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Article

The Impact of Dietary Sugars and Saturated Fats on Body and Liver Fat in a Healthcare Worker Population

by
Sophia Eugenia Martinez-Vazquez
1,*,†,
Ashuin Kammar-García
2,†,
Carlos Moctezuma-Velázquez
3,
Javier Mancilla-Galindo
4,
Ignacio García-Juárez
1 and
Luis Federico Uscanga-Domínguez
1
1
Department of Gastroenterology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City 14080, Mexico
2
Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City 10200, Mexico
3
Department of Medicine, Division of Gastroenterology, University of Alberta, Edmonton, AB T6G 2X8, Canada
4
Institute for Risk Assessment Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2025, 17(8), 1328; https://doi.org/10.3390/nu17081328
Submission received: 7 March 2025 / Revised: 4 April 2025 / Accepted: 7 April 2025 / Published: 11 April 2025
(This article belongs to the Special Issue Nutrients: 15th Anniversary)
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Abstract

:
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent disease influenced by dietary factors. While high sugar and fat consumption are associated with weight gain, their specific impact on liver fat accumulation remains unclear. This study aimed to evaluate the relationship between sugar and saturated fat intake and liver and body fat composition. Methods: A cross-sectional study was conducted from September 2021 to February 2023 in workers from a tertiary care center in Mexico City. Anthropometric measurements, body composition (bioelectrical impedance analysis and skinfold assessment), physical activity, and liver fat (vibration-controlled transient elastography) were measured. Dietary intake was assessed with a 24-h recall questionnaire and analyzed with specialized software. Linear and logistic regression models were fitted to study the relationship between nutrient intake and liver/body fat. Results: A total of 534 healthcare workers (median age: 41.5 years, 61.4% female) were included. Hepatic steatosis was present in 42.5% of participants. Higher carbohydrate intake was associated with increased liver fat (β = 0.23, 95% CI: 0.02–0.45), with each additional 15 g of carbohydrates increasing the odds of steatosis by 5% (OR = 1.053, 95% CI: 1.006–1.102). Fat and sugar intake were associated with higher body fat but not liver fat. Conclusions: Carbohydrate intake was linked to liver fat accumulation, whereas fat and sugar intake were primarily associated with body fat. Tailored dietary recommendations could be informed by these findings. Prospective dietary assessment methods and a nutritional geometry approach could be applied in future studies.

1. Introduction

Nonalcoholic fatty liver disease (NAFLD), now termed metabolic dysfunction-associated steatotic liver disease (MASLD), is a common global health problem affecting 25–35% of the population on average [1,2]. It has been described that diet and sedentary lifestyle influence the development of the disease, with diets high in sugars and fats being important risk factors [3,4,5,6,7,8,9,10,11,12].
The high consumption of energy-dense foods, especially sugars and fats, is associated with body weight gain. In a systematic review of longitudinal studies in adolescents and early adulthood, a diet pattern high in fast foods led to an excess odds of 23% (OR = 1.23; 1.02–1.49) of annual BMI gain of 0.08 kg/m2 [11]. Despite several efforts to determine the role of diet as a whole or dietary components as patterns, some relationships have only been partially explained, such as the consumption of fats and sugars in the diet [2]. Broadly, dietary lipids contribute ~15% to the pool of lipids deposited in the liver, whereas adipose tissue lipolysis and de novo lipogenesis account for 60–80% and 5%, respectively. Insulin regulates the use of adipose tissue sources of energy, which is an efficient process, while hepatocytes are involved in de novo lipogenesis from carbohydrates (especially fructose) under regulation of cytoplasmic transcription factors such as the farnesol X receptor from the PPAR’s family, of which the α and γ variants have anti-inflammatory activity, whereas the receptor suppresses lipogenesis and pro-inflammatory gene expression [13]. Lipid removal occurs through both mitochondrial oxidation and re-esterification to form triglycerides. These triglycerides, if stored as fat droplets, are released as fatty acids in hepatocytes when lipolyzed. In short, insulin resistance in the liver causes steatosis. Furthermore, it is known that excessive carbohydrate intake, particularly simple sugars (fructose and glucose), is linked to increased inflammatory factors and disease progression [2]. However, the quantity of each of these sugars associated with the development of fatty liver disease has not been established.
To date, American societies dedicated to the study of this disease have not issued a cut-off point for both dietary detection (risk factor) and lifestyle treatment regarding carbohydrates [14,15]. European associations did not have a clear recommendation in 2023 on the type and quantity of certain nutrients, including carbohydrates [16]. Nonetheless, it is important to note that a recent systematic review indicates that consumption of four or more servings of sugary drinks per week increases the risk of metabolic dysfunction-associated steatotic liver disease (MASLD) by 45% [17,18]. Regarding fat consumption, European, American, and Asian guidelines [14,17,19] align with the World Health Organization’s (WHO) recommendation to limit saturated fat intake to less than 10% of total energy [16]. To contextualize this, the most widely accepted hypothesis for the development of hepatic steatosis is the “multiple hit” model [13]. This model is characterized by:
  • Steatosis due to excess triglycerides: Accumulation of triglycerides in the liver.
  • Persistent insulin resistance: Leading to decreased glycogen synthesis, increased hepatic fatty acid uptake, altered triglyceride transport, and inhibited beta-oxidation.
  • Pro-inflammatory cytokine activity: Driven by lipotoxicity, apoptosis, inflammasome activation, and mitochondrial dysfunction.
  • Oxidative stress: An imbalance between pro-inflammatory and anti-inflammatory mechanisms that exacerbates insulin resistance in the presence of genetic or environmental susceptibility.
  • Impaired hepatocyte regeneration and apoptosis.
This model explains the occurrence of hepatic steatosis in lean individuals, attributable to the predominance of genetic, metabolic, age-related, and epigenetic factors. In this population, greater heterogeneity in histological disease expression is observed. Therefore, screening from 40 years of age is generally recommended, and management should focus on a total weight loss not exceeding 5% [20]. To date, it remains inconclusive whether the type and quantity of sugars or fats influence fat deposition in specific body segments. Thus, the mechanisms by which body fat contributes to the development of metabolic liver disease are not established, although one study found that abdominal fat deposition is associated with greater metabolic imbalance [21].
The health status of healthcare workers has gained increasing relevance in recent years due to increasing rates of infections, cardiovascular diseases, malignant neoplasms, accidents, and injuries. Additionally, a significant prevalence of mental health issues, including stress and anxiety, has been observed, alongside a high frequency of overweight, respiratory diseases, and gastrointestinal disorders [22,23,24]. Despite the importance of this population group, there are limited reports evaluating hepatic steatosis and the knowledge of this disease within this context. A study conducted among healthcare workers in Brazil, evaluating the association between stress, anxiety, and steatosis, found a 26% prevalence of steatosis, with 15% of cases classified as mild and the remainder as moderate. However, no significant association was identified between the evaluated factors [25]. Regarding the knowledge of risk factors for this disease, a study in Mexico revealed that nearly 90% of healthcare workers consider hepatic steatosis to be a common condition, with metabolic syndrome, particularly obesity, being a relevant risk factor [26]. The scarcity of studies and preventive measures targeting this population is concerning, given their fundamental role in providing healthcare services. Therefore, the objective of this study was to study the relationship between sugar and saturated fat consumption and liver and body fat.

2. Methods

Analytical cross-sectional study conducted from September 2021 to February 2023 in the outpatient clinic of the Department of Gastroenterology, Salvador Zubirán National Institute of Medical Sciences and Nutrition, a tertiary care center in Mexico City.

2.1. Population

Institutional staff (medical, nurses, general clinical care, and administrative) were invited through an internal call. Eligible participants were adults over 18 years of age, of any sex, without a previous diagnosis of fatty liver disease. Participants were included if they self-reported no alcohol use disorder, hepatic steatosis, metabolic disorders under treatment, diabetes, or hypertension under treatment, and if they agreed to voluntarily participate in the interviews and study. Respondents were scheduled for biochemical and anthropometric measurements, questionnaire administration, and elastography at 7:00 A.M., coinciding with the shift change between night and morning duties. The average evaluation time was 2.5 h. All participants were informed and provided consent for participation in this study. Non-inclusion criteria were a diagnosis of cancer, heart disease, liver cirrhosis, hyperthyroidism, hypothyroidism, autoimmune diseases, bariatric or cosmetic surgery, metal prostheses, kidney failure, motor disability, amputations of limbs, use of medications that modify body composition (steroids, antipsychotics, antidepressants), and an alcohol consumption above 20 and 30 g per day for women and men, respectively [17]. Incomplete anthropometric assessments or vibration-controlled elastography were excluded from main analyses, but authorized 24-h reminders from all participants were included.

2.2. Demographic, Clinical, and Biochemical Data

A questionnaire was applied to obtain the demographic characteristics of the study population. Self-reported questions inquired about comorbidities (present/absent): diabetes mellitus, arterial hypertension, prior acute myocardial infarction, rheumatoid arthritis, dyslipidemia, hypothyroidism, insulin resistance, and tobacco use, as well as the self-reported job performed in the health institution, which was classified into medical, nursing, other clinical staff, administrative, and non-specified (when participants decided not to disclose their job task).
Anthropometric measurements were obtained with a SECA model 274 stadiometer (SECA, Hamburg, Germany) for height (precision ±2 mm) and a scale with bioelectrical impedance (mBCA514) for weight (±100 g). Body mass index (BMI) was calculated as the ratio of kg and squared meters (kg/m2) and categorized into universal BMI classes (<16.5, severely underweight; 16.5–18.5, underweight; 18.5–24.9, normal weight; 25–29.9, overweight; 30–34.9, class I obesity; 35–39.9, class II; and ≥40, class III obesity) [27]. All participants had blood samples taken for blood cytometry, blood chemistry, liver enzymes, lipid profile, C-reactive protein (CRP), and insulin to calculate the homeostatic model to assess insulin resistance (HOMA-IR); all laboratory parameters were obtained through the Beckman Coulter equipment (hematological DxH 1061 and series AU5800 for blood chemistry, Brea, CA, USA). We used the validated Laval questionnaire for the Mexican population to record physical activity for 24 h, which allows us to record the number of occasions that activity events occur in a day from fractions of 15 min and the type of activity. The result of each event is multiplied by a constant for the type of physical activity and divided by 60 min to obtain hours. These hours are multiplied by the person’s weight, and thus the energy expenditure for that activity is obtained. The total energy expenditure is obtained by adding all the individual expenses for each activity as Kcals per day [28]. Energy balance (Kcal) was calculated as the difference between the total energy expenditure by the Laval questionnaire and the energy consumption reported during the dietary evaluation. The results of total energy expenditure and energy balance were transformed into Z scores, and their standardized score is presented, as well as the classification of subjects < −1 SD, between −1 and 1 SD, and >1 SD.

2.3. Body Composition Assessment

Body composition measurements were made after the fasting period recommended for biochemical measurements. Multi-frequency bioelectrical impedance (BIA) analyses (11 frequencies) were performed using the SECA mBCA514 equipment. The data obtained from the BIA were total fat mass in kg and percentage and visceral fat in liters (L) [29,30].
All measurements were made with the anthropometric method validated by the International Society for the Advancement of Kinanthropometry by trained personnel who applied the Habitch technique. A Slim Guide caliper was used for body fold measurements (bicipital, tricipital, suprailiac, and subscapular skinfold), and a Lufkin metal tape model W606PM (Lufkin Industries, Missouri City, TX, USA) was used for arm and waist circumferences. Body fat percentage was estimated from the sum of skinfolds by applying the Durnin and Womersley formula [31].

2.4. Liver Fat Assessment

The evaluation of steatosis was performed consecutively with biochemical and body composition measurements to take advantage of the indicated 8-h fasting period. The degree of steatosis and liver fibrosis was assessed by vibration-controlled transient elastography (Fibroscan® 502; Echosens, Paris, France) performed by trained physicians. The cut-off point used to determine steatosis was a controlled attenuation parameter (CAP) ≥ 275 dB/m [32] in accordance with European guidelines. Assessments that had 10 valid measurements and an IQR/med ≤ 30% were included.

2.5. Dietary Assessment

A 24-h multi-step reminder (Supplementary Materials File S1) was used for dietary assessment [33]. The analysis of the main nutrients, their types, and micronutrients was carried out with the Food Processor software v11.11®. The analysis included quantification in grams of total sugars, added sugars, fructose, and saturated fats, in addition to other types of nutrients. In total, 17 types of sugars, 5 types of fats, and total protein were quantified without distinction of their origin, estimated in terms of the amount of Kcal they represent (for each nutrient) and as a percentage of average total energy. The 31 micronutrients were expressed per day in the corresponding and universal dietary unit of measurement.

2.6. Sample Size

The sample size was calculated as the difference between two proportions, considering an overall prevalence of fatty liver of 25.2% [34] and an effect size of 11% associated with the consumption of sugary drinks on liver fat [11]. For this calculation, a confidence level of 95% and a statistical power of 80% were assumed. The minimum sample size needed to detect significant differences was 550 subjects. Calculations were performed using G*Power software, version 3.1.9.7.

2.7. Statistical Analysis

Descriptive data are presented as the median and interquartile range (Q1–Q3) for quantitative variables and frequency and percentage for qualitative variables. The comparisons in clinical, body composition, and laboratory data among participants with and without hepatic steatosis were made with the Mann–Whitney U test for quantitative variables and the Chi-square test or Fisher’s exact test for qualitative variables. The comparison of macronutrient and micronutrient intake was made using the Mann–Whitney U test.
To determine the relationship between nutrient intake and liver and body fat (CAP, body mass index, body fat, and waist circumference), different linear regression models were created for Kcal and each nutrient (carbohydrates, protein, fat, saturated fat, total sugars, added sugar, and fructose, each one in grams and in percentage of energy). For each model, the adjustment was made for age (quantitative), sex, BMI (quantitative), waist circumference (quantitative), and total Kcal (quantitative). The results were presented as the regression coefficient (β) with a 95% confidence interval (95%CI). The variance inflation factor (VIF) was calculated to determine the presence of collinearity in the multivariable models, defined as a value greater than 10.
The three evaluation methods recommended in nutritional epidemiology [35] were used to determine the degree of association between Kcal, nutrient intake, and hepatic steatosis (>275 dB/m):
(1)
Degree of association between nutrient intake (quantitative) and hepatic steatosis.
(2)
Nutrient intake distributed in quartiles, with quartile 1 as reference.
(3)
The association between the total sugar consumption > 10% (model 1) and saturated fat > 7% (model 2) with hepatic steatosis.
Both logistic regression models were adjusted for age (quantitative), sex, BMI (quantitative), waist circumference (quantitative), and total kilocalories (quantitative).
The results of the models are presented as the regression coefficient (β), standard error, odds ratio (OR), 95%CI of the OR, and p-value. The model assumptions were evaluated by residual analysis. The probabilities of developing fatty liver disease were calculated based on the regression coefficients from the multivariate models for each nutrient. The probability estimates were graphed with their 95% CIs for carbohydrate intake per gram and intake per 15 g.
A value of p < 0.05 was considered statistically significant. All analyses were performed using SPSS v21 software. Forest plots were created using GraphPad Prism v.9.1.1. Hepatic steatosis probabilities were obtained with R v. 4.4.3, with the “effects” package.

2.8. Ethical Procedures

Participants received and signed an informed consent form. In the event of not completing all study evaluations, authorization was requested to include all other completed assessments. This study was approved by the institutional research ethics committee with registration number GAS-3794. This study was conducted in compliance with the Declaration of Helsinki while maintaining the anonymity, privacy, and will of participants.

3. Results

A total of 583 eligible adults consented to participate in this study, of whom 49 were not included for analysis as elastography was not available. Of the 534 participants, 61.4% (n = 328) were female, and the median age was 41.5 (IQR: 29.0–52.0). The most frequently reported comorbidities were smoking (7.68%) and hypertension (3.98%). Furthermore, 30.7% (n = 164) of the participants had normal weight (BMI 18–24.9); 41.9% (n = 224) were overweight; 20.6% (n = 110) had class 1 obesity; 4.9% had class 2 obesity (n = 26); and 0.9% (n = 5) had class 3 obesity. A total of 493 participants had complete dietary analysis data. The flow of participants is depicted in Figure 1.
Table 1 presents demographic data, body composition, and results of the most relevant laboratory studies of people with and without hepatic steatosis. People with steatosis had older age and more metabolic alterations and anthropometric indices than those without steatosis. The type of work performed in healthcare was self-reported in four categories, and the response rate was only 46.3%, so most participants could not be classified according to their type of job. In both groups, the frequency of acute myocardial infarction and arthritis was low (≤1 case due to comorbidity per group). There were no differences between groups in urea nitrogen, urea, non-HDL cholesterol, liver function tests, or blood count.
The median CAP of the total sample was 263 dB/m (IQR: 211–304), while the median Kpa was 4.2 (IQR: 3.4–5.3). Of the 534 participants, 227 (42.5%, 95% CI: 38.3–46.7) had hepatic steatosis with a median CAP of 310 (IQR: 292–335) and Kpa of 4.6 (IQR: 3.7–5.8). Of the 307 (57.5%) who did not have steatosis, the median CAP was 193 dB/m (IQR: 221–250) with Kpa of 3.3 (IQR: 4.0–4.8).
Table 2 presents the results of the comparisons of nutrient and micronutrient intake between subjects with and without hepatic steatosis. Intake of energy, protein, total fat, and saturated fat was higher in people without hepatic steatosis. Although carbohydrate consumption was similar between groups, monosaccharide consumption, particularly fructose, was higher in subjects without steatosis. An adjustment was made between the main nutrients by the kilocalories of consumption, thus obtaining the percentage of energy consumed in the form of each nutrient. It was observed that after this adjustment, there were no differences in consumption between the study groups. Regarding micronutrients, no differences were observed between those with and without hepatic steatosis.
Table 3 presents the results of multivariable linear regression models to determine the relationship between nutrient intake and the amount of liver fat, determined by CAP, body fat, visceral fat, and waist circumference. As the percentage of carbohydrate consumption increased, the CAP increased, and therefore the amount of liver fat (β = 0.23, 95%CI: 0.02 to 0.45). Likewise, the increase in the percentage of fat consumption was related to lower values of CAP (β = −0.22, 95%CI: −0.44 to −0.006). The consumption of Kcal, total fat, total sugars, added sugar, and percentage of fat consumption is related to the increase in the percentage of body fat, while lower intakes of total carbohydrates (g), total protein (g), and percentages of carbohydrates were related to increasing body fat. Waist circumference had a weak relationship with Kcal intake, and visceral fat was not associated with any nutrient.
Figure 2 presents the results of logistic regression models for determining the association of nutrient intake with hepatic steatosis. It was observed that carbohydrate intake was associated with higher odds of fatty liver. Each gram of consumption was associated with a 0.3% increase in the odds of having fatty liver (β = 0.003, p = 0.03). We performed a regression model adjusting carbohydrate consumption per 15 g, showing that the odds of having fatty liver increased by 5% (β = 0.51, OR = 1.053, 95%CI: 1.006–1.102, p = 0.03) for every 15 g. In Figure 3 we present the impact of carbohydrate intake on the probability of having hepatic steatosis. It can be observed how the probability of hepatic steatosis increased as carbohydrate consumption increased by 1 g or 15 g.
Supplementary Table S1 presents the results of logistic regression models, considering the distributions of nutritional intake by quartiles. No association was observed between higher intake and increased risk of hepatic steatosis. For the purposes of this analysis, participants were categorized based on adherence to dietary recommendations, specifically those consuming free sugars at less than 10% of total energy intake [20] and saturated fat at less than 7% of total energy intake [36]. Consumption of sugars > 10% (n = 217, 44%, OR = 1.04, 95%CI: 0.67–1.60, p = 0.86) or saturated fats > 7% (n = 451, 91.5%, OR = 0.92, 95%CI: 0.43–1.96, p = 0.83) was not associated with the presence of hepatic steatosis in quartile analyses.
Finally, the frequency of subjects with combined intakes of fats, total sugars, added sugars, and fructose was explored by considering those within quartile 4 of each of the nutrients as high consumption. The frequency of combined high consumption was low for all combinations: fats with total sugars (n = 7, 1.3%), fats with added sugars (n = 6, 1.1%), and fat with fructose (n = 20, 3.7%).

4. Discussion

The objective of this study was to analyze the association between nutrient, sugar, and saturated fat consumption with liver and body fat in the general population. Our results showed that only carbohydrate intake was related to liver fat, with each 15-g serving increasing the odds of developing hepatic steatosis by 5%. Furthermore, higher energy intake from fats and sugars was associated with increased body fat but had no significant effect on waist circumference or visceral fat. These findings provide insight into how diet influences fat accumulation but do not establish its impact on central fat distribution.
In this study, we used information on self-reported comorbidities. Few reports describe the frequency of self-recognition of the disease in general [37] and in-hospital environments [38]. Therefore, these are insightful reports, although future studies could seek to make rigorous confirmation of comorbidities. Participants diagnosed with steatosis were observed to be older and exhibited a higher incidence of metabolic abnormalities, including elevated HOMA, glucose, LDL cholesterol, triglycerides, and insulin, coupled with decreased HDL cholesterol. Anthropometric measurements, particularly waist circumference exceeding 90 cm, demonstrated a strong correlation with MASLD, consistent with previous reports [39,40]. Eight percent had normal weight and steatosis, as reported in the literature [20]. We observed that the probability of presenting hepatic steatosis increases with carbohydrate consumption. Furthermore, we observed an increased probability of body fat accumulation associated with a diet high in fats and sugars, consistent with previous reports [2].
The WHO [36] recommends that a regular diet should contain less than 10% free sugars and minimal saturated fats. Similarly, the National Cholesterol Education Program [41] suggests that a non-atherogenic diet should contain less than 7% saturated fat. However, in this cross-sectional study, we found no association between hepatic steatosis and consumption exceeding these values, which is why we considered 15 g of carbohydrates to be an increase in relevance, which is the recommended portion for carbohydrates in diabetes [42].
Our study revealed an apparent discrepancy between the presence of steatosis and dietary intake, as patients with steatosis reported lower energy, fat, and monosaccharide intake. Several factors could explain this finding. First, patients with steatotic liver disease, who had a higher BMI, may have underreported their intake. Weight bias has been shown to lead to underreporting of calorie intake by nearly 500 kcal in obese individuals [43], and this effect may be even more pronounced among healthcare professionals, who comprised our study population. Considering that we also observed underreporting in the physical activity questionnaire, it was striking that the average energy balance in our study was positive by approximately 600 kcals. One possible explanation for this could be reverse causation if metabolic conditions such as overweight and obesity had prompted dietary modifications. Given that our participants were healthcare personnel, their heightened awareness of health risks may have influenced their dietary behaviors.
Regarding fat intake, our study found an association between higher total fat intake and reduced fatty liver, a finding that remains unclear. Previous studies, including a randomized controlled trial comparing the effects of saturated versus polyunsaturated fatty acids on liver fat, have confirmed that excessive saturated fat intake promotes liver steatosis [44]. However, we could not replicate this association, as saturated fat intake was similar between groups. One possible explanation for these inconsistencies is that the food source of fats may play a more significant role than the nutrient itself. For instance, extra-virgin olive oil, a major source of polyunsaturated fatty acids, has been consistently shown to have a protective effect against MASLD, even in RCTs [45,46]. Therefore, it is essential to develop studies that evaluate food sources, whether in food groups or through dietary patterns.
Similarly, we did not find an association between fructose intake and hepatic steatosis. This may be because the metabolic effects of fructose depend on its food source rather than the nutrient itself. For example, a systematic review and meta-analysis of 51 trials found that the adverse effects of fructose were most pronounced when derived from sugar-sweetened beverages [47].
Our findings suggest that rather than focusing solely on macronutrient composition, other dietary aspects should be considered when assessing the role of nutrition in MASLD. Some authors propose that a diet’s inflammatory potential may be more relevant, as pro-inflammatory diets have been associated with MASLD diagnosis and progression [48]. Consumption patterns may also play a crucial role. For example, intermittent fasting has been suggested as a strategy to reverse lipid accumulation in the blood and liver, particularly in high-fat diets [13]. A recent RCT comparing intermittent fasting with calorie restriction found that time-restricted eating provided additional benefits for MASLD, independent of weight loss [49]. Similarly, dietary patterns seem to be very important, and the Mediterranean diet is widely recommended, with a recent systematic review and meta-analysis of 15 studies supporting its efficacy in reducing intrahepatic lipid content compared to a general low-calorie diet [50].
Other sources of inconsistency may stem from failing to account for factors such as nutrient bioavailability and origin. Additionally, genetics, microbiota composition, and epigenetics likely modulate these associations [2]. In particular, nutrigenetics, the study of how genetic variation affects dietary response, may impact the understanding of the role of specific nutrients on liver health [51]. These complexities highlight the challenge of interpreting the effects of isolated nutrients, as dietary components interact in multifaceted ways. Future studies should consider a nutritional geometry approach, which evaluates the interplay between nutrients, foods, appetite regulation, and metabolic homeostasis [52]. However, even this approach has not yet established an optimal balance of nutrients to support metabolic health.
Our study has limitations that may explain some of the discrepancies between our results and previous findings. The use of a single 24-h dietary recall (R24) to assess intake may have been inaccurate. More precise dietary assessment could be achieved through repeated R24 measurements, and emerging digital tools have been proposed to improve accuracy using automated image-based food recognition [53]. The current recommendation for accurate dietary estimation is the application of four R24 [54]. Due to budgetary limitations, we were restricted to the application of a single questionnaire. The lower caloric intake reported by participants with hepatic steatosis despite their higher metabolic risk suggests that the dietary assessment may have been subject to reporting bias or confounding by indication, in which individuals aware of their metabolic risk may have modified their diet. Similarly, a relationship has been observed between increasing BMI and under-reporting of fat and energy consumption in several assessment modalities, including R24 [55]. This seems to have occurred in our study, as there appears to be under-reporting in subjects who would have a greater metabolic alteration. Lastly, we performed multiple statistical comparisons without p-value adjustment, reason why the results should be interpreted as suggestive, and possibly false positive findings.
The generalizability of our findings may be limited to populations with characteristics similar to healthcare workers in Mexico, who tend to be young, have low physical activity levels, consume low fiber and excessive processed meats and sweetened beverages, and are undergoing a dietary transition toward higher fat, carbohydrate, and sodium intake [56]. Furthermore, the multiple exclusion criteria in our study may restrict the applicability of the findings in other populations. Another limitation was our inability to assess dietary patterns over time or determine the intake of specific dietary components such as omega-3 and omega-6 fatty acids, polyphenols, or carotenoids, which have been recognized as metabolic protective factors [2]. Additionally, we did not collect information on specific food sources. Hepatic steatosis was diagnosed using CAP measurements, which, while more sensitive than ultrasound, may not reliably detect mild steatosis. Moreover, CAP cut-off values vary in the literature, adding another layer of complexity to interpretation. We believe that awareness of self-perception and health recognition, as well as the phenomenology of illness, are terms that should be integrated into the workplace and the education of the healthcare worker community. This is because, in addition to the scarcity of evidence, there is also a significant gap in health systems, and it is not until irreversible outcomes such as disability or mortality occur that those who suffer are taken into account [37,38]. Finally, another limitation of our study was the assessment of alcohol consumption solely through self-reporting. Recent evidence shows that in some cohorts initially classified as pure MASLD, the use of serum alcohol biomarkers reduces the proportion of pure MASLD cases and increases the number of patients reclassified into the MetALD (metabolic and alcohol-related liver disease) group [57].

5. Conclusions

In conclusion, our study found that carbohydrate intake was associated with liver fat accumulation, whereas fat and sugar intake were primarily linked to body fat. Future research should incorporate prospective dietary assessments and a nutritional geometry approach to better understand these relationships. Considering factors such as food sources, dietary patterns, and consumption habits will be essential in elucidating the role of diet in MASLD.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu17081328/s1. Table S1: Multivariable logistic regression models on the relationship of dietary intake quartiles with the presence of hepatic steatosis. File S1: A 24-h recall format.

Author Contributions

S.E.M.-V.: conceptualization, investigation, methodology, project administration, supervision, writing, original draft, review, editing, funding acquisition, and visualization. A.K.-G.: conceptualization, methodology, data curation, formal analysis, supervision, writing, original draft, review, editing, and visualization. C.M.-V.: methodology, investigation, supervision, writing, original draft, review, editing, and visualization. J.M.-G.: methodology, formal analysis, writing, original draft, review, editing, and visualization. I.G.-J.: methodology, supervision, review, editing, and visualization. L.F.U.-D.: conceptualization, supervision, review, editing, funding acquisition, project administration, and visualization. All authors have read and agreed to the published version of the manuscript.

Funding

The publication fees for this article were covered by Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”. There was no funding from public sector agencies, commercial entities, or non-profit organizations for this study. The authors used their own resources for the execution of the project. S.E.M.-V., A.K.-G., I.G.-J. and L.F.U.-D. are part of the National System of Researchers in México and receive economic stimuli for their research activities.

Institutional Review Board Statement

This study was approved by the Ethics Committee of Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán” with a registration number GAS-3794 (31 August 2021), in accordance with the Declaration of Helsinki.

Informed Consent Statement

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

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available because they contain information that could compromise the privacy of research participants.

Acknowledgments

The authors thank José Miguel Corral, Carlos Ignacio Mulia, and Karla Cecilia Ramírez for carrying out all standardized body measurements and supervising the correct filling-out of questionnaires.

Conflicts of Interest

The authors declare that they have no conflicts of interest in conducting or publishing this study.

References

  1. Miao, L.; Targher, G.; Byrne, C.D.; Cao, Y.-Y.; Zheng, M.-H. Current status and future trends of the global burden of MASLD. Trends Endocrinol. Metab. 2024, 35, 697–707. [Google Scholar] [CrossRef] [PubMed]
  2. Berná, G.; Romero-Gomez, M. The role of nutrition in non-alcoholic fatty liver disease: Pathophysiology and management. Liver Int. 2020, 40, 102–108. [Google Scholar] [CrossRef] [PubMed]
  3. Ouyang, X.; Cirillo, P.; Sautin, Y.; McCall, S.; Bruchette, J.L.; Diehl, A.M.; Johnson, R.J.; Abdelmalek, M.F. Fructose consumption as a risk factor for non-alcoholic fatty liver disease. J. Hepatol. 2008, 48, 993–999. [Google Scholar] [CrossRef]
  4. Maersk, M.; Belza, A.; Stødkilde-Jørgensen, H.; Ringgaard, S.; Chabanova, E.; Thomsen, H.; Pedersen, S.B.; Astrup, A.; Richelsen, B. Sucrose-sweetened beverages increase fat storage in the liver, muscle, and visceral fat depot: A 6-mo randomized intervention study. Am. J. Clin. Nutr. 2012, 95, 283–289. [Google Scholar] [CrossRef]
  5. Simons, N.; Veeraiah, P.; Simons, P.I.; Schaper, N.C.; Kooi, M.E.; Schrauwen-Hinderling, V.B.; Feskens, E.J.; Van Der Ploeg, E.; Van Den Eynde, M.D.; Schalkwijk, C.G.; et al. Effects of fructose restriction on liver steatosis (FRUITLESS); a double-blind randomized controlled trial. Am. J. Clin. Nutr. 2021, 113, 391–400. [Google Scholar] [CrossRef]
  6. Zelber-Sagi, S.; Nitzan-Kaluski, D.; Goldsmith, R.; Webb, M.; Blendis, L.; Halpern, Z.; Oren, R. Long term nutritional intake and the risk for non-alcoholic fatty liver disease (NAFLD): A population based study. J. Hepatol. 2007, 47, 711–717. [Google Scholar] [CrossRef]
  7. Asgari-Taee, F.; Zerafati-Shoae, N.; Dehghani, M.; Sadeghi, M.; Baradaran, H.R.; Jazayeri, S. Association of sugar sweetened beverages consumption with non-alcoholic fatty liver disease: A systematic review and meta-analysis. Eur. J. Nutr. 2019, 58, 1759–1769. [Google Scholar] [CrossRef]
  8. Ma, J.; Fox, C.S.; Jacques, P.F.; Speliotes, E.K.; Hoffmann, U.; Smith, C.E.; Saltzman, E.; McKeown, N.M. Sugar-sweetened beverage, diet soda, and fatty liver disease in the Framingham Heart Study cohorts. J. Hepatol. 2015, 63, 462–469. [Google Scholar] [CrossRef] [PubMed]
  9. Schwarz, J.-M.; Noworolski, S.M.; Wen, M.J.; Dyachenko, A.; Prior, J.L.; Weinberg, M.E.; Herraiz, L.A.; Tai, V.W.; Bergeron, N.; Bersot, T.P.; et al. Effect of a High-Fructose Weight-Maintaining Diet on Lipogenesis and Liver Fat. J. Clin. Endocrinol. Metab. 2015, 100, 2434–2442. [Google Scholar] [CrossRef]
  10. Luukkonen, P.K.; Sädevirta, S.; Zhou, Y.; Kayser, B.; Ali, A.; Ahonen, L.; Lallukka, S.; Pelloux, V.; Gaggini, M.; Jian, C.; et al. Saturated Fat Is More Metabolically Harmful for the Human Liver Than Unsaturated Fat or Simple Sugars. Diabetes Care 2018, 41, 1732–1739. [Google Scholar] [CrossRef]
  11. Sobrecases, H.; Lê, K.-A.; Bortolotti, M.; Schneiter, P.; Ith, M.; Kreis, R.; Boesch, C.; Tappy, L. Effects of short-term overfeeding with fructose, fat and fructose plus fat on plasma and hepatic lipids in healthy men. Diabetes Metab. 2010, 36, 244–246. [Google Scholar] [CrossRef]
  12. Schneider, B.C.; Dumith, S.C.; Orlandi, S.P.; Assunção, M.C.F. Diet and body fat in adolescence and early adulthood: A systematic review of longitudinal studies. Ciênc. Saúde Coletiva 2017, 22, 1539–1552. [Google Scholar] [CrossRef] [PubMed]
  13. Perdomo, C.M.; Frühbeck, G.; Escalada, J. Impact of Nutritional Changes on Nonalcoholic Fatty Liver Disease. Nutrients 2019, 11, 677. [Google Scholar] [CrossRef]
  14. Cusi, K.; Isaacs, S.; Barb, D.; Basu, R.; Caprio, S.; Garvey, W.T.; Kashyap, S.; Mechanick, J.I.; Mouzaki, M.; Nadolsky, K.; et al. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings. Endocr. Pract. 2022, 28, 528–562. [Google Scholar] [CrossRef] [PubMed]
  15. Lam, B.P.; Bartholomew, J.; Bau, S.; Gilles, H.; Keller, A.; Moore, A.; Nader, K.; Richards, L.; Henry, L.; Younossi, Z.M. Focused Recommendations for the Management of Metabolic Dysfunction-Associated Steatohepatitis (MASH) by Advanced Practice Providers in the United States. J. Clin. Gastroenterol. 2025, 59, 298–309. [Google Scholar] [CrossRef] [PubMed]
  16. Yki-Järvinen, H.; Luukkonen, P.K.; Hodson, L.; Moore, J.B. Dietary carbohydrates and fats in nonalcoholic fatty liver disease. Nat. Rev. Gastroenterol. Hepatol. 2021, 18, 770–786. [Google Scholar] [CrossRef]
  17. Tacke, F.; Horn, P.; Wai-Sun Wong, V.; Ratziu, V.; Bugianesi, E.; Francque, S.; Zelber-Sagi, S.; Valenti, L.; Roden, M.; Schick, F.; et al. EASL–EASD–EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J. Hepatol. 2024, 81, 492–542. [Google Scholar] [CrossRef]
  18. Zhang, S.; Gu, Y.; Bian, S.; Lu, Z.; Zhang, Q.; Liu, L.; Meng, G.; Yao, Z.; Wu, H.; Wang, Y.; et al. Soft drink consumption and risk of nonalcoholic fatty liver disease: Results from the Tianjin Chronic Low-Grade Systemic Inflammation and Health (TCLSIH) cohort study. Am. J. Clin. Nutr. 2021, 113, 1265–1274. [Google Scholar] [CrossRef]
  19. Eslam, M.; Sarin, S.K.; Wong, V.W.-S.; Fan, J.-G.; Kawaguchi, T.; Ahn, S.H.; Zheng, M.-H.; Shiha, G.; Yilmaz, Y.; Gani, R.; et al. The Asian Pacific Association for the Study of the Liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease. Hepatol. Int. 2020, 14, 889–919. [Google Scholar] [CrossRef]
  20. Sato-Espinoza, K.; Chotiprasidhi, P.; Huaman, M.R.; Díaz-Ferrer, J. Update in lean metabolic dysfunction-associated steatotic liver disease. World J. Hepatol. 2024, 16, 452–464. [Google Scholar] [CrossRef]
  21. Tao, M.; Liu, J.; Chen, X.; Wang, Q.; He, M.; Chen, W.; Wang, C.; Zhang, L. Correlation between serum uric acid and body fat distribution in patients with MAFLD. BMC Endocr. Disord. 2023, 23, 204. [Google Scholar] [CrossRef]
  22. Iyengar, K.P.; Ish, P.; Upadhyaya, G.K.; Malhotra, N.; Vaishya, R.; Jain, V.K. COVID-19 and mortality in doctors. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 1743–1746. [Google Scholar] [CrossRef]
  23. Shin, Y.; Kim, U.J.; Lee, H.A.; Choi, E.J.; Park, H.J.; Ahn, H.S.; Park, H.; Policy Development Committee of NAMOK. Health and Mortality in Korean Healthcare Workers. J. Korean Med. Sci. 2022, 37, e22. [Google Scholar] [CrossRef] [PubMed]
  24. Kunyahamu, M.S.; Daud, A.; Jusoh, N. Obesity among Health-Care Workers: Which Occupations Are at Higher Risk of Being Obese? Int. J. Environ. Res. Public Health 2021, 18, 4381. [Google Scholar] [CrossRef] [PubMed]
  25. Magalhães, V.D.S.; Jost, T.D.A.; Pasqual, H.M.; Becker, A.L.G.; Marques, L.M.; Manica, M.; Delani, B.L.L.; Langaro, J.P.; Afonso, D.T.; Hoppe, L.; et al. Non-alcoholic fatty liver disease and associated risk factors in health care professionals in a community hospital in Brazil. Rev. Bras. Med. Trab. 2020, 18, 449–456. [Google Scholar] [CrossRef] [PubMed]
  26. Vidal-Cevallos, P.; Ordóñez-Vázquez, A.L.; Procopio-Mosso, O.; Cardoso-Arias, R.; Uribe, M.; Chávez-Tapia, N.C. Cross-sectional pilot study to assess primary healthcare workers’ knowledge of nonalcoholic fatty liver disease in a marginalized community in Mexico. Sci. Rep. 2021, 11, 12100. [Google Scholar] [CrossRef]
  27. Weir, C.B.; Jan, A. BMI Classification Percentile and Cut Off Points. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2025. Available online: http://www.ncbi.nlm.nih.gov/books/NBK541070/ (accessed on 28 March 2025).
  28. López Alvarenga, J.C.; Reyes Díaz, S.; Castillo Martínez, L.; Dávalos Ibañez, A. Reproducibilidad y sensibilidad de un cuestionario de actividad física en población mexicana. Salud Pública México 2001, 43, 306–312. [Google Scholar] [CrossRef]
  29. de los Ángeles Espinosa-Cuevas, M.; Rivas-Rodríguez, L.; González-Medina, E.; Atilano-Carsi, X. Vectores de impedancia bioeléctrica para la composición corporal en población mexicana. Rev. Investig. Clin. 2007, 59, 15–24. [Google Scholar]
  30. Jensen, B.; Moritoyo, T.; Kaufer-Horwitz, M.; Peine, S.; Norman, K.; Maisch, M.J.; Matsumoto, A.; Masui, Y.; Velázquez-González, A.; Domínguez-García, J.; et al. Ethnic differences in fat and muscle mass and their implication for interpretation of bioelectrical impedance vector analysis. Appl. Physiol. Nutr. Metab. 2019, 44, 619–626. [Google Scholar] [CrossRef]
  31. Durnin, J.V.G.A.; Womersley, J. Body fat assessed from total body density and its estimation from skinfold thickness: Measurements on 481 men and women aged from 16 to 72 Years. Br. J. Nutr. 1974, 32, 77–97. [Google Scholar] [CrossRef]
  32. Berzigotti, A.; Tsochatzis, E.; Boursier, J.; Castera, L.; Cazzagon, N.; Friedrich-Rust, M.; Petta, S.; Thiele, M. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis—2021 update. J. Hepatol. 2021, 75, 659–689. [Google Scholar] [CrossRef]
  33. Salvador Castell, G.; Serra-Majem, L.; Ribas-Barba, L. What and how much do we eat? 24-h dietary recall method. Nutr. Hosp. 2015, 31 (Suppl. 3), 46–48. [Google Scholar] [CrossRef]
  34. Younossi, Z.M.; Golabi, P.; Paik, J.M.; Henry, A.; Van Dongen, C.; Henry, L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review. Hepatology 2023, 77, 1335–1347. [Google Scholar] [CrossRef] [PubMed]
  35. Willett, W. Issues in analysis and presentation of dietary data. In Nutritional Epidemiology, 3rd ed.; Oxford University Press: New York, NY, USA, 2012; pp. 305–333. Available online: https://academic.oup.com/book/27443/chapter-abstract/197325993?redirectedFrom=fulltext (accessed on 28 March 2025).
  36. World Health Organization. Directriz: Ingesta de Azúcares para Adultos y Niños: Resumen. 2015. Available online: https://iris.who.int/handle/10665/154587 (accessed on 28 March 2025).
  37. Diaz Romero, P. Sobre el Reconocimiento de la Enfermedad Como Experiencia Subjetiva y su Impacto en la Salud Pública. RBD. 2022. Available online: https://revistes.ub.edu/index.php/RBD/article/view/32562 (accessed on 28 March 2025).
  38. Noroña-Salcedo, D.R. Estrés Laboral y Autopercepción del Personal de Salud. 2023. Available online: https://zenodo.org/doi/10.5281/zenodo.10048611 (accessed on 28 March 2025).
  39. Priego-Parra, B.A.; Reyes-Diaz, S.A.; Ordaz-Alvarez, H.R.; Bernal-Reyes, R.; Icaza-Chávez, M.E.; Martínez-Vázquez, S.E.; Amieva-Balmori, M.; Vivanco-Cid, H.; Velasco, J.A.V.-R.; Gracia-Sancho, J.; et al. Diagnostic performance of sixteen biomarkers for MASLD: A study in a Mexican cohort. Clin. Res. Hepatol. Gastroenterol. 2024, 48, 102400. [Google Scholar] [CrossRef]
  40. Pan, Z.; Derbala, M.; AlNaamani, K.; Ghazinian, H.; Fan, J.-G.; Eslam, M. MAFLD criteria are better than MASLD criteria at predicting the risk of chronic kidney disease. Ann. Hepatol. 2024, 29, 101512. [Google Scholar] [CrossRef] [PubMed]
  41. NCEP. National Cholesterol Education Program. Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). NCEP ATP III. Available online: https://www.nhlbi.nih.gov/files/docs/guidelines/atp3xsum.pdf (accessed on 22 October 2024).
  42. Ewers, B.; Vilsbøll, T.; Andersen, H.U.; Bruun, J.M. The dietary education trial in carbohydrate counting (DIET-CARB Study): Study protocol for a randomised, parallel, open-label, intervention study comparing different approaches to dietary self-management in patients with type 1 diabetes. BMJ Open 2019, 9, e029859. [Google Scholar] [CrossRef] [PubMed]
  43. Howes, E.M.; Parker, M.K.; Misyak, S.A.; DiFeliceantonio, A.G.; Davy, B.M.; Brown, L.E.C.; Hedrick, V.E. The Impact of Weight Bias and Stigma on the 24 h Dietary Recall Process in Adults with Overweight and Obesity: A Pilot Study. Nutrients 2024, 16, 191. [Google Scholar] [CrossRef]
  44. Rosqvist, F.; Kullberg, J.; Ståhlman, M.; Cedernaes, J.; Heurling, K.; Johansson, H.-E.; Iggman, D.; Wilking, H.; Larsson, A.; Eriksson, O.; et al. Overeating Saturated Fat Promotes Fatty Liver and Ceramides Compared With Polyunsaturated Fat: A Randomized Trial. J. Clin. Endocrinol. Metab. 2019, 104, 6207–6219. [Google Scholar] [CrossRef]
  45. Donghia, R.; Tatoli, R.; Campanella, A.; Losurdo, G.; Di Leo, A.; De Pergola, G.; Bonfiglio, C.; Giannelli, G. Extra Virgin Olive Oil Reduces the Risk of Non-Alcoholic Fatty Liver Disease in Females but Not in Males: Results from the NUTRIHEP Cohort. Nutrients 2024, 16, 3234. [Google Scholar] [CrossRef]
  46. Rezaei, S.; Akhlaghi, M.; Sasani, M.R.; Barati Boldaji, R. Olive oil lessened fatty liver severity independent of cardiometabolic correction in patients with non-alcoholic fatty liver disease: A randomized clinical trial. Nutrition 2019, 57, 154–161. [Google Scholar] [CrossRef]
  47. Lee, D.; Chiavaroli, L.; Ayoub-Charette, S.; Khan, T.A.; Zurbau, A.; Au-Yeung, F.; Cheung, A.; Liu, Q.; Qi, X.; Ahmed, A.; et al. Important Food Sources of Fructose-Containing Sugars and Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Controlled Trials. Nutrients 2022, 14, 2846. [Google Scholar] [CrossRef] [PubMed]
  48. Sepehrinia, M.; Khanmohammadi, S.; Rezaei, N.; Kuchay, M.S. Dietary inflammatory potential and metabolic (dysfunction)-associated steatotic liver disease and its complications: A comprehensive review. Clin. Nutr. ESPEN 2025, 65, 162–171. [Google Scholar] [CrossRef]
  49. Wang, Y.-Y.; Tian, F.; Qian, X.-L.; Ying, H.-M.; Zhou, Z.-F. Effect of 5:2 intermittent fasting diet versus daily calorie restriction eating on metabolic-associated fatty liver disease—A randomized controlled trial. Front. Nutr. 2024, 11, 1439473. [Google Scholar] [CrossRef]
  50. Dobbie, L.J.; Burgess, J.; Hamid, A.; Nevitt, S.J.; Hydes, T.J.; Alam, U.; Cuthbertson, D.J. Effect of a Low-Calorie Dietary Intervention on Liver Health and Body Weight in Adults with Metabolic-Dysfunction Associated Steatotic Liver Disease (MASLD) and Overweight/Obesity: A Systematic Review and Meta-Analysis. Nutrients 2024, 16, 1030. [Google Scholar] [CrossRef] [PubMed]
  51. Meroni, M.; Longo, M.; Rustichelli, A.; Dongiovanni, P. Nutrition and Genetics in NAFLD: The Perfect Binomium. Int. J. Mol. Sci. 2020, 21, 2986. [Google Scholar] [CrossRef]
  52. Simpson, S.J.; Raubenheimer, D.; Cogger, V.C.; Macia, L.; Solon-Biet, S.M.; Le Couteur, D.G.; George, J. The nutritional geometry of liver disease including non-alcoholic fatty liver disease. J. Hepatol. 2018, 68, 316–325. [Google Scholar] [CrossRef]
  53. Makhsous, S.; Bharadwaj, M.; Atkinson, B.E.; Novosselov, I.V.; Mamishev, A.V. DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients. Sensors 2020, 20, 3380. [Google Scholar] [CrossRef] [PubMed]
  54. Satija, A.; Yu, E.; Willett, W.C.; Hu, F.B. Understanding nutritional epidemiology and its role in policy. Adv. Nutr. 2015, 6, 5–18. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  55. Freedman, L.S.; Commins, J.M.; Moler, J.E.; Willett, W.; Tinker, L.F.; Subar, A.F.; Spiegelman, D.; Rhodes, D.; Potischman, N.; Neuhouser, M.L.; et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake. Am. J. Epidemiol. 2015, 181, 473–487. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  56. Betancourt-Nuñez, A.; Márquez-Sandoval, F.; González-Zapata, L.I.; Babio, N.; Vizmanos, B. Unhealthy dietary patterns among healthcare professionals and students in Mexico. BMC Public Health 2018, 18, 1246. [Google Scholar] [CrossRef]
  57. Krag, A.; Torp, N.; Younossi, Z.M.; Israelsen, M. Reporting discrepancy of alcohol intake affecting estimated prevalence of MetALD and ALD. Lancet Gastroenterol. Hepatol. 2025, 10, 282–284. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Flowchart of the participants.
Figure 1. Flowchart of the participants.
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Figure 2. Multivariable logistic regression models of the relationship between dietary intake and the odds of hepatic steatosis. (A) Nutrients in grams. (B) Nutrients in percentage of total energy. Models adjusted for age, sex, BMI, waist circumference, and total Kcal. Kilocalorie models adjusted for age, sex, BMI, and waist circumference. Black diamond represent the Odds Ratio, and the green line the 95%CI.
Figure 2. Multivariable logistic regression models of the relationship between dietary intake and the odds of hepatic steatosis. (A) Nutrients in grams. (B) Nutrients in percentage of total energy. Models adjusted for age, sex, BMI, waist circumference, and total Kcal. Kilocalorie models adjusted for age, sex, BMI, and waist circumference. Black diamond represent the Odds Ratio, and the green line the 95%CI.
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Figure 3. Impact of carbohydrate intake on the likelihood of developing fatty liver disease. (A) Carbohydrate intake in grams. (B) Carbohydrate intake per 15 g. Probabilities adjusted for age, sex, BMI, waist circumference, and total Kcal. The probability estimate (green line) and its 95% confidence interval (green highlight) are shown.
Figure 3. Impact of carbohydrate intake on the likelihood of developing fatty liver disease. (A) Carbohydrate intake in grams. (B) Carbohydrate intake per 15 g. Probabilities adjusted for age, sex, BMI, waist circumference, and total Kcal. The probability estimate (green line) and its 95% confidence interval (green highlight) are shown.
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Table 1. Clinical, body composition, and laboratory data among participants with and without hepatic steatosis.
Table 1. Clinical, body composition, and laboratory data among participants with and without hepatic steatosis.
Total Sample
(n = 534)		No Steatosis
(n = 307)		With Steatosis
(n = 227)		p Value
Age (years)41.5 (29.0–52.0)36.0 (27.0–51.0)45.0 (34.0–53.0)<0.0001
Sex, n (%)
Women328 (61.4)199 (64.8)129 (56.8)0.061
Smoking, n (%)41 (7.68)18 (5.86)23 (10.13)0.062
Comorbidities, n (%)
Diabetes9 (1.69)3 (0.98)6 (2.64)0.178 *
Hypertension21 (3.93)9 (2.93)12 (5.29)0.166
Dyslipidemia3 (0.56)0 (0.00)3 (1.32)0.077
Hypothyroidism9 (1.69)5 (1.63)4 (1.76)0.999
Insulin resistance4 (0.75)1 (0.33)3 (1.32)0.316
Job categories
Medical staff31 (5.8)20 (6.5)11 (4.8)0.857
Nursing staff46 (8.6)23 (7.5)23 (10.1)
Non-medical clinical staff43 (8.1)26 (8.5)17 (7.5)
Administrative staff126 (23.8)74 (24.1)52 (22.9)
Not disclosed288 (53.9)164 (53.4)124 (54.6)
Body composition
Weight (kg)70.5 (60.6–80.7)64.2 (56.7–74.5)77.8 (69.4–86.4)<0.0001
BMI27.1 (23.8–30.3)25.1 (22.3–27.8)29.4 (27.1–32.8)<0.0001
<18.55 (0.9)5 (1.6)0 (0.0)<0.0001
18–24.9164 (30.7)146 (47.6)18 (7.9)
25–29.9224 (41.9)117 (38.1)107 (47.1)
30–34.9110 (20.6)34 (11.1)76 (33.5)
35–39.926 (4.9)3 (1.0)23 (10.1)
≥405 (0.09)2 (0.7)3 (1.3)
Fat mass (kg)25.1 (18.7–31.5)20.8 (15.5–27.4)29.6 (25.2–35.8)<0.0001
Fat mass (%)35.6 (29.0–42.2)32.9 (26.2–38.7)39.7 (32.6–44.7)<0.0001
Visceral fat (L)2.50 (1.80–3.50)2.00 (1.50–2.70)3.20 (2.48–4.20)<0.0001
Waist circumference (m)0.89 (0.80–0.99)0.84 (0.76–0.92)0.96 (0.89–1.04)<0.0001
Bicipital skinfold (mm)10.0 (7.0–14.0)8.0 (6.0–12.0)12.0 (9.0–17.0)<0.0001
Triceps skinfold (mm)17.0 (12.0–22.0)15.0 (12.0–19.0)20.0 (14.0–25.0)<0.0001
Subscapular skinfold (mm)23.0 (17.0–30.0)19.0 (15.0–25.0)28.0 (23.0–33.0)<0.0001
Suprailiac skinfold (mm)24.0 (17.0–30.0)20.0 (15.0–27.0)28.0 (24.0–35.0)<0.0001
Physical activity
Kcals from PAQ *737.7 (636.5–852.2)699.4 (600.9–814.2)781.3 (697.7–928.2)<0.0001
Z Score PAQ *0.00 (1.00)−0.27 (0.89)0.36 (−0.13)<0.0001
<−1 SD75 (14.0)65 (22.0)10 (4.5)<0.0001
−1 to 1 SD368 (68.9)203 (68.6)165 (74.3)
>1 SD75 (14.0)28 (9.5)47 (21.2)
Energy balance529.5 (203.1–920.9)557.9 (249.8–980.9)463.00 (153.1–829.4)0.013
Z Score Energy balance0.00 (1.00)0.09 (1.01)−0.13 (0.97)0.01
<−1 SD84 (15.7)14 (13.0)44 (19.4)0.04
−1 to 1 SD370 (69.3)216 (70.4)154 (67.8)
>1 SD80 (15.0)51 (16.6)29 (12.8)
Biochemical data
HOMA IR1.58 (1.04–2.62)1.25 (0.81–1.78)2.45 (1.55–3.85)<0.0001
Glucose (mg/dL)89.0 (84.0–96.0)87.0 (82.0–92.0)93.0 (88.0–101.0)<0.0001
Creatinine (mg/dL)0.76 (0.66–0.89)0.74 (0.66–0.87)0.77 (0.66–0.92)0.152
Cholesterol (mg/dL)180.0 (155.0–206.0)177.0 (155.0–202.0)185.0 (156.0–217.0)0.009
Low-density cholesterol (LDL-c; mg/dL)110.0 (89.0–129.0)105.0 (86.0–124.0)116.0 (92.5–134.5)0.001
LDL-c, Martin’s method (mg/dL)107.0 (86.0–126.0)104.0 (83.0–122.0)112.0 (88.0–133.0)0.003
High-density cholesterol (HDL-c; mg/dL)47.0 (40.0–56.0)50.0 (43.0–59.0)43.0 (37.0–51.0)<0.0001
Triglycerides (mg/dL)124.0 (90.0–174.0)106.0 (79.0–142.0)163.0 (114.5–233.0)<0.0001
Total bilirubin (mg/dL)0.64 (0.49–0.85)0.64 (0.49–0.85)0.63 (0.51–0.83)0.762
Alanine aminotransferase (ALT; U/L)21.6 (15.4–32.2)18.1 (13.9–26.0)26.5 (18.0–40.7)<0.0001
Aspartate aminotransferase (AST; U/L)19.6 (17.0–24.8)19.1 (16.9–23.7)21.4 (17.2–26.7)0.002
Gammaglutamyl transferase (U/L)21.4 (15.1–35.3)17.9 (13.1–26.7)27.9 (19.1–42.6)<0.0001
Alkaline phosphatase (U/L)74.0 (61.5–88.0)71.0 (58.0–85.0)78.0 (66.0–92.0)<0.0001
Albumin (g/dL)4.42 (4.23–4.62)4.46 (4.25–4.64)4.38 (4.22–4.59)0.066
Ultra-sensitive C-reactive protein (mg/dL)0.15 (0.08–0.31)0.13 (0.06–0.24)0.20 (0.11–0.41)<0.0001
Insulin (μIU/mL)7.19 (4.95–11.38)5.72 (3.96–8.15)10.43 (6.91–16.02)<0.0001
Platelets (103/μL)249.0 (213.0–289.5)251.0 (214.0–290.0)242.5 (211.0–287.2)0.496
Data are presented as frequency and percentage (%) or as median and interquartile range (Q1–Q3). Quantitative variables were compared with the Mann–Whitney U test, whereas qualitative variables were compared using chi-square test or Fisher’s exact test. * The Laval physical activity questionnaire [28].
Table 2. Comparison of energy and nutrient intake between participants with and without fatty liver collected with 24-h recall.
Table 2. Comparison of energy and nutrient intake between participants with and without fatty liver collected with 24-h recall.
Total Sample
(n = 493)		No Steatosis
(n = 282)		With Steatosis
(n = 211)		p Value
Kilocalories1278.6 (1021.5–1533.9)1326.2 (1050.7–1557.7)1188.7 (991.8–1499.0)0.009
Nutrients
Carbohydrates (g)143.6 (67.5–211.7)145.1 (53.6–216.96)142.9 (78.0–208.5)0.959
Protein (g)55.2 (44.7–69.0)56.4 (46.5–70.5)53.1 (40.8–65.8)0.019
Fat (g)43.1 (30.6–75.2)45.2 (32.5–86.6)41.3 (28.8–62.3)0.008
Kilocalories per carbohydrate574.5 (270.1–846.7)580.2 (214.4–867.8)571.7 (312.2–834.1)0.959
Carbohydrates (%)53.7 (28.2–60.0)51.8 (13.6–60.0)55.4 (41.2–60.0)0.253
Kilocalories from protein221.0 (179.0–276.2)225.9 (186.2–281.9)212.3 (163.5–263.4)0.019
Protein (%)16.6 (15.2–18.9)16.9 (15.4–19.4)16.5 (15.0–18.6)0.241
Kilocalories from fat387.7 (275.7–676.8)407.3 (293.1–780.1)372.0 (259.2–560.9)0.008
Fats (%)26.8 (23.2–49.6)27.8 (23.2–70.5)26.8 (23.1–39.4)0.435
Saturated fat (g)16.8 (11.6–28.6)17.7 (12.1–32.1)15.8 (10.9–24.9)0.022
Kilocalories per saturated fat151.4 (104.3–257.9)159.4 (109.1–288.7)142.4 (98.4–224.9)0.022
Saturated Fat (%)10.6 (9.05–18.35)10.6 (9.11–25.98)10.5 (9.05–14.82)0.587
Monounsaturated fat (g)3.91 (1.56–7.47)3.98 (1.62–7.56)3.50 (1.41–7.24)0.516
Polyunsaturated fat (g)1.76 (0.81–3.36)2.02 (0.90–3.39)1.60 (0.78–3.34)0.232
Trans fats (g)0.00 (0.00–0.12)0.00 (0.00–0.08)0.00 (0.00–0.13)0.250
Types of carbohydrates
Available CH (g)134.2 (57.8–197.5)134.3 (47.5–202.4)134.2 (68.4–190.0)0.886
Total sugars (g)28.0 (17.98–55.39)28.4 (18.61–52.03)28.0 (16.68–55.99)0.352
Added sugar (g)7.53 (3.39–20.14)7.53 (3.52–21.04)7.67 (3.28–19.36)0.649
Total sugars (%)8.57 (6.43–14.73)8.57 (6.43–14.64)8.57 (6.43–16.14)0.728
Added sugars (%)2.44 (1.30–5.65)2.43 (1.30–5.71)2.49 (1.25–5.63)0.845
Monosaccharides (g)7.80 (3.69–12.40)8.44 (4.40–12.72)6.76 (2.33–12.00)0.037
Galactose (g)0.00 (0.00–0.00)0.00 (0.00–0.00)0.00 (0.00–0.00)0.794
Glucose (g)2.91 (1.52–4.71)3.06 (1.99–4.67)2.51 (0.99–4.72)0.049
Fructose (g)4.86 (1.97–8.78)5.42 (2.28–8.78)4.35 (0.96–7.69)0.017
Fructose (%)1.56 (0.75–2.45)1.65 (0.77–2.46)1.50 (0.42–2.45)0.266
Disaccharides (g)2.28 (1.18–3.75)2.37 (1.41–3.75)2.11 (0.71–3.62)0.127
Lactose (g)0.00 (0.00–0.00)0.00 (0.00–0.00)0.00 (0.00–0.00)0.834
Maltose (g)0.00 (0.00–0.01)0.00 (0.00–0.01)0.00 (0.00–0.01)0.404
Other HC (g)75.8 (15.3–142.7)68.7 (15.1–143.3)78.9 (15.6–138.6)0.871
Net HC (g)134.4 (63.1–197.5)134.4 (47.9–202.0)134.4 (68.4–190.0)0.938
Non-digestible HC (g)0.00 (0.00–0.00)0.00 (0.00–0.00)0.00 (0.00–0.00)0.833
Dietary fiber (g)8.57 (4.96–13.36)8.56 (4.97–13.72)8.57 (4.95–13.16)0.807
Starch (g)0.00 (0.00–1.84)0.00 (0.00–1.84)0.00 (0.00–1.83)0.418
Micronutrients
Cholesterol (mg)132.5 (83.4–217.4)141.7 (94.1–221.1)130.2 (78.8–205.9)0.063
Folate (μg)106.9 (58.7–157.1)108.4 (59.4–159.3)104.9 (54.9–156.9)0.458
Folic acid (μg)13.9 (0.00–62.3)7.90 (0.00–66.1)14.28 (0.00–60.1)0.478
Vitamin B1 (mg)0.48 (0.25–0.71)0.49 (0.27–0.77)0.45 (0.24–0.70)0.158
Vitamin B2 (mg)0.88 (0.58–1.28)0.92 (0.58–1.33)0.84 (0.57–1.26)0.519
Vitamin B3 (mg)12.0 (8.26–19.04)12.4 (8.46–19.43)11.6 (8.17–18.67)0.468
Pantothenic acid (mg)0.57 (0.25–1.02)0.57 (0.19–1.02)0.57 (0.27–1.05)0.505
Vitamin B6 (mg)0.85 (0.50–1.34)0.92 (0.50–1.40)0.79 (0.50–1.27)0.158
Vitamin B12 (μg)1.12 (0.41–2.37)1.12 (0.37–2.45)1.07 (0.41–2.35)0.746
Vitamin C (mg)42.3 (13.7–96.4)40.1 (14.6–96.9)42.9 (11.6–96.4)0.952
Vitamin D (μg)0.18 (0.00–0.79)0.19 (0.00–0.74)0.18 (0.00–0.82)0.490
Vitamin E (mg)0.48 (0.23–1.02)0.50 (0.23–1.11)0.47 (0.22–0.95)0.521
Vitamin K (μg)14.7 (5.03–51.57)14.6 (5.19–52.50)15.0 (4.62–49.98)0.483
Biotin (μg)3.59 (0.50–7.27)3.68 (1.33–7.27)3.49 (0.36–7.27)0.401
Vitamin A (IU)426.9 (135.4–856.3)413.5 (124.9–863.7)440.1 (155.9–849.0)0.691
Calcium (mg)916.6 (517.7–1311.1)875.1 (453.7–1299.9)958.4 (572.5–1320.7)0.152
Copper (mg)0.34 (0.21–0.53)0.35 (0.21–0.54)0.32 (0.21–0.51)0.517
Iron (mg)12.68 (7.52–18.20)12.89 (7.35–18.56)12.56 (8.04–17.71)0.929
Magnesium (mg)109.5 (62.3–163.3)114.2 (62.6–173.3)104.2 (61.6–157.5)0.574
Boron (μg)155.1 (0.00–529.19)248.4 (0.00–529.19)122.1 (0.00–529.19)0.102
Chlorine (mg)62.0 (0.0–150.0)84.7 (0.0–150.0)46.8 (0.0–150.0)0.193
Chromium (μg)1.39 (0.00–2.32)1.39 (0.00–2.54)1.05 (0.00–2.32)0.380
Fluoride (mg)0.00 (0.00–0.01)0.00 (0.00–0.01)0.00 (0.00–0.01)0.841
Iodine (μg)1.13 (0.15–33.51)1.42 (0.15–36.74)0.88 (0.12–19.79)0.147
Manganese (mg)0.32 (0.10–0.58)0.32 (0.09–0.58)0.32 (0.10–0.58)0.657
Molybdenum (μg)0.00 (0.00–1.26)0.00 (0.00–1.29)0.00 (0.00–1.22)0.833
Phosphorus (mg)490.8 (305.7–724.6)499.5 (300.4–772.5)486.1 (309.1–684.1)0.669
Potassium (mg)1404.2 (845.2–1957.7)1424.5 (807.9–1986.6)1373.7 (888.0–1953.6)0.833
Selenium (μg)45.58 (30.59–70.16)46.53 (30.36–72.71)44.96 (30.59–68.29)0.562
Sodium (mg)2332.6 (1597.9–3033.6)2335.7 (1589.2–3146.7)2289.8 (1610.1–2968.2)0.578
Zinc (mg)3.73 (1.76–8.89)4.33 (1.66–9.27)3.47 (1.87–8.22)0.496
Data presented as median and interquartile range (Q3–Q1). Comparisons were made using the Mann–Whitney U test. HC: carbohydrates.
Table 3. Multivariable linear regression models of the relationship between dietary intake and liver and body fat.
Table 3. Multivariable linear regression models of the relationship between dietary intake and liver and body fat.
CAP ModelFat ModelModel WaistVisceral Fat Model
NutrientB (95% CI)p-ValueB (95% CI)p-ValueB (95% CI)p-ValueB (95% CI)p-Value
Kilocalories ‡−0.006 (−0.016 to 0.004)0.2040.001 (0.044–0.000)0.002−0.001 (−0.002–0.000)0.0420.000 (0.000–0.000)0.567
Carbohydrates (g) *0.060 (−0.001 to 0.121)0.053−0.002 (0.372–−0.007)0.0030.001 (−0.006–0.007)0.8620.000 (0.000–0.001)0.589
Protein (g) *−0.082 (−0.434 to 0.271)0.649−0.009 (0.541–−0.039)0.0200.002 (−0.034–0.038)0.900−0.001 (−0.005–0.003)0.682
Fat (g) *−0.129 (−0.266 to 0.007)0.0630.006 (0.318–−0.006)0.017−0.001 (−0.015–0.013)0.8450.000 (−0.002–0.001)0.639
Saturated fat (g) *−0.325 (−0.706–0.056)0.0940.015 (0.347–−0.017)0.047−0.006 (−0.045–0.033)0.747−0.001 (−0.005–0.003)0.701
Monounsaturated Fats (g)−0.037 (−0.952–0.878)0.9370.017 (−0.058–0.092)0.6580.047 (−0.045–0.138)0.316−0.007 (−0.017–0.003)0.149
Polyunsaturated Fats (g)0.358 (−1.339–2.055)0.6780.002 (−0.137–0.142)0.9750.083 (−0.087–0.253)0.337−0.006 (−0.024–0.012)0.502
Total sugars (g) *0.054 (−0.087–0.195)0.4530.005 (0.395–−0.007)0.017−0.009 (−0.023–0.006)0.2370.000 (−0.001–0.002)0.812
Added sugar (g) *0.098 (−0.190–0.386)0.5060.018 (0.148–−0.006)0.042−0.012 (−0.041–0.017)0.4250.000 (−0.003–0.003)0.834
Fructose (g) *−0.129 (−0.920–0.661)0.7480.026 (0.448–−0.041)0.092−0.052 (−0.132–0.029)0.210−0.001 (−0.009–0.008)0.884
Protein (%) *−0.285 (−1.336–0.766)0.594−0.019 (0.679–−0.106)0.0690.006 (−0.101–0.113)0.912−0.001 (−0.012–0.010)0.862
Carbohydrates (%) *0.234 (0.019–0.449)0.033−0.013 (0.155–−0.031)0.0050.000 (−0.022–0.022)0.9770.001 (−0.001–0.004)0.247
Fat (%) *−0.220 (−0.435–0.006)0.0440.014 (0.133–−0.004)0.0320.000 (−0.022–0.022)0.995−0.001 (−0.04–0.001)0.263
Saturated Fat (%) *−0.557 (−1.145–0.031)0.0630.037 (0.145–−0.013)0.086−0.003 (−0.064–0.057)0.910−0.003 (−0.010–0.003)0.329
Monounsaturated Fats (%)0.163 (−1.113–1.438)0.8020.012 (−0.093–0.117)0.8180.085 (−0.043–0.212)0.193−0.009 (−0.023–0.005)0.201
Polyunsaturated Fats (%)0.819 (−1.593–3.232)0.505−0.020 (−0.219–0.178)0.8410.127 (−0.115–0.369)0.302−0.007 (−0.033–0.019)0.582
Total sugars (%) *0.165 (−0.293–0.622)0.4800.009 (0.651–−0.029)0.047−0.028 (−0.075–0.018)0.2360.000 (−0.005–0.005)0.954
Added sugar (%) *0.211 (−0.649–1.072)0.6300.034 (0.355–−0.038)0.106−0.030 (−0.118–0.058)0.503−0.001 (−0.011–0.008)0.791
Fructose (%) *−0.719 (−3.298–1.859)0.5840.049 (0.654–−0.167)0.266−0.158 (−0.422–0.106)0.240−0.005 (−0.033–0.023)0.725
‡: Models adjusted for age, sex, BMI, and waist circumference. *: Models adjusted for age, sex, BMI, waist circumference, and total Kcal.
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Martinez-Vazquez, S.E.; Kammar-García, A.; Moctezuma-Velázquez, C.; Mancilla-Galindo, J.; García-Juárez, I.; Uscanga-Domínguez, L.F. The Impact of Dietary Sugars and Saturated Fats on Body and Liver Fat in a Healthcare Worker Population. Nutrients 2025, 17, 1328. https://doi.org/10.3390/nu17081328

AMA Style

Martinez-Vazquez SE, Kammar-García A, Moctezuma-Velázquez C, Mancilla-Galindo J, García-Juárez I, Uscanga-Domínguez LF. The Impact of Dietary Sugars and Saturated Fats on Body and Liver Fat in a Healthcare Worker Population. Nutrients. 2025; 17(8):1328. https://doi.org/10.3390/nu17081328

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Martinez-Vazquez, Sophia Eugenia, Ashuin Kammar-García, Carlos Moctezuma-Velázquez, Javier Mancilla-Galindo, Ignacio García-Juárez, and Luis Federico Uscanga-Domínguez. 2025. "The Impact of Dietary Sugars and Saturated Fats on Body and Liver Fat in a Healthcare Worker Population" Nutrients 17, no. 8: 1328. https://doi.org/10.3390/nu17081328

APA Style

Martinez-Vazquez, S. E., Kammar-García, A., Moctezuma-Velázquez, C., Mancilla-Galindo, J., García-Juárez, I., & Uscanga-Domínguez, L. F. (2025). The Impact of Dietary Sugars and Saturated Fats on Body and Liver Fat in a Healthcare Worker Population. Nutrients, 17(8), 1328. https://doi.org/10.3390/nu17081328

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