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Article

Overnutrition in the Elderly Population: Socio-Demographic and Behavioral Risk Factors in Hungary

by
Battamir Ulambayar
1,
Amr Sayed Ghanem
1 and
Attila Csaba Nagy
1,2,*
1
Department of Health Informatics, Faculty of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary
2
Coordinating Centre for Epidemiology, University of Debrecen Clinical Centre, 4032 Debrecen, Hungary
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(12), 1954; https://doi.org/10.3390/nu17121954
Submission received: 19 May 2025 / Revised: 5 June 2025 / Accepted: 6 June 2025 / Published: 8 June 2025
(This article belongs to the Special Issue Addressing Malnutrition in the Aging Population)
Browse Figure
Versions Notes

Abstract

Background/Objectives: Overnutrition, leading to overweight and obesity, is a growing concern among the elderly, contributing to non-communicable diseases. This study examines socio-demographic, dietary, and lifestyle factors associated with overnutrition in Hungarian adults aged 65 and older. Methods: A cross-sectional analysis was conducted using 2019 European Health Interview Survey data, including 1628 elderly participants. Body mass index (BMI ≥ 25 kg/m2) defined overnutrition. Socio-demographic (gender, income, urbanization, partner status), dietary (fruit, vegetable, water, sweetener, salt intake), and lifestyle (alcohol, smoking, physical activity) factors were analyzed. Chi-square tests and multivariate logistic regression identified associations, with odds ratios (ORs) and 95% confidence intervals (CIs) calculated. Results: Overnutrition prevalence was 72.7%, higher in males (77.8%) than females (69.1%). Urbanization, income, and partner status showed associations. Significant predictors included lower water intake (OR = 0.47, 95% CI: 0.33–0.65 for 1–1.5 L), artificial sweetener use (OR = 1.54, 95% CI: 1.13–2.11), moderate/high salt intake (OR = 1.45, 95% CI: 1.06–1.99), former/never smoking (OR = 2.56, 95% CI: 1.73–3.77), and heavy alcohol use (OR = 4.00, 95% CI: 1.33–12.50). Conclusions: Artificial sweetener use, high salt intake, smoking history, and heavy alcohol consumption are key modifiable predictors of overnutrition, informing targeted interventions for elderly Hungarians.

Graphical Abstract

1. Introduction

Overnutrition, characterized by excessive nutrient intake leading to overweight (BMI ≥ 25) or obesity (BMI ≥ 30), is a growing public health concern, contributing to non-communicable diseases (NCDs) such as type 2 diabetes, cardiovascular disease, and metabolic syndrome [1]. Globally, over 1.9 billion adults are overweight or obese, with elderly populations increasingly affected due to longer life expectancies and lifestyle changes [2]. In Europe, obesity prevalence exceeds 20%, with Central and Eastern European nations facing higher rates due to post-socialist dietary shifts [3]. These trends impose significant economic burdens, with obesity-related NCDs costing billions annually [4]. Beyond physical health, overnutrition impairs mental well-being [5], increasing anxiety and depression among those struggling with weight issues [6].
The aging global population presents unique health challenges, exacerbated by the rising prevalence of overnutrition among older adults [7]. Overnutrition is a significant problem among young people, primarily driven by lifestyle factors [8]. However, aging introduces physiological changes, such as reduced metabolic rate and sarcopenia, which predispose older adults to weight gain [9]. Psychological factors, including loneliness and bereavement, may drive emotional eating, particularly in socially isolated individuals [10]. Limited access to exercise facilities or safe outdoor spaces further restricts physical activity [11].
Historically, public health focused on undernutrition in the elderly, but overnutrition now demands equal attention [12]. Urbanization and globalization expose older adults to high-calorie food choices, increasing obesity risk [13]. This demographic also faces a double burden, with some experiencing concurrent obesity and micronutrient deficiencies or sarcopenic obesity, complicating healthcare delivery by requiring integrated nutritional screening [12,13].
Overnutrition is shaped by socio-cultural, economic, and behavioral factors beyond diet [14]. Economic resources influence nutritional choices, with higher incomes linked to greater dietary diversity and quality, potentially mitigating overnutrition risk [15]. Conversely, low-income individuals rely on cheap, high-calorie processed foods [16]. Urban environments, with abundant fast food, contribute to overnutrition [15,16]. Gender and education also play roles [17,18], with women more vulnerable due to hormonal changes and caregiving demands, and higher education correlating with better nutritional knowledge [19]. Sedentary lifestyles and irregular eating patterns, common in the elderly, exacerbate risks [20]. Structural factors, such as high healthy food prices or poor urban walkability, further limit healthier options [21].
In Hungary, where over 30% of adults are obese and the population is rapidly aging, obesity-related diseases pose a significant health burden and elevate mortality risk [22]. Traditional high-fat diets and post-socialist shifts toward processed foods exacerbate this issue [23]. Hungary’s healthcare system struggles with limited preventive programs, amplifying mortality risks [24]. Existing interventions, such as dietary guidelines, often fail to address elderly-specific needs [25], and research on socio-demographic predictors in Central Europe remains limited [23,24].
A comprehensive approach, incorporating personalized dietary modifications, promotion of physical activity, and lifestyle interventions, is essential for promoting healthy aging [26,27]. Accordingly, this study examines the relationships between socio-demographic, dietary, and lifestyle factors and overnutrition among older adults in Hungary, employing logistic regression to identify predictors for targeted interventions.

2. Materials and Methods

2.1. Study Design and Data

This research employed a cross-sectional approach, utilizing data gathered in Hungary during 2019 as part of the European Health Interview Survey (EHIS). The data were collected under Eurostat’s oversight with a standardized questionnaire, with face-to-face and the sample (n = 5603) was adequately representative of Hungary’s general population. Participation in the EHIS was voluntary. While reasons for non-participation are not documented, possible barriers include poor health, cognitive impairment, or institutionalization, which may have introduced selection bias. Data collection was conducted through face-to-face interviews by trained interviewers using a standardized questionnaire, allowing participants to seek clarification on any unclear questions [28]. From this sample, 1628 individuals aged 65 years or older were selected for inclusion and analysis in this study.

2.2. Variables

Anthropometric data were collected using standardized questions: “How tall are you without shoes?” and “What is your body weight without clothes and shoes?” If participants did not know or could not recall, trained interviewers performed measurements according to EHIS protocol using standardized equipment. Using height and weight data from older adults included in this study, body mass index (BMI) was calculated. A binary variable (outcome variable) was then established, categorizing individuals with a BMI of ≥25 kg/m2 as overweight or obese (indicative of overnutrition) and those with a BMI < 25 kg/m2 as normal weight, consistent with WHO guidelines [29].
The following variables, contained in the EHIS data, were selected as explanatory variables to influence overnutrition in the elderly. Socio-demographic variables included gender, education, household income level, degree of urbanization, and partner status and health-related variables comprised self-perceived health status, and presence of long-term illness. Dietary habits were assessed through weekly consumption frequencies of fruits, vegetables, fruit juice, sugary/soft drinks, sugar-free/diet drinks, sweets/desserts, red meat, white meat, processed meat, fish/seafood, and dairy products. Dietary frequency variables were categorized into daily, several times per week (4–6 times), and occasionally (less than 3 times per week) based on the distribution of responses in our dataset to distinguish between low, moderate, and high intake levels. This categorization aligns with established dietary guidelines for key food groups [30]. Daily water intake was recorded as ≥2 L, 1–1.5 L, or ≤1 L. Sweetener use for hot drinks (coffee, tea) was classified as natural (sugar, honey) or artificial, and salt consumption was dichotomized as never/low or moderate/high. Lifestyle factors included alcohol consumption (heavy, moderate, rare/never), smoking status (active, quit, never smoked), and physical activity. Physical activity was evaluated through general daily activity (mostly sitting/no movement, mostly standing, mostly walking/moderate, mostly heavy physical work), number of days per week with at least 10 min of walking, cycling, sports, or muscle-strengthening exercises, and sleep disturbances in the past two weeks.

2.3. Statistical Analysis

Data were analyzed using descriptive statistics to summarize participant characteristics, dietary habits, and lifestyle factors, stratified by BMI category. Frequencies and percentages were calculated for categorical variables, and chi-square tests were used to assess differences between BMI groups, with p-values indicating statistical significance (p < 0.05). Multivariate logistic regression was employed to investigate associations between overnutrition and socio-demographic, dietary, and lifestyle factors. The model included variables such as gender, income level, degree of urbanization, partner status, long-term illness, fruit and vegetable consumption, water intake, fruit juice intake, sweetener use, salt consumption, smoking status, alcohol use, and days of sports per week based on the significant associations detected by chi-square test. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to estimate the strength of associations, with p-values < 0.05 considered significant. To evaluate the discriminative ability of the logistic regression model, we calculated the area under the receiver operating characteristic (ROC) curve (AUC). STATA IC version 18.0 was used to conduct the statistical analyses used in this analysis [31].

3. Results

The analysis of the study population, as presented in Table 1, provides an overview of the associations between socio-demographic and health-related factors and body weight status, categorized as normal BMI (<25) and overnutrition (BMI ≥ 25). The sample comprised 1628 participants, with a higher proportion of females (59.3%, n = 966) compared to males (40.7%, n = 662). A significant association was observed between gender and body weight status (p < 0.001), with males exhibiting a higher prevalence of overnutrition (77.8%) compared to females (69.1%). The prevalence of overnutrition was relatively consistent across education levels, ranging from 70.1% in the secondary education group to 74.3% in the primary education group. Household income level, divided into lower than average (47.0%, n = 766), average (23.5%, n = 382), and higher than average (29.5%, n = 480), demonstrated a statistically significant association with body weight status (p = 0.032). Participants with higher-than-average income had the lowest prevalence of overnutrition (68.7%), while those with lower-than-average income had the highest (75.4%). The degree of urbanization, categorized as urban (19.5%, n = 317), suburban (20.5%, n = 334), rural (32.6%, n = 530), and remote (27.4%, n = 447), was also significantly associated with body weight status (p = 0.038). Participants in urban areas had the lowest prevalence of overnutrition (68.7%), while those in remote areas had the highest (77.4%). Partner status, divided into living with a partner (53.5%, n = 858) and living without a partner (46.5%, n = 746), showed a significant association with body weight status (p = 0.001). Participants living without a partner had a lower prevalence of overnutrition (68.5%) compared to those living with a partner (76.3%). the presence of a long-term illness, reported by 77.1% (n = 1245) of participants, was significantly associated with body weight status (p = 0.015). Participants without a long-term illness had a lower prevalence of overnutrition (67.6%) compared to those with a long-term illness (74.0%).
Table 2 shows association between eating habits and body weight status among older adults. Fruit consumption per week was significantly associated with body weight status (p = 0.031). Participants consuming fruit less than three times a week had the lowest prevalence of overnutrition (65.9%), compared to those consuming fruit every day (74.0%) or 4–6 times a week (72.2%). Vegetable consumption (excluding potatoes) per week also showed a significant association with body weight status (p = 0.045). Participants consuming vegetables 4–6 times a week had the lowest prevalence of overnutrition (68.5%), compared to those consuming vegetables every day (75.1%) or less than three times a week (70.5%). Daily water consumption showed a highly significant association with body weight status (p < 0.001). Participants who consumed one liter or less of water per day had the lowest prevalence of overnutrition (64.3%), while those who consumed at least two liters had the highest (78.4%), suggesting that polydipsia associated with obesity and metabolic syndrome may contribute to increased water intake in these individuals. Fruit juice consumption per week was significantly associated with body weight status (p = 0.037). In contrast, participants consuming fruit juice 4–6 times a week had the lowest prevalence of overnutrition (62.7%), compared to those consuming it every day (66.7%) or less than three times a week (73.5%). The use of sweeteners for hot drinks (coffee, tea) showed a significant association with body weight status (p = 0.002). Participants with overnutrition use more artificial sweeteners (78.3%) compared to those without overnutrition (69.5%). Finally, salt consumption was significantly associated with body weight status (p = 0.038). Participants with moderate or high salt use had a higher prevalence of overnutrition (76.3%) compared to those with never or low salt use (71.1%).
The analysis of associations between lifestyle behaviors and body weight status among older adults presented in Table 3. Participants with heavy alcohol consumption exhibited the highest prevalence of overnutrition (85.5%), compared to those with moderate (73.2%) or rare/never consumption (71.8%). Smoking status demonstrated a highly significant association with body weight status (p < 0.001). Active smokers had the lowest prevalence of overnutrition (55.8%), while those who had quit smoking had the highest (78.1%), followed by those who never smoked (73.8%). The number of days per week performing sports for at least 10 min was also significantly associated with body weight status (p = 0.026). Participants engaging in sports 4–7 days a week had the lowest prevalence of overnutrition (65.1%), compared to those performing sports 1–3 days (73.2%) or not performing sports (74.0%).
The logistic regression analysis, as presented in Table 4, identifies significant predictors of overnutrition (BMI ≥ 25) among the elderly population in Hungary, focusing on socio-demographic, dietary, and lifestyle factors. Water intake was a significantly associated with overnutrition. Compared to individuals consuming more than 2 L of water daily, those consuming 1.5–2 L had a lower odds of overnutrition (OR = 0.68, 95% CI: 0.48–0.95, p = 0.025), and those consuming 1–1.5 L had an even lower odds (OR = 0.47, 95% CI: 0.33–0.65, p < 0.001). The use of artificial sweeteners for tea or coffee was also significantly associated with overnutrition. Compared to those using natural sweeteners, individuals using artificial sweeteners had higher odds of overnutrition (OR = 1.54, 95% CI: 1.13–2.11, p = 0.006). Salt use was a significant predictor. Individuals with moderate or high salt use had higher odds of overnutrition compared to those with never or low salt use (OR = 1.45, 95% CI: 1.06–1.99, p = 0.020). Smoking status was strongly associated with overnutrition. Compared to active smokers, individuals who had quit smoking had significantly higher odds of overnutrition (OR = 2.32, 95% CI: 1.49–3.61, p < 0.001), as did those who never smoked (OR = 2.56, 95% CI: 1.73–3.77, p < 0.001). Alcohol consumption was also a significant predictor. Compared to heavy drinkers, moderate drinkers had lower odds of overnutrition (OR = 0.26, 95% CI: 0.09–0.80, p = 0.019), as did rare drinkers (OR = 0.25, 95% CI: 0.08–0.75, p = 0.018). To explore potential confounding interactions, we conducted a logistic regression analysis including an interaction term between smoking status and alcohol consumption. However, no statistically significant interaction effect on body weight status was observed (p > 0.05).

4. Discussion

This study aimed to identify relationships between socio-demographic, dietary, and lifestyle factors and overnutrition among Hungarian adults aged 65 years and older. The findings revealed significant associations with gender, long-term illness, socio-economic factors (including income levels, degree of urbanization, and partner status), eating habits (including fruit and vegetable consumption, water and fruit juice intake, use of sweeteners and salt), and lifestyle factors (including alcohol consumption, smoking status, and sports activity). However, after adjusting for all significant variables in multivariate logistic regression, reduced water consumption, use of artificial sweeteners, moderate-to-high salt intake, smoking history (former or never smoked), and heavy alcohol use were significantly correlated with overnutrition among the elderly population in Hungary.
Research shows that the prevalence of overweight and obesity among older adults increases with age, and there are significant gender differences in the prevalence of overnutrition and its impact on health [17,32]. Older women are at increased risk of obesity-related health complications due to physiological changes after menopause [33]. Declining estrogen levels alter metabolic processes and lead to changes in the distribution of fat and lean mass [34]. In many cultures, women often play the primary caregiver role, which affects their diet and access to nutrition. Studies show that both sexes are vulnerable to malnutrition, but older women are more likely to be malnourished due to socio-economic constraints [35], which indirectly contributes to their obesity [36]. The higher prevalence of overnutrition among older Hungarian males compared to females, despite literature suggesting female vulnerability, may reflect Hungary-specific socio-cultural factors, such as dietary patterns and lifestyle risks.
The relationship between socio-economic factors and overnutrition in older adults highlights the complex dynamics that influence dietary patterns, health outcomes, and quality of life in older adults. Research suggests that socio-economic status is an important determinant of nutritional health and has a significant impact on the rate of overnutrition among the elderly. In some countries, wealthy older people tend to have better access to a variety of food options and consume higher-calorie foods, which contributes to the increase in obesity and related health problems [37]. Conversely, individuals from lower socio-economic backgrounds often encounter food insecurity and limited access to nutritious diets, which results in poorer nutritional quality overall [38]. However, being highly educated and having a sufficient income may have a positive impact on the nutritional status of the elderly. Higher levels of education are associated with health literacy, which influences food choices and nutritional awareness [39]. Housing and community factors related to socio-economic status play an important role in influencing health outcomes. Low socio-economic status is associated with reduced opportunities for social participation, which may exacerbate health inequalities among older adults. Social isolation, a common problem among socially disadvantaged groups, can hinder physical activity, which is important for weight control and maintaining health [40]. This is supported by our results that socio-economic variables such as income levels, degree of urbanization, and partner status have been found to be associated with overnutrition among the elderly.
Eating habits, such as meal frequency and the types of foods consumed, are directly related to calorie intake and, consequently, overeating. Studies have shown that older adults who frequently snack, especially those high in sugar and fat, have higher calorie intake than those who eat regularly [41]. Cultural considerations further shape the dietary habits of the elderly population. Research has shown that traditional dietary practices influence food choices and nutritional outcomes in older adults [42,43]. Hungarian traditional cuisine tends to be calorically dense, often featuring meat as the primary ingredient, complemented by hearty sides like potatoes, dumplings, and bread [44]. This characteristic of traditional cuisine may be one reason for the high prevalence of obesity among the elderly. Interestingly, most studies have shown that decreasing fruit and vegetable consumption increases the risk of obesity among older adults [45,46,47], yet our findings contradict this, with higher fruit juice consumption associated with a lower rate of overnutrition. This may be attributed to Hungary’s unique dietary patterns, differences in food preparation methods (vegetables cooked with added fats), and potential biases inherent in self-reported dietary data. Socio-economic factors, or compensatory nutritional habits that mitigate the expected effects of higher fruit and vegetable consumption, as these associations were not observed after adjusting for socio-economic and lifestyle variables in the logistic regression model. Additionally, reverse causation is possible—individuals with higher BMI may increase their fruit and vegetable intake in response to health advice, which could obscure causal relationships.
Our study results revealed that using artificial sweeteners increases overnutrition among older adults by 1.54 times. Most of studies show that these non-nutritive sweeteners contribute to health complications, including weight gain, same as our results. Although people with overnutrition may be more likely to use calorie-free sweeteners for weight loss, one prominent concern is that the use of artificial sweeteners may not effectively promote weight loss or better nutritional outcomes, which is often their intended purpose. Some evidence suggests that artificial sweeteners might paradoxically lead to increased caloric intake due to their effects on appetite and metabolism [48]. The intense sweetness from artificial sweeteners may lead to increased cravings for sweet foods, potentially resulting in overconsumption of both sweet and calorically dense foods [49]. Moreover, certain studies have indicated that those who consume artificially sweetened beverages might experience altered gut microbiota composition, which could predispose them to weight gain and insulin resistance [50]. The metabolic implications are particularly critical for the elderly, as many in this population may already struggle with maintaining a healthy weight due to various factors such as reduced physical activity, hormonal changes, and chronic health issues. Research has shown a correlation between high consumption of artificial sweeteners, such as aspartame and sucralose, and adverse metabolic outcomes, including an increased risk of T2D [51] and cardiovascular diseases [52]. Additionally, long-term studies have suggested that older adults who regularly consume artificially sweetened products may be unaware of the potential health risks associated with their intake. The increasing visibility of diet products marketed towards older adults often leads to a misconception that these items are inherently healthy, prompting frequent consumption [53]. While artificial sweeteners are often promoted as a solution for weight management, evidence suggests that their consumption may be linked to negative health outcomes that contribute to overnutrition in the elderly population. This underscores the need for further research and improved public awareness about the implications of artificial sweeteners on health, particularly among older adults who may rely on these products to manage dietary concerns.
Research indicates that excessive salt intake can contribute to several health concerns, including hypertension, obesity, and other metabolic disorders, particularly in older adults. High dietary salt is known to elevate blood pressure, which is a significant risk factor for cardiovascular diseases and is especially pertinent in the elderly due to age-related increases in vascular stiffness and other cardiovascular changes [54]. Moreover, there is growing evidence that excessive salt intake is associated with overweight and obesity. Studies have shown that high salt intake promotes fat storage and is positively correlated with BMI in older adults [55,56]. A study found a correlation between salt intake and obesity indicators, noting that excessive salt might contribute to fat accumulation and affect metabolic health [57]. High salt intake among the Hungarian population remains a major public health problem, and it is one of the main causes of overnutrition among the elderly, which is consistent with our study results.
The associations between smoking status and overnutrition among elderly reveal significant implications for public health, particularly concerning dietary behaviors and health outcomes in this demographic. Research indicates that individuals categorized as “former smokers” and “never smoked” exhibit higher odds of overnutrition compared to active smokers. The observed odds ratios suggest a potential paradox where those who do not currently smoke may have dietary habits that contribute to obesity. This association is consistent with findings from previous literature. A systematic review explored smoking as a predictor of frailty among older adults, emphasizing how lifelong smoking behavior can influence overall health outcomes, and it supports broader findings that non-smokers or those who quit smoking may adopt different eating patterns, potentially leading to overnutrition following smoking cessation [58]. Interestingly, another study found that elderly smokers typically have lower BMI than non-smokers, suggesting that smoking may play a role in weight management through appetite suppression and increased energy expenditure [59]. This finding contrasts with the higher odds of overnutrition observed in former and never smokers, indicating that cessation of smoking or not smoking at all does not guarantee protective effects against obesity in the elderly. Moreover, Hsieh et al. underlined the necessity of considering confounding variables, including smoking status [60]. Further underscoring these interactions, Lee et al. showed that higher obesity levels were associated with significantly lower quality of life, emphasizing the relevance of smoking cessation in relation to dietary habits and obesity management among elderly populations [61]. Therefore, public health interventions encouraging smoking cessation must be paired with dietary education to help mitigate risks associated with obesity.
The findings indicate that moderate and rare drinkers exhibit significantly lower odds of overnutrition compared to heavy drinkers. This supports the hypothesis that patterns and amounts of alcohol consumption can influence nutritional statuses and overall health outcomes in older adults. Research suggests that light to moderate alcohol consumption may have protective effects against developing metabolic syndrome and its components, such as obesity, hypertension, and dyslipidemia [62], which is possibly linked to the lower odds of overnutrition found in our study. The effects of heavy alcohol consumption are compounded by the potential for developing nutritional deficiencies. Evidence has shown that alcohol can interfere with the metabolism of several key nutrients, leading to imbalances that intensify weight gain and negatively impact overall health [63].
Research indicates that moderate physical activity can enhance metabolism, facilitating better energy use and preventing the accumulation of excess body weight among the elderly [64]. Although unadjusted analysis suggested that higher frequency of sports activity was associated with a lower incidence of overnutrition, this association did not persist in the multivariable logistic regression. This indicates that other factors, such as age, comorbidity burden, or obesity, may mediate or confound the relationship between physical activity and health outcomes in this population. In older adults, barriers to regular physical activity are multifactorial and include physical limitations [65], psychosocial factors [66], and environmental obstacles [67].
Our results showed a significant association between higher water intake and overnutrition, which may reflect reverse causality rather than a direct effect. Older adults with overnutrition often consume more water due to increased caloric intake [68], higher sodium content in their diets [69], and greater metabolic demands associated with higher body mass [70]. Additionally, changes in body composition and fluid balance in obesity, as well as cognitive factors affecting thirst perception, may influence drinking behavior [71]. These considerations suggest that elevated water intake in the group with overnutrition may be a compensatory response to physiological and dietary factors rather than a driver of overnutrition.
This study has some limitations, including the use of self-reported questionnaires, which may lead to bias from inaccurate recall, social desirability, or misreporting. Participants might over- or underestimate their dietary intake and physical activity, causing measurement errors. The cross-sectional design also prevents determining causal relationships between overnutrition and related factors. However, a major strength is the application of advanced statistical methods, like logistic regression, to analyze reliable data collected through a Eurostat-validated questionnaire, which is representative of the Hungarian population.

5. Conclusions

The findings show significant links between lower water intake, artificial sweetener use, moderate-to-high salt intake, smoking status (non-smoker or former smoker), and heavy alcohol consumption with overnutrition in elderly Hungarians. However, water intake is unlikely to be a direct cause of overnutrition and may instead reflect metabolic changes and cognitive factors that drive increased consumption. In contrast, artificial sweetener use, high salt intake, heavy alcohol use, and smoking status emerged as strong, independent predictors of overnutrition, regardless of gender, socio-economic factors, diet, or physical activity, emphasizing their importance as modifiable lifestyle factors.
Our findings support routine screening and lifestyle counseling in nutritional care, particularly for older adults with limited physical activity, and highlight the need for public health strategies in Central and Eastern Europe that promote healthy lifestyles and weight management through community-based programs and improved access to age-appropriate resources. Future longitudinal and interventional studies are recommended to better understand causal relationships and to evaluate the effectiveness of specific public health strategies aimed at modifying these risk factors.

Author Contributions

Conceptualization, B.U. and A.C.N.; methodology, B.U.; validation, B.U.; formal analysis, B.U.; data curation, B.U.; writing—original draft preparation, B.U., A.S.G. and A.C.N.; writing—review and editing, B.U., A.S.G. and A.C.N.; visualization, B.U.; supervision, A.C.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The studies involving humans were approved by Ethics of Committee of the University of Debrecen (5609-2020) on 17 December 2020. The studies were conducted in accordance with the local legislation and institutional requirements.

Informed Consent Statement

Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

Data Availability Statement

The data analyzed in this study are subject to the following licenses/restrictions: The data presented in this study are available upon request from Hungarian Central Statistical Office who performed and supervised the data collection. Requests to access these datasets should be directed to Hungarian Central Statistical Office, www.ksh.hu/?lang=en (accessed on 1 April 2025).

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Demographic and health status data of participants.
Table 1. Demographic and health status data of participants.
Variable CategoryTotal n (%)Body Weight p Value
Normal
BMI < 25		Overnutrition
BMI ≥ 25
GenderMale662 (40.7)146 (22.2)511 (77.8)<0.001
Female966 (59.3)296 (30.9)661 (69.1)
EducationPrimary 830 (51.0)212 (25.7)612 (74.3)0.312
Secondary496 (30.5)143 (29.1)349 (70.1)
High302 (18.5)87 (29.2)211 (70.8)
Household income level Lower than average 766 (47.0)187 (24.6)575 (75.4)0.032
Average 382 (23.5)107 (28.2)272 (71.8)
Higher than average 480 (29.5)148 (31.3)325 (68.7)
Degree of urbanization Urban 317 (19.5)98 (31.2)215 (68.8)0.038
Suburban334 (20.5)91 (27.3)242 (72.7)
Rural530 (32.6)153 (29.1)372 (70.9)
Remote 447 (27.4)100 (22.6)343 (77.4)
Partner statusLiving with a partner858 (53.5)202 (23.7)649 (76.3)0.001
Living without a partner746 (46.5)233 (31.5)506 (68.5)
Self-perceived health status Bad or very bad406 (25.0)111 (27.5)293 (72.5)0.163
Satisfactory854 (52.6)218 (25.8)628 (74.2)
Good or very good363 (22.4)112 (31.1)248 (68.9)
Long-term illnessYes 1245 (77.1)321 (26.0)914 (74.0)0.015
No369 (22.9)119 (32.4)248 (67.6)
Bold values indicate statistical significance (p < 0.05) based on chi-square test.
Table 2. Comparison of eating habits for older people with and without overnutrition.
Table 2. Comparison of eating habits for older people with and without overnutrition.
Variable Name Category Body Weight p Value
BMI < 25BMI ≥ 25
Fruit consumption per weekEvery day309 (26.0)880 (74.0)0.031
4–6 times a week47 (27.8)122 (72.2)
Less than 3 times a week86 (34.1)166 (65.9)
Vegetable (except potato) consumption per weekEvery day217 (24.9)655 (75.1)0.045
4–6 times a week95 (31.5)207 (68.5)
Less than 3 times a week127 (29.5)304 (70.5)
Water consumption per dayAt least 2 L147 (21.6)533 (78.4)<0.001
1–1.5 L148 (28.5)372 (71.5)
1 L or less147 (35.7)265 (64.3)
Fruit juice consumption per weekEvery day43 (33.3)86 (66.7)0.037
4–6 times a week28 (37.3)47 (62.7)
Less than 3 times a week370 (26.5)1028 (73.5)
Sugary and soft drinks per weekEvery day33 (34.0)64 (66.0)0.306
4–6 times a week11 (25.6)32 (74.4)
Less than 3 times a week394 (26.9)1069 (73.1)
Sugar-free and diet drinks per weekMore than once a week42 (22.3)146 (77.7)0.091
Less than once a week399 (28.2)1016 (71.8)
Sweetener for hot drinks (coffee, tea)Natural (sugar, honey)253 (30.5)577 (69.5)0.002
Artificial83 (21.7)299 (78.3)
Sweets and desserts consumption More than once a day130 (28.9)319 (71.1)0.377
Less than once a day311 (26.8)851 (73.2)
Red meat consumptionMore than 4 times a week35 (24.6)107 (75.4)0.300
1–3 times a week152 (25.8)437 (74.2)
Less than once a week254 (29.0)622 (71.0)
White meat consumptionMore than 4 times a week79 (29.9)185 (70.1)0.081
1–3 times a week244 (25.4)716 (74.6)
Less than once a week119 (30.8)267 (69.2)
Processed meat consumptionMore than 4 times a week183 (27.3)486 (72.7)0.708
1–3 times a week138 (26.4)385 (73.6)
Less than once a week121 (28.8)299 (71.2)
Fish and seafood consumptionMore than 4 times a week9 (28.1)23 (71.9)0.422
1–3 times a week19 (21.4)70 (78.6)
Less than once a week412 (27.7)1074 (72.3)
Dairy product consumptionMore than 4 times a week285 (26.9)774 (73.1)0.195
1–3 times a week99 (26.3)278 (73.7)
Less than once a week58 (33.1)117 (66.9)
Salt consumption Never or low salt use331 (28.9)816 (71.1)0.038
Moderate or high salt use 109 (23.7)350 (76.3)
Bold values indicate statistical significance (p < 0.05) based on chi-square test.
Table 3. Comparison of lifestyle factors for older people with and without overnutrition.
Table 3. Comparison of lifestyle factors for older people with and without overnutrition.
Variable Name Category Body Weight p Value
BMI < 25BMI ≥ 25
Alcohol consumptionHeavy 10 (14.5)59 (85.5)0.044
Moderate 93 (26.8)254 (73.2)
Rare or never336 (28.2)855 (71.8)
Smoking status Active92 (44.2)116 (55.8)<0.001
Quit 96 (21.9)343 (78.1)
Never smoked 251 (26.2)709 (73.8)
General characteristics of daily physical activity Mostly sitting or no movement 170 (27.5)448 (72.5)0.996
Mostly standing 30 (28.3)76 (71.7)
Mostly walking or moderate229 (27.2)613 (72.8)
Mostly heavy physical work 8 (27.6)21 (72.4)
Number of days walked for at least 10 min a weekDo not walk88 (27.5)232 (72.5)0.495
1–3 days78 (24.9)235 (75.1)
4–7 days274 (28.4)692 (71.6)
Number of days cycled for at least 10 min a weekDo not cycle322 (27.6)885 (72.4)0.930
1–3 days49 (26.6)135 (73.4)
4–7 days65 (26.6)179 (73.4)
Number of days performing sports for at least 10 min a weekDo not perform sports310 (26.0)883 (74.0)0.026
1–3 days51 (26.8)139 (73.2)
4–7 days75 (34.9)140 (65.1)
Number of days performing muscle strengthening exercises for at least 10 min a weekNo exercise 352 (26.8)961 (73.2)0.101
1–3 days33 (23.6)107 (76.4)
4–7 days48 (34.3)92 (65.7)
Sleep disturbances occurred in last 2 weeksNever204 (26.8)558 (73.2)0.775
In a few days149 (27.2)399 (72.8)
More than a week40 (31.2)88 (68.8)
Almost every day44 (27.2)123 (73.6)
Bold values indicate statistical significance (p < 0.05) based on chi-square test.
Table 4. Logistic regression results for overnutrition in the elderly population in Hungary.
Table 4. Logistic regression results for overnutrition in the elderly population in Hungary.
VariableCategory/LevelOR95% CIp-Value
GenderMale (Reference)
Female0.810.57–1.140.236
Income levels Lower than average (Reference)
Average 0.900.63–1.270.555
Higher than average 0.760.53–1.070.124
Degree of urbanizationUrban (Reference)
Suburban1.240.78–1.950.350
Rural0.850.57–1.280.459
Remote 1.160.75–1.800.494
Partner statusLiving with a partner (Reference)
Living without a partner0.780.57–1.060.118
Long-term illnessYes (Reference)
None0.800.58–1.120.204
Fruit consumptionEvery day (Reference)
4–6 times a week0.980.62–1.550.791
Less than 3 times a week0.680.46–1.010.062
Vegetable consumptionEvery day (Reference)
4–6 times/week0.830.57–1.220.360
1–3 times/week0.920.65–1.300.651
Water intakeMore than 2 L (Reference)
1.5–2 L0.680.48–0.950.025
1–1.5 L0.470.33–0.65<0.001
Fruit juice intake Every day (Reference)
4–6 times/week0.950.43–1.980.860
1–3 times/week1.430.86–2.380.160
Sweetener use for tea or coffeeNatural (Reference)
Artificial1.541.13–2.110.006
Salt useNever or low salt use
Moderate or high salt use 1.451.06–1.990.020
Smoking statusActive (Reference)
Quit 2.321.49–3.61<0.001
Never smoked 2.561.73–3.77<0.001
Alcohol useHeavy drinker (Reference)
Moderate drinker0.260.09–0.80<0.019
Rare drinker0.250.08–0.75<0.018
Days of ten-minute sport per weekDo not perform sports (Reference)
1–3 days0.920.58–1.430.714
4–7 days0.740.48–1.130.171
Bold values indicate statistical significance (p < 0.05). Odds ratios are adjusted for variables in the model. AUC = 0.6708 (95% CI 0.637–0.705).
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Ulambayar, B.; Ghanem, A.S.; Nagy, A.C. Overnutrition in the Elderly Population: Socio-Demographic and Behavioral Risk Factors in Hungary. Nutrients 2025, 17, 1954. https://doi.org/10.3390/nu17121954

AMA Style

Ulambayar B, Ghanem AS, Nagy AC. Overnutrition in the Elderly Population: Socio-Demographic and Behavioral Risk Factors in Hungary. Nutrients. 2025; 17(12):1954. https://doi.org/10.3390/nu17121954

Chicago/Turabian Style

Ulambayar, Battamir, Amr Sayed Ghanem, and Attila Csaba Nagy. 2025. "Overnutrition in the Elderly Population: Socio-Demographic and Behavioral Risk Factors in Hungary" Nutrients 17, no. 12: 1954. https://doi.org/10.3390/nu17121954

APA Style

Ulambayar, B., Ghanem, A. S., & Nagy, A. C. (2025). Overnutrition in the Elderly Population: Socio-Demographic and Behavioral Risk Factors in Hungary. Nutrients, 17(12), 1954. https://doi.org/10.3390/nu17121954

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