1. Introduction
Non-alcoholic fatty liver disease (NAFLD) is characterized by the formation of a cluster of lipid vesicles around the internal structure of hepatocytes. The term was first coined in 1980 by Ludwing J., describing liver diseases that usually develop in alcoholic patients, when these are present in individuals who do not reach a toxic alcoholism level [1]. NAFLD involves a wide variety of liver diseases ranging from steatosis to critical conditions such as cirrhosis [2]. Within its etiology, what is notable is its high frequency in patients with a history of metabolic conditions linked to obesity and type II diabetes [3].
It is estimated that within the general population, between 20% and 30% suffer from NAFLD, and the percentage increases to 70–80% in diabetic individuals [4], which makes it one of the chronic diseases with the greatest impact on a global scale [3]. NAFLD does not have a standard treatment [5] and its characteristics vary according to age, with the elderly being the most vulnerable. With advancing age, liver diseases present greater complications and liver damage than in young individuals [6]. Studies show that diseases such as NAFLD are related to age [7]; however, this is not a significant factor in the degree of steatosis [8], compared to the influence of the type of fatty acid [9].
In Guayaquil, Ecuador, 30 out of every 100 older adults show signs of NAFLD [10], while in Rotterdam, the Netherlands, the number increases to 35 in 100, with increased severity associated with an increase in waist circumference (WC) [11]. Therefore, the aim of this study is to identify the role of waist circumference as a risk factor for non-alcoholic fatty liver disease.
2. Materials and Methods
2.1. Study Design and Participants
The research was a cross-sectional observational study performed between October and May from 2014 to 2016. This study was approved by the ethics committee of Clinica Kennedy in Guayaquil, Ecuador, on 5 March 2014. The participants were selected by a non-probabilistic convenience sampling method from 5 gerontological centers in Guayaquil, Ecuador. Informed consent was obtained from all the individuals who agreed to participate in the study. The sample was selected based on the following inclusion criteria: age ≥ 65 years, able to attend one of the 5 selected gerontological centers, and able to sign the informed consent. Moreover, subjects with the following characteristics were excluded from the study: consumption of alcohol in quantities > 20 g/day, reporting flatulence at the time of WC measurement, individuals with significant cognitive impairment, and history of liver diseases (Hepatitis B, C, autoimmune disease, biliary disease, cirrhosis).
Once the analysis was completed, each one was given a report of these results with a brief explanation; in addition, the food and transportation expenses previously stipulated in the protocol were covered.
2.2. Data Collection
The patients who agreed to participate in the study were scheduled on a specific date for the application of some questionnaires, and the measurement of abdominal circumference and fat mass. Demographic data were collected and the Mini Nutritional Assessment (MNA) [12] was applied to carry out the nutritional screening of the participants, and the percentage of fat mass was obtained with the Tanita scale. For measurement of the waist circumference, a non-elastic plastic measuring tape was used to give a measurement in centimeters of each participant; using two cut-off points as reference [13]: men, greater than 90 cm and women, greater than 80 cm.
The abdominal and pelvic ultrasound was performed by a certified sonographer at the “Fundacion Damas del Honorable Cuerpo Consular” medical center, using a Siemens Sonoline G40 ultrasound. Non-alcoholic liver steatosis was defined by the ultrasonographic as the increase in echogenicity of the liver parenchyma in comparison with the renal cortex and spleen [14,15]. Moreover, the criteria used to define the degree of severity of NAFLD was based on the report by Sherif et al. [16]:
Mild (Grade 1): slight, diffuse increase in fine echoes in liver parenchyma with normal visualization of diaphragm and intrahepatic vessel borders;
Moderate (Grade 2): moderate, diffuse increase in fine echoes with slightly impaired visualization of intrahepatic vessels and diaphragm;
Severe (Grade 3): marked increase in fine echoes with poor or non-visualization of the intrahepatic vessel borders, diaphragm, and posterior right lobe of the liver.
2.3. Statistical Analysis
Descriptive statistics were compiled using frequency tables and measures of central tendency. Normality tests were performed, and parametric or non-parametric tests for association were applied as appropriate. For correlation analysis, Pearson or Spearman tests were performed according to the distribution of the data. Moreover, association analysis was performed with a chi-squared test. In addition, a univariate logistic regression and a multivariate logistic regression were run to assess the factors that may influence the development of NAFLD. The dependent variable was the presence or absence of NAFLD. On the other hand, covariates included age, WC, BMI, and the percentage of fat mass.
3. Results
3.1. Participants
A total of 100 patients who met the inclusion criteria were invited to participate in the study; however, only 99 patients agreed to participate. Table 1 shows the demographic data obtained. The population studied had a mean age of 79 (SD = 4.83) years ranging from 71 to 94. Fifteen percent of the total population were male. Almost all the participants (95%) lived independently. Moreover, 80% of them had three meals a day and 82% were well nourished according to the MNA (mean of 26.3, SD = 2.58, ranging from 19.5 to 30). However, according to the BMI classification and the fat mass classification, the majority of the participants were overweight/obese (72.7% and 68.4%, respectively). The mean BMI was 27.16 (SD = 3.83) ranging from 19.91 to 36.57; and the mean percentage of fat mass was 33.48 (SD = 7.10), ranging from 13.5 to 48.5. Finally, the participants had an average WC of 89.76 (SD = 9.35) cm, ranging from 68 to 119.
3.2. Association of Variables with NAFLD
The presence of NAFLD determined by echography was associated with the anthropometric variables. Table 2 shows the relation between these variables. The mean value of the waist circumference, MNA, percentage of fat mass, and BMI was higher in the NAFLD group in comparison to the non-NAFLD group. Moreover, an association was identified between BMI categories and the presence of NAFLD (see Figure 1). Overweight and obesity were more frequent in people with NAFLD (p = 0.014). On the other hand, associations between sex, independence, complete meals, and age group were non-statistically significant (p > 0.05).
3.3. Logistic Regression Explaining the Absence or Presence of NAFLD
Logistic regression was performed to assess the factors that may increase the possibility to develop NAFLD. Two regressions were performed: a univariate logistic regression and a multivariate logistic regression. The first one was performed to assess the odds ratio for each variable as a unique factor. Then, multivariate logistic regression was performed to assess a predictive model. The method used was “intro” with the variable NAFLD (0 = absence; 1 = present) as the dependent variable.
The univariate regression analysis shows that overweight and obesity are direct factors for developing NAFLD (OR: 4.65 (CI: 1.22–17.62) and 7.33 (CI: 1.72–31.34), respectively). Both were statistically significant with p values = 0.024 and 0.007, respectively. In addition, the percentage of fat mass is a variable that increases the risk for NAFLD 1.11 times (CI: 1.04–1.2); this was statistically significant (p = 0.004).
For the multivariate analysis, the covariates were the anthropometric variables and age recollected from the participants. There were 98 cases analyzed from the total population (1 missing value). The model was statistically significant (X2 = 21.021; Fd = 5; p < 0.001); moreover, the model fits well within the data (X2 = 13.375; Fd = 8; p = 0.100). This model explained the range from 19.3% to 26.9% of the variance with a total correct prediction of 72.4%, a specificity of 87.9%, and a sensitivity of 40.6%. However, only the age and the waist circumference were statistically significant (p = 0.045 and 0.016, respectively). Age presented a negative coefficient (B = −0.115), indicating a decrease in risk with increasing age. On the other hand, the waist circumference presented a positive coefficient (B = 0.092) which indicates an increased risk for NAFLD. Finally, the constant in the model was not statistically significant, which indicates that there are variables that affect the model which is not taken into consideration. Table 3 shows the OR of the evaluated variables.
4. Discussion
In the last decade, non-alcoholic liver steatosis has been classified as an epidemic [17], which is influenced by factors such as age, gender, and diabetes, in addition to the trend towards increased obesity in all age groups [18]. The prevalence of the presence of NAFLD is similar to the one reported by Reyes et al. in Guayaquil, Ecuador (32.3% vs. 30%) [10].
Studies report that NAFLD affects men more than women [19]; however, women of post-menopausal age, who constituted 84.8% of the studied population, are equally susceptible due to inflammatory hepatic changes related to estrogen deficiency [20]. In addition, variables such as age are usually a determining factor for the appearance of NAFLD in more than 40% of adults over 60 [21], making population selection highly important. In contrast to our results, we found no association between the presence of NAFLD and age (p = 0.198). In spite of that, the multivariate logistic regression showed age as a significant factor that may reduce the probability of NAFLD (OR: 0.892; CI: 0.797–0.998; p = 0.045). The literature describes an increase in body fat percentage in proportion to muscular mass due to age-induced sarcopenia [22]. However, our results suggest a decrease in body fat percentage too. This may be due to changes and fat redistribution caused by aging. This was also explored by Kupusinac et al. [23] and Macek et al. [24] who found that people over 60 years of age have less percentage of fat mass in comparison to people 50–60 years of age.
Moreover, some studies have suggested that conditions such as malnutrition have an influence on the development of an aggressive NAFLD [25,26]. However, rapid weight loss has also been reported as beneficial for the improvement in liver steatosis [27], emphasizing diet as the mainstay of disease treatment [28,29]. In this study, no statistically significant relationship was found between the presence of NAFLD and the MNA score (p = 0.152).
Another risk factor that stands out in the development of NAFLD is waist circumference [30], which is usually associated with obese patients, though cases of NAFLD have also been observed in non-obese individuals [31]. In the present study, we found a significant relationship between average WC and the presence of NAFLD (p = 0.001). Average WC presents a seven-point increase in patients with NAFLD (no-NAFLD = 87.5 vs. NAFLD = 94.5). This is consistent with previous studies indicating the relationship between adiposity indicators and NAFLD [8,32,33]. Moreover, logistic regression showed a significant odds ratio for WC (p = 0.018), indicating that people with high WC have a range from 1.017 to 1.197 times the probability to present NAFLD (OR = 1.104).
In addition, some studies present a significant relationship between BMI and the presence of NAFLD [19,32,34,35]. The univariate logistic regression shows a significantly increased risk in patients with overweight and obesity. This is in line with the literature which has established the relationship between obesity, dyslipidemia, metabolic syndrome, and the presence of NAFLD [19,35]. However, even though we found a significant relationship between BMI scores and the presence of NAFLD (26.36 vs. 28.85, p = 0.005), the multivariate logistic regression was not statistically significant (p = 0.319). This indicates that in the presence of the waist circumference, as an evaluation of abdominal adiposity, the BMI categories lose their significance. This effect has already been explored and reported by several authors [36,37,38,39,40,41].
Finally, the percentage of fat mass was another statistically significant variable when analyzing the relationship with the presence of NAFLD (31.98 vs. 36.55, p = 0.009); however, the odds ratio was not significant (p = 0.186). In contrast to our results, some studies have investigated the predictor power of the fat mass for NAFLD with statistically significant values [42,43].
The predictive model expressed the importance of abdominal obesity in the development of NAFLD (see Figure 2). This is supported by the biochemical pathophysiology of NAFLD that has been proposed. The literature suggests that some of the mechanisms of NAFLD are low-grade hepatic inflammation and insulin resistance due to free fatty acids released by the visceral adipose tissue [40,44,45]. Furthermore, there are several cytokines named adipokines which are released by the adipose tissue. Adiponectin and leptin are two of the most studied adipokines, which play a major role in the development of NAFLD. Adiponectin is an important hormone that helps with anti-inflammatory effects and helps in tissues’ insulin sensitization. In people with high visceral adipose tissue, the levels of adiponectin are lower. This decrease in adiponectin levels may induce insulin resistance, which affects the hormone-sensitive lipase, resulting in the release of free fatty acid to hepatic circulation [44,46,47].
Moreover, the hyperinsulinemia state due to insulin resistance produces an inhibition of free fatty acid beta-oxidation with the promotion of hepatic fat accumulation as the principal consequence. Moreover, the lower levels of adiponectin may result in the promotion of a pro-inflammatory state. This, in addition to the secretion of pro-inflammatory cytokines, such as IL-6 and TNF-alpha, induced by free fatty acids will increase the insulin resistance state of the adipocytes [40,44,45,46,47]. This results in a positive feedback loop, which implies a constant development of the NAFLD.
Finally, our model suggests that there is a protective effect of age, which is supported by the changes and fat redistribution produced by aging [23,24]. Considering that lower levels of adipose tissue are correlated with higher levels of adiponectin, it can be deduced that age may express a protective effect due to the lower fat mass which results in higher levels of adiponectin, as reported by some authors [48,49,50].
This study has several limitations. The sample is not representative for the study population, which may limit the generalizability of the results. Many variables which may influence the presence of NAFLD were not taken into consideration for this study, such as skeletal muscle mass and comorbidities related to metabolic syndrome including diabetes mellitus, hypertension, and dyslipidemia [11,19,51]. So, results must be interpreted cautiously as many confounding variables were not analyzed. Furthermore, even though liver biopsy has been considered the gold diagnostic method for NAFLD, its routine use is controversial [52], so less invasive techniques such as ultrasound are chosen [53]. Ultrasonography as a diagnostic method has the disadvantage that it is operator dependent [54], not being effective in accurately determining the degrees of steatosis [55]. The technique has shown a high sensitivity and specificity only in the detection of moderate and severe grades [56]. Finally, the MNA score was considered an indirect marker for unhealthy dietary behavior, but there are better tools to assess the dietary pattern of the patients, such as the food frequency questionnaire, food consumption record, and dietary history [57].
5. Conclusions
NAFLD represents a public health problem, due to population trends showing an increase in risk factors such as obesity, hypertension, and metabolic syndrome. The use of anthropometric measures such as waist circumference is an indicator of risk for the development of the disease. However, even though many studies show the predictor value of variables such as BMI and fat mass percentage, we found an association with the presence of NAFLD, but no predictor capacity. This implies the use of anthropometric measures as a complement to predict the development of NAFLD.
D.C. contributed to the design of the study and the original draft preparation; G.A.-V. and J.M.-R. analyzed and interpreted the data. K.C. and J.M.-R. reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Hospital-Clinica Kennedy in Guayaquil, Ecuador, on 5 March 2014.
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author.
We would like to thank José Zaporta and Myriam Reyes for their collaboration in carrying out this project.
The authors declare that there are no conflict of interest relevant to the present study.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Relationship between BMI categories and the presence or absence of non-alcoholic fatty liver disease.
Enlarge this image.Figure 2. NAFLD mechanism and interaction with anthropometric variables. Increase in waist circumference, along with overweight/obesity and increase in abdominal adiposity will augment the visceral adipose tissue which lead to an incremental release of FFA and a reduction in adiponectin. These predispose to a pro-inflammatory state and insulin resistance, which inhibits FFA beta-oxidation and increases FFA release. As a consequence, there is a hepatic fat accumulation which leads to NAFLD. Finally, due to adipose tissue loss and redistribution, age may lead to a slight incremental increase in adiponectin, which induces an anti-inflammatory state and insulin sensitization. FFA: free fatty acids. Beta-Ox: beta-oxidation. NAFLD: non-alcoholic fatty liver disease.
Characteristics of the participants.
| n = 99 | Percentage (%) | ||
|---|---|---|---|
| Sex | Man | 15 | 15.2% |
| Woman | 84 | 84.8% | |
| Age | 71–75 | 20 | 20.2% |
| 76–80 | 45 | 45.5% | |
| 81–85 | 19 | 19.2% | |
| 86–90 | 13 | 13.1% | |
| 91–95 | 2 | 2.0% | |
| NAFLD | Normal | 67 | 67.7% |
| Grade I | 29 | 29.3% | |
| Grade II | 3 | 3.0% | |
| Lives independently | No | 4 | 4.1% |
| Yes | 94 | 95.9% | |
| Complete meals | Two meals | 19 | 19.2% |
| Three meals | 80 | 80.8% | |
| MNA Classification | Well-nourished | 82 | 82.8% |
| Risk of malnutrition | 17 | 17.2% | |
| BMI Classification | Normal | 27 | 27.3% |
| Overweight | 49 | 49.5% | |
| Obesity | 23 | 23.2% | |
| Fat Mass | Athlete | 2 | 2.0% |
| Fitness | 6 | 6.1% | |
| Acceptable | 23 | 23.5% | |
| Obesity | 67 | 68.4% | |
MNA: Mini Nutritional Assessment; BMI: body mass index; NAFLD: non-alcoholic fatty liver disease.
Association of variables with NAFLD presence.
| Normal | NAFLD | p Value | |
|---|---|---|---|
| Mean (SD) | Mean (SD) | ||
| Age | 79.75 (5.12) | 77.69 (3.86) | 0.198 |
| Waist circumference (cm) | 87.50 (8.32) | 94.50 (9.74) | 0.001 * |
| MNA | 26.00 (2.54) | 26.94 (2.64) | 0.152 |
| BMI | 26.36 (3.58) | 28.85 (3.83) | 0.005 * |
| Percentage of fat mass | 31.98 (7.27) | 36.55 (5.67) | 0.009 * |
MNA: Mini Nutritional Assessment; BMI: body mass index; NAFLD: non-alcoholic fatty liver disease. * p value < 0.01.
Multivariate logistic regression which explained the presence of NAFLD.
| Variables | B | Sig. | OR | OR 95% CI | |
|---|---|---|---|---|---|
| Inferior | Superior | ||||
| Age | −0.115 | 0.045 * | 0.892 | 0.797 | 0.998 |
| Waist circumference (cm) | 0.092 | 0.016 * | 1.097 | 1.017 | 1.183 |
| Overweight | 0.473 | 0.565 | 1.605 | 0.321 | 8.036 |
| Obesity | −0.657 | 0.586 | 0.518 | 0.049 | 5.498 |
| Percentage of fat mass | 0.07 | 0.186 | 1.073 | 0.967 | 1.191 |
| Constant | −2.641 | 0.614 | 0.071 | ||
* p value < 0.05.
References
1. Ludwig, J.; Viggiano, T.R.; McGill, D.B.; Oh, B.J. Nonalcoholic Steatohepatitis: Mayo Clinic Experiences with a Hitherto Unnamed Disease. Mayo Clin. Proc.; 1980; 55, pp. 434-438.
2. Yari, Z.; Hekmatdoost, A. Chapter 20—Dietary Interventions in Fatty Liver. Dietary Interventions in Gastrointestinal Diseases; Watson, R.R.; Preedy, V.R. Academic Press: Cambridge, MA, USA, 2019; pp. 245-255. [DOI: https://dx.doi.org/10.1016/B978-0-12-814468-8.00020-X]
3. Dharmalingam, M.; Yamasandhi, P.G. Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus. Indian J. Endocrinol. Metab.; 2018; 22, pp. 421-428. [DOI: https://dx.doi.org/10.4103/ijem.IJEM_585_17] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30090738]
4. Younossi, Z.M.; Koenig, A.B.; Abdelatif, D.; Fazel, Y.; Henry, L.; Wymer, M. Global Epidemiology of Nonalcoholic Fatty Liver Disease-Meta-Analytic Assessment of Prevalence, Incidence, and Outcomes. Hepatology; 2016; 64, pp. 73-84. [DOI: https://dx.doi.org/10.1002/hep.28431] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26707365]
5. Moctezuma-Velázquez, C. Current Treatment for Non-Alcoholic Fatty Liver Disease. Rev. Gastroenterol. Mex.; 2018; 83, pp. 125-133. [DOI: https://dx.doi.org/10.1016/j.rgmxen.2018.05.014]
6. Alqahtani, S.A.; Schattenberg, J.M. NAFLD in the Elderly. CIA; 2021; 16, pp. 1633-1649. [DOI: https://dx.doi.org/10.2147/CIA.S295524] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34548787]
7. Perumpail, B.J.; Khan, M.A.; Yoo, E.R.; Cholankeril, G.; Kim, D.; Ahmed, A. Clinical Epidemiology and Disease Burden of Nonalcoholic Fatty Liver Disease. World J. Gastroenterol.; 2017; 23, pp. 8263-8276. [DOI: https://dx.doi.org/10.3748/wjg.v23.i47.8263]
8. Sapmaz, F.; Uzman, M.; Basyigit, S.; Ozkan, S.; Yavuz, B.; Yeniova, A.; Kefeli, A.; Asilturk, Z.; Nazligül, Y. Steatosis Grade Is the Most Important Risk Factor for Development of Endothelial Dysfunction in NAFLD. Medicine; 2016; 95, e3280. [DOI: https://dx.doi.org/10.1097/MD.0000000000003280]
9. Gambino, R.; Bugianesi, E.; Rosso, C.; Mezzabotta, L.; Pinach, S.; Alemanno, N.; Saba, F.; Cassader, M. Different Serum Free Fatty Acid Profiles in NAFLD Subjects and Healthy Controls after Oral Fat Load. Int. J. Mol. Sci.; 2016; 17, 479. [DOI: https://dx.doi.org/10.3390/ijms17040479]
10. Reyes, M.; Cabrera, D.; Alvarado, G. Valoración Integral de Adultos Mayores En Centros Gerontológicos de Guayaquil. Rev. Cienc. Unemi; 2017; 10, pp. 116-123. [DOI: https://dx.doi.org/10.29076/issn.2528-7737vol10iss23.2017pp116-123p]
11. Koehler, E.M.; Schouten, J.N.L.; Hansen, B.E.; van Rooij, F.J.A.; Hofman, A.; Stricker, B.H.; Janssen, H.L.A. Prevalence and Risk Factors of Non-Alcoholic Fatty Liver Disease in the Elderly: Results from the Rotterdam Study. J. Hepatol.; 2012; 57, pp. 1305-1311. [DOI: https://dx.doi.org/10.1016/j.jhep.2012.07.028]
12. Nestlé Nutrition Institute. Mini Nutritional Assessment MNA. Available online: https://www.mna-elderly.com/ (accessed on 18 May 2021).
13. Alberti, K.G.M.M.; Zimmet, P.; Shaw, J. IDF Epidemiology Task Force Consensus Group. The Metabolic Syndrome—A New Worldwide Definition. Lancet; 2005; 366, pp. 1059-1062. [DOI: https://dx.doi.org/10.1016/S0140-6736(05)67402-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16182882]
14. Santos, L.F.; Hernández, G.; Varón Puerta, A.; Beltrán, Ó.; Botero, R.C.; Mejía, G. Enfermedad hepática por infiltración grasa no alcohólica: La nueva pandemia del milenio. Rev. Colomb. Gastroenterol.; 2010; 25, pp. 380-398.
15. Ma, X.; Holalkere, N.-S.; R, A.K.; Mino-Kenudson, M.; Hahn, P.F.; Sahani, D.V. Imaging-Based Quantification of Hepatic Fat: Methods and Clinical Applications. RadioGraphics; 2009; 29, pp. 1253-1277. [DOI: https://dx.doi.org/10.1148/rg.295085186] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19755595]
16. Saadeh, S.; Younossi, Z.M.; Remer, E.M.; Gramlich, T.; Ong, J.P.; Hurley, M.; Mullen, K.D.; Cooper, J.N.; Sheridan, M.J. The Utility of Radiological Imaging in Nonalcoholic Fatty Liver Disease. Gastroenterology; 2002; 123, pp. 745-750. [DOI: https://dx.doi.org/10.1053/gast.2002.35354] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12198701]
17. Lazarus, J.V.; Colombo, M.; Cortez-Pinto, H.; Huang, T.T.-K.; Miller, V.; Ninburg, M.; Schattenberg, J.M.; Seim, L.; Wong, V.W.S.; Zelber-Sagi, S. NAFLD—Sounding the Alarm on a Silent Epidemic. Nat. Rev. Gastroenterol. Hepatol.; 2020; 17, pp. 377-379. [DOI: https://dx.doi.org/10.1038/s41575-020-0315-7]
18. Rodríguez, J.H.; Domínguez, Y.A.; Espinal, O.M.M. Prevalence and Trend of Overweight and Obesity in Adults Worldwide. Rev. Cuba. Endoc.; 2019; 30, Available online: https://www.medigraphic.com/cgi-bin/new/resumenI.cgi?IDARTICULO=96712# (accessed on 23 September 2020).
19. Sheka, A.C.; Adeyi, O.; Thompson, J.; Hameed, B.; Crawford, P.A.; Ikramuddin, S. Nonalcoholic Steatohepatitis: A Review. JAMA; 2020; 323, pp. 1175-1183. [DOI: https://dx.doi.org/10.1001/jama.2020.2298]
20. Ballestri, S.; Nascimbeni, F.; Baldelli, E.; Marrazzo, A.; Romagnoli, D.; Lonardo, A. NAFLD as a Sexual Dimorphic Disease: Role of Gender and Reproductive Status in the Development and Progression of Nonalcoholic Fatty Liver Disease and Inherent Cardiovascular Risk. Adv. Ther.; 2017; 34, pp. 1291-1326. [DOI: https://dx.doi.org/10.1007/s12325-017-0556-1]
21. Golabi, P.; Paik, J.; Reddy, R.; Bugianesi, E.; Trimble, G.; Younossi, Z.M. Prevalence and Long-Term Outcomes of Non-Alcoholic Fatty Liver Disease among Elderly Individuals from the United States. BMC Gastroenterol.; 2019; 19, 56. [DOI: https://dx.doi.org/10.1186/s12876-019-0972-6]
22. Chao, Y.-P.; Chen, W.-L.; Peng, T.-C.; Wu, L.-W.; Liaw, F.-Y.; Kao, T.-W. Examining the Association between Muscle Mass, Muscle Function, and Fat Indexes in an Elderly Population. Nutrition; 2021; 83, 111071. [DOI: https://dx.doi.org/10.1016/j.nut.2020.111071]
23. Kupusinac, A.; Stokić, E.; Doroslovački, R. Predicting Body Fat Percentage Based on Gender, Age and BMI by Using Artificial Neural Networks. Comput. Methods Programs Biomed.; 2014; 113, pp. 610-619. [DOI: https://dx.doi.org/10.1016/j.cmpb.2013.10.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24275480]
24. Macek, P.; Terek-Derszniak, M.; Biskup, M.; Krol, H.; Smok-Kalwat, J.; Gozdz, S.; Zak, M. Assessment of Age-Induced Changes in Body Fat Percentage and BMI Aided by Bayesian Modelling: A Cross-Sectional Cohort Study in Middle-Aged and Older Adults. Clin. Interv. Aging; 2020; 15, pp. 2301-2311. [DOI: https://dx.doi.org/10.2147/CIA.S277171] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33335389]
25. Csak, T.; Ganz, M.; Pespisa, J.; Kodys, K.; Dolganiuc, A.; Szabo, G. Fatty Acid and Endotoxin Activate Inflammasomes in Mouse Hepatocytes That Release Danger Signals to Stimulate Immune Cells. Hepatology; 2011; 54, pp. 133-144. [DOI: https://dx.doi.org/10.1002/hep.24341] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21488066]
26. Tsai, J.-H.; Ferrell, L.D.; Tan, V.; Yeh, M.M.; Sarkar, M.; Gill, R.M. Aggressive Non-Alcoholic Steatohepatitis Following Rapid Weight Loss and/or Malnutrition. Mod. Pathol.; 2017; 30, pp. 834-842. [DOI: https://dx.doi.org/10.1038/modpathol.2017.13] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28256569]
27. Mattar, S.G.; Velcu, L.M.; Rabinovitz, M.; Demetris, A.J.; Krasinskas, A.M.; Barinas-Mitchell, E.; Eid, G.M.; Ramanathan, R.; Taylor, D.S.; Schauer, P.R. Surgically-Induced Weight Loss Significantly Improves Nonalcoholic Fatty Liver Disease and the Metabolic Syndrome. Ann. Surg.; 2005; 242, pp. 610-617. discussion 618–620 [DOI: https://dx.doi.org/10.1097/01.sla.0000179652.07502.3f] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16192822]
28. Della Pepa, G.; Vetrani, C.; Lombardi, G.; Bozzetto, L.; Annuzzi, G.; Rivellese, A.A. Isocaloric Dietary Changes and Non-Alcoholic Fatty Liver Disease in High Cardiometabolic Risk Individuals. Nutrients; 2017; 9, 1065. [DOI: https://dx.doi.org/10.3390/nu9101065]
29. Miller, E.F. Nutrition Management Strategies for Nonalcoholic Fatty Liver Disease: Treatment and Prevention. Clin. Liver Dis.; 2020; 15, pp. 144-148. [DOI: https://dx.doi.org/10.1002/cld.918]
30. Pineda, M.G.; Benítez, A.; Figueredo, R.; Ayala, F.; Argüello, R. Frecuencia de hígado graso no alcohólico diagnosticado por ecografía abdominal en pacientes obesos. An. Fac. Cienc. Méd.; 2017; 50, pp. 35-50. [DOI: https://dx.doi.org/10.18004/anales/2017.050(02)35-050]
31. Albhaisi, S.; Chowdhury, A.; Sanyal, A.J. Non-Alcoholic Fatty Liver Disease in Lean Individuals. JHEP Rep.; 2019; 1, pp. 329-341. [DOI: https://dx.doi.org/10.1016/j.jhepr.2019.08.002]
32. Jiménez-Cruz, A.; Gómez-Miranda, L.M.; Díaz Ramírez, G.; Carvalí Meza, N.Y.; Bacardí-Gascón, M. La Adiposidad Como Factor de Riesgo Del Hígado Graso No Alcohólico: Revisión Sistemática. Nutr. Hosp.; 2014; 29, pp. 771-775. [DOI: https://dx.doi.org/10.3305/nh.2014.29.4.7216]
33. Meneses Moreno, D.; Negrin Rangel, E. Esteatosis hepática no alcohólica: Relación con la circunferencia de cintura. Gen; 2017; 71, pp. 74-80.
34. Lizardi-Cervera, J.; Laparra, I.B.; Chávez-Tapia, N.C.; Ostos, M.E.R.; Esquivel, M.U. Prevalencia de hígado graso no alcohólico y síndrome metabólico en población asintomática. Rev. Gastroenterol. Mex.; 2006; 71, pp. 453-459.
35. Polyzos, S.A.; Kountouras, J.; Mantzoros, C.S. Obesity and Nonalcoholic Fatty Liver Disease: From Pathophysiology to Therapeutics. Metabolism; 2019; 92, pp. 82-97. [DOI: https://dx.doi.org/10.1016/j.metabol.2018.11.014] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30502373]
36. Tchernof, A.; Després, J.-P. Pathophysiology of Human Visceral Obesity: An Update. Physiol. Rev.; 2013; 93, pp. 359-404. [DOI: https://dx.doi.org/10.1152/physrev.00033.2011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23303913]
37. Ross, R.; Neeland, I.J.; Yamashita, S.; Shai, I.; Seidell, J.; Magni, P.; Santos, R.D.; Arsenault, B.; Cuevas, A.; Hu, F.B. et al. Waist Circumference as a Vital Sign in Clinical Practice: A Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat. Rev. Endocrinol.; 2020; 16, pp. 177-189. [DOI: https://dx.doi.org/10.1038/s41574-019-0310-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32020062]
38. Kelley, D.E.; McKolanis, T.M.; Hegazi, R.A.F.; Kuller, L.H.; Kalhan, S.C. Fatty Liver in Type 2 Diabetes Mellitus: Relation to Regional Adiposity, Fatty Acids, and Insulin Resistance. Am. J. Physiol. Endocrinol. Metab.; 2003; 285, pp. E906-E916. [DOI: https://dx.doi.org/10.1152/ajpendo.00117.2003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12959938]
39. van der Poorten, D.; Milner, K.-L.; Hui, J.; Hodge, A.; Trenell, M.I.; Kench, J.G.; London, R.; Peduto, T.; Chisholm, D.J.; George, J. Visceral Fat: A Key Mediator of Steatohepatitis in Metabolic Liver Disease. Hepatology; 2008; 48, pp. 449-457. [DOI: https://dx.doi.org/10.1002/hep.22350]
40. Cariou, B. The Metabolic Triad of Non-Alcoholic Fatty Liver Disease, Visceral Adiposity and Type 2 Diabetes: Implications for Treatment. Diabetes Obes. Metab.; 2022; 24, (Suppl. 2), pp. 15-27. [DOI: https://dx.doi.org/10.1111/dom.14651]
41. Park, B.J.; Kim, Y.J.; Kim, D.H.; Kim, W.; Jung, Y.J.; Yoon, J.H.; Kim, C.Y.; Cho, Y.M.; Kim, S.H.; Lee, K.B. et al. Visceral Adipose Tissue Area Is an Independent Risk Factor for Hepatic Steatosis. J. Gastroenterol. Hepatol.; 2008; 23, pp. 900-907. [DOI: https://dx.doi.org/10.1111/j.1440-1746.2007.05212.x]
42. Kim, H.Y.; Baik, S.J.; Lee, H.A.; Lee, B.K.; Lee, H.S.; Kim, T.H.; Yoo, K. Relative Fat Mass at Baseline and Its Early Change May Be a Predictor of Incident Nonalcoholic Fatty Liver Disease. Sci. Rep.; 2020; 10, 17491. [DOI: https://dx.doi.org/10.1038/s41598-020-74659-9]
43. Jones, G.S.; Graubard, B.I.; Alvarez, C.S.; McGlynn, K.A. Prediction of Nonalcoholic Fatty Liver Disease Using Anthropometry and Body Fat Measures by Sex and Race/Ethnicity in the United States. Obesity; 2022; 30, pp. 1760-1765. [DOI: https://dx.doi.org/10.1002/oby.23514] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35945693]
44. Milić, S.; Lulić, D.; Štimac, D. Non-Alcoholic Fatty Liver Disease and Obesity: Biochemical, Metabolic and Clinical Presentations. World J. Gastroenterol.; 2014; 20, pp. 9330-9337. [DOI: https://dx.doi.org/10.3748/wjg.v20.i28.9330] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25071327]
45. Yaqub, S.; Ananias, P.; Shah, A.; Luenam, K.; Jose, A.M.; Melo, J.P.; Turkistani, A.; Mohammed, L. Decoding the Pathophysiology of Non-Alcoholic Fatty Liver Disease Progressing to Non-Alcoholic Steatohepatitis: A Systematic Review. Cureus; 2021; 13, e18201. [DOI: https://dx.doi.org/10.7759/cureus.18201] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34722019]
46. Fang, H.; Judd, R.L. Adiponectin Regulation and Function. Compr. Physiol.; 2018; 8, pp. 1031-1063. [DOI: https://dx.doi.org/10.1002/cphy.c170046] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29978896]
47. Zhao, S.; Kusminski, C.M.; Scherer, P.E. Adiponectin, Leptin and Cardiovascular Disorders. Circ. Res.; 2021; 128, pp. 136-149. [DOI: https://dx.doi.org/10.1161/CIRCRESAHA.120.314458]
48. Tomono, Y.; Hiraishi, C.; Yoshida, H. Age and Sex Differences in Serum Adiponectin and Its Association with Lipoprotein Fractions. Ann. Clin. Biochem.; 2018; 55, pp. 165-171. [DOI: https://dx.doi.org/10.1177/0004563217699233]
49. Obata, Y.; Yamada, Y.; Takahi, Y.; Baden, M.Y.; Saisho, K.; Tamba, S.; Yamamoto, K.; Umeda, M.; Furubayashi, A.; Matsuzawa, Y. Relationship between Serum Adiponectin Levels and Age in Healthy Subjects and Patients with Type 2 Diabetes. Clin. Endocrinol.; 2013; 79, pp. 204-210. [DOI: https://dx.doi.org/10.1111/cen.12041]
50. Koh, S.J.; Hyun, Y.J.; Choi, S.Y.; Chae, J.S.; Kim, J.Y.; Park, S.; Ahn, C.-M.; Jang, Y.; Lee, J.H. Influence of Age and Visceral Fat Area on Plasma Adiponectin Concentrations in Women with Normal Glucose Tolerance. Clin. Chim. Acta; 2008; 389, pp. 45-50. [DOI: https://dx.doi.org/10.1016/j.cca.2007.11.017]
51. Guo, W.; Zhao, X.; Miao, M.; Liang, X.; Li, X.; Qin, P.; Lu, J.; Zhu, W.; Wu, J.; Zhu, C. et al. Association Between Skeletal Muscle Mass and Severity of Steatosis and Fibrosis in Non-Alcoholic Fatty Liver Disease. Front. Nutr.; 2022; 9, 883015. [DOI: https://dx.doi.org/10.3389/fnut.2022.883015]
52. Paul, J. Recent Advances in Non-Invasive Diagnosis and Medical Management of Non-Alcoholic Fatty Liver Disease in Adult. Egypt. Liver J.; 2020; 10, 37. [DOI: https://dx.doi.org/10.1186/s43066-020-00043-x]
53. Park, C.C.; Nguyen, P.; Hernandez, C.; Bettencourt, R.; Ramirez, K.; Fortney, L.; Hooker, J.; Sy, E.; Savides, M.T.; Alquiraish, M.H. et al. Magnetic Resonance Elastography vs Transient Elastography in Detection of Fibrosis and Noninvasive Measurement of Steatosis in Patients With Biopsy-Proven Nonalcoholic Fatty Liver Disease. Gastroenterology; 2017; 152, pp. 598-607.e2. [DOI: https://dx.doi.org/10.1053/j.gastro.2016.10.026] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27911262]
54. Uematsu, T.; Kasami, M.; Kiyohara, Y. B-Mode Ultrasound Imaging, Doppler Imaging, and Real-Time Elastography in Cutaneous Malignant Melanoma and Lymph Node Metastases. Healthcare; 2013; 1, pp. 84-95. [DOI: https://dx.doi.org/10.3390/healthcare1010084] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27429132]
55. Petzold, G. Role of Ultrasound Methods for the Assessment of NAFLD. J. Clin. Med.; 2022; 11, 4581. [DOI: https://dx.doi.org/10.3390/jcm11154581] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35956196]
56. Hernaez, R.; Lazo, M.; Bonekamp, S.; Kamel, I.; Brancati, F.L.; Guallar, E.; Clark, J.M. Diagnostic Accuracy and Reliability of Ultrasonography for the Detection of Fatty Liver: A Meta-Analysis. Hepatology; 2011; 54, pp. 1082-1090. [DOI: https://dx.doi.org/10.1002/hep.24452] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21618575]
57. Baygi, F.; Mohammadi-Nasrabadi, F.; Zyriax, B.-C.; Jensen, O.C.; Bygvraa, D.A.; Oldenburg, M.; Nielsen, J.B. Global Overview of Dietary Outcomes and Dietary Intake Assessment Methods in Maritime Settings: A Systematic Review. BMC Public Health; 2021; 21, 1579. [DOI: https://dx.doi.org/10.1186/s12889-021-11593-z]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Moncayo-Rizzo, Jorge 2
; Cevallos, Karen 2 ; Alvarado-Villa, Geovanny 2
1 Syneos Health, 1951 NW 7th Avenue, Miami, FL 33136, USA
2 Department of Health Sciences, Universidad de Especialidades Espiritu Santo, Guayaquil 092301, Ecuador