| Literature DB >> 35008267 |
Shira Zelber-Sagi1,2, Mazen Noureddin3, Oren Shibolet2,4.
Abstract
The increasing burden of hepatocellular carcinoma (HCC) emphasizes the unmet need for primary prevention. Lifestyle measures appear to be important modifiable risk factors for HCC regardless of its etiology. Lifestyle patterns, as a whole and each component separately, are related to HCC risk. Dietary composition is important beyond obesity. Consumption of n-3 polyunsaturated fatty acids, as well as fish and poultry, are inversely associated with HCC, while red meat, saturated fat, and cholesterol are related to increased risk. Sugar consumption is associated with HCC risk, while fiber and vegetable intake is protective. Data from multiple studies clearly show a beneficial effect for physical activity in reducing the risk of HCC. However, the duration, mode and intensity of physical activity needed are yet to be determined. There is evidence that smoking can lead to liver fibrosis and liver cancer and has a synergistic effect with alcohol drinking. On the other hand, an excessive amount of alcohol by itself has been associated with increased risk of HCC directly (carcinogenic effect) or indirectly (liver fibrosis and cirrhosis progression. Large-scale intervention studies testing the effect of comprehensive lifestyle interventions on HCC prevention among diverse cohorts of liver disease patients are greatly warranted.Entities:
Keywords: alcohol; dietary composition; obesity; physical activity; smoking
Year: 2021 PMID: 35008267 PMCID: PMC8750465 DOI: 10.3390/cancers14010103
Source DB: PubMed Journal: Cancers (Basel) ISSN: 2072-6694 Impact factor: 6.639
Prospective cohort studies and meta-analyses of cohort studies testing the association between dietary factors and patterns and hepatocellular carcinoma.
| Author, Year of Publication (Ref) | Study Design | Study Population and Sample Size | Nutrient/Food Group | Adjusted HR/RR (CI) of Highest Category vs. Lowest Category | Nutrient/Food Intake |
|---|---|---|---|---|---|
| Liu Y., 2021 [ | Prospective cohort | Nurses’ Health Study ( | Plant based low-carbohydrate diet | 0.83 (0.70–0.98) | Per 1 standard deviation increase |
| Carbohydrates from refined grains | 1.18 (1.00–1.39) | Per 1 standard deviation increase | |||
| Plant fat | 0.78 (0.65–0.95) | Per 1 standard deviation increase | |||
| Shah SC., 2021 [ | Prospective cohort | The NIH-American Association of Retired Persons (NIH-AARP) Diet and Health Study ( | Magnesium (diet + supplements) | 0.65 (0.48–0.87) | 4th vs. 1st quartile |
| Luu HN., 2021 [ | Prospective cohort | Singapore Chinese Health | Alternative Health Eating Index-2010 (AHEI-2010) | 0.69 (0.53–0.89) | 4th vs. 1st quartile |
| Alternate | 0.70 (0.52–0.95) | 4th vs. 1st quartile | |||
| Dietary Approaches to Stop Hypertension (DASH) | 0.67 (0.51–0.87) | 4th vs. 1st quartile | |||
| Yang W., 2021 [ | Prospective cohort | Nurses’ Health Study ( | Empirical | 1.89 (1.25–2.87) | 3rd vs. 1st tertile |
| Empirical | 2.05 (1.34–3.14) | 3rd vs. 1st tertile | |||
| Empirical dietary inflammatory pattern (EDIP) | 2.03 (1.31–3.16) | 3rd vs. 1st tertile | |||
| Ji XW., 2021 [ | Prospective cohort | Chinese men ( | Total fat | 1.33 (1.01–1.75) | 4th vs. 1st quartile |
| Saturated fat | 1.50 (1.13–1.97) | 4th vs. 1st quartile | |||
| Monounsaturated fat | 1.26 (0.96–1.65) | 4th vs. 1st quartile | |||
| Polyunsaturated fat | 1.41 (1.07–1.86) | 4th vs. 1st quartile | |||
| Luo Y., 2020 [ | Prospective cohort | Patients with new HCC enrolled in the Guangdong Liver Cancer Cohort ( | Chinese Healthy Eating Index (CHEI-2016) | 0.74 (0.56–0.98) | 3rd vs. 1st tertile |
| Healthy Eating Index-2015 (HEI-2015) | 0.93 (0.71–1.21) | 3rd vs. 1st tertile | |||
| Zhong GC., 2020 [ | Prospective cohort | American adults from the prostate, lung, colorectal and ovarian | Dietary inflammatory index (DII) from food and supplements | 2.05 (1.23–3.41) | 3rd vs. 1st tertile |
| Dietary inflammatory index (DII) from food and supplements | 1.97 (1.13–3.41) | 3rd vs. 1st tertile | |||
| Dietary inflammatory index (DII) from food only | 2.57 (1.44–4.60) | 3rd vs. 1st tertile | |||
| Jayedi A., 2020 [ | Umbrella Review of Meta-Analyses of Prospective Cohort Studies (5 Meta-analyses) | Mixed populations | Fish | 0.65 (0.48–0.87) | per 100 gr/day |
| Zhong GC., 2020 [ | Prospective cohort | American adults from the prostate, lung, colorectal and ovarian | Magnesium (diet + supplements) | 0.44 (0.24–0.80) | 3rd vs. 1st tertile |
| Magnesium (diet + supplements) | 0.83 (0.67–1.01) | Per 100 mg/d | |||
| Dietary magnesium | 0.41 (0.22–0.76) | 3rd vs. 1st tertile | |||
| Dietary magnesium | 0.65 (0.51–0.82) | Per 100 mg/d | |||
| Magnesium (diet + supplements) | 0.37 (0.19–0.71) | 3rd vs. 1st tertile | |||
| Yang W., 2020 [ | Prospective cohort | Nurses’ Health Study ( | Vegetable fats | 0.61 (0.39–0.96) | 17.7 vs. 8.7 (% energy) |
| n-3 PUFA | 0.63 (0.41–0.96) | 0.8 vs. 0.5 (% energy) | |||
| n-6 PUFA | 0.54 (0.34–0.86) | 6.5 vs. 3.7 (% energy) | |||
| Yang W., 2020 [ | Prospective cohort | Nurses’ Health Study ( | High-fat dairy | 1.81 (1.19–2.76) | 2.0 vs. 0.4 serving/day |
| Low-fat dairy | 1.18 (0.78, 1.78) | 1.9 vs. 0.2 serving/day | |||
| Butter | 1.58 (1.06–2.36) | 0.7 vs. 0 serving/day | |||
| Yogurt | 0.72 (0.49–1.05) | 0.2 vs. 0 serving/day | |||
| Kim TL., 2020 [ | Umbrella Review of Meta-analyses of observational studies (2) | Mixed populations | Green tea | 0.87 (0.78–0.98) | High vs. low |
| Guo XF., 2019 [ | Meta-analysis (9 cohorts) | 1,326,176 participants | Vegetable | 0.96 (0.95–0.97) | Per 100 gr/d |
| Ma Y., 2019 [ | Prospective cohort | Nurses’ Health Study ( | Processed red meat | 1.84 (1.16–2.92) | 3rd vs. 1st tertile |
| Total white meat | 0.61 (0.40–0.91) | 3rd vs. 1st tertile | |||
| Unprocessed red meat | 1.06 (0.68–1.63) | 3rd vs. 1st tertile | |||
| Poultry | 0.60 (0.40–0.90) | 3rd vs. 1st tertile | |||
| Fish | 0.70 (0.47–1.05) | 3rd vs. 1st tertile | |||
| Ma Y., 2019 [ | Prospective cohort | Nurses’ Health Study ( | Alternative Healthy Eating Index-2010 (AHEI-2010) | 0.61 (0.39–0.95) | 3rd vs. 1st tertile |
| Tran KT., (2019) [ | Prospective cohort | UK Biobank population ( | Coffee | 0.50 (0.29–0.87) | Any consumption vs. none |
| Instant coffee | 0.51 (0.28–0.93) | Any consumption vs. none | |||
| Ground coffee | 0.47 (0.20–1.08) | Any consumption vs. none | |||
| Kennedy OJ., 2017 [ | Meta-analysis (18 cohorts) | Mixed populations, 2,272,642 participants | Coffee | 0.71 (0.65–0.77) | An extra two cups per day |
| 2 cohorts | Approximately 850,000 participants | Caffeinated coffee | 0.73 (0.63–0.85) | An extra two cups per day | |
| 3 cohorts | Approximately 750,000 participants | Decaffeinated coffee | 0.86 (0.74–1.00) | An extra two cups per day | |
| Gao M., 2015 [ | Meta-analysis (3 cohorts) | Mixed populations, 693,274 participants | Fish | 0.73 (0.56–0.90) | Highest vs. lowest consumption |
| Yang Y., 2014 [ | Meta-analysis (9 cohorts) | Mixed populations, 1,474,309 participants | Vegetables | 0.66 (0.51–0.86) | Highest vs. lowest consumption |
| Luo J., 2014 [ | Meta-analysis | Mixed populations, 2,677,514 participants | Red meat | 1.43 (1.08–1.90) | Highest vs. lowest consumption |
| White meat | 0.70 (0.57–0.86) | Highest vs. lowest consumption | |||
| Fish | 0.74 (0.61–0.91) | Highest vs. lowest consumption | |||
| Bravi F., 2013, [ | Meta-analysis (8 cohorts) | Mixed populations, 378,392 participants | Coffee | 0.64 (0.52–0.7) | No consumption vs. any consumption |
| Fedirko V., 2013 [ | Cohort | European Prospective Investigation into Cancer and | Total sugar | 1.43 (1.17–1.74) | Per 50 gr/day |
| Total dietary fiber | 0.70 (0.52–0.93) | Per 10 gr/day | |||
| Sawada N., 2012 [ | Prospective cohort | Population-based prospective cohort of Japanese subjects | Fish (rich in n-3 PUFA) | 0.64 (0.42–0.96) | 70.6 vs. 9.6 gr/day |
| EPA | 0.56 (0.36–0.85) | 0.74 vs. 0.14 g/day | |||
| DHA | 0.56 (0.35–0.87) | 1.19 vs. 0.28 g/day | |||
| Freedman ND., 2010 [ | Cohort | Men and women of the National Institutes of Health–AARP Diet and Health Study ( | White meat | 0.52 (0.36–0.77) | 65.8 vs. 9.7 g/1000 kcal |
| Red meat | 1.74 (1.16–2.61) | 64.8 vs. 10 g/1000 kcal | |||
| Ioannou GN., 2009 [ | Cohort | General US population from the first National Health and Nutrition Examination Survey ( | Cholesterol | 2.45 (1.3–4.7) | ≥511 vs. <156 mg/d |
Figure 1Pathways involved in alcohol-mediated liver carcinogenesis.
The incidence of HCC in alcoholic cirrhosis.
| Study | Number | Location | Length of Follow Up (Year) | HCC Cases (#) | Incidence |
|---|---|---|---|---|---|
| Torisu et al. [ | 47 | Japan | 6.8 | 9 | 2.1 |
| Kodama et al. [ | 85 | Japan | 3.0 | 6 | 2.5 |
| Mancebo et al. [ | 450 | Spain | 3.5 | 62 | 2.6 |
| N’kontchou et al. [ | 478 | France | 4.2 | 108 | 5.6 |
| Ganne-Carrie et al. [ | 652 | France/Belgium | 2.4 | 43 | 2.9 |
Figure 2Practical summary for prevention by lifestyle habits; behaviors related with increased risk or reduced risk for HCC incidence and outcomes. The evidence for primary prevention is driven from many prospective cohort studies and seems to be more evidence-based than the scarce evidence for tertiary prevention.