The transition to university life may be associated with the purchase of ready-to-eat meals and the consumption of ultra-processed foods (UPF)(Reference Detopoulou, Dedes and Syka1), which are usually highly palatable, energy-dense, unbalanced choices. UPF consumption has been inversely related to adherence to cardioprotective dietary patterns, such as the Mediterranean diet(Reference Detopoulou, Dedes and Syka1) and an ‘early eating’ pattern(Reference Detopoulou, Dedes and Syka1). In parallel, diets rich in UPF are high in sugars, salt, saturated and trans-fatty acids and low in fibre and potassium(Reference Machado, Steele and Levy2). On the contrary, minimally processed foods (MPF) have a higher satiating ability and result in lower postprandial glycaemic peaks than UPF(Reference Fardet3). It is noteworthy that diets rich in UPF have been specifically related to central and visceral fat accumulation(Reference Detopoulou, Dedes and Syka1,Reference Valicente, Peng and Pacheco4) . However, little data exist regarding their further association with other body composition parameters, such as lower muscle mass(Reference Viola, Carvalho and Bragança5).
Bioelectrical impedance analysis (BIA) constitutes a simple, non-invasive, low-cost method of body composition assessment(Reference Ward6). Phase angle (PhA) derives from BIA and relates to cellular health with higher values reflecting better membrane integrity and cell function(Reference Lukaski and Garcia-Almeida7). Its application as a prognostic index is increasing in several diseases such as CVD, cancer and others(Reference Lukaski and Garcia-Almeida7,Reference Detopoulou, Voulgaridou and Papadopoulou8) . In a meta-analysis of 22 studies, subjects with CVD had lower values of PhA than healthy counterparts. Nutritional interventions in patients with cancer can also affect PhA along with muscle strength, according to a recent meta-analysis(Reference Victoria-Montesinos, García-Muñoz and Navarro-Marroco9). Indeed, PhA is a ‘sensitive’ index subjective to changes in cancer therapy(Reference Detopoulou, Tsiouda and Pilikidou10) and monitoring of muscle injury(Reference Lukaski and Garcia-Almeida7). Moreover, its application in healthy individuals is broad, reflecting nutritional status, muscle status and body composition(Reference Lukaski and Garcia-Almeida7). Moreover, PhA is considered an index of cell mass(Reference Lukaski and Garcia-Almeida7), with higher values relating to higher body cell mass and cellular integrity(Reference Lukaski and Garcia-Almeida7), while several factors also affect PhA, such as age, sex and fat-free mass(Reference Gonzalez, Barbosa-Silva and Bielemann11).
Given the utility of PhA as a nutritional index and a proxy of cellular health(Reference Lukaski and Garcia-Almeida7), it is crucial to identify modifiable factors that can affect it. In this context, the potential role of dietary factors in PhA determination has been examined. In general, few studies have assessed the nutritional correlates of PhA. In a previous study of our group, a dietary pattern rich in potatoes, meat and poultry was positively related to PhA in patients with lung cancer(Reference Detopoulou, Tsiouda and Pilikidou12). Similarly, PhA has been positively associated with meat consumption in healthy subjects(Reference Jaremków, Markiewicz-Górka and Hajdusianek13), and it increases after a ketogenic diet irrespectively of weight loss(Reference Barrea, Muscogiuri and Aprano14).
Interestingly, a higher adherence to anti-inflammatory dietary patterns, such as the Mediterranean diet, has been positively related to PhA(Reference Barrea, Muscogiuri and Macchia15), while PhA values have been previously negatively related to inflammation(Reference Barrea, Muscogiuri and Pugliese16–Reference Tomeleri, Cavaglieri and De Souza18). Moreover, serum n-3 fatty acids, with known anti-inflammatory actions, have been positively correlated with PhA(Reference VanderJagt19,Reference VanderJagt, Trujillo and Bode-Thomas20) . UPF consumption is inversely related to Mediterranean diet adherence(Reference Detopoulou, Dedes and Syka1), and it increases inflammatory burden(Reference Tristan Asensi, Napoletano and Sofi21) and oxidative stress(Reference Nestares, Martín-Masot and Flor-Alemany22). Moreover, limited data have associated UPF consumption with lower muscle mass(Reference Viola, Carvalho and Bragança5,Reference Rudakoff, Magalhães and Viola23) .
Taking the effects of UPF and MPF at the cellular level as a starting point, it can be hypothesised that UPF and MPF consumption may differentially affect cellular health, body composition and PhA through modification of the oxidative and inflammatory milieu. Thus, the aim of the present study was to first investigate the relation of UPF and MPF consumption to PhA. The potential validation of such a hypothesis is important, since future clinical interventions aiming at maximising PhA could incorporate modifications in diet quality and changes in UPF and MPF consumption.
Methods
Study design and study sample
This is a cross-sectional study of university students. The measurements were performed in June 2022 and January 2023. The participants of the present study were undergraduate students. The vast majority of them were enrolled at the Department of Nutrition and Dietetics (> 98 %). Participants were selected by convenience sampling. Notices were electronically dispatched to the students by the Department of Nutrition and Dietetics secretariat, while advertisements were prominently displayed in frequented University areas, like the cafeteria and the restaurant zone. The eligibility criteria were (i) being a student at the University. Exclusion criteria were (i) not being a student, (ii) not fulfilling prerequisites for BIA measurement (i.e. no strenuous exercise, caffeine and alcohol intake for 8 h before measurement, no foods, liquids for 3 h before measurements, measurements not performed during menses and +/–3 d to avoid oedemas) and (iii) pregnancy. It is noted that no specific age inclusion/exclusion criteria have been applied. The STROBE-nut reporting checklist is given as an online supplementary material, Supplemental File.
Evaluation of dietary habits and diet quality
A semi-quantitative FFQ was administered(Reference Bountziouka, Bathrellou and Giotopoulou24). The FFQ covered a period of one year and consisted of 69 questions, concerning the frequency of intake of several food groups: dairy products, eggs, starchy vegetables, meat and poultry, fish, legumes, vegetables, fruits, sweets, alcohol, stimulants, fats and oils(Reference Bountziouka, Bathrellou and Giotopoulou24). Most fruits and vegetables included in the FFQ were available throughout the whole year. To capture the seasonal consumption of fruits, two special questions were present, one for each season (summer fruits and winter fruits). The questionnaire, also, contained 7 questions regarding dietary habits, such as the use of oils and butter in cooking, the intake of visible fat from meat, the frequency of ordering food, the frequency of meals, the total number of meals including snacks, the number of main meals, the preference for organic or soy products and the intake of nutritional supplements(Reference Bountziouka, Bathrellou and Giotopoulou24). The possible frequencies, in multiple-choice format, were as follows: ‘never/rarely’, ‘one to three times a month’, ‘one to two times a week’, ‘three to six times a week’, ‘once a day’ and ‘more than twice a day’. For other questions on dietary habits or supplements, a ‘yes’ or ‘no’ format was used(Reference Bountziouka, Bathrellou and Giotopoulou24). The FFQ has been validated in the participant population(Reference Bountziouka, Bathrellou and Giotopoulou24).
One serving of cereals (refined or non-refined) was considered as 1 slice of bread, ½ cup rice or ½ cup pasta. One serving of potatoes was considered as 1 medium potato. One serving of fruit was considered 1 medium fruit or ¼ of a cup of dried fruits. One serving of vegetables was considered as 1 cup of raw vegetables, ½ cup of cooked vegetables, ½ plate of spinach rice/vegetable rice or 2 slices of vegetable pie (e.g. spinach pie). One serving of legumes was considered 1/2 plate of legumes. One serving of fish was considered 60 g of fish. One serving of red meat was considered as 60 g beef/pork lamb/goat, 2 slices of ham, 2 medium sausages or 4 slices of bacon. One serving of poultry was considered as 60 g of chicken or turkey. One serving of eggs was considered 1 egg. One serving of dairy (full-fat or low-fat) was considered as 1 glass of milk or 1 yogurt or 30 g of cheese/cream cheese. One serving of sweets/treats was considered as 1 piece of pastry/tart, 1 whole croissant, 1 medium wafer, 1 slice of cake, 3–4 cookies, 1 ice cream or 1 milkshake. One serving of potato chips/popcorn was considered as 70 g of potato chips/popcorn. One serving of alcohol was considered as 125 ml of wine (1 glass), 240 ml of beer (1 glass) or 1 glass of other alcoholic drinks (30 ml).
Participants’ responses to the FFQ were also used to calculate the Mediterranean Diet Score (MedDietScore)(Reference Panagiotakos, Pitsavos and Stefanadis25). MedDietScore evaluates the individual’s adherence to the Mediterranean Diet, and it is based on the frequency of consumption of the main food groups, on a scale from 0 to 55(Reference Panagiotakos, Pitsavos and Stefanadis25).
In addition, the administered FFQ was used to calculate the percentage of energy derived from MPF and UPF, as defined by the NOVA system(Reference Petrus, do Amaral Sobral and Tadini26). Based on the NOVA classification system, foods are divided into the following 4 categories according to their degree of processing: MPF or non-processed foods, processed cooking ingredients, processed foods and, finally, UPF(Reference Petrus, do Amaral Sobral and Tadini26). MPF or non-processed foods are eaten ‘as they are’ or have undergone negligible industrial processing, such as fresh or dried or frozen fruits and vegetables, fresh, chilled or frozen meat, fish, eggs and dairy products. They are categorised as class ‘1’ foods. UPF, rated as class ‘4’, are those that have undergone a series of industrial processes, such as carbonated soft drinks, most breakfast cereals and pastries(Reference Petrus, do Amaral Sobral and Tadini26). Then, the energy contribution of each category was calculated (as % of total energy intake). The USDA database was used to determine the energy values of foods(27).
Assessment of physical activity
Participants were administered the Hellenic Physical Activity Questionnaire(Reference Kavouras, Maraki and Kollia28). It is a self-completed one-page questionnaire that includes data about the physical activity of the previous seven days and has been adapted to the habits of the particular population(Reference Kavouras, Maraki and Kollia28). The questions are divided into three sub-categories: physical activity at work, physical activity at home and physical activity for leisure, to increase the examinee’s recall(Reference Kavouras, Maraki and Kollia28). Also, for a better assessment of total energy expenditure, habits related to sleep and sedentary life are recorded(Reference Kavouras, Maraki and Kollia28). For each activity, the examinee recorded the amount of time spent on it per day(Reference Kavouras, Maraki and Kollia28). Metabolic equivalents for each of the physical activities were calculated by the examiner(Reference Kavouras, Maraki and Kollia28). The metabolic equivalents were added, and finally, the individual’s energy expenditure was calculated, which was expressed as kilocalories per week and kilocalories per day(Reference Kavouras, Maraki and Kollia28).
Anthropometry and body composition
Body weight was measured with a precision scale (Tanita MC-780, Japan) to the nearest 0·1 kg, and height was measured to the nearest 0·1 cm with a stadiometer (Seca, Hamburg, Germany). The BMI was calculated as weight (in kilograms) divided by height squared (in metres squared) and the students were categorised as underweight, normal weight, or overweight/obese according to WHO criteria. More specifically, underweight students were those with BMI < 18·5 kg/m2, normal weight were those with BMI of 18·5–24·99 kg/m2, overweight were those with BMI of 25–29·99 kg/m2, and obese were those with BMI > 30 kg/m2. Waist and hip circumferences were measured with a non-stretchable measuring tape (Seca, Hamburg, Germany) to the nearest 0·1 cm, in a standing position(29). When measuring the waist circumference, the measuring tape was placed around the narrowest part of the waist, that is between the last rib and above the level of the navel, while, when measuring the hip circumference, it was placed at a level that ensures that maximum hip circumference is measured, which is at the buttock area.
Body composition and PhA were assessed via multi-frequency BIA (Tanita MC-780, Japan). The device used for BIA measurements is a multi-frequency segmental body composition model, measuring at 5 kHz, 50 kHz and 250 kHz. In the present work, we have used PhA automatically calculated by the device at 50 kHz for all participants, while resistance (R) and reactance (Xc) were also provided. The day before BIA measurements subjects refrained from strenuous exercise (for 8 h), and they did not consume beverages high in caffeine (tea, coffee and energy drinks) nor alcohol(29). On the day of measurements, subjects had fasted for at least 3 h (no foods and no liquids)(29). Measurements were not performed during menses and +/–3 d to avoid water retention, which could potentially affect measurements. In order to perform measurements, shoes and socks were removed(29). Measurements were performed in a standing position. More particularly, subjects stepped on the device with the feet touching the electrodes and the weight of clothes was recorded and automatically subtracted from the total weight measured by the device. When indicated, the subject put his hands on the electrodes, slightly separated from the trunk(29), and the measurement was taken.
Lastly, skinfolds were measured at various anatomical sites (biceps, triceps, subscapular and supra-iliac skinfolds) according to standard procedures(29). The same caliper was used for all measurements (Slim Guide Caliper, HaB Essentials). The caliper was operated with the right hand, and measurements were made on the right side of the subject’s body. The examiner with the thumb and forefinger of his left hand had to grasp approximately 1 cm above and below the measurement point at the respective anatomical site and separated the skin from the underlying muscle tissue(29). After four seconds of applying pressure, the reading was recorded to the nearest 0·2 mm(29). The measurement was performed 3 times at each point to minimise measurement error, and the mean value was used. For the measurement of biceps and triceps skinfolds, the midpoint of the arm length (between the acromion and olecranon) was identified and marked(29). Then, vertical folds were taken at mid-biceps and mid-triceps levels(29). The subscapular skinfold measurement was diagonally taken at 1 cm below the inferior angle of the scapula(29), after locating the anatomical site by palpation of the area. For the supra-iliac skinfold, a diagonal fold, just above the front forward protrusion of the hip bone, was considered(29).
All measurements were performed by two members of the technical staff of the Department of Nutrition and Dietetics.
Statistical analysis
For normality testing, the Kolmogorov–Smirnoff test was used. Normally distributed variables are shown as means (sd), and non-normally distributed variables are shown as medians and interquartile range. Categorical variables are presented as frequencies (n, %). For comparisons between men and women, the t test was applied for normally distributed variables or the Mann–Whitney U test for non-normally distributed variables. For categorical variables, the chi-square test was used to compare frequencies between sexes.
Correlations between variables were performed using the Spearman correlation coefficient, since in most variables the normality criterion was not fulfilled. For the calculation of Spearman’s partial correlation coefficients, variables were ranked, and then, the Pearson partial correlation coefficients were calculated, after adjustment for age, sex, physical activity and BMI. Furthermore, Spearman’s partial correlation coefficients were reported in several groups, that is MedDietScore tertiles (low, medium and high adherence to the Mediterranean diet).
All reported P-values are based on two-sided tests, and the level of significance was set at 5 %. IBM SPSS Statistics for Windows 29.0.0.0.241(IBM Corp.) was used for analysis.
Results
Basic characteristics of the participants
In total, 151 students participated, including 114 women and 37 men, aged between 18 and 44. The baseline characteristics of the participants are presented in Table 1. It is noted that overweight and obese subjects were grouped, due to the low frequency of obese subjects. Women had a higher percentage of body fat and higher thickness levels of several skinfolds. Extracellular and intracellular water, as well as lean mass, were higher in male participants. Waist circumference was higher in men. The PhA was 6·8 (6·15–7·35) in men and 5·3 (5·1–5·9) in women (medians, interquartile ranges P < 0·001).
Descriptive characteristics of participants
IQR: interquartile range; MET: Metabolic equivalents; PhA: Phase angle.
Data are presented as means (sd) for normally distributed variables or as median and interquartile range, for non-normally distributed variables. Categorical variables are displayed as n. t test (for normal variables) or Mann–Whitney U test (for non-normal variables) was used to compare values between men and women. The chi-square test (for categorical variables) was used to compare categorical variables between men and women.
Dietary intake of the participants
The dietary intake of the participants is shown in Table 2. The medians and interquartile ranges of the MedDietScore, UPF and MPF intake were 31·00 (26·00–34·00), 13·72 (8·18–33·39) and 59·99 (50·16–70·73), respectively, in the total sample. No sex differences were documented in these parameters, while men had higher red meat intake than women. The correlation coefficients of UPF and MPF with food groups are shown in online supplementary material, Supplemental Tables 1 and 2.
Consumption of food groups and eating habits
IQR: interquartile range; MPF: minimally processed foods; UPF ultra-processed foods.
Data are presented as medians and interquartile ranges, for non-normally distributed variables. Categorical variables are displayed as n (%). Mann–Whitney U test (for non-normal variables) was used to compare values between men and women. The chi-square test (for categorical variables) was used to compare categorical variables between men and women.
Relation of phase angle with dietary variables
In Table 3, the Spearman correlations between PhA and dietary variables are presented. The association of UPF with PhA was significant in women, while the association of MPF with PhA was significant in both sexes. In a multivariate analysis using Pearson’s partial correlations between PhA and dietary variables (ranked variables) after adjustment for age, physical activity, BMI and sex (where applicable), the associations remained significant (Table 4). It is noted that the associations also remained significant in further models adjusted for muscle mass instead of BMI (rho = –0·253, P = 0·004 and rho = 0·228, P = 0·007 for the association of PhA with UPF and MPF, correspondingly). The associations were further tested in stratified analysis per MedDietScore tertile (Fig. 1). It was shown that the inverse association of UPF and PhA was evident in participants who had low and moderate adherence to the Mediterranean diet (i.e. first and second tertile of the MedDietScore). Interestingly, the positive association of MPF consumption and PhA was evident in those with low adherence to the Mediterranean diet (first tertile of the MedDietScore).
Spearman’s correlations between PhA and dietary variables
MedDietScore: Mediterranean diet score; MPF: minimally processed foods; UPF ultra-processed foods.
Spearman’s correlation coefficients between phase angle and dietary parameters.
Pearson’s partial correlations between PhA and dietary variables (ranked variables) after adjustment for age, physical activity, BMI and sex (where applicable)
MPF: minimally processed foods; UPF ultra-processed foods.
Spearman’s correlation coefficients between phase angle and dietary parameters.
Pearson’s partial correlations between PhA and dietary variables (ranked variables) after adjustment for age, physical activity, BMI and sex stratified by MedDietScore tertile
Discussion
The present study firstly documented that the consumption of UPF was negatively related to PhA, while the consumption of MPF was positively associated with PhA in a sample of university students. The associations were more prominent in participants with low-to-moderate adherence to the Mediterranean diet.
The UPF consumption in the present study (13·72 %) was similar to that reported in Italy (10 %)(Reference Bonaccio, Costanzo and Di Castelnuovo30), lower than that reported for Spain(Reference Blanco-Rojo, Sandoval-Insausti and López-Garcia31) and France(Reference Julia, Martinez and Allès32) and much lower than that reported for the UK (more than 55%)(Reference Rauber, Da Costa Louzada and Steele33). Moreover, the intake of UPF was lower than that reported in a previous study of our group of university students conducted in 2018 (mean UPF intake 40·7 %)(Reference Detopoulou, Dedes and Syka1). The median MPF intake was 59·9 % in the present and 44·3 % (mean) in our previous study(Reference Detopoulou, Dedes and Syka1). These differences can be explained by (i) the fact that participants were mainly dietetic students, while in the previous study students from other disciplines participated (dietetic students may have better dietary habits or may selectively misreport unhealthy UPF foods)(Reference Detopoulou, Dedes and Syka1) and (ii) the time period that the present study was conducted (in the COVID-19 epidemic). A recent analysis showed that UPF provided 25·2% of energy in a typical Greek hospital menu(Reference Detopoulou and Panoutsopoulos34).
Regarding PhA, it is noted that most subjects had low PhA compared to reference values(Reference Mattiello, Amaral and Mundstock35). This finding is probably attributed to the measurement procedure since lower PhA values are documented in standing position compared to lying position (with the same device)(Reference Ward and Brantlov36). It is also possible that particularities exist in the present sample and that population-specific reference values would be more appropriate to compare with, as suggested in other studies(Reference Mattiello, Mundstock and Ziegelmann37). In addition, device-specific reference values have been also proposed(Reference Dellinger, Johnson and Benavides38).
As far as the observed associations between UPF, MPF and PhA are concerned, there are limited data in the literature. The vast majority of available data have focused on the relation of UPF with obesity and/or waist circumference(Reference Detopoulou, Dedes and Syka1,Reference Valicente, Peng and Pacheco4) . To our knowledge, there is only one study assessing the relationship between UPF/MPF and PhA in 24 patients with inflammatory bowel disease and 21 controls(Reference Severo, Da Silva Barros and Moraes Mendes39). This study showed non-significant correlations between PhA and the degree of food processing, possibly because of the small sample size and/ or clinical condition(Reference Severo, Da Silva Barros and Moraes Mendes39). In a previous study of our group, a dietary pattern rich in potatoes, meat and poultry was positively related to PhA in patients with lung cancer(Reference Detopoulou, Tsiouda and Pilikidou12). Similarly, PhA has been positively associated with meat consumption in healthy subjects(Reference Jaremków, Markiewicz-Górka and Hajdusianek13) but not all studies agree(Reference Unterberger, Aschauer and Zöhrer40). The association of meat and total protein intake may be related to PhA as it is directly related to muscle mass(Reference Alexandrov, Eelderink and Singh-Povel41). In turn, PhA is positively associated with muscle mass in all age groups(Reference Martins, Alves Junior and Silva42). In this context, the association of meat with PhA (although red meat is not considered a healthy food choice) is possibly explained through the relation of meat intake with muscle mass. It is also noteworthy that the correlation of PhA with meat was evident only in the whole sample and not in sex-specific analysis. This implies that body composition variables may be responsible for the correlation of meat and PhA in the whole sample (i.e. men who have a higher meat intake and a higher muscle mass have higher values of PhA, and thus, a correlation is driven in the whole sample).
In parallel, a higher adherence to the Mediterranean diet (rich in MPF, such as fruits, vegetables and legumes) along with a diet with high antioxidant capacity has been positively related to PhA(Reference Barrea, Muscogiuri and Macchia15,Reference Detopoulou, Fragopoulou and Nomikos43) . In addition, serum long-chain n-3 fatty acids (found mainly in fish) have been positively correlated with PhA(Reference VanderJagt19,Reference VanderJagt, Trujillo and Bode-Thomas20) . Other studies regarding the association of UPF with body composition parameters have shown that the intake of UPF correlates with visceral fat (but not total fat) in women(Reference Hall, Ayuketah and Brychta44) and lower muscle mass in young subjects(Reference Viola, Carvalho and Bragança5). It is noted, however, that the observed associations were independent of muscle mass since this variable was entered in multivariate models.
To better interpret this study’s results, the observed relation of UPF and MPF to PhA should be considered through the prism of food effects on oxidative stress, inflammation and cellular health. Indeed, the intake of UPF has been associated with increased oxidative stress(Reference Nestares, Martín-Masot and Flor-Alemany22) and inflammatory burden(Reference Tristan Asensi, Napoletano and Sofi21). Moreover, trans-fats and simple carbohydrates found in UPF have been documented to promote the translocation of NF-κB and activator protein-1(Reference Calder45) and relate to circulating C-reactive protein, IL-6 and TNF-α(Reference Hadj Ahmed, Kharroubi and Kaoubaa46). Similarly, n-6 fatty acids, often found in processed foods in the form of sunflower or other oils, have been positively related to biosynthetic enzymes of the platelet-activating factor, which is a strong mediator of inflammation(Reference Fragopoulou, Detopoulou and Alepoudea47). On the contrary, a diet rich in antioxidants and in unprocessed foods (i.e. fruits, nuts, herbal drinks, olive oil and whole-wheat products) has been related to reduced inflammatory markers(Reference Detopoulou, Fragopoulou and Nomikos48).
In parallel, PhA is positively related to antioxidant status, as it is correlated with glutathione concentration and superoxide dismutase activity(Reference Severo, Da Silva Barros and Moraes Mendes39), while it is also negatively related to inflammatory markers, including platelet-activating factor(Reference Barrea, Muscogiuri and Pugliese16–Reference Tomeleri, Cavaglieri and De Souza18,Reference Detopoulou, Fragopoulou and Nomikos43) . It can be thus hypothesised that UPF and MPF can differentially affect the antioxidant status and cellular inflammatory milieu, which in turn directly or indirectly affects cellular health and PhA.
Interestingly, we herein first report that the association of UPF and MPF with PhA may be influenced by background diet since the association was mostly present in subjects with low-to-moderate Mediterranean diet adherence. This implies that subjects following an unhealthy dietary pattern could benefit from the reduction of UPF consumption. The importance of background diet has been also shown in a previous study of our group(Reference Psaltopoulou, Panagiotakos and Pitsavos49). Indeed, we have previously shown that the dietary antioxidant capacity related to glycaemic indices only in subjects being away from the Mediterranean diet(Reference Psaltopoulou, Panagiotakos and Pitsavos49). However, these observations need further investigation.
The main strength of the present study pertains to the body composition analysis performed and PhA measurements, which were not available in previous studies of our group concerning UPF intake(Reference Detopoulou, Dedes and Syka1). Moreover, the sample was quite homogenous, since mainly dietetics students participated.
However, several limitations should be reported. This work is a cross-sectional study. Therefore, causal relationships between the factors cannot be substantiated. In addition, the sample consisted exclusively of students of a single university. Most participants were students in the Department of Nutrition, which could impact their dietary choices and nutrition knowledge. Moreover, the present sample consisted mainly of women. All the aforementioned factors limit the generalisability of the present findings. Regarding the participants’ eating habits and diet quality, the assessment was based on the participants’ responses to the self-administered FFQ. Therefore, the results may be affected by the recall ability of the subjects and the possibility of under- or over-reporting cannot be excluded. Several factors influencing BIA measurements have been reported, which are related to the instrument, the technician performing the measurements, especially when patches are applied, the subject and the environmental temperature(Reference Ward6). However, in the present study, the same machine was used. Eight contact electrodes were used, which provide reliable measurements compared to previous TANITA models(Reference Pietrobelli, Rubiano and St-Onge50). On top of this, electrodes are not placed by a technician, which minimises the interference of technician-related errors. In addition, all manufacturers’ recommendations on measurement conditions regarding hydration, food intake, recent physical activity and menstrual cycle were followed(Reference Ward6).
In conclusion, the correlations of PhA with the consumption of UPF (negative correlation) and MPF (positive correlation) were first reported in this study. These associations are possibly due to the interaction of nutrition with inflammation and oxidative stress mechanisms, which in turn affect cellular health and PhA. Future studies with a larger and more representative sample including subjects with a broader age range should be conducted, to affirm or refute our findings.
Acknowledgments
The authors would like to thank all students who helped the measurements and data entry. Special thanks to Tyriakidou Myrsini and Vassilopoulou Georgia.
Financial support
There was no funding for this study.
Conflict of interest
The authors have no conflict of interest.
Authorship
Conceptualisation, P.D. and G.P.; Data curation, P.D.; Formal analysis, P.D., D.L. and O.M.; Investigation, D.L., O.M., V.D., M.T., E.F. and A.G.; Methodology, P.D., E.F. and A.G.; Project administration, V.D. and G.P.; Supervision, G.P.; Writing – original draft, P.D.; Writing – review & editing, M.T., E.F., A.G. and G.P.
The APC are covered by HEAL LINK agreement.
Supplementary material
For supplementary material accompanying this paper, visit https://doi.org/10.1017/S136898002400123X
Ethics of human subject participation
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the Ethics Committee of the School of Health Sciences of the University of Peloponnese (protocol number 129, 7/6/2022). Written informed consent was obtained from all subjects.
Ethical approval
The research protocol was approved by the Ethics Committee of the University (129, 7/6/2022). All participants signed a consent form before participating in the study.
Open access
