Factors associated with NAFLD in the lean population
Significant variables including age, male sex, comorbidities, smoking history, and biochemical indicators identified in the univariate analysis were selected for the binary logistic regression analysis. Since FLI is calculated using the combination of BMI, WC, serum TG, and GGT levels, we applied 3 models to the logistic regression analysis to minimize the potential confounding effects of these parameters. In model I, we selected age, sex, body fat mass, BMI and WC, hypertension history, smoking status, FPG, HbA1c, AST, ALT, UA, TC, TG, HDL, and GGT, but not FLI. Model II included the model I variables; however, we used WC in place of BMI. In model III, FLI was included, but all 4 of its components were not entered. According to model I, significant factors associated with lean-NAFLD included BMI, FPG, TG, UA, and ALT. According to model II, the significant factors were male sex, body fat mass, FPG, UA, TG, and ALT. According to model III, the significant factors were male sex, body fat mass, FPG, UA, ALT, and FLI (Table 3).
Validation of FLI to identify ultrasonographic NAFLD and selection of its optimal cut-off value
The discriminative ability of the significant factors related to lean-NAFLD was determined by comparing their AUROC values. Although FLI for diagnosing lean-NAFLD had moderate accuracy (AUROC of 0.76; 95% confidence interval, 0.73–0.78; sensitivity 60.66%; specificity 79.35%), it had the best discriminative ability to predict lean-NAFLD compared to the other biochemical markers (Table 4). Hence, we considered using FLI for identifying ultrasonographic NAFLD.
Following the methodology of several previous studies (22–24), different cut-off values were tested for their ability to diagnose lean-NAFLD using FLI. The percentages of subjects diagnosed as having NAFLD using FLI cut-off value of 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, and 75 were 4.9%, 4.0%, 3.3%, 2.7%, 2.3%, 1.9%, 1.5%, 1.2%, 0.9%, 0.8%, 0.7%, 0.3%, 0.3%, 0.2%, and 0.2%, respectively (Table 5).
Although a cut-off value of 5 could rule out NAFLD (based on a sensitivity of 91.53%, negative likelihood ratio: 0.22), and FLI 50 could rule in lean-NAFLD (based on a specificity of 98.40; positive likelihood ratio: 7.07). According to the aforementioned report (25), this polar extreme probability (>90%) might lead to additional unnecessary tests. Therefore, we further used the Youden index test and found an optimum cut-off value of 15 with the highest discriminant ability than other values (sensitivity of 61.58%; specificity of 77.37%). Thus, we considered that this value would be more reasonable for clinical practice.
Furthermore, we applied this FLI to our dataset stratified by age (divided ages into <40, 40–59, ≥60 years), sex, BMI (divided into <18.5 and 18.5–24 kg/m2), TG (divided into <150 and ≥150 mg/dL) (26), UA (divided into <6 and ≥6 mg/dL) (27), and FPG value (divided into <100, 100–125, and ≥126 mg/dL) (26). We noted higher sensitivity than all study population with age 40–59 and ≥60 years old (sensitivity: 64.3 and 65.6%, respectively), BMI ≥ 18.5 kg/m2 (sensitivity: 62.8%), FPG = 100–125 and ≥125 mg/dL (sensitivity: 69.4 and 70.0, respectively), and UA ≥ 6 (sensitivity: 79.1%) (see Table 2, Supplementary Digital Content 1, http://links.lww.com/CTG/A43).
In this study, we assessed the risk factors associated with lean-NAFLD and refined the new threshold FLI value using data of patients who underwent routine physical examinations. The major findings of our study are as following: (i) the prevalence of NAFLD among lean participants was 18.5%, which is not low; (ii) male sex, higher values for body fat mass, BMI, FPG, UA, TG, ALT, and FLI were all associated with a risk of lean-NAFLD; (iii) we found that FLI ≥ 15 was a reasonable cut-off value for the prediction of lean-NAFLD, especially in those aged ≥40 years, and having FPG ≥ 100 and UA ≥ 6 in clinical practice. The prevalence of NAFLD has been reported to be 10%–40% worldwide. Among Western countries, the rates ranged from approximately 20% to 40% (28) in the United States and approximately 20%–33% in Europe, with even higher rates in diabetic patients (46%–70%) (29,30). The prevalence of lean-NAFLD (BMI < 24 kg/m2) in this study was 18.5%, which was higher than the prevalence of lean-NAFLD (BMI < 25 kg/m2) in the United States (about 7%–9%) (31,32). However, higher prevalence rates (approximately 19%–35%) (33–35) of lean-NAFLD in Asia have been reported more recently. Our results were comparable with those of a study conducted in Taiwan (17.9% among subjects with BMI around 17.5–22.4 kg/m2) (15) and a study conducted in China (18.33% among subjects with BMI < 24 kg/m2) (36).
Obesity and metabolic syndromes are both well-known factors associated with NAFLD. Expediting body fat expansion and hormone changes in the male population accelerates the production of free fatty acids and adipocytokines. This results in insulin resistance and NAFLD (37). Asians generally have lower BMIs than Westerners, with only 2%–3% of Asians being classified as obese by the current Western criteria (38). In a lean population, Kim et al. (39) demonstrated that their Asian study subjects showed a similar prevalence rate of NAFLD as that for Western people, despite their lower BMIs. Hence, as shown in this study, male sex, body fat mass, and high BMI are still important associated factors for NAFLD, even in the lean population. Conversely, metabolic factors including FPG, UA, TG, and ALT levels were associated with lean-NAFLD in our study. Previous studies have substantiated the link between NAFLD and metabolic factors including hypertriglyceridemia (40), elevated FPG (40,41), and hyperuricemia (42), as well as increased liver enzymes including ALT (43) and GGT (44). These findings are in line with our findings, despite the absence of obesity. Lean people with metabolic aberrances are still at risk for NAFLD.
The lean-NAFLD phenomenon could be explained by variations in the fat distribution among Asian populations. Asian people tend to have greater central obesity and visceral adiposity, despite having normal BMIs (38,45). Additionally, visceral adipose tissue is attributed to insulin resistance and pronounced lipolysis. Consequently, deteriorated glucose and lipid metabolism can lead to NAFLD (46). Visceral adipose tissue has greater lipolytic activity than subcutaneous adipose tissue, which results in fatty acids being delivered to the liver directly. Induction of lipolysis and increased delivery of lipids to the liver worsens insulin resistance in the liver. This aggravates dyslipidemia by increasing TG synthesis (47). Furthermore, Lanaspa et al. (48) concluded that hyperuricemia could produce oxygen-free radical stress and aggravate inflammation (49), which engenders the progression of fatty liver disease. Finally, higher serum ALT and GGT were correlated with intrahepatic oxidative stress and steatosis (50). Hence, as shown in this study, patients with lean-NAFLD had a higher incidence of comorbidities including cardiovascular disease, hypertension, DM, and dyslipidemia than did lean patients without NAFLD.
Our findings support that FLI is a predictor of lean-NAFLD and is superior to other noninvasive markers. Although the positive predictive value of 38.3% in this study was low, it was affected by the low prevalence of NAFLD in our study population. Our findings were comparable to those of previous studies that referred to FLI as a feasible indicator of ultrasonographic NAFLD (51,52). Our results revealed the cut-off value of FLI < 5 for ruling out lean-NAFLD and FLI > 50 for ruling in lean-NAFLD. However, the cut-off value identified in this study is lower than that reported in previous study of Western populations (14). This could be explained by the low BMI and low WC in our study group, as well as by the presence of central adiposity in the lean Asian population (53). The cut-off values for WC and BMI among Asians were disparate due to different ethnicities, dietary habits, and environmental factors (54). Further investigations in larger prospective studies are needed to validate our study results. Owing to the lack of an established nationwide health policy addressing NAFLD, at present, blood testing is more easily accessible and cost-effective than ultrasonography. Hence, for the avoidance of unnecessary tests and clinical practice, we suggest using an FLI value ≥15 as a reasonable cut-off value for screening of lean-NAFLD, especially in those lean participants with metabolic disparities such as serum FPG ≥ 100 mg/dL and UA ≥ 6 mg/dL.
There are some limitations to this study. First, we did not use liver biopsy to diagnose NAFLD. We also cannot detect NAFLD if there is less than 33% fat in the liver and with lower BMI because of the limitations of the ultrasound technique. Moreover, ultrasound findings are operator-dependent and less precise for detecting mild NAFLD. However, in this study, the procedures were performed and verified by a fixed single group of experienced technicians and radiologists, thus decreasing this potential bias dramatically. Second, FLI did not detect advanced fibrosis and steatohepatitis in the original study. Hence, we excluded those patients who had liver cirrhosis history or ultrasonographic detected liver cirrhosis at the beginning of the study, and we used FLI for the prediction of NAFLD rather than steatohepatitis or advanced fibrosis in the lean population. Third, we used BMI < 24 kg/m2 as the cut-off value for defining the lean population in both men and women. These criteria were chosen in accordance with a previous study in Asia.56 However, the bias inherent to the potential presence of sexual dimorphism could not be avoided. Future research is needed to identify truly independent and quantitative markers of steatosis between the sexes. Fourth, we did not obtain insulin resistance and HOMR index data, which were previously reported to be robustly correlated with NAFLD. We also did not check serum insulin levels routinely. A prospective study to verify the FLI value and testing another index for this low prevalence but potentially at-risk population may be needed in the future.
Altogether, FLI was superior to liver function parameters, some metabolic factors, and sex for predicting lean-NAFLD. In addition, the FLI is a relatively easy parameter to evaluate and is a cost-effective, noninvasive marker to screen for NAFLD in lean populations, especially those who have metabolic discrepancies.
CONFLICTS OF INTEREST
Guarantor of the article: Hsien-Chung Yu, MD.
Specific author contributions: C.-L.H. and H.-C.Y.: collected data, draft and revised the manuscript. C.-L.H., K.-H.L., and H.-C.Y.: participated in planning the study design, analyzed and interpreted the data. F.-Z.W. and Y.-H.C.: helped with the statistical analyses and interpreted the data. P.-C.W., Y.-H.C., C.-S.C., W.-H.W., and G.-Y.M.: assisted with collecting data and reviewing the database. All authors have read and approved the final draft submitted.
Financial support: None declared.
Potential competing interests: None declared.
WHAT IS KNOWN
- ✓ Prevalence of NAFLD is increasing among lean population worldwide.
- ✓ NAFLD is an independent risk factor for comorbid metabolic conditions irrespective of patient BMI.
- ✓ The FLI could be considered a marker to screen for lean-NAFLD.
WHAT IS NEW HERE
- ✓ Overall, 18.5% of the lean population in one institution at Southern Taiwan had NAFLD.
- ✓ Lean-NAFLD was associated with male sex, BMI, body fat mass, liver function, and metabolic factors.
- ✓ FLI eclipsed other noninvasive metabolic factors at predicting lean-NAFLD.
- ✓ The FLI bring a better foundation to identify lean-NAFLD.
- ✓ The FLI have a potential to reduce unnecessary screenings.
- ✓ For a greater clinical impact we may expand the use of FLI and other parameters to identify lean-NAFLD in the future.
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