Dietary pattern analysis is an alternative approach to examine the association between diet and nonalcoholic fatty liver disease (NAFLD). This study examined the association of two diet-quality scores, namely Diet Quality Index-International (DQI-I) and Mediterranean Diet Score (MDS) with NAFLD prevalence. Apparently healthy Chinese adults (332 male, 465 female) aged 18 years or above were recruited through a population screening between 2008 and 2010 in a cross-sectional population-based study in Hong Kong. DQI-I and MDS, as well as major food group and nutrient intakes were calculated based on dietary data from a food frequency questionnaire. NAFLD was defined as intrahepatic triglyceride content at ≥5% by proton-magnetic resonance spectroscopy. Multivariate logistic regression models were used to examine the association between each diet-quality score or dietary component and prevalent NAFLD with adjustment for potential lifestyle, metabolic and genetic factors. A total of 220 subjects (27.6%) were diagnosed with NAFLD. DQI-I but not MDS was associated with the prevalence of NAFLD. A 10-unit decrease in DQI-I was associated with 24% increase in the likelihood of having NAFLD in the age and sex adjusted model (95% CI: 1.06–1.45, p = 0.009), and the association remained significant when the model was further adjusted for other lifestyle factors, metabolic and genetic factors [OR: 1.26 (95% CI: 1.03–1.54), p = 0.027]. Multivariate regression analyses showed an inverse association of the intake of vegetables and legumes, fruits and dried fruits, as well as vitamin C with the NAFLD prevalence (p<0.05). In conclusion, a better diet quality as characterized by a higher DQI-I and a higher consumption of vegetables, legumes and fruits was associated with a reduced likelihood of having NAFLD in Hong Kong Chinese.
Citation: Chan R, Wong VW-S, Chu WC-W, Wong GL-H, Li LS, Leung J, et al. (2015) Diet-Quality Scores and Prevalence of Nonalcoholic Fatty Liver Disease: A Population Study Using Proton-Magnetic Resonance Spectroscopy. PLoS ONE 10(9): e0139310. https://doi.org/10.1371/journal.pone.0139310
Editor: Susanne Kaser, Medical University Innsbruck, AUSTRIA
Received: June 17, 2015; Accepted: September 12, 2015; Published: September 29, 2015
Copyright: © 2015 Chan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper.
Funding: The study was supported by funding from the Health and Health Services Research Fund sponsored by the Government of Hong Kong SAR (Reference number 07080081) and the Centre for Nutritional Studies, under the auspices of The Chinese University of Hong Kong. The Centre for Nutritional Studies did have a role in the data collection and analysis.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; DM, diabetes mellitus; DQI-I, Diet Quality Index-International; FFQ, food frequency questionnaire; 1H-MRS, proton-magnetic resonance spectroscopy; HC, hip circumference; IHTG, intrahepatic triglyceride content; MDS, Mediterranean Diet Score; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; SBP, systolic blood pressure; WC, waist circumference
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of abnormal liver biochemistry worldwide. Its prevalence is increasing in Asia [1–3]. The worldwide prevalence of NAFLD in general population is estimated to be 20 to 30% in Western countries and 5 to 18% in Asia . NAFLD may progress to a wide range of liver abnormalities, ranging from nonalcoholic steatohepatitis (NASH), cirrhosis, liver failure and liver cancer. It is also considered as the leading etiology for cryptogenic cirrhosis [4,5]. Substantial evidence supports that NAFLD, obesity and metabolic syndrome are strongly correlated, and NAFLD is an independent cardiovascular risk factor [6,7]. Therefore, determining risk factors for hepatic steatosis are important.
Diet may affect the development of NAFLD, and dietary alterations have been suggested as a cornerstone for the management of NAFLD . However, epidemiological data on the association between diet or nutrient composition and the development of NAFLD are limited. Previous studies on this aspect were mainly conducted in Caucasian populations. Most previous studies were limited by small sample size [9,10] and focused on high-risk population or NAFLD patients [11–14]. Only few studies have included participants from a representative general population [15–17]. Moreover, the examination of diet as a risk factor for NAFLD has traditionally focused on the effect of single foods and nutrients, such as soft drinks and meat , fructose , choline  and n-3 polyunsaturated fatty acids . Recently, a possible link between dietary acid-base load and NAFLD has also been suggested [20,21]. However, since diet is a combination of multiple exposures of food and nutrients, dietary pattern analysis is considered to be an alternative approach to investigate the association between diet and disease risk in epidemiological studies . This approach involves the examination of the whole diet and considers the synergy of food and nutrient consumption, thus it may be more predictive of disease risk in comparison to individual foods or nutrients [22,23].
To our knowledge, only few observational studies [24–29] have been conducted to examine the association between dietary patterns and NAFLD in adults and one prospective study has been done in a population-based cohort of adolescents . In view of the scarcity of evidence on this topic and the fact that Chinese diets are different from Western diets, the present study aimed to examine the association of two diet-quality scores, namely the Mediterranean Diet Score (MDS) and the Diet Quality Index-International (DQI-I) with NAFLD prevalence in 797 Chinese aged 18 years or above, using data from a population-based study in Hong Kong. The MDS was selected because the Mediterranean diet has been widely reported for its many health benefits including its potential protective effect against NAFLD [31,32]. The traditional Chinese diet also shares several similarities with the Mediterranean diet, such that the MDS can be applied to Chinese population . The DQI-I was chosen as it provides an effective means of cross-national comparative work for global understanding of diet quality, and has been used in epidemiological studies to quantify the diet quality in Chinese population [34,35]. We hypothesized that higher diet-quality score was associated with a reduced likelihood of NAFLD.
The present analysis was different from the findings regarding the possible link between dietary acid-base load and NAFLD of the same study population  in regard to the approach of analyzing dietary data. The present analysis used the dietary pattern approach in which the synergy of food and nutrient consumption was considered, while the previous analysis considered the association of a single nutritional factor, namely dietary acidity with the likelihood of NAFLD. Otherwise, both analyses followed the same study protocol (S1 and S2 Files).
Materials and Methods
The study population were 797 subjects who aged 18 years or above and participated in a cross-sectional population screening study for NAFLD. Details of the screening project have been reported previously [36,37] (S1 File) and can be found in the study protocol (S2 File). Those with active malignancy, metallic implants or other contraindications to magnetic resonance imaging, positive hepatitis B surface antigen or antibody against hepatitis C virus, treatment with steatogenic drugs (e.g. corticosteroids and estrogens), secondary causes of fatty liver (e.g. consumption of amiodarone and tamoxifen) and decompensated liver disease (defined as bilirubin above 50 μmol/l, albumin below 35 g/l, platelet count below 150 × 109/l, international normalized ratio above 1.3, or the presence of ascites or varices) (n = 147) were excluded [36,37]. The present study also excluded those with excessive alcohol use (defined as more than 20 g/d day in men and more than 10 g/d in women) (n = 30) and those with incomplete dietary data (n = 95) from the final analysis. The study protocol conformed to the ethical guidelines of the Declaration of Helsinki, and was approved by the Clinical Research Ethics Committee of The Chinese University of Hong Kong. All subjects provided informed written consent.
At the baseline clinic visit, information of drug history, alcohol intake, smoking and past medical history were collected using a standardized questionnaire. Anthropometric measurements including body weight, body height, waist and hip circumferences were obtained using standardized methods. Blood tests for liver biochemistry, glucose and lipids were taken after at least 8 hours of fasting. Metabolic syndrome was defined according to the ethnic-specific criteria by the International Diabetes Federation, which was modified from the National Cholesterol Education Program, Adult Treatment Panel III Guidelines . Details of the clinical assessment have been described previously .
Proton-magnetic resonance spectroscopy (1H-MRS)
1H-MRS was performed to measure intrahepatic triglyceride content (IHTG) within 8 weeks from the baseline visit. Details of the scanning sequence and analysis have been described previously [36,39]. Whole-body 3.0T scanner with a single voxel point-resolved spectroscopy sequence and an echo time of 40 ms and repetition time of 5,000 ms was used. Fatty liver was defined as an IHTG of 5% or more .
Genomic DNA was extracted from 100 μl buffy coat using QIAamp Blood DNA Mini Kit (Qiagen, Germany) and QIAcube System (Qiagen, Germany). Extracted DNA was quantified using Nanodrop 1000 (Thermo Fisher Scientific, USA). For each subject, 20 ng of genomic DNA was used for PNPLA3 rs738409 allelic discrimination using TaqMan® SNP Genotyping Assays (Life Technologies, USA) on the Applied Biosystems 7900HT Fast Real-Time PCR System (Life Technologies, USA). Details have been described elsewhere .
A locally validated food-frequency questionnaire (FFQ) was used to capture food intake and dietary intake over 7 days . The questionnaire contained 297 food items in seven broad categories: bread / pasta / rice; vegetables; fruit; meat / fish / egg; beverages; dimsum / snacks; soups; and oil / salt / sauces. Subjects were asked to complete the questionnaires under the supervision of a trained research staff, with food models, food containers, and a catalogue of pictures of individual food portions provided to facilitate portion size estimation. The amount of cooking oil was calculated based on the usual cooking methods, the usual type of cooking oil and the usual portion of different foods used by the subjects . Daily nutrient intakes and food group intakes were estimated using Food Processor Nutrition analysis and Fitness software version 8.0 (ESHA Research, Salem, Oregon, USA) with the addition of local and Chinese food items [44–46]. All nutrient intakes were energy adjusted by the residual method for regression analysis . Details of the dietary assessment have been described previously .
The Mediterranean Diet Score (MDS)
Adherence to the Mediterranean diet was calculated using the revised method described by Trichopoulou et al. . One score was assigned to consumption of the food groups considered beneficial to health at or above the sex-specific median (vegetables, legumes, fruits and nuts, cereal, fish and monosaturated to saturated lipids ratio). Similarly, one score was also assigned to consumption of the food groups presumed to be detrimental to health (meat, poultry and dairy products) below the median. Because a substantial proportion of subjects did not consume alcohol, the component of ethanol consumption was excluded in the scoring. Therefore, total MDS ranged from 0 (minimal adherence) to 8 (maximal adherence) instead of 0 to 9.
Dietary Quality Index-International (DQI-I)
The Dietary Quality index-International (DQI-I) was calculated according to the method described by Kim et al. . Four major aspects of the diet are assessed in the DQI-I, namely variety, adequacy, moderation and overall balance. The score ranges from 0 to 100 and higher score represents better diet quality. In this study, we did not have enough information to calculate the category of empty-calorie foods under the aspect ‘moderation’. Therefore the range of score for moderation was 0 to 24 instead of 0 to 30 as originally proposed in the calculation, and the DQI-I total score was 0 to 94 instead of 0 to 100. Details of the DQI-I calculation have been described elsewhere .
Statistical tests were performed using the Statistical Package for Social Sciences version 21.0 (SPSS Inc., Chicago, US). Histograms were used to screen for normal distribution. Logarithmic transformation was applied to skewed variables whenever appropriate. Continuous variables were expressed in mean ± SD and compared using the independent t test if they were normally distributed. Skewed variables were expressed in median (IQR) and compared using the Mann-Whitney U test. Categorical variables were compared using chi-squared test or Fisher exact test as appropriate. Hardy–Weinberg equilibrium of alleles was assessed by chi-squared test. Pearson’s correlation or Spearman’s rank correlation was used to examine the correlation between each diet-quality score and food group or nutrient intakes whenever appropriate.
The association between each diet-quality score and IHTG and the presence of NAFLD was analyzed using multivariate linear regression and logistic regression models, respectively. The first model was adjusted for sex and age (continuous). The second model was further adjusted for BMI (continuous), daily energy intake (continuous), current smoker status (yes/no), current drinker status (yes/no), the five individual metabolic components including central obesity (yes/no), triglyceride >1.7 mmol/l (yes/no), reduced HDLC (yes/no), hypertension (yes/no), and impaired fasting glucose or diabetes (yes/no), and the PNPLA3 genotypes. We also examined whether the association between each diet-quality score and the presence of NAFLD varied according to sex, BMI, age, current drinker status, the presence of metabolic syndrome, or the PNPLA3 genotypes. All models were additionally stratified by sex (men and women), age (<50 vs. > = 50 years), BMI (normal weight group <23 vs. overweight and obese group > = 23 kg/m2), current drinker status (yes vs. no), the presence of metabolic syndrome (yes vs. no), and the PNPLA3 genotypes (CC vs. CG vs. GG genotypes). We also investigated potential effect modifications by sex, age, BMI, current drinker status, the presence of metabolic syndrome and the PNPLA3 genotypes by inclusion of cross-product terms to the regression models. No significant interactions were detected for all these stratified variables, thus data were presented based on the results of the whole sample. The above multivariate logistic regression models were repeated to explore the association between each energy adjusted nutrient intake variable or food group intake variable and the prevalence of NAFLD. Due to the skewness in the distribution for most food group intake variables, the food group intake variables were categorized into tertiles based on the distribution of the entire sample. Odds ratios (OR) were computed to compare the middle and upper tertile groups with the lower tertile group. Test for trend was examined by entering tertiles of food group intake as a fixed factor and testing the contrast by using the polynomial option in all models. All tests were 2-sided and P values less than 0.05 were considered statistically significant.
There was no major difference in the baseline characteristics between the included subjects and the excluded subjects (data not shown). The mean ± SD age of 797 subjects was 48.1 ± 10.6 years (range 19–72 years), and 332 (41.7%) were male. All subjects were ethnic Chinese. Two hundred and twenty subjects were diagnosed with NAFLD and the prevalence of NAFLD was 27.6%. Subjects with NAFLD were older, and were likely to be male and current drinkers than subjects without NAFLD (all p<0.05). They also had significantly higher BMI and higher WC than those without NAFLD. The metabolic profiles differed significantly between the two groups. Subjects with NAFLD showed lower age and sex adjusted MDS and DQI-I than subjects without NAFLD (Table 1). There were 311 (39.0%) CC homozygotes, 380 (47.7%) CG heterozygotes and 105 (13.2%) GG homozygotes. The alleles of PNPLA3 rs738409 polymorphism of the total study sample or of the stratified sample by NAFLD status were in Hardy-Weinberg equilibrium (p = 0.511 for total sample, p = 0.533 for non-NAFLD group, p = 0.414 for NAFLD group).
Higher MDS and DQI-I was associated with lower intakes of beverages, dim sum, egg and egg products, fast food, meat, poultry and organ meat. Both scores were positively associated with the intakes of fish and seafood, grains and cereals, and plant-based foods, such as fruits, soy and soy products, and vegetables and legumes (Table 2). MDS and DQI-I were positively associated with percentage of energy from carbohydrate and intakes of dietary fiber and vitamin C, and negatively associated with percentage of energy from total fat and saturated fat, and cholesterol intake (all p<0.05, Table 2).
The multivariate linear regression models suggested that a higher MDS or DQI-I was associated with a lower IHTG (Table 3). Using an IHTG of 5% or more as a cut-off value to define NAFLD, the DQI-I was associated with the prevalence of NAFLD (Table 4). A 10-unit decrease in DQI-I was associated with 24% increase in the likelihood of having NAFLD in the age and sex adjusted model (95% CI: 1.06–1.45, p = 0.009), and the association remained significant when the model was further adjusted for other lifestyle factors, metabolic components and the PNPLA3 genotypes [OR: 1.26 (95% CI: 1.03–1.54), p = 0.027]. The multivariate adjusted association of DQI-I and the prevalence of NAFLD was stronger in male than in female, and in overweight or obese subjects than in normal weight subjects. Similar results were observed between the MDS and the prevalence of NAFLD, although the strength of the associations were not significant in comparison to those of the DQI-I with the NAFLD prevalence (Table 4). Since significant correlations were observed between both diet-quality scores and most food groups or nutrients, the association of each major food group or nutrient with the likelihood of having NAFLD was further examined as to get a more concrete picture of the optimal diet in reducing NAFLD. Among all selected food groups and nutrients examined in the multivariate adjusted models, higher intake of vegetables and legumes, fruits and dried fruits, as well as vitamin C was consistently associated with reduced likelihood of having NAFLD (Table 5).
In this cross-sectional, population based study, lower DQI-I was associated with increased likelihood of having NAFLD in Hong Kong Chinese adults. The association was stronger in male participants compared to female participants, and in individuals who were overweight or obese compared to those were normal weight. Our study also suggested that a higher intake of vegetables and legumes, fruits and dried fruits, as well as vitamin C was associated with a reduced likelihood of having NAFLD.
To our knowledge, only few observational studies [24–29] have been conducted to examine the association between dietary patterns and NAFLD in adults and one prospective study has been done in a population-based cohort of adolescents . While Hasehemi kani et al. reported that higher scores in four different diet-quality indices might protect against NAFLD in a group of Iranian adults attending a gastrointestinal research clinic , Kongtogianni et al. found that greater adherence to the Mediterranean diet as measured using the MDS was associated with less severity of fatty liver disease but was not associated with the likelihood of having NAFLD . A recent cross-sectional, population based study, however, showed that a dietary pattern, characterized by increased intake of alcohol and meat (poultry), and reduced consumption of tea, was associated with higher liver fat content in adults . Only one observational study has previously investigated exploratory dietary patterns and NAFLD risk, which was limited to adolescents . A Western dietary pattern characterized by a high intake of takeaway foods, confectionary, red meat, refined grains, processed meats, chips, sauces, full-fat dairy products, and soft drinks at 14 years of age was prospectively associated with NAFLD at 17 years. Different study design, choices and assessment methods of outcome measures, covariates and confounding factors included in the statistical analysis, as well as methods to generate dietary quality indices or dietary pattern scores in various studies might lead to the mixed findings.
Previous studies have related DQI-I or MDS with cardiovascular risk, obesity and metabolic outcomes, and generally support that better diet quality is associated with better cardiovascular or metabolic outcomes [50,51]. To our knowledge, our study is the first study to report a positive association between DQI-I and NAFLD prevalence. Our findings also suggested that subjects consuming vegetables and legumes, as well as fruits and dried fruits of approximately ≥ 200 g/day each were likely to have 50% reduction in the likelihood of having NAFLD in comparison to subjects in the lowest tertile of consumption of these food groups. These intake levels are in line with the recommended intake of at least 400 g/day of vegetables and fruits by the World Health Organization for the prevention of diet-related chronic diseases . Contrary to our findings that no association was observed between MDS and NAFLD prevalence, several observational studies and clinical trials have preliminarily suggested that higher adherence to the Mediterranean diet might protect against NAFLD [31,32]. Although the Chinese diet has many similar features with the Mediterranean diet, in that vegetable and fruit consumption is high, and fat and meat consumption is low, the consumption of legumes, milk and milk products, nuts, olive oil and wine was less in our cohort than the traditional Mediterranean diet. These differences may be one of the reasons to explain the absence of association between MDS and NAFLD prevalence. Moreover, the various scores differ in many aspects of both indices, such as the items included, the cut-off values used, and the exact method of scoring may be some reasons to explain the different results of DQI-I and MDS in the present study. For example, the scores of various components of the DQI-I are based on recommended reference intake that are beneficial for health, whereas the cut-off value of each component in the MDS is based on the group median intake of each component. The latter scoring system may lead to bias because the MDS calculation is based on cohort- and sex-specific median values across eight food categories of the studied sample, and it may not be related to a healthy level of intake per se .
Our data showed that the association between both diet-quality scores and the prevalence of NAFLD was in general stronger in male than in female, and in overweight or obese subjects than in normal weight subjects. These observations were different from those reported by Koch et al., in which no significant interaction with age, sex, BMI, or type 2 diabetes status was detected between dietary pattern scores and liver fat measured using magnetic resonance imaging as liver signal intensity . The gender- and BMI-specific differences in the association between diet-quality scores and the prevalence of NAFLD in our study could be largely explained by the differences in metabolic factors. Our data also suggested that the association between both diet-quality scores and the prevalence of NAFLD was not affected by the PNPLA3 genotypes. Previous studies focusing on the interaction between the PNPLA3 genotypes and single nutrient intake, such as dietary sugar and essential omega polyunsaturated fatty acids showed that the association of these nutrients with NAFLD might be driven by a predisposing GG genotype [54,55].
The strengths of our study include relatively large sample size, inclusion of subjects from the general population, and the use of 1H-MRS to assess hepatic steatosis. However, our study has several limitations. Our study was of cross-sectional in nature, thus it was not possible to examine the causal relationship between diet-quality scores and the likelihood of having NAFLD. Moreover, the FFQ captured only the short term dietary and food intakes of the subjects. Examining the relationship between diet and risk of chronic diseases using longer-term dietary and food intake data are more useful. Besides, although various common factors and major medical conditions have been adjusted in the analysis, residual potential confounding from other lifestyle factors related to the development of NAFLD, such as physical activity level might still exist ,
A better diet quality as characterized by a higher DQI-I and a higher consumption of vegetables, legumes and fruits was associated with a reduced likelihood of having NAFLD in Chinese adults in Hong Kong.
We would like to thank the following students and research assistants for helping with data collection: Andrew Hayward, Catherine Hayward, Mandy Law, Mia Li, April Wong, Arlinking Ong, Karen Yiu, Shirley Chu, Kris Yuet-Wan Lok, Gwen Lam, Bernice Cheung and Grace Leung. The study was supported by funding from the Health and Health Services Research Fund sponsored by the Government of Hong Kong SAR (Reference number 07080081) and the Centre for Nutritional Studies, under the auspices of The Chinese University of Hong Kong.
Conceived and designed the experiments: RC VWW. Performed the experiments: RC VWW WCC GLW LSL AMC. Analyzed the data: RC VWW JL JW. Wrote the paper: RC VWW JW. Administrative support: DKY MMS FKC HLC.
- 1. Amarapurkar DN, Hashimoto E, Lesmana LA, Sollano JD, Chen PJ, Goh KL. How common is non-alcoholic fatty liver disease in the Asia-Pacific region and are there local differences? J Gastroenterol Hepatol. 2007;22:788–793. pmid:17565631
- 2. Chitturi S, Wong VW, Farrell G. Nonalcoholic fatty liver in Asia: Firmly entrenched and rapidly gaining ground. J Gastroenterol Hepatol. 2011;26:S163–S172.
- 3. Masarone M, Federico A, Abenavoli L, Loguercio C, Persico M. Non alcoholic fatty liver: epidemiology and natural history. Rev Recent Clin Trials. 2014;9:126–133. pmid:25514916
- 4. Caldwell SH, Oelsner DH, Iezzoni JC, Hespenheide EE, Battle EH, Driscoll CJ. Cryptogenic cirrhosis: clinical characterization and risk factors for underlying disease. Hepatology 1999;29:664–669. pmid:10051466
- 5. Farrell GC, Larter CZ. Nonalcoholic fatty liver disease: from steatosis to cirrhosis. Hepatology 2006;43:S99–S112. pmid:16447287
- 6. Hamaguchi M, Kojima T, Takeda N, Nagata C, Takeda J, Sarui H, et al. Nonalcoholic fatty liver disease is a novel predictor of cardiovascular disease. World J Gastroenterol. 2007;13:1579–1584. pmid:17461452
- 7. Wong VW, Wong GL, Yip GW, Lo AO, Limquiaco J, Chu WC, et al. Coronary artery disease and cardiovascular outcomes in patients with non-alcoholic fatty liver disease. Gut 2011;60:1721–1727. pmid:21602530
- 8. Wong VW, Chan RS, Wong GL, Cheung BH, Chu WC, Yeung DK, et al. Community-based lifestyle modification programme for non-alcoholic fatty liver disease: A randomized controlled trial. J Hepatol. 2013;59:536–542. pmid:23623998
- 9. Musso G, Gambino R, De Michieli F, Cassader M, Rizzetto M, Durazzo M, et al. Dietary habits and their relations to insulin resistance and postprandial lipemia in nonalcoholic steatohepatitis. Hepatology 2003;37:909–916. pmid:12668986
- 10. Sathiaraj E, Chutke M, Reddy M, Pratap N, Rao P, Reddy D, et al. A case-control study on nutritional risk factors in non-alcoholic fatty liver disease in Indian population. Eur J Clin Nutr. 2011;65:533–537. pmid:21346716
- 11. Solga S, Alkhuraishe A, Clark J, Torbenson M, Greenwald A, Diehl A, et al. Dietary Composition and Nonalcoholic Fatty Liver Disease. Dig Dis Sci. 2004;49:1578–1583. pmid:15573908
- 12. Allard JP, Aghdassi E, Mohammed S, Raman M, Avand G, Arendt BM, et al. Nutritional assessment and hepatic fatty acid composition in non-alcoholic fatty liver disease (NAFLD): A cross-sectional study. J Hepatol. 2008;48:300–307. pmid:18086506
- 13. Kim C, Kallman J, Bai C, Pawloski L, Gewa C, Arsalla A, et al. Nutritional Assessments of Patients with Non-alcoholic Fatty Liver Disease. Obes Surg. 2010;20:154–160. pmid:18560947
- 14. Ricci G, Canducci E, Pasini V, Rossi A, Bersani G, Ricci E, et al. Nutrient intake in Italian obese patients: Relationships with insulin resistance and markers of non-alcoholic fatty liver disease. Nutrition 2011;27:672–676. pmid:20961734
- 15. Cortez-Pinto H, Jesus L, Barros H, Lopes C, Moura MC, Camilo ME. How different is the dietary pattern in non-alcoholic steatohepatitis patients? Clin Nutr. 2006;25:816–823. pmid:16677739
- 16. Zelber-Sagi S, Nitzan-Kaluski D, Goldsmith R, Webb M, Blendis L, Halpern Z, et al. Long term nutritional intake and the risk for non-alcoholic fatty liver disease (NAFLD): A population based study. J Hepatol. 2007;47:711–717. pmid:17850914
- 17. Oya J, Nakagami T, Sasaki S, Jimba S, Murakami K, Kasahara T, et al. Intake of n-3 polyunsaturated fatty acids and non-alcoholic fatty liver disease: a cross-sectional study in Japanese men and women. Eur J Clin Nutr. 2010;64:1179–1185. pmid:20683463
- 18. Abdelmalek MF, Suzuki A, Guy C, Unalp-Arida A, Colvin R, Johnson RJ, et al. Increased fructose consumption is associated with fibrosis severity in patients with nonalcoholic fatty liver disease. Hepatology 2010;51:1961–1971. pmid:20301112
- 19. Guerrerio AL, Colvin RM, Schwartz AK, Molleston JP, Murray KF, Diehl A, et al. Choline intake in a large cohort of patients with nonalcoholic fatty liver disease. Am J Clin Nutr. 2012;95:892–900. pmid:22338037
- 20. Chan R, Wong VWS, Chu WCW, Wong GLH, Li LS, et al. Higher estimated net endogenous acid production may be associated with increased prevalence of nonalcoholic fatty liver disease in Chinese adults in Hong Kong. PLoS ONE 2015;10:e0122406. pmid:25905490
- 21. Krupp D, Johner SA, Kalhoff H, Buyken AE, Remer T. Long-term dietary potential renal acid load during adolescence is prospectively associated with indices of nonalcoholic fatty liver disease in young women. J Nutr 2012;142:313–319. pmid:22223573
- 22. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3–9. pmid:11790957
- 23. Slattery ML. Defining dietary consumption: is the sum greater than its parts? Am J Clin Nutr. 2008;88:14–15. pmid:18614718
- 24. Hashemi kani A, Moayed Alavian S, Esmaillzadeh A, Adibi P, Azadbakht L. Dietary Quality Indices and Biochemical Parameters Among Patients With Non Alcoholic Fatty Liver Disease (NAFLD). Hepat Mon. 2013;13:e10943. pmid:24065998
- 25. Kontogianni MD, Tileli N, Margariti A, Georgoulis M, Deutsch M, Tiniakos D, et al. Adherence to the Mediterranean diet is associated with the severity of non-alcoholic fatty liver disease. Clin Nutr. 2014;33:678–683. pmid:24064253
- 26. Ferolla SM, Ferrari TC, Lima ML, Reis TO, Tavares WC Jr, et al. Dietary patterns in Brazilian patients with non-alcoholic fatty liver disease: a cross-sectional study. Clinics 2013;68:11–17. pmid:23420151
- 27. Chang JH, Lee HS, Kang EH. A study on dietary habits, nutrient intakes and dietary quality in adults of a health screening and promotion center according to non-alcoholic fatty liver disease. J Nutr Health. 2014;47:330–341.
- 28. Jia Q, Xia Y, Zhang Q, Wu H, Du H, et al. Dietary patterns are associated with prevalence of fatty liver disease in adults. Eur J Clin Nutr.
- 29. Koch M, Borggrefe J, Barbaresko J, Groth G, Jacobs G, Siegert S, et al. Dietary patterns associated with magnetic resonance imaging-determined liver fat content in a general population study. Am J Clin Nutr. 2014;99:369–377. pmid:24305680
- 30. Oddy WH, Herbison CE, Jacoby P, Ambrosini GL, O'Sullivan TA, Ayonrinde OT, et al. The Western dietary pattern is prospectively associated with nonalcoholic fatty liver disease in adolescence. Am J Gastroenterol. 2013;108:778–785. pmid:23545714
- 31. Abenavoli L, Milic N, Peta V, Alfieri F, De Lorenzo A, Bellentani S. Alimentary regimen in non-alcoholic fatty liver disease: Mediterranean diet. World J Gastroenterol. 2014;20:16831–16840. pmid:25492997
- 32. Sofi F, Casini A. Mediterranean diet and non-alcoholic fatty liver disease: new therapeutic option around the corner? World J Gastroenterol. 2014;20:7339–7346. pmid:24966604
- 33. Woo J, Woo KS, Leung SS, Chook P, Liu B, Ip R, et al. The Mediterranean score of dietary habits in Chinese populations in four different geographical areas. Eur J Clin Nutr. 2001;55:215–220. pmid:11305271
- 34. Kim S, Haines PS, Siega-Riz AM, Popkin BM. The Diet Quality Index-International (DQI-I) provides an effective tool for cross-national comparison of diet quality as illustrated by China and the United States. J Nutr. 2003;133:3476–3484. pmid:14608061
- 35. Yu R, Woo J, Chan R, Sham A, Ho S, Tso A, et al. Relationship between dietary intake and the development of type 2 diabetes in a Chinese population: the Hong Kong Dietary Survey. Public Health Nutr. 2011;14:1133–1141. pmid:21466742
- 36. Wong VWS, Chu WCW, Wong GLH, Chan RSM, Chim AML, Ong A, et al. Prevalence of non-alcoholic fatty liver disease and advanced fibrosis in Hong Kong Chinese: a population study using proton-magnetic resonance spectroscopy and transient elastography. Gut 2012;61:409–415. pmid:21846782
- 37. Wong VW, Wong GL, Chu WC, Chim AM, Ong A, Yeung DK, et al. Hepatitis B virus infection and fatty liver in the general population. J Hepatol. 2012;56:533–540. pmid:22027575
- 38. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640–1645. pmid:19805654
- 39. Wong VW, Wong GL, Tsang SW, Fan T, Chu WC, Woo J, et al. High prevalence of colorectal neoplasm in patients with non-alcoholic steatohepatitis. Gut 2011;60:829–36. pmid:21339204
- 40. Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology 2004;40:1387–1395. pmid:15565570
- 41. Shen J, Wong GL, Chan HL, Chan HY, Yeung DK, et al. PNPLA3 gene polymorphism accounts for fatty liver in community subjects without metabolic syndrome. Aliment Pharmacol Ther. 2014;39:532–539. pmid:24417250
- 42. Woo J, Leung SSF, Ho SC, Lam TH, Janus ED. A food frequency questionnaire for use in the Chinese population in Hong Kong: Description and examination of validity. Nutr Res. 1997;17:1633–1641.
- 43. Leung SSF, Woo J, Ho S, Lam TH, Janus ED. Hong Kong Adult Dietary Survey, 1995. Aust J Nutr Diet. 1998;55:S11–S13.
- 44. Yang Y, Wang G, Pan X. China Food Composition 2002. University Medical Press: Peking, 2002.
- 45. Yang Y, Wang G, Pan X. China Food Composition 2004. University Medical Press: Peking, 2004.
- 46. Centre for Food Safety. Nutrient Information Inquiry. Centre for Food Safety: Hong Kong SAR, 2006.
- 47. Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:1220S–1228S. pmid:9094926
- 48. Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med. 2003;348:2599–2608. pmid:12826634
- 49. Woo J, Cheung B, Ho S, Sham A, Lam TH. Influence of dietary pattern on the development of overweight in a Chinese population. Eur J Clin Nutr. 2008;62:480–487. pmid:17327865
- 50. Alkerwi A, Vernier C, Crichton GE, Sauvageot N, Shivappa N, Hebert JR. Cross-comparison of diet quality indices for predicting chronic disease risk: findings from the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX) study. Br J Nutr. 2014;5:1–11.
- 51. George SM, Ballard-Barbash R, Manson JE, Reedy J, Shikany JM, Subar AF, et al. Comparing indices of diet quality with chronic disease mortality risk in postmenopausal women in the Women's Health Initiative Observational Study: evidence to inform national dietary guidance. Am J Epidemiol. 2014;180:616–625. pmid:25035143
- 52. World Health Organization. WHO Technical Report Series, No. 916. Diet, nutrition and the prevention of chronic diseases. Report of the joint WHO/FAO expert consultation. World Health Organization: Geneva, 2003.
- 53. Waijers PM, Feskens EJ, Ocke MC. A critical review of predefined diet quality scores. Br J Nutr. 2007;97:219–231. pmid:17298689
- 54. Santoro N, Savoye M, Kim G, Marotto K, Shaw MM, Pierpont B, et al. Hepatic fat accumulation is modulated by the interaction between the rs738409 variant in the PNPLA3 gene and the dietary omega6/omega3 PUFA intake. PLoS ONE 2012;7:e37827. pmid:22629460
- 55. Davis JN, Le KA, Walker RW, Vikman S, Spruijt-Metz D, Weigensberg MJ, et al. Increased hepatic fat in overweight Hispanic youth influenced by interaction between genetic variation in PNPLA3 and high dietary carbohydrate and sugar consumption. Am J Clin Nutr. 2010;92:1522–1527. pmid:20962157
- 56. Trovato GM, Catalano D, Martines GF, Pirri C, Trovato FM. Western dietary pattern and sedentary life: independent effects of diet and physical exercise intensity on NAFLD. Am J Gastroenterol. 2013;108:1932–1933. pmid:24300872