TNF-α) gene and risk of metabolic syndrome (MS), but their results were contradictory. This meta-analysis aimed to precisely evaluate this association. A systematic literature search was performed in Pubmed database and WanFang Med Online, STATA software 14.0 was used for the meta-analysis. Eleven independent studies containing 3277 cases and 3312 controls were included in our meta-analysis. In overall analysis, significant association was found between -308G/A polymorphism of TNF-α and MS in both allele model  and dominant model . In the subgroup analysis, the A allele was associated with increased risk of MS in Asia group , and decreased risk of MS in Europe group . The A allele also appeared to linked to increased risk of MS in CDS group and IDF groups. No significant association was observed in NCEPATPIII group. Our results suggested that -308G/A of TNF-α gene was a risk factor for MS, but it may played different roles in different ethnics, further studies with larger sample size and more other ethnics should be performed to confirm our conclusions.Many studies tried to assess the relationship between -308G/A polymorphism of tumor necrosis factor alpha ( Among those definitions, the ones made by NCEP:ATPIII and IDF were the most widely used currently. The morbidity of MS is increasing around the world, which makes MS a huge public health burden for many countries9. Many factors are involved in the development of MS. On one hand, environmental factors such as physical inactivity and improper eating habits are essential determinants for MS. On the other hand, genetic factors also play vital roles in the development of MS.Metabolic syndrome (MS) is a multi-component disease characterized by the combination of a series of clinical and biochemical metabolic disorders including insulin resistance, elevated plasma triglyceride levels, decreased HDL-c, hypertension, hyperglycaemia and abdominal obesity. In 1998, the first criteria of MS was made by World Health Organization (WHO)TNF-α) is a multifunctional pro-inflammatory cytokine with both beneficial and destructive functions for human body produced by various kinds of cells such as macrophages, fibroblasts, epithelial cells, adipocyte10. As we known, TNF-α could regulate numerous inflammatory and autoimmune processes, and participates in many life activities such as apoptosis, differentiation and cell recruitment11. Prior studies also demonstrated that TNF-α combined with other types of pro-inflammatory cytokines contributed to the development of type II diabetes, obesity and obesity-induced insulin resistance, which suggested TNF-α played as an essential role in the development of MS15. TNF-α gene is located at 6p21.1–21.3 chromosomal region, which is near to major histocompatibility complex. Wilson et al. firstly identified a G to A variant at 308 upstream of TNF-α gene promoter region in 199216. Subsequent studies has shown that -308G/A had great impact on the transcription activation of TNF-α gene and was relevant with the increased plasma TNF-α plasma levels17. Observational studies supported -308G/A of TNF-α gene was associated with the components of MS such as hypertension and insulin resistance18. Many studies containing different ethnic subjects tried to reveal the relationship between -308G/A of TNF-α gene and MS susceptibility, however, those results were inconsistent.Tumor necrosis factor alpha  AND (polymorphism OR mutation OR variant) AND (metabolic syndrome OR MetS OR MS). The language was restricted to English and Chinese, and the search process was completed on April 6th 2020.A systemic literature search was performed in Pubmed and Wanfang Online database to identify all the potential studies that involved the association between -308G/A polymorphism of TNF-α gene and MS susceptibility; (2) studies should be case–control designed or cohort designed; (3) studies should provide enough data to calculate the odds ratios (ORs) and the corresponding 95% confidence intervals (CIs).The following inclusion criteria should be met when the studies were included in this meta-analysis: (1) studies should examine the association between -308G/A polymorphism of 19.Two authors independently extracted the following information from each study: the first authors, the years of publication, the demographic information of each study, the numbers of case and control groups, and the frequencies of the three types of genotypes in both cases and controls. Newcastle–Ottawa quality assessment scale was used for the quality assessment of each researchTNF-α gene and MS susceptibility. Pooled ORs and 95% CIs were performed under four genetic models: allele model (A vs.G), dominant model (AA + GA vs. GG), recessive model (AA vs. GG + GA), and homozygous model (AA vs. GG), respectively. We used random effect models to calculate all the pooled ORs and corresponding 95%CIs. The Z tests were used to assess the significance of the ORs. Hardy–Weinberg equilibrium (HWE) was tested using Chi-square test in the control groups. Heterogeneity between the studies included in this meta-analysis was evaluated using χ2 based Q tests and I-square (I2) tests. We defined low, moderate, and high degrees of heterogeneity when the values of I2 were 25%, 50%, and 75%, respectively. We carried out sensitive analysis to assess the impact of each study on the pooled ORs and 95% CIs. The Begg’s and Egger’s tests were used to evaluate the publication bias. All tests were two-sided, and a value < 0.05 was considered as statistically significant. STATA software  was used to perform all the statistical tests in this meta-analysis.We calculated the pooled ORs and corresponding 95%CIs to assess the strength of the association between -308G/A polymorphism of TNF-α gene and the components of MS, three studies did not have healthy controls, three studies were for other polymorphism sties of TNF-α gene, two studies were for other disorders, one study with repeating data, one study was meta-analysis, one study with wrong data. Finally, eleven studies containing 3277 cases and 3312 controls were included in our meta-analysis. Eight of the eleven studies were published in English, three ones were in Chinese. The publication time of the eleven studies ranged from 2005 to 2019. There are three types of diagnostic criteria were used for the diagnosis of MS, five studies diagnosed MS using the National Cholesterol Education Program Adult Treatment Panel III (NCEPATP III) definition, four studies adopted International Diabetes Federation (IDF) definition, two studies recruit their subjects using the definition made by Chinese Diabetes Society (CDS). Four different kinds of genotyping methods existed, the numbers of studies used restriction fragment length polymorphism (RFLP) was eight. Golden Gate Assay, Taqman probe, melting curves were respectively used in the remaining three studies. All the studies included in our studies were in HWE (P > 0.05). The general characteristics of the eleven studies and the frequencies of different genotypes extracted from each study were shown in the Table Depending on the literature search strategy mentioned above, 658 articles were initially identified in the Pubmed database and Wanfang Med Online. The sift process was shown in Fig. TNF-α gene and MS susceptibility in four genetic models. Overall, significant association was found in both allele model   Fig.  and domiIn the subgroup analysis performed by the regions of the studies, A allele showed increased risk of MS in Asia group . Inversely, the A allele showed decreased risk of MS in Europe group   and dominant model . Low and moderate degree heterogeneity was found in recessive model  homozygous model , respectively. Sensitive analysis was performed by omitting one study at a time and then the ORs and 95% CI of the remaining studies were recalculated. In this study, the pooled ORs and the 95% CI calculated after excluding single study did not show violent fluctuation compared with the value of overall analysis, suggesting the results of our study were stable. Publication bias analysis was performed using Begg’s and Egger’s tests in all the four genetic models. As shown in Table Results of tests for heterogeneity were also shown in Table 35. TNF-α secreted by M1 pro-inflammatory adipose tissue macrophages is one of the most important pro-inflammatory mediators in various organs of human body and could initiate the NF-κB and JNK pathways involved in the insulin resistance and the apoptosis of the β cells within the pancreatic islets41, which suggested TNF-α may play a central role in the development of MS. Many case–control studies tried to assess the relationship between -308G/A polymorphism of TNF-α gene promoter and the risk of MS, however, their results were inconsistent and with low statistic power due to the small sample sizes of those studies.Prior studies have revealed chronic inflammation may be one of most important promoting factors for the development of MSTNF-α gene promoter and MS risk. Eleven studies containing 3277 cases and 3312 controls were included totally. Our results revealed that the A allele increased 48%, 77% risk for MS in allele model and dominant model in overall study, respectively. In the subgroup analysis, it was of great interests for us to find opposite conclusions in the Asia and Europe subgroup. The A allele distinctly increased MS susceptibilities in allele model , dominant model , and homozygous model  in Asia subgroups, while in Europe subgroup the A allele seemed to play a protective role for MS in allele model , recessive model and homozygous model . Our results showed the complexities for MS once again. Why did the same variant play antipodal roles of MS in different countries? MS is a kind of complex and polygenetic disorder. Different ethnic group may have entirely different genetic background. Silvia et al. performed a meta-analysis to assess the relationship between -308G/A polymorphism and the risk of MS components18. They found ethnicity was also an important factor to influence relationship between -308G/A polymorphism and hypertension, T2DM and obesity. Our study further demonstrated that -308G/A polymorphism could play different roles in the development of MS in different ethnicities on the base of Silvia et al. Some issues should be discussed when we explained this result: firstly, the three studies for Europe included were population-based, while the eight studies from Asia were hospital-based, whether the different methods to include the control populations by each study influence the results of the two subgroups still need to be consulted, even if the minor allele frequencies of A was near to each other between the two the control groups (0.173 in Asia group and 0.151 in Europe group). Secondly, as another risk factor, age also played vital roles in the development of MS, another meta-analysis performed by us revealed the rs9939609 of FTO gene was associated with MS in Chinese adults but not in Chinese children and adolescents42, that means time is needed in the interaction between genetic factors and environment. The subgroup analysis stratified in different age groups could not be performed in our study. Thirdly, in our study, only three studies in Europe were included the sample size was still limited, further studies with larger sample sizes especially from Europe should perform to confirm our conclusions.In this study, we performed a meta-analysis to evaluate the relationship between -308G/A polymorphism of 2 = 80.93%, P < 0.001) and dominant model , while the heterogeneity in recessive model and homozygous model was low  and moderate . The results from overall analysis indicated that the different statistical methods could be a source of heterogeneity. In the subgroup analysis, we found the levels of heterogeneity were dramatically decreased in the Asia and Europe subgroups in all the four genetic models, suggesting that the ethnic may be an important source of heterogeneity. In the subgroup analysis stratified by the diagnostic criteria, lower levels of heterogeneity were found in IDF subgroups and no heterogeneity was found in CDS subgroup, the main heterogeneity came from the NCEP ATP III subgroups, which means diagnostic criteria should be another important source of heterogeneity.The heterogeneity of meta-analysis usually comes from three aspects: clinical heterogeneity, statistical heterogeneity, and methodological heterogeneity. During the overall analysis, high degrees of heterogeneity was found in the allele model (IBegg’s and Egger’s tests were performed to assess the publication bias tests, A P value 0.017 was found in the Egger’s test in dominant model. Although there were no statistical significance was found in the other Begg’s and Egger’s tests, potential publication bias may exist in our study. Because only eleven studies were included in our meta-analysis, limited number of studies may also contributed to the results of the tests for publication bias.Our study still had several limitations should be mentioned: firstly, because we searched the articles only in Chinese and English, researches published in other languages were ignored, that may result in bias; secondly, other risk factors of MS such as physical inactivity, excessive food intake were not be considered in our meta-analysis; thirdly, the total sample size still limited in our research, and only studies from Asia and Europe were included, further studies with larger sample size and more other ethnics should be performed to confirm our conclusions.TNF-α gene promoter and the susceptibility of the MS. Our results suggested -308G/A polymorphism was significantly associated with increased MS susceptibility in overall analysis, in the further analysis a completely apposite result was found in the Asia and Europe subgroup. Our results illuminated the complex impact of -308G/A polymorphism of TNF-α gene promoter played on the MS susceptibility.To our knowledge, this is the first meta-analysis to assess the relationship between the -308G/A polymorphism of


Log Odds Ratios (ORs) per 2.5 unit increase in adiponectin or leptin levels were derived and pooled. All analyses were stratified by study type . 746 papers were retrieved, 34 eligible studies identified, 31 of these could be included in the meta-analysis. Leptin was not consistently associated with overall prostate cancer  and there was weak evidence of a positive association with aggressive disease . There was also weak evidence of a small inverse association of adiponectin with overall prostate cancer , but less evidence of an association with aggressive disease . The magnitude of any effects are small, therefore levels of circulating adiponectin or leptin alone are unlikely to be useful biomarkers of risk or prognosis.Obesity is associated with an increased risk of advanced, recurrent and fatal prostate cancer. Adipokines may mediate this relationship. We conducted a systematic review and meta-analysis of associations of leptin and adiponectin with overall and aggressive prostate cancer. Bibliographic databases were  Although over 60% of men aged 85 have histological evidence of prostate cancer3, the disease is often indolent and most affected men will die of other causes before the cancer progresses4. Identification of markers of aggressive disease is imperative to recognising those cancers likely to progress, enabling radical treatment to be reserved for high-risk cases and minimising morbidity from unnecessary treatments6. In meta-analyses of observational studies, body mass index (BMI) is associated with a modest increased risk of advanced and fatal prostate cancer, and of biochemical recurrence (8–21% increase in risk per 5 kg/m2 increment in BMI)9. However, Mendelian randomisation analysis did not find genetic variants associated with increased BMI to be associated with risk of advanced or high grade disease but did find weak evidence of an association with lower prostate cancer risk10.Prostate cancer is a cause of considerable morbidity and mortality, particularly in industrialised countries where obesity is epidemic11, proliferation12, androgen-independent cells migration13 and expresses angiogenic properties14, while adiponectin inhibits prostate cancer cell proliferation15 and angiogenesis16. We hypothesise that leptin is positively associated with risk of overall and aggressive prostate cancer and adiponectin is inversely associated with risk of overall and aggressive prostate cancer. Here we systematically review the epidemiological evidence on associations of circulating adiponectin and leptin with overall and aggressive (higher grade and/or more advanced stage) prostate cancer and combine study-specific effect estimates in a dose–response meta-analysis.Biologically-active polypeptides synthesised and secreted by white adipose tissue, adipokines, may mediate the association between obesity and prostate cancer progression. In vitro, leptin stimulates growth factor expressionStudies in humans of associations of circulating adiponectin and/or leptin with prostate cancer prevalence, incidence, stage, grade, mortality or other measures of aggressive prostate cancer were identified through systematic searches of the bibliographic databases Medline (1950-April 1st 2017), Embase (1980-April 1st 2017) and Web of Science (1899-April 1st 2017). A comprehensive combination of MeSH and text words was used to search the databases . For the remaining studies and those for which eligibility was unclear, full articles were retrieved and assessed for inclusion by two reviewers independently . Studies were eligible for inclusion if they: presented original peer-reviewed data, included measures of adiponectin and/or leptin in human blood, included data from men with prostate cancer and included a comparison group . Cohort, nested case–control and retrospective case–control studies were eligible. Duplicate publication of study results was identified by comparing study locations, authors, study names, descriptions of the study population, recruitment dates and study designs. Where results were published more than once, the paper which included the highest number of cases, the most detail or the most comparable estimate was selected for inclusion.Data at the level of the study  and the result  were extracted using a standardised extraction form by one author (AB or RL) and check by another (RB). If data necessary to derive a dose–response odds ratio (OR) were not extractable , study authors were contacted for further details.2 increase in BMI  increase in adiponectin or leptin. These ‘dose–response’ ORs were pooled in a meta-analysis, separately for overall prostate cancer risk and risk of aggressive disease . A 2.5 unit increase was selected as this represents approximately one quarter of the adiponectin or leptin distribution and a 2.5 ng/ml increase in leptin corresponds to around a 5 kg/m18. Secondly, if the ORs per quantile of adiponectin or leptin were reported, a dose–response OR was derived using the Greenland and Longnecker method19. For this method, a mean or median of the exposure in each quantile and the number of cases and controls in each quantile, were needed; if neither of these were reported, the mean in each group was estimated using the range18. Thirdly, if the odds ratio per (x) units increase in exposure was given, the OR per 1 unit was calculated by 1 − ((1 − OR)/x). Following conversion to a log odds ratio, this was then multiplied by 2.5 to give the log odds ratio per 2.5 units.Results were reported in three main forms, each of which required a different method of conversion. Firstly, if the difference in means or medians between cases and controls was reported, this was converted to dose–response ORs using the method described by Chêne and Thompson, which assumes an approximately normal distribution of exposureTwo primary meta-analyses were carried out for each exposure: (i) a pooled estimate of the log OR of overall prostate cancer risk per 2.5 unit increase in adiponectin or leptin; and (ii) a pooled estimate of the log odds ratio of aggressive prostate cancer per 2.5 unit increase in adiponectin or leptin. Analyses were stratified by study design; if blood draw was at any time point before biopsy this was considered prospective , and if after biopsy, as cross-sectional. If a paper presented more than one type of result, or more than one logistic regression result , all results were extracted but the results were selected for inclusion in the main analyses using the following order of priority: (A) the effect estimate was: (i) a dose–response OR, (ii) a categorical/quantile OR, (iii) the median or mean difference between cases and controls; (B) the estimate was adjusted for: (i) age only , (ii) hormones/smoking/BMI ; (C) the measure of aggressiveness was: (i) grade (most commonly reported and therefore was most comparable between studies); (ii) stage; (iii) another measure .20) were also calculated and presented for completeness. The I2 statistic was calculated to quantify the percentage of between-study variation due to heterogeneity22; an I2 of 0% indicates the true association is the same between studies. The larger the I2 the higher the proportion of the total variation in study estimates is due to between-study variability and not sampling error, indicating that the true association differs between studies22.As weights applied in fixed-effects analyses are more proportional to the size of the study, the primary analyses were based on the fixed effects estimates, although random effect estimates  ; and v) the method of prostate cancer detection i.e. whether non-PSA screen  detected or detected by PSA-screening. If studies did not specifically report whether cases were PSA or clinically detected, they were classified as method ‘not reported’, apart from those with high mean PSA levels amongst cases or those where cases were sampled before the PSA screening era, which were classified as non-PSA screen detected. Where reported, we extracted effect-estimates stratified by BMI . We also assessed heterogeneity by factors that could affect susceptibility to bias, as there is not a single generally accepted quality assessment tool for observational studies23: study design ; adjustment for confounding ; and type of effect-estimate (comparing pooled OR estimates to pooled mean difference estimates).Subgroup analyses of the main results were used to explore potential sources of heterogeneity: (i) the original results format used to derive the dose–response OR (mean difference or odds ratio)); (ii) the assay method ); (iii) the measure of prostate cancer aggressiveness ; (iv) the mean study-level BMI (< or ≥ 27kgm24 and tests of funnel plot asymmetry (the Egger and the Begg tests)25; as these tests can produce false-positive results when analysing odds ratios25, manual inspection of funnel plots was used as the main indicator of publication bias.We conducted sensitivity analyses where: (i) advanced stage was selected over other measures of aggressive disease and over high grade; (ii) maximally adjusted estimates were selected over minimally adjusted and (iii) both (i) and (ii). Some prospective studies drew blood around the time of diagnosis, at which point the cancer would have already been present which may affect adiponectin or leptin levels . To examine the effect of time of blood draw relative to diagnosis prospective studies were plotted by time of blood draw and an additional sensitivity analysis redefining prospective as > 1 year pre-diagnosis was conducted. An influence analysis, in which each study was excluded from the pooled estimate systematically, was conducted to gauge the influence of individual studies on pooled estimates. Small study effects were explored using funnel plots26.The search was updated to October 2018 to identify if any further studies had been published after the end of the study period. These further studies, plus those from the earlier search which could not be included in the main analyses, had minimal statistical information  extracted and plotted alongside the main results in an albatross plot. Albatross plots allow an approximate estimation of underlying effect sizes and can potentially identify sources of heterogeneity in results from systematic reviews where limited comparable data are availablehttp://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017074010).All analyses were conducted in Stata . The protocol for the review was registered in the PROSPERO international prospective register of systematic reviews: 28 the derived OR for the association between adiponectin and aggressive prostate cancer was implausibly small  and this estimate was excluded . In another study it was unclear what measure of dispersion around the mean was reported and assuming either standard error or standard deviation resulted in implausibly narrow confidence intervals or small effect estimate, respectively. As it was unclear which, if either, was correct this study was excluded29.746 potentially eligible studies were identified, 610 were clearly ineligible  therefore 137 publications were retrieved. 34 studies fulfilled the inclusion criteria  in 7 studies of adiponectin and 1 of leptin. Adiponectin requires a dilution before analysis and it appeared five studies either failed to back-adjusted results for this dilution factor or erroneously reported results in ng/ml rather than µg/ml. When contacted, authors of two out of the five studies33 stated the former was most likely; there was no response from the remaining three studies35. When an adjustment for dilution factor (1:500) was made the results fell into the physiological range and these adjusted results were used in analyses. One study gave no units36 but when contacted the authors stated the units should be ng/ml. These results fell out of the physiological range, even after assuming this was an error and therefore this study was excluded. Another adiponectin study reported results in pg/ml, which the authors stated was a typographical error37. The leptin study reported results in pg/ml and when contacted the author no longer had access to the data38. This was assumed to be a typographical error also.Reported adipokine levels fell outside of the physiological range  and 21 investigated leptin  50.There were more studies from the USA n = 13) than any other country (n = 18) and, despite a wide geographical distribution including Asia, Europe, South and North America, most participants were white , men with BPH (1 study) or a mixture of both (4 studies). Cases in studies of aggressive prostate cancer were men with high grade , higher stage , high volume (1 study), high risk ‘D’Amico’ classification (1 study), biochemical recurrence after prostatectomy (1 study) or a combination score . Controls in studies of aggressive prostate cancer were men with prostate cancer who did not fall into the above categories (i.e. Gleason ≤ 6 or ‘non-aggressive’), except for 5 studies in which controls were healthy men.In main analyses, cases in studies of overall prostate cancer incidence were mostly a mix of men with aggressive and non-aggressive prostate cancer although four studies included men with low grade2 = 54.9%) and cross-sectional studies (52.5%); therefore, pooling of individual study estimates may not be appropriate and these results should be interpreted cautiously.For leptin and overall prostate cancer, the overall fixed effect OR was consistent with the null hypothesis   for which the effect estimate was larger (pooled OR: 1.09 (95% CI 1.03 to 1.16), p = 0.004) than for prospective studies (OR: 1.02 (95% CI 0.99 to 1.05), p = 0.23, I2 0.0%). (Meta-regression p for difference in random effects estimates by study type p = 0.27).There was weak evidence of a small association between leptin and aggressive prostate cancer , though there was considerable heterogeneity amongst the former (I2 = 76.0%). (Meta-regression p for difference in random effects estimates by study type p = 0.26).For adiponectin and overall prostate cancer, the overall pooled estimate indicated a small (4%) decreased risk of prostate cancer per 2.5 µg/ml increase in adiponectin   cross-sectional studies found evidence that adiponectin was inversely associated with risk of aggressive prostate cancer, but larger studies did not. The prospective study estimates were more centred around the null with less evidence of heterogeneity . (Meta-regression p for difference in random effects estimates by study type p = 0.34).Overall, there was little evidence of an association between adiponectin and aggressive prostate cancer  decreased the heterogeneity amongst prospective studies (from 54.9% to 27.4%) but slightly attenuated the affect estimate from 0.97 (0.95–0.99) to 0.98 (0.95–1.00). For leptin and aggressive prostate cancer, cross-sectional study estimates were affected by exclusion of Basaria et al.40: this attenuated the association from 1.09 (1.03–1.16) to 1.04 (0.97–1.12); and Freedland et al.44: this increased the estimate to 1.17 (1.09–1.25).In an influence analysis Table , the ove34 reduced heterogeneity amongst cross-sectional studies from 76.0% to 47.9% but did not materially change the effect estimate. Exclusion of Goktas et al.28 attenuated the effect estimate from 0.89 (0.83–0.95) to 0.95 (0.85–1.06). In studies of aggressive prostate cancer, exclusion of Arisan et al.39 from cross-sectional study analyses did not affect the pooled estimate but reduced heterogeneity from 62.6% to 10.2%.For adiponectin and prostate cancer incidence, exclusion of one study by Lopez Fontana et al.Subgrouping analyses resulted in small numbers in groups so results are interpreted with caution  and in those with BMI ≥ 25: 0.10 (95%CI 0.01–0.78), p for interaction 0.0861.Those studies that conducted BMI-stratified analyses (N = 7), did not provide evidence that the association of leptin with prostate cancer incidence or progression varied by BMI. None of the included studies examined adiponectin and prostate cancer incidence associations stratified by BMI. Two out of three studies that examined BMI-stratified associations of adiponectin with advanced stage or fatal prostate cancer found an inverse association in overweight men only (OR of advanced stage per log adiponectin unit in those with a BMI < 25: 1.48 (95%CI 0.77–2.82) and those with a BMI ≥ 25: 0.62 (0.42–0.90), p interaction: 0.00666. The albatross plots26 in Supplementary Fig. The search was updated to October 2018 and 103 new articles matching search criteria were identified. The abstract screen revealed 83 were ineligible , and a further 15 were trials with no control groups, repeat publications or abstracts. 5 studies were eligibleNo strong, consistent associations between adipokines levels and risk of incident or aggressive prostate cancer were found. Pooled effect estimates from cross-sectional studies tended to be larger, more heterogenous and less stable to sensitivity analyses than those from prospective studies. When just the prospective evidence was considered, all pooled effect estimates were consistent with the null hypothesis accept for a weak inverse association between leptin and overall prostate cancer (3% decreased risk per 2.5 ng/ml increase in leptin).67. It included a small exploratory meta-analysis and, in general, their findings corresponded with ours. However, they reported some suggestive evidence of an inverse association between adiponectin and advanced prostate cancer (meta relative risk 0.81 (95%CI 0.61–1.08) comparing the highest subset of adiponectin). This was based on 4 nested case–control studies, one which was included twice. Our pooled effect estimate for adiponectin and aggressive prostate cancer, which was derived from 7 prospective and 7 cross-sectional studies, did not indicate evidence of an inverse dose–response association . A meta-analysis of genetic polymorphisms in adiponectin, leptin and their receptors found several associations with prostate cancer risk and aggressiveness68. Although this provides evidence that adipokine signaling may be involved in prostate carcinogenesis, the analyses were limited by the number of studies included. Additionally, studies of circulating adipokines in the general populations cannot exclude the possibility that adipokines may be associated with prostate cancer in certain subgroups of the population, such as those with particular metabolic profiles, or through local paracrine signalling of adipokines due to the abundance of periprostatic adipose tissue.A mainly qualitative systematic review of adiponectin, leptin and ghrelin levels with prostate cancer incidence and advanced disease was recently published38 included fewer cases and controls but examined both leptin and adiponectin, measured by multi-analyte profiling. Medina et al.69 focused on adiponectin multimers, measured by ELISA. They found only high-molecular weight adiponectin was associated with prostate cancer incidence . The decision was taken to include the former as it was deemed more comparable with the other studies, but inclusion of the later would not have altered the results.Relevant case-control studies nested within the San Antonio Center for Biomarkers of Risk of Prostate Cancer cohort study were reported in two papers. Baillargeon et al.70. However, the number of studies was too small, and the detail given in some reports insufficient, to explore heterogeneity adequately. There was heterogeneity in the definition of case and control groups; in particular, for aggressive prostate cancer. The majority used Gleason score as an outcome, although some included Gleason 7 as high grade and others did not—a matter that has been much debated in the literature. As a result of the mixture of outcome definitions, some studies may have included men with Gleason 7 in the comparison (‘non-aggressive’) group, potentially leading to an attenuation of any association. There was some evidence of possible reporting bias in studies of adipokines and aggressive prostate cancer, although there are other potential causes of funnel plot asymmetry such as poor study methodology71. Non-linear trends could not be assessed due to the limited number and quality of studies. The detection of prostate cancer is complex and it is recognised that PSA-screening can result in over-diagnosis; we therefore performed a subgroup analysis in which studies based on PSA-screening were meta-analysed separately from those in which cancers were clinically detected. However, several studies did not provide information on how cases were identified, preventing proper exploration of the effect of PSA-screening on associations.Many studies were small and quality of reporting was variable. There was considerable heterogeneity amongst study estimates, particularly cross-sectional evidence, indicating the true effect estimates may vary between studies. Confounding and selection bias are inherent issues in observational data; as a result meta-analysis of such data risks producing spuriously precise pooled effect estimates  Consequently, it is recommended that the quantitative results should be interpreted with caution, and sources of heterogeneity thoroughly explored49; therefore, associations should ideally be examined separately in normal weight and overweight men. This may be particularly important for associations of adiponectin with prostate cancer as adiponectin is an insulin-sensitising hormone and may have a more marked effect in men at higher risk of insulin resistance17. Two out of three studies that did include analyses of adiponectin with aggressive prostate cancer stratified by BMI, found an inverse association with higher prostate cancer stage17, and fatal prostate cancer61, respectively, in obese and overweight men only. Medina et al.69 reported that the association of high-molecular weight adiponectin with prostate cancer incidence was adiposity dependent; positive in normal and overweight men and inverse in obese men. We further explored this by conducting subgroup analyses stratifying by the mean study-level BMI. We did not find further evidence to support this interaction with adiposity. Although an advantage of observational studies is that population subgroups often excluded from trials are more likely to be included70, very few black men were studied. This is particularly important in prostate cancer, as black men are around 2–3 times more likely to develop the disease than white men72. Therefore, this review cannot be considered representative of the wider population at risk.Obesity or hormones such as insulin-like growth factor (IGF)-I may be on the causal pathway and therefore adjustment for such factors (mediators) could lead to over-adjustment. Adiposity may interact with the association of adiponectin and leptin with prostate cancer17  and therefore such analysis may fail to detect an important association. The possibility of reverse causality in these studies (the disease state affecting adipokine levels) cannot be excluded. Finally, and perhaps most importantly, observational data cannot identify causal associations because of the considerable possibility of confounding; any associations found may be markers of another, unmeasured, factor. For example, insulin resistance is a risk factor for prostate cancer and, as adiponectin is an insulin-sensitising hormone, levels are lower in men with insulin resistance.Dose–response meta-analysis assumes a linear relationship between the exposure and outcome. The association of adiponectin and leptin with prostate cancer stage may be U-shaped74. They exhibit slight diurnal variation but levels are relatively stable throughout the day (reaching a nadir at night)75 and over time , or papers that reported one type of estimate. Several authors were contacted to request further information so their data could be included and the response rate to these requests was high. This is important as the strength of an association can affect the amount of detail reported and therefore the likelihood of being able to extract enough data to derive a dose–response OR, which can introduce bias. Our comprehensive search strategy and flexibility in deriving a dose–response OR from limited data will also have helped reduce this bias. Only 3 studies were excluded as the estimates could not be extracted or converted to a plausible dose–response OR. Stratification by point of data collection/study type  provided a means to explore the possibility of reverse causality. It appeared that for leptin in particular, reverse causality or selection bias is a possibility and prospective data may provide a more reliable estimate. Leptin and adiponectin are stable over at least 6 freeze thaw cycles10, but it is not possible to know whether the association of BMI is mediated by leptin or leptin is purely a marker for high BMI. On an individual level, as a biomarker for detection or prognosis, adiponectin or leptin are unlikely to be useful as potential screening tools require exceptionally high ORs to give acceptable detection rates77.We did not find strong evidence to support our hypotheses that leptin is positively associated with risk of overall and aggressive prostate cancer and adiponectin is inversely associated with risk of overall and aggressive prostate cancer. The weak evidence that leptin is inversely associated with overall prostate cancer risk reflects the findings of the Mendelian randomisation analysis of weak evidence of an association of BMI with lower prostate cancer riskSupplementary Information.


CYP2A6) is responsible for the metabolic activation of most tobacco carcinogens. CYP2A6 genetic polymorphism can cause variations in the human metabolism of xenobiotics. We performed this meta-analysis to determine the association between whole-gene CYP2A6 deletion polymorphism (CYP2A6*4) and lung cancer risk.Lung cancer is the most commonly diagnosed cancer worldwide and is the leading cause of cancer death. Smoking is a major contributor to the pathogenesis of lung cancer. Cytochrome P450 2A6 .2=2.49, p=0.96, I2=0%).Nine case-control studies involving 4385 lung cancer cases and 4142 controls were included in the analysis. The random-effects model was used to combine results from individual studies. The pooled odds ratio was 0.39 (95% CI: 0.27–0.56). There was no heterogeneity across studies (χCYP2A6 whole-gene deletion polymorphism decreases the risk of lung cancer. Further research is needed to identify any potential confounding factors that may impact this association.Current evidence from the case-control studies suggests that the  Lung cancer is the most commonly diagnosed cancer worldwide, constituting 11.6% of total cancer cases, and is the leading cause of cancer death 2. Even exposure to secondhand tobacco smoke increases the risk of developing lung cancer among non-smokers4. The pro-carcinogens specific to tobacco are clearly implicated in the progression of lung malignancies6. However, not all smokers will eventually develop lung cancer in their lifetime. These individual differences in lung cancer morbidity could be due to other determinants such as genetic susceptibility8 and environmental factors11. Thus, it is important to identify the genetic variants that influence the risk of lung cancer initiation13.Smoking is a major contributor to the pathogenesis of lung cancer14. Electrophile agents with short lifespans (which are produced in metabolic activation) cross-react with DNA, causing DNA damage and initiating tumours16. Among the CYP isozymes, CYP family 2 subfamily A member 6 (CYP2A6) is responsible for nicotine metabolism and for the metabolic activation of the tobacco-specific pro-carcinogens N-nitrosamine N-nitrosonornicotine (NNN) and 4-(methylnitrosoamino)-1-(3-pyridyl)-1-butanone (NNK), which can eventually contribute to the progression of lung cancer5.Most tobacco carcinogens require metabolic activation, which is mainly executed by the cytochrome P450 (CYP) enzymes17. The difference in DNA sequence may not alter the overall product sufficiently enough to produce a different protein but may affect the specific activity of the enzyme, and binding efficiencies such as those for transcription factors or membrane proteins, or other features and function18. Thus, Cytochrome P450 2A6 (CYP2A6) polymorphism can influence how humans metabolize xenobiotics.Genetic polymorphism is defined as the inheritance of a trait controlled by a single genetic locus with two alleles in which the least common allele has a frequency of approximately ≥1% that can cause variation in the DNA sequence in individuals, groups, or populationsCYP2A6 polymorphism, including whole-gene deletion of CYP2A6 on allele 4 (CYP2A6*4), and the risk of lung cancer among different ethnic populations, but the results have been inconsistent20. Therefore, the present meta-analysis was performed to determine the association between CYP2A6 whole-gene deletion (CYP2A6*4) polymorphism and lung cancer risk.Over the past two decades, several studies have assessed the association between 21. A literature search was performed on five databases, namely PubMed, SAGE, ScienceDirect, the Cochrane Library, and Ovid. The search strategy utilized the PICO  framework to improve searching for clinical questions22. The search terms used were: [‘cytochrome P450 2A6’ OR ‘CYP2A6’] AND [‘lung cancer’ OR ‘pulmonary cancer’ OR ‘respiratory cancer’]. The search was not restricted to any duration or timeline.This meta-analysis was performed based on the PRISMA  statementCYP2A6 whole-gene deletion (CYP2A6*4) polymorphism, and 3) presence of lung cancer. We excluded studies that were not published in English, reviews, case reports or animal studies, or if the full text was not available.Two pairs of reviewers conducted the study selection in two phases after duplicate studies had been excluded. In the first phase, two reviewers (MSAM and MHA) independently screened the titles and abstracts of potential articles to be included in the study. During this phase, irrelevant studies were excluded, and a third reviewer (FHJ) resolved any disagreements. In the second phase, full-text articles were retrieved for detailed evaluation. Studies were included if they were published prior to October 2018 and met the following criteria: 1) observational study design, 2) presence of Articles that met the inclusion and exclusion criteria were retained for a full review. The characteristics of each study were examined and included study design, study location, type of population, sample size, sex, smoking status, matching criteria, genotyping method and genotype, and risk estimate value.23 to examine for the concordance and average NOS score for each study. The NOS is widely used for quality assessment of observational studies25. It evaluates three components to quantify study quality, i.e. selection of study subjects, comparability of study groups and exposure or outcome ascertainment, which consists of eight items with a maximum score of 9 for each study. The scores of each item indicate the methodological quality of the study. A study is categorized as being of high, moderate or low quality, based on a total score of 7–9, 4–6 and 0–3, respectively26.Two authors assessed the quality of the selected articles independently, using the Newcastle-Ottawa Quality Assessment Scale (NOS)27 was used to estimate the pooled effect size from the included studies. Odds ratio (OR) with 95% confidence interval (CI) and a statistical measure of heterogeneity (χ2 and I2) were calculated using Review Manager 5.328. All selected studies were included in the meta-analysis. Subgroup and sensitivity analyses were performed if p<0.10 and I2 ≥50%.The random-effects model35 were included in the meta-analysis. 21. The studies were carried out in Japan, China, Bangladesh, and Italy. Eight studies used Asian samples and only one study34 used Caucasian samples. The search strategy returned a total of 172 articles. Initial screening excluded 35 articles due to duplication. Further screening of titles and abstracts excluded 41 irrelevant studies and nine review articles. The eligibility of the remaining 87 articles was assessed: 40 were excluded for studying different outcomes such as smoking behaviour, 28 were excluded for studying different exposures such as other CYP450 genotypes, eight were excluded as we were unable to extract raw data related to the whole deletion of allele *4/*4, while two articles were excluded because we did not find data related to the whole deletion of allele *4/*4 in the studies. The remaining nine articles34, four were of moderate quality (NOS score: 4.0–6.9)34, and only one study was of low quality (NOS score: <4.0)35. The shape and symmetry of the funnel plot of log OR from the nine studies indicated that there was no publication bias . No heterogeneity was found across the studies . Subgroup analysis according to smoking status , ‘Mixed Smoking Status’  and ‘Unknown Smoking Status’.g status  showed tCYP2A6*4 whole-gene deletion polymorphism and the risk of lung cancer. Nine case-control studies fulfilled the criteria addressing this issue. The analysis, involving 4385 lung cancer cases and 4142 controls, suggested that CYP2A6 polymorphism significantly reduces the risk of lung cancer , with homogeneity observed across studies . Thus, subgroup and sensitivity analyses were not explored.A comprehensive search of different databases was used to yield the most relevant results and incorporated the available epidemiologic evidence to explore the relationship between the CYP2A6 is mainly found in the liver and other tissues such as the nasal epithelium, trachea, lung, and oesophagus37. CYP2A6 metabolizes a few but specific xenobiotics that include nicotine and some tobacco specific nitrosamines that enter the human body. Metabolic activation by CYP2A6 enzymes generally produces a short-lived electrophile agent that reacts with DNA, causing DNA damage and inducing a tumour16. In tobacco smoke, CYP2A6 is the main enzyme activating the tobacco-specific N-nitrosamines NNK and NNN, which are pro-carcinogens6. Thus, in individuals with the inactive CYP2A6 genotype, the CYP2A6 enzyme might not affect metabolic activation of N-nitrosamines and subsequently reduce the risk of lung cancer. Second, CYP2A6 is a major enzyme responsible for nicotine metabolism38, where inactive CYP2A6 causes lower nicotine dependence and thus affects smoking behaviour41.There are two possible explanations for the finding of relative risk reduction as revealed by the analysis. First, 32 recruited only smokers as participants; the remaining studies had mixed populations of smokers and never-smokers. The mixed-smoker status studies yielded non-significant findings or smaller risk estimates, which may be explained by the fact that the probability of lung cancer occurrence may be similar among never-smokers with different CYP2A6 genotypes, where the resulting phenotype is not expressed in people who do not smoke. Thus, when the original studies included both smokers and non-smokers, the association between the CYP2A6 polymorphism genotype and risk of lung cancer may have been attenuated. This may explain why some of the studies did not find any significant association42.Of the included studies, only three studies45. However, in the present meta-analysis, all but one study included different lung cancer histology types; Hosono et al.12 only recruited squamous cell carcinoma cases. The study by Islam et al.29 had the highest proportion of squamous cell carcinoma cases (43.39%), while the other studies had 24.5–28.4% squamous cell carcinoma cases34. These differences in the proportion of histological types might explain the discrepancy of the findings among the studies on CYP2A6 polymorphism and lung cancer risk.Besides that, cigarette smoking is strongly associated with squamous cell carcinoma compared to adenocarcinomaCYP2A6 polymorphism and lung cancer. Therefore, the true relationship between CYP2A6 polymorphism and risk of lung cancer in current-smokers and never-smokers could not be tested in these six studies. In addition, lung cancer might arise due to occupational carcinogen exposure, such as organic dust and silica dust47, which may confound the association between CYP2A6 polymorphism and lung cancer risk. However, none of the included studies adjusted for occupational carcinogen hazard exposure originally. The relationship may be also affected by differences in the demographic characteristics and socioeconomic class of the respondents48, which some of the studies did not mention.The present meta-analysis findings should be interpreted with caution. Six studies did not stratify the smoking status to assess the association between CYP2A6*4 whole-gene deletion have a decreased risk of lung cancer. Further research is needed to identify any potential confounding factors that may impact this association.The evidence from the case-control studies included in the present meta-analysis shows that people with


These results suggest that a propensity for internally-oriented, emotional processing coupled with under-active pain processing could potentially underlie SITBs, but additional research is needed to test this possibility. Separate analyses for types of SITBs suggested that the brain differences associated with deliberate self-harm were consistent with the overall findings. Checkered moderator effects were detected. Overall, the meta-analytic evidence was not robust. More studies are needed to reach definitive conclusions about whether SITBs are associated with brain differences.This meta-analysis aims to evaluate whether the extant literature justifies any definitive conclusions about whether and how SITBs may be associated with brain differences. A total of 77 papers  published through January 1, 2019 that compared individuals with and without SITBs were included, resulting in 882 coordinates. A pooled meta-analysis assessing for general risk for SITBs indicated a lack of convergence on structural differences. When all types of control groups were considered, functional differences in the left posterior cingulate cortex (PCC), right amygdala, left hippocampus, and right thalamus were significant using multi-level kernel density analysis ( Researchers have proposed that SITBs result in part from biological causes in brain systems, and many studies have linked SITBs to specific brain differences4. Multiple researchers have proposed that examining the “suicidal brain” will improve our understanding of SITBs, accelerate the discovery of useful biomarkers, improve the identification of high-risk individuals, and inform therapeutic and pharmacological targets for treatment8. However, the results of neuroimaging studies examining SITBs vary considerably, making it difficult to synthesize evidence on specific brain differences based on qualitative reviews. As such, this study aims to quantitatively summarize existing brain imaging research on SITBs.Self-injurious thoughts and behaviors (SITBs) are major public health concerns9. Other studies reported similar findings and conclusions with regard to the amygdala10, the anterior cingulate cortex11, and the midbrain/pons12, and their associations with traits such as impulsivity, heightened stress reactivity, emotion dysregulation, and pain perception. Given these findings, researchers have proposed that future studies should focus on examining imaging endophenotypes associated with vulnerability to SITBs13. Some researchers further encouraged efforts to identify a biosignature of suicide and develop a biologically-based model of suicide risk15.Among individuals who experience SITBs, recent neuroimaging studies have found differences in multiple brain regions associated with psychological traits that confer risk for SITBs. For instance, structural alterations have been reported in the ventrolateral prefrontal cortex, dorsal prefrontal cortex, and orbitofrontal cortex among suicide attempters, and these differences were considered to reflect deficient cognitive control and impaired decision-making in the context of intense emotions17), but the same findings are rarely detected across multiple studies. For example, multiple studies have found decreased gray matter volumes in suicide attempters, but the location of these differences has varied. One study found this alteration in the left superior temporal lobe and left orbitofrontal cortex18, another study solely discovered this difference in the insula and posterior cingulate regions19, and a third study found broader altered regions including the dorsolateral prefrontal cortex, orbitofrontal cortex, and the parieto-occipital cortex20. Given these inconsistent findings, does the existing literature justify any definitive conclusions about differences in brain structure or function among people with a history of SITBs?Despite evidence linking brain differences with vulnerability to SITBs, neuroimaging studies often yield different results. Most brain imaging studies that examine differences between SITB and non-SITB populations detect some kind of differences  and included diverse imaging methods  and neuropsychological tasks . The samples included in these reviews were also diverse, with psychiatric diagnoses of Schizophrenia, Borderline Personality Disorder, Bipolar Disorder, Major Depressive Disorder, and various affective disorders. The inconsistent conclusions across these reviews leaves open many questions about potential brain structure and functional differences among people with a history of SITBs.According to multiple qualitative reviews of portions of this literature, the literature does justify such conclusions. Notably, however, different reviews reach different conclusions. For instance, reviews have concluded, based on heterogeneous mixes of approximately 10–20 studies each, that SITBs are associated with differences in the ventrolateral, orbital, dorsomedial, dorsolateral, and ventromedial prefrontal cortices, the anterior cingulate gyrus, the amygdala, white matter connection, and gray matter volume24. Quantitative meta-analysis has the advantages of increasing precision in estimating effects, boosting power by combining samples, and evaluating the effects of moderators25. Compared to narrative reviews, however, fewer meta-analyses exist on this topic. Among the efforts, Jollant and colleagues26 summarized 12 structural imaging studies and found a lack of significant differences between suicide attempters and non-attempters, whereas van Heeringen and colleagues27 concluded that suicide attempters have reduced volumes of the rectal gyrus, superior temporal gyrus and caudate nucleus, and increased reactivity of the anterior and posterior cingulate cortices after synthesizing 6 structural and 6 functional imaging studies. It is possible that the small number of studies per analysis has precluded researchers from drawing meaningful conclusions28. Hence, a more comprehensive meta-analysis is needed.A quantitative meta-analytic review may help to resolve some of these questions. With qualitative methods, it is difficult to accurately weight findings and there is often a tendency to overemphasize positive findings28. Without sufficient power, true positive findings might be observed in different brain regions even for identically performed studies29. Therefore, a meta-analysis with a broader scope and larger power is needed to overcome the limitations and reveal a reliable pattern in the literature. Third, recent comprehensive meta-analyses in other domains have surprisingly found that the existing literature does not justify definitive conclusions about brain imaging differences. For example, across 57 studies, a meta-analysis30 found no evidence for brain imaging differences among depressed individuals. These findings did not prove that there are no neural correlates of depression; rather, they showed that the extant literature did not yet justify any definitive conclusions about the neural correlates of depression and clarified directions for future research aimed at investigating these correlates. Given the inconsistency of findings from the SITB brain imaging literature, the present meta-analyses may obtain similar findings and serve a similar function.The primary goal of the present study was to conduct a more comprehensive meta-analytic review of the SITB brain imaging literature. Accordingly, the meta-analysis included 77 papers, an improvement over previous reviews. This analysis will help to determine whether the extant literature justifies any definitive conclusions about brain structure or function differences among people with a history of SITBs. This is important for three reasons. First, as reviewed above, there are many inconsistent findings in the literature. Second, the existing meta-analyses were largely underpowered. Guidelines suggest that at least 20 experiments are needed to reliably detect moderate effect sizes in Activation Likelihood Estimation (ALE), a common method for performing coordinate-based neuroimaging meta-analysesThe present study represents one of the largest efforts to synthesize the evidence on structural and functional brain differences associated with SITBs. We had three major aims. First, a pooled analysis on studies examining the neural correlates of any type of SITBs was conducted to test whether certain brain changes were associated with general vulnerabilities to SITBs. Second, separate meta-analyses were conducted for each type of SITBs to examine whether a unique brain difference exists for different types of SITBs. Third, analyses were conducted to test whether differences in study designs might moderate the findings. The results of this meta-analysis will help to summarize knowledge about the association between SITBs and brain structures and functions, and may serve as a foundation for future work in this area.Most of the contrasts were yielded from studies using fMRI (57.82%), followed by structural MRI (22.00%), SPECT (11.00%), DTI (6.69%), and PET (2.49%). Regarding types of SITBs, 46.49% of the contrasts examined suicide attempt, with the rest studying self-harm regardless of suicidal intent , suicide ideation and plan (13.15%), NSSI (11.34%), suicide death (9.18%), all suicidal thoughts and behaviors (5.33%), and suicide risk (0.23%). More than half of the contrasts (59.64%) adopted task-based paradigms . Regarding control group type, about half of the contrasts (52.27%) used psychiatric controls, with the rest using healthy controls (40.25%) and self-injurious controls .M = 63.68, SD = 40.80). Majority of the samples (70.13%) were adult participants, followed by mixed adult and adolescent (15.58%), child or adolescent (11.69%), mixed adults and elders (1.30%). One study only included elderly participants (1.30%). The average sample age ranged from 13.51 to 79.43, with 29.81 years old as the mean (SD = 11.62). Half of the samples (50.65%) included at least some participants with psychiatric medication at the time of the study, with 27.27% of the samples only including unmedicated participants, and 10.38% of the samples only including medicated participants. The rest of the studies (11.69%) did not mention participants’ medication status. Regarding psychiatric diagnoses, about half of the studies (44.16%) required participants to meet criteria for Major Depressive Disorder (MDD), followed by no specific psychiatric diagnoses required (11.69%), Schizophrenia and other psychotic disorders (11.69%), mood disorders (12.99%), Borderline Personality Disorder (7.79%), other and/or multiple diagnoses , and Bipolar Disorder (5.19%).Among the 77 papers included in this meta-analysis, the sample size ranged from 18 to 272, with 48 as the median  were significantly associated with internally-oriented processes of mentalizing, emotion, and memory, while SITB-related hypoactivation  was significantly associated with pain , indicating a moderator effect Table . This fiWith respect to medication status, no analyses met the stringent 20 experiment minimum  existing neuroimaging research has not found consistent structural brain differences between populations with and without SITBs; (2) the ALE method produced no significant findings regarding functional differences, while in most inclusive analysis, the MKDA method produced a convergence of findings at four locations ; (3) finer-grained meta-analyses for specific types of SITBs showed that deliberate self-harm might be associated with functional differences in the right amygdala, left hippocampus, and left PCC; and (4) moderator analyses showed checkered consistency. These findings together suggest that the extant literature provides some, but far from unanimous, support for neural correlates of SITBs. The major findings and the strengths and limitations of the present study are discussed in more detail below.20, the present meta-analysis did not yield any significant findings with either ALE or MKDA. To our surprise, the meta-analysis did not replicate previous reviews suggesting distinct structural changes associated with SITBs22. A primary difference between this study and previous reviews is that we subscribed to power guidelines in the field28 that required a more stringent minimum of 20 experiments for each analysis. Even though we conducted additional analyses with a more relaxed 10 experiment criterion, more studies are needed to reveal the consistent differences between individuals with and without SITBs. Due to the insufficient number of studies in the literature, it is unclear whether certain structural differences are associated with risk for SITBs in general or for specific types of SITBs. Additionally, it is possible that certain factors moderate structural findings but the limited number of studies prevented us from detecting such effects. For instance, it is unclear whether structural differences associated with SITBs might be primarily manifested in structural connectivity rather than regional volumes. Similarly, it is unclear whether thickness or surface areas might be more relevant to SITBs than volumes. Some researchers further suggest that structural differences might be particularly pronounced among suicide attempters who employed violent methods26. Lastly, in spite of multiple potential explanations for the current null findings, we cannot rule out the possibility that individuals with SITBs might not exhibit consistent structural differences.Despite previous research suggesting structural differences associated with SITBs35. Consistent with its role in the default network, functional profiling of the PCC revealed strong associations with mentalizing, emotion, and memory. Similarly, the hippocampus and amygdala were also shown to be hyper-active in individuals with SITBs and also have well-established roles in memory and emotion38. Collectively, these data suggest that across functional tasks, individuals with SITBs showing a greater propensity for internally-directed processing. On the other hand, MDKA revealed some evidence of hypo-activation of thalamic areas involved in pain in individuals with SITBs. Together, a propensity for internally-oriented, emotional processing coupled with under-active pain processing could form the basis of SITBs. The lack of convergence between the two conceptually similar analyses , however, leads to questions about the robustness of the findings. More studies are needed to shed light on this topic.Our second major finding is that, when all types of control groups were considered, functional differences in the left PCC, right thalamus, right amygdala, and left hippocampus were associated with SITBs based on the MKDA method, but not the ALE method. Many of these regions have not been a focus of the literature on SITBs. If the locations indeed reflect underlying brain differences associated with SITBs, future research might benefit from examining these areas in more detail. The most robust finding across analyses was hyper-activation of the PCC in individuals with SITBs. The PCC is one of the major nodes of default network, which is engaged during mind-wandering, and projects heavily to the memory systemOur third major finding is that the MKDA method, but not the ALE method, found associations between deliberate self-harm and the left PCC, right amygdala, and left hippocampus. We originally intended to conduct separate meta-analyses for each type of SITBs. To our surprise, the majority of the literature focused on studying suicide attempts, with much less focus on suicide ideation, plan, and NSSI. Therefore, we were only able to perform analyses for suicide attempts and deliberate self-harm. Consistent with the pooled analysis of all SITBs, the MKDA with a 15 mm kernel revealed significant hyperactivation in the left PCC, right amygdala, and left hippocampus for deliberate self-harm. It is possible that brain differences might be associated with general risk for SITBs instead of specific types of SITBs. Therefore, differences associated with self-harm regardless of intent might be more consistent with the overall finding than only self-harm with the intent to die . It is also possible that the inclusion of self-harm without intent to die simply boosted the power to detect a significant difference. Given the paucity of research on certain types of SITBs, however, it is unclear whether unique brain changes exist for specific types of SITBs or whether they are associated with a general risk for SITBs. More studies examining self-injurious phenomena other than suicide attempts are needed to provide further insight on this issue.Lastly, moderator analyses demonstrated checkered consistency for the significant functional differences yielded by the pooled meta-analyses. Even though it was within our initial intention to systematically conduct moderator analyses, the unexpectedly limited number of experiments within each moderator category prevented us from fully performing these analyses. However, within the constraints of the literature, we conducted all moderator analyses that met the more relaxed 10 experiment criterion. Significant moderator effects of type of control groups and study paradigms were detected from the MKDA method. However, these moderator effects were not robust as the results were generated from less stringent analyses and inconsistent across MKDA and ALE. More studies are needed detect consistent moderator effects.28, and a limited number of functional findings did. Regarding sample size, It is generally well appreciated that studies with a small sample size might lack the statistical power to detect true effects; however, small sample size also reduces the likelihood that a detected result reflects a true effect39. The median sample size of the studies included in the meta-analysis is 48, which can lead to poor replicability even in one-sample tests40, let alone two-sample tests. On the other hand, increasing sample size will only improve power if there is an underlying group-level effect to find. Recent data indicate that there are multiple neurophysiological subtypes of depression41, and it is possible that SITBs are just as, or even more variable. Furthermore, group-level inferences may not apply to individuals42. Such data suggest that more data are needed at the individual-level. Hence, insufficient power at either the group- and/or individual-levels may contribute to the inconsistent findings in the present meta-analysis.The present findings should be considered within the context of the study’s limitations. It is important to note that a meta-analysis summarizes and reflects the current state of the literature and is therefore largely constrained by the limitations of the literature. First, the statistical power of the present meta-analysis was confined both in terms of the number of experiments and participants. It was surprising that few structural findings met the new power guidelines in the field43. Similarly, high heterogeneity exists regarding preprocessing parameters and the contrasts analyzed, which can lead to vastly different results on the same underlying data44. Moreover, 40.25% of the contrasts used healthy controls instead of psychiatric controls to test for differences associated with SITBs. Considering that individuals with SITBs are likely to meet diagnostic criteria for psychiatric conditions, psychiatric controls would provide a more stringent comparison and reduce the likelihood of detecting differences associated with general psychopathology instead of SITBs. Further, even though the present study was unable to directly examine the effects of specific psychological tasks  employed in the studies due to insufficient statistical power, it is possible that these differences contributed to the checkered consistency of the findings. Of note, heterogeneity and flexibility in study paradigms and analytical decisions might have also obscured previous reviews and meta-analyses, contributing to the mixed conclusions in the literature.Second, the heterogeneity among studies in the literature might have obscured the meta-analytic findings. For example, the thresholds that studies set to control for multiple comparisons vary widely. Insufficiently corrected analyses produce false positives, adding noise to meta-analyses48, but it remains an imperfect method. CBMA creates simulated statistical maps based upon peak activations reported in studies. However, the size and shape of the simulated activation clusters are unrealistic, potentially leading to both false positive and negative results. It would also be prudent to weight activation clusters by their effect size49. However, effect size information was irregularly reported in the present sample. In an ideal scenario, meta-analysis of neuroimaging data would be performed on unthresholded statistical maps, which would at once provide size, shape, and effect size estimates. Although resources such as NeuroVault are becoming increasingly popular50, they are not yet used widely enough to perform meta-analyses in this domain. More consistent data sharing in the future would help to determine whether the present findings were due to limitations of CBMA.In addition to limitations of the literature, it is important to keep in mind limitations of coordinate-based meta-analysis (CBMA). CBMA has been used to identify convergent activations in numerous domains28, but also conducted analyses using the previous criterion for completeness51. The power standard  is considered to be more stringent than previous standards. By following these guidelines, the present study is less likely to yield spurious findings.Despite the limitations, this study also demonstrates several strengths. First, even though this meta-analysis was still underpowered for some sub-analyses and moderator analyses, it demonstrates one of the largest efforts to increase power to detect true underlying effects by including neuroimaging studies on any type of SITBs. Second, the meta-analysis employed two gold-standard coordinate-based meta-analytic methods . This signaled progress over meta-analyses that largely relied on ALE alone and allowed for evaluation of the robustness of findings. Third, we subscribed to the new power guideline in the fieldcurrent empirical evidence for neural correlates of SITBs and whether it justifies any definitive conclusions about brain differences among individuals with SITBs. This study conducted pooled analyses across all SITBs, separate analyses for specific types of SITBs, and moderator analyses of differences among studies. The current state of the literature failed to provide support for structural differences, and provided some, yet far from unequivocal, support for functional differences. The identified differences in the left PCC, right thalamus, right amygdala, and left hippocampus have not been the focus of previous studies, but may offer promising future avenues of exploration. Due to the constraints of the existing literature, it is unclear whether brain differences increase general risk for SITBs or unique differences are associated with specific types of SITBs. Insufficient power, heterogeneity in study paradigm, flexibility in analytical decisions, and limitations of CBMA might have hindered the current study from identifying consistent and robust patterns associated with SITBs. Given the extant literature, more studies are needed to reach definitive conclusions on differences in brain structure and function among people with a history of SITBs. Future studies should consider gathering more group and/or individual-level data, selecting stringent control groups, providing replications of previous research, and adopting standard thresholds and preprocessing parameters.To summarize, the present meta-analysis aimed to evaluate the We identified relevant articles using a range of search terms through January 1, 2019 using PubMed, PsycInfo, and Google Scholar. We intentionally chose a large number of search terms to increase likelihood of identifying relevant articles that may have been missed otherwise. Search terms included variants of “suicide”, “self-injury”, “self-harm”, “self-directed violence”, “self-mutilation”, “deliberate self-harm (DSH)”, and “nonsuicidal self-injury (NSSI)” crossed with variants of “computerized tomography (CT)”, “magnetic resonance imaging (MRI)”, positron emission tomography (PET)”, “single-photon emission computed tomography (SPECT)”, “diffusion tensor imaging (DTI)”, “magnetic resonance spectroscopy (MRS)”, “neuroimaging”, “gray matter”, “white matter”, “serotonin”, “dopamine”, “gamma-aminoburtyric acid”, “noradrenaline”, “norepinephrine”, and “brain”.52. This inclusion criterion is consistent with other meta-analyses of brain imaging studies53.Inclusion required that studies (1) include at least one group of which all individuals exhibit SITBs; (2) include at least one control group; (3) conduct whole-brain analyses; and (4) provide standardized coordinates. The first inclusion criterion is to ensure that the findings are uniquely associated with SITBs instead of general psychopathology, with the second criterion ensuring that each study provided a benchmark for comparison. The third criterion is to prevent Region of Interest (ROI) analyses from biasing the meta-analytic results. Although ROI analyses provide valuable information about the neural correlates of SITBs, they violate the assumption that each voxel has an equal chance of being activated, thus biasing the meta-analytic results toward convergence on the ROIsA total of 1,201 unique papers were identified through database searching. Seventy-seven papers were retained in the present study, yielding a total sample size of 4,903 participants. To reduce nonindependence of multiple papers published on the same samples, only findings from unique contrasts were extracted. For instance, if analyses were first conducted on the entirety of the sample and subsequently repeated on subsamples, only findings from the broader analyses were included as they represented the most inclusive data. A total of 882 unique coordinates were extracted  sample size; (2) imaging techniques; (3) type of SITBs; (4) type of control groups ; (5) psychiatric diagnoses; (6) Montreal Neurological Institute (MNI) or Talaraich coordinates; (7) study paradigm ; (8) sample age, and (9) sample medication status.We extracted the sample size associated with each contrast.27. The types of imaging techniques were determined from each study: Magnetic Resonance Imaging (MRI), functional Magnetic Resonance Imaging (fMRI), Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and Single-Photon Emission Computed Topography (SPECT).Consistent with previous reviews and meta-analysis, we included studies with a range of imaging techniques2 and categorized SITBs examined by each study into: suicide ideation, suicide plan, suicide attempt, suicide death, and nonsuicidal self-injury (NSSI). When a study examined deliberate self-injuries of which the intent to die was unclear, we labeled the type of SITBs as self-harm. When a study examined suicide attempt and other suicidal behaviors  together, the study was considered to have examined all suicidal behaviors. We intentionally included all types of SITBs to conduct a pooled meta-analysis as well as finer-grained analyses to test whether certain brain differences are associated with general risk for SITBs or only specific types of SITBs.We adhered to the terminology proposed by NockA control group was considered a self-injurious control if participants were selected based on prior or current SITBs . A control group was coded as a psychiatric control if participants were drawn because they met certain clinical conditions . When neither eligibility criteria were set by the study, the control group was considered as a healthy control. This code was intended to test whether the stringency of control group might have contributed to the diverse findings in the literature.54, we coded for the primary psychiatric diagnoses of the samples.Given that some evidence suggests that the brain differences associated with SITBs might vary depending on the psychiatric diagnosesWhenever provided, MNI or Talairach coordinates were directly extracted for each contrasts from the studies. If a study did not specify whether they provided MNI or Talairach coordinates, the type of coordinates were inferred based on the statistical software used by the authors.27, we included studies using a wide range of study paradigms. To estimate and control for differences between studies, we first categorized each contrast based on whether they were obtained via resting-state or task-based paradigms. We then categorized task-based paradigms into cognitive tasks, affective tasks, tasks involving pain, and other tasks . Based on both convention in the field and descriptions provided within each study, all tasks reported by studies could be categorized into one of the four categories. For instance, Tower of London Test, Go/No-Go Task, N-Back Task, and Continuous Performance Task were categorized as cognitive tasks. Examples of affective tasks include viewing pictures with negative valence, matching emotional faces, and tasks inducing social rejection. Given an insufficient number of coordinates reported from tasks other than cognitive and affective tasks, they could not be meta-analyzed as a separate category. Therefore, separate analyses were only conducted for cognitive and affective tasks. Even though it was our original intention to code for specific tasks  and to test whether they moderate the findings, we were unable conduct such analyses due to heterogeneity in the literature. Despite the fact that non-neuroimaging meta-analyses have analyzed these tasks when there were at least three studies using the same task55, guidelines suggest a minimum of 20 experiments in each category for coordinate-based meta-analyses28. As such, we were unable to produce finer-grained categorizations.Following convention of prior reviews and meta-analysesThe mean sample age was extracted from each contrast. We also categorized sample age into adult, adolescent, elderly, and mixed samples. A sample was coded as adult if all the participants were at least 18 years old but less than 65 years old, and elderly if all the participants were at least 65 years old. A sample was coded as adolescent if all the participants were under the age of 18. When a sample included both adult and adolescent participants, it was coded as mixed adult and adolescent. Similarly, when a sample included both adult and elderly participants, it was coded as mixed adult and elderly.To assess for potential moderator effects, samples were coded into the following categories based on participants’ psychiatric medication status: none medicated, at least some medicated, or all medicated.58) and Multi-level Kernel Density Analysis (MKDA59). Ideally, meta-analysis of neuroimaging data would be performed on statistical maps. Unfortunately, such maps are rarely available despite current efforts to create map repositories60. In lieu of such maps, CBMA infers statistical maps based upon locations of statistical local maxima (i.e. peaks). Then, spatial-consistency among the inferred statistical maps is assessed. As detailed below, the two methods employed here differ in how the inferred statistical maps are calculated. The use of two different meta-analytic procedures was to ensure that the results did not depend on methodological specifics. To control for multiple comparisons and to reduce Type I error, statistical significance in both meta-analytic methods was determined using cluster-level family-wise error (FWE) correction, as has been recommended for CBMA28. Cluster extents were determined at a height threshold of p < 0.001 using Monte-Carlo permutation methods61.The goal of this meta-analysis was to identify brain areas that were consistently related to SITBs. We addressed this goal using two methods of coordinate-based meta-analysis (CBMA): Activation Likelihood Estimation . ALE assumes that each peak represents a broader activation cluster and that the exact location of each peak/cluster is uncertain. Therefore, each peak is convolved with a Gaussian kernel to form a Gaussian probability density. The kernel has a fixed area-under-the-curve, but the full-width/half-maximum (FWHM) varies according to the sample size of the experiment, with the FWHM values empirically determined58. This results in narrower, higher amplitude peak densities for large sample sizes reflecting greater certainty, and broader, lower amplitude peak densities for small sample sizes reflecting less certainty. To control for the fact that studies vary in whether or not sub-peaks within a cluster are reported, we used the non-additive approach that assigns the maximal density amplitude to a voxel that is activated across multiple clusters57. ALE values are computed for each voxel via the voxel-wise union of the modeled activation maps. Observed ALE values are then compared to a randomly permuted null distribution to determine significance61. Cluster extents were determined at a height threshold of p < 0.001 using previously recommended Monte-Carlo permutation methods of 1000 permutations61.ALE was performed using GingerALE software version 2.359 differs from ALE in terms of the kernel that is convolved with each activation peak. MKDA uses a spherical kernel whose radius is determined by the analyst, whereas ALE uses a Gaussian kernel whose FWHM is empirically determined. At first blush, an empirically determined kernel extent may seem superior to an arbitrarily assigned kernel extent. However, the empirically determined FWHM is based upon data from 21 healthy participants performing a single task with BOLD imaging58. Whether the extents observed there generalize to different populations, imaging modalities, and tasks is unclear. Therefore, the ability to freely choose a kernel extent in MKDA offers assurance that significant/non-significant results are not due to this limitation.MKDA59. For each study, each peak was convolved with the kernel to create a comparison indicator map. The map has values of either 1 (‘a study activated near this voxel’) or 0 (‘a study did not activate near this voxel’). Similar to the non-additive approach to ALE, the nesting of peaks within studies allows that no one study can disproportionally contribute to the significant findings. Each map is weighted by the product of the square root of the study sample size. The weighted average of these maps is then compared to a randomly permuted null distribution to determine significance. Cluster extents were determined at a height threshold of p < 0.001 using the previously recommended Monte-Carlo permutation methods of 5000 permutations59.We conducted analyses with kernel radii at 10 mm and 15 mm, which has been previously recommendedn. Here, we are explicitly focused on two-sample tests. To provide an equivalent one-sample n we used the equation (n1 × n2)/(n1 + n2) following prior guidelines49.Both ALE and MKDA weight studies by sample size. Both methods were developed with one-sample tests in mind and thus the weighting procedures assume a one-sample Both ALE and MKDA use Monte-Carlo procedures to determine the null distribution. By default, both ALE and MKDA restrict random permutation to gray matter. However, this procedure is not appropriate for analyses that are expected to produce results in white matter (e.g. DTI). Therefore, for MKDA analyses of DTI data, we used a white matter, rather than gray matter mask.Supplemental Information.Dataset 1.


Similarly, LDL‐c, TC, TG, and BMI levels are generally increased in T or A alleles carriers. We failed to conduct the meta‐analysis of rs1077835 and rs1077834 due to the limited previous reports. Data from the 1,000 Genomes indicate that the allele frequencies of the four SNPs in total or subpopulations are almost equal to each other. The paired value r2 and D' of the four SNPs are larger than 0.8, which indicate the linkage disequilibrium of the four variants. The analysis of LIPC promoter indicate that C‐514T and G‐250A are, respectively, located in transcriptional factor binding sites of USF1and Pbx1b, which may partly explain the effect of the two SNPs on the decreased LIPC activity in the alleles carriers and the corresponding increased plasma lipids hydrolyzed by LIPC. These results may help us to better understand the different effects of the four SNPs on the plasma lipid levels among subpopulations and offer clues for future clinical treatment of dyslipidemia‐related diseases.By the combination of meta‐analysis, the data of the 1,000 Genomes Project Phase 3, and the promoter sequence of hepatic lipase (LIPC), we performed the cross‐sectional study to explore the associations of four variants  in LIPC promoter with plasma lipid levels. Our results indicate that the first and the next three of the four SNPs are, respectively, reported to be associated with the decreased and increased HDL‐c level. Meta‐analysis of 87 studies with 101,988 participants indicates that HDL‐c level in rs1800588 (C‐514T) ,  With 1,000 Genome data, we evaluated the linkage by the combination of meta‐analysis, the data of the 1,000 Genomes Project Phase 3, and the promoter sequence of hepatic lipase (LIPC), we performed the cross‐sectional study to explore the associations of four variants  in LIPC promoter with plasma lipid levels. As a lipolytic enzyme, LIPC catalyzes the hydrolysis of triglycerides (TG) and phospholipids (PLs) in several plasma lipoproteins. As a ligand‐binding protein, LIPC facilitates the removal of lipoprotein remnant and the hepatic uptake of lipoprotein by anchoring to extracellular  exploring the association between plasma lipids with LIPC SNPs; (b) designed as cohort, case–control, or cross‐sectional studies; and (c) adjusted odds ratio (OR) and 95% confidence intervals (CIs) were reported or could be calculated. In several studies, the adjusted OR was unavailable in the multivariate analysis due to nonsignificant statistical results during univariate analysis. Excluding studies included (a) editorials, letters to the editor, review articles, animal experiments, case reports, and conference abstracts; (b) lipid values were obtained after treatment or management; (c) LIPC combined with other genes; (d) familial combined hyperlipidemia; (e) infrequently reported SNPs; and (f) patients with specific conditions or diseases. Flow diagram for the identification of eligible articles in this meta‐analysis was shown in Figure 2.3SD) was calculated as that of the previous literature . Articles that could not be categorized in accordance with title and abstract were retrieved for full‐text review. The following variables were extracted from these included studies: title, name of the first author, publication year, sample size, ethnic information, study design, age, gender, adjusted OR (or crude OR) and 95% CI, variables adjusted in the multivariate regression model. The values of HDL‐C, LDL‐C, total cholesterol (TC), TG, LIPC activity, and body mass index (BMI) in different subcategory were calculated and combined to produce the overall index with weights reflecting their shares in the total index .Statistical heterogeneity among these studies was evaluated by the chi‐square test with significance set at 33.1Q score and average silhouette score (>0.5), the network was reasonably divided into loosely coupled 15 clusters, and the homogeneity of these clusters was acceptable on average. The clusters #6, #7, and #10 were, respectively, labeled as high‐density lipoprotein metabolism, LIPC gene association, and HDL subfractions, which indicated that the relationships between LIPC and HDL have been investigated in previous publications. About 139 references were retrieved from Web of Science with the searching terms of “LIPC or hepatic lipase” and “SNPs.” Co‐cited clusters view was shown in Figure A total of 2,482 references were retained for bibliometric analysis of references and keywords. As a significant indicator in bibliometric, the co‐citation map of references suggests the scientific relevance of these references , random‐effect model was used to pool overall weighted mean differences (WMDs: kg/m2) of the total 95% CI. Subgroup analyses were performed by gender and race. The values of HDL‐C, LDL‐C, TC, TG, LIPC activity, and BMI in different subcategories were calculated and combined to produce the overall index with weights to reflect the shares of their total index among subcategories. Some of these values showed a statistic difference, while others lacked statistic difference between the carriers and the noncarriers among different subgroups. Detailed information of all subgroups analyses was showed in Tables Eighty‐nine studies with 101,988 participants were included in this meta‐analysis. Seventy‐three studies with 76,798 participants were used to analyze the associations of SNPs C‐514T and plasma lipid levels. Eighteen studies with 25,190 participants were used to analyze the associations of SNPs G‐250A and plasma lipid levels. Two of these studies reported information of both genotypes , suggesting the allele T carriers had a higher plasma HDL‐c level than noncarriers. Besides, in comparison between TT/CC genotype subjects, other outcomes also demonstrated significant difference  (Table p < .001) and TG , p < .001) in C‐514T were higher than those of noncarriers. With the exception of White Caucasian (p > .05), LDL‐c in C‐514T was higher than that of carriers , p = .061).For SNP C‐514T (Table p < .001), suggesting the mean plasma HDL‐c level was higher in subjects with AA genotype than subjects with GG genotype. In other genotype comparisons, mean plasma HDL‐c levels were higher in all subjects with homozygous allele A or A carriers  , p = .043). In addition, subjects with AA genotype had a higher mean plasma TC level than that of subjects with GG genotypes in White Caucasian (AA/GG) . The A carriers had a higher mean plasma TG level than that of the noncarriers  (Table For SNP G‐250A (Table p < .001). The detailed meta‐analysis results and forest plots were demonstrated in Tables Hepatic lipase activities were reported in 12 publications. Data from the only two studies of G‐250A could not be pooled because of different detecting assays  and Egger's regression test   Figure . No publ3.4r2 and D') of the four SNPs indicated that the estimated values of both r2 and D' are larger than 0.8  in total population or subpopulations are almost equal to each other, namely distributed in a similar position (Figure 8 Figure .4Among the four SNPs, C‐514T and G‐250A are repeatedly reported to be associated with the increased HDL‐c level, which does not appear to actually protect individuals from coronary artery disease (van Acker et al., To further explore the possible association of the two SNPs with the transcriptional regulation of LIPC expression, we analyzed the proximal sequence of LIPC promoter. And our results show that C‐514T and G‐250A SNPs are, respectively, located in the transcriptional factor binding sites of USF1 Figure a and PbxThe two SNPs of C‐514T and G‐250A are located in the transcriptional factor binding sites of USF1 and Pbx1b in LIPC promoter, which at least partly explain the observations that LIPC activity is decreased in the alleles carriers and the corresponding increased plasma lipids hydrolyzed by LIPC. As a lipolytic enzyme and ligand‐binding protein, the decreased LIPC activity is not sufficient to completely catalyze the hydrolysis of plasma lipid and lipoprotein, and to fully facilitate the removal of lipoprotein remnant and the hepatic uptake of lipoprotein, which should be accompanied by the increased plasma lipid levels in A or T carriers. These results may help us to better understand the different effects of the four SNPs on the plasma lipid levels among subpopulations and offer new thoughts for clinical treatment of dyslipidemia.The authors declare that they do not have any conflict of interest.M.Y. was responsible for experimental design and manuscript writing. W. W. analyzed these data. H. T. and L. H. prepared Figures Ethical Review: This study does not involve any human or animal testing.Informed Consent: Written informed consent was obtained from all study participants. Click here for additional data file. Click here for additional data file. Click here for additional data file.