FGFR4 Gly388Arg polymorphism in the transmembrane domain of the receptor has been shown to increase genetic susceptibility to cancers. However, its prognostic impact in cancer patients still remains controversial. Herein, we performed this meta-analysis to evaluate the clinicopathological and prognostic impacts of the FGFR4 Gly388Arg polymorphism in patients with cancer. We carried out a computerized extensive search using PubMed, Medline, and Ovid Medline databases up to July 2021. From 44 studies, 11,574 patients were included in the current meta-analysis. Regardless of the genetic models, there was no significant correlation of the FGFR4 Gly388Arg polymorphism with disease stage 3/4. In the homozygous model (Arg/Arg vs. Gly/Gly), the Arg/Arg genotype tended to show higher rate of lymph node metastasis compared with the Gly/Gly genotype : 0.99-1.49, p = 0.06). Compared to patients with the Arg/Gly or Arg/Arg genotype, those with the Gly/Gly genotype had significantly better overall survival (hazard ratios (HR) = 1.19, 95% CI: 1.05-1.35, p = 0.006) and disease-free survival . In conclusion, this meta-analysis showed that the FGFR4 Gly388Arg polymorphism was significantly associated with worse prognosis in cancer patients. Our results suggest that this polymorphism may be a valuable genetic marker to identify patients at higher risk of recurrence or mortality.The fibroblast growth factor-4 receptor (FGFR4) is a member of receptor tyrosine kinase. The  The fibroblast growth factor receptors (FGFRs), a subfamily of transmembrane receptor tyrosine kinase (RTK), composed of four related members FGFR1-4) 2). The aFGFR4 gene is mapped to chromosome 5 (5q35.1) and is highly polymorphic  metastases than the wild-type homozygote (Gly/Gly) in the cohorts of CRC  FGFR4 gensome 5 5q.1 and iss of CRC , 25, 36,s of CRC , 38, PC s of CRC , CRC  . In addisome 5 5q.1 and is(panNET) –46. Thersome 5 5q.1 and ish cancer –65. TherFGFR4 Gly388Arg polymorphism as a prognostic factor in cancers. They found a statistically significant association of the FGFR4 Arg388 allele and overall survival (hazard ratio (HR) = 1.21, 95% confidence interval (CI) 1.05 – 1.40) and LN involvement (odds ratio (OR) = 1.33, 95% CI 1.01-1.74)  . BecauseFGFR4 Gly388Arg polymorphism in any types of cancer. We carried out a computerized extensive search using PubMed, Medline, and Ovid Medline databases up to July 2021. The search used the following keywords variably combined: ‘fibroblast growth factor receptor 4 or FGFR4’, ‘polymorphism or SNP’, ‘Gly388Arg or rs351855’ ‘prognosis or survival’ and ‘cancer or carcinoma or tumor.’ The ‘snowball’ method was adopted to identify additional relevant articles and the reference lists of identified articles were hand-searched  guidelines . We conssearched . In caseFGFR4 Gly388Arg polymorphism with clinicopathological features (LN metastasis or disease stage) or survival outcomes ; (iii) the use of adequate method to assess the FGFR4 Gly388Arg SNP; (iv) adequate data to estimate OR with 95% confidence interval (CI) for pathological parameters and/or HR with 95% CIs for survival; (v) study published only in peer-reviewed journal; and (vi) article written in English.Studies should meet the following eligible criteria: (i) prospective or retrospective cohort study; (ii) study investigating the association of the FGFR4 Gly388Arg polymorphism, LN status, disease stage, survival outcomes , and ORs with 95% CIs for pathological parameters and HRs with their 95% CIs for survival outcomes. When both univariate and multivariate analysis were performed for survival times, the HR with 95% CI from multivariate analysis was selected. Any conflicts were resolved by discussion, with input from the other investigators (JHK and HSK).Two investigators (STP and SYJ), working independently and in parallel, screened the literature and extracted from the eligible articles according to the inclusion criteria. The following data were collected from each article and recorded using the predesigned data-collection form: first author, year of publication, country, ethnicity, inclusion period, sample size, cancer type, genotyping method, genotype counts for the FGFR4 Gly388Arg polymorphism and pathological findings was estimated by the ORs with their 95% CIs in the four genetic models: homozygous [Arg/Arg (AA) vs. Gly/Gly (GG)]; heterozygous (Arg/Gly (AG) vs. GG)); recessive (AA vs. AG+GG); and dominant (AG+AA vs. GG). For the survival analyses, HRs with 95% CIs according to the FGFR4 Gly388Arg polymorphism status were combined. Statistical values were directly obtained from the original articles. If HRs with their 95% CIs were not reported, the Engauge Digitizer software was utilized to calculate them from the corresponding data and Kaplan-Meier curves. The RevMan version 5.4 software was used to combine the data. The heterogeneity across studies was assessed by the Q statistic and 2I inconsistency test. If significant heterogeneity was detected (p  <  0.1 or I2 > 50%), the random-effects model was selected. Otherwise (p ≥ 0.1 and I2 ≤  50%), the fixed-effects model was used. Statistical significance of the pooled HR or OR was determined by Z test. The combined OR or HR > 1.0 implies that cancers harboring the FGFR4 Gly388Arg polymorphism had worse clinicopathological features or survival, respectively.The strength of the association between the Publication biases were evaluated by the Begg’s funnel plot  and the The flow diagram of search process is shown in FGFR4 Gly388Arg polymorphism status. Most studies used formalin-fixed and paraffin-embedded tissue to extract DNA, except for some utilizing fresh frozen tumor tissue or peripheral blood. The impact of the FGFR4 Gly388Arg SNP was most often studied in HNSCC (9 studies), followed by LC (8 studies), BC (7 studies), and CRC (6 studies). Other investigated tumor types were GC, PC, HCC, retinoblastoma, sarcoma, melanoma, lymphoma, bladder cancer, ovarian cancer, cervical cancer, and panNET. Twenty-four studies analyzed the pathological or prognostic parameters according to the four genetic models. Seven studies had a small sample size with less than 100 subjects in total  between patients with the GG genotype and those with the AG or AA genetic type . The association of the FGFR4 Gly388Arg SNP with disease stage was not significant in the other genetic models, either  or those with the AA genotype  Table 2.FGFR4 Gly388Arg SNP and disease-free survival. Patients with the GG genotype showed significantly longer disease-free survival than those with the AG or AA genotype   in patients with HNSCC  or LC  Figure 4In the stratification by the ethnicity, there was a significant association of the common homozygous genotype (GG) with better overall survival in the Asian population , but not in non-Asians  Figure 5The funnel plots of LN metastasis , patients with the AA genotype showed a tendency of higher rate of LN metastases than those with the GG genotype . In terms of survival times, patients with the GG genotype tended to show longer overall survival, compared to those with the AG  or those with the AA genotype . In the dominant model (AG/AA. vs GG), moreover, the Arg388 allele carriers showed a significantly increased hazard of worse overall survival  and disease-free survival . The previous meta-analysis in 2011 by Frullanti et al. reported that there was a significant association between the AA genotype and LN involvement   . They al = 0.04) . These rFGFR4 Arg388 polymorphism with the susceptibility to cancer has mainly been described in PC and BC  and LC . In HNSCC, various tumor locations among studies may explain the negative result on the prognostic value of the FGFR4 Arg388 SNP, since different anatomical locations show different clinical and molecular characteristics  and Caucasians (29.5–30.4%) , but not in non-Asians . These findings suggest that the genetic effect of the FGFR4 Gly388Arg SNP may be different among the ethnicities.The previous studies have reported that there is significantly different in the prevalence of the ucasians .5–30.4% 5–30.4%) . The hig5–30.4%) . In anot5–30.4%) . When weFGFR4 rs351855 G>A polymorphism, there are also other SNPs or mutations of FGFR4 which may affect the risk of cancer development or prognosis of cancer patients. The recent meta-analysis by Moazeni-Roodi et al. revealed that the FGFR4 rs1966265 C>T polymorphism significantly reduced the risk of cancer in the recessive model (TT vs CT+CC) and the rs7708357 G>A variant was significantly associated with increased cancer development in the dominant model (AG+AA vs GG)  . The Y36pathways . In addipathways . Some FGpathways . HoweverThere were some inherent limitations of this meta-analysis. First, there might be a selection bias since we were only able to acquire data from published articles written in English. Second, most studies were performed retrospectively and therefore, may carry the biases of the retrospective design. Third, considerable number of included studies had a relatively small sample size, and most studies utilized univariate statistical method to compare survival outcomes. Forth, because of a paucity of relevant articles, we could not perform the subgroup analyses in other types of cancers than HNSCC and LC. Finally, there was a substantial heterogeneity in the pooled outcomes, which might weaken reliability of the meta-analysis although the random-effects model was adopted.FGFR4 Gly388Arg polymorphism was associated with worse prognosis in cancer patients. Our results suggest that this SNP may be a valuable genetic marker to identify patients at higher risk of recurrence or mortality. Considering the limitations of the current study, however, large prospective researches with genotyping of the whole FGFR4 locus are warranted to reveal the clinicopathological and prognostic roles of the FGFR4 Gly388Arg SNP among various types of cancer, histology, and ethnicity.In conclusion, this meta-analysis elucidated that The original contributions presented in the study are included in the article/JHK and HSK conceived and designed the study. STP and SYJ searched the literatures and extracted the data. HSK and STP carried out the statistical analyses and data interpretation. JHK and HJJ wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) and funded by the Korean government, Ministry of Science and ICT (MSIT) (NRF- 2020R1G1A1005483).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.


In this review, state-of-the-art evidence on the relationship between cannabis use, traffic crash risks, and driving safety were analyzed. Systematic reviews, meta-analyses, and other relevant papers published within the last decade were systematically searched and synthesized. Findings show that meta-analyses and culpability studies consistently indicate a slightly but significantly increased risk of crashes after acute cannabis use. These risks vary across included study type, crash severity, and method of substance application and measurement. Some studies show a significant correlation between high THC blood concentrations and car crash risk. Most studies do not support this relationship at lower THC concentrations. However, no scientifically supported clear cut-off concentration can be derived from these results. Further research is needed to determine dose-response effects on driving skills combined with measures of neuropsychological functioning related to driving skills and crash risk. Cannabis is worldwide the most frequently used illicit drug . Rates o“Cannabis-impaired driving” describes the impairment caused by cognitive and psychomotor effects of Δ9-tetrahydrocannabinol (THC) which negatively influences a driver of a motor vehicle after THC consumption. In contrast, a “cannabis-positive driver” is someone driving a motor vehicle with any detectable THC concentration in blood, oral fluid, or urine with/without showing impairments in his driving. “Driving under the influence of cannabis” (DUIC) is a judicial term which refers to a driver exhibiting a measured reduction in cognitive or psychomotor skills in conjunction with a defined THC concentration in blood, oral fluid, or urine .THC acutely and probably chronically reduces different cognitive and psychomotor abilities needed for driving, like balance, executive function, motor impulsivity and impulse control, perception, psychomotor speed, short-term memory, visual processing, and working memory (reaction time and accuracy) , 6. All Methodological heterogeneity may explain mixed findings. As summarized by Asbridge et al. , severalTiming of cannabis use prior to an event and frequency of cannabis use have different effects on driving skills. While laboratory and experimental studies are often of small sample size, they usually assess specific driving impairment under various but defined doses of smoked or oral cannabis products  in an arn = 26 healthy occasional cannabis users , study types , and kind of crash  regarding DUIC and car crash risk. Third, independent of meta-analyses, we wanted to clarify the relationship between car crashes and different concentrations of THC detected in blood or other body fluids of drivers.http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42016033249.This review is part of an expert report [Cannabis: Potential and Risks: A scientific analysis (CaPRis)] funded by the German Ministry of Health . It follIn accordance with the German Association of the Scientific Medical Societies (AWMF)  this revThe inclusion criteria were systematic reviews or meta-analyses and prospective cohort studies investigating the effects of cannabis use on cognition, intelligence, driving performance, and traffic crashes. All studies published in English or German from 2005 to 2020 were considered. Outcome criteria wasn't specified. Exclusion criteria were non-systematic reviews, reviews without documented systematic literature search, systematic reviews not focusing on cannabis/cannabinoids, animal and molecular studies, expert opinion, and position statements.* OR hemp OR hanf” OR 2) “Mariuana OR Marihuana OR Marijuana.” Search strings were built, pilot-tested, and adopted to different databases. All studies included were rated for their methodological quality using the SIGN-checklist  used were: “Cannabis OR cannabinoidhecklist  revealinhecklist  ranging This review applied a qualitative data synthesis approach. An aggregated data analysis couldn't be used because of the high heterogeneity of primary outcome measures. The study results were interpreted with respect to their sample size, level of evidence, risk of bias, and level of heterogeneity/homogeneity. If there was a case of duplicate primary studies, the following preference criteria was included : the avaThe global literature search identified 470 publications across all databases. After removal of duplicates, 272 manuscripts remained and were screened for eligibility . The litAsbridge et al.  conducteThe meta-analysis of Li et al.  receivedElvik  includedThese studies had a low to moderate methodological quality which was due to differences in controlling for potential confounding variables. Meta-statistics estimated ORs of severity of the crash and computed ORs separately for fatal crashes as well as for injuries and property damage. Cannabis use increased the risk for traffic crash up to 50% while the risk for a fatal crash had an OR of 1.31 . Car crashes with injuries resulted in an OR of 1.26  while the risk for crash-related property damage was significantly increased  which remained significant after controlling for publication bias  were found  and for the Li et al. study, adjusted OR was ~1.55 .In the second meta-analysis, 21 observational studies were included. Revised estimates were in a similar range as the adjusted ORs of study 1 . A statistically significant increase in risk of low-to-moderate magnitude OR 1.36 (1.15–1.61) was reported from the mixed model and an OR of 1.22 (1.1–1.36) in the meta-regression model.Subsample analyses found relatively higher OR estimates for case–control studies OR 1.36 vs. culpability OR 1.12, low OR 1.45 vs. high quality OR 1.39, no limited control of confounders OR 1.52 vs. high confounder adjustment OR 1.17, and not controlling for alcohol intoxication OR 1.79 vs. controlling OR 1.11. In general, OR estimates of the relationships between cannabis use and car crash risk and subgroups are somewhat lower than those reported from previous meta-analyses , n = 10 on self-report only , one on urine analyses and two on “official databases.” Crash risk in DUIC tested via blood analyses had an OR of 1.97, fatal accidents reached an OR of 1.56, and self-reports an OR of 1.94, while the overall effect size for DUIC and crash risk was not statistically significant. Across study types, again, case-control studies presented a higher OR (1.99) than other study types (1.81). Moreover, risk of crashes with injury (OR 2.18) in DUIC and collision (OR 1.81) were higher than those for fatal car crashes (OR 1.73). Using the alternative statistical model, OR estimates were generally 0.2 lower   . The estp = 0.007). The increase in odds was most obvious at higher blood THC concentrations. At 5 ng/mL and above the OR was 3.2 (p = 0.01), and at THC concentrations of 10 ng/mL and above the OR was 10 (p = 0.03). At 1–5 ng/ml, the OR was 1.6 (95% CI 0.9–2.7), 5–9 ng/ml 1.9 (95% CI 0.7–5.0), 5 ng/ml and above the OR was 3.2 , and at THC concentrations of 10 ng/mL and above the OR was 10 . These results indicated increasing odds of culpability with rising concentration. THC was the third most prevalent drug detected in 11.1% of all drivers, following alcohol and stimulants. Only 1% of the 5,000 drivers had THC-only at 10 ng/mL or higher. The authors conclude that their culpability analysis of almost 5,000 injured drivers provided further evidence that elevated odds of culpability correlate with increasing THC levels, particularly those with higher blood concentrations.The same authors recently conducted a culpability study to include 5,000 injured motor vehicle drivers with comprehensive blood toxicology testing . The samn = 10,748 drivers, with known drug and alcohol concentrations, involved in fatal crashes in France from October 2001 to September 2003. A total of 6,766 of these were considered culpable at the crash while n = 3,306 drivers served as controls. In addition, n = 681 drivers were positive for cannabis , including 285 with an illegal blood alcohol-concentration (≥0.5 g/l). Positive cannabis detection was associated with increased risk of responsibility . A significant dose effect was identified; the odds ratio increased from 2.18 (1.22–3.89) for 0 < THC < 1 ng/ml to THC ≥ 5 ng/ml OR 4.72 (3.04–7.33), uncorrected. The effect of cannabis remains significant after adjustment for different cofactors, including alcohol, with which no statistical interaction was observed  and under the influence of cannabis 3.4% (2.9–3.9%). Drivers under the influence of cannabis multiplied their risk for causing a fatal crash by 1.65 (1.16 ± 2.34). Strength of the study is certainly its presentation of separate crash ORs for various doses of cannabis detected in blood samples, after adjusting for age, gender, vehicle category, and time of crash, which replicated results from a previous publication of the same research group . Risk estimates were adjusted for age, gender, and presence of other impairing substances. Odds ratios for all three ranges of blood tested THC were low  , 15–17.n = 51,783 participants, the second meta-analysis from the same year  in oral fluid and plasma, while time influences THC concentrations in plasma and oral fluid differently after repeated oral THC doses . CannabiAlso, cannabinoid blood and plasma concentrations were significantly higher in frequent smokers compared with occasional smokers at most time points for THC and 11-OH-THC . In oralThe third purpose of our analyses on the relationship between THC concentration and car crash risk was addressed by five case-control studies with “high quality (++)” SIGN rating . None ofThe Australian study  indicateCulpability studies may be well suited to compare risk of fatal and non-fatal crashes, because THC blood levels were measured in all five samples. If THC blood levels are compared regarding fatal –28 and nHowever, case-control and culpability studies have limitations. As several of the authors pointed out, estimating the degree of intoxication for cannabis is a more difficult task than for alcohol , 29. As The results of culpability and case-control studies must be compared with findings from experimental and laboratory studies. Laboratory and simulator studies certainly suffer from the limitation that they are conducted in an artificial environment. Thus, experimental data show that drivers attempt to compensate cannabis intoxication by slower driving after smoking cannabis, but their control deteriorates with increasing task complexity , 6. Thesper se limits of 2 or 5 ng/ml, while many European countries have a limit of 1 ng/ml. Advocates of these lower THC concentrations argue that THC concentration drops rapidly after smoking, so a driver impaired with high THC concentrations at the time of driving could show concentrations below 5 ng/ml several hours later if there is a delay in obtaining blood samples  and is probably applicable in practical traffic control situations. Thus, future experimental and laboratory research on cannabinoid's effects on fitness to drive should consider designs which include measures of psychomotor and cognitive functions like reaction times and decision making after long and monotonous drives and divided attention tasks. These cognitive characteristics may be most impaired by cannabis according to available research .Finally, it is important to recognize that an important compound of cannabis-products used is Cannabidiol (CBD). As for driving skills, it is a substance with sedative effects and therefore may play a role in impairing driving abilities and increase risks for accidents. However, no study investigates the influence of CBD alone on car crash risks. Certainly, since CBD is in increasing demand in sales and consumption, further studies are needed to evaluate the relationship between CBD use, driving ability, and car crash risks.In summary, there is unanimous agreement across studies that acute cannabis use significantly increases the risk for car crashes and impairs specific driving skills, and confidence in the results from several types of studies  is rated “moderate” according to CERQual. In meta-analyses, cannabis user's ORs for car crashes are slightly but significantly increased, when several confounders are considered in multivariate analyses. Further, self-report vs. blood test, case control vs. culpability, and non-fatal crashes had higher OR estimates. High quality culpability studies (SIGN) noted that there is a dose effect of higher THC blood concentrations with increased risk for fatal crashes and those with injuries. However, this biological gradient does not provide a clear legal cut off value. Therefore, these cut off values still range between 1 and 5 ng/ml. While there are various problems related to the measure of THC and its metabolite THC-COOH in blood and other tissues within hours after a crash (different body fluids with different time frames of THC levels), these values may not even reflect actual driving skill impairment. Thus, the biological measures alone may be not appropriate to assess fitness to drive and should be combined with ratings of psychomotor and cognitive skills.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.UP, MH, DH, JW, and EH wrote, edited, and supervised the manuscript. MS, BL, and AH contributed on the CAPRIS study by conducting the literature search and evaluating it. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


D-dimer is a fibrin-degrading substance that is soluble and whose degradation is produced by plasma protein-mediated degradation of cross-linked fibrin. Previous investigations have shown a link between D-dimer and the mortality in lung cancer patients. However, different investigations varied whether D-dimer could predict prognosis in these patients.A meta-analysis and systematic review of all available cohort studies were performed on the link between circulating D-dimer levels and survival of lung cancer patients. Relevant studies were searched in Embase, Cochrane Library, and PubMed databases. Data from 540 lung cancer patients from the First Hospital of Soochow University and Sichuan Cancer Hospital were used for external validation.2 = 75.0%). Further stratified analysis showed that higher circulating D-dimer in the advanced lung cancer group was linked to a 1.91-fold risk . Incorporation of other variables, including days of follow-up, country, design, public year, population, disease status, and quality score, into the meta-regression model, indicated that disease status was an additional source of heterogeneity (p < 0.001). External validation of 540 patients also showed that high levels of D-dimer showed a higher risk of overall mortality  and VTE events  in lung cancer patients.We finally obtained 19 eligible cohort studies with pooled HR showing that high D-dimer levels contribute to death in tumor group (HR 1.62, 95% CI: 1.39–1.88, IHigh circulating plasma D-dimer levels independently predict long-term prognosis and the risk of venous thromboembolism in lung cancer. Malignant tumors of the respiratory system are the most frequently occurring malignancies and the primary cause of mortality in patients over 65 years old . StatistD-dimer is a fibrin-degrading substance that is soluble and whose degradation is produced by plasma protein-mediated degradation of cross-linked fibrin. It is regarded as a marker of fibrinolysis and coagulation . As a siHowever, the link between D-dimer and OS of these patients is still controversial. Several studies have shown that lung cancer cases with elevated levels of D-dimer had a poor prognosis , 14, 15.We accordingly conducted a cohort study to collect data including 540 NSCLC patients at our medical center and Sichuan Cancer Hospital between February 1, 2014, and March 1, 2016. The deadline for follow-up was December 1, 2021. The diagnosis of lung cancer was regarding pathological diagnostic criteria. Death data were obtained from the hospital registry, and the time of death was confirmed by telephone follow-up or by reviewing electronic medical records. The median follow-up period was 22.3 months. Informed consent was got from the patients themselves or their immediate family members. All research projects conformed to the guidelines of the ethics committees of Soochow University and Sichuan Cancer Hospital and followed the Declaration of Helsinki.Databases from the Cochrane Library Embase and PubMed were categorized and searched from inception to December 31, 2021. The methods used followed the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) and PRISMA guidelines for systematic analysis reporting statements . SupplemThe inclusion criteria for this meta-analysis and systematic review were as follows. (1) the subjects were human; (2) the data contained plasma or serum D-dimer levels; (3) death was shown as the primary endpoint and VTE events as a secondary endpoint; and (4) confidence intervals (CI) and hazard ratios (HRs) were obtained between D-dimer levels and patient death. We excluded case reports, animal studies, studies that provided insufficient data, or narrative reviews. Three independent reviewers performed literature reviews and database searches. A senior researcher consulted to resolve the above issues in case of disputes.For each potential inclusion, the first author's last country of origin, name, year of publication, gender, sample size, study design, age, serum or plasma D-dimer, adjustment variables, follow-up period, disease outcome, and their corresponding 95% CI and disease status were extracted. The primary endpoint analyzed in the meta-analysis was patient death. Three reviewers also performed data extraction, and senior investigator input was sought when disputes were encountered.We tested the risk of bias of the selected projects according to the Newcastle-Ottawa scale , includi2 statistic, and P-value were used to assess the overall heterogeneity of the included studies. The merging process used a random-effects model for groups with several studies less than or equal to 5 in the subgroup analysis. In contrast, both random-effects models and fixed effects were used to calculate pooled HR or OR when heterogeneity was zero. A fixed-effects model was used. We further investigate the issue of the source of heterogeneity through stratified analysis. Our study also analyzed meta-regression models consistent with standard variables  to analyze heterogeneity similarly. We examined the effect of individual studies on overall heterogeneity by sensitivity analysis. We similarly analyzed funnel plot asymmetries to analyze publication and selective reporting bias. We also performed linear regression analysis by Egger and Begg to determine the statistical significance of the included studies when combined . Forest plots demonstrated the significance of the multifactorial analysis variables on prognosis. All statistical analyses were performed with RStudio (R version 4.0.2). Bilateral P < 0.05 were regarded as statistically significant.We analyzed the correlation of different levels of plasma D-dimer with patient death endpoints by confidence intervals and HR. We referred to various literature's original definitions as the quartiles of baseline plasma D-dimer. Cochran's Q test, Icombined . Fill anWe retrieved 176 records from Cochrane Library, Embase, and PubMed databases. Seventy-three of these records were duplicates, and for the remaining 103 records, we made further checks of the abstracts and titles. Of these records, 13 were not clinical studies, and 67 were unrelated to the subject. After the full-text screening of the final 23 records and removing the four studies that could not extract data , we obtained 19 studies for final analysis. We show a flow chart of the projects' selection process in The baseline information of the 19 projects published from 2007 to 2019 is displayed in 2 = 75.0%, p < 0.01; 2 decline in non-advanced lung cancer  and advanced lung cancer , indicating that this is likely the main source of heterogeneity . In addition, the association between plasma D-dimer and death persisted after inclusion in the new study, as can be shown by data from meta-regression (P = 0.794). The inclusion of other variables, including the year of disclosure, country, days of follow-up, population, quality score, disease status, and design in the meta-regression model, showed that disease status was likely the main source of heterogeneity  and Egger's (p = 0.002) regressions showed the presence of publication bias  in patients with high D-dimer levels . Of these, 165 (31.0%) were female and 375 (69.0%) were male. The mean BMI level was 22.64 ± 3.16. 169 (31.0%) cases were squamous carcinomas, and 346 (64.0%) were adenocarcinomas. Smokers accounted for 52.0% of the included population, with 279 cases . Kaplan-r levels .In this project, by pooling 19 cohort studies on D-dimer and lung cancer involving a total of 5819 patients, we found that high circulating plasma D-dimer levels were associated with the risk of death in lung cancer patients. And external validation not only demonstrated the above correlation and found that its elevated levels were equally related to the occurrence of long-term VTE events. During the analysis, we detected significant heterogeneity, and further subgroup analysis revealed that disease status might be the primary source of heterogeneity (p <0.001). Characteristics such as days of follow-up, country, public year, design, quality score, and population were not strongly correlated for the association of plasma D-dimer with the endpoint event of death in lung cancer patients. The above data suggest that lung cancer patients with higher plasma D-dimer have a poor long-term prognosis.D-dimer is regarded as a biomarker of intravascular fibrin degradation and formation . It is aD-dimers are produced during the generation of fibrin monomers in fibrinogen, which consists of two peripheral D-structural domains and E-structural domain linkage centers. The fibrin network is formed. After conversion, the D-domain is cross-linked by factor XIII, strengthening the fibrin network. Thrombin bound to fibrin is degraded from the fibrin network to soluble fragments. Thereby D dimers are produced. Thus, D-dimer in peripheral blood reflects the degree of fibrinolysis and coagulation and is elevated during thrombosis .The process of coagulation is the conversion of blood from a liquid to a gel, which then forms a blood clot. Recent investigations suggested that patients with lung cancer showed disorders in activating both fibrinolysis and coagulation. Over the past few years, many studies explored the mechanism of hypercoagulability and thrombogenesis in tumor status. Cancer cells cause coagulation relying on their apparent procoagulant activity to activate the coagulation system and deposit fibrin . Meanwhiin vitro, but the function of cancer cells in vivo still needs to be further explored  for the publication of any potentially identifiable images or data included in this article.JL and SY designed and performed research studies, analyzed the data, and wrote the manuscript. XZ, MX, CZ, LG, CY, and XW performed research studies and analyzed data. SC, DZ, and JJ contributed to the research design, data analysis, manuscript writing, and study supervision. All authors contributed to the article and approved the submitted version.This study was supported by the Social Development Project of Jiangsu Province, China in 2016 (no. BE2016672) and the Project of Hygiene and Health Committee of Jiangsu Province, China (no. H2019028).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.


Background: The α-linolenic acid is a plant origin n-3 fatty acid that may reduce the risk of cardiovascular disease. However, the effect of α-linolenic acid (ALA) on the risk of heart failure (HF) remains unclear. In this meta-analysis, we aimed to determine the role of ALA in the risk of incident HF.Methods: Electronic databases were searched for studies up to August 10, 2021. Studies were included for meta-analysis if the adjusted risk of HF in different dietary intake or circulating levels of ALA was reported. We used the random-effects model to calculate the estimated hazard ratios (HRs) and 95% CI for higher ALA.Results: A total of 6 studies (7 cohorts) comprising 135,270 participants were included for meta-analysis. After a median follow-up duration of 10 years, 5,905 cases of HF were recorded. No significant heterogeneity was observed among all the included studies. Random-effects model analyses showed that there was no significant association between ALA and the risk of incident HF, either assessed as quintiles  or per 1 SD increment . Furthermore, we did not observe any association between ALA and the risk of HF in subgroup analyses performed according to age, sex, follow-up duration, and measuring method of ALA.Conclusions: We found no association between ALA and the risk of incident HF, suggesting that ALA might not be effective in the prevention of HF. Heart failure has become a growing global public health burden, with high rates of re-hospitalisation and mortality. Heart failure (HF) incidence increases dramatically with a growing ageing population, it was estimated that nowadays, there were more than 37.7 million HF patients worldwide . AlthougLong-chain (LC) omega-3 polyunsaturated fatty acids (n-3 PUFA), including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), may play a protective role in the incidence and progression of HF, –7 througWe performed the meta-analysis following the recommendations of the Meta-analysis of Observational Studies in Epidemiology (MOOSE) group . Electropost hoc analyses of randomised controlled trials) with adult participants (aged ≥18 years); (2) dietary intakes or circulating levels of ALA were detected; (3) the risk of HF associated with different levels of ALA were reported.We included observational studies for meta-analysis if they met the following criteria: (1) observational studies  they were cross-sectional studies; (2) the risk of HF was unadjusted for confounders; (3) the follow-up duration was <1 year; (4) duplicated articles were reported from the same cohort dataset. If more than one report used data from the same participants' cohort, only the latest published article was included.Two investigators (J.W. and M.Q.) independently conducted article searches and screened the retrieved articles. Full copies of potentially suitable studies were reviewed. Key information of the included studies, such as country, cohort characteristics, methods for measurement of ALA, study sample, sex proportion, mean/median age, HF cases, and follow-up duration was recorded. If the essential message was not reported, we contacted the principal authors for clarification.The Newcastle–Ottawa Quality Assessment Scale for observational cohort studies was used to determine the study quality , which iSD increment in the continuous trait. Therefore, we used two methods to transfer the data into consistent comparisons. We compared the hazard ratio (HR) of HF risk in participants with the highest quintile of ALA with those with the lowest quintile and calculated the HR as per 1 SD increment in ALA. If effect measures were not reported as per quintile or per 1 SD change in the included studies, we converted the data following the previously published methods  and RevMan 5.3 . All We retrieved 1,263 article items from the searched electronic databases. After removing duplication records, two authors independently screened the titles and abstracts. Thirty-one articles were fully evaluated, and 6 studies were included in the pooled analysis finally . In the 2 = 0%, P = 0.77). Random-effects model analyses showed that there was no significant association between ALA and risk of incident HF, either assessed as quintiles (P = 0.36) or per 1 SD increment (P = 0.36). We did not find any evidence of publication bias by the funnel plot (P > 0.1).No statistical heterogeneity observed among all the included studies for the association between ALA and incident HF  and the risk of HF. The null result was further supported by no heterogeneity observed among multiple subgroup comparisons.Basic research on ALA on cardiac function had yielded inconsistent results. In a mice/rat model with doxorubicin-mediated cardiotoxicity, the prophylactic administration of the ALA was cardioprotective by decreasing inflammation, apoptosis, and mitochondrial dysfunction , 25. EnrIn humans, evidence suggests that increasing ALA is associated with favourable cardiometabolic status and lower inflammation status , 33, whiIn contrast, marine n-3 PUFA had been shown to be protective for HF , 13, 36.vs. lowest quintile) or continuous (per 1-SD) increment of ALA. Furthermore, no heterogeneity was observed among the included studies (I2 = 0%) and pooled results were also consistent among comprehensive subgroup analyses. However, some limitations of the current study should be discussed. First, most of the included studies were from the United States, and only one study was from Sweden. The association between ALA and incident HF was still unclear in other populations, especially those with different dietary patterns. Second, we performed this meta-analysis based on the study level. No individual participant data were available, so residual bias cannot be totally avoided. Third, the cardio-protective effects of ALA may be modified by different intakes of LC n-3 PUFAs  and the risk of incident HF. These results suggest that plant origin ALA cannot be regarded as a substitute for LC n-3 fatty acids in the viewpoint for the prevention of HF. Further studies with other populations  are needed to determine whether a high intake of ALA can prevent HF.The original contributions presented in the study are included in the article/JWu, MQ, LS, D-lL, JWe, and YH: research idea and study design. JWu, MQ, and LS: data acquisition. JWu and MQ: data analysis and interpretation. JWu and LS: statistical analysis. JWu and YH: supervision and mentorship. All authors contributed important intellectual content during manuscript drafting or revision and accept accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.This project was supported by the Guangdong Basic and Applied Basic Research Fund (Key Project of Guangdong-Foshan Joint Fund) (2019B1515120044), Science and Technology Innovation Project from Foshan, Guangdong (FS0AA-KJ218-1301-0006), and the Clinical Research Startup Program of Shunde Hospital, Southern Medical University (CRSP2019001). The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.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.


A relationship between this polymorphism and HDL-C and FBG level was observed in the recessive genetic model. In the subgroup analysis, the A allele was associated with impaired lipid profiles  in the Asian population. The influences of health status, design of the study, confounders, and other sources of heterogeneity should be considered when interpreting present findings. Cohort studies with large sample size and in different ethnicities are needed to confirm the relationship between rs1761667 SNP and cardiometabolic risk factors.The cluster of differentiation 36 (CD36) is one of the main receptors implicated in the pathogenesis of the cardiovascular disease. This study aimed to assess the association between CD36 rs1761667 polymorphism and cardiometabolic risk factors including body mass index (BMI), waist circumference (WC), total cholesterol (TC), triglyceride, HDL-C, LDL-C, blood pressure and fasting blood glucose (FBG). PubMed, EMBASE, Scopus, web of science, and Google Scholar were searched up to December 2021. Subgroup and meta-regression analyses were conducted to explore sources of heterogeneity. Eighteen eligible studies (6317 participants) were included in the study. In the overall analysis, a significant association was found between rs1761667 polymorphism of CD36 and TG in allelic ( It has been demonstrated that genetic factors play a role in the development of cardiometabolic risk independent of environmental factors2. The cluster of differentiation 36 (CD36), also known as platelet glycoprotein IV or IIIb, is one of the most important membrane proteins presents on the surface of a wide variety of cell types including adipocytes, macrophages, skeletal and cardiac myocytes, hepatocytes, microvascular endothelial cells, breast, kidney, platelets, microvascular endothelial cells, and epithelial cells in the gut3. It has been shown that CD36 plays a role in inflammatory reactions, angiogenesis, orosensory perception of dietary lipid and fat preference, regulating the metabolic pathways of insulin-resistance, transporting long-chain fatty acids (LCFA) into adipose and muscle tissues, chylomicron synthesis, and energy metabolism4. As a consequence of its functions, CD36 might be associated with a wide range of disorders such as CVD, dyslipidemia, hypertension, diabetes, metabolic syndrome, and cancer6. A genome-wide association study (GWAS) of the four large cohorts  showed that CD36 rs3211928 was significantly associated with stroke. Several studies have been conducted on the association between single-nucleotide polymorphisms (SNPs) in the CD36 gene including rs1761667 (A/G substitution) with CVD8, type 2 diabetes mellitus (T2DM)9, consumption of total fat and fat taste perception, obesity10, and metabolic syndrome11. These studies have been performed in various ethnic populations around the world. However, ambivalent results were obtained regarding the association between genotype distribution of rs1761667 and cardiometabolic risk factors. For instance, Bayoumy et al.11 demonstrated that individuals with AA genotype of the CD36 rs1761667 had a significantly lower degree of dyslipidemia, systolic blood pressure (SBP), and waist circumstance (WC) compared to individuals with AG and GG genotype. Boghdady et al.8 also showed that the AG genotype may be involved in the pathogenesis of coronary artery disease, raised body mass index (BMI), metabolic syndrome, and T2DM. On the other hand, Pioltine et al.12 reported that the SNP rs1761667 in the CD36 gene was not associated with obesity risk.Cardiovascular diseases (CVD) are the most life-threatening conditions which have negative impacts on development and economic growth. Cardiometabolic risk factors including obesity, dyslipidemia, dysglycemia, and elevated blood pressure, increase the risk of cardiometabolic diseases. Impaired cardiometabolic risk factors can be present for years before the clinical symptoms become apparent; therefore, their management is difficult for physiciansTo the best of our knowledge, there is no systematic review and meta-analysis trying to examine the possible relationship between CD36 rs1761667 polymorphism and cardiometabolic risk factors. Thus, the current study aimed to examine the association between this polymorphism and cardiometabolic risk factors including BMI, WC, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), systolic and diastolic blood pressure, and fasting blood glucose (FBG).26. One study reported BMI before pregnancy; thus, it was included for this variable and this article was not used for other risk factors27.In total, 297 publications were identified in the initial search; from which 106 studies were duplicates, and 153 articles did not meet the eligibility criteria after screening titles/abstracts. The full texts of thirty-eight articles were assessed for further consideration and twenty articles were excluded for the following reasons: reported duplicate data (n = 2), had no data on the outcome variables (n = 12), conducted on pregnant women (n = 2), children and adolescents aged below 18 years (n = 2) and did not provide the sufficient data (n = 2) , nine studies were cross-sectional, 23 of which 2 were baseline assessment of clinical trials17  were considered: allelic model (A vs. G), dominant model (AA + GA vs. GG), recessive model (AA vs. GA + GG), homozygous model (AA vs. GG) and heterozygous model (GA vs. GG).27 (n = 2478) reported the data on BMI and five articles24 with 1859 subjects were included in quantitative analysis for WC. As shown in Table 2, 95%, CI: − 0.25, 0.31, p = 0.84, I2 = 31.20%), dominant , recessive , homozygous , and heterozygous  models  and in the heterozygous model, in studies with medium quality  , dominant , recessive , homozygous , heterozygous  models  models and heterogeneity between included studies was moderate to high  and homozygous  models and healthy individuals . Furthermore, serum TC level was significantly higher in adjusted studies under allelic, dominant and heterozygous models  revealed that in overall, there was a significant association between rs1761667 polymorphism and TG values in allelic , recessive  and homozygous  models, the TG level in the AA genotype group was significantly lower than that in G allele carriers, and the same difference was also observed in the above-mentioned models  and allelic models , respectively. Further details about the subgroup analysis are provided in Supplementary Table The analysis of eleven studiesls Table . Subgrou26, AA genotype population had a significantly higher HDL-C level  than G allele carriers in the recessive model . Lower serum HDL-C levels were observed in the studies with the Asian population in allelic , dominant  and heterozygous  models  revealed, no significant association between CD36 rs1761667 polymorphism and LDL-C levels in different genetic models  , recessive  and homozygous  models were associated with an increase in LDL-C levels in healthy participants and there was moderate to high heterogeneity between the included studies. Under the allelic  and homozygous  genetic models, rs1761667 had a significant association with the LDL-C levels in Asian population and there was no heterogeneity between the included studies. The results of subgroup analysis based on the study design and quality indicated that, the concentration of LDL in moderate quality and cross-sectional studies was lower in heterozygous model, whereas this variable was significantly higher in studies that adjusted the association for the potential confounders  than studies with no adjustment   Table . Allelic24 for systolic (SBP) and six studies with 2224 participants25 for diastolic blood pressure (DBP) are illustrated in Table The analysis results for five studies (n = 2112)25 which included a total of 2305 individuals assessed the serum FBG in different genotypes of CD36 rs1761667. According to the pooled analysis, under the recessive genetic model, the homozygous A-allele carriers had a significantly higher FBG concentration compared with G allele carriers , p = 0.001, I2 = 6.03%) and no heterogeneity was observed between included studies (Table p = 0.01)and the between-study heterogeneity was low (I2 = 39.50% and p = 0.19). Under allelic, recessive and homozygous models, a significant correlation was observed between this SNP and circulating FBG levels in the healthy group and cross-sectional studies. The between-study heterogeneity was not observed in these subgroups (I2 = 0.00%). The findings of other subgroup analyses were reported in Supplementary Table Seven studieses Table . No signNo significant association was observed between potential modulators  and effect sizes for the association between CD36 rs1761667 polymorphism and BMI, HDL-C, LDL-C, SBP and DBP using meta-regression analyses. Ethnicity, quality score, design of the study, adjustment of confounders in the homozygote model and Hardy–Weinberg equilibrium were associated with between-study heterogeneity in the allelic model on the relation of CD36 SNP at rs1761667 on the levels of TC was determined using meta-regression , WC (dominant model), triglyceride (dominant and heterozygous models), HDL-C , LDL-C , FBG  which are provided in Supplementary Table No evidence of publication bias was found for BMI, TC and HDL-C levels. Begg’s and Egger’s asymmetry tests suggested evidence of publication bias for the meta-analysis of serum TG and LDL-C levels in the dominant and heterozygous models. However, the results remained unchanged using the trim and fill analysis, which means it is unlikely that publication bias significantly affected imputes estimates Tables , 3 and 428. The CD36 is a multifunctional receptor that both persistent up-regulation and deficiency of this protein can increase the CVD risk. Abnormally up-regulated CD36 can lead to foam cell formation, endothelial apoptosis, exacerbated inflammation, macrophage trapping, and thrombosis. On the other hand, CD36 deficiency promotes metabolic disorders, dyslipidemia  and sub-clinical inflammation, all of which are cardiometabolic risk factors29. Similarly, regarding higher TG levels in individuals with the G/G genotype, previous studies demonstrated this finding and also showed that improvement in plasma TG levels after a fish oil-rich dietary intervention was lower in these individuals compared with G/A and A/A individuals17. In addition, a study in North Indian population showed that the presence of the minor rs1761667-allele A was associated with elevating the risk of developing T2DM9. The results of the ethnicity subgroup showed that Caucasians with AA genotype had a higher FBG concentration than G carriers. This discrepancy in this finding might be owing to different ethnicity, sample sizes, gene-environment and gene–gene interactions, publication bias and clinical heterogeneity. Another possible reason for this contrary finding can be a varied selection of the health status in different populations. On the other hand, the majority of eligible studies had evaluated fasting glucose levels. It was better to apply other glucose indicators (glycated hemoglobin A1c) because fasting blood glucose is affected by many factors such as BMI, psychological stress, smoking habits, potassium intake, etc31. So, it seems that for a definitive interpretation of this result further studies are needed.The results of the present systematic review and meta-analysis unraveled that CD36 rs1761667 polymorphism had a significant association with cardiometabolic risk factors such as circulating TG, HDL-C, and FBG levels. No significant relationship was observed for other comparisons in the overall analyses. A previous cohort study demonstrated that high triglyceride levels were three times more common in CVD patients than elevated cholesterol levels alone32. The deficiency of CD36 occurs rarely in Caucasians and is relatively common (3–10%) in the population of Asian and African descent33. As mentioned before, CD36 deficiency is closely related to the elevated prevalence of metabolic abnormalities, including hyperlipidemia, and increased fasting glucose levels. Moreover, clinical investigations have shown that the rs1761667-A allele decreases the CD36 expression and is associated with upper recognition taste thresholds for fat and decreased lipid taste perception35. The significant relationship between the CD36 SNP rs1761667 variant and lipid levels were shown in Asians also highlights the interaction between the CD36 SNP rs1761667 variant and ethnicity in modulating the plasma lipids. Bayoumy et al.11 noted in an Egyptian population that the minor allele frequency (MAF) of rs1761667 polymorphism was merely 0.25, Noel et al.36 showed that the MAF was 0.46 in a Hispanic population. Previous studies have been reported the influence of interethnic differences in allele frequencies. These variations can contribute to the differences in gene expression and eventually, disease susceptibility. Therefore, it is important to consider the population variations in the allele frequency when trying to identify an association between a polymorphism and the risk of diseases39. Further studies with different ethnicities should be performed to confirm these conclusions.The subgroup analysis demonstrated that the A allele was associated with elevated TC and LDL-C levels and decreased HDL-C levels in Asians. In line with the mentioned conclusion in the present study, some previous studies reported these outcomes and suggested A allele of rs1761667 as a susceptibility factor for high serum cholesterol levels, low HDL-C and atherothrombotic stroke40. CD36 is decreased in pathological cardiac hypertrophy and increased in diabetic cardiomyopathy and atherosclerosis. In a healthy heart, insulin promotes CD36 transportation from the endosome to the cell membrane. Simultaneously, the forkhead box O1 (FOXO1) transcription factor promotes CD36 expression41. Long chain fatty acids (LCFAs) absorbed by CD36 are used for oxidation and storage as lipids in mitochondrial. Whereas in diabetes, the enhancement of insulin strongly activates the PI3K-Akt (phosphatidylinositol 3‑kinase-protein kinase B) pathway that leads to a robust CD36 expression. On the other hand, upregulated mir-320 and down-regulated mir-200b-3p accelerate the CD36 transcription and translation, uptake of LCFAs facilitates in cardiomyocytes eventually. Intracellular LCFAs either enter the mitochondria for producing energy and the by-products-reactive oxygen species (ROS) or forms triglycerides. As we know, triglycerides accumulation can result in insulin resistance. The ROS assembly and insulin resistance deteriorate cardiac function and would trigger diabetic cardiomyopathy42. Therefore, CD36 has different functions in diverse conditions and there is a need for more in-depth study with different health status40. Moreover, a significant association with BMI was detected among subjects with heart diseases in the recessive model. The mechanisms underlying how modifications of CD36 affect fat metabolism and cardiometabolic risk factors still remain to be elucidated; however, some evidence has shown that CD36 plays a role in the metabolism of LDL-C and HDL-C and contributes directly to their regulation44. A systematic review of studies that investigated the association between CD36 and the metabolic complications of obesity, reported that CD36 may be involved in obesity-related complications in humans. Moreover, CD36 deficiency might affect myocardial uptake of LCFAs, delay clearance of plasma fatty acid after an oral meal, and be associated with abnormalities in chylomicron formation45. The CD36 is a membrane transporter of long-chain polyunsaturated fatty acid in many tissues including skeletal muscles, adipocytes and the heart. Dysfunction of this protein might reduce the intramuscular fatty acid oxidation rate. Therefore the availability of fatty acid enhances their storage in adipocytes47. Peroxisome proliferator-activated receptor (PPAR-γ) is a nuclear receptor that regulates adipocyte differentiation and adipogenesis and CD36 is regarded as a key factor in the activation of PPAR-γ and its change might influence PPAR-γ mediated adipocyte differentiation48. It has been suggested that CD36 expression is reduced in circumvallate taste buds among high-fat diet-induced obese rats which leads to a decreased sensitivity to fat taste, as a result the intake of fatty foods increases as a compensatory response 49. Muthuswamy et al.50 reported that a lower CD36 expression (in AA and AG genotype at rs1761667) might be involved in reducing the release of PYY from taste bud cells. The presence of CD36 in gustatory papillae, the main LCFA receptor in taste bud cells51, contributes to dietary fat taste perception and fat preference35. More preference and increased eating of fatty foods have been expressed, which may reflect a decline of oral and gastrointestinal fatty acid sensitivity in obesity52. Based on the results obtained after subgroup analysis according to quality assessment, design of studies, adjustment for confounders, and Hardy–Weinberg equilibrium, high-quality cohort studies considering confounders such as age, sex, BMI, smoking, alcohol consumption and physical activity are needed to confirm the results.The analyses also showed a significant association between increased levels of LDL-C, FBG and A allele of CD36 rs1761667 in healthy participants. It is mentioned that the synthesis and translocation of CD36 are influenced by various stimuli. Modifications of CD36 affect cardiac function via altering the cellular uptake of fatty acids in the myocardium. The increased CD36-induced fatty acid uptake could be harmful or beneficial under various pathological conditions53. Furthermore, some studies in animals indicated that there is a relationship between CD36 genetic background and regulation of blood pressure55. There are some reasons which may explain these inconsistencies. The conflicting results might be due to variations in the health status and the genotyping methods, differences in ethnicity of the populations and their sex, gene–gene interactions in various populations and interactions of rs1761667 polymorphism with other variants in the CD36 gene. Moreover, some environmental factors may affect CD36 expression, as alcohol and fatty acids are recognized to modify the epigenome that includes acetylation of histones, DNA methylation, etc. 57. According to the factors mentioned, observations are still controversial and further studies are needed to arrive at a firm conclusion about the association with blood pressure. However, it is worth noting that studies on CD36 suggest that transcriptional activation, post-translational modification and localization alterations in this protein may create new approaches for the treatment of CVD40.The current evidence showed no significant association of this CD36 SNP with blood pressure; however, subgroup analysis based on adjustment of confounders demonstrated a significant association between decreased DBP and studies which did not consider confounding variables into account. Molecular studies suggested that CD36 contributes to the production of nitric oxide. Since reducing nitric oxide activity in the renal medulla is associated with hypertension, it is proposed that a decreased CD36 in renal cells may be related to hypertension The present meta-analysis has some strengths. To the best of our knowledge, this is the first meta-analysis about the relationship between genetic locus 7q11 rs1761667 polymorphism and cardiometabolic risk factors which tried to include all related publications. Notably, several subgroup analyses and meta-regression were performed to assess the potentially different relation between rs1761667 polymorphism and cardiometabolic risk factors. There are a number of limitations that should be considered when interpreting the current study’s results. First, some information  was unavailable in studies to perform more detailed subgroup analysis. Age is another risk factor that plays an important role in the enhancement of cardiovascular risk factors and the development of CVD. Unfortunately, conducting subgroup analysis based on age groups was not possible in the present study. In addition, further research considering other environmental and genetic factors is still needed to come up with a more apprehensive estimation of CD36 rs1761667 polymorphism with cardiometabolic risk factors. Second, the number of studies available for some outcome variables and a number of subgroups was fairly small. Furthermore, the total sample size of Asian and European studies are still limited in our research. Another problem is the fact that some of the obtained relations  were affected by the removal of one or several studies in the sensitivity analysis. In addition, we could not find any GWAS finding on rs1761667; therefore, it was not possible to compare the results obtained in this study with GWAS result. A high level of heterogeneity was also observed between the studies included in analyses for different genetic models. Thus, the findings should be interpreted with caution.In summary, the results of the current systematic review and meta-analysis indicated that CD36 rs1761667 polymorphism was significantly associated with decreased TG and elevated HDL-C and FBG concentration in the overall analysis. Asian populations with different genotypes have different levels of lipids , which may affect the susceptibility of the disease in these people. The influence of ethnicity, confounders, and health status on pooled effects should be considered when interpreting the association between rs1761667 polymorphism and elevated cardiometabolic risk factors. Furthermore, cohort studies with a large sample size which take confounders into account are needed to confirm the associations found between rs1761667 SNP and cardiovascular risk factors.58. The study protocol was also registered in the prospective register of systematic reviews (PROSPERO) [protocol code: CRD42021253789].This systematic review and meta-analysis is reported in accordance with the PRISMA  statementRelevant articles were identified through online search of the literature in PubMed/MEDLINE, EMBASE, Scopus, ISI (Web of Science) and Google Scholar up to December 2021 without any publication date, language, and any other restrictions. The keywords used to search were: rs1761667 OR “− 31,118 G > A” OR “− 31,118 G > A” OR “− 31,118 G > A” OR “− 31,118 G > A”. The research was also updated by adding the following keywords in the all fields "GWAS", "Genome-wide association studies", "Genome wide association studies", "Genome wide association study", "Genome-wide association study", "GWA study", "whole genome association study", "WGA study", "WGAS", "Whole-genome association study", "whole genome association studies", "whole-genome association studies", "WGA", "Whole Genome Association Analysis", "Whole-Genome Association Analysis", "Genome Wide Association Analysis", "Genome-Wide Association Analysis" to did not lose any article  that focused on CD36 rs1761667 polymorphism and cardiometabolic biomarkers such as anthropometric indices , lipid profile markers , blood pressure and FBG were included in the present review. Furthermore, articles with or without deviation from the Hardy–Weinberg equilibrium (HWE) were included. If the studies were done in pregnant, lactating women, children, adolescents aged < 18 years, and also if they had no outcomes of interest, were excluded from the review. In the case of repeated publications on the same study, we selected the one which included a higher number of participants.For each eligible study, data were extracted on the author’s last name, publication year, country, participants’ characteristics , method of genotyping, HWE and mean ± standard deviation (SD) of desired outcomes. Two reviewers (ASA and ZY) independently screened the titles and abstracts, assessed the full text of the relevant articles, and extracted the data. Any possible disagreements or discrepancies were resolved by group discussion.59 and its modified version adapted for cross-sectional studies (seven items)60 with a maximum score of 9 and 10, respectively. The NOS was used for assessing the risk of bias in clinical trials because their baseline assessments were considered in this meta-analysis. According to the obtained NOS scores, case–control studies were classified into three levels: low quality (0–4 points), medium quality (5–6) and high quality (7–9 points) and cross sectional studies were classified into four levels: unsatisfactory (0–4 points), satisfactory (5–6 points), good (7–8 points) and very good (9–10 points) (as previously performed62). Any discrepancies were addressed by discussion to reach a consensus.Two authors independently evaluated the methodological quality of eligible studies. The quality of each study was assessed by using the Newcastle–Ottawa (NOS) Scale for case–control (eight items)2) statistic (which is an estimate ranging from 0 to 100%). The heterogeneity was regarded as low, moderate, and high when the values of I2 were 25%, 50%, and exceeded 75%, respectively63. Subgroups analyses according to ethnicity, health status , Hardy–Weinberg equilibrium, quality score , design of the study  and adjustment of confounders were performed to detect sources of between-study heterogeneity and also meta-regression analysis was used to identify potential sources of heterogeneity. Sensitivity analysis was used to assess whether the overall association depended on a specific study64. The presence of the publication bias was investigated by visual inspection of the funnel plots in case there were < 10 studies in each analysis, and also through statistical asymmetry tests (Begg's adjusted rank correlation and Egger`s tests) for meta-analysis of 10 or more effect sizes65. In the case of asymmetry, Duval and Tweedie’s trim and fill analysis was applied for more adjustment of publication bias66. All statistical analyses were performed by using STATA version 11.2 . Two-tailed p ≤ 0.05 were considered statistically significant.The raw difference in means and its 95% confidence interval (CI) was calculated by five genetic comparison models: allelic model (A vs. G), dominant model (AA + GA vs. GG), recessive model (AA vs. GA + GG), homozygous model (AA vs. GG) and heterozygous model (GA vs. GG) to determine the effect size for the meta-analysis. The fixed-effect model was carried out to combine the subgroup-specific estimates when studies reported separate results across different subgroups and the pooled effect size was used for meta-analysis. A random-effects model which takes the between-study heterogeneity into account was used to derive weighted mean difference (WMD) as the summary estimate and its confidence limit. The heterogeneity between studies was evaluated using Cochran's Q test and the I-squared (ISupplementary Information 1.Supplementary Information 2.


Osteoarthritis (OA) is an important health issue in elderly people. Many studies have suggested that genetic factors are important risk factors for OA, of which tumor necrosis factor-α (TNF-α) is one of the most examined genes. Moreover, several studies have investigated the relationship between TNF-α G-308A polymorphisms and OA risk, but consistent results have not been obtained.This study examines the association between TNF-α G-308A polymorphisms and knee OA. Moreover, meta-analysis and trial sequential analysis (TSA) was used to determine whether this is a susceptibility gene for knee OA.Between 2015 and 2019, 591 knee OA cases and 536 healthy controls were recruited. The Kellgren–Lawrence grading system was used to identify the knee OA cases. A meta-analysis was conducted including related studies published until 2020 from PubMed, Embase, and previous meta-analysis to improve the evidence level of the current study. The results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CI) to evaluate the effect of this polymorphism on knee OA risk. The TSA was used to estimate the sample sizes required in this issue.n = 2182) were sufficient to obtain a definite conclusion.A nonsignificant association was found between the AA genotype and knee OA  in the present case–control study, and analysis of other genetic models showed a similar trend. After adding the critical case–control samples for Asians, the TNF-α G-308A, AA genotype exhibited 2.57 times more risk of developing arthritis when compared with the GG + GA genotype , and the cumulative samples for TSA (The results of this meta-analysis revealed that the TNF-α G-308A, AA genotype is a susceptible genotype for OA in the Asian population. This study integrated all current evidence to arrive at this conclusion, suggesting that future studies on Asians are not required. Osteoarthritis (OA) is the most common joint disease that is the main cause of disability among elderly people globally . GeneticTumor necrosis factor-α (TNF-α) is located in the major histocompatibility complex III region 6p21.3) on chromosome 6 . TNF-α pp21.3 on Twelve studies have currently investigated the association between G-308A polymorphism and knee OA , 12–22. This study was approved by the Institutional Review Board (TSGH-2-102-05-028) of the Tri-Service General Hospital (TSGH). Volunteers signed the consent form after the investigators explained the study.This study enrolled 1,127 participants (591 cases and 536 controls) comprising individuals ≥65 years old. All received Taipei City senior medical check-ups between January 2015 and December 2019 at the TSGH, a medical teaching hospital at the National Defense Medical Center, Taipei, Taiwan. The check-up is a government-driven welfare program for individuals ≥65 years old who are registered Taipei City residents for >1 year.Patient information was examined while the participants underwent check-ups. All participants who received the study information, understood the process, and provided written consent were enrolled. Participants were excluded if sufficient blood samples were drawn. The exclusion criteria were patients who had no knee X-ray or genotype data.Approximately 10 mL of peripheral blood was intravenously extracted from participants by a physician or nurse. Genomic DNA was isolated using standard procedures with proteinase K  digestion and phenol/chloroform methods. The rs1800629 SNP was genotyped using the iPLEX Gold SNP genotyping method. Genotyping was performed under blind conditions. At least 10% of the samples were randomly selected for repeated genotyping to validate the results, and the concordance rate was 99%.t-test. Differences in genotype and allele frequencies between knee OA patients and healthy controls were tested using the χ2 test. Odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of knee OA were calculated using logistic regression. The primary analysis was the association test based on the recessive model assumption [p value of <0.05 was considered significant. R 3.4.4 was used for statistical analyses.Continuous variables of the general demographic data were expressed as mean and standard deviation using Student’s sumption . The othThe PRISMA checklist and Meta-analysis on Genetic Association Studies Checklist are described in Two reviewers (Chih-Yun Huang and Sui-Lung Su) were responsible for the independent extraction of literature data in this study. The data extracted included the last name of the first author, publication year, country, ethnicity of the study population, and gene distribution in the case and control groups. All extracted papers were assessed using the Newcastle–Ottawa Scale, and the details are shown in I2 test was used to assess heterogeneity. I2 >50% denoted moderate to high heterogeneity. Egger’s regression and a funnel plot were used to examine the symmetry after combination. The primary analysis was the association test based on the recessive model assumption [All included papers were described using appropriate proportions or mean values. The current meta-analysis used ORs with 95% CIs to examine the correlation between TNF-α G-308A and OA. The sumption . The othsumption  and “metsumption  packagessumption . TSA wasp < 0.001), and the age and body mass index of the case group were higher than those of the control group (p < 0.001 and p = 0.019). p = 0.091) and did not exhibit a significant correlation with OA . The dominant and recessive models were further used to validate the results and similarly did not find any significant differences after correcting for covariates. Therefore, the current case–control study found that TNF-α G-308A does not have a significant correlation with OA. The samples from the case–control study were included in the meta-analysis and further employed TSA validation to see if definite conclusions could be obtained for Asian samples to increase the power of evidence for the meta-analysis.The sample size for Caucasians was 1,533 patients after TSA estimation without significant correlation between TNF-α G-308A and OA, and the cumulative sample size did not reach the target sample size . Therefore, definite results could not be obtained in the meta-analysis of Caucasians . The cumThe study by Han et al. showed an OR ratio that was too high . HoweverTSA estimation was performed after exclusion. Consequently, 981 cumulative Caucasian samples were noted without significant correlation between TNF-α G-308A and OA, and the sample size was insufficient to obtain a definite conclusion . The cumThe current case–control study identified no significant correlation between TNF-α G-308A and OA risk. This result is consistent with some of the previous studies , 18–20. The current meta-analysis found an increased risk by using the recessive model analysis. However, a subgroup analysis found that the relationship between TNF-α-308 polymorphism and OA risk only existed among Asians and not among Caucasians, which is consistent with the results of previous meta-analyses , 18, 19.In the precision medicine era, genomic and environmental factors that are significantly related to disease phenotypes may be used as biomarkers for risk prediction and early disease diagnosis –46. WellThe current study has three major strengths: (1) none of the previous meta-analyses estimated whether the cumulative sample size is sufficient to obtain a definite conclusion. This study employed TSA estimation and found that definite conclusions can be obtained based on the Asian sample size. (2) This study used the random-effects model to combine results and avoid serious errors caused by model selection based on heterogeneity . (3) In In conclusion, the definite null relationship between TNF-α G-308A and knee OA was validated in this case–control study. However, this meta-analysis demonstrated that the AA genotype of the TNF-α G-308A polymorphism increased the risk of OA in Asians. A decisive conclusion could be obtained, for which the current case–control samples provided critical evidence. In contrast, TNF-α G-308A polymorphism was not associated with knee OA risk in Caucasians. Further studies with a large sample size are necessary to validate whether TNF-α gene polymorphism contributes to knee OA susceptibility in Caucasians.S1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 FigI2 (heterogeneity) = 0%.A trial sequential analysis was performed using a dominant model assumption. Detailed settings: significance level = 0.05, power = 0.8, least extreme odds ratio to be detected = 1.5, minor allele frequency = 0.069, and (TIF)Click here for additional data file.S2 FigI2 (heterogeneity) = 0%.A trial sequential analysis was performed using a dominant model assumption. Detailed settings: significance level = 0.05, power = 0.8, least extreme odds ratio to be detected = 1.5, minor allele frequency = 0.12, and (TIF)Click here for additional data file.S3 FigI2 (heterogeneity) = 0%.A trial sequential analysis was performed using a dominant model assumption. Detailed settings: significance level = 0.05. power = 0.8, least extreme odds ratio to be detected = 1.5, minor allele frequency = 0.069, and (TIF)Click here for additional data file.


Previous studies showed inconsistent results regarding associations between diabetes mellitus (DM) and risk of Parkinson's disease (PD). The study aimed to make a meta‐analysis to clarify whether DM is a risk factor for PD.We searched for articles regarding the effect of DM on risk of PD and published before July 2020 with search terms as follows: (“diabetes mellitus” OR “diabetes”) AND (“Parkinson's disease” OR “PD”) in the following databases: PubMed, Web of Science, MEDLINE, EMBASE, and Google Scholar. We used STATA 12.0 software to compute multivariate odds ratio (OR) or relative risk (RR) and 95% confidence intervals (CI) regarding the association between DM and risk of PD.I2 = 92.4%, p < .001). Subgroup study showed that DM was associated with higher risk of PD in cohort studies , whereas no significant association was indicated between DM and risk of PD in case–control studies . Sensitivity analysis showed no changes in the direction of effect when any one study was excluded from all meta‐analyses. In addition, Begg's test, Egger's test, and funnel plot showed no significant risks of publication bias.The present study finally included 7 case–control studies  and 9 cohort studies  exploring the association between DM and risk of PD. The meta‐analysis indicated that DM was related to elevated risk of PD (OR/RR = 1.15, 95% CI 1.03–1.28, In conclusion, we have tried to determine whether prior onset of DM may contribute to the risk of developing PD. More and more large‐scale prospective studies should be conducted to evaluate the relationship between DM and PD. We have tried to determine whether prior onset of DM may contribute to the risk of developing PD. More and more large‐scale prospective studies should be conducted to evaluate the relationship between DM and PD. Selection criteria were shown as follows: (a) studies that provided sufficient information regarding odds ratio (OR) or relative risk (RR) and 95% confidence intervals (CI) associated with DM and risks of PD; and (b) studies whose OR or RR and 95% CI could be calculated from the data in the studies. In addition, included studies could not be reviews, meta‐analyses, or case reports. All the abstracts and full texts were read independently by two investigators (Wei Liu and Jianfeng Tang).2.2Two investigators (Wei Liu and Jianfeng Tang) independently used an Excel file to extract data from finally included studies. These data included author, publication year, study design, study location, sample sizes, gender of participants, event for analysis, adjustment factors, and results.2.3Q test and inconsistency index (I2) to assess heterogeneities between studies. When the heterogeneity was high , random‐effects models were used to compute the results; when the heterogeneity was low , fixed‐effects models were used to compute the results. Additionally, subgroup studies (for different study types) were performed to detect the source of the heterogeneity. The stabilization of study was evaluated by using sensitivity analysis. Publication bias was evaluated by using Begg's test, Egger's test, and funnel plot.We used STATA 12.0 software to compute the results extracted from finally included studies. We used 33.1Figure 3.2I2 = 92.4%, p < .001; Figure I2 = 93.9%, p < .001; Figure I2 = 82.3%, p < .001; Figure p = .444; Egger's test: p = .077; Figure The meta‐analysis indicated that DM was related to elevated risk of PD (OR/RR = 1.15, 95% CI 1.03–1.28, 4In this meta‐analysis based on 16 studies analyzing the relationship between DM and PD in over 3,800,000 individuals, we noticed that patients with DM were associated with a 15% increase in the risk of developing PD, compared with patients without DM. In subgroup analyses, cohort studies showed positive correlation between DM and PD, while case–control studies suggested no association.The potential mechanisms of the role of DM in PD developing have been growing concern. Many risk factors for DM are overlapped with those for PD. Advanced age is a main risk factor for both DM and PD. Meanwhile, both DM and PD are related to chronic inflammation, which performs an important function in the occurrence and development of these diseases (De Virgilio et al., Our results of the meta‐analysis are consistent with some previous studies (De Pablo‐Fernandez et al., Although we included the published literature on the association between DM and PD as much as possible to increase the credibility of analysis results and decrease the risk of bias, some limitations should be noticed. First, the exclusion of unpublished literature may result in a potential bias because of the lack of negative results. Second, we cannot get detailed information related to covariates of PD, such as family history of DM patients, cigarette smoking, and alcohol drinking.In a word, we have tried to determine whether prior onset of DM may contribute to the risk of developing PD. More and more large‐scale prospective studies should be conducted to evaluate the relationship between DM and PD.No conflict of interest.Wei Liu participated in research design, the writing of the paper, the performance of the research, and data analysis. Jianfeng Tang participated in research design and data analysis.The present study was a meta‐analysis. No ethical statement is provided.https://publons.com/publon/10.1002/brb3.2082.The peer review history for this article is available at Table S1Click here for additional data file.


Background: The hemoglobin glycation index (HGI) has been proposed as a marker to quantify inter-individual variation in hemoglobin glycosylation. However, whether HGI is associated with an increased risk of diabetic complications independent of glycated hemoglobin (HbA1c) remains unclear. This meta-analysis aimed to determine the association between HGI and the risk of all cause mortality and composite cardiovascular disease (CVD).Methods: PubMed, and EMBASE databases were searched for related studies up to March 31, 2021. Observational studies reported associations between HGI levels and composite CVD and all cause mortality were included for meta-analysis. A random effect model was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CI) for higher HGI.Results: A total of five studies, comprising 22,035 patients with type two diabetes mellitus were included for analysis. The median follow-up duration was 5.0 years. After adjusted for multiple conventional cardiovascular risk factors, an increased level of HGI was associated with a higher risk of composite CVD  and all cause mortality . However, when further adjusted for HbA1c, the association between HGI and risk of composite CVD  and all cause mortality  became insignificant.Conclusions: High HGI was associated with an increased risk of composite CVD and all cause mortality after adjustment for multiple conventional cardiovascular risk factors. However, the association was mainly mediating by the level of HbA1c. Prevalence of type 2 diabetes mellitus (T2DM) has increased dramatically, with ~422 million adults diagnosed with T2DM worldwide . CardiovGlycated hemoglobin (HbA1c) is an index that reflects the mean blood glucose (MBG) for the prior 2–3 months . HbA1c iThere has been contentious about adopting the HGI as a marker of risk stratification in patients with diabetes. It had been proposed that high HGI is associated with vascular complications in patients with and without diabetes –15. In aAccording to the recommendations of the Meta-analysis of Observational Studies in Epidemiology group , we searpost-hoc analysis of randomized controlled trials (RCTs), evaluating the associations between HGI and risk of CVD or all cause mortality; (2) the risk of endpoints were reported in high HGI compared with low HGI patients , or evaluated when HGI was defined as a continuous metric; (3) multivariable-adjusted relative risks (RRs), or hazard ratios (HRs) and 95% confidence intervals (CIs) were reported; (3) the follow up duration ≥1 years. We excluded studies if: (1) they were cross-sectional studies without follow-up evaluation; (2) the association between HGI and risk of endpoints was not adjusted for other cardiovascular risk factors, and (3) identical outcomes were derived from the same population.We included studies for meta-analysis if they met the following criteria: (1) cohort study, nest case-control study or Two reviewers (JW and DL) screened the titles and abstracts of the retrieved reports, and reviewed the full copies of potentially relevant studies. Study information such as study design, ethnicity, participant's characteristics, sample size and proportion of female, follow-up duration, adjusted confounders, and endpoints assessment were recorded. Quality assessment of the included studies was conducted based on the Newcastle–Ottawa Quality Assessment Scale for cohort studies , in whicThe primary outcomes were the risk of composite cardiovascular events and all-cause mortality associated with HGI. The associations between the HGI and risk of outcomes were reported in different ways in the included studies, such as per standard-deviation (SD) increment in the continuous trait; or per tertiles or quartiles in HGI levels. To enable a consistent approach to the meta-analysis, we calculated the HRs per 1 SD increment in the level of HGI. If the included studies did not report effect measures as per 1 SD change, we converted the results according to the previously reported methods , 27.I2 statistics were used to test heterogeneity among studies, and an I2 > 50% was considered to be with significant heterogeneity. We used the inverse variance approach with a random-effects model to combine the log HRs and corresponding standard errors (SEs). We also performed a sensitivity analysis by omitting one study and recalculating the estimates of HRs at a time. Publication bias was evaluated by inspecting funnel plots for the primary outcomes in which the natural log HR was plotted against SEs, and further tested using Egger's and Begg's tests. Subgroup analyses were performed according to participant's age, follow-up duration, and baseline CVD.Adjusted HRs and 95% CI were extracted for the meta-analysis. If a study reported multiple results based on different statistical adjusted models, results adjusted for the most number of variables were extracted for analysis. If RRs, but not HRs were reported, the RRs were considered as approximate HRs and included for meta-analysis . To explP < 0.05 was considered as with statistical significance.Analyses were performed using Stata 12.0  and RevMan 5.3 . A A total of 121 manuscripts were retrieved from the electronic databases. After screening of the titles and abstracts, 12 qualified for a full review, and five studies were included for meta-analysis finally  17–21).–21.17–21post-hoc analyses of RCTs enrolled patients from multiple countries worldwide. All the studies included patients with T2DM, while differed in with/without CVD. According to quality assessment criteria, 4 studies were graded as good quality, and only one as fair . Random-effects models analyses showed that per 1 SD increment of HGI was associated with a 14% higher risk of composite CVD after adjusted for multiple cardiovascular risk factors  (P > 0.05).All the included five studies provided a multi-variable adjusted risk of composite CVD associated with HGI before adjusted for HbA1c. Significant heterogeneity among all studies was observed (= 0.007) . We did = 0.007) , or usinP = 0.007)  .2 = 51%, P = 0.10). Random-effects models analyses showed that when adjusted for multiple cardiovascular risk factors and HbA1c, the association between HGI and risk of composite CVD became insignificant  (P = 0.37) (Four studies reported the risk of composite CVD associated with HGI after adjusted for multiple cardiovascular risk factors plus HbA1c. There was significant heterogeneity among studies (I= 0. 79) . Similar = 0.37) .We performed several sensitivity analyses and confirmed that the results were not influenced by the use of fixed-effects models compared with random-effects models, or recalculating the HRs by omitting one study at a time.P for subgroup heterogeneity = 0.01, I2 = 84.5%). There were no significant associations for HGI and composite CVD risk among all other subgroup analyses.The results of subgroup analyses for the risk of composite CVD and HGI were presented in In this study, we included five observational studies, including more than 22,000 T2DM patients with a median follow-up duration of 5 years for meta-analysis. We found that high HGI was associated with an increased risk of composite CVD, and all cause mortality after adjustment of multiple cardiovascular risk factors. However, when further adjusted for levels of HbA1c, the association between HGI and poor prognosis was disappeared.Although still with controversies, several mechanisms had been proposed to explain the association between HGI and the risk of CVD. First, patients with high HGI had a longer duration of diabetes and combined cardiovascular risk factors . Second,Recently, Zhang et al. had reported a meta-analysis, which showed that for each 1-unit increment in HGI, there was a 20 and 13% increase of composite CVD and all cause mortality, respectively . Our anaOur study has several limitations. First, this is a meta-analysis based on the study level. We did not have individual participant data. Therefore, residual confounders could not be avoided. However, all the included studies had adjusted for multiple conventional cardiovascular risk factors. Second, the calculation of HGI differed among the included studies, which would cause significant heterogeneity among the studies. Third, it had been proposed that the variance in HbA1c was different in Asians and Westerns . HoweverOur results showed that although high HGI was associated with an increased risk of composite CVD and all cause mortality after adjustment for multiple conventional cardiovascular risk factors. However, the association was mainly mediating by the level of HbA1c. Considering that HbA1c can be easily evaluated in most laboratories worldwide, and the calculation of the HGI is complicated, clinical use of HGI in T2DM cannot be routinely recommended.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.J-dW, D-lL, YX, M-yC, and H-hC: research idea and study design. J-dW, D-lL, DS, and H-qH: data acquisition. J-dW and D-lL: data analysis and interpretation. J-dW and YX: statistical analysis. J-dW: supervision and mentorship. All authors contributed important intellectual content during manuscript drafting or revision and accept accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.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.