Abstract
Low endogenous testosterone and sex hormone-binding globulin (SHBG) concentrations have been reported to be associated with metabolic syndrome (MetS) and non-alcoholic fatty liver disease (NAFLD). However, little is known about the relationships between testosterone or SHBG and liver fibrosis in NAFLD. Thus, we aimed to clarify the relationships between serum testosterone or SHBG concentration and fibrosis-4 (FIB-4) index, a marker of liver fibrosis. Serum testosterone was assayed in various forms (total testosterone [TT], calculated free testosterone [cFT], calculated bioavailable testosterone [cbT], and SHBG) and metabolic markers were also measured in 363 Japanese men (mean age 51.1 ± 8.7 years) at routine health examinations. We then attempted to identify the factors contributing to liver fibrosis by investigating the associations between the metabolic markers, including testosterone, and FIB-4 index. People with a relatively high FIB-4 index (≥1.3) demonstrated lower cFT, cbT, homeostasis model assessment (HOMA)-β, low-density lipoprotein-cholesterol, and blood urea nitrogen, but higher SHBG, than those with a lower FIB-4 index (<1.3). There were no significant differences in HbA1c, fasting glucose concentration, HOMA-R, or metabolic syndrome prevalence between the two groups. Binary regression analysis revealed that SHBG ≥52 nmol/L and cFT <8.0 ng/dL were statistically significant risk factors for FIB-4 index ≥1.3. Receiver operating characteristic analysis revealed that cFT <7.62 ng/dL (area under the curve [AUC] = 0.639) and SHBG ≥49.8 nmol/L (AUC = 0.649) were the strongest risk factors for FIB-4 index ≥1.3. In contrast to previous findings showing low SHBG concentrations in NAFLD, we provide evidence that high SHBG and low bioactive testosterone are associated with liver fibrosis.
NON-ALCOHOLIC FATTY LIVER DISEASE (NAFLD) is defined as a state of fat accumulation in the liver without a history of alcohol overuse. Because both NAFLD and metabolic syndrome (MetS) have a common basis in insulin resistance, NAFLD is considered to be a manifestation of MetS. NAFLD can progress from a situation of fat deposition alone (non-alcoholic fatty liver; NAFL) to inflammation (non-alcoholic steatohepatitis; NASH), fibrosis, liver cirrhosis, and finally often to liver cancer [1, 2]. Therefore, the diagnosis of liver fibrosis is clinically important. However, because liver biopsy is required for the definitive diagnosis of liver fibrosis, non-invasive indicators, such as the NAFLD fibrosis score (NFS) [3], BARD score [4], aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (AAR) [5] and fibrosis-4 (FIB-4) index [6], have been used as indices of hepatic fibrosis in recent years. In particular, the FIB-4 index proposed by Sterling et al. [6] has been reported to be more useful for NAFLD compared with seven other fibrosis indices [7]. In a recent meta-analysis, the use of a cut-off value of 1.30 for the FIB-4 index improved the accuracy of a diagnosis of liver fibrosis using the FIB-4 index above those associated with the NFS and BARD scores [8]. Furthermore, the FIB-4 index is easy to determine, because the parameters used in the calculation (age, serum AST activity, ALT activity, and platelet count [PLT]) are routinely measured.
Recent studies have demonstrated that a sex hormone, testosterone, is closely associated with MetS and type2 diabetes mellitus (T2DM). Testosterone circulates in three forms: sex hormone-binding globulin (SHBG)-bound (35–75%), albumin-bound (25–65%), and free (FT), with the sum of these three being referred to as total testosterone (TT) [9]. Relatively low levels of testosterone occur in association with MetS and T2DM in men [10-12], while in addition, a low concentration of serum SHBG is predictive for type 2 diabetes in men and women [13].
A meta-analysis of 55 observational studies revealed that MetS in men was associated with TT and FT concentrations, and that SHBG was associated with MetS in both men and women [14]. We previously reported that TT was the most useful type of serum testosterone measurement to act as a marker of MetS in middle-aged men [15]. A decrease in SHBG caused by hyperinsulinemia [16] results in a decrease in the largest testosterone fraction, the SHBG-bound hormone, thus leading to a reduction in TT concentration. While a reduction in the serum concentration of bioactive testosterone can also be measured, the above mechanism explains why TT is the most sensitive and useful marker of MetS.
The relationships between NAFLD and the types of serum testosterone measurement have also been reported in a recent meta-analysis [17], in which a low TT was shown to be positively associated with NAFLD in men, but inversely related in women, while a low SHBG concentration was reported to be associated with a high risk of NAFLD both in men and women. This relationship makes sense if NASH arises against a background of MetS. However, the relationships between liver fibrosis indicators and the various types of testosterone measurement remain unclear. There has only been a single report that the FIB-4 index in T2DM is negatively correlated with FT [18], and no reports regarding the relationships between liver fibrosis indicators and SHBG.
Therefore, the purpose of this study was to determine the relationship between liver fibrosis, using a moderate cut-off value of 1.30 for the FIB-4 index, and the various types of testosterone measurement, including SHBG, in healthy subjects. The results indicate that high SHBG and low bioactive testosterone are associated with FIB-4 index. In particular, SHBG, which is principally synthesized in the liver and secreted into the bloodstream, appears to represent a promising marker for the detection of liver fibrosis.
Materials and Methods
Subjects
The study protocol was approved by the Institutional Review Boards of Fukuoka University Hospital and Iizuka Hospital. Written informed consent was obtained from all subjects before their participation in the study and the study protocol conformed to the principles of the Declaration of Helsinki.
The first 684 subjects who visited the Department of Preventive Medicine at Iizuka Hospital for a health check-up were recruited. One hundred and sixty-three women were excluded, as well as 60 male subjects, because they were taking medication for diabetes mellitus and/or hyperlipidemia, which would have made the data unreliable. Patients who were taking antihypertensive drugs, and who were therefore considered to be hypertensive, were not excluded, irrespective of their blood pressure on the day of the examination. Of the remaining 461 male subjects, there was sufficient serum remaining to measure hormone concentrations in samples from 365 participants. Of these, two additional participants for whom waist circumference data were not available were also excluded. Therefore, data from 363 asymptomatic and healthy men aged 51.1 ± 8.7 years (mean ± SD) were analyzed.
Evaluation of serum markers
Fasting blood samples were collected in the morning and analyzed for lipid content, including serum total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), and markers of glucose metabolism, including fasting blood glucose (FBG), fasting immunoreactive insulin (F-IRI), glycohemoglobin (HbA1c), and homeostasis model assessment of insulin resistance (HOMA-R) and beta-cell function (HOMA-β). Insulin sensitivity was estimated using HOMA-R, which was calculated as [F-IRI (μU/mL) × FBG (mmol/L)]/22.5 [19]. HOMA-β, an index of insulin secretory capability, was calculated as [F-IRI (μU/mL) × 20]/[fasting plasma glucose (mmol/L) – 3.5] [19]. In addition, anthropometric data, including height, body mass, body mass index (BMI), and waist circumference were collected, and blood pressure was measured. Blood samples were also used to quantify the various types of serum testosterone, including TT, cFT, cbT, and SHBG. The method used to calculate cFT and cbT is available on the International Society for the Study of the Aging Male (ISSAM) website (http://www.issam.ch/freetesto.htm).
As an index of liver fibrosis, FIB-4 index was calculated as [age (years) × AST (IU/L)]/[PLT (×109/L)] × √ALT(IU/L)] [6]. The AST/ALT ratio (AAR) was calculated using the measured serum AST and ALT activities [5], and a diagnosis of MetS was made according to international (International Diabetes Federation [IDF], 2009 version) [20] and domestic (Japanese) criteria [21]. The IDF criteria require three or more of the following to be present to make a diagnosis: i) systolic and/or diastolic BP ≥130/85 mmHg, ii) serum FBG ≥100 mg/dL, iii) serum TG ≥150 mg/dL, iv) HDL-C ˂40 mg/dL, and v) waist circumference ≥90 cm. The Japanese criteria for diagnosis stipulate a waist circumference of ≥85 cm and two or more of the following: i) systolic and/or diastolic BP ≥130/85 mmHg, ii) FBG ≥110 mg/dL, iii) serum TG ≥150 mg/dL, and/or HDL-C ˂40 mg/dL.
Measurement of testosterone, SHBG, adiponectin, and 25(OH)-vitamin D
Serum concentrations of TT and SHBG were measured with the collaboration of SRL Co. Ltd. (Tokyo, Japan). TT was measured using an electro-chemiluminescence immunoassay (ECLIA) (ECL TESTO II, Roche, Mannheim, Germany) and SHBG was measured by immunoradiometric assay (IRMA) using the IRMA-Count SHBG kit (Siemens Japan KK, Tokyo, Japan). The detection limits for TT and SHBG stated by the manufacturers were 0.025 ng/mL and 0.04 nmol/L, respectively. The intra-assay coefficients of variation for TT and SHBG were <10% and 2.8–5.3%, respectively. Serum cbT and cFT were calculated using the Free & Bioavailable Testosterone Calculator at http://www.issam.ch/freetesto.htm, on the basis of the measured TT and SHBG values. Serum adiponectin concentration was measured using a human adiponectin ELISA kit (Otsuka Pharmaceutical Co., Ltd., Tokyo Japan) and 25-(OH) vitamin D3 (25-OHVD3) was measured by Siemens Japan (Tokyo) using a Chemilumi 25(OH)VD3 kit.
Statistical analyses
Data are expressed as mean ± standard deviation (SD), or median with interquartile range (25%–75%), or number with percentage. Continuous variables were evaluated using the unpaired t-test or Mann-Whitney test according to the data distribution. Categorical variables were evaluated using Fisher’s exact test. Pearson’s correlation was used to evaluate the relationships between FIB-4 index and clinical variables. Binary regression analysis was performed to identify predictors of FIB-4 index ≥1.3, and to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs). Receiver operating characteristic (ROC) curve analysis was used to determine the cut-off values for continuous parameters that could be used as predictors of medical endpoints, and their area under the curve (AUC) and 95% confidence interval (CI) were calculated. All statistical analyses were performed using SPSS (IBM) version 18.0, and p < 0.05 was considered statistically significant.
Results
Table 1 shows the clinical characteristics of the 363 subjects and a comparison of the values in patients with a FIB-4 index <1.30 (N = 280, 77.1%) and in those with a FIB-4 index ≥1.30 (N = 83, 22.9%). The mean FIB-4 index was 1.01 [0.78–1.27]. There were 280 (77.1%), 80 (22.0%), and 3 (0.9%) men with normal (FIB-4 index <1.30), intermediate (1.3 ≤ FIB-4 index < 2.6), and high (FIB-4 index ≥2.6) FIB-4 indexes, respectively. Significantly lower HOMA-β (p = 0.002), cFT (p < 0.001), cbT (p < 0.001), LDL-C (p = 0.013), white blood cell count [WBC] (p < 0.001), red blood cell count [RBC] (p = 0.004), PLT (p < 0.001), and AAR (p < 0.001), but higher SHBG (p < 0.001), AST (p < 0.001), blood urea nitrogen (BUN) (p = 0.002), and age (p < 0.001) were found for the FIB-4 index ≥1.3 group than for the FIB-4 index <1.3 group. A significant reduction in HOMA-β, rather than an increase in HOMA-R, was also noted with the progression of NAFLD to fibrosis [22]. The concentration of 25-(OH)VD3, unlike that of SHBG, was not different between the groups. In addition, importantly, there were no significant differences in the number of individuals with MetS between the groups, assessed using either the IDF or Japanese criteria.
Table 1
Basal data of subjects stratified according to fibrosis-4 index
|
All cases
N = 363 |
FIB-4 index ≥1.30
N = 83 |
FIB-4 index <1.30
N = 280 |
p values |
| Age, years |
51.1 ± 8.7 |
59.1 ± 7.4 |
48.7 ± 7.6 |
<0.0011) |
| BMI, kg/m2 |
23.8 ± 3.1 |
23.7 ± 3.4 |
23.9 ± 3.0 |
0.7331) |
| Waist, cm |
86.7 ± 8.6 |
86.9 ± 9.0 |
86.6 ± 8.5 |
0.8061) |
| SBP, mmHg |
131.4 ± 19.7 |
132.7 ± 21.9 |
131.0 ± 19.0 |
0.4491) |
| DBP, mmHg |
81.9 ± 13.0 |
82.7 ± 13.6 |
81.7 ± 12.8 |
0.5071) |
| TT, ng/mL |
4.98 ± 1.60 |
4.95 ± 1.64 |
4.98 ± 1.59 |
0.8911) |
| SHBG, nmol/L |
52.2 ± 22.4 |
59.4 ± 21.1 |
50.1 ± 22.4 |
<0.0011) |
| cFT, ng/dL |
7.86 ± 2.33 |
6.99 ± 1.97 |
8.11 ± 2.37 |
<0.0011) |
| cbT, ng/mL |
1.84 ± 0.54 |
1.63 ± 0.46 |
1.90 ± 0.54 |
<0.0011) |
| Adiponectin, μg/mL |
4.24 [3.16–6.41] |
4.62 [3.29–7.15] |
4.09 [3.11–6.10] |
0.0982) |
| Blood sugar, mg/dL |
100.2 ± 11.9 |
102.3 ± 14.2 |
99.6 ± 11.1 |
0.0661) |
| HbA1c, % |
5.41 ± 0.23 |
5.44 ± 0.58 |
5.41 ± 0.46 |
0.5761) |
| Insulin, μU/mL |
5.54 [3.64–7.02] |
4.91 [3.05–7.66] |
5.60 [3.82–8.02] |
0.0642) |
| HOMA-R |
1.36 [0.88–2.06] |
1.21 [0.73–1.92] |
1.40 [0.90–2.10] |
0.1282) |
| HOMA-β |
54.8 [38.5–79.0] |
46.0 [30.2–68.2] |
57.4 [41.1–81.8] |
0.0022) |
| Total protein, g/dL |
7.09 ± 0.38 |
7.14 ± 0.41 |
7.07 ± 0.37 |
0.1671) |
| Albumin, g/dL |
4.32 ± 0.23 |
4.28 ± 0.23 |
4.33 ± 0.23 |
0.1261) |
| AST, IU/L |
23.3 ± 8.6 |
26.9 ± 10.6 |
22.2 ± 7.6 |
<0.0011) |
| ALT, IU/L |
22.0 [17.0–33.0] |
21.0 [17.0–27.5] |
23.0 [16.0–34.0] |
0.3262) |
| γGTP, IU/L |
34.0 [22.0–52.5] |
32.0 [25.0–48.0] |
34.0 [22.0–55.0] |
0.9632) |
| S-Creatinine, mg/dL |
0.85 ± 0.13 |
0.87 ± 0.15 |
0.85 ± 0.12 |
0.1211) |
| BUN, mg/dL |
13.8 ± 3.4 |
14.9 ± 3.7 |
13.5 ± 3.3 |
0.0021) |
| Uric acid, mg/dL |
5.97 ± 1.21 |
5.98 ± 1.26 |
5.96 ± 1.20 |
0.8871) |
| WBC, /μL |
5,786 ± 1,515 |
5,295 ± 1,218 |
5,932 ± 1,564 |
<0.0011) |
| RBC, ×104/μL |
479 ± 38 |
468 ± 39 |
482 ± 37 |
0.0041) |
| Hematocrit, % |
44.8 ± 2.9 |
44.4 ± 3.0 |
44.9 ± 2.9 |
0.2231) |
| Platelet, ×104/μL |
23.4 ± 5.3 |
19.3 ± 4.6 |
24.6 ± 4.9 |
<0.0011) |
| Triglycerides, mg/dL |
127 ± 67 |
123 ± 64 |
129 ± 68 |
0.5071) |
| LDL-C, mg/dL |
119 ± 30 |
112 ± 30 |
121 ± 30 |
0.0131) |
| HDL-C, mg/dL |
54.1 ± 11.7 |
55.3 ± 13.5 |
53.8 ± 11.1 |
0.2981) |
| CRP, mg/dL |
0.04 [0.02–0.09] |
0.05 [0.03–0.10] |
0.04 [0.02–0.08] |
0.1602) |
| 25-(OH)VD3, ng/mL |
19.6 ± 7.0 |
20.7 ± 6.8 |
19.3 ± 7.0 |
0.1091) |
| Mets (IDF), n (%) |
96 (26.4) |
20 (24.1) |
76 (27.1) |
0.6713) |
| Mets (JPN), n (%) |
85 (23.4) |
23 (27.7) |
62 (22.1) |
0.3043) |
| AAR |
0.95 [0.74–1.20] |
1.14 [0.93–1.41] |
0.90 [0.70–1.13] |
<0.0012) |
Data are expressed as mean ± SD, median [quartile 25%–75% value], or number (%). P values were determined by 1) unpaired t test, 2) Mann-Whitney test, or 3) Fisher’s exact test. FIB-4, fibrosis-4; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TT, total testosterone; SHBG, sex hormone-binding globulin; cFT, calculated free testosterone; cbT, calculated bioavailable testosterone; HbA1c, glycohemoglobin; HOMA-R, Homeostasis model assessment for insulin resistance; HOMA-β, homeostasis model assessment for β cell function; AST, aspartate aminotransferase; ALT, alanine aminotransferase; AAR, AST/ALT ratio; γGTP, γ-glutamyl transpeptidase; BUN, blood urea nitrogen; WBC, white blood cell count; RBC, red blood cell count; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; CRP, C-reactive protein; 25-(OH)VD3, 25-(OH) vitamin D3; MetS, metabolic syndrome
Table 2 shows the data for men with and without MetS, defined using the IDF criteria. As expected, BMI, waist circumference, SBP, DBP, AST, ALT, γ-glutamyl transpeptidase (γ-GTP), and TG were significantly higher in individuals with MetS than in those without. In addition, and consistent with our previous findings [14], serum TT, SHBG, cFT, cbT, and adiponectin were significantly lower in individuals with MetS than in those without. Interestingly, significantly lower AAR was calculated for individuals with MetS than for those without. However, there was no significant difference in FIB4-index between the groups.
Table 2
Main data of subjects categorized according to the presence of metabolic syndrome, diagnosed using the IDF criteria
|
with MetS (IDF)
N = 96 |
without MetS (IDF)
N = 267 |
p value |
| Age, years |
51.8 ± 7.93 |
50.9 ± 8.98 |
0.320 |
| BMI, kg/m2 |
26.6 ± 3.17 |
22.8 ± 2.38 |
<0.001 |
| Waist, cm |
94.7 ± 8.19 |
83.8 ± 6.74 |
<0.001 |
| SBP, mmHg |
143.6 ± 18.5 |
127 ± 18.1 |
<0.001 |
| DBP, mmHg |
88.8 ± 10.4 |
79.4 ± 12.9 |
<0.001 |
| TT, ng/mL |
4.19 ± 1.28 |
5.26 ± 1.60 |
<0.001 |
| SHBG, nmol/L |
44.4 ± 19.1 |
55.0 ± 22.8 |
<0.001 |
| cFT, ng/dL |
7.26 ± 2.16 |
8.07 ± 2.35 |
0.003 |
| cbT, ng/mL |
1.73 ± 0.52 |
1.87 ± 0.54 |
0.035 |
| Adiponectin, μg/mL |
3.75 [2.70–5.00] |
4.50 [3.33–7.05] |
<0.001 |
| Blood sugar, mg/dL |
109 ± 15.2 |
97.1 ± 8.48 |
<0.001 |
| Insulin, mIU/mL |
8.93 [6.13–11.65] |
4.70 [3.26–6.65] |
<0.001 |
| HOMA-R |
2.30 [1.68–3.11] |
1.10 [0.78–1.58] |
<0.001 |
| HOMA-β |
71.8 [48.8–102.4] |
50.1 [36.2–71.4] |
<0.001 |
| HbA1c, % |
5.65 ± 0.64 |
5.33 ± 0.39 |
<0.001 |
| AST, IU/L |
26.6 ± 11.0 |
22.1 ± 7.15 |
<0.001 |
| ALT, IU/L |
32.5 [25.0–42.3] |
19.0 [15.0–28.0] |
<0.001 |
| γGTP, IU/L |
45.0 [35.3–89.3] |
29.0 [21.0–45.0] |
<0.001 |
| HDL-C, mg/dL |
48.1 ± 9.81 |
56.3 ± 11.6 |
<0.001 |
| Triglycerides, mg/dL |
180 ± 81.7 |
108 ± 48.5 |
<0.001 |
| CRP, mg/dL |
0.07 [0.04–0.13] |
0.03 [0.02–0.09] |
<0.001 |
| FIB-4 index |
0.94 [0.77–1.25] |
1.03 [0.79–1.28] |
0.304 |
| AAR |
0.74 [0.60–0.91] |
1.05 [0.83–1.28] |
<0.001 |
Data are expressed as mean ± SD, median [quartile 25%–75% value], or number (%). P values were determined by unpaired t test or Mann-Whitney test. MetS, metabolic syndrome; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TT, total testosterone; SHBG, sex hormone-binding globulin; cFT, calculated free testosterone; cbT, calculated bioavailable testosterone; HOMA-R, homeostasis model assessment for insulin resistance; HOMA-β, homeostasis model assessment for β cell function; HbA1c, glycohemoglobin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; γGTP, γ-glutamyl transpeptidase; HDL-C, high density lipoprotein cholesterol; CRP, C-reactive protein; FIB-4, fibrosis-4; AAR, AST/ALT ratio.
Next, binary regression analysis was performed to identify predictors of a FIB-4 index ≥1.3, and to calculate the ORs and 95% CIs (Table 3). An FBG of 110 mg/dL is used as the cut-off value for the diagnosis of MetS [21] and an LDL-C concentration of 140 mg/dL is the cut-off value used by the Japan Atherosclerosis Society [23]. Because no cut-off levels for cFT and cbT are available for the diagnosis of MetS, median serum concentrations for cFT of <8.0 ng/dL, cbT of <1.8 ng/mL, and SHBG of ≥52 nmol/L were tentatively selected for use in logistic regression analyses. Similarly, because no cut-off values for adiponectin, WBC, RBC, BUN or age are available for the diagnosis of MetS, median values for serum adiponectin of <5.0 μg/mL, WBC of <5,800/μL, RBC of <480 × 104/μL, BUN of ≥14 mg/dL and age ≥50 years were tentatively selected for use in logistic regression analyses. An AAR of ≥0.8 has previously been used as a cut-off value for NAFLD [5]. Unadjusted binary regression analysis revealed that SHBG, cFT, cbT, WBC, RBC, BUN, and LDL-C are statistically significant markers of FIB-4 index ≥1.3, and after adjustment, SHBG (p = 0.022), cFT (p < 0.001), WBC (p = 0.004), RBC (p = 0.039), and LDL-C (p = 0.017) remained statistically significant (Table 3). Finally, ROC curve analysis was performed to determine the cut-off values for these predictors. This showed that SHBG >49.8 nmol/L was the strongest predictor of FIB-4 index ≥1.3 (AUC = 0.649, p < 0.001) (Table 4).
Table 3
Predictors of FIB-4 index ≥1.3, determined using binary logistic regression analysis
| Variables |
Before adjustment |
After adjustment |
| OR (95%CI) |
p values |
OR (95%CI) |
p values |
| SHBG ≥52 nmol/L * |
2.33 (1.41–3.85) |
<0.001 |
1.89 (1.10–3.23) |
0.022 |
| cFT <8.0 ng/dL * |
3.41 (1.92–6.04) |
<0.001 |
3.27 (1.79–5.95) |
<0.001 |
| cbT <1.8 ng/mL * |
3.11 (1.81–5.34) |
<0.001 |
Not apply |
|
| Adiponectin <5.0 μg/mL * |
0.66 (0.40–1.08) |
0.096 |
Not apply |
|
| Blood sugar ≥110 mg/dL |
1.40 (0.73–2.71) |
0.309 |
Not apply |
|
| HOMA-β ≥60 * |
0.64 (0.39–1.07) |
0.088 |
Not apply |
|
| WBC <5,800/μL * |
2.42 (1.43–4.11) |
0.001 |
2.29 (1.31–4.03) |
0.004 |
| RBC <480 × 104/μL * |
2.23 (1.34–3.73) |
0.002 |
1.79 (1.03–3.10) |
0.039 |
| BUN ≥14 mg/dL * |
1.85 (1.12–3.05) |
0.015 |
1.60 (0.93–2.74) |
0.088 |
| LDL-C <140 mg/dL |
1.96 (1.01– 3.85) |
0.047 |
2.44 (1.18–5.00) |
0.017 |
| Age ≥50 years* |
11.13 (5.18–23.95) |
<0.001 |
Not apply |
|
| AAR ≥0.8 |
5.71 (2.65–12.31) |
<0.001 |
Not apply |
|
The Chi-square value was 8.327 in the Hosmer-Lemeshow test (p = 0.402). * The mean value in all cases. OR, odds ratio; CI, confidence interval; SHBG, sex hormone-binding globulin; cFT, calculated free testosterone; cbT, calculated bioavailable testosterone; HOMA-β, homeostasis model assessment for β cell function; WBC, white blood cell count; RBC, red blood cell count; BUN, blood urea nitrogen; LDL-C, low density lipoprotein cholesterol; AAR, AST/ALT ratio.
Table 4
Cut-off values for predictors and areas under the curve for fibrosis-4 index ≥1.3, determined by receiver operating characteristic curve analysis
| Predictors |
Cut-off values |
AUC (95%CI) |
p values |
| SHBG |
49.8 nmol/L |
0.649 (0.585–0.713) |
<0.001 |
| cFT |
7.62 ng/dL |
0.639 (0.574–0.705) |
<0.001 |
| WBC |
5,550/μL |
0.618 (0.552–0.684) |
0.001 |
| RBC |
478 × 104/μL |
0.598 (0.529–0.668) |
0.006 |
| LDL-C |
116 mg/dL |
0.589 (0.520–0.658) |
0.014 |
AUC, area under the curve; CI, confidence interval; SHBG, sex hormone-binding globulin; cFT, calculated free testosterone; WBC, white blood cell count; RBC, red blood cell count; LDL-C, low density lipoprotein cholesterol.
Discussion
In the present study, we found that relatively high SHBG and low cFT are predictors of a FIB-4 index of ≥1.3, using binary logistic regression analysis. ROC analysis established an SHBG of 49.8 nmol/L and a cFT of 7.62 ng/dL as cut-off values for the prediction of FIB-4 index ≥1.3, with AUC values of 0.649 and 0.639, respectively. To our knowledge, this study is the first to establish the relationships between measures of testosterone status, especially SHBG, and FIB-4 index, a liver fibrosis index.
There have been several reports that low serum testosterone and SHBG concentrations are associated with NAFLD, as well as MetS [14, 16, 24, 25]. In men, blood testosterone concentration has been reported to be inversely associated with fat accumulation [10, 11, 26, 27], while reducing endogenous testosterone in young adult males by the administration of GnRH analogs has been reported to result in an increase in body fat percentage and a reduction in resting energy expenditure [28]. Similarly, the incidence of MetS has been reported to be increased by androgen deprivation therapy in prostate cancer patients [29]. Furthermore, endogenous testosterone has been suggested to exert an anti-inflammatory effect, because inflammatory cytokine levels are suppressed by testosterone administration in hypogonadotropic men [30]. The fat accumulation and inflammation are thought to be brought about by a low testosterone level, resulting in insulin resistance, an underlying feature of both MetS and NAFLD.
The inverse relationships between SHBG and MetS, and between SHBG and NAFLD, may be explained by the fact that SHBG production in the liver can be downregulated by insulin, as evidenced by experiments in cultured HepG2 cells [16], or inflammatory cytokines (tumor necrosis factor α and interleukin-1β) [31]. Although SHBG production is usually up-regulated by adiponectin-AMP-activated protein kinase signaling in HepG2 cells [31, 32], this mechanism may be deficient in MetS or NAFLD because of the lower adiponectin secretion. Furthermore, it has recently been reported that SHBG overexpression protects against high fructose diet-induced fatty liver disease in mice and that exogenous SHBG inhibits lipogenesis by reducing PPARγ expression via the extracellular signal-regulated kinase (ERK)-1/2-mitogen-activated protein kinase (MAPK) pathway in HepG2 cells [33].
In the present study, cFT was inversely associated with the FIB-4 index of liver fibrosis. However, SHBG concentration showed a positive relationship with FIB-4 index, which was the opposite relationship to that observed between NAFLD and SHBG, as discussed above. The precise mechanisms for the inverse relationship between cFT and FIB-4 index and the positive relationship between SHBG and FIB-4 index are unclear, but some speculation is possible. High circulating concentrations of inflammatory cytokines have been reported to be risk factors for the progression of NAFLD to NASH and hepatic fibrosis, and testosterone is well known to have an anti-inflammatory effect in the liver [34]. Thus, our finding that a relatively low level of bioactive testosterone is closely associated with a higher FIB-4 index is logical. Alternatively, high SHBG concentration may reflect less fat deposition in the liver.
Importantly, Miyaaki et al. reported that the amount of liver fat may decrease with the progression of fibrosis in patients with hepatic steatosis [35]. Furthermore, Stefan et al. reported that serum SHBG increases as liver fat mass decreases [36] and SHBG has been shown to increase in parallel with a decrease in intrahepatic fat mass, assessed using ultrasound [24, 37], computed tomography [38, 39], or magnetic resonance imaging [40]. Interestingly, these clinical observations may be explained by the recent finding that SHBG has anti-inflammatory and lipolytic effects on cultured adipocytes and macrophages [41]. Thus, the increase in SHBG alongside that of FIB-4 index may reflect a compensatory increase, which may lead to a reduction in intrahepatic fat mass and retard the progression of inflammation and liver fibrosis. The inverse correlation between serum albumin and SHBG (r = –0.17, p < 0.002, data not shown) may also suggest a compensatory increase in SHBG in liver fibrosis. Although 25(OH)VD3 is synthesized in the liver, like SHBG, no significant relationship with FIB-4 index was shown, suggesting that there is a specific pathologic association between fibrosis and SHBG.
It was also interesting that FIB-4 index was not affected by the presence or absence of MetS, in contrast to the significant difference identified in AAR. The FIB-4 index may reflect the relatively progressive stages of NAFLD, compared with AAR.
In binary logistic regression analysis, relatively lower levels of RBC, WBC, and LDL-C were also found to be risk factors for FIB-4 index ≥1.3. These findings are compatible with the fact that liver fibrosis is commonly associated with hypersplenism or a reduction in hepatic cholesterol synthesis, respectively.
There were several limitations to our study. First, because it was a cross-sectional study, we cannot draw definitive conclusions regarding pathogenesis on the basis of the identified association between SHBG and FIB-4 index. Second, our data are limited to middle-aged men, and because it has been suggested that the relationship between NAFLD and testosterone is sex-specific [16], the relationship between FIB-4 index and testosterone status may be different in women. Finally, we did not perform liver biopsies, meaning that we did not accurately ascertain the extent of the liver fibrosis or intrahepatic fat deposition.
In conclusion, approximately 23% of middle-aged men undergoing a medical examination had a FIB-4 index ≥1.3. Of the various testosterone measurements, FIB-4 index showed a negative correlation with FT and a positive relationship with SHBG. Considering the previous reports showing an inverse correlation between SHBG and NAFLD, the positive correlation between SHBG and FIB-4 index identified here was unexpected, but an increase in SHBG may represent a protective mechanism against liver fibrosis. Furthermore, SHBG may represent a useful marker of liver fibrosis.
Acknowledgments
This research was partially supported by a Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science (JSPS) (ID: 23390248). We thank Mr. Takahito Kaji for assistance with the statistical analysis. We thank Mrs. K. Kusamoto, M. Ochi, T. Onimaru, and R. Makita in the Department of Preventive Medicine of Iizuka Hospital for their invaluable help with this study. We also thank Mark Cleasby, PhD, from Edanz Group (www.edanzediting.com/ac) for editing drafts of this manuscript.
Disclosure
The authors declare no conflict of interest relevant to this manuscript.
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