Assessing Diet Quality in Hemodialysis with Brief Screeners: Multicenter Findings on Dietary Diversity and Ultraprocessed Foods

Assessing Diet Quality in Hemodialysis with Brief Screeners: Multicenter Findings on Dietary Diversity and Ultraprocessed Foods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Diet Quality in Hemodialysis with Brief Screeners: Multicenter Findings on Dietary Diversity and Ultraprocessed Foods Andrea C Sczip, Jyana G Morais, Adaiane Calegari, Tatiana S Kruger, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8969021/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose Rapid and low-burden tools to assess diet quality are needed in clinical settings characterized by high nutritional risk and limited consultation time, such as hemodialysis (HD) care. We aimed to assess dietary diversity and ultra-processed food (UPF) intake using validated brief dietary screeners in patients undergoing HD, identify their determinants, and compare dietary patterns between dialysis and non-dialysis days. Methods In this multicenter cross-sectional study, patients on HD from seven dialysis units answered two validated dietary screeners assessing diversity of unprocessed and minimally processed foods and UPF intake. Screeners were applied on one dialysis day and one non-dialysis day. Scores ranged from 0 to 13, and median values were used to classify participants into higher versus lower dietary diversity and UPF intake. Multivariable logistic regression models were used to identify independent determinants. Results Among 297 participants (mean age 52.1 ± 14.1 years; 57.9% men), the median dietary diversity score was 6 (4.5–7). Higher dietary diversity was independently associated with older age, shorter dialysis vintage, and absence of obesity. The median UPF score was 2.5 (2–4), with higher UPF intake associated with younger age, male sex, and obesity. Dietary diversity did not differ between dialysis and non-dialysis days, whereas UPF intake was significantly higher on dialysis days. Conclusion Patients undergoing HD exhibited low dietary diversity and higher UPF intake on dialysis days. Brief dietary screeners were able to detect clinically relevant dietary patterns and determinants, supporting their use for routine nutritional monitoring and targeted counseling in HD care. dietary diversity ultraprocessed foods hemodialysis dietary screeners Figures Figure 1 Figure 2 Introduction Assessing dietary quality in patients on hemodialysis (HD) is essential due to the high prevalence of protein–energy wasting, micronutrient deficiencies, and elevated cardiovascular and inflammatory risk in this population. Poor dietary quality has been linked to increased production of uremic toxins, alterations in gut microbiota, metabolic disturbances, malnutrition, reduced lean tissue mass, and greater dietary monotony [ 1 – 3 ]. Current CKD nutrition guidelines recommend a 3-day food record, including dialysis and nondialysis days, as the preferred method to evaluate dietary intake in HD patients. However, food records are time-consuming, dependent on literacy and memory, and prone to substantial underreporting [ 4 ]. In a previous Brazilian study using this method, nearly three-quarters of participants were excluded due to significant underreporting [ 5 ]. These limitations underscore the need for simpler, culturally adapted tools that can be feasibly integrated into routine HD care. Brief dietary screeners have emerged as pragmatic alternatives for rapidly capturing key dimensions of diet quality in clinical and public health settings. These instruments impose minimal respondent burden, perform well in low-literacy contexts, and allow risk stratification when comprehensive dietary assessment is not feasible [ 6 – 8 ]. Their clinical utility may be particularly relevant in settings such as HD units, where dietitians face high patient loads and limited time for individualized counseling [ 9 ]. Dietary diversity is a fundamental component of diet quality, reflecting the principle that no single food provides all essential nutrients [ 10 ]. Indicators such as the Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) have demonstrated strong performance as proxies for micronutrient adequacy across diverse populations [ 11 – 13 ]. In Brazil, this concept was adapted into a validated screener aligned with the NOVA classification, focusing on unprocessed or minimally processed foods [ 14 , 15 ]. A screener assessing UPF intake was also developed and validated in Brazil [ 14 ]. UPFs are industrial formulations rich in sodium, additives, and inorganic phosphates—components particularly harmful for individuals on HD [ 16 , 17 ]. Our previous analyses using this screener showed that UPF intake, especially of animal-based products, was an independent determinant of hyperphosphatemia in patients on HD [ 18 ]. Despite the availability of validated brief dietary screeners, evidence on their application to assess diet quality in HD patients remains scarce. Moreover, the organization of HD care, including long treatment sessions, commuting time, and reliance on meals or snacks consumed during dialysis, may differentially influence dietary patterns on dialysis versus non-dialysis days. Therefore, this multicenter study applied two validated brief dietary screeners to: (1) identify sociodemographic and clinical determinants of dietary diversity and UPF intake in patients undergoing HD; and (2) compare dietary patterns between dialysis and non-dialysis days. Materials and Methods Participants and setting This multicenter cross-sectional study was conducted in seven dialysis units in Southern Brazil. Adult patients (≥ 18 years) receiving maintenance HD three times weekly for ≥ 90 days were eligible. Patients with cognitive or visual impairments preventing questionnaire completion were excluded. Of 820 eligible individuals, a convenience sample of at least 50% per clinic was invited to ensure representation across shifts, resulting in 309 invitations. Study protocol Between November 2021 and February 2023, dietitians from the participating clinics conducted interviews with all study participants. The dietary diversity screener was adapted from the structure and scoring of the MDD-W and included 13 subgroups of unprocessed or minimally processed foods (NOVA Group 1)[ 14 ]. The UPF screener assessed 13 subgroups of UPFs (NOVA Group 4) and is used in the national VIGITEL surveillance system [ 19 ]. This tool has demonstrated good agreement with dietary share of UPF[ 20 ]. Both screeners collect intake from the previous day and their score varies from 0 to 13 (Supplement 1). Each participant was interviewed on two separate days, with a maximum interval of two weeks, and both questionnaires were administered at each interview. The first interview was conducted in person during a dialysis session to assess dietary intake on a non-dialysis day, and the second was conducted by telephone to assess dietary intake on a dialysis day. To mitigate potential bias from weekend eating habits, interviews were not conducted during the first dialysis session of the week. Scores from dialysis and non-dialysis days were compared. The final diversity and UPF scores were obtained by averaging the scores from both days, and the median score of each questionnaire was used as a cutoff to classify patients into higher and lower categories of dietary diversity and UPFs intake. Prevalence rates for the intake of each food subgroup were calculated. To evaluate fruit and vegetable consumption, we determined the percentage of participants who did not consume any of the two fruit subgroups or the four vegetable subgroups. Demographic, clinical variables, and laboratory test results, were extracted from electronic medical records. Ethics approval was granted by the local ethical committee (NO: 4.842.525) and informed consent was obtained from all patients. Statistical Analysis Statistical analysis was conducted using SPSS software version 21.0 for Windows (SPSS, Inc., Chicago, IL). Results were presented as percentages, means with standard deviations, or medians with interquartile ranges, depending on the distribution of the variables. The Wilcoxon Signed-Rank Test was used to compare scores between dialysis and non-dialysis days. To compare variables between groups, the Student's t-test was applied for normally distributed data, while the Mann-Whitney U test was used for non-normally distributed data. The chi-square test was employed for categorical variables. Logistic regression analysis was performed to identify determinants of higher dietary diversity and UPF scores, including all variables with p < 0.1 from univariate analyses. Statistical significance was set at p < 0.05. Results A total of 297 patients participated in the study (three refused participation and nine did not respond to the telephone interview). Table 1 provides an overview of their main characteristics. The majority were white men with at least eight years of formal education. Table 1 Main charateristics of the study population and comparisons across subgroups according to diversity and ultraprocessed foods scores (n = 297) Age (years) ≥ 60 n (%) Study population (n = 297) Diversity score ≤ 6 (n = 178) Diversity score > 6 (n = 119) UPF score ≤ 2.5 (n = 153) UPF score > 2.5 (n = 144) 52.1 ± 14.1 94 (31.6) 50.0 ± 13.7 47 (26.4) 52.2 ± 14.2*** 47 (39.5)** 56.2 ± 13.2 65 (42.9) 47.8 ± 13.7*** 29 (20.1)*** Male n (%) 172 (57.9) 96 (53.9) 73 (63.9)* 81 (52.9) 91 (63.2)* Dialysis vintage (m) 38 (18–86) 41 (19–98) 32 (14–60)** 34 (14–68) 41 (20–91)* Skin color white n (%) 225 (75.8) 135 (75.8) 90 (75.6) 116 (75.8) 109 (75.7) Diabetes n (%) 87 (29.3) 49 (27.5) 38 (31.9) 46 (30.1) 41 (28.5) Education (years at school) ≥ 8 years n (%) 9.5 (6–12) 216 (72.7) 9 (6–12) 128 (71.9) 10 (6–12) 88 (73.9) 9 (6–12) 107 (69.9) 10 (8–12) 109 (75.7) Body mass index (Kg/m 2 ) ≥ 30 n (%) 25.3 (22.3–28.5) 57 (19.2) 25.4 (22.6–29.3) 40 (22.5) 25.2 (21.9–28.0) 17 (14.3)* 25.2 (22.7–28.3) 23 (15) 25.7 (21.7–29.0) 34 (23.6)* Per capta income > 1 (mw) n (%) 174 (58.6) 102 (57.3) 72 (60.5) 88 (57.5) 86 (59.7) Serum potassium (mEq/L) 5.2 ± 0.7 5.2 ± 0.7 5.2 ± 0.7 5.2 ± 0.7 5.2 ± 0.8 Interdialytic weight gain (%) 2.9 (1.9–4.0) 2.9 (2.1–4.0) 2.7 (1.5–3.9) 2.7 (1.6–3.8) 3.1 (2.1–4.3)** UPF: ultraprocessed foods; mw: minimum wage; *p < 0.1 vs lower score; **p < 0.05 vs lower score; ***p < 0.01 vs lower score Figure 1 A shows intake frequency across food groups. Cereals were the most consumed group, followed by meat, milk, and legumes. On nondialysis days, 36% consumed no fruits and 34% consumed no vegetables; similar proportions were observed on dialysis days. Figure 1 B displays UPF intake, with margarine, ultraprocessed breads, and processed meats being the most common items. There was no significant difference in the dietary diversity score between days with and without dialysis treatment (6 [ 4 – 7 ] vs. 6 [ 5 – 7 ]; p = 0.08). UPF scores had identical medians (3 [ 2 – 4 ] vs. 3 [ 2 – 4 ]), but were significantly higher on dialysis days (p = 0.04) as illustrated by the prevalence data shown in Fig. 1 B. Figure 2 shows the distribution of the population according to dietary diversity and UPF scores. In multivariable models (Tables 2 and 3 ), higher dietary diversity was independently associated with older age, shorter dialysis vintage, and absence of obesity. Higher UPF intake was independently associated with younger age, male sex, and obesity. Table 2 Logistic regression analysis of the determinants of higher dietary diversity score (R 2 = 0.10) Variables OR (95% CI) p Age (years) 1.03 (1.01–1.05) 0.004 Male 1.37 (0.84–2.24) 0.21 Dialysis vintage (m) 0.99 (0.99–1.00) 0.03 BMI ≥ 30 Kg/m 2 0.52 (0.27–0.98) 0.04 OR: odds ratio; CI: confidence interval; BMI: body mass index. Table 3 Logistic regression analysis of the determinants of higher UPF score (R 2 = 0.17) Variables OR (95% CI) p Age (years) 0.95 (0.93–0.97) < 0.001 Male 1.94 (1.17–3.20) 0.01 Dialysis vintage (m) 1.00 (0.99–1.01) 0.16 BMI ≥ 30 Kg/m 2 2.33 (1.24–4.39) 0.008 UPF: ultraprocessed foods; OR: odds ratio; CI: confidence interval; BMI: body mass index Discussion This multicenter study demonstrates that patients undergoing hemodialysis exhibit low dietary diversity and modest UPF intake, with distinct sociodemographic and clinical determinants. Importantly, dietary diversity did not differ between dialysis and non-dialysis days, whereas UPF intake was higher on dialysis days. Using brief dietary screeners, we identified meaningful dietary patterns that align with findings from large epidemiological studies, supporting the clinical applicability of these tools in HD care. The brief dietary screeners used in this study showed good discriminatory capacity despite their simplicity. They identified associations—such as lower dietary diversity among younger and obese individuals and higher UPF intake among younger, obese, and male individuals—that mirror patterns consistently observed in population-based studies [ 21 – 23 ]. This concordance suggests that brief screeners are capable of capturing relevant dimensions of diet quality even when compared with more comprehensive dietary assessment methods. In settings such as HD units, where time constraints and patient burden limit the feasibility of detailed dietary assessment, these tools may offer a practical alternative for routine monitoring. The incorporation of brief dietary screeners into routine HD care is particularly relevant in Brazil, where dietitians often manage high patient volumes and multiple clinical responsibilities [ 9 , 24 ]. In this context, rapid tools that allow early identification of patients at nutritional risk can support prioritization of individualized counseling and more efficient allocation of clinical resources. Dietary diversity among patients undergoing HD was low in this cohort. Cereals, meat, milk, and legumes were the most frequently consumed food groups, consistent with dietary patterns observed in national surveys [ 25 ]. Over a third of participants reported not consuming any items from fruit or vegetable subgroups, a finding consistent with both national [ 3 , 26 ], and international studies [ 27 , 28 ]. In the general Brazilian population, only 21.4% meet the World Health Organization's recommended intake of fruits and vegetables [ 29 ]. To our knowledge, this is the first study to evaluate dietary diversity specifically in individuals on HD using a validated diversity screener. The median Dietary Diversity Index of 6, with 60% of participants consuming six or fewer of the 13 food subgroups, suggests that many patients may not achieve adequate micronutrient intake. Although no cut-off has been established for this tool, the MDD-W uses a threshold of ≥ 5 out of 10 subgroups as a proxy for micronutrient adequacy [ 11 ]. The similarly low mean MDD-W score in the Brazilian population (4.66 ± 1.36) [ 12 ] underscores that low dietary diversity is a national challenge but may have even greater implications in HD due to increased nutritional vulnerability. Older age, shorter dialysis vintage, and absence of obesity were independently associated with higher dietary diversity. The inverse association with obesity may reflect the lower energy density of unprocessed and minimally processed foods, a pattern previously observed using NOVA-based dietary assessments [ 30 ]. The lack of association with education and income in our study may be due to the limited socioeconomic variability in this cohort, where most individuals had similarly low socioeconomic status. The relationship between dialysis vintage and dietary diversity remains unexplored and warrants further study. Overall UPF intake was relatively low, with a median score of 2.5 out of 13. Margarine and processed bread were the most frequently consumed items, particularly on dialysis days, likely influenced by meals or snacks provided in dialysis centers. Prior research also observed poorer dietary quality on dialysis days among elderly HD patients [ 3 ]. This finding highlights dialysis days as a critical window for nutritional intervention and an opportunity to improve the quality of foods offered within HD facilities. Approximately one-quarter of our sample consumed soft drinks, cookies, desserts, and processed meats on both dialysis and non-dialysis days, consistent with patterns in the broader Brazilian population [ 31 ]. Younger age was strongly associated with higher UPF intake—a trend previously observed in studies of both patients on HD [ 32 ] and the general population [ 31 ]. Each year decrease in age was associated with a 5% increase chance of higher UPFs intake, and men had nearly twice the odds of higher intake compared with women. This pattern has been consistently observed in both large epidemiological studies [ 31 , 33 ], and in patients on HD [ 32 ]. These demographic patterns have been recognized as major targets for public health interventions aimed at reducing UPF consumption [ 31 ]. Obesity was another strong determinant of higher UPF intake. This association has been documented globally and may reflect the highly palatable, energy-dense, and behaviorally reinforcing characteristics of UPFs [ 22 , 34 ]. In Brazil, rising UPF availability has contributed to substantial increase in obesity prevalence [ 22 ]. Evidence from prospective cohorts in Europe and North America also associates UPF intake with weight gain and higher BMI [ 35 , 36 ]. Among Brazilian HD patients, UPF consumption has previously been linked to abdominal obesity [ 37 ], reinforcing the metabolic implications of UPFs in this population. In line with our previous work showing that UPF intake was associated with hyperphosphatemia in HD patients[ 18 ] we additionally observed an association with higher interdialytic weight gain. This may reflect increased thirst and fluid intake due to the high sodium content of many UPFs [ 38 ]. Although table salt remains the primary source of sodium in Brazil, UPFs have substantially increased their contribution to overall sodium intake in recent years [ 39 ], driven by changes in dietary practices and reduced consumption of homemade meals based on natural foods [ 40 ]. The growing evidence linking UPF consumption to adverse health outcomes—including obesity, cardiovascular disease, mortality, and chronic kidney disease progression—has increased recognition of the need for targeted clinical counseling as well as population-level policy measures [ 41 ]. Recommended strategies include reducing the cost of fruits and vegetables and implementing stricter regulations on advertising, labeling, and taxation of UPFs [ 42 ]. For patients on HD, improving the nutritional quality of meals provided within dialysis centers is an especially actionable opportunity. This study has some limitations, including its cross-sectional design, which precludes causal inference, and the non-random selection of participants, which may limit generalizability. The study was conducted in a single region of Brazil, and dietary practices may differ in other geographic or cultural contexts. Despite these limitations, notable strengths include the multicenter design and the use of brief, validated dietary screeners that minimized respondent burden and enabled efficient assessment of dietary patterns in a population for whom more complex instruments may be impractical. In conclusion, patients undergoing hemodialysis exhibited low dietary diversity, with higher UPF intake on dialysis days. Older age, shorter dialysis vintage, and absence of obesity were associated with greater dietary diversity, whereas younger age, male sex, and obesity were associated with higher UPF intake. Brief dietary screeners represent efficient tools for identifying patients at nutritional risk and can support targeted counseling aimed at promoting unprocessed and minimally processed foods while reducing UPF consumption. Declarations Funding The authors did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for the submitted work. Availability of Data and Materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Authors’ Contributions Conceptualization: Andrea C Sczip; Fabiana B Nerbass; Fellype C Barreto. Methodology: Andrea C Sczip; Fabiana B Nerbass; Fellype C Barreto. Investigation: Andrea C Sczip; Fabiana B Nerbass; Jyana G Morais; Adaiane Calegari; Tatiana S Kruger; Jorgiane C Oliveira; Natália K Scatone; Rafaela G dos Santos. Formal analysis: Andrea C Sczip; Fabiana B Nerbass. Writing – original draft preparation: Andrea C Sczip. Writing – review and editing: Andrea C Sczip; Jyana G Morais; Adaiane Calegari; Tatiana S Kruger; Jorgiane C Oliveira; Natália K Scatone; Rafaela G dos Santos; Fabiana B Nerbass; Fellype C Barreto. Supervision: Fabiana B Nerbass; Fellype C Barreto. All authors read and approved the final manuscript. Ethics Approval and Consent to Participate The study involving human participants and their data was approved by the local Research Ethics Committee (approval No. 4.842.525). The study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants prior to inclusion in the study. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. Use of Generative AI and AI-Assisted Technologies During the preparation of this work, the authors used ChatGPT to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References Sualeheen A, Khor B-H, Balasubramanian GV, et al. Benchmarking Diet Quality to Assess Nutritional Risk in Hemodialysis Patients: Applying Adequacy and Moderation Metrics of the Hemodialysis-Healthy Eating Index. J Ren Nutr. 2022;32:726–38. https://doi.org/10.1053/j.jrn.2022.02.002 . Stanford J, Charlton K, Stefoska-Needham A, et al. Associations Among Plant-Based Diet Quality, Uremic Toxins, and Gut Microbiota Profile in Adults Undergoing Hemodialysis Therapy. J Ren Nutr. 2021;31:177–88. https://doi.org/10.1053/j.jrn.2020.07.008 . Martins AM, Bello Moreira AS, Canella DS, et al. Elderly patients on hemodialysis have worse dietary quality and higher consumption of ultraprocessed food than elderly without chronic kidney disease. Nutrition. 2017;41:73–9. https://doi.org/10.1016/j.nut.2017.03.013 . Ikizler TA, Burrowes JD, Byham-Gray LD, et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am J Kidney Dis. 2020;76:S1–107. https://doi.org/10.1053/j.ajkd.2020.05.006 . Fernandes AS, Ramos CI, Nerbass FB, Cuppari L. Diet Quality of Chronic Kidney Disease Patients and the Impact of Nutritional Counseling. J Ren Nutr. 2018;28:403–10. https://doi.org/10.1053/j.jrn.2017.10.005 . Vadiveloo M, Lichtenstein AH, Anderson C, et al. Rapid Diet Assessment Screening Tools for Cardiovascular Disease Risk Reduction Across Healthcare Settings: A Scientific Statement From the American Heart Association. Circ Cardiovasc Qual Outcomes. 2020;13:E000094. https://doi.org/10.1161/HCQ.0000000000000094 . England CY, Andrews RC, Jago R, Thompson JL. A systematic review of brief dietary questionnaires suitable for clinical use in the prevention and management of obesity, cardiovascular disease and type 2 diabetes. Eur J Clin Nutr. 2015;69:997–1003. https://doi.org/10.1038/ejcn.2015.6 . FAO. (2018) Dietary Assessment: A resource guide to method selection and application in low resource settings. Rome. Nerbass FB, Antunes A, de Cuppari A L. Dietitians’ practices in dialysis units in Brazil: nutritional assessment and intervention. Brazilian J Nephrol. 2024. https://doi.org/10.1590/2175-8239-JBN-2023-0092en . Verger EO, Le Port A, Borderon A, et al. Dietary Diversity Indicators and Their Associations with Dietary Adequacy and Health Outcomes: A Systematic Scoping Review. Adv Nutr. 2021;12:1659–72. https://doi.org/10.1093/advances/nmab009 . Martin-Prevel Y, Arimond M, Allemand P, et al. Development of a Dichotomous Indicator for Population-Level Assessment of Dietary Diversity in Women of Reproductive Age. Curr Dev Nutr. 2017;1. https://doi.org/10.3945/cdn.117.001701 . cdn.117.001701. Gómez G, Monge-Rojas R, Vargas-Quesada R et al. (2024) Exploring the FAO Minimum Dietary Diversity Indicator as a Suitable Proxy of Micronutrient Adequacy in Men and Women Across Reproductive and Non-reproductive Ages in 8 Latin American Countries. Food Nutr Bull. https://doi.org/10.1177/03795721241242920 FAO, 360 FHI. Minimum Dietary Diversity for Women. A Guide for Measurement; 2016. Sattamini IF, Monteiro CA. Instrumentos de avaliação da qualidade de dietas: desenvolvimento, adaptação e validação no Brasil. Universidade de São Paulo; 2019. Monteiro CA, Levy RB, Claro RM, et al. A new classification of foods based on the extent and purpose of their processing. Cad Saude Publica. 2010;26:2039–49. https://doi.org/10.1590/S0102-311X2010001100005 . Mc Causland FR, Waikar SS, Brunelli SM. Increased dietary sodium is independently associated with greater mortality among prevalent hemodialysis patients. Kidney Int. 2012;82:204–11. https://doi.org/10.1038/ki.2012.42 . Nerbass FB, Canzi ER, Araujo R dos A, et al. Differences in phosphatemia and frequency of consumption of dietary sources of phosphorus in hemodialysis patients in southern and northern Brazil. Brazilian J Nephrol. 2019;41:83–8. https://doi.org/10.1590/2175-8239-jbn-2018-0063 . Sczip AC, Morais JG, Calegari A, et al. Hyperphosphatemia in Patients on Hemodialysis May be Driven by the Consumption of Ultraprocessed Foods. J Ren Nutr. 2025;35:780–6. https://doi.org/10.1053/j.jrn.2025.07.007 . Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. (2019) Vigitel Brasil 2018: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Brasília. Sattamini IF. Instrumentos de avaliação da qualidade de dietas: desenvolvimento, adaptação e validação no Brasil. Universidade de São Paulo; 2019. Monteiro dos Santos JE, Crispim SP, Murphy J, de Camargo Cancela M. Health, lifestyle and sociodemographic characteristics are associated with Brazilian dietary patterns: Brazilian National Health Survey. PLoS ONE. 2021;16:e0247078. https://doi.org/10.1371/journal.pone.0247078 . Louzada ML, Steele EM, Rezende LFM, et al. Changes in Obesity Prevalence Attributable to Ultra-Processed Food Consumption in Brazil Between 2002 and 2009. Int J Public Health. 2022;67:1–8. https://doi.org/10.3389/ijph.2022.1604103 . Costa C dos, Sattamini S, Steele IF EM, et al. Consumo de alimentos ultraprocessados e associação com fatores sociodemográficos na população adulta das 27 capitais brasileiras (2019). Rev Saude Publica. 2021;55:47. https://doi.org/10.11606/s1518-8787.2021055002833 . Nerbass FB, Antunes A, de Cuppari A L. Demographic and occupational profile of dietitians working in dialysis centers in Brazil. Brazilian J Nephrol. 2024;46:56–61. https://doi.org/10.1590/2175-8239-jbn-2023-0017en . IBGE. Instituto Brasileiro de Geografia e Estatística. (2020) Pesquisa de orçamentos familiares 2017–2018: análise do consumo alimentar pessoal no Brasil. Rio de Janeiro. Vaz IMF, Freitas ATV, de Peixoto S, do RG M, et al. Food intake in patients on hemodialysis. Rev Nutr. 2014;27:665–75. https://doi.org/10.1590/1415-52732014000600002 . Chiu YF, Chen YC, Wu PY, et al. Association between the hemodialysis eating index and risk factors of cardiovascular disease in hemodialysis patients. J Ren Nutr. 2014;24:163–71. https://doi.org/10.1053/j.jrn.2013.12.006 . Kim H, Lim H, Choue R. A Better Diet Quality is Attributable to Adequate Energy Intake in Hemodialysis Patients. Clin Nutr Res. 2015;4:46. https://doi.org/10.7762/cnr.2015.4.1.46 . Ministério da Saúde. Vigitel Brasil 2023 - Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico. Brasília, DF: Ministério da Saúde; 2023. Santos FSdos, Martinez Steele E, Costa C dos S, et al. Nova diet quality scores and risk of weight gain in the NutriNet-Brasil cohort study. Public Health Nutr. 2023;26:2366–73. https://doi.org/10.1017/S1368980023001532 . dos Santos Costa C, de Faria FR, Gabe KT, et al. Nova score for the consumption of ultra-processed foods: description and performance evaluation in Brazil. Rev Saude Publica. 2021;55:1–9. https://doi.org/10.11606/s1518-8787.2021055003588 . Marques NMP, Cattafesta M, Soares FLP, et al. Consumption of minimally processed and ultraprocessed foods by individuals on hemodialysis in southeastern Brazil. J Hum Growth Dev. 2022;32:237–51. https://doi.org/10.36311/jhgd.v32.13856 . dos Santos Costa C, Steele EM, de Faria FR, Monteiro CA. Score of ultra-processed food consumption and its association with sociodemographic factors in the Brazilian National Health Survey, 2019. Cad Saude Publica. 2022;38:1–11. https://doi.org/10.1590/0102-311X00119421 . Avesani CM, Cuppari L, Nerbass FB, et al. Ultraprocessed foods and chronic kidney disease—double trouble. Clin Kidney J. 2023;16:1723–36. https://doi.org/10.1093/ckj/sfad103 . Cordova R, Kliemann N, Huybrechts I, et al. Consumption of ultra-processed foods associated with weight gain and obesity in adults: A multi-national cohort study. Clin Nutr. 2021;40:5079–88. https://doi.org/10.1016/j.clnu.2021.08.009 . Sullivan VK, Appel LJ, Anderson CAM, et al. Ultraprocessed Foods and Kidney Disease Progression, Mortality, and Cardiovascular Disease Risk in the CRIC Study. Am J Kidney Dis. 2023;82. https://doi.org/10.1053/j.ajkd.2023.01.452 . Gering SJ, Martins CA, Mara N, et al. The Consumption of Ultra-Processed Foods Is Associated with Abdominal Obesity in Individuals on Hemodialysis in Brazil. Obesities. 2024;212–25. https://doi.org/10.3390/obesities4030019 . Nerbass FB, Morais JG, dos Santos RG, et al. Factors associated to salt intake in chronic hemodialysis patients. J Bras Nefrol ʹorgão Soc Bras e Latino-Americana Nefrol. 2013;35:87–92. https://doi.org/10.5935/0101-2800.20130015 . Nilson EAF, Andrade GC, Claro RM, et al. Sodium intake according to NOVA food classification in Brazil: trends from 2002 to 2018. Cad Saude Publica. 2024;40. https://doi.org/10.1590/0102-311XEN073823 . Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Departamento de Atenção Básica. (2014) Guia alimentar para a população brasileira / Ministério da Saúde, Secretaria de Atenção à Saúde, Departamento de Atenção Básica., 2nd ed. Ministério da Saúde. Lane MM, Gamage E, Du S et al. (2024) Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ e077310. https://doi.org/10.1136/bmj-2023-077310 Popkin BM, Barquera S, Corvalan C, et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol. 2021;9:462–70. https://doi.org/10.1016/S2213-8587(21)00078-4 . Additional Declarations No competing interests reported. Supplementary Files Supplement1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8969021","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602759978,"identity":"dacee882-ab22-41fd-b38c-94d982fc7a79","order_by":0,"name":"Andrea C Sczip","email":"","orcid":"","institution":"Universidade Federal do Paraná, Programa de Pós- Graduação em Medicina Interna e Ciências da Saúde","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"C","lastName":"Sczip","suffix":""},{"id":602759979,"identity":"471a8d35-ca1d-47b1-9dbf-5e356e20d9e5","order_by":1,"name":"Jyana G Morais","email":"","orcid":"","institution":"Fundação Pró-Rim","correspondingAuthor":false,"prefix":"","firstName":"Jyana","middleName":"G","lastName":"Morais","suffix":""},{"id":602759981,"identity":"5b1d92cb-536a-4908-ae8c-294ca42f14ae","order_by":2,"name":"Adaiane Calegari","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adaiane","middleName":"","lastName":"Calegari","suffix":""},{"id":602759985,"identity":"6a2a5b82-c33a-4e86-9eff-d760bd22672b","order_by":3,"name":"Tatiana S Kruger","email":"","orcid":"","institution":"Fundação Pró-Rim","correspondingAuthor":false,"prefix":"","firstName":"Tatiana","middleName":"S","lastName":"Kruger","suffix":""},{"id":602759990,"identity":"de0253f3-da92-4b97-add1-36565c5483bf","order_by":4,"name":"Jorgiane C Oliveira","email":"","orcid":"","institution":"Fundação Pró-Rim","correspondingAuthor":false,"prefix":"","firstName":"Jorgiane","middleName":"C","lastName":"Oliveira","suffix":""},{"id":602759993,"identity":"16a52fc1-fa7d-4e2e-aa6c-fbbd437317e7","order_by":5,"name":"Natália K Scatone","email":"","orcid":"","institution":"Fundação Pró-Rim","correspondingAuthor":false,"prefix":"","firstName":"Natália","middleName":"K","lastName":"Scatone","suffix":""},{"id":602759994,"identity":"17730f96-8d7d-47ba-a098-a9cc0ee58d7a","order_by":6,"name":"Rafaela G Santos","email":"","orcid":"","institution":"Jaraguá do Sul","correspondingAuthor":false,"prefix":"","firstName":"Rafaela","middleName":"G","lastName":"Santos","suffix":""},{"id":602759995,"identity":"2c2eb975-d48d-4288-9092-d12b982e3c5c","order_by":7,"name":"Fabiana B Nerbass","email":"","orcid":"","institution":"Fundação Pró-Rim","correspondingAuthor":false,"prefix":"","firstName":"Fabiana","middleName":"B","lastName":"Nerbass","suffix":""},{"id":602760000,"identity":"2ab27cf8-5878-4feb-adcf-74e2126b1fe1","order_by":8,"name":"Fellype Carvalho Barreto","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal do Paraná, Programa de Pós- Graduação em Medicina Interna e Ciências da Saúde","correspondingAuthor":true,"prefix":"","firstName":"Fellype","middleName":"Carvalho","lastName":"Barreto","suffix":""}],"badges":[],"createdAt":"2026-02-25 14:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8969021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8969021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104546893,"identity":"9a8b2f2d-4051-418e-a1ff-d6c4de2cd18c","added_by":"auto","created_at":"2026-03-13 07:30:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102841,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of intake on dialysis and non-dialysis day for dietary diversity subgroups (1A) and ultraprocessed foods subgroups (1B).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8969021/v1/6d4bfeddf9f08ac7c23f88fe.jpg"},{"id":104781444,"identity":"185b9789-483f-45a7-9324-c589b45d99b0","added_by":"auto","created_at":"2026-03-17 07:55:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81307,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of participants by diversity score (2A) and ultraprocessed foods score (2B).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8969021/v1/4d18910bbb36e9e43dd5f039.jpg"},{"id":104784648,"identity":"4c0da45f-ce1d-4a63-8f14-69dcfc861f1e","added_by":"auto","created_at":"2026-03-17 08:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8969021/v1/f12faff1-053b-4d89-9c45-ee5fbf15dcf6.pdf"},{"id":104546896,"identity":"e09e0ac8-2c13-40b9-a82a-82729e9fc8bd","added_by":"auto","created_at":"2026-03-13 07:30:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21096,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8969021/v1/fbe3e94c98b39111cd342180.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Diet Quality in Hemodialysis with Brief Screeners: Multicenter Findings on Dietary Diversity and Ultraprocessed Foods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAssessing dietary quality in patients on hemodialysis (HD) is essential due to the high prevalence of protein\u0026ndash;energy wasting, micronutrient deficiencies, and elevated cardiovascular and inflammatory risk in this population. Poor dietary quality has been linked to increased production of uremic toxins, alterations in gut microbiota, metabolic disturbances, malnutrition, reduced lean tissue mass, and greater dietary monotony [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Current CKD nutrition guidelines recommend a 3-day food record, including dialysis and nondialysis days, as the preferred method to evaluate dietary intake in HD patients. However, food records are time-consuming, dependent on literacy and memory, and prone to substantial underreporting [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In a previous Brazilian study using this method, nearly three-quarters of participants were excluded due to significant underreporting [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These limitations underscore the need for simpler, culturally adapted tools that can be feasibly integrated into routine HD care.\u003c/p\u003e \u003cp\u003eBrief dietary screeners have emerged as pragmatic alternatives for rapidly capturing key dimensions of diet quality in clinical and public health settings. These instruments impose minimal respondent burden, perform well in low-literacy contexts, and allow risk stratification when comprehensive dietary assessment is not feasible [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Their clinical utility may be particularly relevant in settings such as HD units, where dietitians face high patient loads and limited time for individualized counseling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDietary diversity is a fundamental component of diet quality, reflecting the principle that no single food provides all essential nutrients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Indicators such as the Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) have demonstrated strong performance as proxies for micronutrient adequacy across diverse populations [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In Brazil, this concept was adapted into a validated screener aligned with the NOVA classification, focusing on unprocessed or minimally processed foods [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA screener assessing UPF intake was also developed and validated in Brazil [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. UPFs are industrial formulations rich in sodium, additives, and inorganic phosphates\u0026mdash;components particularly harmful for individuals on HD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our previous analyses using this screener showed that UPF intake, especially of animal-based products, was an independent determinant of hyperphosphatemia in patients on HD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the availability of validated brief dietary screeners, evidence on their application to assess diet quality in HD patients remains scarce. Moreover, the organization of HD care, including long treatment sessions, commuting time, and reliance on meals or snacks consumed during dialysis, may differentially influence dietary patterns on dialysis versus non-dialysis days.\u003c/p\u003e \u003cp\u003eTherefore, this multicenter study applied two validated brief dietary screeners to: (1) identify sociodemographic and clinical determinants of dietary diversity and UPF intake in patients undergoing HD; and (2) compare dietary patterns between dialysis and non-dialysis days.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and setting\u003c/h2\u003e \u003cp\u003eThis multicenter cross-sectional study was conducted in seven dialysis units in Southern Brazil. Adult patients (\u0026ge;\u0026thinsp;18 years) receiving maintenance HD three times weekly for \u0026ge;\u0026thinsp;90 days were eligible. Patients with cognitive or visual impairments preventing questionnaire completion were excluded. Of 820 eligible individuals, a convenience sample of at least 50% per clinic was invited to ensure representation across shifts, resulting in 309 invitations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy protocol\u003c/h3\u003e\n\u003cp\u003eBetween November 2021 and February 2023, dietitians from the participating clinics conducted interviews with all study participants. The dietary diversity screener was adapted from the structure and scoring of the MDD-W and included 13 subgroups of unprocessed or minimally processed foods (NOVA Group 1)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The UPF screener assessed 13 subgroups of UPFs (NOVA Group 4) and is used in the national VIGITEL surveillance system [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This tool has demonstrated good agreement with dietary share of UPF[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Both screeners collect intake from the previous day and their score varies from 0 to 13 (Supplement 1).\u003c/p\u003e \u003cp\u003eEach participant was interviewed on two separate days, with a maximum interval of two weeks, and both questionnaires were administered at each interview. The first interview was conducted in person during a dialysis session to assess dietary intake on a non-dialysis day, and the second was conducted by telephone to assess dietary intake on a dialysis day. To mitigate potential bias from weekend eating habits, interviews were not conducted during the first dialysis session of the week.\u003c/p\u003e \u003cp\u003eScores from dialysis and non-dialysis days were compared. The final diversity and UPF scores were obtained by averaging the scores from both days, and the median score of each questionnaire was used as a cutoff to classify patients into higher and lower categories of dietary diversity and UPFs intake.\u003c/p\u003e \u003cp\u003ePrevalence rates for the intake of each food subgroup were calculated. To evaluate fruit and vegetable consumption, we determined the percentage of participants who did not consume any of the two fruit subgroups or the four vegetable subgroups.\u003c/p\u003e \u003cp\u003eDemographic, clinical variables, and laboratory test results, were extracted from electronic medical records. Ethics approval was granted by the local ethical committee (NO: 4.842.525) and informed consent was obtained from all patients.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS software version 21.0 for Windows (SPSS, Inc., Chicago, IL). Results were presented as percentages, means with standard deviations, or medians with interquartile ranges, depending on the distribution of the variables. The Wilcoxon Signed-Rank Test was used to compare scores between dialysis and non-dialysis days. To compare variables between groups, the Student's t-test was applied for normally distributed data, while the Mann-Whitney U test was used for non-normally distributed data. The chi-square test was employed for categorical variables. Logistic regression analysis was performed to identify determinants of higher dietary diversity and UPF scores, including all variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 from univariate analyses. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 297 patients participated in the study (three refused participation and nine did not respond to the telephone interview). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of their main characteristics. The majority were white men with at least eight years of formal education.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain charateristics of the study population and comparisons across subgroups according to diversity and ultraprocessed foods scores (n\u0026thinsp;=\u0026thinsp;297)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003cp\u003e\u0026ge; 60 n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;297)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiversity score \u0026le; 6 (n\u0026thinsp;=\u0026thinsp;178)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiversity score \u0026gt; 6 (n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUPF score\u0026thinsp;\u0026le;\u0026thinsp;2.5\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;153)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUPF score\u0026thinsp;\u0026gt;\u0026thinsp;2.5\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;144)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003cp\u003e94 (31.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003cp\u003e47 (26.4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2***\u003c/p\u003e \u003cp\u003e47 (39.5)**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003cp\u003e65 (42.9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7***\u003c/p\u003e \u003cp\u003e29 (20.1)***\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (63.9)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91 (63.2)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis vintage (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (18\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (19\u0026ndash;98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (14\u0026ndash;60)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (14\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41 (20\u0026ndash;91)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin color white n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109 (75.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41 (28.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years at school)\u003c/p\u003e \u003cp\u003e\u0026ge; 8 years n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (6\u0026ndash;12)\u003c/p\u003e \u003cp\u003e216 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6\u0026ndash;12)\u003c/p\u003e \u003cp\u003e128 (71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6\u0026ndash;12)\u003c/p\u003e \u003cp\u003e88 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (6\u0026ndash;12)\u003c/p\u003e \u003cp\u003e107 (69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003cp\u003e109 (75.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e\u0026ge; 30 n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3 (22.3\u0026ndash;28.5)\u003c/p\u003e \u003cp\u003e57 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4 (22.6\u0026ndash;29.3)\u003c/p\u003e \u003cp\u003e40 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.2 (21.9\u0026ndash;28.0)\u003c/p\u003e \u003cp\u003e17 (14.3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.2 (22.7\u0026ndash;28.3)\u003c/p\u003e \u003cp\u003e23 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.7 (21.7\u0026ndash;29.0)\u003c/p\u003e \u003cp\u003e34 (23.6)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer capta income\u0026thinsp;\u0026gt;\u0026thinsp;1 (mw) n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86 (59.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum potassium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterdialytic weight gain (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9 (1.9\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 (2.1\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (1.5\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7 (1.6\u0026ndash;3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1 (2.1\u0026ndash;4.3)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eUPF: ultraprocessed foods; mw: minimum wage; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 vs lower score; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs lower score; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 vs lower score\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows intake frequency across food groups. Cereals were the most consumed group, followed by meat, milk, and legumes. On nondialysis days, 36% consumed no fruits and 34% consumed no vegetables; similar proportions were observed on dialysis days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB displays UPF intake, with margarine, ultraprocessed breads, and processed meats being the most common items.\u003c/p\u003e \u003cp\u003eThere was no significant difference in the dietary diversity score between days with and without dialysis treatment (6 [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] vs. 6 [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; p\u0026thinsp;=\u0026thinsp;0.08). UPF scores had identical medians (3 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] vs. 3 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]), but were significantly higher on dialysis days (p\u0026thinsp;=\u0026thinsp;0.04) as illustrated by the prevalence data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the distribution of the population according to dietary diversity and UPF scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn multivariable models (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), higher dietary diversity was independently associated with older age, shorter dialysis vintage, and absence of obesity. Higher UPF intake was independently associated with younger age, male sex, and obesity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of the determinants of higher dietary diversity score (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.10)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37 (0.84\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis vintage (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30 Kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.27\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eOR: odds ratio; CI: confidence interval; BMI: body mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of the determinants of higher UPF score (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.17)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.93\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.17\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis vintage (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30 Kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33 (1.24\u0026ndash;4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eUPF: ultraprocessed foods; OR: odds ratio; CI: confidence interval; BMI: body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multicenter study demonstrates that patients undergoing hemodialysis exhibit low dietary diversity and modest UPF intake, with distinct sociodemographic and clinical determinants. Importantly, dietary diversity did not differ between dialysis and non-dialysis days, whereas UPF intake was higher on dialysis days. Using brief dietary screeners, we identified meaningful dietary patterns that align with findings from large epidemiological studies, supporting the clinical applicability of these tools in HD care.\u003c/p\u003e \u003cp\u003eThe brief dietary screeners used in this study showed good discriminatory capacity despite their simplicity. They identified associations\u0026mdash;such as lower dietary diversity among younger and obese individuals and higher UPF intake among younger, obese, and male individuals\u0026mdash;that mirror patterns consistently observed in population-based studies [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This concordance suggests that brief screeners are capable of capturing relevant dimensions of diet quality even when compared with more comprehensive dietary assessment methods. In settings such as HD units, where time constraints and patient burden limit the feasibility of detailed dietary assessment, these tools may offer a practical alternative for routine monitoring.\u003c/p\u003e \u003cp\u003eThe incorporation of brief dietary screeners into routine HD care is particularly relevant in Brazil, where dietitians often manage high patient volumes and multiple clinical responsibilities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this context, rapid tools that allow early identification of patients at nutritional risk can support prioritization of individualized counseling and more efficient allocation of clinical resources.\u003c/p\u003e \u003cp\u003eDietary diversity among patients undergoing HD was low in this cohort. Cereals, meat, milk, and legumes were the most frequently consumed food groups, consistent with dietary patterns observed in national surveys [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Over a third of participants reported not consuming any items from fruit or vegetable subgroups, a finding consistent with both national [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and international studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the general Brazilian population, only 21.4% meet the World Health Organization's recommended intake of fruits and vegetables [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to evaluate dietary diversity specifically in individuals on HD using a validated diversity screener. The median Dietary Diversity Index of 6, with 60% of participants consuming six or fewer of the 13 food subgroups, suggests that many patients may not achieve adequate micronutrient intake. Although no cut-off has been established for this tool, the MDD-W uses a threshold of \u0026ge;\u0026thinsp;5 out of 10 subgroups as a proxy for micronutrient adequacy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The similarly low mean MDD-W score in the Brazilian population (4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] underscores that low dietary diversity is a national challenge but may have even greater implications in HD due to increased nutritional vulnerability.\u003c/p\u003e \u003cp\u003eOlder age, shorter dialysis vintage, and absence of obesity were independently associated with higher dietary diversity. The inverse association with obesity may reflect the lower energy density of unprocessed and minimally processed foods, a pattern previously observed using NOVA-based dietary assessments [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lack of association with education and income in our study may be due to the limited socioeconomic variability in this cohort, where most individuals had similarly low socioeconomic status. The relationship between dialysis vintage and dietary diversity remains unexplored and warrants further study.\u003c/p\u003e \u003cp\u003eOverall UPF intake was relatively low, with a median score of 2.5 out of 13. Margarine and processed bread were the most frequently consumed items, particularly on dialysis days, likely influenced by meals or snacks provided in dialysis centers. Prior research also observed poorer dietary quality on dialysis days among elderly HD patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This finding highlights dialysis days as a critical window for nutritional intervention and an opportunity to improve the quality of foods offered within HD facilities. Approximately one-quarter of our sample consumed soft drinks, cookies, desserts, and processed meats on both dialysis and non-dialysis days, consistent with patterns in the broader Brazilian population [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eYounger age was strongly associated with higher UPF intake\u0026mdash;a trend previously observed in studies of both patients on HD [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and the general population [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Each year decrease in age was associated with a 5% increase chance of higher UPFs intake, and men had nearly twice the odds of higher intake compared with women. This pattern has been consistently observed in both large epidemiological studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and in patients on HD [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These demographic patterns have been recognized as major targets for public health interventions aimed at reducing UPF consumption [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity was another strong determinant of higher UPF intake. This association has been documented globally and may reflect the highly palatable, energy-dense, and behaviorally reinforcing characteristics of UPFs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In Brazil, rising UPF availability has contributed to substantial increase in obesity prevalence [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Evidence from prospective cohorts in Europe and North America also associates UPF intake with weight gain and higher BMI [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Among Brazilian HD patients, UPF consumption has previously been linked to abdominal obesity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], reinforcing the metabolic implications of UPFs in this population.\u003c/p\u003e \u003cp\u003eIn line with our previous work showing that UPF intake was associated with hyperphosphatemia in HD patients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] we additionally observed an association with higher interdialytic weight gain. This may reflect increased thirst and fluid intake due to the high sodium content of many UPFs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although table salt remains the primary source of sodium in Brazil, UPFs have substantially increased their contribution to overall sodium intake in recent years [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], driven by changes in dietary practices and reduced consumption of homemade meals based on natural foods [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe growing evidence linking UPF consumption to adverse health outcomes\u0026mdash;including obesity, cardiovascular disease, mortality, and chronic kidney disease progression\u0026mdash;has increased recognition of the need for targeted clinical counseling as well as population-level policy measures [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Recommended strategies include reducing the cost of fruits and vegetables and implementing stricter regulations on advertising, labeling, and taxation of UPFs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For patients on HD, improving the nutritional quality of meals provided within dialysis centers is an especially actionable opportunity.\u003c/p\u003e \u003cp\u003eThis study has some limitations, including its cross-sectional design, which precludes causal inference, and the non-random selection of participants, which may limit generalizability. The study was conducted in a single region of Brazil, and dietary practices may differ in other geographic or cultural contexts. Despite these limitations, notable strengths include the multicenter design and the use of brief, validated dietary screeners that minimized respondent burden and enabled efficient assessment of dietary patterns in a population for whom more complex instruments may be impractical.\u003c/p\u003e \u003cp\u003eIn conclusion, patients undergoing hemodialysis exhibited low dietary diversity, with higher UPF intake on dialysis days. Older age, shorter dialysis vintage, and absence of obesity were associated with greater dietary diversity, whereas younger age, male sex, and obesity were associated with higher UPF intake. Brief dietary screeners represent efficient tools for identifying patients at nutritional risk and can support targeted counseling aimed at promoting unprocessed and minimally processed foods while reducing UPF consumption.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Andrea C Sczip; Fabiana B Nerbass; Fellype C Barreto.\u003cbr\u003e\u0026nbsp;Methodology: Andrea C Sczip; Fabiana B Nerbass; Fellype C Barreto.\u003cbr\u003e\u0026nbsp;Investigation: Andrea C Sczip; Fabiana B Nerbass; Jyana G Morais; Adaiane Calegari; Tatiana S Kruger; Jorgiane C Oliveira; Nat\u0026aacute;lia K Scatone; Rafaela G dos Santos.\u003cbr\u003e\u0026nbsp;Formal analysis: Andrea C Sczip; Fabiana B Nerbass.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; original draft preparation: Andrea C Sczip.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; review and editing: Andrea C Sczip; Jyana G Morais; Adaiane Calegari; Tatiana S Kruger; Jorgiane C Oliveira; Nat\u0026aacute;lia K Scatone; Rafaela G dos Santos; Fabiana B Nerbass; Fellype C Barreto.\u003cbr\u003e\u0026nbsp;Supervision: Fabiana B Nerbass; Fellype C Barreto.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involving human participants and their data was approved by the local Research Ethics Committee (approval No. 4.842.525). The study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eUse of Generative AI and AI-Assisted Technologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSualeheen A, Khor B-H, Balasubramanian GV, et al. Benchmarking Diet Quality to Assess Nutritional Risk in Hemodialysis Patients: Applying Adequacy and Moderation Metrics of the Hemodialysis-Healthy Eating Index. J Ren Nutr. 2022;32:726\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.jrn.2022.02.002\u003c/span\u003e\u003cspan address=\"10.1053/j.jrn.2022.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanford J, Charlton K, Stefoska-Needham A, et al. Associations Among Plant-Based Diet Quality, Uremic Toxins, and Gut Microbiota Profile in Adults Undergoing Hemodialysis Therapy. J Ren Nutr. 2021;31:177\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.jrn.2020.07.008\u003c/span\u003e\u003cspan address=\"10.1053/j.jrn.2020.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartins AM, Bello Moreira AS, Canella DS, et al. Elderly patients on hemodialysis have worse dietary quality and higher consumption of ultraprocessed food than elderly without chronic kidney disease. Nutrition. 2017;41:73\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nut.2017.03.013\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2017.03.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkizler TA, Burrowes JD, Byham-Gray LD, et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am J Kidney Dis. 2020;76:S1\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.ajkd.2020.05.006\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2020.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes AS, Ramos CI, Nerbass FB, Cuppari L. Diet Quality of Chronic Kidney Disease Patients and the Impact of Nutritional Counseling. J Ren Nutr. 2018;28:403\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.jrn.2017.10.005\u003c/span\u003e\u003cspan address=\"10.1053/j.jrn.2017.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVadiveloo M, Lichtenstein AH, Anderson C, et al. Rapid Diet Assessment Screening Tools for Cardiovascular Disease Risk Reduction Across Healthcare Settings: A Scientific Statement From the American Heart Association. Circ Cardiovasc Qual Outcomes. 2020;13:E000094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/HCQ.0000000000000094\u003c/span\u003e\u003cspan address=\"10.1161/HCQ.0000000000000094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngland CY, Andrews RC, Jago R, Thompson JL. A systematic review of brief dietary questionnaires suitable for clinical use in the prevention and management of obesity, cardiovascular disease and type 2 diabetes. Eur J Clin Nutr. 2015;69:997\u0026ndash;1003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ejcn.2015.6\u003c/span\u003e\u003cspan address=\"10.1038/ejcn.2015.6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO. (2018) Dietary Assessment: A resource guide to method selection and application in low resource settings. Rome.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNerbass FB, Antunes A, de Cuppari A L. Dietitians\u0026rsquo; practices in dialysis units in Brazil: nutritional assessment and intervention. Brazilian J Nephrol. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/2175-8239-JBN-2023-0092en\u003c/span\u003e\u003cspan address=\"10.1590/2175-8239-JBN-2023-0092en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerger EO, Le Port A, Borderon A, et al. Dietary Diversity Indicators and Their Associations with Dietary Adequacy and Health Outcomes: A Systematic Scoping Review. Adv Nutr. 2021;12:1659\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/advances/nmab009\u003c/span\u003e\u003cspan address=\"10.1093/advances/nmab009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin-Prevel Y, Arimond M, Allemand P, et al. Development of a Dichotomous Indicator for Population-Level Assessment of Dietary Diversity in Women of Reproductive Age. Curr Dev Nutr. 2017;1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3945/cdn.117.001701\u003c/span\u003e\u003cspan address=\"10.3945/cdn.117.001701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. cdn.117.001701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez G, Monge-Rojas R, Vargas-Quesada R et al. (2024) Exploring the FAO Minimum Dietary Diversity Indicator as a Suitable Proxy of Micronutrient Adequacy in Men and Women Across Reproductive and Non-reproductive Ages in 8 Latin American Countries. Food Nutr Bull. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/03795721241242920\u003c/span\u003e\u003cspan address=\"10.1177/03795721241242920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO, 360 FHI. Minimum Dietary Diversity for Women. A Guide for Measurement; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSattamini IF, Monteiro CA. Instrumentos de avalia\u0026ccedil;\u0026atilde;o da qualidade de dietas: desenvolvimento, adapta\u0026ccedil;\u0026atilde;o e valida\u0026ccedil;\u0026atilde;o no Brasil. Universidade de S\u0026atilde;o Paulo; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Levy RB, Claro RM, et al. A new classification of foods based on the extent and purpose of their processing. Cad Saude Publica. 2010;26:2039\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/S0102-311X2010001100005\u003c/span\u003e\u003cspan address=\"10.1590/S0102-311X2010001100005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMc Causland FR, Waikar SS, Brunelli SM. Increased dietary sodium is independently associated with greater mortality among prevalent hemodialysis patients. Kidney Int. 2012;82:204\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ki.2012.42\u003c/span\u003e\u003cspan address=\"10.1038/ki.2012.42\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNerbass FB, Canzi ER, Araujo R dos A, et al. Differences in phosphatemia and frequency of consumption of dietary sources of phosphorus in hemodialysis patients in southern and northern Brazil. Brazilian J Nephrol. 2019;41:83\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/2175-8239-jbn-2018-0063\u003c/span\u003e\u003cspan address=\"10.1590/2175-8239-jbn-2018-0063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSczip AC, Morais JG, Calegari A, et al. Hyperphosphatemia in Patients on Hemodialysis May be Driven by the Consumption of Ultraprocessed Foods. J Ren Nutr. 2025;35:780\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.jrn.2025.07.007\u003c/span\u003e\u003cspan address=\"10.1053/j.jrn.2025.07.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Secretaria de Vigil\u0026acirc;ncia em Sa\u0026uacute;de. (2019) Vigitel Brasil 2018: vigil\u0026acirc;ncia de fatores de risco e prote\u0026ccedil;\u0026atilde;o para doen\u0026ccedil;as cr\u0026ocirc;nicas por inqu\u0026eacute;rito telef\u0026ocirc;nico. Bras\u0026iacute;lia.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSattamini IF. Instrumentos de avalia\u0026ccedil;\u0026atilde;o da qualidade de dietas: desenvolvimento, adapta\u0026ccedil;\u0026atilde;o e valida\u0026ccedil;\u0026atilde;o no Brasil. Universidade de S\u0026atilde;o Paulo; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro dos Santos JE, Crispim SP, Murphy J, de Camargo Cancela M. Health, lifestyle and sociodemographic characteristics are associated with Brazilian dietary patterns: Brazilian National Health Survey. PLoS ONE. 2021;16:e0247078. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0247078\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0247078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouzada ML, Steele EM, Rezende LFM, et al. Changes in Obesity Prevalence Attributable to Ultra-Processed Food Consumption in Brazil Between 2002 and 2009. Int J Public Health. 2022;67:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/ijph.2022.1604103\u003c/span\u003e\u003cspan address=\"10.3389/ijph.2022.1604103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta C dos, Sattamini S, Steele IF EM, et al. Consumo de alimentos ultraprocessados e associa\u0026ccedil;\u0026atilde;o com fatores sociodemogr\u0026aacute;ficos na popula\u0026ccedil;\u0026atilde;o adulta das 27 capitais brasileiras (2019). Rev Saude Publica. 2021;55:47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11606/s1518-8787.2021055002833\u003c/span\u003e\u003cspan address=\"10.11606/s1518-8787.2021055002833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNerbass FB, Antunes A, de Cuppari A L. Demographic and occupational profile of dietitians working in dialysis centers in Brazil. Brazilian J Nephrol. 2024;46:56\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/2175-8239-jbn-2023-0017en\u003c/span\u003e\u003cspan address=\"10.1590/2175-8239-jbn-2023-0017en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBGE. Instituto Brasileiro de Geografia e Estat\u0026iacute;stica. (2020) Pesquisa de or\u0026ccedil;amentos familiares 2017\u0026ndash;2018: an\u0026aacute;lise do consumo alimentar pessoal no Brasil. Rio de Janeiro.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaz IMF, Freitas ATV, de Peixoto S, do RG M, et al. Food intake in patients on hemodialysis. Rev Nutr. 2014;27:665\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1415-52732014000600002\u003c/span\u003e\u003cspan address=\"10.1590/1415-52732014000600002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu YF, Chen YC, Wu PY, et al. Association between the hemodialysis eating index and risk factors of cardiovascular disease in hemodialysis patients. J Ren Nutr. 2014;24:163\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.jrn.2013.12.006\u003c/span\u003e\u003cspan address=\"10.1053/j.jrn.2013.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim H, Lim H, Choue R. A Better Diet Quality is Attributable to Adequate Energy Intake in Hemodialysis Patients. Clin Nutr Res. 2015;4:46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7762/cnr.2015.4.1.46\u003c/span\u003e\u003cspan address=\"10.7762/cnr.2015.4.1.46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinist\u0026eacute;rio da Sa\u0026uacute;de. Vigitel Brasil 2023 - Vigil\u0026acirc;ncia de Fatores de Risco e Prote\u0026ccedil;\u0026atilde;o para Doen\u0026ccedil;as Cr\u0026ocirc;nicas por Inqu\u0026eacute;rito Telef\u0026ocirc;nico. Bras\u0026iacute;lia, DF: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos FSdos, Martinez Steele E, Costa C dos S, et al. Nova diet quality scores and risk of weight gain in the NutriNet-Brasil cohort study. Public Health Nutr. 2023;26:2366\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980023001532\u003c/span\u003e\u003cspan address=\"10.1017/S1368980023001532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edos Santos Costa C, de Faria FR, Gabe KT, et al. Nova score for the consumption of ultra-processed foods: description and performance evaluation in Brazil. Rev Saude Publica. 2021;55:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11606/s1518-8787.2021055003588\u003c/span\u003e\u003cspan address=\"10.11606/s1518-8787.2021055003588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarques NMP, Cattafesta M, Soares FLP, et al. Consumption of minimally processed and ultraprocessed foods by individuals on hemodialysis in southeastern Brazil. J Hum Growth Dev. 2022;32:237\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.36311/jhgd.v32.13856\u003c/span\u003e\u003cspan address=\"10.36311/jhgd.v32.13856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edos Santos Costa C, Steele EM, de Faria FR, Monteiro CA. Score of ultra-processed food consumption and its association with sociodemographic factors in the Brazilian National Health Survey, 2019. Cad Saude Publica. 2022;38:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0102-311X00119421\u003c/span\u003e\u003cspan address=\"10.1590/0102-311X00119421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvesani CM, Cuppari L, Nerbass FB, et al. Ultraprocessed foods and chronic kidney disease\u0026mdash;double trouble. Clin Kidney J. 2023;16:1723\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ckj/sfad103\u003c/span\u003e\u003cspan address=\"10.1093/ckj/sfad103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordova R, Kliemann N, Huybrechts I, et al. Consumption of ultra-processed foods associated with weight gain and obesity in adults: A multi-national cohort study. Clin Nutr. 2021;40:5079\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clnu.2021.08.009\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2021.08.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan VK, Appel LJ, Anderson CAM, et al. Ultraprocessed Foods and Kidney Disease Progression, Mortality, and Cardiovascular Disease Risk in the CRIC Study. Am J Kidney Dis. 2023;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.ajkd.2023.01.452\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2023.01.452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGering SJ, Martins CA, Mara N, et al. The Consumption of Ultra-Processed Foods Is Associated with Abdominal Obesity in Individuals on Hemodialysis in Brazil. Obesities. 2024;212\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/obesities4030019\u003c/span\u003e\u003cspan address=\"10.3390/obesities4030019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNerbass FB, Morais JG, dos Santos RG, et al. Factors associated to salt intake in chronic hemodialysis patients. J Bras Nefrol ʹorg\u0026atilde;o Soc Bras e Latino-Americana Nefrol. 2013;35:87\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5935/0101-2800.20130015\u003c/span\u003e\u003cspan address=\"10.5935/0101-2800.20130015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilson EAF, Andrade GC, Claro RM, et al. Sodium intake according to NOVA food classification in Brazil: trends from 2002 to 2018. Cad Saude Publica. 2024;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0102-311XEN073823\u003c/span\u003e\u003cspan address=\"10.1590/0102-311XEN073823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Secretaria de Aten\u0026ccedil;\u0026atilde;o \u0026agrave; Sa\u0026uacute;de. Departamento de Aten\u0026ccedil;\u0026atilde;o B\u0026aacute;sica. (2014) Guia alimentar para a popula\u0026ccedil;\u0026atilde;o brasileira / Minist\u0026eacute;rio da Sa\u0026uacute;de, Secretaria de Aten\u0026ccedil;\u0026atilde;o \u0026agrave; Sa\u0026uacute;de, Departamento de Aten\u0026ccedil;\u0026atilde;o B\u0026aacute;sica., 2nd ed. Minist\u0026eacute;rio da Sa\u0026uacute;de.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane MM, Gamage E, Du S et al. (2024) Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ e077310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj-2023-077310\u003c/span\u003e\u003cspan address=\"10.1136/bmj-2023-077310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopkin BM, Barquera S, Corvalan C, et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol. 2021;9:462\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2213-8587(21)00078-4\u003c/span\u003e\u003cspan address=\"10.1016/S2213-8587(21)00078-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrire","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Nutrire](https://www.springer.com/journal/41110)","snPcode":"41110","submissionUrl":"https://submission.nature.com/new-submission/41110/3","title":"Nutrire","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"dietary diversity, ultraprocessed foods, hemodialysis, dietary screeners","lastPublishedDoi":"10.21203/rs.3.rs-8969021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8969021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003e Rapid and low-burden tools to assess diet quality are needed in clinical settings characterized by high nutritional risk and limited consultation time, such as hemodialysis (HD) care. We aimed to assess dietary diversity and ultra-processed food (UPF) intake using validated brief dietary screeners in patients undergoing HD, identify their determinants, and compare dietary patterns between dialysis and non-dialysis days.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this multicenter cross-sectional study, patients on HD from seven dialysis units answered two validated dietary screeners assessing diversity of unprocessed and minimally processed foods and UPF intake. Screeners were applied on one dialysis day and one non-dialysis day. Scores ranged from 0 to 13, and median values were used to classify participants into higher versus lower dietary diversity and UPF intake. Multivariable logistic regression models were used to identify independent determinants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 297 participants (mean age 52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1 years; 57.9% men), the median dietary diversity score was 6 (4.5\u0026ndash;7). Higher dietary diversity was independently associated with older age, shorter dialysis vintage, and absence of obesity. The median UPF score was 2.5 (2\u0026ndash;4), with higher UPF intake associated with younger age, male sex, and obesity. Dietary diversity did not differ between dialysis and non-dialysis days, whereas UPF intake was significantly higher on dialysis days.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePatients undergoing HD exhibited low dietary diversity and higher UPF intake on dialysis days. Brief dietary screeners were able to detect clinically relevant dietary patterns and determinants, supporting their use for routine nutritional monitoring and targeted counseling in HD care.\u003c/p\u003e","manuscriptTitle":"Assessing Diet Quality in Hemodialysis with Brief Screeners: Multicenter Findings on Dietary Diversity and Ultraprocessed Foods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 07:30:08","doi":"10.21203/rs.3.rs-8969021/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"142670009785540297432266918969778970222","date":"2026-04-16T00:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T11:02:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T11:00:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T03:17:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrire","date":"2026-02-25T14:43:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrire","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Nutrire](https://www.springer.com/journal/41110)","snPcode":"41110","submissionUrl":"https://submission.nature.com/new-submission/41110/3","title":"Nutrire","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"700ccc06-a2aa-4867-9160-834ef324b7f8","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T07:30:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 07:30:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8969021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8969021","identity":"rs-8969021","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}