Modeling the Natural History of Non-Alcoholic Fatty Liver Disease:
Impact of Reduction in Body Mass Index and/or
Cholesterol on the Prevalence of NAFLD

Mika Aizawa
Graduate School of Biomedical & Health Sciences, Department of Health Informatics,
Hiroshima University, Kasumi 1-2-3 Minami-ku, Hiroshima, 734-8553, Japanaizawa6911@gmail.com
Seiichi Inagaki
International University of Health and Welfare, 4-3 Kozunomori, Narita City, Chiba 286-0048,
Japans.inagaki@iuhw.ac.jp
Michiko Moriyama
Graduate School of Biomedical & Health Sciences, Department of Chronic Care and Family
nursing, Hiroshima University,
Kasumi 1-2-3 Minami-ku, Hiroshima, 734-8553, Japan
morimich@hiroshima-u.ac.jp
Masayuki Kakehashi
Graduate School of Biomedical & Health Sciences, Department of Health Informatics,
Hiroshima University, Kasumi 1-2-3 Minami-ku, Hiroshima, 734-8553, Japan
kakehashi@hiroshima-u.ac.jp

ABSTRACT
Non-alcoholic fatty liver disease (NAFLD) is a manifestation of metabolic syndrome which
may lead to liver cancer. As there has been a recent increase in obesity, NAFLD is a growing
public health concern. In this study, we constructed the microsimulation model for NAFLD
which allows us to estimate the effectiveness of reduction in elevated body mass index (BMI)
and/or cholesterol on the prevalence of NAFLD. The study population was approximately
2,000 Japanese males who were aged 18-69 years and observed from 2012 to 2016. NAFLD
was presented in individuals if the presence of fatty liver was accompanied by the absence ora little alcohol consumption. The predictor variables and their transition probabilities
manipulating the model for NAFLD were determined by longitudinal multivariate regression
analysis. Forty-year projection for NAFLD in the initial cohort comprised of subjects aged 2029 years and they provided the baseline trajectory of the prevalence of NAFLD. Similar
projections were carried out under the various prevention scenarios, followed by thecomparison with the baseline trajectory. The magnitude of the effectiveness was strongly
dependent on the types of measures. If all the population had normal BMIs and/or cholesterol
profiles, the prevalence of NAFLD simply decreased to 3% (aged 60-69 years) while the1baseline prevalence peaked at 20% (aged 35-44 years) before decreasing to 12% (aged 60-69
years). In contrast, the prevalence of fatty liver including NAFLD simply decreased to reacha plateau of 8% (aged 30 years or over) while the baseline prevalence peaked at 32% (aged45-54 years) before decreasing to 29% (aged 60-69 years) in the projection period. Reductionin the risks of elevated BMI and/or LDL/HDL was predicted to significantly decrease theprevalence of NAFLD; however, its effectiveness was shown to be insufficient to eliminate
NAFLD. The details on the limitations affecting the prediction of NAFLD outcome were alsopresented.
KEYWORDS: Microsimulation, Non-Alcoholic Fatty Liver Disease, Metabolic Syndrome,
Body Mass index, Cholesterol,
JEL classification: C15, C63, I19

1. INTRODUCTION
Non-alcoholic liver disease (NAFLD) is a hepatic manifestation of the metabolic syndrome[1], which can lead to end stage liver diseases including liver cancer [2,3]. Due to pandemicof obesity and metabolic syndrome, NAFLD is widespread with steadily rising trends in many
countries [4-7]. In Japan as well, NAFLD is becoming the most common form of chronic liverdisease and is a growing public health concern [8-10], when compared with alcoholic liverdisease or virus-induced hepatitis. Due to the absence of subjective symptoms neither noeffective pharmacological therapy is available [11,12], and prevention of NAFLD byimproving lifestyle factors are important [13-15]. Meanwhile, to prevent metabolic syndrome
nationwide in Japan, metabolic screening and health education has been implemented since2008 [16]. However, little is known about the quantitative effectiveness of the measures on
the outcome of the prevalence of NAFLD.
Recently, health-related microsimulation models have been developed and are being usedfocusing on health insurance, medical care expenditure and population disease burden [17-19].
The models, for example, provide information to guide health policy decisions by predicting
trends in disease incidence under alternative health policy scenarios, and comparing theeffectiveness and cost-effectiveness of measures [20,21]. For these purposes, various models
have been developed for the following non-communicable diseases, coronary heart disease,stroke, type 2 diabetes, obesity, cancer, respiratory diseases, and vascular dementia [22-28].
Simulation models can predict long-term disease outcomes and assess experimentally theeffectiveness of alternative measures. Therefore, simulation studies on chronic diseases are
increasing when compared with cost and time demanding long-term cohort studies on chronic2diseases. However, no model for NAFLD has been developed to predict the outcome of theprevalence of NAFLD in association with lifestyle-related risk factors including obesity.
In this study, we have constructed a microsimulation model that predicts the outcome of theprevalence of NAFLD for Japanese adult males. This model estimates the effectiveness of
reduction in the risks, elevated BMI and/or cholesterol, on the incidence of NAFLD. Ourmodel was developed using the following scheme: Modelling procedures included specifying
both a model structure and parameters. They were specified by the possible predictor variables
for NAFLD and their update rules. Variable selection and identification of update rules were
performed by longitudinal multivariate regression analysis utilizing the sample source data.
NAFLD projection was carried out utilizing real population data to describe the baseline orcounterfactual trajectory. The obtained trajectories were subsequently compared to estimate
quantitative effectiveness of the measures on the outcome of the prevalence of NAFLD.
We developed an unprecedented model for NAFLD associated with lifestyle related risks,elevated BMI and/or cholesterol profile. However, to distinguish NAFLD from ALD byalcohol consumption at each time may not be consistent with histologically confirmed
NAFLD. This problem will be addressed in the discussion section.
2. METHODS
2.1 Study Population
The study population consisted of Japanese adult males aged 18-69 who participated in annual
health examinations, and completed questionnaires regarding their health and lifestyle, whichwere observed from 2012 to 2016 (n=1908, 1958, 2046, 2079 and 2269). The entire study
population consisted of employees of Fujikura Ltd., one of the representative manufacturers
in the power & telecommunication industry of Japan.
2.2 Characteristics of the Data
Sample source, a set of all the data from the study population, included the following variables:
demographic variables (age and presence or absence of shift-working), health status variables(diagnosis of fatty liver, blood pressure, body mass index (BMI) and laboratory variables(triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoproteincholesterol (HDL-C), hemoglobin A1c (HbA1c)), presence or absence of medication for
hypertension, dyslipidemia or insulin resistance and lifestyle variables (presence or absenceof regular exercise and amount and frequency of alcohol consumption). We describe
LDL/HDL, the ratio of LDL-C to HDL-C, hereafter.
The diagnosis of fatty liver was carried out by physicians on ultrasonography [29]. Blood3

pressure, BMI and laboratory data were measured by standard procedures. Medication, regularexercise and amount and frequency of alcohol consumption were self-reported. For example,
if the alcoholic beverage was other than Japanese sake, the gross alcohol consumption wasconverted to the amount of Japanese sake with equal alcohol content.
Alcohol consumption was measured as the mean ethanol consumption per day, given by thefollowing equation (1):
Alcohol consumption (g/day) = 180 * m * 0.15 * n/7(1)

where m=1, 1.5, 2.5 and 3 if the consumption of Japanese sake (ml/day) is “less than 180”,“180-less than 360”, “360-less than 540” or “540 or over”, respectively, and n=7, 3.5 and 0 if
the frequency of drinking alcohol is “every day”, “often” or “seldom or never”, respectively.
2.3 Diagnosis of NAFLD
According to the guidelines for the diagnosis of NAFLD [30], the NAFLD cases wereidentified as fatty liver with a history of absence or a little amount of alcohol consumption(less than 30 g/day). The cases of fatty liver with elevated alcohol consumption (30g/day orover) was included in alcoholic liver disease (ALD) cases [30]. Patients who had viral
infection-induced liver disease, “hepatitis B or hepatitis C”, and/or history of pancreatectomy
were also excluded [30].
Projection for NAFLD required initial values including presence or absence of fatty liver,
however, no diagnosis of fatty liver was available for individuals of the initial cohort aged 2029 years as they did not participate in the fatty liver screening. Prior to projection, we predicted
the presence or absence of fatty liver in individuals in the initial cohort by applying criteria
obtained in the population aged 30-69 years.
.
2.4 Construction of the model
2.4.1 Identification of Predictor Variables and their Update Rules
Our longitudinal study revealed that BMI, LDL/HDL, age, presence or absence of fatty liver
and alcohol consumption were the potential predictor variables for NAFLD model (Figure 1).
The equations to update each predictor variable for NAFLD model were also provided. (Figure2). (These methods are similar to our previous study which is now under review)
2.4.2 Model Structures
Annual updates of the presence or absence of NAFLD were conducted in the following two
4

steps, the presence or absence of fatty liver and alcohol consumption was annually updated
separately, which was followed by judging if the presence of fatty liver was accompanied by
the absence or a little alcohol consumption (less than 30 g/day). Therefore, the predictivemodel for NAFLD comprised of the predictive model for fatty liver and the predictive model
for alcohol consumption because NAFLD is determined by the combination of presence orabsence of fatty liver and alcohol consumption [7]. Furthermore, the predictive model for fatty
liver included sub-models for the risk factors, BMI or LDL/HDL (Figure 2). Those obtained
were closed models where no demographic ins and outs were allowed.
2.4.3 Cohort Setting and Projection
Two types of theoretical cohorts, a validation cohort and an initial cohort, were established.
The validation cohort (n=932) comprised of Japanese adult males aged 30-69 years from thesample source. Using the data in 2012 from the validation cohort, the prevalence of NAFLD
was predicted in each year from 2013 to 2016, which was followed by comparison withobserved prevalence. At the same time, the predictor variables, BMI (kg/m2) (=0 if < 25, =1 if≧25), LDL/HDL (=0 if < 2.0, =1 if ≧2.0) and alcohol consumption (g/day) (=0 if < 30, =1
if ≧30), were also projected and described. This description included the proportion ofobesity, dyslipidemia and alcohol drinkers, respectively. These obtained trajectories were
subsequently compared with the observed ones. The trajectory of the calculated prevalence
was given by the means which were generated in the 10-times Monte Carlo simulations for all
the individuals at every year step. The obtained means were described with error bars
indicating the mean ± 2SDs.
The initial cohort (n=1000) comprised of Japanese adult males randomly selected from thestudy population aged 20-29 from 2012 to 2016. The prevalence of NAFLD was projected for
40 years to describe the life-course pattern (from 20 to 69 years old) of the baseline prevalenceof NAFLD. Furthermore, NAFLD projection for the initial cohort was conducted undervarious prevention scenarios, which were followed by a comparison with the baseline
prevalence of NAFLD to estimate quantitative effectiveness of the measures against NAFLD.
Five types of prevention scenarios were assumed:(a) intervention performed for all the population, 100% of future obesity and dyslipidemia
incidents prevented completely(b) intervention performed for only the population at risk, 100% of future obesity anddyslipidemia incidents prevented(c) intervention performed for only the population at risk, 50% of future obesity anddyslipidemia incidents prevented(d) intervention performed for only the population at risk, 10% of future obesity and5dyslipidemia incidents prevented(e) intervention performed for all obese population, stochastic reduction in BMIs of
individuals by -1.1±2.6 (kg/m2) in the next year stepFor each scenario, simulation was conducted in the following rules.
(a) If updated BMI and/or LDL/HDL were the cut-off values or over in individuals, 100% ofthem were supposed to have 24.9 of BMI and/or 1.9 of LDL/HDL(b) If the subjects at risk of elevated BMI and/or LDL/HDL, all of them were supposed to have24.9 of BMI and/or 1.9 of LDL/HDL in the next year step(c) If the subjects at risk of elevated BMI and/or LDL/HDL, 50% of them were randomly
supposed to have 24.9 of BMI and/or 1.9 of LDL/HDL in the next year step(d) If the subjects at risk of elevated BMI and/or LDL/HDL, 10% of them were randomly
supposed to have 24.9 of BMI and/or 1.9 of LDL/HDL in the next year step(e) If the subjects at risk of elevated BMI, 100% of them were supposed to have reduced BMI
by random real number with N (-1.1, 2.62) in the next year step.

Figure1: The required variables to predict presence or absence of NAFLD for individuals and their updates

Figure2: Regression equations for updated variables required to predict presence or absence of NAFLD for individuals

62.4.4 Performing Monte Carlo Simulations
All the simulation was performed using mathematical software, “Mathematica 11.0”. The
outcome of the presence of fatty liver was predicted by Bernoulli trial P(X=1) = p where X=1
if the individuals have fatty liver, and the expectation value (p) was given by the following
equations (2) and (3). Alcohol consumption (g/day) (=0 if < 30, =1 if ≧30) was also predictedin the same manner using the equations (2) and (4).
p(V(t+1)) = 1/(1+exp(-Sc))
If V=FL (fatty liver), Sc = C0f + C1f [FL(t)] + C2f [BMI(t)] + C3f [LDL/HDL(t)]If V=ALC (alcohol consumption), Sc = C0a + C1a [ALC(t)] + C2a [age(t)]

(2)
(3)
(4)

Updates of BMI and LDL/HDL were conducted by the following equations (5) and (6).
[BMI(t+1)] = C0f+C1f [FL(t)] +C2f [BMI(t)] +C3f [LDL/HDL(t)] +εb(5)[LDL/HDL(t+1)] = C0f+C1f [FL(t)] +C2f [BMI(t)] +C3f [LDL/HDL(t)] +εl(6)2where ε is the real number with N (0, SD ), SD: the standard deviation of the residuals
ε was incorporated in the equation to predict BMI or LDL/HDL in the next year step.
2.5 Statistical Analysis
All statistical analyses were performed using the software, “IBM SPSS Statistics 24”.
Quantitative data were expressed as mean values (± standard deviation). The normality of
distribution of continuous variables was tested using the Shapiro-Wilk test. Multivariate
logistic (or linear) regressions were conducted depending on the properties of predictedvariables, binary or numerical. A two-tailed P value < 0.05 was regarded to be statisticallysignificant.
2.6 Ethics Statement
The study protocol was approved by the Ethics Committee of Hiroshima University, Japan.
The data utilized in this analysis were obtained from Fujikura Ltd. through opt-out procedure.
3. RESULTS
3.1 Predictor variables and their update rules
The possible predictor variables for NAFLD and their update rules were identified bylongitudinal multivariate regression analysis utilizing the data from the sample source. The
characteristics are shown in Table 1. The summary of the predictor variables for NAFLD andtheir predictive equations are presented in Table 2.
7

3.2 Projection and Validation
Projection was carried out for the validation cohort from 2012 to 2016 to describe thetrajectory of the prevalence of NAFLD as well as obesity, dyslipidemia and alcohol drinkers.
Each observed value virtually fell in the variance (±2SDs) of calculated value in the same
year step (Figure 3).
Table 1 Baseline Characteristics of Sample Source in 2012

Age
BMI (kg/m2)*
LDL-C (mg/dl)
HDL-C (mg/dl)
Triglyceride (mg/dl)
Hemoglobin A1c (%)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)

Mean
40.4
23.6
122.3
56.4
128.1
5.4
122.2
56.4

SD
9.5
3.5
29.6
13.7
94.9
0.50
15.6
13.7

Positive (%)
Ageless than 20
20-30
30-40
40-50
50-60
60 and overDiagnosis of fatty liver
Medication
Hypertension
Insulin Resistancehyperlipidemia
Smoking
Alcohol consumption(≥30g/day)
Regular exercise
Shift-workers

0.9
15.8
30.3
35.7
16.2
1.1
27.9
9.0
2.0
4.4
33.8
34.2
20.8
43.3

Table 2 The Characteristics of regression models for predicted variables including coefficients* of predictor variables* Non-standardized coefficient (B) of the predictor variables in the regression models
FL: Fatty liver, ALC: Alcohol consumption

3.3 Baseline and Counterfactual Projections
The baseline characteristics of the initial cohort are shown in Table 3. NAFLD/fatty liver BMI
and LDL/HDL were projected for 40 years to describe the trajectory of the baseline prevalenceof NAFLD/fatty liver. Similarly, projections were conducted under the scenarios (a)-(e) to givecounterfactual prevalence of NAFLD/fatty liver. The comparison among the trajectories given
8

by the simulations revealed that the magnitude of the effectiveness was remarkably dependentof the types of prevention measures. If all the population had normal BMIs and/or cholesterol
profiles, the prevalence of NAFLD simply decreased to 3% (aged 60-69 years) while thebaseline prevalence peaked at 20% (aged 35-44 years) before decreasing to 12% (aged 60-69
years). In contrast, the prevalence of fatty liver including NAFLD simply decreased to reacha plateau of 8% (aged 30 years or over) while the baseline prevalence peaked at 32% (aged45-54 years) before decreasing to 29% (aged 60-69 years) in the projection period. (Figure 4).

Figure 3: Projected Prevalence of the Validation Cohort:(1) Fatty liver and NAFLD, (2) Alcohol drinkers, (3) Obesity and (4) Dyslipidemia

Table 3 Baseline Characteristics of the Initial cohort

Diagnosis of fatty liver
Medication
Hypertension
Insulin Resistancehyperlipidemia
Smoking
Alcohol consumption (≥30g/day)
Regular exercise
Shift-workers

Positive (%)
12.3 *Age
BMI (kg/m2)*
LDL-C (mg/dl)
HDL-C (mg/dl)
Triglyceride (mg/dl)
Hemoglobin A1c (%)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)

0.4
0.0
0.4
21.3
24.0
23.4
29.3

Mean
26.8
22.4
107.4
57.2
89.6
5.1
116.3
70.0

* BMI (kg/m2) = (weight (kg)) / (height (m)) ^ 2* estimated value

9

SD
2.3
3.4
27.6
11.8
61.8
0.3
12.7
9.3

4. DISCUSSION
We constructed the microsimulation model in order to predict the outcome of the prevalenceof NAFLD for Japanese adult males. The model estimated the quantitative effectiveness of
reduction in the risks, elevated BMI and/or cholesterol, on incidence of NAFLD. Our model
conducted baseline and counterfactual projections for the prevalence of NAFLD and showeda reduction in the risks of elevated BMI and/or LDL/HDL decreased the prevalence of NAFLD.

Figure 4: Projected Prevalence of Initial Cohort: (1) NAFLD, (2) Fatty liver (NAFLD + ALD)

The NAFLD risk factors were classified by level of exposure. The cut-off value of BMI was
assumed to be 25kg/m2 as is common in Japan [31]. That of alcohol consumption was 30g/day
for adult male [30]. Since it has been recognized that the onset of NAFLD was associated withLDL and inversely associated with HDL [32,33], LDL/HDL was produced as one of the
NAFLD/fatty liver risk factors in our model. The cut-off value of LDL/HDL (=2.0) was set
because a previous study reported that the exposure of LDL/HDL equals to 2.0 or oversignificantly leads to sclerosis in association with metabolic syndrome [34-36].
Longitudinal regression analysis clearly showed that BMI and LDL/HDL were significant
predictor variables. The evidence for this was consistent with the facts that elevated BMI
and/or cholesterol were the possible risk factors for NAFLD [32,33]. HbA1c was also reported
to be a possible risk factor for NAFLD [37], however, the results showed it had no additional
predictive power in our regressions and was dropped during model selection.
Since limited epidemiological findings were available to validate the prevalence of NAFLD/
fatty liver, all we could conduct was internal validation where simulated values were comparedwith observed values of the data from the study population in 2012-2016.
The subjects with metabolic syndrome have received 6-month intervention of health education
under the current measures. It was reported that the intervention reduced BMIs of subjects
10with metabolic syndrome by -1.1±2.6 (kg/m2) and no significant increase in BMI was observedfor 6 months after the intervention [38]. Projection under scenario (e) showed the prevalenceof NAFLD and it peaked at 16% (aged 30-39 years) before decreasing to 9% (aged 60-69
years), while the baseline prevalence peaked at 20% (aged 35-44 years) before decreasing to
12% (aged 60-69 years). These results can be used to estimate the effectiveness of current
measures against metabolic syndrome in all Japanese adult males. Metabolic screening has
been conducted only 50% of the people aged 40 years or over, neither health education has
been conducted only 17% of the metabolic subjects as of 2016 [16]. Therefore, theeffectiveness of the current measures on prevention of NAFLD is probably very small.
Our simulation indicated that complete reduction in the risks of elevated BMI and/or
LDL/HDL decreased the prevalence of NAFLD and fatty liver to 3% and 8%, respectively in
40-year projection for the initial population aged 20-29 years. However, its effectiveness wasshown to be insignificant to eliminate NAFLD.
This result may partly be associated with the existence of lean NAFLD found in some of the
population, who had no risk of elevated BMI [39-41]. Further prevention strategies other thanobesity and/or dyslipidemia may be required. Moreover, further studies are required including
those focusing on medical care expenditure and population disease burden in order tomaximize total benefits.
5. LIMITATIONSThe NAFLD model was a closed model where no demographic ins and outs were allowed.
The diagnosis of fatty liver was available only for the population aged 30-69 years although
we need NAFLD information of the population aged 20-29 years to carry out further
simulations. Accordingly, presence or absence of fatty liver in the initial cohort aged 20-29
years was estimated by applying criteria obtained in the population aged 30-69 years.
Projection of NAFLD for the population aged 20-29 years might reduce accuracy. In contrast,
projection for NAFLD was not carried out after 70 years because it was suspected thatextrapolation cannot be applicable.
Our model was constructed using Japanese full-time workers data. The population comprisedof full-time workers may be slightly different in the epidemiology of NAFLD/fatty livercompared with other populations comprised of those who work part-time or who are
unemployed. This is because the employment condition variations may cause the differencein the prevalence of obesity and dyslipidemia [42]; further study is required comparing BMIs
11

and cholesterol profiles of the study population with those of whole Japanese adult male.
Moreover, gender may also affect the cut-off values of alcohol consumption, BMI and/or
LDL/HDL as well as their predictive equations when this model is applied to other populations[43-45]. In this case, external validation and modification of the model will be required.
Distinguishing NAFLD from ALD by alcohol consumption may be accompanied by a certain
inaccuracy due to the following reasons; (1) alcohol consumption was self-reported, (2)validity of the cutoff value remains controversial [46,47], and (3) alcohol consumption in
individuals may possibly change in their course of life. For the last reason, elevated alcohol
consumption in the subjects with NAFLD may change the diagnosis of NAFLD into FLD andvice versa. Therefore, the diagnosis of NAFLD by alcohol consumption is not always assured
to be consistent with the histological diagnosis
We admit that the method to distinguish NAFLD from ALD by alcohol consumption includes
some uncertainty. However, since cost-effective and noninvasive methods to accurately
distinguish NAFLD have not yet been established [48], the classification based on alcohol
consumption is a cost-effective and noninvasive method to determine NAFLD with a certain
level of significance. The emergence of innovative methods to provide accurate diagnosis of
NAFLD is expected in the future.
6. CONCLUSION
We have constructed a microsimulation model that predicts the outcome of the prevalence of
NAFLD for Japanese adult males and estimated the effectiveness of reduction in the risks,elevated BMI and/or LDL/HDL, on incidence of NAFLD. Our model showed that a reductionin the risks for NAFLD, elevated BMI and/or LDL/HDL, significantly decreased theprevalence of NAFLD, however, its effectiveness was shown to be insufficient to eliminate
NAFLD. Further studies are required including those focusing on medical care expenditureand population disease burden in order to maximize total benefits.
ACKNOWLEDGEMENT
This study was funded by a Grant-in-Aid for Scientific Research B (Grant Number 15H03339)
from the Ministry of Education, Culture, Sports, Science and Technology, Japan. The authors
have no financial relationships to disclose. The first and the last authors take complete
responsibility for the integrity of the data and the accuracy of the data analysis.
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