Next Article in Journal
Metabolic Effects of Vitamin B1 Therapy under Overnutrition and Undernutrition Conditions in Sheep
Previous Article in Journal
Diet-Induced Hypothalamic Inflammation, Phoenixin, and Subsequent Precocious Puberty
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 13
Issue 10
10.3390/nu13103462
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Relationship between Dietary Fatty Acid Intake with Nonalcoholic Fatty Liver Disease and Liver Fibrosis in People with HIV

by
Cristiane Fonseca de Almeida
1,2,
Paula Simplicio da Silva
1,2,
Claudia Santos de Aguiar Cardoso
1,2,
Nathalia Gorni Moreira
2,
Julliana Cormack Antunes
1,2,
Michelle Morata de Andrade
3,
Julio Silva
3,
Marina Campos Araujo
4,
Wilza Arantes Ferreira Peres
5,
Pedro Emmanuel Alvarenga Americano do Brasil
1,6,
Ronaldo Ismerio Moreira
7,
Sandra W. Cardoso
7,
Valdilea G. Veloso
7,
Beatriz Grinsztejn
7,
Patricia Dias de Brito
1,2 and
Hugo Perazzo
7,*
1
Grupo de Pesquisa Clínica em Nutrição e Doenças Infecciosas (GPClin_Nut), Instituto Nacional de Infectologia Evandro Chagas—Fundação Oswaldo Cruz (FIOCRUZ), 21040-360 Rio de Janeiro, Brazil
2
Serviço de Nutrição (SENUT), Instituto Nacional de Infectologia Evandro Chagas—Fundação Oswaldo Cruz (FIOCRUZ), 21040-360 Rio de Janeiro, Brazil
3
Plataforma de Pesquisa Clínica, Instituto Nacional de Infectologia Evandro Chagas—Fundação Oswaldo Cruz (FIOCRUZ), 21040-360 Rio de Janeiro, Brazil
4
Escola Nacional de Saúde Pública—Fundação Oswaldo Cruz (FIOCRUZ), 21031-210 Rio de Janeiro, Brazil
5
Instituto de Nutrição Josué de Castro, Universidade Federal do Rio de Janeiro (UFRJ), 21941-902 Rio de Janeiro, Brazil
6
Laboratório de Pesquisa em Imunização e Vigilância em Saúde, Instituto Nacional de Infectologia Evandro Chagas—Fundação Oswaldo Cruz (FIOCRUZ), 21040-360 Rio de Janeiro, Brazil
7
Laboratório de Pesquisa Clínica em DST/AIDS (LAPCLIN-AIDS), Instituto Nacional de Infectologia Evandro Chagas—Fundação Oswaldo Cruz (FIOCRUZ), 21040-360 Rio de Janeiro, Brazil
*
Author to whom correspondence should be addressed.
Nutrients 2021, 13(10), 3462; https://doi.org/10.3390/nu13103462
Submission received: 6 July 2021 / Revised: 20 September 2021 / Accepted: 20 September 2021 / Published: 29 September 2021
(This article belongs to the Special Issue Nutrition in Liver Diseases and Transplantation)
Browse Figures
Versions Notes

Abstract

:
We aimed to evaluate the relationship between food intake of lipids with nonalcoholic fatty liver disease (NAFLD) and/or liver fibrosis in people living with HIV/AIDS (PLWHA). In this cross-sectional study, transient elastography was used to detect the presence of NAFLD and/or liver fibrosis. The dietary intake of fats and fatty acids (FA) were assessed by two 24 h dietary recalls (24-HDR) (n = 451). Multivariate logistic regression models were performed. Participants with higher intake of total fat were associated with higher odds for NAFLD compared to those with lower consumption [adjusted odds ratio (aOR) = 1.91 (95% confidence interval (95% CI) 1.06–3.44)]. Furthermore, participants with intermediate intake of n6-PUFA (n6-poly-unsaturated FA) and lauric FA had lower odds for NAFLD, respectively aOR = 0.54 (95% CI 0.3–0.98) and aOR = 0.42 (95% CI 0.22–0.78). Additionally, a higher intake of myristoleic FA (fourth quartile) was a significant protective factor for NAFLD [aOR = 0.56 (95% CI 0.32–0.99)]. Participants with higher intake of lauric FA [0.38 (95% CI 0.18–0.80)], myristic FA [0.38 (0.17–0.89)], palmitoleic FA [0.40 (0.19–0.82)] and oleic FA [0.35 (0.16–0.79)] had positively less odds of having liver fibrosis. On the other hand, higher intake of n-6 PUFA was significantly associated with fibrosis [aOR = 2.45 (95% CI 1.12–5.32)]. Dietary assessment of total fat and FA should be incorporated into HIV care as a tool for preventing NAFLD and fibrosis in PLWHA.

1. Introduction

Globally, 38 million people have been living with the human immunodeficiency virus (HIV) [1]. The use of early combined antiretroviral therapy (c-ART) has been decreasing the incidence of opportunistic diseases and increased the life expectancy in people living with HIV/AIDS (PLWHA) [2]. In contrast, the prevalence of non-communicable diseases has been dramatically increasing in PLWHA in the last decade [3]. Non-alcoholic fatty liver disease (NAFLD) is characterized by abnormal accumulation of fat in the liver in the absence of abusive alcohol intake. Clinical presentation of NAFLD can range from simple steatosis to nonalcoholic steatohepatitis (NASH) that can progress to cirrhosis and its complications, such as hepatocellular carcinoma. The presence of advanced liver fibrosis is the main predictor of mortality in individuals with NAFLD [4]. Several studies have been reporting the burden of NAFLD and/or liver fibrosis in PLWHA [5,6,7].
Dietary habits seem to play an important role in the pathogenesis of NAFLD. The Western diet has been associated with high levels of inflammatory cytokines [8] and a higher prevalence of NAFLD in the general population [9]. On the other hand, the Mediterranean diet can reduce fatty liver and improve insulin resistance status [10]. However, the influence of specific nutrients has not been fully elucidated [11]. Among dietary factors, total fat intake and analysis of the specific subtype of fatty acid (FA) intake might be relevant due their functional and metabolic distinct effects [12]. This might be reinforced because the dietary FA composition impacts liver metabolism, leading to triglyceride accumulation in the liver tissue [13]. However, this relationship has not been completely studied, especially in PLWHA. Studies conducted in Brazil showed that patients with NAFLD had high energy and lipid consumption [14]. Additionally, studies have demonstrated that PLWHA presented more likely an unhealthy food intake pattern [15]. However, the relationship between dietary fat intake and their subtype of fatty acid with NAFLD and liver fibrosis has not been studied in PLWHA. Therefore, the aim of this study was to evaluate the relationship between dietary fatty acid intake and NAFLD and/or the presence of liver fibrosis in HIV mono-infected individuals.

2. Materials and Methods

2.1. Study Design and Participants

This cross-sectional study analyzed data collected at the baseline visit from the longitudinal PROSPEC-HIV study (NCT02542020) that has been conducted at Evandro Chagas National Institute of Infectious Diseases (INI/FIOCRUZ, Rio de Janeiro, Brazil) [16]. All participants with HIV infection enrolled in the PROSPEC-HIV study from June 2015 to January 2019 were eligible for this analysis. Participants with viral hepatitis co-infection defined by positive HCV-antibody or positive HBsAg; excessive alcohol consumption defined by the Alcohol Use Disorders Identification Test (AUDIT) score ≥8 [17]; use of lipids supplements or missing laboratory/inconsistent data on dietary assessment were excluded. This study was approved by the Ethical Committee from INI/FIOCRUZ (IRB 32889514.4.0000.5262). All participants signed an informed consent upon enrollment in the PROSPEC-HIV study.

2.2. Clinical Assessment and HIV Infection History

Clinical records collected at baseline visit of PROSPEC HIV study included age, sex at birth, self-reported skin-color [18], years of study and presence of co-morbidities. Dyslipidemia, hypertension, type-2 diabetes and metabolic syndrome were defined according to the International Diabetes Federation [19]. Anthropometric measures, such as weight, height and waist circumference were measured by trained research assistants. Participants were considered as lean, overweight and obese if body mass index (BMI) < 25 Kg/m2, BMI = 25 to 29.99 Kg/m2 and BMI ≥ 30 Kg/m2, respectively [20]. A bioelectrical impedance analyzer (Biodynamics® 450, Sao Paulo, Brazil) with 4-electrode (hand-feet) and frequency of 50 kHz was used to assess body fat percentage. All bioelectrical impedances were performed by a single operator in fasted participants in supine position [21]. The following data were available at the INI/FIOCRUZ HIV clinical cohort: (i) date of first positive HIV antibody test; (ii) date of initiation of any antiretroviral drug; (iii) dates of start and end of combined antiretroviral therapy (c-ART) and (iv) CD4+ T-lymphocyte count and HIV viral load from the closely day of clinical visit.

2.3. Laboratory Tests and Transient Elastography

Blood tests were performed after an overnight fasting and analyzed in a centralized laboratory using an analyzer Dimension-RxL-Max (Siemens Healthcare Diagnostic, Hoffman Estates, IL, USA). Liver tests, such as alkaline phosphatase, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma-glutamyltransferase (GGT) were measured using an enzymatic assay. Glucose was measured using the hexokinase method; total and high-density lipoprotein (HDL) cholesterol and triglycerides were determined using enzymatic methods. Low-density lipoprotein (LDL) cholesterol was calculated using the Friedewald equation [16]. Insulin was determined using chemiluminescent immunoassay (CLIA) and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) was calculated by the formula: [fasting insulin (mIU/L) × fasting glucose (mg/dL)]/405 [22].
Transient elastography (TE) by FibroScan (EchoSens, Paris, France) was performed by a single experienced (>2000 examinations) operator (HP) to detect the presence of NAFLD and/or liver fibrosis following a previously described validated procedure [16]. The results defined as a median of 10 valid measures and expressed in kPa were considered as reliable for analysis if the following criteria had been met: (1) at least 10 valid measurements; (2) an interquartile range (IQR) lower than 30% of the median of liver stiffness measurement (LSM) for fibrosis or Controlled Attenuation Parameter (CAP) for steatosis; and (3) a success rate of more than 60%. The results of XL probe were considered in participants with unreliable TE exams with the M probe. NAFLD was defined by CAP ≥ 248 dB/m [23]. Presence of significant liver fibrosis (METAVIR stage F ≥ 2) was defined by LSM ≥ 7.1 kPa or ≥6.2 kPa with M or XL probe, respectively [24].

2.4. Dietary Data

The dietary intake of macronutrients, fat subtypes and FA were assessed by the 24 h dietary recall (24-HDR) method. Briefly, a nutritionist investigator requested the participants to self-report all foods and beverages consumed through the last 24 h. These reports must include details of food preparation and type of oil or fat used, as well as amount of food consumed in household measurements [25]. The 24-HDR was applied using the Automated Multiple-Pass method to structure the interview and to increase the accuracy of the report, minimizing any memory bias [26]. In addition, the 24-HDR was performed in two non-consecutive days: a face-to-face interview during the clinical visit and a remote interview by telephone a few days later. Data of each food and/or beverage item reported by the participant were converted to milligrams/grams and/or milliliters/liters, and these data were entered into a nutritional analysis software (Diet Win Professional Plus 3.0® package software) that uses the Brazilian nutrient database, known as TACO (“Tabela Brasileira de Composição de Alimentos”). The implausibility in self-report intake was verified when individual report less than three foods items. In addition, the 24-HDR that had extreme values (outliers) of caloric intake were reviewed by boxplot graphs to evaluate possible inconsistent data.
The statistical modeling technique Multiple Source Method (MSM) was used to estimate the usual intake of nutrients of participants and to correct the intrapersonal dietary variability (https://msm.dife.de, accessed on 7 July 2019). The use of this approach to correct this variability avoids the need of multiple dietary interviews to estimate individual dietary intake [27]. Nutrients were adjusted by energy density method (the ratio between usual nutrient intake and total usual energy intake) expressed as a percentage to evaluate the relative contribution of these nutrients to the diet [28]. Fiber intake was calculated per 1000 kcal using the following formula: total fiber (g) × 1000 kcal/total energy intake.

2.5. Statistical Analysis

Categorical variables were reported as absolute (n) and relative frequency (%) and continuous variables as median and interquartile range (IQR). We used Chi-square and Mann—Whitney tests to compare proportions and medians, respectively. All nutrients were analyzed in proportion of energy intake (E%). Direct Acyclic Graphs (DAGs) were created with assumptions on the relationship among co-variables and outcomes (NAFLD or fibrosis) using the DAGitty as a browser-based environment (http://www.dagitty.net/, accessed on 6 December 2019) (Figure 1). DAG, as illustrated in Figure 1, is a theoretical model described through a graph that permits qualitative and visual assessment of confounding factors. These DAGs supported our decision about the most parsimonious models for NAFLD and fibrosis to avoid collinearity and confounding. Logistic multivariate models considered occurrence of NAFLD and liver fibrosis as outcomes, each nutrient alone (in quartiles) as independent variables (assuming quartile 1, lowest consumption as the reference), and age, sex and duration of c-ART as confounders as well as usual energy intake (kcal) to minimize the underreporting of the food intake method. Statistical analyses were performed using R version 3.6.3 and considering p-values < 0.05 as statistically significant.

3. Results

3.1. Study Characteristics

A total of 727 participants with HIV infection were included in the PROSPEC-HIV study from June 2015 to January 2019. For this analysis, participants were excluded due to viral hepatitis coinfection (n = 95), abusive alcohol intake (n = 123), use of lipid supplement (n = 6), 24-HDR with missing (n = 4) or inconsistent dietary data (n = 4) or missing data of serum insulin (n = 44). The flowchart of the study population is depicted in Figure 2.
A total of 451 participants [60.3 female, median age of 45 (IQR, 36–53) years, 33.9% with metabolic syndrome, median BMI = 25 (IQR, 23–29) Kg/m2, 96.7% under c-ART during a median time of 7 (IQR, 4–14) years] were included in this analysis. Table 1 describes clinical and demographic characteristics of the participants. CAP and LSM values were unreliable with M and XL probes in 9% (n = 39) and 2% (n = 8) of participants, respectively. Therefore, the association of lipid dietary intake with NAFLD and significant fibrosis was assessed in 412 and 443 HIV mono-infected participants, respectively. The prevalence of NAFLD and significant fibrosis were 37% (95% CI, 32–41) [n = 152] and 16% (95% CI, 12–20) [n = 72], respectively. Supplementary Tables S1 and S2 describe the socio-demographic and clinical characteristics of participants with NAFLD and liver fibrosis.

3.2. Relationship between Dietary Intake and NAFLD or Liver Fibrosis

The Table 2 summarizes the association between quartiles of usual intake of nutrients with NAFLD in HIV participants. Considering the multivariate models, higher usual intake of total carbohydrates (highest quartile) was associated with lower odds for NAFLD [aOR = 0.44 (95% CI 0.24–0.8); p = 0.01] when compared to the lower intake range (reference quartile). Furthermore, participants with intermediate intake of fiber (third quartile), n6-PUFA (n6-poly-unsaturated FA) (second quartile), lauric FA (third quartile) had significantly lower odds for NAFLD when compared to the reference quartile, respectively aOR = 0.51 (95% CI 0.27–0.96); p = 0.04; aOR = 0.54 (95% CI 0.3–0.98); p = 0.04, and aOR = 0.42 (95% CI 0.22–0.78); p = 0.01. Additionally, a higher intake of myristoleic FA (fourth quartile) was a significant protective factor for NAFLD [aOR = 0.56 (95% CI 0.32–0.99), p = 0.05]. In contrast, participants with higher (fourth quartile) usual intake of total fat had higher odds for NAFLD compared to those with lower consumption [aOR = 1.91 (95% CI 1.06–3.44), p = 0.03].
The association between usual intake of nutrients in quartiles with occurrence of liver fibrosis in HIV mono-infected participants is summarized in Table 3. After adjustment for confounding factors, the usual intake of protein had only a statistical non-significant trend [aOR = 2.13 (95% CI 0.96–4.70); p = 0.06] to be associated with liver fibrosis. In multivariate models, participants with moderate usual intake of lauric FA [second quartile; aOR = 0.38 (0.18–0.80); p = 0.01], myristic FA [third quartile; aOR = 0.38 (0.17–0.89), p = 0.03], palmitoleic FA [third quartile; aOR = 0.40 (0.19–0.82); p = 0.01] and oleic FA [third quartile; aOR = 0.35 (0.16–0.79); p = 0.79] had lower risk of presence of liver fibrosis compared to those with low usual intake (lowest quartile) of these FAs. On the other hand, intermediate usual intake of n-6 PUFA (third quartile) was significantly associated with the presence of liver fibrosis compared to low intake [aOR = 2.45 (95% CI 1.12–5.32); p = 0.02].

4. Discussion

This study highlighted the association of dietary fat intake with the presence of NAFLD and/or fibrosis in PLWHA. To the best of our knowledge, this is the one of the first studies that has demonstrated the role of FA intake, and that high ingestion of total fat can increase the odds of NAFLD in PLWHA, independently of energy intake, age, sex and duration of c-ART. We demonstrated that participants with high usual intake of total fat had 91% more odds of having NAFLD.
A high-fat diet can be a trigger for liver fatty infiltration [29], might cause dysbiosis [30] and increase intestinal permeability leading to accumulation of triglycerides in hepatocytes contributing to NAFLD [31]. Our findings were aligned with a Korean study that showed a higher odds for NAFLD, determined by ultrasonography, in individuals with higher fat intake, quantified by food frequency questionnaire (FFQ) [32]. Similarly, high levels of Fatty Liver Index, a serological biomarker for detection of steatosis, were associated with a higher intake of total fat in a Dutch population [33]. In addition, a Brazilian cross-sectional study that assessed dietary intake in a limited sample of 96 participants with NAFLD using 24-HDR reported that most individuals consumed a higher total fat amount than recommended [14].
In contrast with previous publications, our study did not report association between total saturated fat intake and NAFLD. Instead, we described that moderate consumption of lauric FA was significantly associated with a lower odd of NAFLD and liver fibrosis in PLWHA. Lauric is a saturated medium-chain FA (MCFA) which is directly transported to the liver, where it is rapidly metabolized by β oxidation and also provokes a thermogenic response [34]. An experimental study reported that mice fed with lauric FA diet had lower obesity-related metabolic disorders and lower levels of plasma markers of liver function (alanine and aspartate aminotransferases) than mice fed with palmitic FA [35]. The present study also reported that participants with HIV who had moderated their consumption of myristic FA had less likely odds of having liver fibrosis compared to those with low intake. This might be explained by the fact that myristic, a saturated FA found in coconut and milk products, seems to be more rapidly metabolized (both β-oxidation and elongation) in hepatocytes [36].
The relationship between a high intake of monounsaturated fatty acid (MUFA) and improvement on lipid profile has been extensively described in previous studies that reported the benefits of the Mediterranean diet [10,37,38]. The present study reinforces this concept since we demonstrated that a moderate consumption of myristoleic FA, an MUFA, was a protective nutrient for NAFLD associated with lower odds of NAFLD. Additionally, moderate consumption of palmitoleic and oleic MUFAs were associated with a reduction of at least 60% in the odds for developing liver fibrosis. Several studies demonstrated that a diet rich in oleic acid can improve plasma lipid profile, inflammatory cytokines (INF-, IL-6), insulin sensitivity and macrophage infiltration, reducing histological features of NAFLD and liver fat [13,39,40]. Besides, previous studies reported that palmitoleic FA could impact glucose metabolism improving and/or preventing insulin resistance and type-2 diabetes [41].
Few studies have investigated the associations of n-6 PUFA intake with NAFLD. In our study, participants with a moderate consumption of n-6 PUFA had less likely NAFLD. This result is in agreement with a cross-sectional study that investigated the association of n-6 PUFA intake with NAFLD in adults using data from the National Health and Nutrition Examination Survey (NHANES). Those authors also used 24-HDR and demonstrated that n-6 PUFA intake was inversely associated with NAFLD [42]. In contrast, we reported that moderate consumption of n-6 PUFA increased the risk of liver fibrosis, probably related a pro-inflammatory activity [43]. These results were aligned with a study by Cortez-Pinto et al. which demonstrated that patients with biopsy-proven NASH had a significantly higher intake of n-6 PUFA and higher n6/n3 ratio, determined by FFQ, compared to controls [44].
We demonstrated that the moderate consumption of some fatty acids was associated with lower odds of NAFLD or liver fibrosis, but this was not observed in higher quartile. We suppose that the effect of dose—response might not be adequate for association of fatty acids with NAFLD or liver fibrosis because the moderate intake has a beneficial effect over excessive consumption.
We reported that a higher usual intake of total carbohydrate was associated with lower odds for NAFLD. Although the literature has shown that high intakes of dietary sugars have been associated with increased risk for NAFLD, there is no consensus about the effects of total carbohydrates on this liver disease [11]. Studies assessing nutrient intake and dietary patterns have showed that a high consumption of monosaccharides and disaccharides (fructose and sucrose) was positively associated with NAFLD [45,46]. We were unable to analyze the different subtypes of carbohydrates ingested, but we can suggest that our result reflects a high intake of polysaccharides, originating from beans and cereals, which are very common in Brazilian eating habits and are also sources of dietary fiber [47].
Another piece of evidence presented in this study revealed that a moderate consumption of dietary fiber was associated with lower odds for NAFLD, which remains in agreement with previous publications [46,48]. The benefits of dietary fiber have been extensively validated in overall metabolic health due to improvement of insulin sensitivity. Additionally, dietary fiber can prevent/control obesity through its effects on satiety, reducing the frequency of eating and the portion of food [49]. The fermentation of fiber, due to the interaction with gut microbes, can provide short-chain FA, key microbial metabolites that promote a protective and nourishing role for colonocytes, ensuring the preservation of the intestinal barrier and consequently protecting liver function [48].
The major limitations of our study are the cross-sectional study design and the lack of liver biopsy as the reference for the presence of NAFLD and/or liver fibrosis. Our study design does not allow us to conjecture any conclusions about the causality between dietary intake and incidence of NAFLD and/or fibrosis. In the present study, the presence of liver steatosis, for the definition of NAFLD, and the presence of fibrosis were defined using an extensive validated non-invasive method, such as transient electrography [50,51]. The same threshold of CAP measurement (≥248 dB/m) was used independent of the probe because LSM would be 1.5 to 2.0 kPa lower by the XL probe compared to M probe [52], but CAP seems to be similar in both probes [53]. A potential criticism would be the lack of physical activity assessment using validated questionnaires. We acknowledge that when informing the participant about the presence of liver steatosis and/or fibrosis during the clinical visit, it could affect in the second 24-HDR, due to significant changes in dietary habits. However, this source of bias was mitigated since we did not notice any important difference in food energy intake between first and second 24-HDRs. We are aware that 24-HDR is a self-reporting instrument for dietary intake assessment that might lead to underreporting, and to minimize this bias, we used the Automated Multiple-Pass method. Nevertheless, this is a practical and validated method that is considered the least biased to examine association between diet and disease and has been widely used in epidemiological and dietary monitoring studies [25]. Finally, we assume that two 24-HDRs might be insufficient to evaluate usual fat intake. However, we adjusted nutrients for total energy intake to minimize misreporting [53], as well as the potential variability on dietary intake using a well-established statistical method, such as MSM, to estimate usual intake [28]. The 24-HDR is a validated method that has been recommended as the least biased of the self-reporting instruments when compared to the other instruments such as FFQ and food record [25,54]. Alternative measurements which would be easier to implement in clinical practice are dietary screeners, which allow the assessment of aspects of the diet, such as specifics nutrients, rather than the total diet [55]. The lack of biochemical analysis of fatty acids is also a limitation of our study. Additionally, our study design hinders the evaluation of whether food intake can lead to higher prevalence of NAFLD in PLWHA compared to controls, since the PROSPEC-HIV was not set to include uninfected individuals. Studies comparing prevalence of NAFLD or food intake of PLWHA compared to controls (uninfected individuals) remain lacking in Brazil. However, the diet quality seems to be lower in PLWHA, and this population presents high prevalence of inappropriate food intake, despite the fact that PLWHA have undergone the same culture and influences as the general population [56,57]. The last point to highlight is the statistical methodological choices. We used the “Multivariate Nutrient Density Method” [58] due to the need to adjust the total energy consumption methodology (as mentioned earlier). This choice of statistical model highlighted the nutrients of interest (lipids) and avoided the discussion of diet, i.e., the influence of other nutrient intake on the correlation of lipid nutrients and their results. Thus, this choice can be considered a limitation of the study.
The main strengths of this study remain the dietary intake evaluation in a well-characterized large sample of people with HIV mono-infection, the quality methodology of data analysis and the use of DAG, supporting the choice of confounding variables. Clinical assessment, TE exams, bioelectrical impedances and blood samples were performed on the same day in the PROSPEC study. Additionally, all TE exams were performed for a single experimented operator in fasting patients, and blood analyses were performed in a centralized laboratory.

5. Conclusions

In conclusion, the current study showed that a higher usual intake of total fat increased the risk of NAFLD. Additionally, consumption of specific FAs was associated with lower and/or higher odds for presence of liver diseases in HIV mono-infected participants. These results reinforced the role of diet in the pathogenesis of NAFLD and/or liver fibrosis in PLWHA. Dietary assessment of total fat and FA could be incorporated into HIV care, and this strategy should be used as a tool for preventing NAFLD and fibrosis in PLWHA. Additionally, dietary supplementation of specific fatty acids, such as myristoleic FA, could be important in nutritional care of PLWHA.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/nu13103462/s1, Table S1: Clinical and demographic characteristics of participants with HIV mono-infection in INI/FIOCRUZ and nonalcoholic fatty liver disease (NAFLD) [CAP ≥ 248 dB/m; n = 152, prevalence = 37%], Rio de Janeiro, Brazil, Table S2: Clinical and demographic characteristics of participants with HIV mono-infection in INI/FIOCRUZ and significant liver fibrosis (stage F ≥ 2) [LSM ≥ 7.1 kPa or ≥6.2 kPa with M or XL probe; n = 72, prevalence = 16%], Rio de Janeiro, Brazil.

Author Contributions

Conceptualization and methodology: C.F.d.A., P.D.d.B. and H.P.; patient recruitment and data collection: C.F.d.A., C.S.d.A.C., M.M.d.A., P.S.d.S. and H.P.; data entry in Diet Win software: N.G.M. and J.C.A.; data analysis: J.S., P.E.A.A.d.B. and R.I.M.; writing—original draft preparation: C.F.d.A., P.D.d.B. and H.P.; interpretation of results and critical review of the manuscript: C.F.d.A., M.C.A., W.A.F.P., S.W.C., V.G.V., B.G., P.D.d.B. and H.P.; writing—review and editing: C.F.d.A., M.C.A., W.A.F.P., P.D.d.B. and H.P.; study supervision: S.W.C., V.G.V., B.G.; funding acquisition: V.G.V., B.G. and H.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), grant number E-26/110.268/2014; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) grant numbers 405.211/2016-3, 407.410/2013-9 and 301.520/2014-3. The APC was funded by the Post-Graduation Program in Infectious Diseases from INI/FIOCRUZ. This study was partially supported by the Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES) - Finance Code 001.” The funders had no role in study design; data collection and analysis; decision to publish the results; or preparation of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Ethical Committee from INI/FIOCRUZ (IRB 32889514.4.0000.5262) on 16 July 2016. All participants signed an informed consent form upon enrollment in the PROSPEC-HIV study.

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the investigators and members of LAPCLIN-AIDS (Laboratório de Pesquisa Clínica em IST/AIDS) and SENUT (Serviço de Nutrição) from INI/FIOCRUZ (Instituto Nacional de Infectologia Evandro Chagas—Fundacão Oswaldo Cruz) for their dedication to the PROSPEC-HIV study. In addition, we express our most sincere appreciation to all patients for volunteering to participate in the PROSPEC-HIV.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ALT, alanine aminotransferase; ART, antiretroviral therapy; AST, aspartate aminotransferase; AUDIT, Alcohol Use Disorders Identification Test; BMI, body mass index; CAP, controlled attenuation parameter; CI, confidence interval; E%, energy percent; FA, fatty acid; FFQ, frequency food questionnaire; GGT, gamma-glutamyltransferase; 24-HDR, 24 h dietary recall; HDL, high-density cholesterol; IQR, interquartile range; LDL, low-density cholesterol; LSM, liver stiffness measurement; MUFA, mono-unsaturated FA; nonalcoholic NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; OR, odds ratio; PUFA, poly-unsaturated FA; Q, quartile; ULN, upper limit of normal; WHO, World Health Organization.

References

  1. UNAIDS 2020 Global AIDS Update. Available online: https://www.unaids.org/en/resources/documents/2020/global-aids-report (accessed on 14 November 2020).
  2. Grinsztejn, B.; Luz, P.M.; Pacheco, A.G.; Santos, D.V.G.; Velasque, L.; Moreira, R.I.; Guimarães, M.R.C.; Nunes, E.P.; Lemos, A.S.; Ribeiro, S.R.; et al. Changing Mortality Profile among HIV-Infected Patients in Rio de Janeiro, Brazil: Shifting from AIDS to Non-AIDS Related Conditions in the HAART Era. PLoS ONE 2013, 8, e59768. [Google Scholar] [CrossRef] [PubMed]
  3. Castilho, J.L.; Escuder, M.M.; Veloso, V.; Gomes, J.O.; Jayathilake, K.; Ribeiro, S.; Souza, R.A.; Ikeda, M.L.; de Alencastro, P.R.; Tupinanbas, U.; et al. Trends and Predictors of Non-communicable Disease Multimorbidity among Adults Living with HIV and Receiving Antiretroviral Therapy in Brazil. J. Int. AIDS Soc. 2019, 22, e25233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Dulai, P.S.; Singh, S.; Patel, J.; Soni, M.; Prokop, L.J.; Younossi, Z.; Sebastiani, G.; Ekstedt, M.; Hagstrom, H.; Nasr, P.; et al. Increased Risk of Mortality by Fibrosis Stage in Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis. Hepatology 2017, 65, 1557. [Google Scholar] [CrossRef] [PubMed]
  5. Vuille-Lessard, É.; Lebouché, B.; Lennox, L.; Routy, J.; Costiniuk, C.T.; Pexos, C.; Giannakis, A.; Szabo, J.; Klein, M.B.; Sebastiani, G. Nonalcoholic Fatty Liver Disease Diagnosed by Transient Elastography with Controlled Attenuation Parameter in Unselected HIV Monoinfected Patients. Available online: https://pubmed.ncbi.nlm.nih.gov/27603289/ (accessed on 7 December 2020).
  6. Pembroke, T.; Deschenes, M.; Lebouché, B.; Benmassaoud, A.; Sewitch, M.; Ghali, P.; Wong, P.; Halme, A.; Vuille-Lessard, E.; Pexos, C.; et al. Hepatic Steatosis Progresses Faster in HIV Mono-Infected than HIV/HCV Co-Infected Patients and Is Associated with Liver Fibrosis. J. Hepatol. 2017, 67, 801–808. [Google Scholar] [CrossRef] [Green Version]
  7. Aepfelbacher, J.A.; Balmaceda, J.; Purdy, J.; Mattingly, A.; Zambell, K.; Hawkins, K.; Chairez, C.; Curl, K.A.; Dee, N.; Hadigan, C. Increased Prevalence of Hepatic Steatosis in Young Adults With Lifelong HIV. J. Infect. Dis. 2019, 220, 266–269. [Google Scholar] [CrossRef] [Green Version]
  8. Phillips, C.; Shivappa, N.; Hébert, J.; Perry, I.; Phillips, C.M.; Shivappa, N.; Hébert, J.R.; Perry, I.J. Dietary Inflammatory Index and Biomarkers of Lipoprotein Metabolism, Inflammation and Glucose Homeostasis in Adults. Nutrients 2018, 10, 1033. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Cantero, I.; Abete, I.; Babio, N.; Arós, F.; Corella, D.; Estruch, R.; Fitó, M.; Hebert, J.R.; Martínez-González, M.Á.; Pintó, X.; et al. Dietary Inflammatory Index and Liver Status in Subjects with Different Adiposity Levels within the PREDIMED Trial. Clin. Nutr. 2018, 37, 1736–1743. [Google Scholar] [CrossRef] [Green Version]
  10. Ryan, M.C.; Itsiopoulos, C.; Thodis, T.; Ward, G.; Trost, N.; Hofferberth, S.; O’Dea, K.; Desmond, P.V.; Johnson, N.A.; Wilson, A.M. The Mediterranean Diet Improves Hepatic Steatosis and Insulin Sensitivity in Individuals with Non-Alcoholic Fatty Liver Disease. J. Hepatol. 2013, 59, 138–143. [Google Scholar] [CrossRef] [PubMed]
  11. Parry, S.A.; Hodson, L. Influence of Dietary Macronutrients on Liver Fat Accumulation and Metabolism. J. Investig. Med. 2017, 65, 1102–1115. [Google Scholar] [CrossRef]
  12. Vergani, L. Fatty Acids and Effects on In Vitro and In Vivo Models of Liver Steatosis. Curr. Med. Chem. 2019, 26, 3439–3456. [Google Scholar] [CrossRef]
  13. Jurado-Ruiz, E.; Varela, L.M.; Luque, A.; Berná, G.; Cahuana, G.; Martinez-Force, E.; Gallego-Durán, R.; Soria, B.; de Roos, B.; Romero Gómez, M.; et al. An Extra Virgin Olive Oil Rich Diet Intervention Ameliorates the Nonalcoholic Steatohepatitis Induced by a High-Fat “Western-Type” Diet in Mice. Mol. Nutr. Food Res. 2017, 61, 1600549. [Google Scholar] [CrossRef]
  14. Ferolla, S.; Ferrari, T.; Lima, M.; Reis, T.; Tavares, W., Jr.; Couto, O.; Vidigal, P.; Fausto, M.; Couto, C. Dietary Patterns in Brazilian Patients with Non-Alcoholic Fatty Liver Disease: A Cross-Sectional Study. Clinics 2013, 68, 11–17. [Google Scholar] [CrossRef]
  15. Deresz, L.F.; de Brito, C.; Schneider, C.D.; Rabito, E.I.; Ikeda, M.L.R.; Lago, P.D. Dietary Intake and Cardiovascular Risk among People Living with HIV/AIDS. Ciênc. Amp Saúde Coletiva 2018, 23, 2533–2542. [Google Scholar] [CrossRef] [PubMed]
  16. Perazzo, H.; Cardoso, S.W.; Yanavich, C.; Nunes, E.P.; Morata, M.; Gorni, N.; da Silva, P.S.; Cardoso, C.; Almeida, C.; Luz, P.; et al. Predictive Factors Associated with Liver Fibrosis and Steatosis by Transient Elastography in Patients with HIV Mono-Infection under Long-Term Combined Antiretroviral Therapy. J. Int. AIDS Soc. 2018, 21, e25201. [Google Scholar] [CrossRef] [PubMed]
  17. Saunders, J.B.; Aasland, O.G.; Babor, T.F.; De La Fuente, J.R.; Grant, M. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II. Addiction 1993, 88, 791–804. [Google Scholar] [CrossRef]
  18. Travassos, C.; Laguardia, J.; Marques, P.M.; Mota, J.C.; Szwarcwald, C.L. Comparison between Two Race/Skin Color Classifications in Relation to Health-Related Outcomes in Brazil. Int. J. Equity Health 2011, 10, 35. [Google Scholar] [CrossRef] [Green Version]
  19. Alberti, K.G.M.M.; Zimmet, P.; Shaw, J. Metabolic Syndrome—A New World-wide Definition. A Consensus Statement from the International Diabetes Federation. Available online: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1464-5491.2006.01858.x (accessed on 18 August 2019).
  20. WHO. Obesity: Preventing and Managing the Global Epidemic. Available online: http://www.who.int/entity/nutrition/publications/obesity/WHO_TRS_894/en/index.html (accessed on 18 May 2018).
  21. Kyle, U.G.; Bosaeus, I.; De Lorenzo, A.D.; Deurenberg, P.; Elia, M.; Gómez, J.M.; Heitmann, B.L.; Kent-Smith, L.; Melchior, J.-C.; Pirlich, M.; et al. Bioelectrical Impedance Analysis—Part I: Review of Principles and Methods. Clin. Nutr. Edinb. Scotl. 2004, 23, 1226–1243. [Google Scholar] [CrossRef]
  22. Boyko, E.J.; Jensen, C.C. Do We Know What Homeostasis Model Assessment Measures?: If Not, Does It Matter? Diabetes Care 2007, 30, 2725–2728. [Google Scholar] [CrossRef] [Green Version]
  23. Boursier, J.; Vergniol, J.; Guillet, A.; Hiriart, J.-B.; Lannes, A.; Bail, B.L.; Michalak, S.; Chermak, F.; Bertrais, S.; Foucher, J.; et al. Diagnostic Accuracy and Prognostic Significance of Blood Fibrosis Tests and Liver Stiffness Measurement by FibroScan in Non-Alcoholic Fatty Liver Disease. J. Hepatol. 2016, 65, 570–578. [Google Scholar] [CrossRef] [PubMed]
  24. Wong, V.W.-S.; Vergniol, J.; Wong, G.L.-H.; Foucher, J.; Chan, A.W.-H.; Chermak, F.; Choi, P.C.-L.; Merrouche, W.; Chu, S.H.-T.; Pesque, S.; et al. Liver Stiffness Measurement Using XL Probe in Patients with Nonalcoholic Fatty Liver Disease. Am. J. Gastroenterol. 2012, 107, 1862–1871. [Google Scholar] [CrossRef] [PubMed]
  25. Thompson, F.E.; Kirkpatrick, S.I.; Subar, A.F.; Reedy, J.; Schap, T.E.; Wilson, M.M.; Krebs-Smith, S.M. The National Cancer Institute’s Dietary Assessment Primer: A Resource for Diet Research. J. Acad. Nutr. Diet. 2015, 115, 1986–1995. [Google Scholar] [CrossRef] [Green Version]
  26. Moshfegh, A.J.; Rhodes, D.G.; Baer, D.J.; Murayi, T.; Clemens, J.C.; Rumpler, W.V.; Paul, D.R.; Sebastian, R.S.; Kuczynski, K.J.; Ingwersen, L.A.; et al. The US Department of Agriculture Automated Multiple-Pass Method Reduces Bias in the Collection of Energy Intakes. Am. J. Clin. Nutr. 2008, 88, 324–332. [Google Scholar] [CrossRef]
  27. Verly, E., Jr.; Oliveira, D.C.R.S.; Fisberg, R.M.; Marchioni, D.M.L. Performance of Statistical Methods to Correct Food Intake Distribution: Comparison between Observed and Estimated Usual Intake. Br. J. Nutr. 2016, 116, 897–903. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Freedman, L.S.; Schatzkin, A.; Midthune, D.; Kipnis, V. Dealing with Dietary Measurement Error in Nutritional Cohort Studies. J. Natl. Cancer Inst. 2011, 103, 1086–1092. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Vilar, L.; Oliveira, C.P.M.S.; Faintuch, J.; Mello, E.S.; Nogueira, M.A.; Santos, T.E.; Alves, V.A.F.; Carrilho, F.J. High-Fat Diet: A Trigger of Non-Alcoholic Steatohepatitis? Preliminary Findings in Obese Subjects. Nutrition 2008, 24, 1097–1102. [Google Scholar] [CrossRef] [PubMed]
  30. Campo, L.; Eiseler, S.; Apfel, T.; Pyrsopoulos, N. Fatty Liver Disease and Gut Microbiota: A Comprehensive Update. J. Clin. Transl. Hepatol. 2019, 7, 56. [Google Scholar] [CrossRef] [Green Version]
  31. Mokhtari, Z.; Gibson, D.L.; Hekmatdoost, A. Nonalcoholic Fatty Liver Disease, the Gut Microbiome, and Diet. Adv. Nutr. 2017, 8, 240. [Google Scholar] [CrossRef]
  32. Shim, P.; Choi, D.; Park, Y. Association of Blood Fatty Acid Composition and Dietary Pattern with the Risk of Non-Alcoholic Fatty Liver Disease in Patients Who Underwent Cholecystectomy. Ann. Nutr. Metab. 2017, 70, 303–311. [Google Scholar] [CrossRef]
  33. Rietman, A.; Sluik, D.; Feskens, E.J.M.; Kok, F.J.; Mensink, M. Associations between Dietary Factors and Markers of NAFLD in a General Dutch Adult Population. Eur. J. Clin. Nutr. 2018, 72, 117–123. [Google Scholar] [CrossRef]
  34. McCarty, M.F.; DiNicolantonio, J.J. Review: Lauric Acid-Rich Medium-Chain Triglycerides Can Substitute for Other Oils in Cooking Applications and May Have Limited Pathogenicity. Open Heart 2016, 3, e000467. [Google Scholar] [CrossRef] [Green Version]
  35. Saraswathi, V.; Kumar, N.; Gopal, T.; Bhatt, S.; Ai, W.; Ma, C.; Talmon, G.A.; Desouza, C. Lauric Acid versus Palmitic Acid: Effects on Adipose Tissue Inflammation, Insulin Resistance, and Non-Alcoholic Fatty Liver Disease in Obesity. Biology 2020, 9, 346. [Google Scholar] [CrossRef]
  36. Yoo, H.J.; Jung, K.J.; Kim, M.; Kim, M.; Kang, M.; Jee, S.H.; Choi, Y.; Lee, J.H. Liver Cirrhosis Patients Who Had Normal Liver Function Before Liver Cirrhosis Development Have the Altered Metabolic Profiles Before the Disease Occurrence Compared to Healthy Controls. Front. Physiol. 2019, 10, 1421. [Google Scholar] [CrossRef]
  37. Moosavian, S.P.; Arab, A.; Paknahad, Z. The Effect of a Mediterranean Diet on Metabolic Parameters in Patients with Non-Alcoholic Fatty Liver Disease: A Systematic Review of Randomized Controlled Trials. Clin. Nutr. ESPEN 2020, 35, 40–46. [Google Scholar] [CrossRef] [PubMed]
  38. Zelber-Sagi, S.; Salomone, F.; Mlynarsky, L. The Mediterranean Dietary Pattern as the Diet of Choice for NAFLD.; Evidence and Plausible Mechanisms. Liver Int. Off. J. Int. Assoc. Study Liver 2017, 37, 936–949. [Google Scholar] [CrossRef] [Green Version]
  39. Chen, X.; Li, L.; Liu, X.; Luo, R.; Liao, G.; Li, L.; Liu, J.; Cheng, J.; Lu, Y.; Chen, Y. Oleic Acid Protects Saturated Fatty Acid Mediated Lipotoxicity in Hepatocytes and Rat of Non-Alcoholic Steatohepatitis. Life Sci. 2018, 203, 291–304. [Google Scholar] [CrossRef] [PubMed]
  40. Errazuriz, I.; Dube, S.; Slama, M.; Visentin, R.; Nayar, S.; O’Connor, H.; Cobelli, C.; Das, S.K.; Basu, A.; Kremers, W.K.; et al. Randomized Controlled Trial of a MUFA or Fiber-Rich Diet on Hepatic Fat in Prediabetes. J. Clin. Endocrinol. Metab. 2017, 102, 1765–1774. [Google Scholar] [CrossRef] [Green Version]
  41. Frigolet, M.E.; Gutiérrez-Aguilar, R. The Role of the Novel Lipokine Palmitoleic Acid in Health and Disease. Adv. Nutr. 2017, 8, 173S–181S. [Google Scholar] [CrossRef]
  42. Cui, J.; Li, L.; Ren, L.; Sun, J.; Zhao, H.; Sun, Y. Dietary N-3 and n-6 Fatty Acid Intakes and NAFLD: A Cross-Sectional Study in the United States. Asia Pac. J. Clin. Nutr. 2021, 30, 87–98. [Google Scholar] [CrossRef]
  43. Juárez-Hernández, E.; Chávez-Tapia, N.C.; Uribe, M.; Barbero-Becerra, V.J. Role of Bioactive Fatty Acids in Nonalcoholic Fatty Liver Disease. Nutr. J. 2016, 15, 72. [Google Scholar] [CrossRef] [Green Version]
  44. Machado, M.V.; Cortez-Pinto, H. Non-Alcoholic Fatty Liver Disease: What the Clinician Needs to Know. World J. Gastroenterol. WJG 2014, 20, 12956–12980. [Google Scholar] [CrossRef]
  45. Jensen, T.; Abdelmalek, M.F.; Sullivan, S.; Nadeau, K.J.; Green, M.; Roncal, C.; Nakagawa, T.; Kuwabara, M.; Sato, Y.; Kang, D.-H.; et al. Fructose and Sugar: A Major Mediator of Nonalcoholic Fatty Liver Disease. J. Hepatol. 2018, 68, 1063. [Google Scholar] [CrossRef] [Green Version]
  46. Zolfaghari, H.; Askari, G.; Siassi, F.; Feizi, A.; Sotoudeh, G. Intake of Nutrients, Fiber, and Sugar in Patients with Nonalcoholic Fatty Liver Disease in Comparison to Healthy Individuals. Int. J. Prev. Med. 2016, 7, 98. [Google Scholar] [CrossRef]
  47. Souza, A.d.M.; Pereira, R.A.; Yokoo, E.M.; Levy, R.B.; Sichieri, R. Alimentos mais consumidos no Brasil: Inquérito Nacional de Alimentação 2008–2009. Rev. Saúde Pública 2013, 47, 190s–199s. [Google Scholar] [CrossRef]
  48. Krawczyk, M.; Maciejewska, D.; Ryterska, K.; Czerwińka-Rogowska, M.; Jamioł-Milc, D.; Skonieczna-Żydecka, K.; Milkiewicz, P.; Raszeja-Wyszomirska, J.; Stachowska, E. Gut Permeability Might Be Improved by Dietary Fiber in Individuals with Nonalcoholic Fatty Liver Disease (NAFLD) Undergoing Weight Reduction. Nutrients 2018, 10, 1793. [Google Scholar] [CrossRef] [Green Version]
  49. Barber, T.M.; Kabisch, S.; Pfeiffer, A.F.H.; Weickert, M.O. The Health Benefits of Dietary Fibre. Nutrients 2020, 12, 3209. [Google Scholar] [CrossRef] [PubMed]
  50. Karlas, T.; Petroff, D.; Sasso, M.; Fan, J.-G.; Mi, Y.-Q.; de Lédinghen, V.; Kumar, M.; Lupsor-Platon, M.; Han, K.-H.; Cardoso, A.C.; et al. Individual Patient Data Meta-Analysis of Controlled Attenuation Parameter (CAP) Technology for Assessing Steatosis. J. Hepatol. 2017, 66, 1022–1030. [Google Scholar] [CrossRef] [PubMed]
  51. Bota, S.; Herkner, H.; Sporea, I.; Salzl, P.; Sirli, R.; Neghina, A.M.; Peck-Radosavljevic, M. Meta-analysis: ARFI Elastography versus Transient Elastography for the Evaluation of Liver Fibrosis. Liver Int. 2013, 33, 1138–1147. [Google Scholar] [CrossRef]
  52. Myers Robert, P.; Aaron, P.; Richard, K.; Gilles, P.; Melanie, B.; Mark, L.; Andres, D.; David, W.; Pam, C.; Magdy, E. Controlled Attenuation Parameter (CAP): A Noninvasive Method for the Detection of Hepatic Steatosis Based on Transient Elastography. Liver Int. 2012, 32, 902–910. [Google Scholar] [CrossRef] [PubMed]
  53. Subar, A.F.; Freedman, L.S.; Tooze, J.A.; Kirkpatrick, S.I.; Boushey, C.; Neuhouser, M.L.; Thompson, F.E.; Potischman, N.; Guenther, P.M.; Tarasuk, V.; et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J. Nutr. 2015, 145, 2639. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Park, Y.; Dodd, K.W.; Kipnis, V.; Thompson, F.E.; Potischman, N.; Schoeller, D.A.; Baer, D.J.; Midthune, D.; Troiano, R.P.; Bowles, H.; et al. Comparison of Self-Reported Dietary Intakes from the Automated Self-Administered 24-h Recall, 4-d Food Records, and Food-Frequency Questionnaires against Recovery Biomarkers. Am. J. Clin. Nutr. 2018, 107, 80. [Google Scholar] [CrossRef] [Green Version]
  55. Carmen, P.R.; Juan, M.F.L.; Pilar, R.S.; Javier, A.B. Screeners and Brief Assessment Methods. Nutr. Hosp. 2015, 31 (Suppl. 3), 91–97. [Google Scholar] [CrossRef]
  56. Weiss, J.J.; Sanchez, L.; Hubbard, J.; Lo, J.; Grinspoon, S.K.; Fitch, K.V. Diet Quality Is Low and Differs by Sex in People with HIV. J. Nutr. 2019, 149, 78–87. [Google Scholar] [CrossRef] [PubMed]
  57. Giudici, K.V.; Duran, A.C.F.L.; Jaime, P.C. Inadequate Food Intake among Adults Living with HIV. Sao Paulo Med. J. 2013, 131, 145–152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Willett, W. Nutritional Epidemiology, 3rd ed.; Monographs in epidemiology and biostatistics; Oxford University Press: Oxford, UK; New York, NY, USA, 2013; ISBN 978-0-19-975403-8. [Google Scholar]
Nutrients 13 03462 g001 550
Figure 1. Direct Acyclic Graph for association of dietary fats and fatty acids with liver fibrosis and NAFLD in HIV patients, Rio de Janeiro, Brazil.
Figure 1. Direct Acyclic Graph for association of dietary fats and fatty acids with liver fibrosis and NAFLD in HIV patients, Rio de Janeiro, Brazil.
Nutrients 13 03462 g001
Nutrients 13 03462 g002 550
Figure 2. Flow chart of patient recruitment, Rio de Janeiro, Brazil.
Figure 2. Flow chart of patient recruitment, Rio de Janeiro, Brazil.
Nutrients 13 03462 g002
Table
Table 1. Clinical and demographic characteristics of included participants with HIV mono-infection in INI/FIOCRUZ. Rio de Janeiro, Brazil.
Table 1. Clinical and demographic characteristics of included participants with HIV mono-infection in INI/FIOCRUZ. Rio de Janeiro, Brazil.
VariablesAll (n = 451)
Social and demographic
Female sex a272 (60.3)
Age, years b45 (36–53)
Self-reported skin color a
White214 (47.5)
Brown139 (30.8)
Black94 (20.8)
Others4 (0.9)
Education a < 8 years of study 209 (46.4)
Comorbidities
Diabetes mellitus a46 (10.2)
Hypertension a100 (22.2)
Dyslipidemia a78 (17.3)
Metabolic syndrome a150 (33.9)
Biochemistry
ALT, IU/L b29 (23–43)
AST, IU/L b25 (20–33)
Alkaline phosphatase, IU/L b89 (70–111)
GGT, IU/L b45 (32–70)
Total cholesterol, mg/dL b185 (158–219)
LDL—cholesterol, mg/dL b112 (90–138)
HDL—cholesterol, mg/dL b43 (35–54)
Triglycerides, mg/dL b124 (84–171)
Fasting glucose, mg/dL b93 (88–100)
Insulin, um/L11 (8–16)
HOMA-IR3 (2–4)
Nutritional Status
Body mass index, (kg/m2) b25 (23–29)
Lean [<25 Kg/m2] a207 (45.9)
Overweight [25–29.99 Kg/m2] a153 (33.9)
Obesity [≥30 Kg/m2] a91 (20.2)
Body fat, (%), by bioimpedance b30 (24–35)
Waist circumference, (cm) b87 (79–95)
HIV history and characteristics
Duration of HIV infection, years b10 (5–17)
CD4+ T-lymphocyte count (cells/m3) b665 (421–881)
Detectable HIV RNA viral load (>40 cópias/mm3) a74 (16.1)
Current c-ART use a436 (96.7)
Duration of c-ART, years b7 (4–14)
Data expressed as n (%) a or median (IQR) b. ALT, alanine transaminase; ART, antiretroviral therapy; AST, aspartate transaminase; BMI, body mass index; GGT, gamma-glutamyltransferase, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; LDL, low-density lipoprotein; NAFLD, nonalcoholic fatty liver disease; waist circumference.
Table
Table 2. Logistic multivariate model considering dietary intake and presence of nonalcoholic fatty liver disease (NAFLD) [CAP ≥ 248 dB/m] in participants with HIV mono-infection (n = 412)—INI/FIOCRUZ, Rio de Janeiro, Brazil.
Table 2. Logistic multivariate model considering dietary intake and presence of nonalcoholic fatty liver disease (NAFLD) [CAP ≥ 248 dB/m] in participants with HIV mono-infection (n = 412)—INI/FIOCRUZ, Rio de Janeiro, Brazil.
NAFLD
VariablesUnivariate ModelMultivariate Models
OR (95% CI)aOR (95% CI)
Energy; kcal
Q1 < 1587.76ReferenceReference
Q2 (1587.76–1952.72)0.80 (0.46–1.39)1.00 (0.50–2.00)
Q3 (1952.72–2299.45)0.73 (0.42–1.28)1.07 (0.43–2.68)
Q4 > 2299.450.68 (0.39–1.21)1.43 (0.36–5.69)
Carbohydrate; % kcal
Q1 < 49.07ReferenceReference
Q2 (49.07–53.16)0.62 (0.35–1.07)0.56 (0.31–1.01)
Q3 (53.16–56.79)0.56 (0.32–0.99)0.56 (0.31–1.01)
Q4 > 56.790.48 (0.27–0.85)0.44 (0.24–0.80)
Protein; % kcal
Q1 < 14.49ReferenceReference
Q2 (14.49–16.12)1.19 (0.67–2.12)0.99 (0.54–1.82)
Q3 (16.12–17.93)1.37 (0.77–2.45)1.18 (0.64–2.18)
Q4 > 17.931.71 (0.97–3.04)1.41 (0.75–2.65)
Total fat; % kcal
Q1 < 28.56ReferenceReference
Q2 (28.56–31)0.95 (0.53–1.68)0.97 (0.53–1.76)
Q3 (31–34.38)0.70 (0.39–1.26)0.65 (0.35–1.21)
Q4 > 34.381.81 (1.04–3.17)1.91 (1.06–3.44)
Fiber, density; g/1000 kcal
Q1 < 8.47ReferenceReference
Q2 (8.47–10.13)0.87 (0.49–1.52)0.71 (0.39–1.29)
Q3 (10.13–11.80)0.68 (0.38–1.21)0.51 (0.27–0.96)
Q4 > 11.801.13 (0.65–1.98)0.88 (0.48–1.60)
Saturate fat; % kcal
Q1 < 8.36ReferenceReference
Q2 (8.36–9.59)0.91 (0.51–1.61)0.89 (0.49–1.62)
Q3 (9.59–10.77)0.80 (0.45–1.41)0.85 (0.46–1.55)
Q4 > 10.771.45 (0.83–2.54)1.62 (0.9–2.92)
PUFA fat; % kcal
Q1 < 5.87ReferenceReference
Q2 (5.87–7.23)0.63 (0.36–1.11)0.57 (0.31–1.02)
Q3 (7.23–8.28)0.68 (0.38–1.19)0.60 (0.33–1.08)
Q4 > 8.280.77 (0.44–1.35)0.70 (0.39–1.26)
MUFA fat; % kcal
Q1 < 7.47ReferenceReference
Q2 (7.47–8.38)1.04 (0.59–1.83)0.78 (0.43–1.43)
Q3 (8.38–9.48)0.74 (0.42–1.34)0.58 (0.31–1.07)
Q4 > 9.481.32 (0.75–2.31)0.93 (0.49–1.76)
Trans FA; % kcal
Q1 < 0.28ReferenceReference
Q2 (0.28–0.37)0.77 (0.44–1.37)0.80 (0.44–1.46)
Q3 (0.37–0.46)1.34 (0.77–2.35)1.28 (0.71–2.31)
Q4 > 0.460.81 (0.46–1.43)0.68 (0.37–1.25)
Cholesterol; % kcal
Q1 < 0.1ReferenceReference
Q2 (0.1–0.12)1.33 (0.80–2.23)1.25 (0.73–2.15)
Q3 (0.12–0.14)0.99 (0.55–1.79)0.84 (0.44–1.6)
Q4 > 0.141.14 (0.65–1.98)0.96 (0.51–1.79)
n-6 PUFA; % kcal
Q1 < 3.47ReferenceReference
Q2 (3.47–4.34)0.57 (0.32–1.01)0.54 (0.30–0.98)
Q3 (4.34–5.28)0.73 (0.41–1.27)0.66 (0.37–1.20)
Q4 > 5.280.64 (0.36–1.13)0.59 (0.32–1.08)
n-3 PUFA; % kcal
Q1 < 0.41ReferenceReference
Q2 (0.41–0.53)0.75 (0.42–1.31)0.71 (0.40–1.29)
Q3 (0.53–0.67)0.72 (0.41–1.28)0.65 (0.36–1.19)
Q4 > 0.670.89 (0.51–1.56)0.76 (0.42–1.38)
Lauric FA (12:00); % kcal
Q1 < 0.08ReferenceReference
Q2 (0.08–0.13)0.69 (0.40–1.19)0.80 (0.45–1.40)
Q3 (0.13–0.18)0.42 (0.23–0.77)0.42 (0.22–0.78)
Q4 > 0.180.74 (0.44–1.27)0.75 (0.43–1.32)
Myristic FA (14:00); % kcal
Q1 < 2.36ReferenceReference
Q2 (2.36–3.27)1.23 (0.70–2.16)1.25 (0.69–2.24)
Q3 (3.27–5.14)1.00 (0.56–1.79)1.09 (0.59–2.00)
Q4 > 5.141.34 (0.76–2.36)1.26 (0.70–2.29)
Palmitic FA (16:00); % kcal
Q1 < 2.58ReferenceReference
Q2 (2.58–3.09)1.23 (0.70–2.15)1.30 (0.73–2.33)
Q3 (3.09–3.63)1.30 (0.74–2.27)1.20 (0.67–2.15)
Q4 > 3.630.89 (0.50–1.58)0.82 (0.45–1.50)
Stearic FA (18:00); % kcal
Q1 < 1.06ReferenceReference
Q2 (1.06–1.3)1.13 (0.65–1.99)1.28 (0.72–2.30)
Q3 (1.3–1.57)1.27 (0.73–2.22)1.19 (0.66–2.14)
Q4 > 1.570.87 (0.49–1.55)0.81 (0.44–1.49)
Arachidic FA (20:00); % kcal
Q1 < 0.035ReferenceReference
Q2 (0.035–0.04)0.89 (0.53–1.51)0.76 (0.44–1.33)
Q3 (0.04–0.05)1.06 (0.61–1.83)0.87 (0.49–1.55)
Q4 > 0.051.37 (0.66–2.83)1.02 (0.47–2.21)
Myristoleic FA (14:1); % kcal
Q1 < 0.02ReferenceReference
Q2 (0.02–0.03)0.78 (0.46–1.31)0.72 (0.42–1.25)
Q3 (0.03–0.04)0.53 (0.29–0.99)0.63 (0.33–1.21)
Q4 > 0.040.70 (0.41–1.19)0.56 (0.32–0.99)
Palmitoleic FA (16:1); % kcal
Q1 < 0.17ReferenceReference
Q2 (0.17–0.21)0.80 (0.45–1.43)0.80 (0.44–1.45)
Q3 (0.21–0.26)1.27 (0.75–2.16)1.06 (0.61–1.84)
Q4 > 0.261.08 (0.61–1.91)0.87 (0.47–1.60)
Oleic FA (18:1); % kcal
Q1 < 4.29ReferenceReference
Q2 (4.29–5.09)0.97 (0.55–1.71)0.97 (0.54–1.75)
Q3 (5.09–5.94)0.86 (0.49–1.52)0.71 (0.39–1.29)
Q4 > 5.941.08 (0.61–1.89)0.87 (0.47–1.58)
Linoleic FA (18:2-n6); % kcal
Q1 < 3.45ReferenceReference
Q2 (3.45–4.33)0.61 (0.35–1.08)0.59 (0.33–1.08)
Q3 (4.33–5.26)0.75 (0.43–1.32)0.70 (0.39–1.26)
Q4 > 5.260.66 (0.38–1.17)0.63 (0.34–1.14)
Linolenic FA (18:3-n3); % kcal
Q1 < 0.4ReferenceReference
Q2 (0.4–0.51)0.71 (0.40–1.26)0.68 (0.38–1.24)
Q3 (0.51–0.63)0.77 (0.44–1.34)0.67 (0.37–1.21)
Q4 > 0.630.88 (0.50–1.55)0.77 (0.43–1.40)
n6/n3 PUFA ratio; g
Q1 < 7.45ReferenceReference
Q2 (7.45–8.21)0.61 (0.35–1.07)0.59 (0.33–1.06)
Q3 (8.21–9.18)0.66 (0.37–1.16)0.69 (0.38–1.25)
Q4 > 9.180.84 (0.48–1.46)0.96 (0.54–1.71)
Multivariate models adjusted by usual energy intake, age, gender and duration of c-ART. ART, antiretroviral therapy; CAP, controlled attenuation parameter; E%, energy percent; FA, fatty acid; g, gram; kcal, kilocalories; MUFA, mono-unsaturated FA; NAFLD, nonalcoholic fatty liver disease; PUFA, poly-unsaturated FA; Q, quartile.
Table
Table 3. Logistic multivariate model considering dietary intake and presence of liver fibrosis (stage F ≥ 2) [LSM ≥ 7.1 kPa or ≥ 6.2 kPa with M or XL probe] in participants with HIV mono-infection (n = 443)—INI/FIOCRUZ. Rio de Janeiro, Brazil.
Table 3. Logistic multivariate model considering dietary intake and presence of liver fibrosis (stage F ≥ 2) [LSM ≥ 7.1 kPa or ≥ 6.2 kPa with M or XL probe] in participants with HIV mono-infection (n = 443)—INI/FIOCRUZ. Rio de Janeiro, Brazil.
Fibrosis
VariablesUnivariate ModelMultivariate Models
OR [95%IC]aOR [95%IC]
Energy; kcal
Q1 < 1587.76ReferenceReference
Q2 (1587.76–1952.72)0.97 (0.49–1.89)0.97 (0.43–2.20)
Q3 (1952.72–2299.45)0.82 (0.41–1.64)0.80 (0.26–2.46)
Q4 > 2299.450.50 (0.23–1.08)0.48 (0.08–2.79)
Carbohydrate; % kcal
Q1 < 49.07ReferenceReference
Q2 (49.07–53.16)1.33 (0.65–2.70)1.37 (0.67–2.82)
Q3 (53.16–56.79)1.25 (0.61–2.59)1.33 (0.64–2.76)
Q4 > 56.790.99 (0.47–2.09)0.97 (0.46–2.07)
Protein; % kcal
Q1 < 14.49ReferenceReference
Q2 (14.49–16.12)1.94 (0.91–4.18)1.75 (0.81–3.82)
Q3 (16.12–17.93)1.11 (0.48–2.54)0.97 (0.41–2.28)
Q4 > 17.932.61 (1.24–5.49)2.13 (0.96–4.70)
Total fat; % kcal
Q1 < 28.56ReferenceReference
Q2 (28.56–31)1.45 (0.75–2.80)1.55 (0.79–3.05)
Q3 (31–34.38)0.69 (0.33–1.46)0.70 (0.33–1.49)
Q4 > 34.380.64 (0.30–1.37)0.64 (0.30–1.39)
Fiber; g/1000 kcal
Q1 < 8.47ReferenceReference
Q2 (8.47–10.13)0.77 (0.37–1.61)0.65 (0.30–1.38)
Q3 (10.13–11.80)0.83 (0.40–1.72)0.69 (0.33–1.46)
Q4 > 11.801.21 (0.61–2.39)0.95 (0.47–1.95)
Saturated fat; % kcal
Q1 < 8.36ReferenceReference
Q2 (8.36–9.59)1.03 (0.52–2.02)1.04 (0.53–2.08)
Q3 (9.59–10.77)0.88 (0.44–1.77)0.96 (0.47–1.96)
Q4 > 10.770.60 (0.28–1.28)0.63 (0.29–1.36)
PUFA fat; % kcal
Q1 < 5.87ReferenceReference
Q2 (5.87–7.23)1.67 (0.84–3.33)1.61 (0.80–3.25)
Q3 (7.23–8.28)0.94 (0.44–2.00)0.89 (0.41–1.91)
Q4 > 8.281.03 (0.49–2.19)0.96 (0.45–2.06)
MUFA fat; % kcal
Q1 < 7.47ReferenceReference
Q2 (7.47–8.38)0.68 (0.33–1.41)0.54 (0.26–1.14)
Q3 (8.38–9.48)0.85 (0.42–1.70)0.67 (0.33–1.38)
Q4 > 9.480.84 (0.42–1.68)0.54 (0.25–1.17)
Trans FA; % kcal
Q1 < 0.28ReferenceReference
Q2 (0.28–0.37)0.87 (0.40–1.90)0.89 (0.41–1.96)
Q3 (0.37–0.46)1.37 (0.65–2.88)1.24 (0.58–2.65)
Q4 > 0.461.90 (0.94–3.84)1.68 (0.82–3.45)
Cholesterol; % kcal
Q1 < 0.1ReferenceReference
Q2 (0.1–0.12)1.20 (0.63–2.32)1.10 (0.57–2.15)
Q3 (0.12–0.14)1.05 (0.49–2.24)0.87 (0.40–1.92)
Q4 > 0.141.29 (0.65–2.57)1.03 (0.49–2.18)
n-6 PUFA; % kcal
Q1 < 3.47ReferenceReference
Q2 (3.47–4.34)2.10 (0.96–4.59)2.10 (0.95–4.63)
Q3 (4.34–5.28)2.57 (1.19–5.54)2.45 (1.12–5.32)
Q4 > 5.281.53 (0.68–3.47)1.40 (0.61–3.21)
n-3 PUFA; % kcal
Q1 < 0.41ReferenceReference
Q2 (0.41–0.53)0.66 (0.31–1.42)0.64 (0.30–1.39)
Q3 (0.53–0.66)1.34 (0.67–2.66)1.27 (0.63–2.54)
Q4 > 0.6551.08 (0.53–2.19)0.94 (0.46–1.93)
Lauric FA (12:00); % kcal
Q1 < 0.08ReferenceReference
Q2 (0.08–0.13)0.34 (0.16–0.72)0.38 (0.18–0.80)
Q3 (0.13–0.18)0.47 (0.23–0.98)0.49 (0.24–1.02)
Q4 > 0.180.60 (0.31–1.16)0.63 (0.32–1.22)
Myristic FA (14:00); % kcal
Q1 < 2.36ReferenceReference
Q2 (2.36–3.27)0.97 (0.50–1.87)0.98 (0.50–1.91)
Q3 (3.27–5.135)0.36 (0.16–0.83)0.38 (0.17–0.89)
Q4 > 5.1350.85 (0.43–1.69)0.80 (0.40–1.60)
Palmitic FA (16:00); % kcal
Q1 < 2.58ReferenceReference
Q2 (2.58–3.09)0.71 (0.36–1.41)0.73 (0.36–1.46)
Q3 (3.09–3.625)0.66 (0.32–1.34)0.62 (0.30–1.28)
Q4 > 3.6250.71 (0.36–1.41)0.64 (0.32–1.30)
Stearic FA (18:00); % kcal
Q1 < 1.06ReferenceReference
Q2 (1.06–1.3)0.76 (0.38–1.51)0.82 (0.41–1.64)
Q3 (1.3–1.57)0.50 (0.23–1.07)0.48 (0.22–1.04)
Q4 > 1.570.95 (0.48–1.87)0.91 (0.46–1.80)
Arachidic FA (20:00); % kcal
Q1 < 0.035ReferenceReference
Q2 (0.035–0.04)0.93 (0.48–1.81)0.81 (0.41–1.6)
Q3 (0.04–0.05)1.00 (0.50–1.98)0.83 (0.41–1.69)
Q4 > 0.051.29 (0.53–3.14)0.98 (0.39–2.46)
Myristoleic FA (14:1); % kcal
Q1 < 0.02ReferenceReference
Q2 (0.02–0.03)0.65 (0.34–1.27)0.63 (0.32–1.24)
Q3 (0.03–0.04)0.51 (0.23–1.14)0.59 (0.26–1.34)
Q4 > 0.040.71 (0.37–1.38)0.65 (0.33–1.27)
Palmitoleic FA (16:1); % kcal
Q1 < 0.17ReferenceReference
Q2 (0.17–0.21)0.78 (0.40–1.52)0.80 (0.40–1.58)
Q3 (0.21–0.26)0.46 (0.23–0.95)0.40 (0.19–0.82)
Q4 > 0.260.68 (0.34–1.35)0.52 (0.25–1.09)
Oleic FA (18:1); % kcal
Q1 < 4.29ReferenceReference
Q2 (4.29–5.09)0.74 (0.37–1.46)0.71 (0.36–1.43)
Q3 (5.09–5.94)0.40 (0.18–0.88)0.35 (0.16–0.79)
Q4 > 5.940.89 (0.46–1.73)0.70 (0.35–1.41)
Linoleic FA (18:2-n6); % kcal
Q1 < 3.45Reference Reference
Q2 (3.45–4.33)1.61 (0.75–3.44)1.63 (0.75–3.51)
Q3 (4.33–5.23)2.17 (1.04–4.53)2.08 (0.99–4.38)
Q4 > 5.231.30 (0.59–2.84)1.19 (0.54–2.64)
Linolenic FA (18:3-n3); % kcal
Q1 < 0.4ReferenceReference
Q2 (0.4–0.51)0.68 (0.31–1.46)0.67 (0.31–1.45)
Q3 (0.51–0.63)1.32 (0.67–2.61)1.25 (0.63–2.49)
Q4 > 0.631.04 (0.51–2.14)0.92 (0.44–1.91)
n6/n3 PUFA ratio, g
Q1 < 7.445ReferenceReference
Q2 (7.445–8.21)1.69 (0.82–3.51)1.69 (0.81–3.52)
Q3 (8.21–9.18)1.57 (0.75–3.30)1.62 (0.77–3.44)
Q4 > 9.181.20 (0.56–2.60)1.27 (0.58–2.78)
Multivariate models adjusted by usual energy intake, age, gender and duration of c-ART. ART, antiretroviral therapy; E%, energy percent; kcal, kilocalories; LSM, liver stiffness measurement; MUFA, mono-unsaturated FA; NAFLD, nonalcoholic fatty liver disease; PUFA, poly-unsaturated FA; Q, quartile.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

de Almeida, C.F.; da Silva, P.S.; Cardoso, C.S.d.A.; Moreira, N.G.; Antunes, J.C.; de Andrade, M.M.; Silva, J.; Araujo, M.C.; Peres, W.A.F.; do Brasil, P.E.A.A.; et al. Relationship between Dietary Fatty Acid Intake with Nonalcoholic Fatty Liver Disease and Liver Fibrosis in People with HIV. Nutrients 2021, 13, 3462. https://doi.org/10.3390/nu13103462

AMA Style

de Almeida CF, da Silva PS, Cardoso CSdA, Moreira NG, Antunes JC, de Andrade MM, Silva J, Araujo MC, Peres WAF, do Brasil PEAA, et al. Relationship between Dietary Fatty Acid Intake with Nonalcoholic Fatty Liver Disease and Liver Fibrosis in People with HIV. Nutrients. 2021; 13(10):3462. https://doi.org/10.3390/nu13103462

Chicago/Turabian Style

de Almeida, Cristiane Fonseca, Paula Simplicio da Silva, Claudia Santos de Aguiar Cardoso, Nathalia Gorni Moreira, Julliana Cormack Antunes, Michelle Morata de Andrade, Julio Silva, Marina Campos Araujo, Wilza Arantes Ferreira Peres, Pedro Emmanuel Alvarenga Americano do Brasil, and et al. 2021. "Relationship between Dietary Fatty Acid Intake with Nonalcoholic Fatty Liver Disease and Liver Fibrosis in People with HIV" Nutrients 13, no. 10: 3462. https://doi.org/10.3390/nu13103462

APA Style

de Almeida, C. F., da Silva, P. S., Cardoso, C. S. d. A., Moreira, N. G., Antunes, J. C., de Andrade, M. M., Silva, J., Araujo, M. C., Peres, W. A. F., do Brasil, P. E. A. A., Moreira, R. I., Cardoso, S. W., Veloso, V. G., Grinsztejn, B., de Brito, P. D., & Perazzo, H. (2021). Relationship between Dietary Fatty Acid Intake with Nonalcoholic Fatty Liver Disease and Liver Fibrosis in People with HIV. Nutrients, 13(10), 3462. https://doi.org/10.3390/nu13103462

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop