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      Gastrointestinal cancers in lean individuals with non‐alcoholic fatty liver disease: A systematic review and meta‐analysis

      1 , 2 , 3 , 4
      Liver International
      Wiley

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          Abstract

          Background & Aims

          Obesity and non‐alcoholic fatty liver disease (NAFLD) are known risk factors for gastrointestinal (GI) cancers. However, GI carcinogenesis in lean NAFLD patients remains unclear. This systematic review and meta‐analysis aims to investigate the association between lean NAFLD and GI cancer risk.

          Methods

          PubMed, Embase and Cochrane Library databases were systematically searched (from inception date to April 2023) for cohort studies assessing GI cancers in lean (body mass index [BMI] < 25 kg/m 2 or < 23 kg/m 2 in Asians) and non‐lean (BMI ≥25 kg/m 2 or ≥ 23 kg/m 2 in Asians) NAFLD individuals. Data from eligible studies were extracted, and meta‐analysis was carried out using a random effects model to obtain risk ratios (RRs) with 95% confidence intervals (CIs). Subgroup analyses, meta‐regressions and sensitivity analyses were also performed. This study was registered in PROSPERO (CRD42023420902).

          Results

          Eight studies with 56,745 NAFLD individuals (11% were lean) and 704 cases of incident GI cancers were included. Lean NAFLD was associated with higher risk of hepatic (RR 1.77, 95% CI 1.15–2.73), pancreatic (RR 1.97, 95% CI 1.01–3.86) and colorectal cancers (RR 1.53, 95% CI 1.12–2.09), compared to non‐lean NAFLD. No significant differences were observed for oesophagus, gastric, biliary and small intestine cancers.

          Conclusions

          This study shows that lean NAFLD patients have an increased risk of liver, pancreatic and colorectal cancers compared to non‐lean NAFLD patients, emphasizing the need to explore tailored cancer prevention strategies for this specific patient group. Further research is required to explore the mechanisms underlying the association between lean NAFLD and specific GI cancers.

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          Most cited references54

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Quantifying heterogeneity in a meta-analysis.

            The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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              Meta-analysis in clinical trials

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                Author and article information

                Contributors
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                Journal
                Liver International
                Liver International
                Wiley
                1478-3223
                1478-3231
                January 2024
                October 13 2023
                January 2024
                : 44
                : 1
                : 6-14
                Affiliations
                [1 ] Department of Internal Medicine Federal University of Rio de Janeiro Rio de Janeiro Brazil
                [2 ] Department of Internal Medicine SUNY Downstate Health Sciences University Brooklyn New York USA
                [3 ] Department of Experimental Medicine Sapienza University Rome Italy
                [4 ] Section of Endocrinology, Diabetes and Metabolism University and Azienda Ospedaliera Universitaria Integrata of Verona Verona Italy
                Article
                10.1111/liv.15763
                37833849
                8b0040c4-b954-4b29-8075-9b86db06de5f
                © 2024

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