Canadian Journal of Gastroenterology and Hepatology

Canadian Journal of Gastroenterology and Hepatology / 2018 / Article
Special Issue

Nonalcoholic Fatty Liver Disease

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Research Article | Open Access

Volume 2018 |Article ID 5323061 | 11 pages | https://doi.org/10.1155/2018/5323061

Speculation of the Time-Dependent Change of FIB4 Index in Patients with Nonalcoholic Fatty Liver Disease: A Retrospective Study

Academic Editor: Branka Filipović
Received25 Oct 2017
Accepted22 Jan 2018
Published12 Mar 2018

Abstract

Aim. To speculate on the time-dependent change of FIB4 index in patients with nonalcoholic fatty liver disease (NAFLD) and its increase-decrease rate per year, simply and accurately. Methods. In all 23 patients with NAFLD with the value of FIB4 index over 1.30 at the peak, the period from the first to each examination date was calculated and this period (years) was regarded as . Next, the mean value of FIB4 index during the past year to each examination date was regarded as . In every , the minimum and the maximum value were found out. Between corresponding to this minimum and corresponding to this maximum , the correlation between and was analyzed as the main subject. Then, the slope of each correlation was investigated, because it should indicate increase-decrease rate per year. Results. In all 23 patients, the correlations as the main subject were recognized and the mean absolute value of correlation coefficient was . As for the slope, the mean absolute value was (/year). Conclusion. Simply and accurately, the time-dependent change of FIB4 index and its increase-decrease rate per year could be approximately speculated.

1. Introduction

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease worldwide [15]. A liver biopsy still remains the gold standard for the diagnosis of nonalcoholic steatohepatitis (NASH), but it is difficult to perform liver biopsies in all patients with NAFLD. Therefore many noninvasive methods for estimating liver fibrosis have been developed; these are direct markers and the scoring systems, such as type IV collagen 7S [6, 7], hyaluronic acid [8, 9], aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (AAR) [9, 10], NAFLD fibrosis score [11], BARD score [12], NAFIC score [7], and so on.

FIB4 index has been developed to predict liver fibrosis in patients with HIV/HCV coinfection [13] and it is also useful for estimating liver fibrosis in patients with NAFLD [1416]. However there were few reports analyzing the transition of FIB4 index during all the clinical period in patients with NAFLD. Probably for the dispersion of the data, it has been difficult to estimate the accurate value.

In this study the correlation between the period from the first to each examination date and the mean value of FIB4 index during the past year to each examination date was analyzed. This correlation was thought to be the time-dependent change of the mean FIB4 index during the past one year and in the present study the correlation was proved to be extremely strong. Moreover, increase-decrease rate per year could be derived from the slope of the correlation in the scatter diagram.

In this retrospective study, the aim was to speculate approximately on the time-dependent change of FIB4 index and its increase-decrease rate per year, simply and accurately.

2. Methods

2.1. Patients

A total of 23 patients between October 1999 and June 2017 were enrolled with the following criteria: negative HBs antigen, negative HCV antibody, and negative anti-mitochondrial antibody [17]. Serum CRP levels were continuously negative. Patients whose values of anti-nuclear antibody (ANA) showed more than 1 : 160 were excluded [18]. The peak ALT levels were over 40 (U/L) for males or over 30 (U/L) for females [1921]. The peak value of FIB4 index was over 1.30 [14, 15, 22] in every patient. Fatty liver was diagnosed with ultrasonography and/or computed tomography. Drug induced liver injury and hereditary liver diseases were denied by the interview. Patients who consumed alcohol over 30 g per day for males or over 20 g per day for females were excluded [14, 23, 24]. Patients whose observed period in the clinic was less than two years were excluded. Finally patients whose maximum interval between examinations was more than one year were excluded.

All procedures in this study were conducted with the declaration of Helsinki (1964). The written informed consent was not applicable, because this is a retrospective study. In this study, direct data of AST, ALT, age, and platelet count were only used in patient characteristics and it was not possible to identify individuals.

2.2. Correlations
2.2.1. The Main Correlations

First, the period from the first to each examination date was calculated and this period (years) and was regarded as . Next, the mean value of FIB4 index during the past one year to each examination date (the mean FIB4 index YTD) was regarded as . Because of using the mean value during the past one year as , less than 1.00 (years) and corresponding to this were excluded; the minimum value in every was more than 1.00 (years). In every , the minimum value and the maximum value were found out. Between corresponding to this minimum and corresponding to this maximum , the correlation between and was analyzed in every patient. This correlation was defined as the main correlation. There are two possibilities; either the values of correlation coefficient are positive or these are negative. The group with positive value of was defined as FIB4 index-increasing group and the group with negative value of was also defined as FIB4 index-decreasing group.

2.2.2. The After-Main Correlations

Then, another correlation was analyzed, except for the data during the period of the main correlation. However, both ends of the data in the main correlation were not excluded.

After the period of the main correlation, it was checked whether the period to the last examination date was more than 1.00 (years) or not. Only when this period was more than one year, the analysis was performed. The first data of this analysis was automatically the last data in the main correlation. In FIB4 index-increasing group, the minimum value was newly found out in this period. Yet, in FIB4 index-decreasing group, the maximum value was newly found out. In both groups, from the maximum in the main correlation to corresponding to newly found out, the correlation between and was analyzed. This correlation was defined as the after-main correlation.

2.2.3. The Before-Main Correlations

Finally, before the period of the main correlation, it was checked whether the period from the first examination date was more than 2.00 (years) or not, because less than 1.00 (years) had been excluded. Only when this period was more than two years, the analysis was performed. The last data of this analysis was automatically the first data in the main correlation. In FIB4 index-increasing group, the maximum value was newly found out in this period. Yet in FIB4 index-decreasing group, the minimum value was newly found out. In both groups, from corresponding to newly found out to the minimum in the main correlation, the correlation between and was analyzed. This correlation was defined as the before-main correlation.

2.2.4. A Total of the Correlations Recognized in the Study

The cumulative correlations recognized in this study were shown.

2.3. Slopes of Correlations

In every patient, increase-decrease rate per year of the mean FIB4 index YTD was derived from the slope of the main correlation. In the same way it was also derived from each slope of the after-main correlation and/or the before-main correlation, if these correlations were recognized.

2.4. Statistics Analysis

Each correlation between two parameters was evaluated by Pearson’s correlation. A value () less than 0.05 was considered statistically significant. It was conducted by Microsoft Excel for MAC 2011.

3. Results

3.1. Patient Characteristics

12 out of 23 patients (52.2%) were male. In 20 patients the values of ANA were less than 1 : 40 [18] and in three patients these were 1 : 40, 1 : 40, and 1 : 160, respectively, and the values of anti-smooth muscle antibody were all less than 1 : 40 and also immunoglobulin G levels were all within the upper normal limit of the clinic [25]. In 17 patients computed tomography scans were performed. In all patients, the mean value of the peak ALT levels was (U/L) and that of the peak value of FIB4 index was . In 10 patients the peak values of FIB4 index were more than 2.67 [14, 21], yet in nine patients those were less than 2.00. In all patients, the mean value of platelet count at the bottom was (×109/L). Of 23 patients, 19 consumed no alcohol and the remaining four were all males (Table 1).


Patients Laboratory findings
At the first examinationPeak valueAt the last examination
(bottom value only as for platelet count)

Gender (male)12 (52.2%)
Age (years)58.2 ± 8.5NA68.8 ± 9.5
AST (U/L)40 ± 2657 ± 3028 ± 10
ALT (U/L)49 ± 3572 ± 3526 ± 12
GGT (U/L)NA94 ± 8345 ± 44
FIB4 index1.66 ± 0.782.84 ± 1.342.04 ± 0.82
AAR0.88 ± 0.221.52 ± 0.341.17 ± 0.31
Platelet count (×109/L)215 ± 68165 ± 45207 ± 63
Type IV collagen 7S (ng/mL)NA5.2 ± 2.04.4 ± 1.4
M2BPGiNA1.09 ± 0.860.88 ± 0.74

Mean ± SD

Continuous variables were shown as mean ± standard deviation. At the last examination both type IV collagen 7S and M2BPGi were examined in all 23 patients. , number of patients; NA, no analysis; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyl transpeptidase; AAR, AST/ALT ratio; M2BPGi, Mac-2 binding protein.
3.2. Correlations
3.2.1. The Main Correlations

In all 23 patients the main correlations were recognized (Figure 1) and the mean absolute value of was (Table 2). Each was shown in Table 2. Of 23 patients, 17 were categorized in FIB4 index-increasing group and the mean value of was (0.69 to 0.99). In 11 of these 17, the values of were more than 0.90. On the other hand, six of 23 patients were categorized in FIB4 index-decreasing group and the mean value of was − (−0.97 to −0.91). In all these six patients, the absolute values of were more than 0.90. Therefore, in 17 out of 23 patients, the absolute values of were more than 0.90. In a total of 23 patients, the mean value of interval between examinations was (years), that is, (days), and the mean value of the total clinical period was (years) (Table 2). Since less than 1.00 (years) were excluded, the total analyzed period was (years) (Table 2). The period in which the main correlation was recognized (the main correlation’s period) was (years) and the mean ratio of the main correlation’s period to the total analyzed period was % (27% to 98%).


PatientsSlope Interval between examinationsThe main correlation’s periodRatioTotal analyzed periodTotal clinical period
Case number Positive valueNegative valueAbsolute valuePositive valueNegative valueAbsolute value(years)(days)(years)(%) (years) (years)

Case  10.40980.4098 2 × 10−510.980.980.960.12448.26412.813.9
Case  20.33430.33431 × 10−200.980.980.960.08292.4288.79.7
Case  30.26980.26985 × 10−330.960.960.920.18669.59410.111.1
Case  40.19230.19239 × 10−80.990.990.980.12441.2721.62.7
Case  50.16180.16180.010.960.960.920.19701.1323.34.4
Case  60.0840.0842 × 10−80.930.930.870.431568.87312.113.6
Case  70.07620.07629 × 10−70.820.820.670.19713.4359.610.9
Case  80.07580.07581 × 10−420.920.920.850.165814.79315.917.3
Case  90.07550.07553 × 10−440.910.910.830.09359.96315.817.1
Case  100.07080.07082 × 10−530.940.940.880.1142129812.213.5
Case  110.06520.06524 × 10−430.960.960.910.15549.48411.212.5
Case  120.06290.06291 × 10−320.840.840.710.134714.19614.816
Case  130.06080.06086 × 10−100.80.80.640.27979.18011.412.5
Case  140.03490.03495 × 10−80.690.690.470.1384.8677.18.2
Case  150.03020.03021 × 10−130.740.740.550.27213.99814.215.3
Case  160.02990.02996 × 10−110.890.890.790.291057.67510.111.2
Case  170.02560.02566 × 10−480.950.950.90.11409.56115.516.6
Case  18−0.0890.0890.004−0.950.950.90.21761.2532.33.5
Case  19−0.09840.09845 × 10−12−0.910.910.820.11392.9436.87.9
Case  20−0.10780.10780.005−0.950.950.890.41452474.25.2
Case  21−0.12370.12376 × 10−5−0.940.940.880.13461.3632.13.1
Case  22−0.3070.3072 × 10−11−0.970.970.930.12442375.56.6
Case  23−0.36810.36812 × 10−11−0.950.950.90.12453.22711.912.9

Mean ± SD0.1212 ± 0.1114−0.1823 ± 0.11170.1371 ± 0.11470.90 ± 0.09−0.94 ± 0.020.91 ± 0.080.83 ± 0.130.17 ± 0.0964 ± 336.6 ± 4.564 ± 239.5 ± 4.510.7 ± 4.6

Each value was shown in total clinical period; continuous variables were shown as mean ± standard deviation. Slope, the slope of the correlation; , a value; , correlation coefficient; , a squared value of ; The main correlation’s period, the period in which the main correlation was recognized; Ratio, the ratio of the main correlation’s period to the total analyzed period; Total analyzed period, the period from the earliest examination date at least a year after the first examination to the last examination date; Total clinical period, the period from the first to the last examination date.
3.2.2. The After-Main Correlations

In 11 out of all 23 patients, each period to the last examination after the main correlation was more than 1.00 (years). In eight of these 11, the after-main correlations were seen. The mean absolute value of was and each was shown in Table 3. In the remaining three of these 11, that is, in patients of cases , , and 18, the correlations were not recognized statistically. In these three patients, numbers of analyzed data were four, five, and five, respectively, and the correlations were not recognized by and .83, by and .82, and by and , respectively (Table 3).


PatientsPhase with the before-main correlationPhase with the main correlationPhase with the after-main correlation
Case number Period (years)Ratio (%)SlopeAbsolute value of SlopePeriod (years)Ratio (%)Slope (%)Absolute value of

Case  11.18−0.51330.0006−0.910.9190.4098713.628−0.2735 2 × 10−17−0.950.9533
Case  21.113−0.43043 × 10−9−0.960.96160.3343301.922−0.26881 × 10−14−0.950.9528
Case  3No existence0.269859No existence
Case  4No existence0.192310No existence
Case  50.824−0.24750.005−0.970.9750.16185No recognition(0.17)(−0.83)-(4)
Case  6Impossible to analyze(2)0.084181.412−0.38750.004−0.980.985
Case  70.77−0.0820.0001−0.990.9950.076224331−0.06190.001−0.850.8511
Case  8No recognition(0.34)(−0.86)-(3)0.0758101No existence
Case  9No existence0.07551121.17−0.34462 × 10−7−0.940.9415
Case  10No existence0.0708113No existence
Case  11Impossible to analyze(2)0.065280No existence
Case  12No existence0.0629116No existence
Case  13217−0.10930.0008−0.990.9950.060840No existence
Case  141.420−0.11181 × 10−6−0.950.95120.034949No existence
Case  15No existence0.030272No existence
Case  160.55−0.15350.04−0.9980.99830.029930No recognition(0.09)(−0.82)-(5)
Case  173.422−0.04011 × 10−10−0.810.81410.025696No existence
Case  18No existence−0.0896No recognition(0.07)(0.85)-(5)
Case  191150.15121 × 10−80.980.9813−0.0984300.7110.13080.0040.950.956
Case  20No recognition(0.27)(0.73)-(4)−0.10786No existence
Case  21No existence−0.123710No existence
Case  221.5270.35956 × 10−50.90.912−0.307191.1210.2290.0040.880.888
Case  23No existence−0.3681224.8410.18352 × 10−140.940.9430

The mean ± SD1.3 ± 0.716 ± 70.95 ± 0.051.9 ± 1.322 ± 110.93 ± 0.04

Continuous variables were shown as mean ± standard deviation. Period, the period in which each correlation was recognized; Ratio, the ratio of each correlation’s period to the total analyzed period; Slope, the slope of each correlation; , a value; , correlation coefficient; , number of data in each correlation; No existence, the correlation did not exist; Impossible to analyze, the analysis was impossible; No recognition, the correlation was not recognized statistically and both and were shown as figures in parentheses.
3.2.3. The Before-Main Correlations

In 14 out of all 23 patients, each period from the first examination before the main correlation was more than 2.00 (years). In 10 of these 14, the before-main correlations were seen. The mean absolute value of was and each was shown in Table 3. In the remaining four of these 14, that is, in patients of cases , , 11, and 20, the correlations were not recognized statistically. In two patients of cases and , numbers of analyzed data for the correlations were both two and it was impossible to analyze. In the remaining two patients of cases and , numbers of analyzed data were three and four and the correlations were not recognized by and .86 and by and , respectively (Table 3).

3.2.4. A Total of the Correlations Recognized in the Study

The cumulative number of all correlations recognized in this study was 41 (Table 3). The mean absolute value of was . In 32 of 41 correlations the absolute values of were over 0.90 and in only three of 41 they were less than 0.80 (0.688 to 0.799).

3.3. Slopes of Correlations

In all 23 main correlations, the values of increase-decrease rate per year of the mean FIB4 index YTD were shown as the slope in Table 2. In them the mean absolute value of the slope was (/year). In 17 correlations categorized in FIB4 index-increasing group, the mean value of the slope was (/year), yet in six ones categorized in FIB4 index-decreasing group, it was − (/year). Then, in a total of 41 correlations, the mean absolute value of the slope was (/year). In 22 positive correlations, the mean value of the slope was (/year), yet in 19 negative correlations, it was − (/year). All 41 correlations were shown in Figure 2. In order to demonstrate the slopes clearly, the main correlations were shown without -intercept in Figure 2.

4. Discussion

In the present study the correlations between the period from the first to each examination and the mean FIB4 index YTD were analyzed. The results just would mean the time-dependent change of the mean FIB4 index YTD. All 23 enrolled patients had at least one phase with the main correlation (Figure 1 and Table 2) and the mean absolute value of was . In 17 of these 23 (74%) the absolute values of were over 0.90. Meanwhile, 10 of 23 patients had only one phase with the main correlation (Figure 2(a)) and the remaining 13 had several phases (Figures 2(b)2(d)). As a result, a total of 41 correlations were recognized in the study and the mean absolute value of was . In 32 of all the 41 correlations (78%) the absolute values of were over 0.90 (Table 3). In addition, the authors will show the reason why there were some correlations with low absolute values of . For example, out of all 41, three correlations (7.3%) with the absolute values of less than 0.80 were recognized and all of them were the main correlations. They were enumerated in Figure 3. In all the three correlations the mean FIB4 index YTD gradually increased and then at once reached the peak, which was so-called “second peak point.” After this point to the last point of data in the correlation, there was the bottom point, which was so-called “second bottom point.” In these correlations the mean FIB4 index YTD moved like an italic type of “.” In such a condition, the absolute value of would probably become low.

In statistics, the coefficient of determination, which is calculated as a squared value of   , determines how enough the outcomes could be explained by the hypotheses. In this study, the mean value of in the main correlation was and that in a total of 41 correlations was . The value of like these could not be ignored, even if the number was 23 or 41. Moreover, it had been explained why there were some correlations with low absolute values of . Statistically it was thought to be sufficient to speculate how the mean FIB4 index YTD moved.

There was another important thing about the movement of the mean FIB4 index YTD. In three of five patients with three phases (Figure 2(b)), the mean FIB4 index YTD showed decreasing firstly, increasing secondly, and decreasing finally, yet in two of these five (Figure 2(b)), it showed increasing, decreasing, and increasing. On the other hand, in eight patients with two phases (Figures 2(c)-2(d)), it showed firstly decreasing and finally increasing or firstly increasing and finally decreasing. From the viewpoint of the movement, the most important thing was that there was a turning point in which the mean FIB4 index YTD changed from increasing to decreasing or from decreasing to increasing. This means that the mean FIB4 index YTD moved like a wave. Even in the main correlations, these waves were seen and the typical ones had been picked up in Figure 3.

Now, developing this study’s methods, there would be a possibility. The possibility is that the methods will be applicable for any partial period. In order to validate it, the analysis only has to be performed, not from the first examination date and/or not to the last examination date. For example, in all the 23 patients the period was newly set from the closest date after half the total clinical period to that date after three-quarters. Limiting to this period, the analysis was newly performed through this study’s methods. Out of 23 patients, seven whose analyzed period remained less than two years were excluded, because it was necessary for a year to calculate the mean FIB4 index YTD. In all the remaining 16 out of 23, the new main correlations were analyzed. In the patient of case there were only two pieces of data and it was impossible to analyze the correlation and in the patient of case there were three pieces of data and the correlation was not statistically recognized by and . However, in the remaining 14 patients the new main correlations were recognized and the mean absolute value of was . In addition, in all 14, the absolute values of were over 0.80. From this result, it was thought that the methods might be applicable for any partial period (Table 4).


Case number of the patientSlopeAbsolute value of slopeAbsolute value of

Case  10.4150.4156 × 10−100.925
Case  20.42960.42969 × 10−80.9713
Case  30.62450.62450.0010.899
Case  60.11860.11860.0450.826
Case  7−0.10150.10150.0350.95
Case  80.34610.34614 × 10−110.9913
Case  90.43830.43831 × 10−120.9916
Case  100.09210.09213 × 10−90.8925
Case  110.11920.11921 × 10−100.9619
Case  120.10620.10625 × 10−160.9531
Case  14−0.04580.04580.0040.917
Case  15−0.10940.10948 × 10−60.9610
Case  170.13040.13040.0040.888
Case  230.37070.37070.0040.859

The mean ± SD0.2462 ± 0.17660.92 ± 0.05

Continuous variables were shown as mean ± standard deviation. In all the 23 patients the period was newly set from the closest date after half the total clinical period to that date after three-quarters. Of 23 patients, seven whose analyzed period remained less than two years were excluded. In the remaining 16 patients, the new main correlations were analyzed. In two of these 16, the correlations were not recognized. In case the correlation could not be analyzed because of only two data and in case number of data was three and the correlation was not recognized by and . Slope, the slope of the correlation; , a value; , correlation coefficient; , number of data to analyze the correlation.

The trigger of the start of the present study was a case report of a patient with NAFLD that the authors have already reported previously [26]. In this report we analyzed the correlation in a partial period between time (years) and the direct data of FIB4 index and we showed in that period that the direct data of FIB4 index decreased with the rate of 0.15 per year, statistically proven by the general linear regression model; FIB4 index = 4.90 – 0.15 × time (years) (). This patient was enrolled as a patient of case in the present study. If this study’s methods were applicable for any partial period, similar outcome should be obtained. As expected, the mean FIB4 index YTD decreased with the rate of 0.15 per year (Figure 4); the mean FIB4 index YTD = 5.02 − 0.15 × time (years) ( and ). This was very similar to our previous formula and has become extremely low.

In this way, it was very easy to estimate increase-decrease rate per year of the mean FIB4 index YTD. It was the value of the slope of each correlation on the scatter diagram. As for the main correlations, the mean absolute rate per year was (Table 2). In this viewpoint, since the difference of FIB4 index between 1.30 and 2.67 [14, 27] is 1.37, it would take about 10 years by the mean absolute rate per year.

Meanwhile, the limitations of this study should be shown. Firstly, interval between examinations in this study was (years) and it would be rather short. If the interval was longer, the dispersion of the data could not be minimized and the strong correlation might not be recognized. In fact, the after-main correlations or the before-main correlations were not recognized statistically in some patients, probably for the lack of number of data. About this, if the interval in the correlations had been shorter, the number of data would have increased and the correlation might have been seen. Anyway, when there were at least six pieces of data for the analysis, the correlations were all recognized in this study.

Secondly, it took a year from the first examination to calculate the data and also took another year to analyze the data.

Thirdly, in this study histological findings were not performed. Certainly, it was speculated that, compared to the earlier studies, liver fibrosis in the patients of this study would be rather advanced. Several proofs should be shown as follows. In this study the mean value of FIB4 index at the peak was (Table 1). Shah et al. reported that, for advanced fibrosis (stage 3-4), a FIB4 > or = 2.67 had an 80% positive predictive value [14]. Moreover, the mean value of platelet count at the bottom was (×109/L) (Table 1). Kaneda et al. reported that the platelet count was found to be an independent predictor of cirrhosis and a cut-off value of 16 × 104/microL for the platelet count was associated with an optimal combination of sensitivity (100%) and specificity (95%) [8]. In addition, the mean value of type IV collagen 7S at the peak was (ng/mL) (Table 1). It was reported that in patients with NASH the type IV collagen 7S domain was significantly elevated in patients with advanced fibrosis by multiple regression analysis [6] and Sumida et al. have developed NAFIC score in biopsy-proven patients with NAFLD to differentiate NASH from NAFLD, using the cut-off of type IV collagen 7S ≥ 5.0 (ng/mL) [7]. Then, the mean value of M2BPGi at the peak was (Table 1). Nishikawa et al. reported that in NASH patients the median values in each fibrosis stage were 0.7 COI in F1, 0.7 COI in F2, 1.2 COI in F3, and 2.4 COI in F4 [28] and Lai et al. also reported that the AUROC of the COI for the diagnosis of fibrosis stages ≥1, ≥2, ≥3, and 4 was 0.61, 0.71, 0.74, and 0.84, respectively [29]. These facts would show liver fibrosis in the enrolled patients would be rather advanced.

However, it is a problem whether the time-dependent change of FIB4 index corresponds to that of fibrosis by a liver biopsy. About this there was a retrospective study. McPherson et al. reported the following [30]. In 108 patients who had serial biopsies (median interval 6.6 years, range 1.3–22.6), there was a significant relationship between the change in fibrosis between biopsies and the change in both NAFLD fibrosis score [11] and FIB-4 score. They compared patients with histological evidence of increasing fibrosis stage (progressors) to subjects whose fibrosis remained stable or regressed (nonprogressors) and in progressors FIB-4 score was changed from at baseline biopsy to at follow-up one, yet in nonprogressors it was changed from to [30].

Nevertheless, there were few studies about the relationship between change in FIB4 index and change in fibrosis conducted by paired biopsies and therefore further verifications should be done. However, in the process of verifying it, this study’s methods might be useful, because the dispersion of a single direct data of FIB4 index probably would be a considerable problem. It is difficult to perform biopsies to all patients with NAFLD and in such a condition the methods to minimize the dispersion of the data would be helpful.

To consider the risk of liver fibrosis based on grasping the whole picture of the movement of the mean FIB4 index YTD would be one of the practical benefits in the study. For all practical purposes, the latest correlation means either the main correlation or the after-main correlation and if the value of the slope of that correlation were positive, the progression of liver fibrosis would be concerned. Especially in a patient whose last data of the mean FIB4 index YTD in that correlation shows a value over 2.67, advanced liver fibrosis should be well considered. On the other hand, in a patient with a negative value of the slope of the latest correlation, even if the last data of the mean FIB4 index YTD shows a value over 2.67, it would be a little difficult to assess the risk. In fact, in a patient of case , a sever complication was gradually improved in such a condition [26]. However, in such a patient careful treatment should be done to prevent the progression of liver fibrosis. Finally, if the value of the slope is negative and also the last data of the mean FIB4 index YTD is less than 1.30 in the latest correlation, the risk of liver fibrosis is considered to be low.

We hope that the methods in this study will be the benefits to patients with NAFLD and in the future the methods will be compared to other markers and modalities for estimating liver fibrosis, with increased number of patients.

5. Conclusion

This study demonstrated that in patients with NAFLD the correlations between the period from the first to each examination date and the mean value of FIB4 index during the past one year to each examination date were strongly recognized. Approximately, the time-dependent change of FIB4 index and its increase-decrease rate per year could be speculated simply and accurately.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

  1. Z. M. Younossi, A. B. Koenig, D. Abdelatif, Y. Fazel, L. Henry, and M. Wymer, “Global epidemiology of nonalcoholic fatty liver disease—meta-analytic assessment of prevalence, incidence, and outcomes,” Hepatology, vol. 64, no. 1, pp. 73–84, 2016. View at: Publisher Site | Google Scholar
  2. M. Ekstedt, H. Hagström, P. Nasr et al., “Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up,” Hepatology, vol. 61, no. 5, pp. 1547–1554, 2015. View at: Publisher Site | Google Scholar
  3. E. H. Van Den Berg, M. Amini, T. C. M. A. Schreuder et al., “Prevalence and determinants of non-Alcoholic fatty liver disease in lifelines: A large Dutch population cohort,” PLoS ONE, vol. 12, no. 2, Article ID e0171502, 2017. View at: Publisher Site | Google Scholar
  4. J. Uhanova, G. Minuk, F. Lopez Ficher, and N. Chandok, “Nonalcoholic Fatty Liver Disease in Canadian First Nations and Non-First Nations Patients,” Canadian Journal of Gastroenterology and Hepatology, vol. 2016, Article ID 6420408, 2016. View at: Publisher Site | Google Scholar
  5. D. Kim and W. R. Kim, “Nonobese Fatty Liver Disease,” Clinical Gastroenterology and Hepatology, vol. 15, no. 4, pp. 474–485, 2017. View at: Publisher Site | Google Scholar
  6. M. Yoneda, H. Mawatari, K. Fujita et al., “Type IV collagen 7s domain is an independent clinical marker of the severity of fibrosis in patients with nonalcoholic steatohepatitis before the cirrhotic stage,” Journal of Gastroenterology, vol. 42, no. 5, pp. 375–381, 2007. View at: Publisher Site | Google Scholar
  7. Y. Sumida, M. Yoneda, H. Hyogo et al., “A simple clinical scoring system using ferritin, fasting insulin, and type IV collagen 7S for predicting steatohepatitis in nonalcoholic fatty liver disease,” Journal of Gastroenterology, vol. 46, no. 2, pp. 257–268, 2011. View at: Publisher Site | Google Scholar
  8. H. Kaneda, E. Hashimoto, S. Yatsuji, K. Tokushige, and K. Shiratori, “Hyaluronic acid levels can predict severe fibrosis and platelet counts can predict cirrhosis in patients with nonalcoholic fatty liver disease,” Journal of Gastroenterology and Hepatology, vol. 21, no. 9, pp. 1459–1465, 2006. View at: Publisher Site | Google Scholar
  9. N. A. Palekar, R. Naus, S. P. Larson, J. Ward, and S. A. Harrison, “Clinical model for distinguishing nonalcoholic steatohepatitis from simple steatosis in patients with nonalcoholic fatty liver disease,” Liver International, vol. 26, no. 2, pp. 151–156, 2006. View at: Publisher Site | Google Scholar
  10. P. Angulo, J. C. Keach, K. P. Batts, and K. D. Lindor, “Independent predictors of liver fibrosis in patients with nonalcoholic steatohepatitis,” Hepatology, vol. 30, no. 6, pp. 1356–1362, 1999. View at: Publisher Site | Google Scholar
  11. P. Angulo, J. M. Hui, G. Marchesini et al., “The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD,” Hepatology, vol. 45, no. 4, pp. 846–854, 2007. View at: Publisher Site | Google Scholar
  12. S. A. Harrison, D. Oliver, H. L. Arnold, S. Gogia, and B. A. Neuschwander-Tetri, “Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease,” Gut, vol. 57, no. 10, pp. 1441–1447, 2008. View at: Publisher Site | Google Scholar
  13. R. K. Sterling, E. Lissen, N. Clumeck et al., “Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection,” Hepatology, vol. 43, no. 6, pp. 1317–1325, 2006. View at: Publisher Site | Google Scholar
  14. A. G. Shah, A. Lydecker, K. Murray, B. N. Tetri, M. J. Contos, and A. J. Sanyal, “Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease,” Clinical Gastroenterology and Hepatology, vol. 7, no. 10, pp. 1104–1112, 2009. View at: Publisher Site | Google Scholar
  15. S. McPherson, S. F. Stewart, E. Henderson, A. D. Burt, and C. P. Day, “Simple non-invasive fibrosis scoring systems can reliably exclude advanced fibrosis in patients with non-alcoholic fatty liver disease,” Gut, vol. 59, no. 9, pp. 1265–1269, 2010. View at: Publisher Site | Google Scholar
  16. Y. Sumida, M. Yoneda, H. Hyogo et al., “Validation of the FIB4 index in a Japanese nonalcoholic fatty liver disease population,” BMC Gastroenterology, vol. 12, article 2, 2012. View at: Publisher Site | Google Scholar
  17. A. Komori, A. Tanaka, H. Takikawa et al., “Guidelines for the management of primary biliary cirrhosis: The Intractable Hepatobiliary Disease Study Group supported by the Ministry of Health, Labour and Welfare of Japan,” Hepatology Research, vol. 44, Supplement 1, pp. 71–90, 2014. View at: Publisher Site | Google Scholar
  18. R. Vuppalanchi, R. J. Gould, L. A. Wilson et al., “Clinical significance of serum autoantibodies in patients with NAFLD: Results from the nonalcoholic steatohepatitis clinical research network,” Hepatology International, vol. 6, no. 1, pp. 379–385, 2012. View at: Publisher Site | Google Scholar
  19. S. S. Kunde, A. J. Lazenby, R. H. Clements, and G. A. Abrams, “Spectrum of NAFLD and diagnostic implications of the proposed new normal range for serum ALT in obese women,” Hepatology, vol. 42, no. 3, pp. 650–656, 2005. View at: Publisher Site | Google Scholar
  20. A. L. Fracanzani, L. Valenti, E. Bugianesi et al., “Risk of severe liver disease in nonalcoholic fatty liver disease with normal aminotransferase levels: a role for insulin resistance and diabetes,” Hepatology, vol. 48, no. 3, pp. 792–798, 2008. View at: Publisher Site | Google Scholar
  21. M. N. Kabbany, P. K. C. Selvakumar, K. Watt et al., “Prevalence of Nonalcoholic Steatohepatitis-Associated Cirrhosis in the United States: An Analysis of National Health and Nutrition Examination Survey Data,” American Journal of Gastroenterology, vol. 112, no. 4, pp. 581–587, 2017. View at: Publisher Site | Google Scholar
  22. S. E. Mahady, P. Macaskill, J. C. Craig et al., “Diagnostic Accuracy of Noninvasive Fibrosis Scores in a Population of Individuals With a Low Prevalence of Fibrosis,” Clinical Gastroenterology and Hepatology, vol. 15, no. 9, pp. 1453–1460.e1, 2017. View at: Publisher Site | Google Scholar
  23. V. Ratziua, S. Bellentanib, and H. Cortez-Pintoc, “A position statement on NAFLD/NASH based on the EASL 2009 special conference,” Journal of Hepatology, vol. 53, no. 2, pp. 372–384, 2010. View at: Publisher Site | Google Scholar
  24. European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), and European Association for the Study of Obesity (EASO), “EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease,” Journal of Hepatology, vol. 64, no. 6, pp. 1388–1402, 2016. View at: Google Scholar
  25. E. M. Hennes, M. Zeniya, A. J. Czaja et al., “Simplified criteria for the diagnosis of autoimmune hepatitis,” Hepatology, vol. 48, no. 1, pp. 169–176, 2008. View at: Publisher Site | Google Scholar
  26. H. Miyata and S. Miyata, “A case of hepatopulmonary syndrome derived from nonalcoholic fatty liver disease with severe liver fibrosis, in which hypoxia could be recovered by the improvement of liver fibrosis,” Kanzo (Acta Hepatologica Japonica), vol. 55, no. 8, pp. 479–487, 2014. View at: Publisher Site | Google Scholar
  27. T. Wada and M. Zeniya, “Background of the FIB-4 index in japanese non-alcoholic fatty liver disease,” Internal Medicine, vol. 54, no. 2, pp. 127–132, 2015. View at: Publisher Site | Google Scholar
  28. H. Nishikawa, H. Enomoto, Y. Iwata et al., “Clinical significance of serum Wisteria floribunda agglutinin positive Mac-2-binding protein level in non-alcoholic steatohepatitis,” Hepatology Research, vol. 46, no. 12, pp. 1194–1202, 2016. View at: Publisher Site | Google Scholar
  29. L.-L. Lai, W.-K. Chan, P. Sthaneshwar, N. R. N. Mustapha, K.-L. Goh, and S. Mahadeva, “Serum Wisteria floribunda agglutinin-positive Mac-2 binding protein in non-alcoholic fatty liver disease,” PLoS ONE, vol. 12, no. 4, Article ID e0174982, 2017. View at: Publisher Site | Google Scholar
  30. S. McPherson, T. Hardy, E. Henderson, A. D. Burt, C. P. Day, and Q. M. Anstee, “Evidence of NAFLD progression from steatosis to fibrosing-steatohepatitis using paired biopsies: implications for prognosis and clinical management,” Journal of Hepatology, vol. 62, no. 5, pp. 1148–1155, 2015. View at: Publisher Site | Google Scholar

Copyright © 2018 Hiroshi Miyata and Satoru Miyata. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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