investigate what risk factors/conditions might be linked to SI prevalence using meta-regression. Pooled analysis demonstrated that many demographic, clinical, and other risk factors are associated with SI. Older age, female gender, Asian, and African-American races were associated with a higher incidence of SI, whereas White, Caucasian, and Hispanic races were not associated with higher SI risk. Many commonly observed risk factors and conditions may also be significantly associated with SI occurrence, including obesity, diabetes mellitus, hypothyroidism, chronic liver disease, and renal failure. Depression was negatively associated with SAMS, perhaps because of under-reporting in these patients.202–205 Smoking and anticoagulant drugs were not associated with SI; however, the use of alcohol, exercise, antiarrhythmic agents, and CCB was positively associated with SI. Finally, as previously reported, higher doses of statins were associated with a greater prevalence of SI.7Because statins are the gold standard for the treatment of dyslipidaemia and in the management of elevated CV risk, the most important issue during the diagnosis and management of patients with SI is the urgent need to continue statin therapy. To predict the risk of SI and to be effective in lipid management, it is critically important to know the risk factors and conditions that might increase the risk of SI.I2 = 93–99%; unknown confounding may have led to this), although this was anticipated because of the broad scope of this systematic analysis, and due to very large data, we could not test the influence analysis that would resolve the effect size of different weight across the studies. The statistical examination of potential publication bias through Egger and funnel plots is not appropriate because studies with <100 patients were excluded from this systematic review.Our meta-analysis has some limitations. Heterogeneity between studies was present in our analysis . It is also important to emphasize the importance of the nocebo/drucebo effect that was not examined in the included studies and might have distorted the final results to some extent (it might be responsible even for >50% of SAMS).The data obtained do not allow us to draw conclusions in relation to the doses of other drugs used in the included studies that could have interacted with statin therapy. Nor can we draw conclusions relating to the stage or severity of diseases such as those affecting the liver, kidney, and thyroid. Finally, our analysis cannot be used to suggest appropriate management techniques (e.g. doses of drugs and/or the severity of the diseases when statins might be used without increasing the risk of SI).Based on the data from >4 million patients, we demonstrated that the overall prevalence of SI is relatively low, especially when SI is objectively determined using the recognized international definitions. These results support the concept that the prevalence of complete SI is often overestimated and highlights the need for a very careful assessment of patients with SI, to decrease the risk of unnecessary statin discontinuation, and suboptimal lipid-lowering therapy. Clinicians should use these results to encourage adherence to statin therapy in their patients.European Heart Journal online.ehac015_Supplementary_DataClick here for additional data file.


Numerous studies report an association between coffee or caffeine consumption and pregnancy loss; however, the nature and strength of this relationship have not been clearly established. Based on recent studies, our meta-analysis aimed to test whether a dose–response relationship between coffee or caffeine consumption and pregnancy loss exists.We searched for articles in PubMed, Web of Science, and Scopus published until May 2022. Two independent reviewers extracted data and rated the quality of the evidence using the GRADE approach. We applied a random-effects, one-stage dose–response meta-analysis.P < 0.001), respectively. A dose–response analysis suggested that an increase of a cup of coffee per day during pregnancy was associated with 3% increased risk of pregnancy loss; 100 mg of caffeine per day during pregnancy was also associated with 14 and 26% increased risk of pregnancy loss in cohort and case-control studies, respectively. A non-linear dose–response association was observed between coffee intake and the risk of pregnancy loss.A total of 34 articles (18 cohort studies and 16 case-control studies) were included in this review. Results showed a significantly higher risk of pregnancy loss for coffee consumption before  and during pregnancy , and for coffee consumption during pregnancy in case-control studies . Findings from this meta-analysis demonstrated that caffeine intake during pregnancy was associated with a significantly higher risk of pregnancy loss in cohort  and case-control studies (Pooled ES: 2.39; 95% CI: 1.69–3.37, This study confirms that coffee or caffeine consumption raises the risk of pregnancy loss. Researchers are encouraged to conduct more studies to explore the underlying mechanisms and active compounds in coffee and caffeine.www.crd.york.ac.uk/prospero/], identifier [CRD42021267731].[ Fetal deaths account for a high percentage of perinatal deaths and can be categorized into stillbirth and spontaneous abortion (or miscarriage) , 2. MiscCaffeine is a 1,3,7-trimethylxanthine; in the world, it is one of the most common substances consumed in pharmacologically active amounts and is found in beverages like coffee, tea, soda, solid milk chocolate, and products containing cacao . Hoyt etThe previous meta-analysis including studies published until 2015 , 22 was This systematic review and meta-analysis were performed based on PRISMA  and MOOSE  guidelines , 29. TheA systematic literature search was conducted on Medline/PubMed, ISI Web of Knowledge, and Scopus to include observational studies published until May 2022. The following keywords were used in a comprehensive literature search: [“caffeine*” OR “coffee*”] in combination with  AND [“prospective*” OR “retrospective*” OR “observational” OR “longitudinal” OR “cohort*” OR “relative risk” OR “hazard ratio” OR “odds ratio” OR “follow-up” OR “follow up” OR “population-based” OR “hr” OR “rr”]. The reference lists of full-text publications were also screened to identify any relevant studies. The search was not restricted by publication date or language. Endnote (version 20) was used for the management of records downloaded from databases. Two reviewers (AJ and MA) screened the titles and abstracts of each relevant study. Potentially eligible studies were reviewed independently by the reviewers. Discrepancies were resolved by discussion, and if necessary, a third author (LA) was consulted to reach consensus.The two researchers independently checked the studies’ eligibility based on our inclusion and exclusion criteria, and differences were resolved by arbitration or consensus. Original studies were selected if they met the following inclusion criteria:1.Study design: cohort or case-control studies.2.Participants: pregnant women.3.Exposure: dietary caffeine or coffee intake.4.Outcome: pregnancy loss including miscarriage, abortion, and stillbirth .Two independent reviewers (AJ and SN) extracted the relevant data independently. Any disagreements and differences were resolved by the study supervisor (LA). The following data were extracted from each included study by use of a standardized data-collection form: the first author’s last name, publication year, country, study design, length of follow-up, sample size, the incident of pregnancy loss, measurement of exposure, method of dietary intake assessment and outcome, comparison categories, and effect sizes  with 95% CI with the maximum number of adjustments. Disagreements were resolved through discussion with the senior author (LA).The risk of bias in the included studies was assessed by two authors using the ROBINS-I tool . This toI2 were used as indicators of heterogeneity. We used a random-effects model (n > 5) to assess heterogeneity , body mass index (n = 7), smoking (n = 17), education (n = 13), alcohol consumption (n = 17), and previous spontaneous abortion . Some others also adjusted for energy intake (n = 1) and other dietary variables (n = 1).Most cohorts controlled for some conventional risk factors, including age , with no significant heterogeneity among the studies , with no significant heterogeneity among the cohort studies , with moderate heterogeneity among the cohort studies , indicated a significant positive association between caffeine intake and the risk of pregnancy loss . Notably, 11 studies reported on the association in case-control studies . This was also the case in the linear dose–response meta-analysis when examining an additional one cup of coffee per day  in the dose–response analysis of caffeine intake during pregnancy and abortion risk, a significant non-linear association was observed in both cohort  and that consuming ≥4 servings/day had 20% increased risk of SAB  but not for low (<2 cups) and moderate (2–3 cup) consumption in subgroup analyses   . Our incWe found a positive association between caffeine consumption during pregnancy and the risk of pregnancy loss. Our findings are in agreement with several past studies , 16, 21.Smoking is related to caffeine intake and is a known risk factor for pregnancy loss. Therefore, smoking is a potentially important confounder of the association between caffeine intake and pregnancy loss. Pregnancy symptoms  are more common in healthy pregnancies than in pregnancies that end in spontaneous abortion. Women with viable pregnancies may be more likely to reduce their caffeine consumption in response to these symptoms during pregnancy. Symptoms during pregnancy can affect the interpretation of the relationship between caffeine and pregnancy loss.As mentioned, the main component of coffee is caffeine, which has several effects on the human body, especially pronounced during the pregnancy period. Caffeine can increase catecholamine secretion and reduce uterine and placental blood flow due to its vasoconstrictive effects, resulting in fetal hypoxia and growth and developmental defects. Moreover, caffeine can directly affect the fetal cardiovascular function and initiate tachycardia . HoweverOur study is a comprehensive and up-to-date meta-analysis that investigated the association of maternal coffee and caffeine with the risk of pregnancy loss. Also, we used the one-stage weighted effect method. There are several strengths of our study. We tried to reduce the effects of confounding, searched databases without language restrictions, and included a large number of studies by modulating the effects of confounders in the meta-analysis. Additionally, we evaluated coffee and caffeine consumption separately and evaluated time points during and before pregnancy. Moreover, we conducted a one-stage dose–response analysis to determine linear and non-linear relationships between the variables, performed a subgroup analysis to eliminate the possible effects of confounders , and performed Egger’s asymmetry test to assess the effect of publication bias.The limitations of this study include potential measurement error of caffeine intake in the primary studies, the use of case-control studies, the possibility of recall and selection bias, and evaluation of coffee consumption as a source of caffeine. Regarding the measurement error of caffeine intake, the studies examined in our meta-analysis used interviews and self-report as well as a food frequency questionnaire (FFQ), which are accurate and reliable .This meta-analysis suggests that increased coffee and caffeine consumption in pregnancy may be associated with an increased risk of pregnancy loss. More research is needed to explore the underlying mechanisms and active compounds in coffee and caffeine.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.AJ and MA designed the study and independently carried out the literature search and screening of manuscripts. SN analyzed the data. AJ and HS assessed risk of bias and quality of evidence. AJ, SN, SS, and FK wrote the manuscript. PS edited the English and commented on the manuscript. LA supervised and revised the study. All authors have read and approved the final version of the manuscript.


I2 = 77%). Each additional 50 grams WG consumption per day was associated with a 23% reduced risk of T2D. The nonlinear association of WG and T2D revealed that 60 grams WG intake per day would give the highest benefit to prevent T2D (Pnonlinearity < 0.001). The findings were not affected by any individual study. No evidence of publication bias was documented. In conclusion, a high intake of WG was associated with a lower risk of T2D. Randomized controlled trials are needed to confirm our results.This study aimed to quantitatively summarize earlier findings on the association between whole grain (WG) intake and type 2 diabetes (T2D) risk. We searched related keywords on PubMed/Medline, Scopus, and Google Scholar up to October 2021. Prospective observational studies investigating the association between WG intake and risk of T2D were included. The random‐effects model calculates the summary relative risks by contrasting categories and linear and nonlinear dose–response associations. Eleven prospective cohort studies, including 463,282 participants and 37,249 type 2 diabetes incidents, were analyzed. The pooled relative risk (RR) for the highest versus the lowest WG intake category indicated a 21% decrease in T2D risk : 0.73–0.85,  Type 2 diabetes, manifested by chronic hyperglycemia, has been increasing dramatically in the world. Poor diet is one of the most important lifestyle factors which is linked to the risk of diabetes. We noticed whole grains as remarkable elements of a healthy diet and reviewed earlier publications in this regard. We also quantitatively evaluated the dose–response association between total whole grain intake and incidence of type 2 diabetes in prospective cohort studies of apparently healthy adults. To include studies in the dose–response analysis, we considered publication studies with sufficient data on the number of cases and person‐years and adjusted RRs across ≥3 categories of whole grain intake. Studies reported data on the same population were considered, and only the most updated version with a higher number of incident cases was included.Reviews and meta‐analyses, research notes, letters, ecological studies, nonprospective observational studies, and interventional studies were excluded. We also excluded food pattern‐related articles in which whole grain intake was not considered separately.2.3To extract the required data for analysis, included eligible studies were fully reviewed by two independent authors (FG and SMM). Any discrepancies between the two investigators were resolved by consultation with the corresponding author (AE). The main data we needed was first author's name, year of publication, study location, name of the cohort, mean/median years of follow‐up, general demographic features (such as age and gender), number of participants and incident diabetes cases, method of dietary assessment, quantity of whole grain intake, and reported risk estimates (in the form of RR or HR) along with 95% CIs for each category of whole grain intake in the most adjusted model. In studies with multiple effect sizes, we pooled risk estimates using the fixed‐effect model, and the overall effect size was used for further analyses.The primary studies' risk of bias in the current meta‐analysis was assessed using the Newcastle–Ottawa Scale (NOS) by two independent authors (SMM and FG)  to conduct all the analyses.We considered RRs and 95% CIs as the effect size of all included studies. Reported risks in HRs and ORs were also calculated as equal to RR  and irrelevant publications based on screening for title and abstract (n = 2134), 249 citations were thoroughly reviewed, and eventually, nine articles were eligible for inclusion in the current meta‐analysis. We have explained the reasons for excluding studies in Table Based on the literature search process, shown in Figure 3.1.1n = 9 studies)    . We explored for the sources of this heterogeneity in subgroups with meta‐regression. We found that follow‐up duration, number of cases, location of the study, method of WG assessment (Single measurement of diet at study baseline versus repeated measurement of diet by FFQ), and adjustment for energy intake, alcohol intake, family history of type 2 diabetes, and BMI were potential sources of between‐study heterogeneity. Furthermore, in the sensitivity analysis, we found that the exclusion of individual studies one by one did not affect the overall findings, indicating our findings' robustness (Table Eleven prospective cohort studies (9 publications), including 436,282 participants and 37,249 cases of type 2 diabetes, were included in this meta‐analysis. The pooled RR from the random‐effects model for comparing the highest versus lowest category of whole grain intake was 0.79 (95% CI 0.73–0.85), suggesting a 21% reduced risk of type 2 diabetes with the greatest intake of whole grains   Figure  Due to t) Figure  and thei3.4p = .93).Assessment of publication bias by visual inspection of the funnel plot indicated no evidence of asymmetry in the association between whole grains intake and risk of type 2 diabetes Figure . Also, E4Principle findings of the current meta‐analysis of prospective cohort studies suggested that higher whole grains consumption was associated with a 21% reduction in type 2 diabetes incidence. Evidence from the dose–response analysis showed that each 50 g/day increments in WG intake might reduce the risk of type 2 diabetes by 23%. Based on the nonlinear dose–response meta‐analysis, 60 grams WG consumption was found as the optimal amount for type 2 diabetes prevention; however, higher amounts did not offer additional benefits.Our meta‐analysis has several strengths and advantages over previous studies. We conducted a comprehensive updated meta‐analysis of WG intake in relation to type 2 diabetes incidence. Compared to prior studies, we included more relevant studies with a higher number of cases and longer follow‐up duration. More than half of included studies lasted ten years or more. Instead of WG products or specific subtypes, we tried to analyze total WG intake. We also performed both linear and nonlinear dose–response meta‐analysis on included data. The future design of the included studies has decreased the risk of recall and selection bias. According to the Newcastle–Ottawa scale (NOS), all the included papers were ranked as medium or high. However, the final result of our meta‐analysis might be affected by some limitations. First of all, published studies have reported intakes of dry weight WG (Hu et al., A meta‐analysis of randomized clinical trials revealed that WG intake might beneficially affect systematic inflammation (Hajihashemi & Haghighatdoost, Potential mechanisms of the association between WG intake and reduced risk of type 2 diabetes might be related to their nutrient content. WGs include fibers, phytochemicals, vitamins, minerals, lignans, or phytic acid Fardet, . DietaryIn conclusion, the current meta‐analysis demonstrated that WG consumption was inversely associated with the risk of type 2 diabetes incidence. Higher WG intake was associated with a 23% lower occurrence of type 2 diabetes. We also found that the optimal intake of whole grains was about 50–60 grams per day. Given the basis of this review on observational studies, further studies, in particular long‐term randomized clinical trials, are necessary to reach a causal relationship.The authors declare that they do not have any conflict of interest.This study does not involve any human or animal testing.Appendix S1Click here for additional data file.


Objective: The pathogenesis of elevated uric acid (UA) levels in patients with cardiac syndrome X (CSX) is unclear, and the results presented in recent papers on UA levels in patients with CSX are controversial. Therefore, we conducted a meta- analysis to assess the relationship between UA levels and CSX.Methods: Three databases, including the Web of Science, EMBASE and PubMed, were systematically searched until January 2022. Fixed-effect and random-effects models were used to analyze the relationship between UA levels and CSX. Subgroup analysis and sensitivity analysis were also performed.Results: Six studies involving 406 CSX patients and 267 non-CSX were included. Our results showed a significant relationship between UA levels and CSX, with a pooled SMD of 0.68 . We also found a close relationship between UA levels and CSX for patients ≥ 55 years old , for patients < 55 years old , for women ≥ 60% , for women < 60% , for BMI ≥ 28 Kg/m2 , for BMI < 28 Kg/m2 , for publication years ≥ 2012 , for publication years < 2012 , and for Turkey . Sensitivity analysis showed that the pooled results remained consistent after removing any one study or converting the random-effects model to fixed-effects model.Conclusion: Our results indicated a strong association between high UA levels and CSX. However, more well-designed studies are needed to investigate whether early treatment of hyperuricemia can reduce the incidence of CSX. Cardiac syndrome X (CSX), also known as microvascular angina (MVA), usually presents with angina pectoris or angina-like symptoms, ST-segment depression on electrocardiogram (ECG) or exercise treadmill test, however with completely normal coronary angiography . PatientUric acid (UA) is the final product of purine metabolism. UA reduces the synthesis of nitric oxide (NO) by taking advantage of the oxidation of the accompanying superoxide and inhibiting the action of NO synthase. This leads to impaired vascular endothelial relaxation . High UAStandard guidelines for meta-analysis (PRISMA) were applied . StudiesInclusion criteria were as follows: All participants were over 18 years old; studies were limited to humans; studies featured raw data and were published in English. The diagnosis of CSX was based on one of the following three conditions 1) typical angina pectoris, ST segment depression during exercise load test and normal coronary angiography; 2) index of microcirculatory resistance ≥ 25; 3) coronary flow reserve ≤ 2.5. The control group displayed no CSX. There was no significant difference between the control group and the CSX group with respect to age and body mass index (BMI).Exclusion criteria were as follows: Patients with coronary heart disease, acute coronary syndrome, left ventricular hypertrophy, atrial fibrillation, congenital heart disease, renal and hepatic failure, acute and chronic inflammatory diseases, excessive alcohol consumption and history of diuretic use. Also not included were duplicate studies, abstracts, letters, reviews and case reports.Information extracted from each study included the first author’s name, publication year, country, study design, sample, sample size, age, sex, BMI, and UA levels (mean ± SD) for both the CSX group and the control group.I2 index. I2 values < 25%, 25–50%, 50–75% and 75–100% were considered to be homogeneous, low, medium and high heterogeneity levels, respectively.When the I2 value was > 50%, the random effects model (REM) was applied. Otherwise, the fixed effects model (FEM) was used. The pooled standard mean difference (SMD) of different studies and corresponding 95% confidence intervals (CIs) were used to estimate the link between UA levels and CSX. Sensitivity analysis was repeated and the impact of each study in the meta-analysis was evaluated by deleting different individual studies each time or converting the random-effects model to fixed-effects model.We used the Newcastle Ottawa scale (NOS) to assess the quality of each study . Meta-anFirst, 108 potentially relevant studies were found from our database and 0 from reference lists using our search strategy. Secondly, 36 studies were retained after excluding duplicate studies. After screening titles and abstracts, nine studies that did not meet the inclusion and exclusion criteria were excluded. Third, after scanning the entire body of the 27 retained studies, 21 studies were excluded due to lack of useful data. Finally, six studies were included in the meta-analysis . The ste14reported results separately for patients with normal coronary flow (group A) and patients with slow coronary flow (group B). Another study . Due to moderate heterogeneity, REM was used to calculate the pooled data.The forest map results of the relationship between UA levels and CSX are shown in p < 0.00001) based on the FEM. Meanwhile, age < 55 years old was significant with a SMD of 0.68   .p = 0.0004); Women < 60% was significant with a SMD of 0.61   .2 and BMI < 28 Kg/m2 was significant with SMD of 0.61  and 0.75 , respectively (BMI ≥ 28 Kg/mectively .p = 0.0003) and 0.73 , respectively   .I2 = 71%), and the results significantly affected the pooled results (I2dropped from 71% to 0) after excluding Elbasan’s study (Elbasan 2013A and Elbasan 2013B). Therefore, we hypothesized that the study of Elbasan may be the source of heterogeneity in this meta-analysis. Sensitivity analysis indicated that SMD ranged from 0.82 (0.58–1.06) to 0.57 (0.29 to 0.85). The risk of publication bias was analyzed by Begg’s test (p > 0.05) and Egger’s regression test (p > 0.05). Results suggested that there was no significant publication bias in the meta-analysis.Our study showed moderate heterogeneity  Oxidative stress. UA is an endogenous antioxidant, which can protect against the damage caused by oxygen free radicals to the cardiovascular system. However, when patients form atherosclerotic plaques, the antioxidant capacity of UA is compromised, causing LDL and lipid peroxidation and resulting in vascular damage and accelerated plaque formation  Inflormation . 3) Vascormation . UA is aormation .I2 decreased from 71% to 0, which may be a source of heterogeneity. The reason may be that the population and study design of Elbasan’s study are different from other studies.Many studies have shown that UA is an independent risk factor for cardiovascular disease in women , and stuSo far, there is no consistent conclusion regarding the association of UA levels with CSX, and our study is the first attempt to use a meta-analysis to demonstrate the mechanism behind CSX. However, our study also has limitations. First, the sample size is not enough, which may affect the accuracy of the results. Therefore, more large-scale studies are needed for confirmation. Secondly, although we explored the source of heterogeneity through subgroup analyses, we were unable to detect heterogeneity from other aspects due to insufficient data. Further, we had a limited number of studies that met our inclusion criteria. Thirdly, our results may be biased due to different measurement methods and instruments used to detect UA levels. In addition to this, because meta-analysis is often subject to publication bias, even though we found no evidence of significant publication bias in our analyses, it is worth noting that a few negative studies may have been overlooked. Finally, we cannot determine a reliable cut-off point for UA tests because we do not have raw data to draw ROC curves.Our study showed that patients with CSX had elevated UA levels. In the future, more well designed studies will be needed to verify that treatment of high UA levels leads to a reduced occurrence of CSX.


A significant association was also observed in multiple pre-specified sensitivity and subgroup analyses. Similar results were observed in the group containing a large sample size , and the group has a small sample size ; the pooled R.R was 1.23  and 1.56 , respectively. With the Bayesian method, the effect size was 1.23  for the large sample size group and 1.57  for the small sample size group. Smoking is associated with a significant increase in fracture risk for men. Thus, smoking cessation would also greatly reduce fracture risk in all smokers, particularly in men.Past studies indicate that men are more likely to smoke and be at higher risk of smoking-related conditions than women. Our research aimed, through meta-analysis, to assess the association between smoking and fracture risk in men. The following databases were searched, including MEDLINE, EMBASE, Scopus, PsycINFO, ISI Web of Science, Google Scholar, WorldCat, and Open Grey, for identifying related studies. A random-effects model was used to pool the confounder-adjusted relative risk (R.R.). Frequentist and Bayesian hierarchical random-effects models were used for the analysis. The heterogeneity and publication bias were evaluated in this study. Twenty-seven studies met the inclusion criteria. Overall, smoking is associated with a significantly increased risk of fracture in both the frequentist approach  and the Bayesian approach . Significant heterogeneity was observed in the meta-analysis (Higgin's I As the global population ages, osteoporotic fractures are expected to increase significantly in the coming decades2. Worldwide, the number of people aged 50 years or older and who were at high risk of osteoporotic fracture was around 158 million in 2010, and that number is expected to double by 20403. In the United States, data from 2013–2014 indicated that around 8.3% of adults should have received osteoporosis treatment because they were at a 20% or greater 10-year risk of major osteoporotic fractures3. From 2006 to 2025, annual osteoporotic fracture events and costs for affected populations in the United States are projected to grow by more than 48%2. Thus, osteoporotic fracture prevention is essential for both high-risk individuals and for society in general.Osteoporotic fractures are a major cause of morbidity and disability in older people, often leading to their premature death4. Recent data suggested that there are still approximately 34.2 million adult smokers in this country5. Specifically, men are more likely to smoke than women in the US since 16.7% of adult males and 13.6% of adult females smoke cigarettes6. In addition, smoking accounted for an estimated 3.1 million years of potential life lost for male smokers and 2.0 million years for female smokers during 2000–20047, suggesting that men are at higher risk of smoking-related conditions than women. Prior studies found that smoking was associated with a significantly increased risk of fractures9. Smoking increases the risk of spine and hip fracture to 32% and 40% in men, respectively10. The one-year mortality rates were as high as 20.6%11 and 37.1%12 among male smokers with spine and hip fractures. Hence, a reliable estimate of the association between smoking and fractures in men is crucial, which might help to improve their recognition of the dangers of smoking.Smoking is the single most preventable cause of disease, disability, and death in the United States13, that study was completed five years ago. As a result, several large-scale eligible cohort studies published in recent years were not included19. As well, the prior meta-analysis only focused on hip fracture13, while more current related research has well documented that smoking harms overall bone physiology, thus leading to increased fractures in many other skeletal regions20. Therefore, a comprehensive and updated meta-analysis about the association between smoking and fractures in men is needed. Therefore, our current meta-analysis aimed to include both frequentist and Bayesian approaches in order to quantify all eligible cohort studies that assessed the association between smoking and fractures in men. With the Bayesian method, we can estimate the probabilities that smoking increases fracture risk by more than 0%, 10%, and 20%, which the frequentist approach is unable to provide. Therefore, using both classical and advanced methodology in our meta-analysis allows us to gather more comprehensive and accurate information about the effects of smoking on fractures among men.Although a prior meta-analysis was conducted and found a significant association between smoking and hip fractures in men21, with reference to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)22. The study objectives, primary outcomes, literature search strategy, inclusion and exclusion criteria, study selection methods, data extraction, and data synthesis were all defined in advance in the meta-analysis research protocol (in Supplementary). We also pre-specified the sensitivity and subgroup analyses that we planned to conduct this meta-analysis in the protocol.This meta-analysis was conducted in accordance with the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelinesmen, fractures, osteoporosis, smoking, cigarette, and tobacco, with no restrictions on language, year of publication, or publication status. Using the same strategy, we also conducted literature searches of EMBASE, PsycINFO, SCOPUS, and ISI Web of Science. The above search terms were adapted for other database searches, according to the syntax of each specific database. The last literature search was conducted on April 28, 2021. We also searched Google Scholar, WorldCat Dissertations, and Open Grey. Experienced librarians were consulted to ensure the comprehensiveness of the literature search. Two investigators (M.W. and Y.B.) independently examined reference lists from the original studies and related meta-analyses and reviews.Two investigators (Y.X. and Y.B.) conducted a comprehensive literature search. Electronic searches were conducted on MEDLINE, using the following terms: The following criteria were used to screen relevant references: (1) prospective or retrospective cohort studies designs; (2) reported smoking status ; (3) had risk estimates for any fracture or provided sufficient information to estimate fracture risk; and (4) reported results for men. At the initial selection stage, the two investigators independently screened each article's title and abstract retrieved from the electronic search. Only those citations that both reviewers deemed irrelevant were excluded. References with a disagreement between the two reviewers were included for a further full review. In the second phase of the study selection, each reference's full content obtained during the screening stage was reviewed and assessed by the investigators independently. For duplicate publications from the same study cohort, we included in our meta-analysis the study with the largest sample size or effect size adjusted for the largest number of confounders. Disagreements or uncertainties were discussed and resolved through adjudication from a third investigator (Y.X.) when needed. We only included studies that reported relative risk (R.R.) or hazard ratio (H.R.) of fracture associated with smoking or those with the necessary data for calculating R.R. in the current meta-analysis. The agreement between investigators was evaluated using the κ statistic, a robust statistic for inter-rater reliability testing.23. No major disagreements or discrepancies arose between the two investigators; minor differences were resolved by rechecking the original reports and by discussion. As recommended by the MOOSE study group21, the quality scores were not used as weights in the meta-analysis. However, quality scores were used in the subgroup analysis (score > 7 versus ≤ 7).The methodologic quality of each included study was scored independently by the two researchers (M.W. and Y.B.), using the Newcastle–Ottawa Scale24 did not report adequate data to compute the effect size. We attempted to contact the corresponding author for additional information but were unsuccessful.The two reviewers (M.W. and Y.B.) performed data extraction independently. Before the study, a standard data abstraction form was developed. The following information was recorded: titles, authors, types of publication , characteristics of study , characteristics of participants , assessment of exposure (smoking), method of ascertainment of outcomes, outcomes , and risk estimates . When multiple estimates were presented in the original studies, the estimates with most confounders were adjusted, and the estimate of current smokers was chosen for overall pooled analysis when applicable. Corresponding estimates from the subgroup analyses in the original studies were abstracted when appropriate. One study25 was used to calculate the pooled R.R. and variance. In the Bayesian meta-analysis, Gaussian distribution with an unknown effect size (θi) and known within-study variance i). The set of θi across the original studies was also assumed to follow a Gaussian distribution, with an unknown mean (μ) and across-study variance (τ2), where μ was the estimate of the overall log R.R. and τ2 was a measure of the between-study variation. The prior distribution of τ2 was assumed to follow an improper uniform distribution, and the prior distribution for τ2 was assumed to be non-informative. The probabilities that current smoking use increases fracture risk by more than 0%, 10%, or 20% were estimated and reported. Heterogeneity was assessed with Cochran's Q statistic and Higgins's index27. Univariate and multiple meta-regression analysis was performed to explore heterogeneity. Baujat plot was used to identify studies that had high heterogeneity28, and the effect size was estimated after removing outlying/influential studies.The summary measures used in this meta-analysis were confounder-adjusted R.R. or H.R. for fractures. For studies that reported the estimates by subgroups only, the overall effect size was estimated by a meta-analysis of the reported subgroup's estimates. Before we pooled the data, R.R. or H.R. was transformed into their natural logarithms in order to stabilize the variance and normalize the distribution. We derived H.R. or R.R. natural logarithm variance from the corresponding 95% CIs provided in the original reports. Both frequentist and Bayesian hierarchical random-effects models were utilized for the synthesis analysis. In the frequentist meta-analysis, the DerSimonian-Laird methodSeveral pre-specified sensitivity analyses were conducted to assess the robustness of our estimates. The effects of current smoking on fracture risk were calculated with different inclusion criteria, including reporting R.R./H.R., using medical records/hospital dataset, using hip fracture as the outcome, using clinical vertebral fracture as the outcome, and studies focusing on people older than 60 years old. Subgroup meta-analysis stratified by characteristics identified study location, length of follow-up, sample size, year of publication, and quality score. We also conducted a cumulative meta-analysis by performing sequential random-effects pooling, beginning with the earliest qualified report. Each subsequent meta-analysis summarized all eligible reports from the preceding years. To demonstrate the effect of adding reports on the pooled effect size, we presented results chronologically in a forest plot.A funnel plot created by plotting R.R.s against their standard errors was utilized to examine the potential for publication bias. We also used the Egger test to examine the significance of publication bias. Furthermore, the trim-and-fill method was employed to estimate and adjust unpublished studies' potential effects on the estimated effect size. We used R statistical software  for the data analysis. A p-value of 0.05 or less was considered statistically significant.30. Therefore, we combined the two studies as one, and twenty-seven studies were included in the current meta-analysis. Three original studies33 by Drs. Nguyen et al.31, Felsenberg et al.33, and De Laet et al.32, included in a meta-analysis conducted by Dr. Kanis et al.34, also met our inclusion criteria. However, the three studies were updated by Drs. Nguyen et al.35, Roy et al.36, and van der Klift et al.37 with larger study samples, respectively. Thus we included the three corresponding updated studies37 in the meta-analysis. The agreement between the two investigators was good at this second stage (κ = 0.83). All included studies were published in English.The study flow diagram is illustrated in Fig. 53. For these included studies, the median follow-up time was 9 years, with a range of 2.7–30 years, and the median sample size was 6582, with a range from 258 to 1,174,232. Except for two studies52, the other twenty-five studies reported using R.R./H.R. However, one of the 25 studies reported H.R. using current smokers rather than non-smokers as the reference16. Therefore, we used non-smokers as the reference group and calculated the R.R. based on the provided data for the three mentioned studies52. The potential confounding effects of age were adjusted in 21 studies, and these studies also reported effect size with multiple risk factors adjusted53. Twenty-one studies were conducted outside of North and South America52. Thirteen studies only focused on the association between current smoking and hip fractures53. The quality score of the 27 included studies ranged from 4 to 9.The characteristics of the 27 included studies are summarized in Table 1p < 0.01), and the Higgins I2 index was 83%. The pooled estimates varied slightly but remained significant when they included studies with different eligibility criteria for the analysis . Moreover, studies published after 2010 had a similar R.R.  to those published before or in 2010 . Except for the sample size group, no significant between-group difference was observed in the subgroup analyses. The estimated effect sizes in the subgroup with the Bayesian method are shown in Supplementary Table 2 indicate that two variables, location and reported R.R./H.R., could explain the, 31.34% and 58.61% of the heterogeneity, respectively. There was no multicollinearity between the two mentioned variables, and the heterogeneity in this study was further assessed with multiple meta-regression. After adjusting the two variables and their interaction, the significant heterogeneity among included studies was still observed . The Baujat plot was then used to detect outlier/influential studies, and two41 were identified  also indicated significant publication bias in our current meta-analysis. Hence, we employed the trim-and-fill correction to adjust for the publication bias. However, the overall effect size remained significant after the correction .Publication bias was examined by plotting the log R.R.s between smokers and non-users against their standard errors for each study Fig. . Visual This meta-analysis summarizes 27 cohort studies to assess the association between smoking and fracture risk among men. Both the frequentist method  and the Bayesian method  showed a significant association between smoking and increased fracture risk. The association between smoking and fracture risk was consistent in all sensitivity analyses with different inclusion criteria, various subgroup analyses, and analysis after excluding two outlier/influential studies, which suggests consistency and robustness of findings in this meta-analysis. Furthermore, although the cumulative meta-analysis showed that the pooled estimate fluctuated during 1993–2009, the study also showed a consistent and significant association between smoking and an increased risk of fractures since 2010. This finding suggests that the addition of future studies would have a limited impact on the overall estimate. Additionally, the 95% CIs were increasingly narrower when studies were organized chronologically, which further demonstrates the robustness of our results.13. However, the previous meta-analysis was published five years ago and thus was unable to integrate findings from recently published, more extensive studies57. Moreover, the previous meta-analysis only assessed smoking and hip fracture association. Therefore, the association between smoking and other, more specific fracture outcomes remains unknown. However, we updated the meta-analysis by including the most recent qualified study reports in the present study. We also quantified the association between smoking and overall fracture, along with vertebral fractures. In addition, we included two crucial updated study reports in the present meta-analysis, both with large sample sizes. Dr. Forsen and colleagues published an eligible study in 199458, which was included in the previous meta-analysis. The same research group published another updated report with the same data in 1998; the corresponding updates were included in the present meta-analysis. We also replaced the original study by Paganini-Hill et al.59 in the previous meta-analysis, with an updated report by White et al.43, in the present meta-analysis. Although both study reports used the same data source, the newer version was more comprehensive. It assessed the association between smoking and hip, wrist, and spine fracture, while the older one only focused on hip fracture, so the current study included the updated findings. Compared to the previous meta-analysis, we employed both the frequentist and Bayesian approaches to evaluate the association between smoking and fracture risk. The results from the two methods were consistent in our study. In addition, the Bayesian meta-analysis provided the probabilities that smoking increases fracture risk by 10% and 20%; such results help male smokers to recognize that smoking is linked to elevated fracture risk. Our findings are also consistent with a prior meta-analysis by Dr. Kanis and his colleagues34 that current male smokers had a significantly higher fracture risk than non-current smokers. Compared to Dr. Kanis and colleagues' meta-analysis, our meta-analysis not only included all additional recent eligible studies but also replaced three original studies by Drs. Nguyen et al.31, Felsenberg et al.33, and De Laet et al.32 in Dr. Kanis's meta-analysis with the corresponding updated studies by Drs. Nguyen et al.35, Roy et al.36, and van der Klift et al.37, respectively. The three updated studies had a larger sample size, which might contribute to a more precise estimate because the variance and standard error decrease as the sample size increaseds60. Thus, our meta-analysis is likely to yield a more accurate estimate of the effect size.Our meta-analysis results are consistent with the previous meta-analysis conducted by Dr. Wu et al., that smoking increases the risk of hip fracture in men61. Low BMD is the primary cause of osteoporotic fracture risk and is a measure widely used in clinical practice to identify patients at an increased risk of fracture56. The biological plausibility of BMD loss due to smoking can be linked to the effects of nicotine and cadmium in cigarette smoke on bone cells61. In addition, smoking is associated with decreased vitamin D levels. People with low vitamin D are more likely to have low BMD and are at a higher risk of suffering a fracture62. On the other hand, smoking is also associated with reducing calcium absorption54, also leading to increased fracture risk. Another potential reason is that smoking has been considered a risk factor for injury57, which is linked to fractures. A study in elderly persons found a 28% increase in smokers' accidental injury over non-smokers, and smoking and nicotine are inhibitory factors in wound and fracture healing55. Smoking also interferes with tissue repair processes, leaving tissue more susceptible to injury and fracture55.The underlying mechanism of how smoking influences fracture risk is not fully understood. One potential reason could be the decreased bone mineral density (BMD) caused by smokingOur study has several limitations. First, two studies with self-reported data and one without specification about the outcome measures were included. The data from the three studies might be less reliable compared to other data derived directly from medical records. After removing the three studies, the effect size of smoking on fracture risk decreased slightly. Second, due to the different questionnaire designs from the included studies, we could not examine the dose–response relationship between smoking and the risk of fractures. Third, publication bias is suspected in the current meta-analysis, as indicated by the funnel plot and Egger test. However, the pooled estimate remained significant after we adjusted for publication bias by using the trim-and-fill method. Finally, the adjustment for confounders in all the included articles varies, which may exaggerate or underestimate the findings. Nevertheless, this limitation unlikely altered our meta-analyses conclusion; the consistent findings from sensitivity and subgroup analyses suggested that our current study findings are reliable and robust.In summary, our comprehensive meta-analysis found a significant association between smoking and increased risk of fractures. Our findings were consistent in both frequentist and Bayesian approaches, as well as all subgroup analyses, sensitivity analysis, and the analysis with publication bias correction. More importantly, our results have crucial implications in public health, with the most apparent being that quitting smoking can reduce an individual's risk of bone fracture, both now and later in life.Supplementary Information.


Congenital heart defect (CHD) is the leading cause of birth defects globally, which results in a great disease burden. It is still imperative to detect the risk factors of CHD. This umbrella review aimed to comprehensively summarize the evidence and grade the evidence of the associations between non-genetic risk factors and CHD.Databases including Medline, Embase, Web of Science, Cochrane Library, and four Chinese databases were searched from inception to 18 Jan 2022. The reference lists of systematic reviews (SR) and meta-analyses (MA) were screened, which aimed to explore the non-genetic risk factors of CHD. Subsequently, titles and abstracts of identified records and full texts of selected SR/MA were screened by two independent reviewers based on predefined eligibility criteria. A priori developed extraction form was used to abstract relative data following the PRISMA 2020 and MOOSE guidelines. The risk of bias was assessed with the AMSTAR2 instrument. Data were synthesized using fixed-effects and random-effects meta-analyses, respectively. Finally, the evidence on the association of non-genetic risk factors and CHD was graded using Ioannidis’s five-class evidence grade.A total of 56 SRs, encompassing 369 MAs, were identified. The risk factors included relative factors on air pollution, reproductive-related factors, parental age and BMI, parental life habits, working and dwelling environment, maternal drug exposure, and maternal disease. Based on AMSTAR2 criteria, only 16% (9/56) of SRs were classified as “Moderate”. One hundred and two traceable positive association MAs involving 949 component individual studies were included in further analysis and grading of evidence. Family genetic history, number of abortions, maternal obesity, especially moderate or severe obesity, decoration materials, harmful chemicals, noise during pregnancy, folic acid supplementation, SSRIs, SNRIs, any antidepressants in the first trimester, maternal DM (including both PGDM and GDM), and gestational hypertension were convincing and highly suggestive factors for CHD. After sensitivity analyses based on cohort studies, some grades of evidence changed.The present umbrella review will provide evidence-based information for women of childbearing age before or during pregnancy to prevent CHD. In addition, sensitivity analysis based on cohort studies showed the changed evidence levels. Therefore, future SR/MA should concern the sensitivity analysis based on prospective birth cohort studies and case-control studies.The online version contains supplementary material available at 10.1186/s12884-022-04600-7. Birth defects are growing parts of the global disease burden for under 18 years old because of the fall in infectious diseases and improvements in children nutrition, in which congenital heart defects (CHD) is the leading cause of birth defects globally , especiaAs for the risk of CHD, the current consensus is that the development of CHD is determined by both genetic and environment factors. Although the genetic algorithms for cardiac defects have been constructed, the risk assessment of CHD based on non-genetic risk factors is still imperative because non-genetic risk factors can be prevented more easily . In ordeIn addition, a China Birth Cohort aimed to assess the risk of CHD was initiated in 2017, 500,000 pregnant women have been enrolled and following up by far , 15. In This review was conducted according to the rules for conducting umbrella reviews and published approach , 17, andThe Chinese and English databases were systematically searched, including Medline, Embase, the Cochrane Library, Web of Science databases, Wangfang, CNKI, VIP, and Sinomed databases from database inception to 18 January 2022. All studies aimed to explore the potential environmental risk factors of CHD were captured. Initial free-text keywords and Medical Subject Headings or EmTree terms included ‘congenital heart defects’, ‘tetralogy of fallot’, ‘cyanotic heart’, ‘aortic coarctation’, ‘heart valve diseases’, ‘hypoplastic syndrome’, ‘pulmonary atresia’, ‘interruption of the aortic arch’, ‘valve stenosis’, ‘pulmonary atresia’, ‘systematic review’, and ‘meta-analysis’. To provide comparable results, we used the syntax applied in the previous comprehensive Cochrane reviews  .The detThe systematic reviews (SR) or meta-analyses (MA) of individual observational studies  were eligible, which aimed to examine the associations between environmental risk or protective factors and CHD (including any kind of specific classification of CHD). The exclusion criteria included: (1) SR/MA focused only on genetic risk factors of CHD; (2) SR/MA focused on risk factors which influenced treatment and prognosis of CHD; (3) SR/MA aimed to study the impact of adult CHD on other diseases; (4) SR/MA of epidemiological descriptive studies of CHD; (5) SR/MA that did not present study specific data (relative risks (RR), odds ratio (OR), 95% confidence intervals, and numbers of cases/population). The language was restricted to English and Chinese. SR presented separate MA on more than one eligible outcome , ventricular septal defects (VSD), and coarctation of the aorta (COA)) were assessed separately. Given that more than one MA focus on the same scientific association, the one with the largest number of included component studies was selected, but sensitivity analyses and comparisons were conducted to assess the concordance of the summary associations  in these duplicate meta-analyses .Individual studies of SR and MA were firstly screened based on titles and abstracts. If a judgment could not be made based on titles and abstracts, we proceeded to read the full text. Both the screening process and data extraction were performed independently by four investigators . Senior investigators (X.N.) resolved discrepancies through discussions.For each eligible MA, three independent investigators  firstly extracted data including: name of first author, year of publication, country, factor, outcome, number of included component studies, search date, study population, combined effect value reported with 95% CI, the model of analysis (fix/random model), and method of bias assessment.Four independent investigators  then extract the following information for each component included study of eligible MA: first author and published year of corresponding MA, first name of component study, year of publication, study design, factor, outcome (including CHD and any kind of specific classification), comparison level, population size of each component study, number of case and control for case-control study, number of exposure and non-exposure group for cohort study, effect size reported with 95% CI. For the purpose of mitigating the risk of introducing newly defined factors not originally present in the literature, we restricted the data extraction to only the factors that each individual meta-analysis or systematic review had originally introduced and did not combine similar factors if the meta-analysis or systematic review had considered and analyzed them separately.https://amstar.ca/Amstar-2.php). AMSTAR2 ranks the quality of a SR from critically low to high according to 16 predefined items [The methodological quality of each included SR was independently assessed by two group of raters  with the Assessment of Multiple SysTemAtic Reviews (AMSTAR) 2 tool , which had just released in 2022 for R .I2, with values > 50% indicative of high heterogeneity [p-value of < 0.10 [p < 0.10) was adopted [t distribution.Considering the MA with negative association did not show statistical significance and the requirement of Egger test (number of included studies: k ≥ 3), we only focused on selected MA with positive association for further synthesis and analysis. For each eligible MA, we estimated the summary effect sizes and 95% CI through both fixed-effects and random-effects models. We also estimated the prediction interval (PI) and its 95%CI, which further accounts for between-study effects and estimates the certainty of the association if a new study addresses the same association –26. Betwogeneity . We calcf < 0.10 , where sf < 0.10 . Because adopted . ConsideIn addition, we addressed temporality with a sensitivity analysis that includes only prospective studies because the temporality of the association is critical to minimize reverse causation in an umbrella review of potential risk and protective factors , 31.p<10− 6, I2<50%, 95% prediction interval excluding null value, largest study has significantly result, no small study effect, no excess significance bias; (2) Highly suggestive: number of all included studies>1000 cases, random-effect p<10− 6, largest study has significantly result; (3) Suggestive: number of all included studies>1000 cases, random-effect p<10− 3; (4) Weak: significant association with p<0.05; (5) NS: Not significant associated [All the evidences were categorized into five categories as follows: (1) Convincing: number of all included studies>1000 cases, random-effect sociated .Overall, 9923 potentially eligible records were identified. After screening titles and abstracts, full-text evaluation was carried out for 214 records. Among them, 101 studies needed to extract data to determine whether it was the largest and latest study of the specific association that can be included. Finally, 56 SRs with 369 MAs were fulfilled the inclusion criteria , HLHS (n = 20), ASD/VSD (n = 17), septal defects (n = 16), TOF (n = 12), RVOTO (n = 10), COA (n = 7), CTD (n = 7), OFT defects (n = 6), VSD (n = 6), TGA (n = 4), and AVSD (n = 3). Table p < 0.005, and 26 (25%) associations reached p < 10− 6. Large estimates of heterogeneity (I2 > 50%) in meta-analysis were detected for 38 association (37%). Moreover, small-study effects were showed in 19 associations (19%), and there were 24 associations (24%) with evidence of excess significance. Overall, 5 (5%) of all the factors showed convincing (Class I) evidence, 13 (13%) showed highly suggestive (Class II) evidence, 21 (21%) showed suggestive (Class III) evidence, 63 (62%) showed weak (Class IV) evidence. For presentation purposes, the sections below only summarized the evidence grade for 68 factors of CHD.One-hundred and two MAs involving 949 component individual studies were included for data synthesis and grading the evidence, in which 271 individual studies were cohort studies and other 678 were case-control studies. Among them, the outcome of 802 studies were CHD, the rest were about ASD , and number of abortions were highly suggestive (Class II) evidence. In-vitro-fertilization (IVF) or Intracytoplasmic sperm injection (ICSI) pregnancy, especially singleton IVF/ICSI, history of (spontaneous) abortion, maternal parity, gravidity number, these five factors showed suggestive (Class III) evidence of increasing the risk for CHD. Monochorionic (MC) twins, either with or without twin–twin transfusion syndrome (TTTS), MC twins with TTTS vs. MC twins without TTTS, ICSI vs IVF (in fresh transplantation cycle), intermarriage, maternal or fetal abnormalities detected, history of induced abortion, and gravidity had class IV evidence as a risk factor for CHD Table .Fig. 2ThRegarding the maternal BMI, maternal moderate or severe obesity identified convincing (Class I) evidence and maternal obesity showed highly suggestive (Class II) compared with normal BMI, while maternal overweight had class IV evidence as a risk factor for CHD. In terms of parental age, paternal age (≥40 years and 35–39 years) and maternal age (≥35 years) showed suggestive (Class III) evidence of increasing the risk for CHD . While the weak (Class IV) evidence included the Lithium exposure in the first trimester compared with unexposed women or history exposed patients with bipolar disorder, solvents exposure, paternal occupational exposure to adverse substances, and high intake of caffeinated products. Regarding the parental smoking, both maternal active and passive smoking, paternal smoking, especially paternal active smoking were classed as grade III evidence. However, both paternal light smoking (10–19 cigarettes/day) and heavy smoking (≥20 cigarettes/day) were showed as weak (Class IV) evidence compared with nonsmoker. In terms of family financial situation, maternal educational attainment and family income showed suggestive (Class III) evidence and weak (Class IV) evidence, respectively Table .Fig. 4ThFolic acid supplementation showed convincing (Class II) evidence, which was only protective factor for CHD. On the contrary, Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) showed convincing (Class I) evidence as risk factors for CHD. Any antidepressants in the first trimester were class as grade II. Other eligible drug exposure, including fluconazole in the first trimester, ß − blockers in the first trimester, oral hormone pregnancy tests, sertraline, citalopram, bupropion, nitrate  were class IV risk factors for CHD , including both pregestational diabetes mellitus (PGDM), gestational diabetes mellitus (GDM), together with gestational hypertension were classed as highly suggestive evidence (Class II). Fever showed suggestive (Class III) evidence. Finally, malnutrition during pregnancy, infection of the reproductive system, cytomegalovirus, rubella virus, influenza, viral infection, respiratory infection all had class IV evidence. Chronic disease before pregnancy was also a class IV risk factor for CHD Table .Fig. 6Thp < 0.05. Overall, 16 factors remained the same level of evidence with umbrella review based on both cohort studies and case-control studies. In addition, 13 factors , maternal educational attainment, maternal passive smoking, maternal active smoking, β-blockers in the first trimester, SNRI, SSRI, oral hormone pregnancy tests, and GDM) downgraded while one (any antidepressant in the first trimester) upgraded.The cohort studies were separated for sensitivity analyses involving 37 factors, in which seven factors  did not conduct data quantitative synthesis because of only one eligible cohort , and gestational hypertension were convincing and highly suggestive factors for CHD.Although there have been published two umbrella reviews, Zhang’s study lacked of not only some important factors, maternal DM for instance, but also assessment of robustness based on sensitivity analysis ; while LCompared with the results of published umbrella reviews, most of the summary results are consistent and grade of factors stay the same. Regarding the inconsistent results, partly because we chose latest published largest MA for specific association, in which the component individual studies were different from articles of Zhang’s and Korean research groups , 8. On tOur results suggest a substantial number of factors that may be considered as predictors in CHD . The most obvious advantage of studying risk factors, particularly those that are environmental and potentially modifiable, is that it can provide crucial knowledge on prevention strategies . The conThe main strength of this umbrella review lies in the systematic search strategy, good quality control during data extraction, and rigorous data analysis and synthesis. However, this review does have some limitations. Firstly, as we described in the methods, this umbrella review could only conduct secondary analysis based on the associations which has been investigated, published and systematically reviewed or meta-analyzed. Take maternal MC twins with TTTS as an example, since there was only one MA  in Gijtenbeek’s article focused on this association , even ifThe present umbrella review will provide evidence-based information for women of childbearing age before or during pregnancy to prevent CHD. In addition, the sensitivity analysis based on cohort studies showed the changed evidence levels. Therefore, future SR/MA should concern the sensitivity analysis based on prospective birth cohort studies and case-control studies.Additional file 1 : Supplementary materials.