Skip to main content

Association between the severity of metabolic dysfunction-associated fatty liver disease and the risk of colorectal neoplasm: a systematic review and meta-analysis

Abstract

Background

The severity of metabolic dysfunction-associated fatty liver disease (MAFLD) reportedly plays a part in the etiology of colorectal tumors. However, there is no consensus.

Methods

Studies relevant with the impact of MAFLD severity on the risk of colorectal neoplasms published before 24th April 2022 were screened. The pooled odds ratio (OR) with corresponding 95% confidence intervals (95% CI) was obtained using standard and cumulative meta-analyses. Subgroup, meta-regression, and sensitivity analyses were carried out to identify heterogeneity.

Results

Fourteen studies with data from 37,824 MAFLD patients were included. The prevalence of colorectal neoplasms escalated with the progression of MAFLD compared to simple steatosis (OR = 1.93; 95% CI = 1.42–2.62). The magnitude and direction of the effect on these outcomes remained largely constant over time. Even after limiting the meta-analysis to 8 studies with available adjusted OR (aOR), the findings still suggested that MAFLD severity was positively related to colorectal neoplasms (aOR = 3.03; 95% CI = 2.02–4.53). Severe MAFLD was more likely to cause left colon tumors (OR = 3.86, 95% CI = 2.16–6.91) than right colon neoplasms (OR = 1.94, 95% CI = 1.15–3.28).

Conclusion

The severity of MAFLD was independently related to colorectal neoplasms and severe MAFLD was more likely to cause left colon tumors.

Introduction

Metabolic dysfunction-associated fatty liver disease (MAFLD), previously named non-alcoholic fatty liver disease (NAFLD), involves approximately 25 % of the adults worldwide [1]. MAFLD was significantly associated with a majority of tumorigenic cases (90%), especially colorectal neoplasms which are also common worldwide [2,3,4]. Therefore, MAFLD causes considerable health and economic burden globally and frequently leads to inferior quality of life. MAFLD includes two histologically different phases with distinct prognoses: non-alcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH); the latter encompasses different liver tissue lesions, including fibrosis, cirrhosis, and liver cancer [5].

The colorectal area is divided anatomically into the left colon and the right colon, which is separated by the splenic flexure. The definition of advanced colorectal neoplasia is an adenomatous polyp with a diameter of more than 10 mm and/or villous histology and/or high-grade dysplasia or adenocarcinoma.

Many systematic reviews have shown the link between MAFLD and a high risk of colorectal tumors [6,7,8,9]. Only two of them briefly assessed the association between the severity of MAFLD and colorectal tumors as a secondary research objective, and only three and seven studies were respectively included in the two meta-analyses [6, 8]. There is still some uncertainty regarding whether the presence of severe steatosis, NASH or advanced fibrosis is more likely to cause colorectal neoplasms compared to mild liver disease [10]. Here, a meta-analysis was conducted for the first time to uncover the potential relationship between different severities of MAFLD, including hepatic steatosis, inflammation and fibrosis, and colorectal neoplasms (colorectal adenomas or/and advanced colorectal neoplasia), which may promote the prevention and detection of colorectal neoplasms. This meta-analysis also evaluated the site-specific effects of the varying severity of MAFLD on colorectal tumors.

Methods

This meta-analysis was reported following the guidelines of the Meta-analysis Of Observational Studies in Epidemiology [11]. Registration of the study protocol was done in advance (NO. CRD42021269830).

Methodology of searching

Studies published on PubMed, EMBASE, Cochrane Library, Web of Science and China National Knowledge Infrastructure (CNKI) from inception to 24th April 2022 were retrieved using various combinations of MeSH and non-MeSH terms related to MAFLD and colorectal neoplasm. The search strategy details are shown in Supplemental Table 1. Language and region were not restricted. To search for eligible studies fully, references from relevant articles were also reviewed.

Study selection

Criteria for eligibility included the following: 1) observational studies (cross-sectional, case-control, or cohort studies) that investigated the association between the severity of MAFLD and colorectal tumors; 2) odds ratio (OR) with 95% confidence interval (CI), or enough raw data to calculate OR with 95% CI were provided; 3) colorectal adenomas and advanced colorectal neoplasia were confirmed by colonoscopy; 4) MAFLD was diagnosed via imaging or biopsy; 5) MAFLD severity was assessed by biopsy, imaging steatosis degree or non-invasive fibrosis scoring systems; 6) no restrictions on race, sex, ethnicity or comorbidities of research subjects; 7) due to the lack of relevant studies, congress abstracts that met the above inclusion criteria were also incorporated; 8) when studies on the same population were published multiple times, only the most recent or comprehensive publication was chosen.

The criteria for excluding studies were as follows: 1) laboratory studies, letters, summaries, reviews, meta-analyses, commentaries, and case reports; 2) studies that include patients with other competing causes (viral infections, drugs, alcohols) of chronic liver diseases; 3) studies where participants were candidate liver transplant recipients with cirrhosis; 4) duplicate studies; 5) studies conducted in pediatric populations.

Two reviewers independently checked each study. Discussions among the two reviewers and the paper’s other author were held to resolve disagreements.

Data extraction

Based on a standardized form, the following data were summarized: the number of patients with MAFLD; first author; publication date; sex-related data; country of study; study design; methods used for MAFLD diagnosis; assessment methods for the severity of MAFLD; the outcome of interest (colorectal adenomas or advanced colorectal neoplasia); covariates; Newcastle–Ottawa Scale (NOS)/Agency for Healthcare Research and Quality (AHRQ) scores.

Quality assessment

Two authors evaluated the quality of the eligible researches separately. Any disagreements were resolved via a re-valuation of the studies by another reviewer. Case-control and cohort studies were appropriate for the NOS scale, while cross-sectional studies were assessed using the AHRQ scale [12]. The NOS evaluates the quality of a study based on 3 criteria: selection, comparability, and outcome. Studies that received a six-star rating or higher were denoted as high quality in this paper. The AHRQ scale grades the quality of articles as “low” (score of 0–3), “moderate” (4–7), or “high” (8–11) based on 11 items [13].

Statistical analysis

Analysis of the data was performed with Stata version 16.0 SE (Stata Corp, College Station, TX) and Review Manager version 5.3 (RevMan, the Cochrane Collaboration, Oxford, UK). The OR was used as the effect size for binary variables, and each effect size provided its 95% CI. If a study had multiple adjustment models, the one that maximally adjusted the confounding factors were selected. The pooled ORs and the 95% CIs were calculated to show the effect of MAFLD severity on the occurrence of colorectal neoplasm. The final outcomes were visualized as forest plots. Statistical significance was denoted by P values below 0.05 (two-sided).

Quantitative heterogeneity was evaluated by Q-based I2, where the Q-statistic was made up of the weighted sum of the squared values of the study effect size deviation from the overall mean effect size. The I2 index measured the proportion of heterogeneity that is unknown or unexplained [14], and I2 > 50% or P < 0.05 meant the presence of significant heterogeneity. In the absence of non-negligible heterogeneity, the fixed-effects model was applied to pool studies; otherwise, the random-effects model was selected [15]. Cumulative meta-analysis treated the data as a continuous unity and conducted separate meta-analyses each time a new study was included. It reflected the trend of the estimator of effect size over time to measure the time taken for the research subjects to reach sufficient stability [16]. Subgroup analyses were conducted in order to explain some possible causes of heterogeneity, allowing effect sizes of studies within a subgroup to be compared and assessing if heterogeneity was reduced through subgroup analyses [14]. Meta-regression analysis was conducted to evaluate potential regulatory influences of the variables on between-study heterogeneity [17]. To find the outlier studies and determine the firmness of the original results, sensitivity analyses were carried out based on the removal of one study at a time. The funnel plot, Begg’s test, and Egger’s test were performed to judge the possibility of publication bias [18, 19].

Results

Features of selected studies

The detailed selection process are presented in Fig. 1. 1027 records in all were retrieved after the initial search (234 from PubMed, 349 from Embase, 3 from the Cochrane library, 272 from the Web of Science, and 169 from CNKI), 320 were duplicate. 674 records were excluded following a careful review of titles and abstracts. Of the remaining 33 articles, 19 met the exclusion criteria. Finally, 14 studies were included.

Fig. 1
figure 1

The PRISMA flow diagram

Table 1 lists the detailed information of the 14 studies [20,21,22,23,24,25,26,27,28,29,30,31,32]. Total 37,824 MAFLD patients in these studies all underwent a screening colonoscopy. MAFLD was diagnosed by either biopsy or imaging techniques [liver biopsy, n = 4 studies; ultrasonography, n = 7 studies; Fibro Touch, n = 1 study; Transient Elastography (TE) with Controlled Attenuation Parameter (CAP), n = 1 study; biopsy or ultrasonography, n = 1 study]. 11 studies were from Asia (South Korea, China, and Thailand), two from North America (the United States), and one from Europe (Romania). Eight studies adjusted potential confounding factors, whereas six studies did not provide the adjusted OR. In order to assess the severity of MAFLD, five studies explored the stage of liver fibrosis, four evaluated the fatty liver grade, and five determined the presence and absence of NASH. Ten cross-sectional studies scored at least eight stars on the AHRQ, one case-control study scored seven stars and three cohort studies scored at least six stars on the NOS.

Table 1 The characteristics of the included studies (Page 9; line 139)

Main outcomes of standard and cumulative meta-analysis

Fourteen articles were included to assess the impact of severity of hepatic steatosis and fibrosis on the occurrence of colorectal neoplasms; 11 articles [20,21,22, 25,26,27,28,29,30,31] on colorectal adenomas and eight studies [22,23,24,25,26, 29, 30, 32] on advanced colorectal neoplasia. The pooled effect estimate was statistically significant (OR = 1.93; 95% CI = 1.42–2.62), along with obvious heterogeneity (I2 = 75.5 > 50%, P = 0.000 < 0.05; Fig. 2). Hence, the random-effects model was selected throughout this study. Additionally, the pooled effect estimate showed a higher risk of both colorectal adenomas (OR = 1.61; 95% CI = 1.12–2.32) and advanced colorectal neoplasia (OR = 2.34; 95% CI = 1.42–3.87) in patients with greater severity of MAFLD. The meta-analysis of eight studies, which provided the aOR, also revealed that severe MAFLD had a positive impact on colorectal adenomas (aOR = 2.60; 95% CI = 1.42–4.75) as well as advanced colorectal neoplasia (aOR = 3.45; 95% CI = 1.88–6.32). However, the heterogeneity was still high (Fig. 3). A cumulative meta-analysis showed that this evidence had been available since 2011 and that additional data had provided further accuracy of point estimates, without changing either the direction or magnitude of the effect (Fig. 4).

Fig. 2
figure 2

Forest plot of the relationship between the severity of MAFLD and colorectal neoplasms

Fig. 3
figure 3

The meta-analysis of eight studies providing the aOR

Fig. 4
figure 4

Cumulative meta-analysis of 14 studies

Subgroup analyses

Study design, study region, and the classification methods for the severity of MAFLD in the included studies differed greatly, all of which could be underlying factors affecting study outcomes. Therefore, the subgroups based on the above factors were established to determine the source of heterogeneity.

Study design

Higher prevalence of colorectal adenomas (OR = 1.67, 95% CI = 1.21–2.31, I2 = 42.3%, P = 0.096) and advanced colorectal neoplasia (OR = 2.88, 95% CI = 1.56–5.33, I2 = 81.6%, P = 0.000) were found in patients with greater severity of MAFLD than in controls in the cross-sectional studies, whereas no significant differences in the cohort studies were observed. One case-control study relevant to the relationship between severe MAFLD and colorectal adenomas indicated a positive result (OR = 9.52, 95% CI = 1.71–52.93; Fig. 5).

Fig. 5
figure 5

Subgroup analysis by study design

Study region

The subgroups of Asia, Europe, and North America were analyzed in accordance with the study region. Severe MAFLD led to an higher prevalence of colorectal adenomas in the subgroups of Europe (OR = 9.52, 95% CI = 1.71–52.93) and North America (OR = 2.23, 95% CI = 1.02–4.89, I2 = 0.0%, P = 0.850), but not in the Asia subgroup (OR = 1.42, 95% CI = 0.96–2.09, I2 = 67.3%, P = 0.003). However, severe MAFLD seemed more likely to develop advanced colorectal neoplasia in Asian countries with an overall OR of 2.34 (1.42, 3.87) (Fig. 6).

Fig. 6
figure 6

Subgroup analysis by study region

Classification methods for the severity of MAFLD

When assessing the severity of MAFLD by the degree of liver fibrosis, the total ORs of colorectal adenomas and advanced colorectal neoplasia were 1.54 [95% CI (1.17–2.02)] and 3.20 [95% CI (1.63–6.26)], respectively. However, when evaluating steatosis grade and the presence and absence of NASH, no significant differences were found for both colorectal adenomas and advanced colorectal neoplasia (Fig. 7).

Fig. 7
figure 7

Subgroup analysis by classification methods for the severity of MAFLD

Meta-regression

Since no specific source of heterogeneity could be identified in subgroup analyses, all patients with MAFLD were subjected to univariate meta-regression based on sample size and gender ratio. The findings indicated that the sex ratio played a role in the data heterogeneity (Adjusted R2 = 60.72%; I2 = 38.96%; P = 0.030; 95%CI = 0.941–0.996; Fig. 8A). The sample size did not work in the heterogeneity exploration (Fig. 8B). Owing to the lack of relevant reports, meta-regression analyses according to mean age, race, mean transaminase levels, etc., were not conducted.

Fig. 8
figure 8

Univariate meta-regression according to sex ratio (A); and sample size (B)

Sensitivity analyses

By sequentially eliminating each study, sensitivity analyses were carried out to assess their impact on the overall result. Figure 9 showed that the pooled effect and 95% CI did not change significantly, which indicated the stability of the original results.

Fig. 9
figure 9

Sensitivity analyses conducted in cross-sectional studies (A); and case-control and cohort studies (B)

Site-specific prevalence of colorectal tumors

Two studies explored the link between the severity of MAFLD and the location of colorectal adenomas [29, 33]. One study quantified the relationship between MAFLD and the location of advanced colorectal neoplasia [29]. The results revealed that regardless of whether it was on the left or right side, the risk of colorectal tumors in patients with severe liver disease was higher than in controls (Fig. 10A; Fig. 10B). Moreover, left colon tumors were more likely to be caused by severe MAFLD (Left: OR = 3.86, 95% CI = 2.16–6.91, I2 = 0%, P = 0.49; Right: OR = 1.94, 95% CI = 1.15–3.28, I2 = 0%, P = 0.62).

Fig. 10
figure 10

Forest plots of the relationship between the severity of MAFLD and left colon tumors (A); and right colon tumors (B)

Publication bias

The funnel plot of pooled OR for colorectal neoplasms showed symmetry (Fig. 11). Begg’s (P = 0.889) and Egger’s test (P = 0.489) also showed a non-significant results (Fig. 11). As a result of insufficient studies included, the funnel plots were inapplicable. Hence, statistical tests were conducted on the publication bias of pooled OR for tumor location and showed no indications of publication bias (Begg’s test & Left: P = 1.000; Begg’s test & Right: P = 1.000; Egger’s test & Left: P = 0.521; Egger’s test & Right: P = 0.497; Fig. 12; Fig. 13).

Fig. 11
figure 11

Publication bias of the 14 studies

Fig. 12
figure 12

Publication bias of studies exploring the link between the severity of MAFLD and left colon tumors

Fig. 13
figure 13

Publication bias of studies exploring the link between the severity of MAFLD and right colon tumors

Discussion

The clinical and economic burden of MAFLD and colorectal neoplasms is considerable since the prevalence of the two diseases is high among the general public. However, most studies focus on the relationship between MAFLD and colorectal neoplasms. Further researches on the relationship between the severity of MAFLD and colorectal tumors are limited. It is the first research that systematically investigate the prevalence of colorectal neoplasms in patients with different MAFLD severities. Results showed that in comparison to patients with simple steatosis, milder liver fibrosis, and less liver fat, the incidence of colorectal adenomas increased by 1.61 times in severe MAFLD patients, and the incidence of advanced colorectal neoplasia increased by 2.34-fold. These outcomes largely exhibited the same direction and magnitude of effect over time. Furthermore, the pooled effect estimate for eligible studies that were fully adjusted for confounding factors was higher, indicating an independent relationship between the severity of MAFLD and colorectal neoplasms. Additionally, this meta-analysis discovered that left colon tumors are more likely to be caused by severe MAFLD. However, due to the scarcity of related studies, this conclusion was deemed untrustworthy. Additional verification is required.

Numerous studies have demonstrated that four main mechanisms, namely insulin resistance, chronic inflammation, adipocytokines, and intestinal microecology alteration, mediate the association between MAFLD and colorectal adenomas or colorectal cancer (CRC) [34,35,36,37,38,39,40]. Hyperinsulinemia due to insulin resistance can both directly stimulate neoplastic growth of the colonic mucosa and indirectly lead to colorectal tumors by increasing insulin-like growth factor-1 level [41, 42]. Other pro-inflammatory cytokines can contribute to the development of MAFLD and colorectal tumors by inducing metabolic liver inflammation and insulin resistance through various complex inflammatory signaling pathways, such as IL-6 and TNFα [43,44,45]. As adipocytokines, adiponectin and leptin play opposite roles in the proliferation and migration of colorectal tumor cells [41, 43, 46]. When serum adiponectin levels decreases in MAFLD, leptin is more potent to exert a carcinogenic effect [42, 47]. Further, low levels of plasma adiponectin are especially in relation to the risk of KRAS-mutant CRC [48]. Gut microbiota dysbiosis increases intestinal permeability thus causing liver inflammation and damage, accelerates a chronic systemic inflammatory state, as well as produces genotoxins that interfere with the regulation of the intestinal cell cycle [49,50,51]. The severity of MAFLD is in close relation to the risk of colorectal tumors, possibly because inflammatory state, insulin resistance, decreased serum adiponectin levels, and intestinal bacterial overgrowth are more common and severe with the progression of MAFLD histology [37, 38, 52, 53].

There was significant heterogeneity among the studies included. Subgroup analysis revealed that despite the lack of uniform non-invasive methods for stratifying fibrosis, there was a higher incidence of colorectal neoplasms in MAFLD patients with advanced liver fibrosis than those without. This may be because significant fibrosis implies the end stage of MAFLD. Besides, patients with NASH had a higher risk of colorectal adenomas and advanced colorectal neoplasia than patients with simple steatosis. But this result lack statistical significance, perhaps because of the high heterogeneity among related studies. However, an interesting finding was that severe MAFLD confirmed by imaging techniques did not show any relationship with colorectal adenomas and advanced colorectal neoplasia, and there was almost no heterogeneity among related studies. This might be due to the unreliable classification of the degree of liver fat based on ultrasound techniques [54]. Conventional abdominal ultrasound examination lacks corresponding objective indicators, and the results of the diagnosis are affected by the patient’s body mass index (BMI), subcutaneous fat thickness, instrument sensitivity, and gain adjustment, resulting in the large discrepancies among different observers about MAFLD grading, especially in the evaluation of moderate and severe MAFLD [55, 56]. Current international guidelines do not recommend using ultrasound to stratify the severity of MAFLD [5]. Despite being considered as the gold standard in staging liver disease, the invasive nature of liver biopsy limits its use. To address this issue, non-invasive approaches have thus been developed. Even if computed tomography and magnetic resonance imaging can accurately detect and quantify liver fat, multiple limitations such as radiation, low availability, and high cost might affect the diagnostic feasibility [57, 58]. Therefore, increased non-invasive indexes of MAFLD have appeared. The included studies in this meta-analysis used the most widely applied complex score models, including the NAFLD fibrosis score (NFS) and fibrosis-4 (FIB-4) index to explore the link between the severity of liver fibrosis and colorectal tumors [59, 60]. However, advanced fibrosis are late manifestations. Detecting a progressive disease at an earlier stage would be beneficial. The indirect indexes of steatosis developed in recent years include the Fatty Liver Index [61], the Lipid Accumulation Product [62], the Hepatic Steatosis Index [63]. However, these indicators are not well suited for the diagnosis of steatosis grades [64]. Therefore, developing mature non-invasive scoring systems for liver fat quantification is necessary.

In this meta-analysis, a significant relationship between the severity of MAFLD and colorectal neoplasms was found in cross-sectional studies, but not in cohort studies. Due to the fact that only three cohort studies were included in this study, it was difficult to reflect the real relationship. Further evidence from prospective cohort studies are required to confirm whether the severity of MAFLD has a influence on the risk of colorectal tumors. Besides, studies performed in non-Asian regions showed a statistically significant pooled effect for colorectal adenomas, while those in Asian regions showed inconsistent findings. As for the association between the severity of MAFLD and advanced colorectal neoplasia, all relevant studies were performed in the Asian region. The result showed that severe MAFLD led to an increased ocurrence of advanced colorectal neoplasia compared to mild MAFLD. There is a need for more research in non-Asian population to clarify the role of MAFLD severity in the advanced colorectal neoplasia.

MAFLD patients of different severity levels lack formal guidelines or recommendations regarding routine colorectal neoplasm screening, despite the fact that the close relationship between them has been confirmed by many clinical studies. Besides, studies discovered that the severity of MAFLD is related to the poor prognosis of colorectal cancer. Severe MAFLD independently increased the risk of liver metastasis from CRC and colorectal CRC-specific mortality [65, 66]. There are reasons to believe that MAFLD patients, especially those with severe liver disease, could substantially benefit from more earlier or frequent colonoscopy. However, before implementation, the cost-effectiveness of regular colonoscopy screening still needs to be considered and validated. Further evaluation is also required to determine the right time for initiating such screening.

Study strengths and limitations

This meta-analysis provide the most comprehensive and up-to-date assessment on the relationship between the severity of MAFLD and colorectal tumors. Wide regional coverage was involved, including Asia, Europe, and North America. A variety of statistical methods were combined to confirm the reliability of the outcomes. Based on the comprehensive search, it is unlikely that any published studies have been omitted, and neither funnel plots nor formal statistical tests indicate a publication bias. The study also has some limitations. First, some inherent limitations of cross-sectional studies led to the impossibility to accurately determine the incidence of future events. The lack of well-designed prospective studies resulted in the true causality between liver disease severity and colorectal tumors cannot be confirmed. Second, nearly half of the eligible studies did not fully adjust important confounding factors (such as obesity, metabolic syndrome, drug use, family history of cancer, etc.), so the risk of bias could not be ruled out, which could affect the reliability of the result. Third, significant heterogeneity among the eligible studies made it necessary to be cautious in interpreting some of the results of this meta-analysis. To systematically investigate and identify possible statistical heterogeneity sources, subgroup, meta-regression and sensitivity analyses were conducted. While meta-regression found that the heterogeneity was partly caused by the sex ratio, it was not possible to identify all possible heterogeneity due to the lack of detailed reports. The pooled subject data from large prospective studies is necessary for more thorough analysis of heterogeneity, as these become available over time. Fourth, MAFLD was diagnosed through liver biopsy in only five studies among the included studies. Liver biopsy provides the most accurate outcomes for diagnosing and staging MAFLD. However, invasive examinations are often not accepted by asymptomatic MAFLD patients. Furthermore, most of the included studies were from Asian countries. As the body fat distribution, genetic background, and living habits might significantly affect on the development of tumors in Asian and non-Asian individuals, the European and American populations should be studied in greater detail in prospective cohort studies.

Conclusion

According to the findings of this study, MAFLD severity is independently related to colorectal adenomas and advanced colorectal neoplasia. Additionally, the left colon tumors are more likely to be caused by severe MAFLD, compared to the right colon tumors. Hence, patients with greater severity of MAFLD need a regular colonoscopy to detect colorectal tumors early and increase life expectancy. Perhaps regular colonoscopy screening in the future could help reduce the economic burden on society. A mechanism for this association needs to be investigated further.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

AHRQ:

Agency for Healthcare Research and Quality

aOR:

Adjusted OR

BMI:

Body mass index

CAP:

Controlled Attenuation Parameter

CI:

Confidence interval

CNKI:

China National Knowledge Infrastructure

CRC:

Colorectal cancer

FIB-4:

Fibrosis-4

MAFLD:

Metabolic dysfunction-associated fatty liver disease

NAFL:

Non-alcoholic fatty liver

NAFLD:

Non-alcoholic fatty liver disease

NASH:

Nonalcoholic steatohepatitis

NFS:

NAFLD fibrosis score

NOS:

Newcastle-Ottawa Scale

OR:

Odds ratio

TE:

Transient Elastography

References

  1. Eslam M, Newsome PN, Sarin SK, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international Expert consensus statement. J Hepatol. 2020;73(1):202–9. https://doi.org/10.1016/j.jhep.2020.03.039.

    Article  PubMed  Google Scholar 

  2. Johdi NA, Sukor NF. Colorectal Cancer immunotherapy: options and strategies. Front Immunol. 2020;11:1624. https://doi.org/10.3389/fimmu.2020.01624.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Armstrong MJ, Adams LA, Canbay A, Syn W. Extrahepatic complications of nonalcoholic fatty liver disease. Hepatology. 2014;59(3):1174–97. https://doi.org/10.1002/hep.26717.

    CAS  Article  PubMed  Google Scholar 

  4. Mantovani A, Scorletti E, Mosca A, Alisi A, Byrne CD, Targher G. Complications, morbidity and mortality of nonalcoholic fatty liver disease. Metabolism. 2020;111S:154170. https://doi.org/10.1016/j.metabol.2020.154170.

    CAS  Article  PubMed  Google Scholar 

  5. EASL-EASD-EASO clinical practice guidelines for the Management of non-Alcoholic Fatty Liver Disease. J Hepatol. 2016;64(6):1388–402. https://doi.org/10.1016/j.jhep.2015.11.004.

  6. Chen W, Wang M, Jing X, Wu C, Zeng Y, Peng J, et al. High risk of colorectal polyps in men with non-alcoholic fatty liver disease: a systematic review and meta-analysis. J Gastroenterol Hepatol. 2020;35(12):2051–65. https://doi.org/10.1111/jgh.15158.

    Article  PubMed  Google Scholar 

  7. Chen J, Bian D, Zang S, Yang Z, Tian G, Luo Y, et al. The association between nonalcoholic fatty liver disease and risk of colorectal adenoma and Cancer incident and recurrence: a Meta-analysis of observational studies. Expert Rev Gastroent. 2019;13(4):385–95. https://doi.org/10.1080/17474124.2019.1580143.

    CAS  Article  Google Scholar 

  8. Mantovani A, Dauriz M, Byrne CD, Lonardo A, Zoppini G, Bonora E, et al. Association between nonalcoholic fatty liver disease and colorectal Tumours in asymptomatic adults undergoing screening colonoscopy: a systematic review and Meta-analysis. Metabolism. 2018;87:1–12. https://doi.org/10.1016/j.metabol.2018.06.004.

    CAS  Article  PubMed  Google Scholar 

  9. Abdelfatah M, Elebiary A, Ali EG, Shill M, Kandil H. Association between nonalcoholic fatty liver disease and colorectal adenoma: updated Meta-analysis. Gastroenterology. 2015;1481(4):S1051–2.

    Article  Google Scholar 

  10. Hagström H, Kechagias S, Ekstedt M. Risk for hepatic and extra-hepatic outcomes in nonalcoholic fatty liver disease from the symposium: non-alcoholic fatty liver disease (NAFLD). J Intern Med. 2021. https://doi.org/10.1111/joim.13343.

  11. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of observational studies in epidemiology (MOOSE) group. JAMA. 2000;283(15):2008–12. https://doi.org/10.1001/jama.283.15.2008.

    CAS  Article  PubMed  Google Scholar 

  12. Li C, Cheng G, Sha T, Cheng W, Yan Y. The relationships between screen use and health indicators among infants, toddlers, and preschoolers: a Meta-analysis and systematic review. Int J Environ Res Public Health. 2020;17(19). https://doi.org/10.3390/ijerph17197324.

  13. Mao DH, Miao JK, Zou X, Chen N, Yu LC, Lai X, et al. Risk factors in predicting prognosis of neonatal bacterial meningitis-a systematic review. Front Neurol. 2018;9:929. https://doi.org/10.3389/fneur.2018.00929.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ruppar T. Meta-analysis: how to quantify and explain heterogeneity? Eur J Cardiovasc Nur 2020; 19(7):646–652. https://doi.org/10.1177/1474515120944014.

  15. Cai Z, Fan X. A comparison of fixed-effects and random-effects models for multivariate Meta-analysis using an SEM approach. Multivariate Behav Res. 2020;55(6):839–54. https://doi.org/10.1080/00273171.2019.1689348.

    Article  PubMed  Google Scholar 

  16. Mullen B, Muellerleile P, Bryant B. Cumulative Meta-analysis: a consideration of indicators of sufficiency and stability. Pers Soc Psychol B. 2001;27(11):1450–62. https://doi.org/10.1177/01461672012711006.

    Article  Google Scholar 

  17. Harbord RM, Higgins J. Meta-Regression in Stata. Stata J. 2008;8(4):493–519. https://doi.org/10.1177/1536867X0800800403.

    Article  Google Scholar 

  18. Egger M, Davey SG, Schneider M, Minder C. Bias in Meta-analysis detected by a simple, Graphical Test. BMJ. 1997;315(7109):629–34. https://doi.org/10.1136/bmj.315.7109.629.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Begg CB, Berlin JA. Publication Bias and dissemination of clinical research. J Natl Cancer Inst. 1989;81(2):107–15. https://doi.org/10.1093/jnci/81.2.107.

    CAS  Article  PubMed  Google Scholar 

  20. Blackett JW, Verna EC, Lebwohl B. Increased prevalence of colorectal adenomas in patients with nonalcoholic fatty liver disease: a cross-sectional study. Dig Dis. 2020;38(3):222–30. https://doi.org/10.1159/000502684.

    Article  PubMed  Google Scholar 

  21. Chuan L, Chang J, Zhao J, Li L, Yang X, Yu D. Association between nonalcoholic fatty liver disease and colorectal adenomatous polyps. J Clin Hepatol. 2020;36(6):1299–303.

    Google Scholar 

  22. Cho Y, Lim SK, Joo SK, et al. Nonalcoholic steatohepatitis is associated with a higher risk of advanced colorectal neoplasm. Liver Int. 2019;39(9):1722–31. https://doi.org/10.1111/liv.14163.

    Article  PubMed  Google Scholar 

  23. Kim MC, Park JG, Jang BI, Lee HJ, Lee WK. Liver fibrosis is associated with risk for colorectal adenoma in patients with nonalcoholic fatty liver disease. Medicine (Baltimore). 2019;98(6):e14139. https://doi.org/10.1097/MD.0000000000014139.

    Article  Google Scholar 

  24. Kim GA, Lee HC, Choe J, et al. Association between non-alcoholic fatty liver disease and Cancer incidence rate. J Hepatol. 2018;68(1):140–6. https://doi.org/10.1016/j.jhep.2017.09.012.

    Article  Google Scholar 

  25. Piyachaturawat P, Thanapirom K, Aniwan S, Rerknimitr R, Kullavanijaya P, Patanasakpinyo C, et al. The impact of nonalcoholic fatty liver disease on prevalence of colorectal adenomas and advanced adenomas in Thai patients with average risk for colorectal Cancer screening. Gastroenterology. 2016;150(4):S1145. https://doi.org/10.1016/S0016-5085(16)33863-X.

    Article  Google Scholar 

  26. Lee T, Yun KE, Chang Y, Ryu S, Park DI, Choi K, et al. Risk of colorectal neoplasia according to fatty liver severity and presence of gall bladder polyps. Dig Dis Sci. 2016;61(1):317–24. https://doi.org/10.1007/s10620-015-3873-8.

    Article  PubMed  Google Scholar 

  27. Yang W, Yu X, Wang Y, Yang Z. Colorectal adenomatous polyps and non-alcoholic fatty liver disease. Zhonghua Gan Zang Bing Za Zhi. 2014;22(1):66–8.

    CAS  PubMed  Google Scholar 

  28. Tantau A, Tantau M. The prevalence of colorectal adenomas and advanced neoplasms detected through PAN-Chromocolonoscopy in patients with non-alcoholic fatty liver disease (NAFLD). A Prospective Study. Gastroenterology. 2014;146(5):S689.

    Article  Google Scholar 

  29. Wong V, Wong G, Tsang S, et al. High prevalence of colorectal neoplasm in patients with non-alcoholic steatohepatitis. Gut. 2011;60(6):829–36. https://doi.org/10.1136/gut.2011.237974.

    Article  PubMed  Google Scholar 

  30. Liu M, Dai F, Peng Q. Relationship between severity of liver fibrosis and colorectal adenomatous polyp in nonalcoholic fatty liver disease. Chin J Integr Tradit West Med Digestion. 2022;30(1):36–41.

    Google Scholar 

  31. Seo JY, Bae JH, Kwak MS, Yang JI, Chung SJ, Yim JY, Lim SH, Chung GE. The Risk of Colorectal Adenoma in Nonalcoholic or Metabolic-Associated Fatty  Liver Disease. Biomedicines. 2021;9(10):1401. https://doi.org/10.3390/biomedicines9101401.

  32. Ahn JS, Sinn DH, Min YW, et al. Non-Alcoholic Fatty Liver Diseases and Risk of Colorectal Neoplasia. Aliment Pharmacol Ther. 2017;45(2):345-53. https://doi.org/10.1111/apt.13866.

  33. Touzin NT, Bush KN, Williams CD, Harrison SA. Prevalence of colonic adenomas in patients with nonalcoholic fatty liver disease. Ther Adv Gastroenterol. 2011;4(3):169–76. https://doi.org/10.1177/1756283X11402118.

    Article  Google Scholar 

  34. Kang HW, Kim D, Kim HJ, et al. Visceral obesity and insulin resistance as risk factors for colorectal adenoma: a cross-sectional, case-control study. Am J Gastroenterol. 2010;105(1):178-87. https://doi.org/10.1038/ajg.2009.541.

  35. Wei EK, Giovannucci E, Fuchs CS, Willett WC, Mantzoros CS. Low plasma adiponectin levels and risk of colorectal Cancer in men: a prospective study. J Natl Cancer Inst. 2005;97(22):1688–94. https://doi.org/10.1093/jnci/dji376.

    CAS  Article  PubMed  Google Scholar 

  36. Khan RS, Bril F, Cusi K, Newsome PN. Modulation of insulin resistance in nonalcoholic fatty liver disease. Hepatology. 2019;70(2):711–24. https://doi.org/10.1002/hep.30429.

    CAS  Article  PubMed  Google Scholar 

  37. Cobbina E, Akhlaghi F. Non-alcoholic fatty liver disease (NAFLD) - pathogenesis, classification, and effect on drug metabolizing enzymes and transporters. Drug Metab Rev. 2017;49(2):197–211. https://doi.org/10.1080/03602532.2017.1293683.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Polyzos SA, Kountouras J, Mantzoros CS. Adipokines in nonalcoholic fatty liver disease. Metabolism. 2016;65(8):1062–79. https://doi.org/10.1016/j.metabol.2015.11.006.

    CAS  Article  PubMed  Google Scholar 

  39. Safari Z, Gérard P. The links between the gut microbiome and non-alcoholic fatty liver disease (NAFLD). Cell Mol Life Sci. 2019;76(8):1541–58. https://doi.org/10.1007/s00018-019-03011-w.

    CAS  Article  PubMed  Google Scholar 

  40. Gkolfakis P, Dimitriadis G, Triantafyllou K. Gut microbiota and non-alcoholic fatty liver disease. Hepatob Pancreat Dis. 2015;14(06):572–81.

    Article  Google Scholar 

  41. Parizadeh SM, Parizadeh SA, Alizade-Noghani M, et al. Association between non-alcoholic fatty liver disease and colorectal Cancer. Expert Rev Gastroent. 2019;13(7):633–41. https://doi.org/10.1080/17474124.2019.1617696.

    CAS  Article  Google Scholar 

  42. Sanna C, Rosso C, Marietti M, Bugianesi E. Non-alcoholic fatty liver disease and extra-hepatic cancers. Int J Mol Sci. 2016;17(5). https://doi.org/10.3390/ijms17050717.

  43. Ho GYF, Wang T, Gunter MJ, et al. Adipokines linking obesity with colorectal Cancer risk in postmenopausal women. Cancer Res. 2012;72(12):3029–37. https://doi.org/10.1158/0008-5472.CAN-11-2771.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Kim S, Keku TO, Martin C, Galanko J, Woosley JT, Schroeder JC, et al. Circulating levels of inflammatory cytokines and risk of colorectal adenomas. Cancer Res. 2008;68(1):323–8. https://doi.org/10.1158/0008-5472.CAN-07-2924.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Invest. 2006;116(7):1793–801. https://doi.org/10.1172/JCI29069.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Sugiyama M, Takahashi H, Hosono K, et al. Adiponectin inhibits colorectal Cancer cell growth through the AMPK/mTOR pathway. Int J Oncol. 2009;34(2):339–44.

    CAS  PubMed  Google Scholar 

  47. Tilg H, Diehl AM. NAFLD and extrahepatic cancers: have a look at the Colon. Gut. 2011;60(6):745–6. https://doi.org/10.1136/gut.2011.239392.

    Article  PubMed  Google Scholar 

  48. Inamura K, Song M, Jung S, et al. Prediagnosis plasma adiponectin in relation to colorectal Cancer risk according to KRAS mutation status. J Natl Cancer Inst. 2016;108(4). https://doi.org/10.1093/jnci/djv363.

  49. Adams LA, Anstee QM, Tilg H, Targher G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut. 2017;66(6):1138. https://doi.org/10.1136/gutjnl-2017-313884.

    Article  PubMed  Google Scholar 

  50. Hoffmanová I, Sánchez D, Tučková L, Tlaskalová-Hogenová H. Celiac disease and liver disorders: from putative pathogenesis to clinical implications. Nutrients. 2018;10(7). https://doi.org/10.3390/nu10070892.

  51. Mandal P. Molecular mechanistic pathway of colorectal carcinogenesis associated with intestinal microbiota. Anaerobe. 2018;49:63–70. https://doi.org/10.1016/j.anaerobe.2017.12.008.

    CAS  Article  PubMed  Google Scholar 

  52. Muhidin SO, Magan AA, Osman KA, Syed S, Ahmed MH. The relationship between nonalcoholic fatty liver disease and colorectal Cancer: the future challenges and outcomes of the metabolic syndrome. J Obes. 2012;2012:637538. https://doi.org/10.1155/2012/637538.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. Hui JM, Hodge A, Farrell GC, Kench JG, Kriketos A, George J. Beyond insulin resistance in NASH: TNF-alpha or adiponectin? Hepatology. 2004;40(1):46–54. https://doi.org/10.1002/hep.20280.

    CAS  Article  PubMed  Google Scholar 

  54. Stern C, Castera L. Non-invasive diagnosis of hepatic steatosis. Hepatol Int. 2017;11(1):70–8. https://doi.org/10.1007/s12072-016-9772-z.

    Article  PubMed  Google Scholar 

  55. Ozturk A, Grajo JR, Gee MS, Benjamin A, Zubajlo RE, Thomenius KE, et al. Quantitative hepatic fat quantification in non-alcoholic fatty liver disease using ultrasound-based techniques: a review of literature and their diagnostic performance. Ultrasound Med Biol. 2018;44(12):2461–75. https://doi.org/10.1016/j.ultrasmedbio.2018.07.019.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Williamson RM, Perry E, Glancy S, et al. The use of ultrasound to diagnose hepatic steatosis in type 2 diabetes: intra- and Interobserver variability and comparison with magnetic resonance spectroscopy. Clin Radiol. 2011;66(5):434–9. https://doi.org/10.1016/j.crad.2010.09.021.

    CAS  Article  PubMed  Google Scholar 

  57. Schiavone C, Piscaglia F, Iannetti G, Cantisani V. What ultrasound operators must be well aware of in a World with raising burden of non alcoholic fatty liver disease? Ultraschall Med 2019; 40(1):7–10. https://doi.org/10.1055/a-0808-8062.

  58. Cicero AFG, Gitto S, Fogacci F, Rosticci M, Giovannini M, D'Addato S, et al. Fatty liver index is associated to pulse wave velocity in healthy subjects: data from the Brisighella heart study. Eur J Intern Med. 2018;53:29–33. https://doi.org/10.1016/j.ejim.2018.03.010.

    Article  PubMed  Google Scholar 

  59. Adams LA, George J, Bugianesi E, Rossi E, De Boer WB, van der Poorten D, et al. Complex non-invasive fibrosis models are more accurate than simple models in non-alcoholic fatty liver disease. J Gastroenterol Hepatol. 2011;26(10):1536–43. https://doi.org/10.1111/j.1440-1746.2011.06774.x.

    CAS  Article  PubMed  Google Scholar 

  60. Musso G, Gambino R, Cassader M, Pagano G. Meta-analysis: natural history of non-alcoholic fatty liver disease (NAFLD) and diagnostic accuracy of non-invasive tests for liver disease severity. Ann Med. 2011;43(8):617–49. https://doi.org/10.3109/07853890.2010.518623.

    Article  PubMed  Google Scholar 

  61. Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6. https://doi.org/10.1186/1471-230X-6-33.

  62. Bedogni G, Kahn HS, Bellentani S, Tiribelli C. A simple index of lipid Overaccumulation is a good marker of liver steatosis. BMC Gastroenterol 2010; 10. https://doi.org/10.1186/1471-230X-10-98.

  63. Lee JH, Kim D, Kim HJ, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Digest Liver Dis. 2010;42(7):503–8. https://doi.org/10.1016/j.dld.2009.08.002.

    CAS  Article  Google Scholar 

  64. Fedchuk L, Nascimbeni F, Pais R, Charlotte F, Housset C, Ratziu V. Performance and limitations of steatosis biomarkers in patients with nonalcoholic fatty liver disease. Aliment Pharmacol Ther. 2014;40(10):1209–22. https://doi.org/10.1111/apt.12963.

    CAS  Article  PubMed  Google Scholar 

  65. Kondo T, Okabayashi K, Hasegawa H, Tsuruta M, Shigeta K, Kitagawa Y. The impact of hepatic fibrosis on the incidence of liver metastasis from colorectal Cancer. Brit J Cancer. 2016;115(1):34–9. https://doi.org/10.1038/bjc.2016.155.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. Chen ZF, Dong XL, Huang QK, Hong WD, Wu WZ, Wu JS, et al. The combined effect of non-alcoholic fatty liver disease and metabolic syndrome on colorectal carcinoma mortality: a retrospective in Chinese females. World J Surg Oncol. 2018;16(1):163. https://doi.org/10.1186/s12957-018-1461-z.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Natural Science Foundation of Shandong Province (ZR2019MH112).

Author information

Authors and Affiliations

Authors

Contributions

YZ designed the research, searched articles, extracted data, interpreted outcomes, and wrote the paper. RC searched articles, extracted data, and interpreted outcomes. ZT and YG contributed to data interpretation. All authors approved the final manuscript.

Corresponding author

Correspondence to Yanjing Gao.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zeng, Y., Cao, R., Tao, Z. et al. Association between the severity of metabolic dysfunction-associated fatty liver disease and the risk of colorectal neoplasm: a systematic review and meta-analysis. Lipids Health Dis 21, 52 (2022). https://doi.org/10.1186/s12944-022-01659-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-022-01659-1

Keywords

  • Metabolic dysfunction-associated fatty liver disease
  • Colorectal adenoma
  • Colorectal neoplasm
  • Severity
  • Meta-analysis