Non-alcoholic fatty liver disease (NAFLD) is a major cause of liver disease worldwide, with a prevalence of as high as 30 % in the general population and up to 70 % in patients with type 2 diabetes mellitus(Reference Kwok, Choi and Wong1). NAFLD is characterised by the hepatic accumulation of lipids in the absence of excessive alcohol consumption or established liver disease(Reference Chalasani, Younossi and Lavine2). The spectrum of abnormalities in NAFLD can range from simple steatosis to non-alcoholic steatohepatitis, which may progress to liver fibrosis, cirrhosis and hepatocellular carcinoma(Reference Vernon, Baranova and Younossi3,Reference Musso, Gambino and Cassader4) . However, its clinical burden is not confined to the liver. For example, studies have shown that NAFLD was associated with increased risks of diabetes, CVD and all-cause mortality(Reference Targher, Day and Bonora5–Reference Simon, Roelstraete and Khalili7). While various clinical trials are in progress, there is currently no standard treatment for NAFLD(Reference Chalasani, Younossi and Lavine8). Dietary factors associated with NAFLD are potentially modifiable and thus represent targets for primary prevention of the condition(Reference Berna and Romero-Gomez9,Reference Deng, Zhong and Zhong10) .
Organ meats, such as animal liver and gut, are mainly consumed in Asia and the Middle East, due to the prevailing fact that they are cheap and delicious and provide several nutrients, such as folic acid, vitamin A and vitamin B12 (Reference Karimi, Jessri and Houshiar-Rad11–Reference Yang, Wang and Pan14). Despite these advantages, several characteristics of organ meats might be involved in causing disease, especially NAFLD. First, organ meats are high in saturated fat and cholesterol(Reference Zeng, Fan and Xue15), both of which have been associated with an increased risk of NAFLD(Reference McGettigan, McMahan and Orlicky16). Second, organ meats are rich in N-glycolylneuraminic acid, a chemical that can potentially incite inflammation(Reference Ji, Wang and Chen17), while chronic inflammation plays important role in the development of NAFLD(Reference Luo and Lin18). Finally, organ meats contain high levels of heme Fe. In vivo and human studies, dietary heme Fe can increase oxidative stress and lipid peroxidation, implying that heme Fe may increase the risk of NAFLD(Reference Romeu, Aranda and Giralt19,Reference Gueraud, Tache and Steghens20) . Therefore, we hypothesised that high consumption of organ meats might increase the risk of NAFLD.
Although previous studies have shown that high red meat consumption was associated with an increased risk of NAFLD(Reference Zelber-Sagi, Ivancovsky-Wajcman and Fliss Isakov21,Reference Hashemian, Merat and Poustchi22) , epidemiological evidence linking intake of organ meats to the risk of the disease was still very scarce. Only two cross-sectional studies have reported that higher consumption of organ meats was associated with a higher prevalence of NAFLD(Reference Hashemian, Merat and Poustchi22,Reference Shi, Liu and Li23) . To our knowledge, there is no prospective study examining the association between organ meats and NAFLD. Thus, the present study aimed to prospectively examine the association of organ meat consumption with the risk of NAFLD in a large population of Chinese adults.
Methods
Study design and population
The Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort study is a dynamic prospective cohort study launched in 2007 focusing on the associations between chronic low-grade systemic inflammation and the health status of a population living in Tianjin, China(Reference Zhang, Gu and Bian24,Reference Zhang, Gan and Zhang25) . In the TCLSIH cohort study, participants were recruited from the Tianjin general population of men and women (excluding pregnant women) aged 18 years or older who participated in annual health examinations including abdominal ultrasound and blood draw, and who had completed questionnaires regarding their smoking and alcohol consumption habits and disease history since January 2010. Moreover, a detailed dietary and lifestyle questionnaire was administered to randomly selected participants from this population since May 2013. All participants were invited to complete follow-up assessments during annual health examinations (including ultrasonography) that happened in the same month every year. Participants are followed from the date of diet questionnaire completion to 31 December 2019. All participants had completed a written informed consent form, which was witnessed and formally recorded. This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects/patients were approved by the Institutional Review Board of Tianjin Medical University (reference number: TMUhMEC 201430).
In the current analysis, we included a total of 30 391 participants. To minimise reverse causal bias and reduce the impact of measurement errors, we excluded participants with extreme values for total energy intake (below the 2·5 or over the 97·5 percentiles) or those with missing data on the exposure variables (n 2618) or those with a history of CVD (n 1588), cancer (n 210), alcoholic fatty liver disease (n 1129), other liver diseases (n 178) and NAFLD (n 7097) at baseline. We further excluded those who were lost to follow-up (n 2003, retention rate: 88·8 %). Data from 15 568 participants were available for the final analysis. The study population flow chart is described in Fig. 1.
Fig. 1. Selection of study participants. NAFLD, non-alcoholic fatty liver disease.
Diagnosis of non-alcoholic fatty liver disease
Abdominal sonography was performed using the TOSHIBA SSA-660A (Toshiba) by experienced radiologists. Ultrasound images were assessed by senior hepatologists with over 10 years of experience. The ultrasound technician and hepatologist were unaware of the study aims and blinded to the participant’s information. All participants were unaware of the presence of steatosis before completing the FFQ. The diagnosis of fatty liver was based on at least two of three abnormal findings on abdominal ultrasonography, diffusely increased echogenicity (‘bright’) liver with liver echogenicity greater than kidney or spleen, vascular blurring and deep attenuation of ultrasound signal(Reference Farrell, Chitturi and Lau26). NAFLD was defined according to ultrasound-diagnosed fatty liver disease after excluding excessive alcohol consumption (≥ 210 g/week for men and ≥ 140 g/week for women), chronic hepatitis B or C and long-term steatogenic medicine use.
Assessment of dietary intake
Diet was assessed using a 100-item FFQ with specified serving sizes that were described by natural portions or standard weight and volume measures of the servings commonly consumed in this study population(Reference Zhang, Fu and Zhang27). All participants were asked how often, on average, they had consumed a particular amount of a specific type of food during the previous month. Daily energy and nutrient intakes were extracted from the questionnaires using the China Food Composition database that includes information on nutrient content per gram or serving per product(Reference Yang, Wang and Pan14). The reproducibility and validity of the FFQ in measuring food intake have been described in detail previously(Reference Zhang, Gu and Bian24). The Spearman correlation coefficients between the FFQ and dietary records were 0·49 for energy and 0·68 for organ meats. Spearman’s correlation coefficients between the two FFQ collected about 3 months apart were 0·68 for energy and 0·70 for organ meats. Our previous study showed that despite the FFQ investigating the dietary habits during the last month, the long-term dietary intake of the participants could be inferred(Reference Zhang, Wu and Bian28). To evaluate overall diet quality, principal component analysis with orthogonal rotation was conducted to extract dietary patterns. Eigenvalues (> 1·5), scree plots, factor interpretability and percentage of variance were used to identify key patterns. Food items with absolute factor loadings ≥ 0·45 were considered to be the main contributors to the dietary pattern. Factors were named descriptively according to the food items showing high loading (absolute value) as follows: vegetable-rich pattern, sugar-rich pattern and animal food pattern. These patterns were consistent with dietary patterns previously derived in the TCLSIH cohort study(Reference Xia, Wang and Yu29).
On our FFQ, organ meats included animal liver, kidney, lung and large/small intestine. To correct for potential measurement error, organ meat consumption was adjusted for total energy intake using the nutrient density method and expressed as g/4184 KJ/d (1000 kcal/d)(Reference Freedman, Schatzkin and Midthune30). Because the majority of participants (40·8 %) almost never consumed organ meats, we set the reference group as ‘almost never’. The remaining participants with organ meat consumption were ranked into tertiles.
Assessment of other covariates
Data on potential covariates, including age, sex, smoking status, alcohol drinking status, education level, occupation, family history of disease (including CVD, hypertension, hyperlipidaemia and diabetes), hypertension, diabetes and hyperlipidaemia, were collected with self-administered questionnaires at the baseline survey. Physical activity (PA) in the most recent week was assessed using the short form of the International Physical Activity Questionnaire(Reference Craig, Marshall and Sjostrom31). Total PA was estimated as MET-h/week. Height and body weight were measured using a standard protocol, and BMI was calculated as weight (kg)/height (m)2. Waist circumference (cm) was assessed at the level of the umbilicus with participants standing and breathing normally.
Blood samples were collected from the antecubital vein in siliconised vacuum plastic tubes after an overnight fast. Fasting blood glucose, total cholesterol, TAG, LDL-cholesterol and HDL-cholesterol were measured using an automatic biochemical analyser (Roche Cobas 8000 modular analyser). Participants were classified as having diabetes if their fasting blood glucose was ≥7·0 mmol/l or they had a self-reported history of diabetes. Participants were defined as having hyperlipidaemia if they had increased levels of blood lipids (total cholesterol ≥ 5·17 mmol/l, or TAG ≥ 1·7 mmol/l, or LDL-cholesterol ≥ 3·37 mmol/l) or they took lipid-lowering medication(32). Blood pressure was measured from the upper right arm using the TM-2655 oscillometric device (A&D). Hypertension was defined as having a self-reported history of hypertension or systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg(Reference Chobanian, Bakris and Black33).
Statistical analysis
Baseline characteristics of the analytic sample were presented as mean values and standard deviations for continuous variables and percentages for categorical variables. This study calculated each individual’s person-years from the date of the return of the baseline questionnaire to the date of the first NAFLD diagnosis, end of follow-up (December 2019) or lost to follow-up, whichever occurred first. Cox proportional hazards models were applied to calculate hazard ratios and 95 % CI for the association of energy-adjusted organ meat consumption (g/1000 kcal/d) and risk of NAFLD. The proportional hazards assumption was evaluated with a likelihood ratio test comparing the model with and without an interaction term between follow-up time and exposure. In multivariate models, we adjusted for age (continuous), sex (men or women), smoking status (current/past/never), drinking status (everyday/sometime/past/never), BMI (continuous), PA (continuous), educational level (college graduate or not), occupation (managers, professionals or others), household income (< or ≥ 10 000 Yuan), hypertension (yes or no), hyperlipidaemia (yes or no), diabetes (yes or no) and family history of disease (including CVD, hypertension, hyperlipidaemia and diabetes (each yes or no)), total energy intake (continuous), vegetable intake (continuous), fruit intake (continuous), seafood intake (continuous), soft drink intake (< 1 serving/week, 1 serving/week, 2–3 servings/week and ≥ 4 servings/week) and red meat intake (continuous).
To avoid reverse causality bias, we did a sensitivity analysis by excluding NAFLD cases that occurred within the first years of follow-up. In addition, the final multivariable model was rerun by adjusting for vegetable-rich pattern, sugar-rich pattern and animal food pattern instead of vegetable, fruit, seafood, soft drink and red meat consumption. Moreover, we stratified the participants by potential effect modifiers, including age (< 40 or ≥ 40 years), sex (male or female), BMI (< 24·0 or ≥ 24·0 kg/m2), PA (< or ≥ 23 MET/week), smoking status (current/past/never), drinking status (everyday/sometime/past/never), hypertension (yes or no), hyperlipidaemia (yes or no) and diabetes (yes or no). The interactions were tested using the likelihood ratio test comparing models with and without cross-product terms.
All analyses were performed using SAS version 9.3 for Windows (SAS Institute Inc.). All statistical tests were two-sided, and P < 0·05 was considered statistically significant.
Results
During a median follow-up of 4·2 years of 15 568 participants, we identified 3604 incident NAFLD cases. Table 1 displays baseline characteristics of participants by NAFLD status. Participants with NAFLD were older, tended to be men, had higher BMI, waist circumference, total cholesterol, TAG, LDL-cholesterol, systolic blood pressure, diastolic blood pressure, fasting blood glucose, total energy intake, PA, organ meat intake and lower HDL-cholesterol (all P < 0·001). They also were more likely to be current smokers and ex-smokers, current drinkers, tended to have more co-morbidities, and had a family history of CVD, hypertension and diabetes. Furthermore, participants with NAFLD had lower education levels.
Table 1. Baseline characteristics of the participants according to NAFLD status
(Mean values and standard deviations, n 15 568)
NAFLD, non-alcoholic fatty liver disease; WC, waist circumference; TC, total cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; PA, physical activity; MET, metabolic equivalent.
* P values were calculated using t test for continuous variables and χ2 test for categorical variables.
† Continuous variables are expressed as mean values and standard deviations and categorical variables are expressed as percentages.
The median (range) intakes of organ meats in each tertile were 1·53 (0·59–2·24), 3·12 (2·24–4·43) and 7·87 (4·43–66·8) g/1000 kcal/d, respectively. The associations between organ meat consumption and NAFLD risk were analysed in three adjustment models. In the age-, sex- and BMI-adjusted model, organ meat consumption was positively associated with the risk of NAFLD (comparing the third tertile with almost never eating: hazard ratio = 1·17; 95 % CI 1·07, 1·29, P for trend < 0·001) (Table 2). After further adjustment for non-dietary NAFLD risk factors, we observed similar results. Additional adjustment for intakes of vegetables, fruits, seafoods, soft drinks and red meats, such association remained statistically significant but attenuated (comparing the third tertile with almost never eating: hazard ratio = 1·11, 95 % CI 1·01, 1·22, P for trend < 0·05).
Table 2. The association of organ meat consumption with NAFLD
(Hazard ratios and 95 % confidence intervals, n 15 568)
NAFLD, non-alcoholic fatty liver disease.
* Obtained using multivariable Cox regression model.
† Model 1 was adjusted for age, sex and BMI.
§ Hazard ratios (95 % CI) (all such values).
¶ Model 2 was adjusted for age, sex, BMI, smoking status, drinking status, education level, occupation, household income, physical activity, total energy intake, family history of disease (including CVD, hypertension, hyperlipidaemia and diabetes), hypertension, hyperlipidaemia and diabetes.
Model 3 was adjusted for variables in model 2 plus intakes of vegetable, fruit, seafood, soft drink and red meat.
Because the majority of participants (40·8 %) almost never consumed organ meats, we set it as ‘almost never’ as the reference group. The remaining participants with organ meat consumption were ranked into tertiles.
We found no evidence that the associations between meat intake and NAFLD varied by age (P for interaction = 0·69), sex (P for interaction = 0·42), BMI (P for interaction = 0·66), smoking status (P for interaction = 0·59), drinking status (P or interaction = 0·87), PA (P for interaction = 0·71), hyperlipidaemia (P for interaction = 0·68), diabetes (P for interaction = 0·87) and HBP (P for interaction = 0·64) (Table 3).
Table 3. Association between organ meat consumption and risk of NAFLD stratified by major covariates
(Hazard ratios and 95 % confidence intervals)
NAFLD, non-alcoholic fatty liver disease; PA, physical activity; MET, metabolic equivalent.
* Obtained using multivariable Cox regression model. Adjusted for age, sex, BMI, smoking status, drinking status, education level, occupation, household income, physical activity, total energy intake, family history of disease (including CVD, hypertension, hyperlipidaemia and diabetes), hypertension, hyperlipidaemia, diabetes, intakes of vegetable, fruit, seafood, soft drink and red meat.
† P for interaction was calculated using likelihood ratio test.
‡ Hazard ratios (95 % CI) (all such values).
In the sensitivity analysis, the associations remained similar when we excluded incident NAFLD cases in the first year of follow-up (n 861, online Supplementary Table S1). In the fully adjusted model, compared with almost never eating, the highest tertile of intake was significantly associated with a higher risk of the NAFLD (hazard ratio = 1·15; 95 % CI 1·02, 1·29; P for trend = 0·01). Similar results were observed when adjusting for three main dietary patterns instead of intakes of vegetable, fruit, seafood, soft drink and red meat consumption (online Supplementary Table S2).
Discussion
The prospective findings from the present study support the hypothesis that organ meat was associated with a modestly increased risk of incident NAFLD in Chinese adults, independent of other dietary or non-dietary NAFLD risk factors. To our knowledge, this was the first large prospective cohort study investigating the association between organ meat consumption and the risk of NAFLD.
In this large-scale population-based study, we adjusted for multiple potentially confounding factors, including age, sex, BMI, smoking status, drinking status, PA, socio-economic status, personal and family history of disease, and total energy intake. These adjustments did not change the positive association between organ meat consumption and NAFLD. In addition, to test for the potential influence of nutritional quality of overall diets on the association between organ meat consumption and NAFLD, we additionally adjusted vegetable, fruit, soft drink, seafood and red meat intake. However, the adjustment for these dietary factors also did not change the positive association between organ meat consumption and NAFLD, implying that the association between organ meat and NAFLD was independent of these dietary factors.
In our study, the mean consumption of organ meat was 6·61 g/d, similar to that of Chengdu, China(Reference Shi, Liu and Li23). Our findings are in line with a previous case–control study, which showed that NAFLD patients consumed significantly more organ meat than controls (9·7 v. 3·4 g/d, P < 0·05)(Reference Shi, Liu and Li23). In this study, however, energy intake was not taken into account as a covariate. Another study in Iran observed similar results, showing that organ meat consumption was associated with the increased odds of NAFLD (OR Q4 v. Q1 = 1·70, 95 % CI 1·19, 2·44, P for trend = 0·0025)(Reference Hashemian, Merat and Poustchi22). It is worth noting that, in this study, ultrasound assessments were not performed at baseline but in 6 years after the dietary data collection, and the participants might have changed their diet during this period. Therefore, it was not clear whether participants had fatty liver disease at baseline. Our present cohort study provides prospective evidence that organ meat consumption was associated with an increased risk of NAFLD.
Several hypotheses could be put forward to explain our findings. Firstly, organ meats are commonly high in saturated fat and cholesterol. Saturated fat ingestion was reported to augment hepatic lipid storage and impaired insulin sensitivity, both of which were accompanied by regulation of hepatic gene expression and signalling that predispose to the development of NAFLD(Reference Hernandez, Kahl and Seelig34). Among hepatic lipid species, cholesterol is considered a major lipotoxic molecule in non-alcoholic steatohepatitis development(Reference Ioannou35). Recently, studies in animal models have demonstrated that dietary cholesterol could induce both reactive oxygen species and proinflammatory cytokines, thereby promoting NAFLD development(Reference Zhang, Coker and Chu36,Reference Comhair, Garcia Caraballo and Dejong37) . A second interpretation concerns the heme Fe in organ meats. Heme Fe may exert effects on the synthesis and secretion of insulin and interfere with insulin receptors, thus reducing insulin sensitivity(Reference Huang, Jones and Luo38). This could lead to NAFLD because insulin resistance is the key dysfunction in this disease(Reference Rinella39). In addition, heme Fe was associated with increased oxidative stress and lipid peroxidation(Reference Rajpathak, Crandall and Wylie-Rosett40). A large prospective cohort study also found that heme Fe intake was associated with a higher risk of chronic liver disease(Reference Freedman, Cross and McGlynn41). Finally, the sialic acid, N-glycolylneuraminic acid, in organ meats has been hypothesised to generate a proinflammatory cascade(Reference Ji, Wang and Chen17). It is worth noting that the human liver is an organ that can readily incorporate dietary N-glycolylneuraminic acid(Reference Samraj, Pearce and Laubli42,Reference Tangvoranuntakul, Gagneux and Diaz43) .
The strengths of the present study include its large sample size, prospective study design and the robustness of the results in a series of analyses. However, several limitations need to be considered when interpreting our results. First, NAFLD diagnosis was based on abdominal ultrasound rather than the gold standard liver biopsy. However, a meta-analysis showed that the sensitivity and specificity of ultrasonography for assessment of fatty liver, compared with CT, MRI or magnetic resonance spectroscopy, were 93·6 % (95 % CI 60·5, 99·3) and 80·1 % (95 % CI 53·3, 93·4), respectively(Reference Hernaez, Lazo and Bonekamp44). Moreover, to reduce inter- and intra-observer variation, all ultrasonographic exams were examined by board-certified radiologists using standard methods. Second, bias in self-reported organ meat consumption is inevitable, and such bias might have led to some degree of non-differential misclassification of exposure, which could have attenuated the observed associations. Third, organ meat consumption was collected once at baseline, which did not take into account the changes in dietary habits during the follow-up time. Therefore, future studies need to evaluate the association of long-term changes in organ meat consumption with subsequent risk of NAFLD. Fourth, as in all observational studies, although we have carefully controlled for the potential confounding factors, we cannot exclude the possibility of residual confounding. Finally, since the study results only represent the study region, caution must be taken when generalising the findings to other populations. Therefore, further studies are needed to verify the results in other populations.
In conclusion, in this large cohort study of Chinese adults, we found a positive association between organ meat consumption and the risk of NAFLD. Further large-scale population-based studies are needed to confirm the generalisability of these findings in different populations and settings.
Acknowledgements
The authors gratefully acknowledge all the people who have made this study.
This study was supported by grants from the National Natural Science Foundation of China (No. 81941024, 81872611, 82103837 and 81903315), Tianjin Major Public Health Science and Technology Project (No. 21ZXGWSY00090), National Health Commission of China (No. SPSYYC 2020015), Food Science and Technology Foundation of Chinese Institute of Food Science and Technology (No. 2019-12), 2014 and 2016 Chinese Nutrition Society (CNS) Nutrition Research Foundation—DSM Research Fund (Nos. 2016-046, 2014-071 and 2016-023), China.
The authors’ responsibilities were as follows: H. L. analysed the data and wrote the paper. H. L., X. Z., S. R., A. T., G. M., Q. Z., L. L., H. W., Y. G., S. Z., T. Z., X. W., J. D., Z. C., X. Z., X. R., S. S., X. W., M. Z., Q. J. and K. S. conducted the research. K. N. designed the research and had primary responsibility for the final content. All authors had full access to all the data in the study and read and approved the final manuscript.
There are no conflicts of interest.
Supplementary material
For supplementary material referred to in this article, please visit https://doi.org/10.1017/S0007114522000629
