Lifestyle and Hepatocellular Carcinoma What Is the Evidence and Prevention Recommendations
Abstract
:Simple Summary
Abstract
1. Introduction
2. Role of Diet and Lifestyle in General in the Prevention of Hepatocellular Carcinoma
3. The Role of Obesity in HCC
4. The Role of Dietary Composition beyond BMI
4.1. Types of Dietary Fat, Meat, and Fish
4.2. Added (Free) Sugars
5. Role of Micronutrients, Fruits, and Vegetables in HCC
6. Coffee and Tea
7. Dietary Patterns
8. Role of Physical Activity in HCC
8.1. Physical Activity and General Cancer Risk
8.2. Physical Activity and HCC in Animal and Cellular Models
8.3. Physical Activity and HCC Incidence and Prevention in Humans
8.4. Physical Activity Following HCC Treatment in Humans
9. Role of Alcohol in HCC
9.1. Alcohol as a Carcinogen
9.2. The Amount of Alcohol as a Risk Factor
9.3. Gender, Obesity, and Type 2 Diabetes as Additional Synergistic Risk Factors with Alcohol
9.4. Viral Hepatitis and Alcohol-Associated HCC
9.5. Change of HCC Risk after Alcohol Cessation
10. Role of Smoking in HCC
11. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
References
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Author, Year of Publication (Ref) | Study Design Cohort Study/Meta-Analysis of Cohort Studies | Study Population and Sample Size | Nutrient/Food Group | Adjusted HR/RR (CI) of Highest Category vs. Lowest Category | Nutrient/Food Intake Categories Which Were Compared (Highest Category vs. Reference Category) |
---|---|---|---|---|---|
Liu Y., 2021 [5] | Prospective cohort | Nurses’ Health Study (n = 88,770 women). The Health Professionals Follow-up Study (n = 48,197 men) | 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 [6] | Prospective cohort | The NIH-American Association of Retired Persons (NIH-AARP) Diet and Health Study (n = 536,359) | Magnesium (diet + supplements) | 0.65 (0.48–0.87) | 4th vs. 1st quartile |
Luu HN., 2021 [7] | Prospective cohort | Singapore Chinese Health Study (n = 63,2570) | Alternative Health Eating Index-2010 (AHEI-2010) | 0.69 (0.53–0.89) | 4th vs. 1st quartile |
Alternate Mediterranean Diet (aMED) | 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 [8] | Prospective cohort | Nurses’ Health Study (n =70,055 women). Health Professionals Follow-up Study (n = 49,261 men) | Empirical lifestyle pattern score for hyperinsulinemia (ELIH) | 1.89 (1.25–2.87) | 3rd vs. 1st tertile |
Empirical lifestyle pattern score for insulin resistance (ELIR) | 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 [9] | Prospective cohort | Chinese men (n = 59 998) | 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 [10] | Prospective cohort | Patients with new HCC enrolled in the Guangdong Liver Cancer Cohort (n = 887) | Chinese Healthy Eating Index (CHEI-2016) | 0.74 (0.56–0.98) Outcome: HCC specific mortality | 3rd vs. 1st tertile |
Healthy Eating Index-2015 (HEI-2015) | 0.93 (0.71–1.21) Outcome: HCC specific mortality | 3rd vs. 1st tertile | |||
Zhong GC., 2020 [11] | Prospective cohort | American adults from the prostate, lung, colorectal and ovarian cancer screening trial (n = 103,902) | Dietary inflammatory index (DII) from food and supplements | 2.05 (1.23–3.41) Outcome: PLC incidence | 3rd vs. 1st tertile |
Dietary inflammatory index (DII) from food and supplements | 1.97 (1.13–3.41) Outcome: PLC mortality (n = 102) | 3rd vs. 1st tertile | |||
Dietary inflammatory index (DII) from food only | 2.57 (1.44–4.60) Outcome: PLC incidence | 3rd vs. 1st tertile | |||
Jayedi A., 2020 [12] | 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 [13] | Prospective cohort | American adults from the prostate, lung, colorectal and ovarian cancer screening trial (n = 104,025) | Magnesium (diet + supplements) | 0.44 (0.24–0.80) Outcome: PLC incidence | 3rd vs. 1st tertile |
Magnesium (diet + supplements) | 0.83 (0.67–1.01) Outcome: PLC incidence | Per 100 mg/d | |||
Dietary magnesium | 0.41 (0.22–0.76) Outcome: PLC incidence | 3rd vs. 1st tertile | |||
Dietary magnesium | 0.65 (0.51–0.82) Outcome: PLC incidence | Per 100 mg/d | |||
Magnesium (diet + supplements) | 0.37 (0.19–0.71) Outcome: PLC mortality | 3rd vs. 1st tertile | |||
Yang W., 2020 [14] | Prospective cohort | Nurses’ Health Study (n =88,657 women). Health Professionals Follow-up Study (n = 49,826 men) | 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 [15] | Prospective cohort | Nurses’ Health Study (n = 93,427 women). Health Professionals Follow-up Study (n = 51,418 men) | 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 [16] | 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 [17] | Meta-analysis (9 cohorts) | 1,326,176 participants | Vegetable | 0.96 (0.95–0.97) | Per 100 gr/d |
Ma Y., 2019 [18] | Prospective cohort | Nurses’ Health Study (n = 92,389 women). Health Professionals Follow-up Study (n = 50,468 men). | 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 [19] | Prospective cohort | Nurses’ Health Study (n = 121,700 women). Health Professionals Follow-up Study (n = 51,529 men) | Alternative Healthy Eating Index-2010 (AHEI-2010) | 0.61 (0.39–0.95) | 3rd vs. 1st tertile |
Tran KT., (2019) [20] | Prospective cohort | UK Biobank population (n = 471,779) | 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 [21] | 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 [22] | Meta-analysis (3 cohorts) | Mixed populations, 693,274 participants | Fish | 0.73 (0.56–0.90) | Highest vs. lowest consumption |
Yang Y., 2014 [23] | Meta-analysis (9 cohorts) | Mixed populations, 1,474,309 participants | Vegetables | 0.66 (0.51–0.86) | Highest vs. lowest consumption |
Luo J., 2014 [24] | Meta-analysis (7 cohorts) | 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, [25] | Meta-analysis (8 cohorts) | Mixed populations, 378,392 participants | Coffee | 0.64 (0.52–0.7) | No consumption vs. any consumption |
Fedirko V., 2013 [26] | Cohort | European Prospective Investigation into Cancer and Nutrition cohort (n = 477,206) | 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 [27] | Prospective cohort | Population-based prospective cohort of Japanese subjects (n = 90,296) | 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 [28] | Cohort | Men and women of the National Institutes of Health–AARP Diet and Health Study (n = 495,006) | 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 [29] | Cohort | General US population from the first National Health and Nutrition Examination Survey (n = 9221) | Cholesterol | 2.45 (1.3–4.7) | ≥511 vs. <156 mg/d |
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Zelber-Sagi, S.; Noureddin, M.; Shibolet, O. Lifestyle and Hepatocellular Carcinoma What Is the Evidence and Prevention Recommendations. Cancers 2022, 14, 103. https://doi.org/10.3390/cancers14010103
Zelber-Sagi S, Noureddin M, Shibolet O. Lifestyle and Hepatocellular Carcinoma What Is the Evidence and Prevention Recommendations. Cancers. 2022; 14(1):103. https://doi.org/10.3390/cancers14010103
Chicago/Turabian StyleZelber-Sagi, Shira, Mazen Noureddin, and Oren Shibolet. 2022. "Lifestyle and Hepatocellular Carcinoma What Is the Evidence and Prevention Recommendations" Cancers 14, no. 1: 103. https://doi.org/10.3390/cancers14010103