Abstract
Aim
Physical activity plays an important role in the development of non-alcoholic fatty liver disease (NAFLD). However, the optimal intensity and dose of physical activity for the treatment of NAFLD have yet to be found. In the present study, we aimed to provide a dose–response association between physical activity and NAFLD in a Chinese population.
Methods
We recruited 543 patients with NAFLD diagnosed by abdominal ultrasonography, and 543 age-matched and sex-matched controls. The amount of physical activity, sedentary time and energy intake was collected through a structured questionnaire. Logistic regression analyses were performed to investigate the association between physical activity and NAFLD.
Results
After adjusting for hypertension, diabetes, body mass index, fasting blood glucose, energy intake and sedentary time, the total amount of physical activity was found to be inversely associated with NAFLD in a dose-dependent manner in men (>3180 metabolic equivalent of energy [MET]-min/week vs ≤1440 MET-min/week: OR 0.60, 95% CI 0.40 to 0.91, p for trend=0.01). In addition, both moderate-intensity and vigorous-intensity physical activity were effective in reducing the risk of NAFLD, independent of confounding variables in men (moderate-intensity physical activity: >684 MET-min/week vs none: OR 0.58, 95% CI 0.40 to 0.86, p for trend=0.01; vigorous-intensity physical activity: >960 MET-min/week vs none: OR 0.63, 95% CI 0.41 to 0.95, p for trend=0.02).
Conclusions
Physical activity was inversely associated with risk of NAFLD in a dose-dependent manner in men. Vigorous-intensity and moderate-intensity physical activity were both beneficial to NAFLD, independent of sedentary time and energy intake.
Keywords: lipid disorders, hepatology, public health, sports medicine
Strengths and limitations of this study.
This study had a considerable sample size and several potential confounding variables, such as energy intake and sedentary time, were taken into account.
The intensity of physical activity was measured in terms of metabolic equivalent of energy (MET) and dose of physical activity was presented in the form of MET-min/week
This study was a case–control design; thus, the causal association between physical activity and non-alcoholic fatty liver disease could not be precisely identified.
This study was a case–control study, recall bias was inevitable and randomised controlled trial studies are therefore required for more accurate results.
Introduction
Non-alcoholic fatty liver disease (NAFLD) is defined as fat accumulation in more than 5% of hepatocytes, without competing liver disease such as viral hepatitis or autoimmune hepatitis.1 It encompass a broad spectrum of hepatic dysfunction ranging from simple hepatic lipid accumulation (steatosis) to non-alcoholic steatohepatitis, fibrosis, cirrhosis and, finally, hepatocellular carcinoma.2 A meta-analysis indicated that 25.24% of global population have NAFLD,3 similar to the prevalence rate in China of 20%.4 Observation studies showed that patients with NAFLD have a higher risk of developing extrahepatic complications such as cardiovascular disease, diabetes and metabolic syndrome.5–7 Therefore, NAFLD is recognised as a global health burden and it is crucial to explore effective prevention and treatment strategies.
Physical activity as a lifestyle modification plays an important role in the development of NAFLD. Previous studies found an inverse relationship between physical activity and the risk of NAFLD,8 9 and randomised controlled trials also demonstrated that physical activity improved liver enzyme function and reduced fat accumulation.10–13 A meta-analysis of 20 randomised controlled trials (RCTs) showed that levels of alanine aminotransferase (ALT), gamma-glutamyltranspeptidase (GGT), aspartate aminotransferase (AST) and intrahepatic fat of the intervention group were significantly better than the control group.14 However, physical activity is a complex concept and includes type, intensity, frequency and duration. Many studies only consider the frequency of physical activity, and this does not reflect the dose. In addition, most studies had a limited sample size and the data on physical activity were retrieved from populations with diverse demographic characteristics. Therefore, the optimal intensity and dose of physical activity for the treatment of NAFLD have yet to be found. For example, a report from the Korean suggested that exercising more than twice a week and for more than 30 min can decrease the risk of hepatic steatosis.15 Another study, from USA found that moderate-intensity exercise might reduce the risk of hepatic steatosis, but did not make a specific recommendation about the desired.16
In the present study, metabolic equivalent of energy (MET) was used as a measure of physical activity. We aimed to explore the dose–response relationship between physical activity and NAFLD in a Chinese population, taking into consideration confounding variables such as energy intake and sedentary time.
Methods
Patient and public involvement
This study is a case–control design focused on a Chinese Han population between 18 and 70 years old. Subjects were recruited from a health examination centre of Nanping First Affiliated Hospital of Fujian Medical University from October 2015 to September 2017. All subjects underwent abdominal ultrasound and blood biochemical tests. Once cases and controls have been linked to the NAFLD, a letter of invitation and information about the study will be sent to each potential case and control to obtain consent. Eligible subjects will be interviewed face-to-face by investigators to collect data. In addition, all methods were performed in accordance with the relevant guidelines and regulations.
Sample size calculation
This study is a case–control design; thus, we estimate the sample size based on the case–control study formula for 1:1 frequency matching. By consulting the literature,17 we estimate OR 0.7, ρ0=0.6. The calculated sample size was Ncase=Nconrtol=508. Finally, 1086 subjects (543 cases and 543 controls) were recruited in this study.
Outcome: eligibility of NAFLD cases and controls
NAFLD was diagnosed by the presence of at least two of the following three abnormal findings on abdominal ultrasonography18: (1) increased echogenicity of the liver near-field region with deep attenuation of the ultrasound signal; (2) hyperechogenity of liver tissue (‘bright liver’), as often compared with hypoechogenity of the kidney cortex; and (3) vascular blurring. Exclusion criteria were as follows: (1) alcohol consumption >140 g/week for men and >70 g/week for women; (2) presence of hepatitis B surface antigen or hepatitis C antibodies; (3) use of hepatotoxic drugs (such as tamoxifen, amiodarone, valproate and methotrexate)19 which can induce hepatic fat accumulation; (4) hepatic disease which can induce hepatic fat accumulation; (5) hepatic disease such as Wilson’s disease, autoimmune hepatitis and haemochromatosis. A total of 543 newly diagnosed patients with NAFLD were enrolled; and 543 controls were selected by frequency matching according to age (±5 years) and gender from a healthy population who underwent abdominal ultrasonography examination during the same period.
Exposure: physical activity measurements
Physical activity during the past 7 days was quantified through a questionnaire based on the International Physical Activity Questionnaire, adapted to the characteristics of Nanping residents.20 It includes four domains (transportation-related, work-related, household-related and leisure time-related). Each domain includes specific activities which correspond to various intensities of exercise (light, moderate and vigorous intensity). Participants were asked to estimate information on the frequency and duration spent in specific activities during the past 7 days. Sedentary time was measured by the single question, ‘During the past seven days, how much time did you usually spend sitting on a day?’
The intensity of physical activity was defined in terms of MET. According to a standard reference, each kind of activity was assigned a specific MET value: low-intensity physical activities were defined as <3 METs, moderate-intensity activities defined as 3–6 METs and vigorous-intensity activities defined as >6 METs.21 The dose of specific physical activity was quantified by the frequency and duration and presented in the form of MET-min per week (MET-min/week=duration ×frequency per week×MET value). The total dose of physical activity equals the sum of the doses for each specific activity.
Potential confounders
Face-to-face investigation was performed by uniformly trained investigators. Data were collected in the following four categories, using a structured questionnaire for the first two:
Demographic characteristics including age, gender, education, income, marriage status and history of diabetes.
Health-related behaviours including smoking status, alcohol drinking, tea consumption, total energy intake.
Total energy intake was assessed by semiquantitative food frequency questionnaire,22 which had been specifically developed and validated for the southern Chinese population.23 Participants were asked to estimate information on the average frequency of consumption of selected foods and the estimated portion size over the previous year, ignoring any recent changes. Intakes of food were converted into gram per day. Each food item was assigned a specific energy according to Food Nutrition Facts Table and total energy intake was the sum of the energy of various foods ingested in a day.24
Smokers were defined as those who had smoked at least one cigarette per day during the previous 6 months. Tea consumption was defined as drinking one or more cups of tea per day during the previous 6 months.
Anthropometric assessment including height, body weight and blood pressure.
Body mass index (BMI) was calculated as body weight (kg)/height2 (m2), and classified into four categories: lean ≤18.5 kg/m2, normal 18.6–23.9 kg/m2, overweight 24.0–27.9 kg/m2, obese ≥28.0 kg/m2.25
For blood pressure measurement, participants were first asked to rest for 10 min. Then, the trained investigators measured blood pressure twice on seated participants using a standard mercury sphygmomanometer, and the mean of the two measurements was considered as the participant’s blood pressure. Hypertension was defined as systolic arterial blood pressure ≥140 mm Hg or diastolic arterial blood pressure ≥90 mm Hg.26
Biochemical examinations after a 12-hour overnight fast
Biochemical parameters included serum AST, ALT, GGT, serum fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL) and high-density lipoprotein (HDL).
Blood samples were collected between 08:00 and 10:00 after fasting overnight (12 hours). Blood biochemical analysis was carried out by the medical laboratory department of Nanping First Affiliated Hospital of Fujian Medical University.
Statistical analysis
The χ2 test was used to assess categorical variables and the Mann-Whitney U-test was used for continuous variables. An unconditional logistic regression model was employed to progressively reduce the confounding effect of the relationship between physical activity and NAFLD risk. The bivariate spearman correlation was conducted to explore the association between physical activity and biochemical parameters. All statistical analyses were conducted using SPSS V.23.0. The p value was defined as two-tailed and set at <0.05.
Results
A total of 1086 subjects (543 cases and 543 controls) were recruited. Seven hundred and forty-two (68.3%) were men, 344 (3.7%) were women. Baseline characteristics are shown in table 1. The prevalence of hypertension (30.0%), overweight or obesity (66.5%) and diabetes (4.8%) were higher in subjects with NAFLD (each p<0.05). And they tend to have tea consumption (p=0.04). Serum levels of GGT, ALT, AST, TC, TG and FBG were also higher than in the control population (each p<0.05). Whereas HDL were lower in the cases (p<0.05). There was no difference in age, gender, income, marriage status, smoking status, education level, sedentary time or serum level of LDL between the two groups.
Table 1.
Baseline characteristics of cases and controls
| Variable | Case | Control | Z/χ2 | P value |
| No (%) or median (quartiles) | No (%) or median (quartiles) | |||
| Age (years) | 48 (39–54) | 48 (39–54) | −0.03 | 0.97 |
| Gender | <0.01 | 1 | ||
| Male | 371 (68.3) | 371 (68.3) | ||
| Female | 172 (31.7) | 172 (31.7) | ||
| Blood pressure (mm Hg) | 20.60 | <0.001* | ||
| <140/90 | 380 (70.0) | 444 (81.8) | ||
| ≥140/90 | 163 (30.0) | 99 (18.2) | ||
| BMI (kg/m2) | 208.51 | <0.001* | ||
| ≤18.5 | 3 (0.6) | 20 (3.7 | ||
| 18.6–23.9 | 179 (33.0) | 388 (71.5) | ||
| 24.0–27.9 | 284 (52.3) | 129 (23.8) | ||
| ≥28.0 | 77 (14.2) | 6 (1.1) | ||
| Diabetes | 5.35 | 0.02* | ||
| No | 517 (95.2) | 531 (97.8) | ||
| Yes | 26 (4.8) | 12 (2.2) | ||
| Education level | 5.52 | 0.06 | ||
| Primary education | 274 (50.5) | 286 (52.7) | ||
| Secondary education | 158 (29.1) | 126 (23.2) | ||
| Bachelor degree | 111 (20.4) | 131 (24.1) | ||
| Income (¥) | 1.44 | 0.49 | ||
| <1000 | 33 (6.1) | 35 (6.4) | ||
| 1000– 2000 | 161 (29.7) | 178 (32.8) | ||
| ≥2000 | 349 (64.3) | 330 (60.8) | ||
| Tea consumption | 4.40 | 0.04* | ||
| No | 338 (62.2) | 239 (44.0) | ||
| Yes | 205 (37.8) | 304 (56.0) | ||
| Smoking habit | 0.24 | 0.62 | ||
| No | 140 (25.8) | 131 (24.1) | ||
| Yes | 403 (74.2) | 412 (75.9) | ||
| Marital status | 2.65 | 0.10 | ||
| Single or divorced | 53 (9.8) | 70 (12.9) | ||
| Married | 490 (90.2) | 473 (87.1) | ||
| Sedentary time (hours/day) | 2.98 | 0.23 | ||
| <4 | 167 (30.8) | 184 (33.9) | ||
| 4–8 | 250 (46.0) | 255 (47.0) | ||
| ≥8 | 126 (23.2) | 104 (19.2) | ||
| Energy intake (kJ) | 2227.34 (1778.78–2664.85) | 2106.85 (1696.41–2600.52) | −2.32 | 0.02* |
| GGT (IU/L) | 32 (23.00–45.00) | 23 (17.00–32.00) | −10.18 | <0.001* |
| ALT (IU/L) | 27 (20.00–38.00) | 20 (15.00–25.00) | −11.47 | <0.001* |
| AST (IU/L) | 24 (20.00–28.00) | 22 (18.00–25.00) | −5.69 | <0.001* |
| TC (mmol/L) | 5.19 (4.64–5.77) | 5.03 (4.53–5.53) | −2.76 | 0.06 |
| TG (mmol/L) | 1.85 (1.29–2.54) | 1.18 (0.87–1.59) | −13.48 | <0.001* |
| FBG (mmol/L) | 5.37 (5.03–5.84) | 5.20 (4.90–5.53) | −6.16 | <0.001* |
| HDL (mmol/L) | 1.21 (1.06–1.37) | 1.34 (1.18–1.48) | −9.16 | <0.001* |
| LDL (mmol/L) | 3.27 (2.63–3.79) | 3.17 (2.68–3.74) | −0.61 | 0.54 |
ALT, alanine aminotransferase; AST, serum aspartate aminotransferase; BMI, body mass index; FBG, serum fasting blood glucose; GGT, gamma-glutamyltranspeptidase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglycerides.
*P <0.05.
In total population, there is no significant dose–response association between physical activity and NAFLD after adjusting for BMI, hypertension, diabetes, fasting blood glucose, energy intake and sedentary time (see online supplementary table S1). Because the prevalence of NAFLD differed between men and women, we then used a gender-specific model in the further analysis.
bmjopen-2018-026854supp001.pdf (333.8KB, pdf)
In men, after adjusting for BMI, hypertension, diabetes, fasting blood glucose and sedentary time in multivariate logistic model 3, physical activity was associated with the risk of NAFLD in a dose-dependent manner (>3180 MET-min/week vs ≤1440 MET-min/week: OR 0.61, 95% CI 0.41 to 0.92, p for trend=0.02). After further adjusting for energy intake, this association was maintained (>3180 MET-min/week vs ≤1440 MET-min/week: OR 0.60, 95% CI 0.40 to 0.91, p for trend=0.01, table 2). In women, there exists no relationship between physical activity and NAFLD (see online supplementary table S2).
Table 2.
Association between physical activity and NAFLD in male
| Variable | Case | Control | Univariate model | Multivariate model 1 | Multivariate model 2 | Multivariate model 3 |
| (MET-min/week) | No (%) | No (%) | OR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) |
| Total amount of physical activity | ||||||
| ≤1440 | 153 (41.2) | 124 (33.4) | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| 1440–3180 | 104 (28.0) | 124 (33.4) | 0.68 (0.48 to 0.97) | 0.62 (0.41 to 0.93)* | 0.62 (0.41 to 0.93)* | 0.62 (0.41 to 0.92)* |
| >3180 | 114 (30.7) | 123 (33.2) | 0.75 (0.53 to 1.06) | 0.62 (0.41 to 0.92)* | 0.61 (0.41 to 0.92)* | 0.60 (0.40 to 0.91)* |
| P value for trend | 0.09 | 0.02* | 0.02* | 0.01* | ||
| Light-intensity physical activity | ||||||
| ≤525 | 121 (32.6) | 125 (33.7) | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| 525–1500 | 127 (34.2) | 125 (33.7) | 1.05 (0.74 to 1.49) | 1.00 (0.67 to 1.49) | 1.00 (0.67 to 1.50) | 1.03 (0.69 to 1.55) |
| >1500 | 123 (33.2) | 121 (32.6) | 1.05 (0.74 to 1.50) | 0.96 (0.64 to 1.43) | 0.96 (0.64 to 1.44) | 0.95 (0.63 to 1.44) |
| P value for trend | 0.79 | 0.83 | 0.84 | 0.82 | ||
| Moderate-intensity physical activity | ||||||
| None | 204 (55) | 170 (45.8) | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| ≤684 | 74 (19.9) | 79 (21.3) | 0.78 (0.54 to 1.14) | 0.79 (0.52 to 1.21) | 0.79 (0.51 to 1.21) | 0.78 (0.51 to 1.20) |
| >684 | 93 (25.1) | 122 (32.9) | 0.64 (0.45 to 0.89)* | 0.59 (0.40 to 0.86)* | 0.58 (0.39 to 0.86)* | 0.58 (0.40 to 0.86)* |
| P value for trend | 0.01* | 0.01* | 0.01* | 0.01* | ||
| Vigorous-intensity physical activity | ||||||
| None | 272 (73.3) | 251 (67.7) | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| ≤960 | 28 (7.5) | 35 (9.4) | 0.74 (0.44 to 1.25) | 0.77 (0.42 to 1.42) | 0.77 (0.42 to 1.42) | 0.77 (0.42 to 1.41) |
| >960 | 71 (19.1) | 85 (22.9) | 0.77 (0.54 to 1.10) | 0.65 (0.43 to 0.98)* | 0.65 (0.43 to 0.98)* | 0.63 (0.41 to 0.95)* |
| P value for trend | 0.12 | 0.03* | 0.03* | 0.02* | ||
Multivariate model 1: adjusted for BMI, hypertension, diabetes and fasting blood glucose.
Multivariate model 2: adjusted for BMI, hypertension, diabetes, fasting blood glucose and sedentary time.
Multivariate model 3: adjusted for BMI, hypertension, diabetes, fasting blood glucose, sedentary time and energy intake.
aOR, adjusted OR; BMI, body mass index; MET, metabolic equivalent of energy; NAFLD, non-alcoholic fatty liver disease.
*P<0.05.
We also calculated the distribution of the three energy nutrients (carbohydrate, fat and protein) in cases and controls stratified by gender (see online supplementary table S3 and S4). After adjusting for the carbohydrate, total fat and protein, the association between physical activity and NAFLD was maintained in men (see online supplementary table S5 and S6). However, daily diets contain a variety of foods, not individual nutrients or individual foods, and there are complex interactions between different nutrients or foods. Based on individual food or nutrient studies, the association between diet and NAFLD cannot be accurately assessed. Thus, we finally analysed only total energy intake in the final multivariate logistic model.
Then, we further analysed the association between various intensities of physical activity and the risk of NAFLD. In men, the moderate-intensity and vigorous-intensity levels were inversely associated with the risk of NAFLD, independent of the confounding variables: (moderate-intensity physical activity: >684 MET-min/week vs none: OR 0.58, 95% CI 0.40 to 0.86, p for trend=0.01; vigorous-intensity physical activity: >960 MET-min/week vs none: OR 0.63, 95% CI 0.41 to 0.95, p for trend=0.02, table 2). In women, there is no association between various intensity of physical activity and NAFLD (see online supplementary table S3).
According to the Physical Activity Guidelines for Americans (PAGA) released by the US Department of health and human service (USDHHS),27 more than 150 min of moderate-intensity physical activity per week or 75 min of vigorous-intensity physical activity per week is beneficial to health; we divided physical activity into different levels. The dose–response association was shown: men who underwent moderate-intensity or vigorous-intensity physical activity had a significantly lower risk of NAFLD (moderate-intensity physical activity ≥2.5 hours vs none: OR 0.63, 95% CI 0.43 to 0.92; p for trend=0.01; vigorous-intensity physical activity ≥1.25 hours vs none: OR 0.66, 95% CI 0.45 to 0.96; p for trend=0.03, table 3).
Table 3.
Association between moderate-intensity or vigorous-intensity physical activity and NAFLD in men
| Variable | Case | Control | Univariate model | Multivariate model 1 | Multivariate model 2 | Multivariate model 3 |
| (MET-min/week) | No (%) | No (%) | OR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) |
| Moderate-intensity physical activity | ||||||
| None | 204 (55.0) | 170 (45.8) | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| ~2.5 hours | 67 (18.1) | 75 (20.2) | 0.74 (0.51 to 1.10) | 0.73 (0.47 to 1.13) | 0.72 (0.47 to 1.12) | 0.72 (0.46 to 1.12) |
| ≥2.5 hours | 100 (27.0) | 126 (34.0) | 0.66 (0.47 to 0.92)* | 0.63 (0.43 to 0.91)* | 0.62 (0.43 to 0.91)* | 0.63 (0.43 to 0.92)* |
| P value for trend | 0.01* | 0.01* | 0.01* | 0.01* | ||
| Vigorous-intensity physical activity | ||||||
| None | 272 (73.3) | 251 (67.7) | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| ~1.25 hours | 9 (2.4) | 12 (3.2) | 0.69 (0.29 to 1.67) | 0.73 (0.26 to 2.07) | 0.73 (0.26 to 2.07) | 0.74 (0.26 to 2.08) |
| ≥1.25 hours | 90 (24.3) | 108 (29.1) | 0.77 (0.55 to 1.07) | 0.68 (0.47 to 0.98)* | 0.68 (0.46 to 0.98)* | 0.66 (0.45 to 0.96) |
| P value for trend | 0.10 | 0.04* | 0.04* | 0.03* | ||
Multivariate model 1: adjusted for BMI, hypertension, diabetes and fasting blood glucose.
Multivariate model 2: adjusted for BMI, hypertension, diabetes, fasting blood glucose and sedentary time.
Multivariate model 3: adjusted for BMI, hypertension, diabetes, fasting blood glucose, sedentary time and energy intake.
aOR, adjusted OR; MET, metabolic equivalent of energy; NAFLD, non-alcoholic fatty liver disease.
* P<0.05.
We explored the association between physical activity and biochemical indicators. In patients with NAFLD, subjects who undergo a higher total amount of physical activity tend to have significantly lower levels of GGT (p=0.02). In the control population, greater physical activity was significantly associated with greater AST (p=0.001) (table 4).
Table 4.
Association between total amount of physical activity and biochemical indicators in men
| Variable | Case | Control | ||
| Physical activity (MET-min/week) | P value | Physical activity (MET-min/week) | P value | |
| Correlation coefficient | Correlation coefficient | |||
| GGT (IU/L) | −0.13 | 0.02* | 0.05 | 0.33 |
| ALT (IU/L) | −0.09 | 0.09 | 0.10 | 0.05 |
| AST (IU/L) | 0.01 | 0.80 | 0.17 | 0.001* |
| TC (mmol/L) | 0.01 | 0.87 | 0.01 | 0.80 |
| TG (mmol/L) | −0.04 | 0.47 | −0.04 | 0.40 |
| HDL (mmol/L) | 0.05 | 0.35 | 0.03 | 0.53 |
| LDL (mmol/L) | −0.03 | 0.59 | 0.02 | 0.76 |
ALT, alanine aminotransferase; AST, serum aspartate aminotransferase; BMI, body mass index; FBG, serum fasting blood glucose; GGT, gamma-glutamyltranspeptidase; HDL, high density lipoprotein; LDL, low density lipoprotein; TC, total cholesterol; TG, triglycerides.
P<0.05.
Discussion
Physical activity is a complex concept including type, intensity, frequency and duration. The parameters used to define the intensity of physical activity fall into two categories: absolute or relative. Absolute intensity refers to the rate of energy expenditure during physical activity and is usually presented as MET. MET is a widely used physiological concept defined as the ratio of work metabolic rate to a standard resting metabolic rate of 1 kcal/kg/hour (1 MET=3.5 mL O2/kg/min=1 kcal/kg/hour).28 Moderate-intensity physical activity corresponds to 40%–60% of VO2 max or 4–6 METs. Vigorous-intensity physical activity corresponds to ≥60% of VO2 max or >6 METs.29 Since different methods are used to assess physical activity in the literature, the optimal intensity and dose of physical activity for the treatment of NAFLD have yet to be determined.
In the present study, the intensity of physical activity was measured in terms of MET; and dose of physical activity was presented in the form of MET-min/week. We observed an inverse dose–response association between physical activity and the risk of NAFLD, independent of potential confounding variables. In men, patients with more than 3180 MET-min/week total physical activity had a 40% lower risk of NAFLD compared with those with less than 1440 MET-min/week. In addition, we also found that moderate-intensity and vigorous-intensity physical activity were beneficial to NAFLD in men. When the dose of physical activity was divided according to the PAGA released by the USDHHS27 (more than 150 min of moderate-intensity physical activity per week or 75 min of vigorous-intensity physical activity per week is beneficial to health), the dose–response association was maintained. However, the relationship between physical activity and NAFLD has not been observed in women. The gender difference observed in this study may be explained by sex hormones. Mechanism research have found that sex hormones can upregulate insulin receptor expression and increase receptor phosphorylation protein kinase levels, thereby enhancing insulin signalling and preventing NAFLD.30 31 Furthermore, some sex hormones and their derivatives are strong endogenous antioxidants, which can inhibit the production of lipid peroxides in the liver and reduce its concentration, and play a protective role in the liver.32 Several studies using maximal heart rate or percentage of VO2 max to define the intensity of physical activity indirectly supported our findings. One other cross-sectional study has also found a dose–response association between physical activity and NAFLD risk in terms of MET.33 This study suggested that men with a dose of more than 5760 MET-min/week had a 31% lower risk of NAFLD compared with those with less than 498 MET-min/week. In women, the association was weaker. However, the study population was heterogeneous, meaning that the results should be interpreted with caution and that optimal dose of physical activity should be tailored to the patient’s clinical characteristics, fitness status and preferences.
The mechanism by which physical activity improves NAFLD is unclear, although several potential mechanisms have been suggested. First, insulin sensitivity is a plausible explanation,34 via increasing expression of glucose transport protein and synthase activity of muscle glycogen, and decreasing the accumulation of serum triglyceride. Second, physical activity decreases visceral adiposity, which in turn decreases free fatty acid influx to the liver. Third, physical activity is known to upregulate the intake of glucose and lipid oxidation in skeletal muscle, which in turn depletes the accumulation of fatty acid in the liver.35 In the present study, we observed that increased physical activity was associated with decreased GGT levels in men with NAFLD, and men with higher physical activity tend to have higher AST levels in controls. Nevertheless, more studies are still needed to confirm the association between physical activity and NAFLD and potential mechanisms should be explored.
Strengths and limitations
There were several advantages to the current study. First, several potential confounding variables, including energy intake and sedentary time, were taken into account. With the development of technology and a better economy, people tend to spend more time in sedentary activities: one study showed that sitting time was positively associated with risk of NAFLD, even in subjects with a high level of physical activity.36 Similarly, another study indicated that regular participation in high levels of physical activity does not fully protect against the risks associated with prolonged bouts of sedentary behaviours.37 Other known risk factors of NAFLD are energy intake and BMI. Several previous studies have found that patients with NAFLD tend to have higher energy intake, and a energy-restricted diet was found to have great benefits for weight loss and improving BMI.38–41 However, few studies have considered sedentary time and energy intake at the same time when investigating the association between physical activity and NAFLD. The potential confounding effect of these factors may reduce the power to detect associations between physical activity and the risk of NAFLD.
A second advantage to our study was that we used the well-known parameter MET to quantify the intensity of physical activity; and also quantified dose of physical activity as frequency and duration. We found a dose–response association between physical activity and risk of NAFLD, which could provide evidence for a clinical treatment guideline for NAFLD.
A third advantage was that this study had a considerable sample size and could thus provide substantial statistical power to assess the effect of physical activity on NAFLD.
However, several limitations should be considered. First, this study was a case–control design; thus, the causal association between physical activity and NAFLD could not be precisely identified. Second, the level of physical activity was self-reported: subjects often have difficulty in recalling physical activity undertaken in the past 7 days and tend to underestimate the time spent in specific activities. Therefore, misclassification bias was inevitable and could have affected the calculated association between physical activity and NAFLD. RCT studies are therefore required for more accurate results. Third, liver biopsy is the gold standard for quantitative diagnosis of NAFLD. However, it is an invasive examination; there exists the possibility of postoperative blood and bile leakage, and there are sampling errors; therefore, it does not apply to routine screening. In the current study, NAFLD was diagnosed by abdominal ultrasonography. Ultrasound examination currently is the preferred method for the initial screening of NAFLD with its advantages of no scratching, no radiation damage, reproducibility and low price. It is based on the enhancement or attenuation of intrahepatic echo and the progression of intravascular blood vessels. In moderate to severe steatosis, the sensitivity and specificity of ultrasound diagnosis are high (78.4%–90.8% and 76.9%– 90.9%, respectively).42 However, ultrasound diagnosis is susceptible to individual differences, checking instrument performance and parameter selection, operating experience and many other factors, so ultrasound quantitative diagnosis of fatty liver still has limitations. This diagnosis mainly depends on the subjective judgement of the operator, and there is no objective and unified quantitative index. Also, it is difficult to identify liver fibrosis and liver fat. Each method has its own advantages and disadvantages. It is hoped that with the advancement of science and technology, better non-invasive diagnostic methods will emerge.
Conclusions
The present study found that high physical activity was inversely associated with the risk of NAFLD in a dose-dependent manner in men, with moderate-intensity and vigorous-intensity physical activity having the greatest effect on reducing risk.
Supplementary Material
Acknowledgments
We thank all study participants for their cooperation. We also thank our staff for recruiting subjects and their technical assistance.
Footnotes
YFL and FH contributed equally.
Contributors: YFL and FH are joint first authors. X-EP obtained funding. X-EP, ZJH, XL and SHX designed the study. XTP and YFL collected the data. YLW and YH were involved in data cleaning and verification. YFL and XTP analysed the data. YFL and FH drafted the manuscript. X-EP and ZJH contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors have read and approved the final manuscript.
Funding: This work was supported by the National Natural Science Foundation of China (grant number 81473047 and 81401657), the Training Program Foundation for Middle-Aged and Young Talents from Sanitation System of Fujian Province (grant number 2014-ZQN-ZD-23) and the Joint Innovation Project of Science and Technology of Fujian Province (grant number 2017Y9104).
Competing interests: None declared.
Ethics approval: Local ethics committees of Fujian Medical University (ethics number 2014096)
Provenance and peer review: Not commissioned; externally peer reviewed.
Data sharing statement: Data are stored in Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China. Data are available upon request from XianE Peng; email address: Peng;fmuxe@163.com (X.E.P.).
Patient consent for publication: Not required.
References
- 1. Torres DM, Williams CD, Harrison SA. Features, diagnosis, and treatment of nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol 2012;10:837–58. 10.1016/j.cgh.2012.03.011 [DOI] [PubMed] [Google Scholar]
- 2. Rinella ME. Nonalcoholic fatty liver disease: a systematic review. JAMA 2015;313:2263–73. 10.1001/jama.2015.5370 [DOI] [PubMed] [Google Scholar]
- 3. Younossi ZM, Koenig AB, Abdelatif D, et al. . Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73–84. 10.1002/hep.28431 [DOI] [PubMed] [Google Scholar]
- 4. Li Z, Xue J, Chen P, et al. . Prevalence of nonalcoholic fatty liver disease in mainland of China: a meta-analysis of published studies. J Gastroenterol Hepatol 2014;29:42–51. 10.1111/jgh.12428 [DOI] [PubMed] [Google Scholar]
- 5. Motamed N, Rabiee B, Poustchi H, et al. . Non-alcoholic fatty liver disease (NAFLD) and 10-year risk of cardiovascular diseases. Clin Res Hepatol Gastroenterol 2017;41:31–8. 10.1016/j.clinre.2016.07.005 [DOI] [PubMed] [Google Scholar]
- 6. Gaggini M, Morelli M, Buzzigoli E, et al. . Non-alcoholic fatty liver disease (NAFLD) and its connection with insulin resistance, dyslipidemia, atherosclerosis and coronary heart disease. Nutrients 2013;5:1544–60. 10.3390/nu5051544 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Ortiz-Lopez C, Lomonaco R, Orsak B, et al. . Prevalence of prediabetes and diabetes and metabolic profile of patients with nonalcoholic fatty liver disease (NAFLD). Diabetes Care 2012;35:873–8. 10.2337/dc11-1849 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Miele L, Dall’armi V, Cefalo C, et al. . A case-control study on the effect of metabolic gene polymorphisms, nutrition, and their interaction on the risk of non-alcoholic fatty liver disease. Genes Nutr 2014;9:383 10.1007/s12263-013-0383-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Katsagoni CN, Georgoulis M, Papatheodoridis GV, et al. . Associations between lifestyle characteristics and the presence of nonalcoholic fatty liver disease: A case-control study. Metab Syndr Relat Disord 2017;15:72–9. 10.1089/met.2016.0105 [DOI] [PubMed] [Google Scholar]
- 10. Houghton D, Thoma C, Hallsworth K, et al. . Exercise reduces liver lipids and visceral adiposity in patients with nonalcoholic steatohepatitis in a randomized controlled trial. Clin Gastroenterol Hepatol 2017;15:96–102. 10.1016/j.cgh.2016.07.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Zhang HJ, Pan LL, Ma ZM, et al. . Long-term effect of exercise on improving fatty liver and cardiovascular risk factors in obese adults: A 1-year follow-up study. Diabetes Obes Metab 2017;19:284–9. 10.1111/dom.12809 [DOI] [PubMed] [Google Scholar]
- 12. Sullivan S, Kirk EP, Mittendorfer B, et al. . Randomized trial of exercise effect on intrahepatic triglyceride content and lipid kinetics in nonalcoholic fatty liver disease. Hepatology 2012;55:1738–45. 10.1002/hep.25548 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Hallsworth K, Thoma C, Hollingsworth KG, et al. . Modified high-intensity interval training reduces liver fat and improves cardiac function in non-alcoholic fatty liver disease: a randomized controlled trial. Clin Sci 2015;129:1097–105. 10.1042/CS20150308 [DOI] [PubMed] [Google Scholar]
- 14. Katsagoni CN, Georgoulis M, Papatheodoridis GV, et al. . Effects of lifestyle interventions on clinical characteristics of patients with non-alcoholic fatty liver disease: A meta-analysis. Metabolism 2017;68:119–32. 10.1016/j.metabol.2016.12.006 [DOI] [PubMed] [Google Scholar]
- 15. Korean Association for the Study of the L. KASL clinical practice guidelines: management of nonalcoholic fatty liver disease. Clin Mol Hepatol 2013;19:325–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Chalasani N, Younossi Z, Lavine JE, et al. . The diagnosis and management of non-alcoholic fatty liver disease: Practice guideline by the american gastroenterological association, american association for the study of liver diseases, and american college of gastroenterology. Gastroenterology 2012;142:1592–609. 10.1053/j.gastro.2012.04.001 [DOI] [PubMed] [Google Scholar]
- 17. Qiu S, Cai X, Sun Z, et al. . Association between physical activity and risk of nonalcoholic fatty liver disease: a meta-analysis. Therap Adv Gastroenterol 2017;10:701–13. 10.1177/1756283X17725977 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Farrell GC, Chitturi S, Lau GK, et al. . Guidelines for the assessment and management of non-alcoholic fatty liver disease in the Asia-Pacific region: executive summary. J Gastroenterol Hepatol 2007;22:775–7. 10.1111/j.1440-1746.2007.05002.x [DOI] [PubMed] [Google Scholar]
- 19. C N. Drug-induced hepatic steatosis. %A Amacher DE. Seminars in liver disease 2014;34:205–14. [DOI] [PubMed] [Google Scholar]
- 20. Bassett DR. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35:1396 10.1249/01.MSS.0000078923.96621.1D [DOI] [PubMed] [Google Scholar]
- 21. Ainsworth BE, Haskell WL, Herrmann SD, et al. . 2011 Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc 2011;43:1575–81. 10.1249/MSS.0b013e31821ece12 [DOI] [PubMed] [Google Scholar]
- 22. Shahar D, Shai I, Vardi H, et al. . Development of a semi-quantitative Food Frequency Questionnaire (FFQ) to assess dietary intake of multiethnic populations. Eur J Epidemiol 2003;18:855–61. 10.1023/A:1025634020718 [DOI] [PubMed] [Google Scholar]
- 23. Relative Validity of a Semi-quantitative Food Frequency Questionnaire Versus 3 day Weighed Diet Records in Middleaged Inhabitants in Chaoshan Area. China.pdf. [PubMed] [Google Scholar]
- 24. Yuexin Y, Guang W, Xingchang P. China food composition: Peking University Medical Press, 2009:1–360. [Google Scholar]
- 25. He Y, Jiang B, Wang J, et al. . BMI versus the metabolic syndrome in relation to cardiovascular risk in elderly Chinese individuals. Diabetes Care 2007;30:2128–34. 10.2337/dc06-2402 [DOI] [PubMed] [Google Scholar]
- 26. Chalmers J, MacMahon S, Mancia G, et al. . 1999 World health organization-international society of hypertension guidelines for the management of hypertension. Guidelines sub-committee of the world health organization. Clin Exp Hypertens 1999;21:1009–60. 10.3109/10641969909061028 [DOI] [PubMed] [Google Scholar]
- 27. Tucker JM, Welk GJ, Beyler NK. Physical activity in U.S.: adults compliance with the Physical Activity Guidelines for Americans. Am J Prev Med 2011;40:454–61. 10.1016/j.amepre.2010.12.016 [DOI] [PubMed] [Google Scholar]
- 28. Ekelund U, Poortvliet E, Yngve A, et al. . Heart rate as an indicator of the intensity of physical activity in human adolescents. Eur J Appl Physiol 2001;85:244–9. 10.1007/s004210100436 [DOI] [PubMed] [Google Scholar]
- 29. Fletcher GF, Ades PA, Kligfield P, et al. . Exercise standards for testing and training: a scientific statement from the American Heart Association. Circulation 2013;128:873–934. 10.1161/CIR.0b013e31829b5b44 [DOI] [PubMed] [Google Scholar]
- 30. Park S, Hong SM, Ahn IL, et al. . Estrogen replacement reverses olanzapine-induced weight gain and hepatic insulin resistance in ovariectomized diabetic rats. Neuropsychobiology 2010;61:148–61. 10.1159/000285780 [DOI] [PubMed] [Google Scholar]
- 31. Babaei P, Mehdizadeh R, Ansar MM, et al. . Effects of ovariectomy and estrogen replacement therapy on visceral adipose tissue and serum adiponectin levels in rats. Menopause Int 2010;16:100–4. 10.1258/mi.2010.010028 [DOI] [PubMed] [Google Scholar]
- 32. Topçuoglu A, Uzun H, Balci H, et al. . Effects of estrogens on oxidative protein damage in plasma and tissues in ovariectomised rats. Clin Invest Med 2009;32:133–43. 10.25011/cim.v32i2.6031 [DOI] [PubMed] [Google Scholar]
- 33. Kwak MS, Kim D, Chung GE, et al. . Role of physical activity in nonalcoholic fatty liver disease in terms of visceral obesity and insulin resistance. Liver Int 2015;35:944–52. 10.1111/liv.12552 [DOI] [PubMed] [Google Scholar]
- 34. Winn NC, Liu Y, Rector RS, et al. . Energy-matched moderate and high intensity exercise training improves nonalcoholic fatty liver disease risk independent of changes in body mass or abdominal adiposity - a randomized trial. Metabolism 2018;78:128–40. 10.1016/j.metabol.2017.08.012 [DOI] [PubMed] [Google Scholar]
- 35. Zhang HJ, He J, Pan LL, et al. . Effects of moderate and vigorous exercise on nonalcoholic fatty liver disease: a randomized clinical trial. JAMA Intern Med 2016;176:1074–82. 10.1001/jamainternmed.2016.3202 [DOI] [PubMed] [Google Scholar]
- 36. Ryu S, Chang Y, Jung HS, et al. . Relationship of sitting time and physical activity with non-alcoholic fatty liver disease. J Hepatol 2015;63:1229–37. 10.1016/j.jhep.2015.07.010 [DOI] [PubMed] [Google Scholar]
- 37. Matthews CE, George SM, Moore SC, et al. . Amount of time spent in sedentary behaviors and cause-specific mortality in US adults. Am J Clin Nutr 2012;95:437–45. 10.3945/ajcn.111.019620 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Gonzalez C, de Ledinghen V, Vergniol J, et al. . Hepatic steatosis, carbohydrate intake, and food quotient in patients with NAFLD. Int J Endocrinol 2013;2013:1–4. 10.1155/2013/428542 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Golbidi S, Daiber A, Korac B, et al. . Health benefits of fasting and caloric restriction. Curr Diab Rep 2017;17:123 10.1007/s11892-017-0951-7 [DOI] [PubMed] [Google Scholar]
- 40. Hashemi Kani A, Alavian SM, Esmaillzadeh A, et al. . Dietary quality indices and biochemical parameters among patients with Non Alcoholic Fatty Liver Disease (NAFLD). Hepat Mon 2013;13:e10943 10.5812/hepatmon.10943 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Cheng Y, Zhang K, Chen Y, et al. . Associations between dietary nutrient intakes and hepatic lipid contents in NAFLD Patients Quantified by ¹H-MRS and Dual-Echo MRI. Nutrients 2016;8:527 10.3390/nu8090527 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Bohte AE, van Werven JR, Bipat S, et al. . The diagnostic accuracy of US, CT, MRI and 1H-MRS for the evaluation of hepatic steatosis compared with liver biopsy: a meta-analysis. Eur Radiol 2011;21:87–97. 10.1007/s00330-010-1905-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
bmjopen-2018-026854supp001.pdf (333.8KB, pdf)