The risk of bias was assessed by the Newcastle–Ottawa Scale for cohort studies, and the quality of evidence was investigated by the GRADE tool. The analysis included 11 publications from eight cohorts, including data from 27,346 survivors and seven dietary indices. Both approaches yielded a similar effect size, but cohort-based analysis had a wider CI. Pre-diagnosis diet quality was not associated with both outcomes. However, better post-diagnosis diet quality significantly reduced all-cause mortality by 21%  and marginally reduced breast cancer-specific mortality by 15% . Subgroup analysis showed that adhering to the Diet Approaches to Stop Hypertension and Chinese Food Pagoda guidelines could reduce breast cancer-specific mortality. Such reduction could be larger for older people, physically fit individuals, and women with estrogen receptor-positive, progesterone receptor-negative, or human epidermal growth factor receptor 2-positive tumors. The risk of bias in the selected studies was low, and the quality of evidence for the identified associations was low or very low due to imprecision of effect estimation, inconsistent results, and publication bias. More research is needed to precisely estimate the effect of diet quality on mortality. Healthcare providers can encourage breast cancer survivors to comply with healthy dietary recommendations to improve overall health. .This systematic review examined the effect of diet quality, defined as adherence to healthy dietary recommendations, on all-cause and breast cancer-specific mortality. Web of Science, Medline, CINAHL, and PsycINFO databases were searched to identify eligible studies published by May 2021. We used a random-effects model meta-analysis in two different approaches to estimate pooled hazard ratio (HR) and 95% confidence interval (CI) for highest and lowest categories of diet quality: (1) each dietary quality index as the unit of analysis and (2) cohort as the unit of analysis. Heterogeneity was examined using Cochran’s Q test and inconsistency I In 2020, approximately 2.3 million women were diagnosed with breast cancer, accounting for 11.7% of all new cancer cases worldwide, and 685,000 women died from breast cancer, accounting for 6.9% of all cancer deaths , a majorSun et al.  showed tTo these suggestions, researchers examined the effect of adhering to the dietary recommendations for overall health on cancer outcomes. Multiple meta-analysis studies showed that following a healthy diet improved overall survival among cancer survivors, including breast cancer. In 2020, Morze et al.  conducteIn 2017, Schwingshackl et al.  updated Recently, seven more studies ,35,36,37The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to structure the present study. The review protocol was registered in the International Prospective Register of Systematic Reviews , an open-access online database of prospectively registered systematic reviews in health and social care (CRD42021260135).Two independent reviewers conducted a comprehensive search using the key search terms to maximize the identification of cohort studies that examined the associations between diet quality and breast cancer recurrence and mortality. Web of Science and EBSCOhost  online search platforms were searched on 2 June 2021, using the following search parameters: breast cancer, dietary quality, recurrence, mortality, and prognosis. The full search strategies are shown in ® software  [® recognizes the duplicated records from search results and removes them automatically. It also streamlines the screening and data extraction processes by having all search results and selection criteria in one place. By recording responses from two reviewers independently and simultaneously, it increases the study selection’s efficiency and validity. All reviewers were trained to use the program before starting the screening. After each study’s title and abstract were screened, the full text was screened for inclusion. Each study was reviewed by two reviewers and consensus was research. Studies were included in the review if they met the criteria stated in The search results from all databases were first uploaded to Covidencestralia) , and incThe quality of the studies was assessed using the modified Newcastle–Ottawa Scale for cohort studies . This to®. Disagreements were infrequent and were resolved through discussion by consensus with all authors. The following data were extracted from each study:Study characteristics: title, first author, year of publication, country of study, cohort name, study design, sample size, study aim, and follow-up periods.Population characteristics: age, race/ethnicity, smoking, body mass index, menopausal status, and tumor characteristics (stage and estrogen receptor (ER) status).Exposure: a diet quality index used, the dietary assessment tools  and the dietary assessment timing regarding breast cancer diagnosis (before/at or after diagnosis) and target of diet assessed (pre-diagnostic diet or post-diagnostic diet).Comparison: high versus low dietary quality score.Outcome: recurrence and mortality , ascertainment methods , RR/HR and 95% CI comparing high vs. low index score, covariates included in the multivariable model, and overall findings from the study.Two reviewers extracted the data using a predesigned data extraction form created by authors on CovidenceAfter extraction, the principal study author was contacted if the original publication had missing information essential to the current study.The meta-analysis was performed by combining the multivariable-adjusted HR/RR of the association between the diet quality score and cancer outcomes from each study using the DerSimonian–Laird random-effects pooling model. We employed two approaches in a meta-analysis. (1) Index-based analysis: the overall effect of a healthy diet on cancer outcomes was summarized by pooling the results from each diet quality index/score using an index as the unit of analysis. (2) Cohort-based analysis: the overall effect was summarized with a cohort being the unit of analysis. If one study evaluated multiple diet quality indices, an average effect from all indices was first estimated using a random-effects model. If numerous publications came from the same cohort using a different diet quality index, the summary effect for the cohort combining all dietary indices across multiple publications was obtained before the meta-analysis.Q test and inconsistency I2 test. Sensitivity analyses were conducted, excluding one study at a time to clarify whether the results were robust or sensitive to the influence of a single study. Statistical analyses were performed using the Comprehensive Meta-Analysis software  and R (version 4.1.2) with the metafor package, and a two-tailed p < 0.05 was considered statistically significant.Subgroup analysis was conducted to investigate potential sources of heterogeneity. First, the effect of the timing of the dietary assessment was assessed to determine whether pre-and post-diagnosis diet quality had a differential impact on cancer outcomes. Second, the effect of each diet quality index was evaluated to identify which dietary quality index had the most favorable impact on cancer outcomes. Third, patient characteristics and tumor characteristics were evaluated to identify the subgroup of women who could benefit more than others. Heterogeneity between studies was quantitatively assessed by the Cochran Overall, we screened a total of 2471 abstracts, 33 of which were reviewed in full for eligibility. We excluded 22 full-text publications, leaving 11 for data extraction and quality assessment. The detailed steps of the systematic search and selection process are shown in n = 8) [n = 3) [The characteristics of included studies are summarized in n = 8) ,35,36,37 [n = 3) ,33,34 co [n = 3) ,33 and Wn = 3) [n = 10) [n = 9) [n = 2) [n = 3) [n = 10) [n = 1) [Dietary intake was assessed before/at (n = 3) ,34,37 or[n = 10) ,35,36,37 [n = 2) ,33 metho [n = 3) ,34,37, p [n = 1)  was asse [n = 1) ,33,35,36 [n = 1) ,35,36 an [n = 1) .Food components included in each diet quality index are summarized in The disease recurrence was ascertained from the self-reported questionnaire or medical records, and deaths were confirmed from family members, postal service, or linking to the National Death Index or state/national vital status data. Causes of death were examined from the death certificate. Overall, there were 727 breast cancer recurrences and 5001 deaths from all causes, including 2472 breast cancer deaths among 27,346 breast cancer survivors. A total of three studies ,36 examiThree studies ,35,36 re2 = 4.5%, n = 24). This effect remained consistent for post-diagnosis diet quality scores , but not for pre-diagnosis diet quality scores  as shown in Three studies ,34,37 re2 = 16.83%, n = 7). The effect of pre-diagnosis was not significantly associated with all-cause mortality .As the results from Kim et al.  and Izan2 = 47.2%, n = 22). When pre-diagnosis diet quality scores and post-diagnosis diet quality scores were evaluated separately, the results were both nonsignificant; however, post-diagnosis diet quality showed a potentially larger effect  than pre-diagnosis diet quality  as shown in 2 = 0.0%, n = 3) and post-diagnosis diet  as shown in Three studies ,34,37 reIn addition, Sun et al. study  reportedAmerican Cancer Society (ACS) score: Two studies ,37 evaluChinese Food Pagoda (CHFP) score: Only one study  from the2 = 0.0%, n = 3) and breast cancer-specific mortality  with evidence of little heterogeneity.Diet Approaches to Stop Hypertension (DASH) score: Three studies ,35,36 ev2 = 0.0%, n = 9) and for breast cancer-specific mortality . More heterogeneity was observed than other diet quality indices, and only two studies [2 = 48.5%, n = 6, not shown in 2 = 36.4%, n = 6, not shown in Healthy Eating Index (HEI)/Alternative HEI (AHEI): Overall, a higher HEI/AHEI score was associated with a reduced risk for all-cause mortality : Three studies ,34,36 in2, of postmenopausal status, and those with ER-positive, progesterone receptor (PR)-negative, or human epidermal growth factor receptor (HER) 2-positive tumors compared to their respective counterparts. A total of six studies ,34,35,36p = 0.006).Sensitivity analysis showed that removing one of each included study in the meta-analysis did not have a significant impact on the estimated overall effect size (data not shown). A funnel plot for the analysis between the post-diagnosis diet quality and all-cause mortality showed substantial evidence of missing studies with a negative result from a small sample size . While the CHFP dietary guidelines showed a significant inverse relationship to all-cause and breast cancer-specific mortality, interpretation should be made with caution due to limited data from only one study in China. Both versions of CHFP (CHFP-2007 and CHFP-2016) guidelines encourage higher intakes of vegetables, fruits, grains, and fish along with lower intakes of meat and poultry. It should be noted that most of the diet quality indices included in the current review are based on dietary guidelines for general health rather than cancer-specific health, and these guidelines encourage consuming foods or dietary components high in antioxidants, which help regulate inflammation and immune responses. Diets high in anti-inflammatory properties showed promising results in reducing the risk of breast cancer mortality [Among the diet quality indices included, CHFP and DASH showed the most favorable results for breast cancer-specific mortality. The DASH guideline encourages high intakes of magnesium, potassium, calcium, fiber, and healthy unsaturated fats through grains, fish, and nuts and limited intake of saturated fats from red and processed meats and dairy products in addition to lower intakes of sodium. Higher fiber intakes from grains can help maintain healthy gut microbiota , which eortality ,49,50.We identified that better diet quality might have a more significant impact on specific groups of women, such as older women, those with a high BMI, and those with ER-positive tumors. Breast cancers found in older women are more likely to be ER-positive, and they are more likely to die from other causes than breast cancer. Moreover, those with a high BMI have an increased risk of dying from other causes, possibly due to other comorbidities. Therefore, management of comorbidities through a healthy diet can be an excellent strategy to reduce the risk of death among older cancer survivors with ER-positive tumors or a high BMI. In addition, two studies ,35 showeAlthough we focused on the relationship between diet quality and cancer outcomes, it is also important to consider other factors that influence diet quality among cancer survivors, including age, gender, education, and income levels. Individuals with low socioeconomic status (SES) are less likely to adhere to the healthy dietary guidelines  due to mInconsistent results across the studies may be explained by the differences in the study designs, including methods, timing, and frequency of dietary assessment and follow-up period in the included studies. Although FFQs are a convenient, inexpensive way to measure dietary intake in a large population-based study, it is prone to recall bias and measurement errors depending on the food items included in the questionnaire . While 2Most studies assessed diet intake at a single point in time, assuming consistent dietary intake over time within individuals, except for two studies ,31 that Only a few studies assessed the effect of individual dietary components on cancer outcomes, but with little evidence. This finding is consistent with the results from the most recent WCRF/AICR update, which stated that none of the individual dietary components were convincing or probable for reducing breast cancer-specific mortality or death from other causes . The revThere are several limitations of the current analysis that should be considered. First, although we took the estimate from the fully adjusted multivariable model, other unmeasured confounders could not be ruled out as treatment and disease severity were not included in some studies. Second, most of the studies were conducted in the United States, with most of the sample population being white; thus, the estimates may not be directly applicable to other populations where they have different dietary intakes and risks of death. Third, included studies were all observational cohort studies; therefore, future randomized clinical trials are needed to provide more robust evidence for a relationship between healthy eating and breast cancer outcomes. Fourth, the possibility of publication bias should be considered because the small studies with null/negative results were less likely to be published. In addition, reports from theses, dissertations, and conference abstracts were not included in the current analysis. Fifth, few studies examined breast cancer recurrence as the main outcome. More research is needed to improve the body of evidence on the relationship between healthy eating and breast cancer outcomes.There are several strengths of the current analysis worth noting. First, this is the most extensive meta-analysis of cohort studies, including 11 publications from eight cohorts, which provides greater statistical power. Another strength includes employing a cohort-based analysis approach, which allows the inclusion of all available studies but avoids over- or under-representation of a singular study by counting each study only one time. Previous meta-analysis studies counted one particular study multiple times if it included multiple dietary indices or excluded one when it was based on the same cohort as the other. Third, the current analysis included the prospective cohort studies with high quality, increasing the quality of evidence. Lastly, our study focused on a diet quality index/score that is based on a priori-defined healthy dietary recommendation rather than data-driven dietary patterns. These predefined dietary recommendations help cancer survivors easily understand how to improve their diet quality.Our research demonstrates that following a healthy dietary recommendation after a cancer diagnosis can reduce mortality from all causes, including breast cancer. Additional research in large populations with different racial/ethnic backgrounds is needed to confirm our findings regarding the observed beneficial effect of DASH guidelines on breast cancer-specific mortality. It is important to increase the awareness of and compliance with healthy dietary recommendations that target overall health among cancer survivors, given that there are still no dietary guidelines specific to them. Encouraging them to adopt other healthy lifestyles, such as being physically active, avoiding drinking, and stopping smoking, will maximize the benefit of a healthy diet among breast cancer survivors.


This systematic review and meta-analysis was conducted to identify the potential risk factors for postoperative delirium in geriatric patients with hip fracture.PubMed, EMBASE, and Cochrane Library were searched from inception until December 31st, 2021. A combined searching strategy of subject words and free words was adopted. Studies involving risk factors for postoperative delirium in elderly patients undergoing hip fracture surgeries were reviewed. Qualities of included studies were assessed using the Newcastle–Ottawa Scale. Data were pooled and a meta-analysis was performed using Review Manager 5.3.A total of 37 studies were included. The following risk factors were significant: advanced age (per year increase) , age>80 years , male , preoperative cognitive impairment , preoperative dementia , preoperative delirium , diabetes , preoperative functional dependence , ASA level (per level increase) , ASA level≥3, low albumin , medical comorbidities , Parkinson's disease  and surgery delay>48 h .Clinicians should be alert to patients with those risk factors. To identify the risk factors more precisely, more research studies with larger sample size and better design should be conducted. The incidence of hip fracture is increasing concurrently with the aging of the population. It has been estimated that in many countries, the number of hip fractures will rise from 1.7 million in 1990 to 6.3 million in 2050  Statement ] AND (“Delirium”[MeSH Terms] OR [“deliri*”[Title/Abstract] OR “confus*”[Title/Abstract] OR “pocd”[Title/Abstract] OR “postoperative cognitive disorder”[Title/Abstract])].PubMed, EMBASE, and Cochrane Library were searched from inception until December 31st, 2021. A combined searching strategy of subject words and free words was adopted. The concrete searching strategy for PUBMED is as follows: AND (“Risk Factors”[MeSH Terms] OR [“correlatThe inclusion criteria were as follows: (1) Types of studies: retrospective or prospective cohort design; (2) Types of participants: all the patients are older than 60 years old and undergo surgeries for hip fracture; (3) Outcomes: risk factors merely for postoperative delirium, and the risk factors are reported in ≥2 studies. (4) Data: available odds ratio (OR) or relative risk (RR) with 95% confidence interval (95% CI) as a result of a multivariate logistic regression.The exclusion criteria were as follows: (1) Types of studies: those studies that are not cohort design or whose concrete description could not be extracted, editorial reviews, systematic reviews, conference abstracts, letters and comments; (2) Types of participants: including patients younger than 60 years old or undergoing other types of surgeries; (3) Outcomes: assessed risk factors for postoperative delirium are reported in less than 2 studies or risk factors for preoperative delirium or perioperative delirium; (4) Data: no available odds ratio (OR) or relative risk (RR) with 95% confidence interval (95% CI) as a result of a multivariate logistic regression.Two reviewers independently scanned the titles and abstracts of potentially included studies. Once the studies met the inclusion criteria, full texts of articles were reviewed thoroughly. The following variables were extracted from each study: first author's name, publication year, country, diagnosis of delirium and numbers of cases and controls, mean age of cases and controls, numbers of males and females in cases and controls, and significant risk factors. The extracted data were entered in a standardized data collection form. Any discrepancy about the data were resolved by discussion or consulting a senior reviewer.The quality of included studies was assessed by 2 reviewers with the Newcastle–Ottawa Scale (NOS) . Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled across the studies to estimate the risk factors of postoperative delirium. In studies where the incidence of delirium is low, the RR could be regarded as equal to the OR. Statistical heterogeneity was assessed with the P and IOne thousand seven hundred ten articles were identified from the search of the databases and 1 paper was identified from other sources. One thousand two hundred sixty-two studies remained when the duplicates were removed. Then, the title and the abstracts of the 1262 citations were scanned to exclude those which did not meet the inclusion criteria. As a consequence, 1175 citations were excluded. Next, the 87 remained studies were carefully full-text-reviewed to recognize those which could reach the inclusion criteria. At last 37 citations , 11 studies were conducted in North America (9 in the USA and 2 in Canada), 7 studies were conducted in Europe  and the remaining 2 were conducted in Australia and New Zealand. The Confusion Assessment Scale (CAM) was the most frequently used scale (in 20 studies) for the diagnosis of postoperative delirium in hip fracture patients. The incidence of postoperative delirium varies among the range from 10.09% to 51.28%. In eight studies, the mean age in delirium patients an non-delirium patients are both older than 80, and in almost all of the studies, female patients are more than male patients in both groups . The meta-analysis of the pooled studies suggest that advanced age (per year increase) is a significant risk factor for the development of POD in geriatric patients after hip fracture surgeries . The meta-analysis of the pooled studies suggests that age>80 years is a significant risk factor for the development of POD in geriatric patients after hip fracture surgeries . The meta-analysis of the pooled studies suggests that male is a significant risk factor for the development of POD . The meta-analysis of the pooled studies suggest that preoperative cognitive impairment is a significant risk factor for the development of POD . The meta-analysis of the pooled studies suggest that preoperative dementia is a significant risk factor for the development of POD . The meta-analysis of the pooled studies suggest that preoperative delirium is a significant risk factor for the development of POD . The meta-analysis of the pooled studies suggest that preoperative diabetes is a significant risk factor for the development of POD . The meta-analysis of the pooled studies suggests that preoperative functional dependence is a significant risk factor for the development of POD . The meta-analysis of the pooled studies suggests that higher ASA level (per level increase) is a significant risk factor for the development of POD in geriatric patients after hip fracture surgeries . The meta-analysis of the pooled studies suggest that ASA level≥3 is a significant risk factor for the development of POD in geriatric patients after hip fracture surgeries , medical comorbidities , Parkinson's disease  and surgery delay>48 h  are significant risk factors for POD in geriatric patients undergoing hip fracture surgeries, while general anesthesia , smoking , longer surgical duration  are not significant risk factors.Another 7 factors including general anesthesia, medical comorbidities, Parkinson's disease, smoking, surgery delay>48 h, longer surgical duration were analyzed. The results in A sensitivity analysis was conducted respectively for the analysis of age (per year increase), male, preoperative dementia and ASA level≥3. We excluded each study one by one to explore whether a single study significantly impacts the heterogeneity or the results. Overall, we found that when it comes to factors including age (per year increase), male, preoperative dementia, the heterogeneity and the results were not significantly affected by any single study and that although when analyzing the ASA level≥3, after we excluding the study by Wang Y et al., the heterogeneity decreased significantly, the result changed little. Therefore, the result of our meta-analysis was relatively robust.POD is prevalent among geriatric patients undergoing hip fracture surgeries. POD is a common, acute, under-recognized adverse event and is associated with significant morbidity and mortality in hospitalized elderly patients, Considering these serious complications, it is essential to recognize the risk factors of delirium to prevent it after surgery   or relative risk (RR) with 95% confidence interval (95% CI) as a result of a multivariate logistic regression was extracted.There are several limitations in this meta-analysis: (1) significant heterogeneity was found within the selected studies. (2) only articles published in English were included. (3) different assessment time and assessment scale for delirium may introduce bias. (4) for certain identified factors, only small numbers of included studies were available, and as a result, the statistical power might not be enough to detect potential associations.Based on all the relevant studies, some risk factors for POD in geriatric inpatients with hip fracture were identified. Possible significant risk factors include advance age, male, preoperative cognitive impairment, preoperative delirium, preoperative dementia, diabetes, preoperative functional dependence, high ASA level, low albumin, medical comorbidities, Parkinson's disease and surgical delay. Clinicians should be alert to patients with those factors. To identify the risk factors more precisely, more research studies with larger sample size and better design should be conducted.The original contributions presented in the study are included in the article/All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This work was supported by Winfast Charity Foundation (granted number: YL20220225).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.


BMP15) gene detected some single-nucleotide polymorphisms (SNPs) such as c.963AGG GA vs. AA), recessive (GG vs. GA AA), additive (GG vs. AA) andco-dominant (GG AA vs. GA) model of inheritance. Data were analyzed underrandom-effects models based on the AA genotype could be positively connected with litter size in goats.Litter size is an important economic trait in the goat industry. Previousstudies on the bone morphogenetic protein 15 ( They play an important economic role in developing countries. The goat population is increasing indeveloping countries due to their different food consumption patterns andlower water requirements in comparison with other livestock species such ascattle and sheep . Goats are raised for meat,milk and hair, particularly mohair or cashmere production, and it is clear thathighly productive goats can improve the quality and increase the quantity of thementioned products .BMP15) that regulates the proliferation and differentiation ofgranulosa cells by stimulating their mitosis, stopping the expression of theFSH gene receptor and expressing the stimulation of the ligand. The proteinplays a major role in female fertility in mammals .This gene is located on the X chromosome and comprises two exons with 1185base pairs in total . Studies performed in differentprolific goat breeds have indicated that some BMP15 variants increase ovulation ratesand subsequently litter size . A mutation study of thisgene suggested that even exchanging an amino acid that does not cause alarge alteration in the product sequence can lead to a large impact on theactivity of the product, followed by the ovulation rate .In recent years, the improvement of reproductive traits, such as litter size(LS), has become one of the great interests of breeders and local farmers, andconsequently, research efforts have been made to unravel these traits' geneticbasis . Although litter size is a complex traitinfluenced by numerous genes and environmental factors, some major geneshave been identified to influence litter size . Amongthem is the bone morphogenetic protein 15 . Somemeta-analyses have been conducted on litter size in goats  and milk-related traits in cattle  and smallruminants . To the best of our knowledge, nometa-analysis has been conducted on association of detected SNPs in the22.1The preferred reporting items for systematic reviews and meta-analyses(PRISMA) checklist criteria were used to identify eligible studies for thismeta-analysis. Two investigators  independently searcheddatabases including Springer, ScienceDirect, Wiley and PubMed to detectstudies relevant to our question using combination of search terms asfollows: “BMP15”, “SNP”, “polymorphism”, “prolificacy”, “littersize”, “capra hircus” and “goat”. Furthermore, we explored all Chineseand Persian journals and databases to find articles published in differentlanguages. In addition, we scrutinized reference lists of extracted articlesto assure that no articles were missed. All articles which were in the formof an abstract or review and also any kind of duplication were removed, andthe quality of remaining full-text articles was appraised by twoinvestigators. Finally, the third investigator (Amir Rashidi) resolved all conflicts anddisagreements for inclusion and exclusion of studies.2.2Studies were eligible if they met the following criteria: (1) report onc.963A2.3The data included in our meta-analysis were extracted from selected studiesbased on designated inclusion and exclusion criteria. The extracted dataincluded the first name of the author, year of publication, goat breed andsample size, LSM, and standard error reported for eachgenotype.Cochrane Handbook for Systematic Reviews of Interventions .Considering that the standard deviations are needed to analyze data, weemployed the following equation to calculate SD from sample sizes of genotypesand standard errors of the LSM:2.4GG vs. GA AA), dominant (GG GAvs. AA), additive (GG vs. AA) and co-dominant (GG AA vs. GA) geneticmodels.ReviewManager v5.0 software was used to analyze data collected fromdifferent studies employing recessive , isrecommended. It is necessary to mention search dates and exact keywordthreads.The screening and eligibility stage should be based on the inclusion andexclusion criteria studies. Criteria might differ for the initial screening compared to the full-text screening, but both need to bereported in detail. At least two investigators should study and decide on theselection of eligible articles, with a plan for disagreement resolution andcalculating disagreement rates. The list of studies excluded at thefull-text screening stage, with reasons for their exclusion, and a full listof studies included in the final dataset, with their basic characteristics,are reported. We recorded the figures and tables as well as reportedintermediate calculations, transformations, simplifications and assumptionsmade during data extraction. These details make identifying mistakes easier andmodify reproducibility. Documentation included a summary of the dataset,information on data and study details that authors reported, a shortexplanation of software used for analyses. Therefore, wecreated a PRISMA diagram , which records the startinginformation from the studies and leads to the final dataset .A total of 38 articles were identified through search on databasesincluding PubMed, ScienceDirect, Wiley Online Library, CNKI (Chinese) andMagiran (Iranian).In addition to the abstracts, a total of five duplicate studies were removed.Then, we screened the remaining publications to exclude irrelevant studies,resulting in deletion of nine articles which did not investigate the SNPand/or trait of interest. Furthermore, in some studies polymorphisms havebeen reported, but their association with litter size was not evaluated;thus these studies were also rejected. In conclusion, five studies involving978 goats were selected to be included in our meta-analysis, three of which werewritten in English, one in Persian and the last one in Chinese. Among the selected studies, two pieces of research were conducted ondifferent breeds of goats; thus each breed was evaluated as a separate studyin the meta-analysis. Characteristics of included studies are presented in Table 1.3.2Table 2 involves Cochran's 3.3\opttitle{Meta-analysis of the relationship between the c.963A$\textgreater$Gpolymorphism and litter size}The results of meta-analysis of association between the SNP and trait ofinterest under four genetic models are summarized in Tables 3–7. Theestimates did not show any association between the c.963A3.4The sensitivity analysis was performed to investigate the robustness andvalidity of the meta-analysis using a leave-one-out approach. We did notobserve any difference in pooled results of SMDs before and after removingone study in dominant, recessive, additive and co-dominant genetic models.The funnel plots for studies drawn in all genetic models are depicted inFig. 2. As is observable, the shape of all plots indicates nopublication bias under all four employed models. On the contrary,sensitivity analysis showed significant difference in litter size bydropping studies performed by Dong and Du (2010) on the Lubei White breed and byFeng et al. (2014) and Moghadaszadeh et al. (2015) under the additive model. Furthermore, the results of Egger's regression testobtained for all four comparison models showed no evidence of publicationbias at the level of 4BMP15 gene is essential for femalefertility, so any knowledge of its function allows breeders to improvethe ovulation rate and litter size in farm animals. In addition, the study ofgenes encoding reproductive proteins is also important for capturinginformation on genetic disorders associated with reproduction traits . A reduction in the It is important to understand the genetic regulation of reproduction traitsin livestock . The BMP15, in goats has been examined . BMP15 is an X chromosomal geneknown as the FecX gene (fecundity X gene) that is associated with litter size. Given the large effect of BMP15 mutations on ovulationand litter size, it can be regarded as a major gene for reproduction in farmanimals .Association of the litter size with some important genes, especiallyBMP15 is essential for follicular development, and it also plays a key role inregulating ovulation in rats .In research on genetic mutations and effects on the ovulation rate in sheep,results have shown that BMP15 mutations affecting fertility inCele black sheep in Xinjiang, China. The result showed that mutations arevery useful and play an important role in breeding purposes in sheep.Niu et al. (2021) worked on the importance of BMP15 as a major gene .Results of a study on Luzhong mutton sheep stated the association betweenlitter size and BMP15 and lead to amino acidchanges in S300G and L329V, were associated with some fertilitycharacteristics. In another study by Dong and Du (2010), the A901G SNP wasinvestigated, which is in the same location as the A963G SNP; however they haveused a different name to refer to it.Jiao et al. (2007) and Chu et al. (2007) reported that novel SNPs (A963G)and (C1050G), which were identified in exon 2 of BMP15 gene (exon 2) in theJining Grey, Lubei White and Yimeng Black goats was discovered to have a significantassociation with litter size.Interestingly, the mutation of the A963G of the GG and GAcombined genotypes and those with the AA genotype . However, weobserved significant association of A963G polymorphism with litter sizeunder the dominant and additive models (Tables 4 and 6). In Table 4, thediamond lies entirely to the left side of the line of no effect, suggesting asignificant difference in litter size between animals with GG genotype differs from the AA genotype under an additivemodel (Table 6). For all genetic models, a random-effects model was used toanalyze data because the obtained GG GA combined genotypes and the AA genotype under a dominant genetic modelby fitting the random-effects model. Higgins and Thompson (2002) suggestedthat random-effects models affect the confidence interval and the estimationof effective size. Further, heterogeneity probably results in a differentpooled estimation, a wider confidence interval and a larger Nevertheless, the GG GA and AA genotypes under a dominantmodel  on the Lubei White breed and by Feng et al. (2014) andMoghadaszadeh et al. (2015) influenced overall results of the meta-analysis byincreasing the In conclusion, the meta-analysis we have conducted has some advantages: (1) the data used in this meta-analysis were collected from all studiespublished in several languages; (2) for our meta-analysis study we have usedfour different genetic models to investigate association betweenc.963A5AA increases litter size in goats, but consideringthe limitations aforementioned, it is necessary to be careful in explainingthe results of this meta-analysis.Ultimately, the findings of the present meta-analysis study showedsignificant association between c.963A


The protective effect persisted when considering women  and men  separately, although this was not significant for the latter. No difference was found when comparing Roux-en-Y gastric bypass and sleeve gastrectomy. Bariatric surgery reduces the incidence of colorectal cancer in individuals with obesity independently from gender and surgical procedure. Prospective large cohort studies are needed to confirm these findings.Bariatric surgery has shown to be effective in producing sustained weight loss and the resolution of obesity related medical problems. Recent research focused on the role of obesity and adipose tissue in tumorigenesis, finding a strong crosslink through different mechanisms and highlighting an increase in cancer incidence in individuals with obesity. The aim of this meta-analysis is to find if bariatric surgery reduces the incidence of colorectal cancer in patients with obesity. We performed a meta-analysis including 18 studies (PROSPERO ID: CRD4202235931). Bariatric surgery was found to be significantly protective toward colorectal cancer incidence in individuals with obesity (HR: 0.81,  With sustained long-term weight loss and remission or improvement of comorbidities associated with obesity, bariatric surgery (BS) is regarded as the most effective treatment for morbid obesity and its associated medical problems [Colorectal cancer (CRC) is the world’s third most frequent cancer and the second major source of cancer-related deaths, with an estimated 1.8 million new cases and 881,000 deaths in 2018 . Epidemiproblems . These fproblems ,8,9, butWe performed a systematic review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and AMSTAR  guidelines ,11, and We used the PROSPERO platform to register the protocol of this research (ID: CRD4202235931). The PubMed, Scopus, Embase, and Cochrane Library databases were screened without time restrictions up to 31 August 2022.Full texts were obtained through the University of Milan (Università degli Studi di Milano) digital library and the University of Nice (Université Côte d’Azur) digital library, or by direct contact with the authors. In the search strategy we also included studies and previous reviews on the topic, and their references were screened to find additional relevant studies according to selection criteria. Following PRISMA recommendations, we realized a specific population (P), intervention (I), comparator (C), outcome (O), and study design (S) (PICOS) framework to define study eligibility:Population (P): adult individuals (≥18 years old), diagnosed with morbid obesity, followed-up for at least 3 years to investigate the insurgence of CRC;Intervention (I): BS;Comparison (C): simple observation or any behavioral or medical treatment;Outcomes (O): risk to develop a CRC during the follow-up period;Study design (S): retrospective and prospective comparative studies with at least 10 patients per group.All non-comparative studies describing the risk to develop CRC in populations of individuals with morbid obesity undergoing BS or not were not eligible for inclusion. Studies not reporting a specific risk measure to develop CRC were also excluded. Only studies with the full text available and English-language research were considered for analysis.In the first step, 1407 articles were identified by the literature search. Duplicates found through different databases were removed using Mendeley reference software . A screening of the titles and abstracts of 723 records was then performed, based on an a screening form that was created to guide study selection. Investigators  were blinded to each other’s decisions. The risk of bias was assessed for the selected studies according to the ROBINS-I tool as recommended by the Cochrane Collaboration . This toInvestigators  collected risk of bias data according to the methodology proposed by Higgins . FinallyA computerized spreadsheet  was developed to collect information about study design and methodology, participant demographics and baseline characteristics, obesity treatment details, risk of colorectal cancer insurgence, and survival.Primary outcome was represented by identifying the relative risk (RR) of developing CRC in a population of morbidly obese patients who underwent BS compared to morbidly obese patients who did not have BS.In the same subset, secondary outcomes consisted in defining the RR of colonic and rectal cancer separately. Moreover, a subgroup analysis considering CRC RR for men and women separately and depending on the type of BS performed (SG vs. RYGB) were realized. Finally, we identified the hazard ratio (HR) of colorectal cancer in individuals with obesity undergoing BS or not.We performed a meta-analysis of binary outcomes and hazard ratios, so primary and secondary outcome measures were expressed in terms of RR or HR and 95% Confidence Intervals (CI) for cancer risk.Fixed and random effects models based on the Mantel-Haenszel method were built displaying the impact of heterogeneity on the results. When low heterogeneity (<25%) was found, a fixed-effects model was chosen to compute the outcome. The eventual presence of outliers was investigated; if any were found, its effect size was excluded.2 statistic and Cochran’s Q test; cut-off values of 25%, 50%, and 75% were considered as low, moderate, and high, respectively [Heterogeneity between studies was quantified by Iectively . SensitiTo explore publication bias we developed funnel plots. Egger’s test of the intercept was then used to quantify the funnel plots’ asymmetry when feasible. A statistical analysis was conducted with RStudio software (RStudio: Integrated Development Environment for R—ver. 2022.07.02) , using tAs shown in Data to evaluate the primary outcome was retrieved from 17 studies ,33,34,35p = 0.018, I2 = 98%). A forest plot is shown in Individuals with morbid obesity who underwent BS had a 54% reduction in the risk of developing CRC during follow-up .A sensitivity analysis was conducted due to high heterogeneity. Four studies ,27,30,35Only three studies reported data concerning colonic and rectal cancer separately ,27,34. p = 0.2444, I2 = 89%).For colonic cancer, a meta-analysis showed a tendency in favor of BS patients, although this was not significant .Similarly, considering rectal cancer, a non-significant tendency in the advantage of BS patients was found .From literature research we identified five studies ,25,27,34p = 0.0014, I2 = 90%) in women with a history of BS. Six studies [We found a significant 46%) reduction of risk of CRC . Only three [Considering the type of bariatric procedure, SG and RYGB were the most common. A binary outcome meta-analysis on five studies ,24,31,35ly three ,31,35 stp = 0.2974, I2 = 77%). However, after sensitivity analysis and exclusion of the research by Mackenzie et al. [p = 0.0142, I2 = 66%), as shown in Finally, we selected those articles reporting HR estimates of developing CRC in individuals with obesity whether they had a history of BS or not ,31,33,34Most of the included studies resulted in having a low risk of bias after assessment through the ROBINS-I tool. A moderate risk of bias was identified in nine ,25,32,34p = 0.9807).To assess publication bias, we generated contour-enhanced funnel plots for the primary outcome . The EggThe tumorigenic effect of obesity has been widely investigated in the last decades and particularly in the last years, along with the recent explosion of the “obesity epidemic”. As BS has been shown to be associated with significant and durable weight loss and remission of obesity-linked comorbid conditions, the possibility that BS could be effective in reducing the risk of cancer has gained increasing interest. Currently, it is accepted that obesity is a risk factor for several cancers including endometrial, ovarian, female breast, colorectal, pancreatic, esophagus, and kidney cancer . Many laOn the other hand, recent literature has shown a possible relationship between the anatomical gut modifications due to BS and an increased risk of colorectal cancer . As a maIn the present study, we aimed to focus only on CRC incidence to also explore possible subsets additionally favoring cancer incidence. As mentioned earlier, meta-analyses have been previously performed on this topic. Firstly, Almazeedi et al.  reviewedIn our meta-analysis we included 18 studies involving more than 12 million patients, and we found that individuals with morbid obesity who underwent BS had a 54% reduction in the risk of developing CRC during follow-up. However, this effect must be analyzed, taking into account the heterogeneity of the study characteristics, the populations, and the results of the studies included in the meta-analysis. Firstly, although most of the studies on the topic found a protective effect of BS, others came to opposite conclusions. Derogar et al.  indicateSecond, when focusing on the characteristics of follow-up, an important between-studies heterogeneity is noticed, with follow-up time varying from 3 to more than 20 years, strongly influencing the results in a strictly “time-dependent” condition like cancer. As a matter of fact, cancer incidence increases when follow-up is longer, and it is obvious that studies with a short follow-up have limited reliability compared to those with very long follow-up. Previous meta-analyses on this subject compare the incidence of colorectal cancer in populations with very different follow-ups and, consequently, very different chances to identify colorectal cancer. In these studies, odds ratios (OR) and risk ratios (RR), which are strictly time dependent, were used to measure the risk of colorectal cancer. This represents the most consistent limitation, as studies reporting risk estimates depending on time cannot be effectively compared when follow-up time is different, especially in cases of highly dissimilar time spans. An important novelty introduced by this meta-analysis concerns how we succeeded in overcoming this issue. The risk of colorectal cancer in the two populations  have been estimated through a risk measure which is independent of time. Our meta-analysis is based on hazard ratio (HR) as a measure of colorectal cancer risk. HR is time independent and permits the identification of the risk of developing colorectal cancer at the moment of bariatric surgery, independently of follow-up time. This makes populations with different follow-ups directly comparable, with no methodological bias. We selected those articles reporting HR estimates of developing CRC in individuals with obesity, whether they underwent BS or not ,19,20,21A gender-based subgroup analysis also found a protective role played by BS, as expected from the main outcome. The fact that no risk difference was found between patients who underwent SG and those who had RYGB confirms that the reduction of the risk of CRC mainly depends on the reduction of body weight, which translates to the reduction of the amount of adipose tissue and its related tumorigenic effect and not on a procedure-specific mechanism. As a matter of fact, recent research has shown how white adipose tissue is responsible for initiation and progress through different processes in individuals with obesity . First oAnother important novelty introduced by our meta-analysis compared to the previous ones is that in the last 2 years, a number of large new cohort studies, which were not included even in the last meta-analysis, have been published on this subject. As a matter of fact, our meta-analysis has a four-fold increased population  compared to the previous one. Moreover, these recent cohort studies are based on very different populations, amplifying the applicability of our results. In addition, these same studies, compared to the ones included in previous meta-analyses, have contrasting results, not always in accordance with a protective effect of bariatric surgery, which made it interesting to realize a new analysis.2 or 40 kg/m2) were set, different patients’ characteristics were analyzed to compare populations, and different bariatric procedures were realized; the study by McCawley et al. [Although subset analyses were performed to overcome biases, our study still have some limitations depending on the above-mentioned factors. One important source of between study heterogeneity is represented by the different inclusion criteria used to create the study cohorts. For example, different BMI cut-offs (35 kg/my et al.  only incBS has proven to be effective in inducing weight loss, obesity-related comorbidity remission and also in reducing cancer incidence. This meta-analysis confirms the protective role of BS against CRC through both RR and HR evaluation, which is independent of gender and the type of surgical procedure performed. Further studies are needed to confirm these results.


Thyroid cancer (TC) is one of the most common malignant tumours of the endocrine system. Thyroid-stimulating hormone (TSH) is known as being a risk factor for TC, but other thyroid-related hormones are inconsistently associated with TC. The purpose of this study was to comprehensively evaluate the relationships between thyroid-related hormones and the risk of TC.This study utilized searches of PubMed, Embase, Web of Science and Cochrane library up to the date of March 31st, 2022. Additionally, we performed a systematic review of related original studies combining overall and dose–response meta-analyses.A total of 30, 5 and 7 articles were included in the meta-analyses of TSH, Free triiodothyronine (FT3), free thyroxine (FT4) and TC risk with 58437, 6813 and 7118 participants respectively. An increased risk of TC was associated with high TSH exposure  in the overall meta-analysis. For every 1 mU/L increase in TSH, the risk of TC increased by 16%. However, in those studies that used healthy subjects as controls, the association was not statistically significant(P=0.62). Additionally, high serum FT3 demonstrated a reduced risk of TC, with a combined OR of 0.86 in the fixed-effect model . In addition, a statistically significant increase in TC risk was found when FT4 concentrations reached a certain threshold (approximately 2.2 ng/dL) in the dose-response meta-analysis.Significant associations between thyroid-related hormones and the risk of TC were found in this study. Further research is needed to understand the underlying mechanisms. Thyroid cancer (TC) is one of the most common malignant cancers of the endocrine system. The most common pathological types of TC include papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC), which are differentiated thyroid carcinomas (DTCs). In recent years, the rapid increase in the incidence of TC has made it an important public health problem . In the The aetiology of TC currently remains unclear. A large number of studies have reported of related factors of TC, such as age, female sex, radiation exposure, and BRAF V600E mutation, which are more or less related to the pathogenesis and clinical features of TC –6. In reCurrent thyroidology clinical studies that measured thyroid status mainly refer to TSH levels rather than thyroid hormones. In the normal hypothalamic-pituitary-thyroid axis, TSH controls the production and secretion of triiodothyronine and thyroxine and inversely regulates TSH release. After hydrolysis, more than 99% of triiodothyronine and thyroxine form noncovalent bonds with plasma proteins in the circulating blood, thus leaving only a small fraction of free triiodothyronine (FT3) and free thyroxine (FT4), which enter the target cell and bind to the receptor to exert effects . A studyGiven the high incidence of TC, a comprehensive systematic review and meta-analysis of the associations between thyroid-related hormone levels and the risk of TC is warranted, especially for thyroid hormones (FT3 and FT4), which have not been reported thus far in the literature. Therefore, we synthesized data from the original study and performed overall and dose–response meta-analyses to investigate the association between thyroid-related hormone  concentrations and the risk of TC.We searched the PubMed, Embase, Web of Science and Cochrane library for all articles on the association between thyroid-related hormones and the risk of TC. The following keywords were used: “thyroid-stimulating hormone”, “thyrotropin”, “thyroid-stimulating hormone”, “free triiodothyronine”, “free thyroxine”, “TSH”, “FT3”, “FT4”, and “thyroid cancer/carcinoma” . This study also used a reference list of published papers and literature reviews to complement other relevant studies. Relevant articles were evaluated by checking their titles and abstracts, and all of the articles that met the “inclusion criteria” were included in this study.The studies that were included in this meta-analysis met all of the following criteria: (1) epidemiological studies  on preoperative serum thyroid-related hormone concentrations and TC risk in the population; (2) reports of the odds ratio (OR) or relative risk (RR) estimates with the corresponding 95% confidence intervals (CIs) or having sufficient information to calculate them; (3) having clear sample size information; and (4) original articles published in English. The following study types were excluded: systematic reviews; meta-analyses, case reports, and case series. Eligible studies were screened by two authors. If there was a disagreement, it was assessed and resolved by a third person.Two authors carefully extracted data from all of the eligible studies according to the inclusion criteria. If two authors could not reach a consensus, a third author was added to evaluate the article. The following data were extracted from each study: first author name, year of publication, country, cancer type, quality score, number of cases and total population, thyroid-related hormone levels, and model correction factors, and other factors.via discussion.Three authors independently assessed the quality of the selected studies according to the Newcastle–Ottawa Scale (NOS) , which iThe meta-analysis was performed with the use of STATA 12.0  and R software to facilitate the pooling of results across the studies.P<0.05 was considered to be statistically significant. The heterogeneity among the different studies was verified by using the chi-square-based Q-test. If P>0.10, which indicated a lack of heterogeneity, the fixed-effect model was used. Otherwise, the random-effect model (the DerSimonian and Laird method)  could be  method) . Thus, sr method . Restricvia a funnel plot and Egger’s linear regression test. P <0.05 in the Egger’s linear regression was considered to indicate the presence of a potential publication bias. In the sensitivity analysis, we deleted one single study each time, and the new result reflected the influence of the individual dataset on the pooled OR.The publication bias was estimated By using different keyword combinations, 1164 articles were searched from database . After duplicate studies were removed (n =422), 742 remaining studies were screened. 611 articles were excluded after screening the titles and abstracts. After further review of the full text, 98 articles were excluded . Finally, 32 articles met the inclusion criteria. 30, 5, and 7 articles were included in the meta-analysis of TSH, FT3, FT4, and TC risk, respectively. The process of literature screening is shown in A total of 30 studies with 14,487 cases and 43,950 controls were included in the meta-analysis for serum TSH and TC risks. Most of the studies were retrospective cross-sectional studies. The controls in 25 studies were benign thyroid patients, and 5 studies included healthy controls. Moreover, 10 studies were conducted in the Chinese population. Serum TSH was a continuous variable in 12 studies, a binary variable in 4 studies, and a multiclass variable in 14 studies.2 = 91.1%). Similar results were also found in studies conducted in Non-Chinese and Chinese population in random-effects models  (2 = 90.2%). For those studies with healthy individuals as controls, there was no statistical relationship between the risk of TC and TSH levels   Figure 214 studies with TSH as a multiclass variable were further examined for the dose–response relationship of TC risk and TSH. Subsequently, a nonlinear relationship was found. Within a given range of serum TSH concentrations, the risk of TC increased as TSH concentrations increased. According to the fractional polynomial model, the OR estimates for TC were 2.08 (95% CI: 1.48-2.91) and 2.71 (1.87-3.89) when serum TSH levels were 2.0 mU/L and 4.0 mU/L, respectively. In addition, this study also conducted a meta-analysis of 12 studies in which TSH was a continuous variable. The results showed that for every 1 mU/L increase in TSH, the risk of TC increased by 16% .5 studies involving 6,813 participants were included in the meta-analysis of serum FT3 and TC. The control groups in 4 studies included benign patients. Moreover, 3 studies were conducted in Chinese population. As shown in 7 studies with 7,118 participants were included in the meta-analysis of serum FT4 and TC. 3 studies were conducted in Chinese population. The overall meta-analysis results are shown in P=0.54 for TSH and TC; P=0.07 for FT3 and TC; P=0.82 for FT4 and TC), thus indicating that no significant bias existed. We deleted a single study involved in this meta-analysis each time to investigate the influence of single study data on the pooled ORs. The results showed that the pooled ORs did not change dominantly in any model, which indicated that the pooled estimates were stable  to investigate the association between preoperative thyroid-related hormone levels and the risk of TC. Second, compared with other meta-analyses of TSH and the risk of TC, this study was more comprehensive and provides updates for the latest literature in recent years. Additionally, the effect of thyroid hormones on the risk of TC was analysed for the first time. Our meta-analysis also had some limitations. For example, nonpublished data, non-English-language studies, and missed studies may exist and may have influenced our results. Besides, retrospective studies cannot determine the causal link between thyroid-related hormones and disease risk. Furthermore, the control population included in the study may differ in terms of country, health status and demographic characteristics, which may confound the results. Subgroup analysis was conducted for the source of controls in this study but does not fully explain the heterogeneity of the findings. Last but not the least, TC predominates in women and has been trending younger in recent years. Due to limited relevant studies, subgroup analysis by gender and age was not performed in this study. Whether there are gender, age, and other demographic differences in the association between thyroid-related hormones and TC risk needs to be further explored.In conclusion, the existing data included in this meta-analysis provide evidence favouring a significant relationship between high TSH and FT4 concentrations, and low FT3 concentrations contribute to the increased risk of TC. Further prospective research is required to clarify the impact of thyroid-related hormones on the initiation of TC.The original contributions presented in the study are included in the article/YW collected data and drafted manuscripts. YL and ZW completed the search and selection of the database, reviewed the articles and extracted the data. QZ analysed the data and submitted the paper. RF and YJ designed the study and revised the article. All authors contributed to the article and approved the submitted version.


The development of risk prediction models for breast cancer lymphedema is increasing, but few studies focus on the quality of the model and its application. Therefore, this study aimed to systematically review and critically evaluate prediction models developed to predict breast cancer-related lymphedema.PubMed, Web of Science, Embase, MEDLINE, CNKI, Wang Fang DATA, Vip Database, and SinoMed were searched for studies published from 1 January 2000 to 1 June 2021. And it will be re-run before the final analysis. Two independent investigators will undertake the literature search and screening, and discrepancies will be resolved by another investigator. The Prediction model Risk Of Bias Assessment Tool will be used to assess the prediction models’ risk of bias and applicability.Seventeen studies were included in the systematic review, including 7 counties, of which 6 were prospective studies, only 7 models were validation studies, and 4 models were externally validated. The area under the curve of 17 models was 0.680~0.908. All studies had a high risk of bias, primarily due to the participants, outcome, and analysis. The most common predictors included body mass index, radiotherapy, chemotherapy, and axillary lymph node dissection.The predictive factors’ strength, external validation, and clinical application of the breast cancer lymphedema risk prediction model still need further research. Healthcare workers should choose prediction models in clinical practice judiciously.PROSPERO CRD42021258832The online version contains supplementary material available at 10.1186/s13643-022-02084-2. In 2020, about 2.3 million new cases of female breast cancer were diagnosed and have surpassed lung cancer as the most commonly diagnosed in the world , 2. BreaBCRL has different aspect risk factors, including demographic, physiological, biochemical, and treatment-related factors , 9. In mA prediction model is a formal combination of multiple predictors. It calculates specific risk values for individuals so that it can predict the risk of related outcomes with intuitive data through quantitative methods . It is aAll steps of this study were carried out according to the guide to systematic review and meta-analysis of the prediction model . This syPubMed, Web of Science, Embase, MEDLINE, CNKI, Wang Fang DATA, Vip Database, and SinoMed were searched for studies published on 1 January 2000 and updated on 1 June 2021 in English and Chinese. We combined the following search terms which were used in referring to the PICOTS framework: the population (Breast Cancer), exposure (Lymphedema), and intervention (prediction model). Keywords were adjusted across databases. More details of the search strategy are given in the The review question was defined according to the PICOTS framework  based on the recommendations in the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Any disagreement between reviewers was resolved by consensus. The key items to be extracted from each included study are 11 domains, including the source of data, participants, outcome(s) to be predicted, candidate predictors, sample size, missing data, model development, model performance, model evaluation, results, interpretation, and discussion; this information can be used to describe or assess the risk of bias or applicability. In addition, we extracted the general characteristics of the included studies, including author, publication year, and presentation of the model.The bias risk and applicability of the study are evaluated by the bias risk assessment tool of the prediction model (PROBAST) . The risThe study retrieved 5668 titles through a systematic search. After the title and abstract were screened, twenty-seven studies were retained for full test assessment and 17 prediction models were ultimately selected for inclusion  and 12 studies were published in the past 5 years. Most studies included various factors to establish the prediction model of breast cancer lymphedema: Yuan et al.’s study [n=6, 35.29%), six were retrospective cohort studies, four were cross-sectional studies, and one was a randomized controlled trial.The study included 19,224 breast cancer patients from 7 countries . Other studies were five Cox models, one used linear regression, and one used machine learning to estimate the probability of lymphedema  models were internally validated, including 6 (35.29%) bootstrap validation, 2 (12.5%) random split-sample validation, and 2 cross-validation. Four (23.53%) models were externally validated. Most studies assessed discrimination with concordance statistics (c-statistic) or the receiver operating characteristic (ROC) curve, and the ROC was 0.68~0.96 in the training cohort and 0.702~0.804 in the validation cohort. Only 11 (64.70 %) reported calibration, 5 studies assessed calibration with Hosmer–Lemeshow tests, and only the study of Wei et al.  assessedMore than half of the studies were at high risk of bias principally due to issues in the participants’ domain and analysis domain. The overall and domain-specific ratings for risk of bias and applicability are reported in Fig. n=12, 70.59%), there is a high risk in the bias risk of the criteria for participant selection . For the risk of bias caused by different definitions and measurements of the predictors, the source data are mainly multi-center study data, and the model included in this study has no multi-center research data, and some studies have clarified the definition of relevant predictors. In this domain, there is a high risk of bias from the evaluation of predictive factors about knowledge of outcome data, and there is no “blinding” for predictor assessments [Bias in the predictors’ domain mostly was a low concern for risk . The unclear items mainly focus on the researchers who are unclear about the information of predictors and whether the interval between predictor assessment and outcome determination is appropriate. The included studies do not mention these two parts. Knowledge of predictor results may influence determination and lead to bias [n=10, 58.82%) used the circumference difference to diagnose lymphedema, which was a semi-objective indicator. There is no clear definition of the follow-up interval and the time to determine the outcome. The follow-up time of Penn et al. [Of all studies, most were unclear for risk in the outcome domain  should be at least 20 , 46. AndThe applicability of the study is assessed for the participants, predictors, and outcome domains. Only one of the models included in this study is of low concern regarding applicability. The models established by Yuan et al., Wang et al., Kwan et al., and Basta et al. , 35, 36 In this systematic review of BCRL prediction models, we identified 17 model development studies. The development of the prediction model included was deemed to be at a high risk of bias owing to a combination of poor reporting and methodological conduct for participant selection, predictor description, and statistical methods used, but most models reported moderate to excellent predictive performance.Several aspects could influence the occurrence of lymphedema for breast cancer. Due to the different populations, candidate predictors, and modeling methods in the primary studies, the final predictors are included in prediction models. More importantly, the methods for handling continuous and categorical predictors included in each study are different. For the level of lymph node dissection, most are divided into I~III; this classification is rarely applied in clinical practice at present. There are studies on the number of lymph node dissection stratification, but the number is very different. For example, Kim et al.’s study  is boundIn recent years, there are more and more researches on predictive models in medicine . HoweverThe external validation of the model by different authors is one of the methods to promote the application of the model in clinical practice, but the operability of the model is worth thinking about. The comprehensive report of the parameters of the model is the primary condition for other researchers to use the existing model, and the TRIPOD statement also has corresponding requirements for presenting important data so that the model can facilitate external validation by other researchers after publication , such asTo our best knowledge, this study is the first to systematically review and appraise the prediction model for breast cancer-related lymphedema. And in our study, the research plan and information registration are carried out before the study, and the normative research and report are carried out through the Cochrane manual and CHARMS.There are potential limitations to our study. First, due to the differences in diagnostic criteria, included predictive factors, modeling methods, and evaluation indexes, it is inappropriate to carry out the meta-analysis. Second, all model studies on BCRL prediction were included at different modeling methods, and PROBAST might not be suitable for the model evaluation of machine learning. Finally, we restricted our focus to Chinese- and English-related databases; there may be prediction models for other languages that are not examined here.Several prediction models for BCRL are currently available and they all report good discriminative performance. However, these models have a high risk of bias and lack external validation. Therefore, further studies aimed at validating models externally to evaluate the extrapolation of the model. And the development of prediction models is expected to improve the transparent reporting of the study, so that the model will facilitate external validation by other researchers and contribute to the clinical application after publication. Eligible prediction models will help to identify high-risk groups of breast cancer lymphedema early, can enhance patient care, and promote rational allocation of limited medical resources.Additional file 1. Database Search Strategy in English.


To investigate the association of risk of offspring autism spectrum disorder (ASD) with both maternal and paternal rheumatoid arthritis (RA).The Embase, Medline, Cochrane Library databases were searched for studies that investigated the association of parental RA with risk of offspring ASD. The primary outcome was the associations of maternal/paternal RA with the risk of offspring ASD. Subgroup analyses were conducted based on the timing of maternal RA diagnosis  and geographical location  of studies.p < 0.001, 10 studies), while there was no association of paternal RA with the risk of offspring ASD . Subgroup analysis demonstrated no correlation between diagnosis of maternal RA before childbirth and the risk of offspring ASD , while there was a significant association of maternal RA regardless of the timing of diagnosis with the risk of offspring ASD . Subgroup analysis on geographical location showed a significant association of maternal RA with the risk of offspring ASD regardless of the study location .Ten studies published between 2005 and 2022 involving 6,177,650 participants were analyzed. Pooled results revealed a significant association between maternal RA and the risk of ASD (OR = 1.246, Our findings supported an association between maternal RA and an elevated risk of ASD in offspring. However, given the limited numbers of studies investigating the risk of offspring ASD in mothers diagnosed with RA before childbirth, further studies are warranted to elucidate this issue.www.crd.york.ac.uk/prospero/], identifier [CRD42022358470].[ Autism spectrum disorder (ASD), a type of neurodevelopmental disorder, is characterized mainly by deficits in social communication and the presence of repetitive and restrictive behaviors . The gloAlthough available evidence endorses the importance of early treatment for children suffering from ASD , early sA variety of autoimmune or autoinflammatory diseases in parents, such as inflammatory bowel diseases and inflammatory arthritis, were found to be associated with higher risks of ASD in offspring . In partTherefore, to fill this gap in knowledge, the current updated meta-analysis aimed at investigating the association of the risk of offspring ASD with both maternal RA and paternal RA as well as assessing the difference in risk of offspring ASD between mothers who were diagnosis with RA prior to childbirth and those with the diagnosis of RA being made in any time frame.This meta-analysis was reported based on recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The protocol was previously registered in PROSPERO (CRD42022358470).Three independent databases including Embase, Medline, and Cochrane Library were searched for the identification of studies that investigated the association of parental RA with the risk of offspring ASD. We also searched Google Scholar and the reference lists of the included studies to retrieve other relevant articles. There were no language and ethnicity restrictions applied for determining study eligibility. The database research was conducted from inception until September 7, 2022. The major terms used for literature search included “Rheumatoid Arthritis” AND “Autism” for literature search. Medical Subject Headings (MeSH), and Embase Subject Headings (Emtree) were also used to facilitate searching. The detailed search strategies for these databases are shown in The titles and abstracts of the retrieved articles were independently screened by two authors for eligibility based on the following criteria: (1) observational studies such as cohort or population-based investigations that reported an association between parental RA and risk of offspring ASD; (2) availability of information about odds ratio (OR), relative risk (RR), hazard ratio (HR), or sufficient details  for risk assessment. We excluded the following forms of publications: letters, conference abstracts, meta-analyses, case reports, reviews, and animal experimental studies. For two or more institution-based cohort studies that reported findings on the same or overlapping population, the one with the largest sample size was selected. A third reviewer was consulted for any disagreement.The following details were extracted from all eligible studies: publication-specific information  and study characteristics including population size, country, follow-up duration, number of cases, and details for risk calculation . If a study reported both unadjusted and adjusted data, we collected the latter for analysis.The primary outcomes included the association of maternal RA with the risk of offspring ASD and that of paternal RA with offspring ASD risk regardless of parental age and the timing of RA diagnosis. Two subgroup analyses were performed. The first subgroup investigation was conducted to evaluate the potential impact of pre-childbirth maternal RA  on the risk of offspring ASD after separating the maternal participants into those diagnosed with RA before childbirth and those whose diagnosis of RA was made without referring to the timing of childbirth, followed by comparing their differences in the risk of offspring ASD relative to that in their respective controls . The second subgroup analysis focused on the effect of geographical location  on the risk of offspring ASD. The diagnosis of RA and ASD was in accordance with that of each study.Two reviewers independently judged the risk of bias in the retrieved studies based on the Newcastle-Ottawa Scale (NOS), which categorizes the possible sources of bias in a cohort study into eight items pertinent to selection, comparison, and outcome. Disagreements were solved by a third reviewer. A nine-point scale was used to grade the included studies .2 statistics in which an I2 over 50% denotes substantial heterogeneity  V3 software , pooled effect size was estimated using reported patient number or raw data of OR. HR and RR were directly considered to be OR as previously reported , taking ogeneity . Leave-oogeneity , 29. A pn = 6), conference abstract (n = 1), and failure to meet the inclusion criteria (n = 3). Finally, 10 studies were eligible for inclusion in this systematic review  (p = 0.595)  , 30, 32.= 0.595) .p = 0.253, I2 = 2.127%)  , 21, 29.p = 0.192, I2 = 0%, four studies) with consistent findings on sensitivity analysis   , 22, 29.studies) .p = 0.002, I2 = 0%, six studies) (p = 0.06).Findings of subgroup analysis based on geographical location  are shown in studies) . Sensitistudies)   (p = 0.155).Focusing on Asian countries, the pooled results also disclosed a significant link between maternal RA and the risk of offspring ASD  . NeverthAlthough a previous meta-analysis demonstrated an elevated risk of offspring ASD in mothers suffering from autoimmune diseases, that study only included five studies for RA and investigated only the maternal side . Our metConsistent with the results of a previous meta-analysis that demonstrated a 39% increase in risk of ASD in offspring of mothers diagnosed with ASD compared with those without RA , our stuThere are several possible mechanisms that may explain this elevated risk of offspring ASD in mothers with RA. First, genetic inheritance, which is considered to be one of most important causes of ASD, is supported by several studies that found a shared genetic liability between autoimmune diseases and ASD, such as human leukocyte antigen (HLA) alleles –35. Intep = 0.116) possibly due to the limited numbers of studies . In contrast, despite the apparently smaller increase in risk from a mixed population comprising women diagnosed with RA before and after childbirth (OR: 1.25), the increase was probably attributable to a larger number of studies  with a narrower confidence interval.Taking into consideration the importance of environmental factors, especially pregnancy, in the etiology of ASD , 37, we in utero exposure in mothers with autoimmune diseases were associated with abnormalities in fetal neurodevelopment  on the risk of offspring ASD.The original contributions presented in the study are included in the article/C-KS and Y-SC: conceptualization and literature search. I-WC and H-JC: methodology. WC and R-FT: trial selection. H-YF: data analysis. H-YF and C-WL: data extraction. C-KS, Y-SC, and K-CH: writing—original draft preparation. K-CH and C-KS: writing—review and editing. All authors had read and agreed to the published version of the manuscript.


Consistent results were found in subgroup analyses between Asian and Caucasian populations in studies with community and hospital-derived controls as well as in studies with PCR-RFLP and direct sequencing . In conclusion, current evidence does not support an association between MTR A2756G polymorphism and CHD susceptibility.The association between methionine synthase (MTR) A2756G (rs1805087) polymorphism and the susceptibility to congenital heart disease (CHD) has not been fully determined. A meta-analysis of case-control studies was performed to systematically evaluate the above association. Studies were identified by searching the PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and WanFang databases from inception to June 20, 2021. Two authors independently performed literature search, data extraction, and quality assessment. Predefined subgroup analyses were carried out to evaluate the impact of the population ethnicity, source of healthy controls , and methods used for genotyping on the outcomes. A random-effects model was used to combine the results, and 12 studies were included. Results showed that MTR A2756G polymorphism was not associated with CHD susceptibility under the allele model (odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.86 to 1.07, P = 0.43, I Congenital heart disease (CHD) is a common birth defect in newborns that has been associated with increased morbidity and mortality for infants , 2. AccuThe Meta-analysis of Observational Studies in Epidemiology  StatemenElectronic databases including PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and WanFang were searched with a combination of the following terms: (1) "MTR" OR "Methionine synthase" OR "A2756G" OR "rs1805087"; (2) "Heart Defects, Congenital" OR "congenital heart abnormalities" OR "congenital heart abnormality" OR "congenital heart malformation" OR "congenital heart defect" OR "congenital heart disease" OR "congenital heart defects" OR "congenital heart diseases" OR "congenital anomalies" OR "birth defect"; and (3) "Polymorphism, Single Nucleotide" OR "Genotype" OR "Alleles" OR "polymorphism" OR "genetic variant" OR "genetic variants" OR "genetic polymorphism" OR "genetic" OR "Genetic Variation" OR "SNP" OR "mutation" OR "variation" OR "variant" OR "single nucleotide polymorphism". Studies published in English or Chinese were considered. The reference lists of related original and review articles were manually searched for potentially eligible studies. The final literature search was performed on June 20, 2021.Studies fulfilling all of the following criteria were included: (1) case-control studies published as full-length articles; (2) included patients with confirmed CHD diagnosis and healthy participants as controls; (3) MTR A2756G polymorphism was evaluated and regarded as exposure; and (4) reported the association between the MTR A2756G polymorphism status and susceptibility to CHD. Reviews, editorials, studies reporting the maternal genotype of the patients and controls, studies without healthy controls, and studies without detailed genotype data were excluded from the current meta-analysis.The literature search, data extraction, and quality assessment of the included studies were performed by two authors independently according to the predefined criteria. Discrepancies were resolved by consensus. The extracted data were as follows: (1) name of the first author, publication year, and country; (2) participant characteristics, including the ethnicity of the population and source of healthy controls; (3) genotyping methods; and (4) distributions of participants with MTR A2756G genotype status . The quality of each study was evaluated using the Newcastle-Ottawa Scale (NOS) . This sc2 statistic [2 > 50% indicated significant heterogeneity. A random-effects model was used to synthesize the hazard ratio (HR) data because this model is considered a more generalized method that incorporates potential heterogeneity among the included studies [For each study, the Hardy-Weinberg equilibrium (HWE) was tested to examine possible biases in genotype distribution. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were applied as the general measure for the association between the MTR A2756G genotype status and CHD susceptibility. The pooled ORs and 95% CIs were calculated for five genetic models: allele model (G versus A), heterozygote model (AG versus AA), homozygote model (GG versus AA), dominant model (GG + AG versus AA), and recessive model (GG versus AG + AA). Cochrane’s Q test was used to evaluate the heterogeneity among the included cohort studies as well as to estimate the Itatistic . I2 > 50 studies . Sensiti studies . Predefi studies . RevMan Fig 1 shows the process for the database search. Briefly, 351 articles were obtained during the initial literature search of the databases after excluding duplicates. Among them, 329 articles were excluded for lack of relevance after screening the titles and abstracts. Subsequently, 22 articles underwent full-text review. Of these, 10 articles were further excluded for the reasons listed in Fig 1. Finally, 12 studies were obtained for this meta-analysis [analysis –26.Table 1. One article included three comparisons from different centers in China; therefore, the datasets were included in the meta-analysis separately [Table 2).The characteristics of the included studies are summarized in parately . Overallparately , 19. Regparately , 24, andparately , 25, 26.2 = 4%; Fig 2A), heterozygote model , homozygote model , dominant genetic model , or recessive genetic model . No significant heterogeneity was observed for the above meta-analyses . Further sensitivity analyses by excluding one dataset at a time showed consistent results . Consistent results were found in subgroup analyses between Asian and Caucasian populations in studies with community and hospital-derived controls as well as in studies with PCR-RFLP and direct sequencing .Pooled results from the 14 datasets on 12 case-control studies showed that MTR A2756G polymorphism was not associated with CHD susceptibility under the allele model .Funnel plots for the association between MTR A2756G polymorphism and susceptibility to CHD in different models are shown in In this meta-analysis of case-control studies, we found that the MTR A2756G polymorphism is not significantly associated with CHD susceptibility. The reliability of the findings was evidenced by the consistency of the results for five genetic models, as well as the results of sensitivity and subgroup analyses. Specifically, sensitivity analysis by excluding one study at a time showed that the results were not primarily driven by any of the included studies. Subgroup analyses showed that there was no significant influence from the predefined study characteristics on the association, including the ethnicity of the participants, source of healthy controls, and genotyping methods. Taken together, the current evidence from case-control studies does not support a significant association between MTR A2756G polymorphism and CHD susceptibility.To the best of our knowledge, only one previous meta-analysis evaluated the potential association between the MTR A2756G polymorphism and CHD susceptibility . The preAlthough the cause of the majority of CHD cases is unknown, advances in genetic CHD studies provide increasing evidence for genetic causes underlying CHD and have identified critical biological pathways involved in CHD, including chromatin remodeling, Notch signaling, cilia function, RAS signaling, and gut immunodeficiency etc. , 44–47. The meta-analysis conducted in this study was based on association studies only. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the genetic variants identified by GWAS can only explain a small proportion of the heritability of complex diseases. Association analysis (including SNP studies) is a major tool for genomic studies of complex diseases and has been used for decades. Although novel technologies have been developed to uncover hidden genetic variants, association analysis still lacks the power to determine the mechanisms of diseases due to its inability to identify causal signals, which are quite different from association signals. Another reason is that the widespread networks established from integrated omics analysis are undirected. Because association analysis has limited power to unravel the mechanisms of complex diseases, it is necessary to shift the paradigm of genomic analysis from association analysis to causal inference analysis . Causal Artificial intelligence (AI) and machine learning (ML) have recently received enormous attention due to the successful application of deep neural networks in many fields, including medical science , 56. HowOur study also has limitations. Firstly, eight of the included studies were performed in China and the results were mostly from Chinese populations. Only two studies included Caucasian populations; therefore, the results should be validated in large-scale studies. The association between MTR A2756G polymorphism and CHD susceptibility in other ethnic groups such as Africans should be investigated in the future. Secondly, outcomes according to the individual forms of CHD were rarely reported among the included studies. Therefore, we were unable to evaluate the possible associations between MTR A2756G polymorphism and individual forms of CHD. Future studies are warranted. In addition, this is a meta-analysis based on study-level data rather than data for individual patients. Therefore, the influence of age, sex, and other characteristics on the association between MTR A2756G polymorphism and susceptibility to CHD remains unknown. Finally, the results were based on estimates with univariate analysis. An imbalance in participant characteristics between the patients and controls may confound the results.In conclusion, the results of this meta-analysis indicated that current evidence from case-control studies does not support an association between MTR A2756G polymorphism and CHD susceptibility.


In our article ‘European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis’  we synthesize the effect of agroforestry on biodiversity. Boinot et al.  criticise our approach arguing that our definitions of agroforestry and biodiversity are too narrow; that we use inappropriate control sites for primary studies lacking distance to the treatment sites; that there are too few studies for a meta-analysis in silvoarable systems; and that local practice should be emphasized. We agree on the importance of local practices and that more studies would improve the robustness of our meta-analytical results. However, our conclusions are robust to removing studies criticised for inappropriate controls. We also recognize the problem of different definitions of agroforestry and using species richness as sole proxy for biodiversity. We appreciate being given the opportunity to clarify our results and to trigger future discussions about definitions and the interpretation of results from meta-analysis.The online version contains supplementary material available at 10.1186/s12862-022-02016-7. In our recent meta-analysis , we haveAgroforestry is a land-use system combining agriculture or pasture with woody elements. Some definitions emphasize a mixture of trees with crops and/or animals on the same land and a combined production system , 4, whilWe agree with Boinot et al. that biodiversity has more dimensions than species richness alone. However, species richness is one very important aspect of biodiversity and showed to be related to abundance and biomass , 11. It Boinot et al. state that 22 of 28 studies found some effects of agroforestry on abundance, species composition or functional groups and hence our conclusion about biodiversity are not valid. Although we assume that their point was to emphasize the multidimensionality of biodiversity, we want to draw attention to the danger of such an argument. Vote counting is a statistically flawed and unreliable research synthesis method . We haveBoinot et al. criticise that some of our case-control comparisons on which the calculation of effect sizes is based, underestimate the effect of agroforestry on biodiversity. Indeed, we have included studies for which control locations were close to the treatment sites and this may potentially underestimate the effect of agroforestry on biodiversity. We have tested whether this was the case by excluding the sites that Boinot et al. regard as inappropriate [for code and data see Additional file For meta-analyses, a minimum of primary studies is required to compute a meaningful summary effect . Boinot However, in the same breath, Boinot et al. conclude that our meta-analysis ‘most likely underestimates the positive effects’. We wonder about such a conclusion on ‘likely positive effects’ that are necessarily based on the same few primary studies that are said to be too few for meta-analysis. In the absence of good primary studies, no general conclusions can be drawn in either direction and it is unjustified to assume that there must be a strong positive effect of agroforestry on biodiversity. We however think that even with a low number of primary studies, a quantitative synthesis in form of a meta-analysis will outcompete the statement of each individual study and allow more general and evidence-based conclusions . Thus a The effect of agroforestry on biodiversity may be influenced by local differences as Boinot et al. point out. We agree that local differences should be considered, if they have an influence on the impact of agroforestry on biodiversity. We want to remind that a fundamental idea of evidence-based practice is the identification of a causal link between an impact, e.g. a conservation measure, and an outcome of interest . These dAdditional file 1.Data for the meta-analysis on biodiversity.Additional file 2. Description of data tables and R-Code of the analysis.