Abstract
Background
The study assessed the associations between dietary indices and anthropometric parameters in participants enrolled in the Third Age University (U3A) in Türkiye.
Methods
A cross-sectional study was conducted on 272 U3A participants. Dietary quality was assessed using the Healthy Eating Index-2015 (HEI-2015), Diet Quality Index-International (DQI-I), and Household Dietary Diversity Score (HDDS), which were calculated from 24-h dietary recall data. Adherence to the Mediterranean diet was evaluated using the Mediterranean Diet Adherence Scale (MEDAS). Anthropometric and body composition measurements were collected. All analyses controlled for relevant socio-demographic factors and nutrition education.
Results
Higher scores for both DQI-I and HDDS were linked to lower body mass index and waist circumference (all p < 0.05). Further, greater adherence to the Mediterranean diet was inversely associated with body fat percentage, with participants exhibiting higher adherence having a lower likelihood of increased body fat percentage (OR = 0.831; %95CI: 0.697–0.989; p < 0.05).
Conclusion
Diet quality among participants in U3As was predominantly moderate to high. Taken together, these findings indicate that high adherence to the Mediterranean diet and higher dietary diversity may be linked to improvements in anthropometric measurements and a reduced risk of obesity in this population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26268-w.
Keywords: Aged, Diet quality, Obesity, Anthropometric measurements, Third Age University
Introduction
The global increase in life expectancy has led to an increased proportion of the population aged 60 and over in the world. It is projected that by 2050, people aged 60 and over will make up approximately 22.0% of the global population [1]. Universities of the Third Age (U3As) provide educational opportunities for people aged 60 or older, promoting active ageing through lifelong learning. Inspired by similar initiatives in France, England, Poland, Portugal, Malta, Brazil, the United States of America, Australia, New Zealand, Taiwan and China, the “Tazelenme” University (TAU) was established in 2016 under the leadership of the Department of Gerontology and the Application and Research Centre for Ageing Studies at Akdeniz University [2, 3]. U3As promote intellectual engagement, self-improvement and life satisfaction by addressing the educational needs of individuals in the older age range. Lifelong learning plays a crucial role in improving overall well-being in the face of the physiological, psychological and social challenges associated with ageing, such as declining health, retirement, financial constraints and changes in social status [3].
Nutrition plays a vital role in shaping the ageing process and maintaining overall health in the ageing population. Ensuring that the nutritional needs of the older people are met and promoting a healthy and balanced diet are imperative for their general health, functional independence, and overall quality of life [4]. Surprisingly, there is a paucity of research on the nutritional status and dietary quality of U3A members. U3A programs can positively improve the dietary habits of older people by increasing their social interaction. Research has shown greater participation in social and cognitive leisure activities was related to better diet quality in community dwelling older individuals [5]. Including healthy and balanced nutrition education in the U3A’s programme can encourage the preference of healthier and nutritious foods through social interaction and friendship.
Diet quality indices, the most widely used of which are the Healthy Eating Index (HEI), the Diet Quality Index-International (DQI-I), the Household Dietary Diversity Score (HDDS) and the Mediterranean Diet Adherence Score (MEDAS), are essential tools for assessing the nutritional status of individuals by evaluating dietary patterns, nutrient intakes and adherence to dietary guidelines. These indices provide a comprehensive understanding of dietary adequacy and its impact on health [6, 7]. Their use is particularly valuable in the older population, where adequate nutrition plays a vital role in maintaining health, functional independence, mental health and overall well-being [4]. Mental health factors such as stress, loneliness and depression may have a negative effect on dietary habits in elderly individuals. Chegini et al. [8] reported that higher diet quality may be associated with lower depression symptoms. As such, programmes that increase social interaction, such as U3A, can indirectly improve the quality of the diet by supporting mental health.
Ageing changes body composition, including muscle loss, increased fat mass and altered metabolism. Consequently, Body Mass Index (BMI) often increases due to fat accumulation and vertebral compression. However, BMI is less accurate in the population aged 60 and over because of height loss, and its inability to differentiate between peripheral and visceral obesity. This can lead to misdiagnosis of normal weight central obesity [9, 10]. In contrast, waist circumference (WC) and body fat percentage (BF%) are more accurate measures of obesity, assessing fat distribution and overall adiposity. WC is particularly useful for identifying central obesity associated with cardiometabolic risks such as hypertension and cardiovascular diseases, while BF% assesses adiposity better than BMI. Therefore, combining WC and BF% with BMI is essential to provide a comprehensive assessment of obesity and the health risks it poses in older individuals [11].
This study aims to assess the nutritional status and dietary quality of participants aged 60 years and older enrolled in the TAU in Akdeniz University, Türkiye. In particular, the study focuses on assessing adherence to the Mediterranean diet, and diet quality using the HEI and the DQI-I, and dietary diversity using the HDDS. We hypothesise that there is an association between diet quality and anthropometric measurements among U3A members in TAU. Results from this study will help develop evidence-based, targeted dietary recommendations for older individuals attending U3A.
Methods
Study sample and data collection
This single-center, cross-sectional observational study was conducted according to the guidelines for strengthening the reporting of observational studies in epidemiology (STROBE). Participants were randomly selected from 850 older people aged 60 years and over enrolled in the U3A of Akdeniz University, Türkiye, to ensure an unbiased sample. The randomization process was carried out using a computer-generated randomization algorithm. Each participant had an equal chance of being selected. A priori power analysis was conducted using G*Power 3.1.9.7 to determine the minimum sample size required. For a logistic regression analysis, an alpha level of 0.05 and a statistical power of 80% were selected. The expected outcome prevalence was set at 50%, based on preliminary data from a previous study conducted in the same Tazelenme University population, which reported a combined overweight prevalence of 53.7% [12]. To detect a clinically relevant effect size represented by an odds ratio of 0.70, the calculation indicated a minimum of 264 participants. To accommodate this requirement and ensure robust power, a sample of 264 participants was targeted. Simple randomization was thus applied to the 272 individuals who were notified of the study and met the inclusion and exclusion criteria. Inclusion criteria required participants to be 60 years of age or older, defined as members at the TAU on the campus of Antalya Akdeniz University, and to provide voluntary informed consent. Participants who withdrew, did not provide written consent, did not follow the study protocol, or had specific chronic conditions known to severely alter dietary intake and metabolism (e.g., end-stage renal disease, active cancer treatment, liver failure etc.) were excluded. Data collection was performed using a questionnaire administered through face-to-face interviews.
The study was approved and conducted in accordance with the ethical principles of the Declaration of Helsinki by the Clinical Research Ethics Committee of Antalya Training and Research Hospital, University of Health Sciences, Türkiye (Decision No: 3/16, Date: 03.02.2022). Written informed consent was obtained from all participants prior to enrolling them in the study.
Questionnaire Administration and Anthropometric Measurements
The questionnaire collected information on several parameters, including sociodemographic characteristics, health status, nutrition education and anthropometric measurements.
24-h dietary recall data
The team of researchers consisting of dietitians and they collected 24-h dietary recall data records using the "Food and Nutrient Photo Catalogue" for home consumption and "National menu planning and implementation guide for mass nutrition systems" for meals consumed outside the home [13]. Daily energy and nutrient intake data analyzed using the Nutrition Information System 9 (BeBIS 9) computer program [14].
Evaluation of dietary indices
In this study, we used data collected from the 24-h dietary recalls calculating the following dietary quality indices, the Healthy Eating Index-2015 (HEI-2015), the Diet Quality Index-International (DQI-International), and the Household Dietary Diverse Scale (HDDS). Additionally, adherence to the Mediterranean diet was evaluated using the separately administered 14-item Mediterranean Diet Adherence Scale (MEDAS).
Healthy Eating Index-2015 (HEI-2015): The HEI-2015 is an update of the HEI-2010, in which the concept of empty calories is replaced by specific assessments of saturated fat and added sugars, resulting in 13 components. HEI-2015 assesses diet quality on a scale of 0–100, with higher scores reflecting better diet quality. HEI calculation was performed in accordance with the guidelines [15]. HEI-2015 scores < 50 show “poor”, 51–80 as “moderate”, and ≥ 80 as “good-quality diets”.
Diet Quality Index-International (DQI-I): The DQI-I evaluates diet quality based on diversity, adequacy, moderation and overall balance [16]. The total DQI-I score ranges from 0 to 100, with higher scores indicating better diet quality. For descriptive purposes, the participants were classified into two groups based on the median DQI-I score: lower diet quality (≤ 62.25), and higher diet quality (> 62.25). For all logistic regression analyses, however, DQI-I was treated as continuous variable, to maximize statistical power and preserve the granularity of the data.
Household Dietary Diverse Scale (HDDS): The HDDS is a snapshot of a household's economic ability to access a variety of foods [17]. The higher the HDDS, the greater the association with socio-economic status and household food security. HDDS is evaluated based on the total number of 12 food groups consumed, and it is evaluated as low when the ≤ is 3, medium when it is 4–6, and high when it is ≥ 7 [18].
Evaluation of Mediterranean diet adherence
Mediterranean diet adherence was evaluated with the 14-item Mediterranean Diet Adherence Scale (MEDAS). The MEDAS measures dietary habits based on 14 components of the Mediterranean diet, and each of the 14 items is scored 1 or 0, depending on adherence to specific criteria such as use of olive oil, consumption of fruit and vegetables, preference for white meat and limitation of red meat, animal fats and sugary drinks. Higher scores indicate greater adherence to the Mediterranean diet, with scores categorized as low (≤5), moderate (6–9), and high (≥10) [19, 20].
Anthropometric measurements
The measurements were conducted in the Anthropometry Laboratory of the Department of Nutrition and Dietetics using a bioelectrical impedance analyzer (BIA) and stadiometer. Height measurements adhered to standard procedures [21], with BMI calculated from weight and height data. The criteria used to assess BMI were based on the cut-off values recommended for older people [22]. Waist circumference measured with an inflexible tape measure to within 0.1 cm. The cut-off values for waist circumference were evaluated using values recommended for older people [23]. Body fat percentage (BF%) was measured using a segmental body composition bioelectrical impedance analyzer (BC-418, Tanita Corp., Tokyo, Japan) following standardized technique [10]. To classify obesity based on BF%, we used age and gender specific cut-off points [24].
Statistical analysis
Descriptive statistics were expressed as median (interquartile range (IQR)) or mean (standard deviation (SD)), depending on data distribution, and as frequency (percentage) for categorical variables. Differences in anthropometric measures of dietary quality indices were analyzed using ANOVA for normally distributed data (HEI-2015, DQI-I) and Kruskal–Wallis test for non-normally distributed variables (HDDS, MEDAS). Post-hoc comparisons using the Tukey test for ANOVA and the Mann–Whitney U test with Bonferroni correction for Kruskal–Wallis analyses. The association between dietary indices and BMI categories was assessed using multinomial logistic regression, while the relationships between dietary indices and waist circumference (WC) and body fat percentage (BF%) were evaluated using binomial logistic regression. A two-step approach was used to develop logistic regression models. Model 1 adjusted for age and gender, whereas model 2 incorporated additional potential confounders including marital status, education level, income, smoking status, presence of chronic illness, nutritional education. The selection of control variables was based on an extensive literature review, which demonstrated their potential influence on both diet quality and anthropometric outcomes. The order of inclusion of variables was determined by model fit indices Nagelkerke R2 and significance testing to ensure robust estimation of associations. Logistic regressions were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for obesity-related classifications. Statistical significance was set at p < 0.05. Analyses were performed using IBM SPSS Statistics 25.0 (IBM Corp., Armonk, NY, USA).
Results
The characteristics of the study participants are shown in Table 1. The majority were women (66.5%), aged 60–69 (68.8%), 61.0% were married, 72.0% had at least a secondary education, and 62.9% reported an income equal to their expenses. In addition, 89.0% were non-smokers. 75.7% had a chronic disease and 71.7% reported regular use of medication. More than a third of the participants (36.0%) received nutritional education. The median BMI was 28.0 kg/m2, with 52.2% of participants having overweight and 12.9% having obesity. A higher prevalence of abdominal obesity, defined by waist circumference, was observed among women (39.2%) compared to men (15.4%). Similarly, the median body fat percentage was higher in women (36.7%) than in men (26.1%). Overall, 18.8% of the study population was identified as having obesity according to body fat percentage criteria.
Table 1.
Characteristics of study participants
| Valuables | Men (n = 91, 33.5%) | Women (n = 181, 66.5%) | Total (n = 272, 100.0%) |
|---|---|---|---|
| Age groups (years) | |||
| 60–69 | 52 (57.1%) | 135 (74.6%) | 187 (68.8%) |
| 70 or above | 39 (42.9%) | 46 (25.4%) | 85 (31.3%) |
| Marital Status | |||
| Married | 63 (69.2%) | 103 (56.9%) | 166 (61.0%) |
| Single | 28 (30.8%) | 78 (43.1%) | 106 (39.0%) |
| Education Status | |||
| Primary school | 12 (13.2%) | 38 (21.0%) | 50 (18.4%) |
| Middle school | 3 (3.3%) | 23 (12.7%) | 26 (9.6%) |
| High school | 25 (27.5%) | 64 (35.4%) | 89 (32.7%) |
| University | 45 (49.5%) | 52 (28.7%) | 97 (35.7%) |
| Graduate | 6 (6.6%) | 4 (2.2%) | 10 (3.6%) |
| Income status | |||
| Income > expenses | 5 (5.5%) | 8 (4.4%) | 13 (4.8%) |
| Income = expenses | 56 (61.5%) | 115 (63.5%) | 171 (62.9%) |
| Expenses > income | 30 (33.0%) | 58 (32.0%) | 88 (32.4%) |
| Smoking | |||
| No | 79 (86.8%) | 163 (90.1%) | 242 (89.0%) |
| Yes | 12 (13.2%) | 18 (9.9%) | 30 (11.0%) |
| Chronic Illness | |||
| No | 28 (30.8%) | 38 (21.0%) | 66 (24.3%) |
| Yes | 63 (69.2%) | 143 (79.0%) | 206 (75.7%) |
| Medicine use | |||
| No | 30 (33.0%) | 47 (26.0%) | 77 (28.3%) |
| Yes | 61 (67.0%) | 134 (74.0%) | 195 (71.7%) |
| Nutritional education | |||
| No | 59 (64.8%) | 115 (63.5%) | 174 (64.0%) |
| Yes | 32 (35.2%) | 66 (36.5%) | 98 (36.0%) |
| BMI (kg/m2) (Median, IQR) | 27.8 (4.7) | 28.2 (4.9) | 28.0 (4.6) |
| BMI classification | |||
| Underweight (< 23) | 5 (5.5%) | 15 (8.3%) | 20 (7.4%) |
| Normal (23–26.9) | 31 (34.1%) | 44 (24.3%) | 75 (27.6%) |
| Overweight (27–33) | 47 (51.6%) | 95 (52.5%) | 142 (52.2%) |
| Obesity (> 33) | 8 (8.8%) | 27 (14.9%) | 35 (12.9%) |
| Waist circumference (cm)(Mean, SD) | 101.1 (9.0) | 95.1 (11.1) | 97.8 (13.4) |
|
Waist circumference classification Obesity (Men ≥ 110 cm; Women ≥ 98 cm) |
14 (15.4%) | 71 (39.2%) | 85 (31.3%) |
| Body fat % (Median, IQR) | 26.1 (7.9) | 36.7 (7.3) | 33.5 (11.8) |
|
Body fat classificaiton Obesity (Men ≥ 30%; Women ≥ 42%) |
20 (22.0%) | 31 (17.1%) | 51 (18.8%) |
| HEI-2015 (Mean, SD) | 61.8 (11.7) | 62.1 (12.2) | 62.1 (17.0) |
| HEI −2015 classification | |||
| Poor (< 50) | 12 (13.2%) | 36 (19.9%) | 48 (17.6%) |
| Moderate (51–80) | 72 (79.1%) | 136 (75.1%) | 208 (76.5%) |
| Good (> 80) | 7 (7.7%) | 9 (5.0%) | 16 (5.9%) |
| DQI-I (Mean, SD) | 61.4 (6.6) | 61.8 (7.3) | 62.3 (9.2) |
| DQI-I classification | |||
| Lower diet quality (≤ 62.25) | 49 (53.8%) | 87 (48.1%) | 136 (50.0%) |
| Higher diet quality (> 62.25) | 42 (46.2%) | 94 (51.9%) | 136 (50.0%) |
| HDDS (Median, IQR) | 9.0 (2.0) | 9.0 (2.0) | 9.0 (2.0) |
| HDDS classification | |||
| Low diversity (0–3) | 0 (0.0%) | 1 (0.6%) | 1 (0.4%) |
| Medium diversity (4–6) | 5 (5.5%) | 7 (3.9%) | 12 (4.4%) |
| High diversity (7–12) | 86 (94.5%) | 173 (95.6%) | 259 (95.2%) |
| MEDAS (Median, IQR) | 8.0 (3.0) | 8.0 (2.0) | 8.0 (2.0) |
| MEDAS classification | |||
| Low adherence (0–5) | 10 (11.0%) | 15 (8.3%) | 25 (9.2%) |
| Medium adherence (6–9) | 54 (59.3%) | 128 (70.7%) | 182 (66.9%) |
| High adherence (10–14) | 27 (29.7%) | 38 (21.0%) | 65 (23.9%) |
BMI Body Mass Index, HEI-2015 Healthy eating index-2015, DQI-I Dietary Quality Index International, HDDS Household Dietary Diversity Scale, MEDAS Mediterranean Diet Adherence Scale
Diet quality was assessed using four indices. According to the HEI-2015, the majority of participants (76.5%) had a diet categorized as "needs improvement," while only 5.9% achieved a "good" score. Slightly more women (51.9%) than men (46.2%) were classified in the 'better' diet quality group based on the DQI-I. The HDDS revealed high dietary diversity for most participants (95.2%). Adherence to the Mediterranean diet, measured by MEDAS, was medium for 66.9% and high for 23.9% of the study population. Furthermore, as illustrated in Fig. 1, the distributions of HEI-2015, DQI-I, HDDS, and MEDAS scores are compared across the two age groups (60–69 years and ≥ 70 years).
Fig. 1.
Distribution of dietary quality indices and MEDAS across age groups (60–69 and 70 years or over) in U3A members. Box plots show the scores for the (a) Healthy Eating Index-2015 (HEI-2015), b Diet Quality Index-International (DQI-I), c Household Dietary Diversity Score (HDDS), and d Mediterranean Diet Adherence Scale (MEDAS) scores between participants aged 60–69 years and those aged 70 years or over
Table 2 shows the analysis of dietary quality and anthropometric measures by BMI, waist circumference and body fat percentage. Analysis of diet quality and anthropometric measures revealed no significant differences in HEI-2015 across BMI categories, waist circumference groups, or body fat percentage groups (p = 0.305, p = 0.513, p = 0.899; respectively). However, the DQI-I scores showed significant variation across BMI categories (p = 0.019), with post-hoc analysis indicating a borderline significant difference between underweight and overweight participants (p = 0.050). No significant differences were found for waist circumference (p = 0.110) or for the percentage of body fat (p = 0.832). HDDS showed significant variation across BMI categories (p = 0.046), although post-hoc analyses did not identify specific pairwise differences after multiple comparison adjustment. Similarly, significant differences were observed in HDDS across waist circumference categories (p = 0.004). In contrast, no significant variation in HDDS was found across body fat percentage categories (p = 0.545). Finally, MEDAS showed no significant differences in adherence between BMI categories (p = 0.637) or waist circumference groups (p = 0.807), although a significant difference was found for body fat (p = 0.048), with those with higher body fat less adherent to the Mediterranean diet.
Table 2.
Association between dietary quality and anthropometric measurements among third age university students
| Dietary quality | BMI | WC | BF% | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Underweight (< 23kg/m2) (n = 20) | Normal (23–26.9kg/m2) (n = 75) | Overweight (27–33 kg/m2) (n = 142) | Obesity (> 33 kg/m2) (n = 35) | p | Men < 110 cm; Women < 98 cm (n = 187) |
Men ≥ 110 cm; Women ≥ 98cm (n = 85) |
p | Men < 30%; Women < 42% (n = 221) |
Men ≥ 30%; Women ≥ 42% (n = 51) |
p | |
| HEI-2015, mean (SD) | 61.4 (14.0) | 59.8 (11.0) | 62.8 (12.4) | 63.4 (10.5) | 0.305 | 61.6 (11.8) | 62.7 (12.4) | 0.513 | 62.0 (11.8) | 61.8 (13.1) | 0.899 |
| DQI-I, mean (SD) | 58.6 (5.9)a | 60.9 (7.3)a,b | 62.9 (6.6)b | 60.5 (8.1)a,b | 0.019 | 61.2 (6.7) | 62.7 (7.8) | 0.110 | 61.7 (7.0) | 61.5 (7.3) | 0.832 |
| HDDS, median (IQR) | 9.0 (1.0) | 9.0 (2.0) | 9.0 (2.0) | 9.0 (2.0) | 0.046 | 9.0 (2.0) | 9.0 (1.0) | 0.004 | 9.0 (2.0) | 9.0 (1.0) | 0.545 |
| MEDAS, median (IQR) | 9.0 (3.8) | 8.0 (2.0) | 8.0 (3.0) | 8.0 (3.0) | 0.637 | 8.0 (3.0) | 8.0 (2.0) | 0.807 | 8.0 (2.5) | 7.0 (3.0) | 0.048 |
BMI Body Mass Index, WC Waist Circumference, BF% Body Fat %, BMI Body Mass Index, HEI-2015 Healthy eating index-2015, DQI-I Dietary Quality Index International, HDDS Household Dietary Diversity Scale, MEDAS Mediterranean Diet Adherence Scale
Values are expressed as mean ± standard deviation for normally distributed variables (HEI, DQI-I) and median (interquartile range) for non-normally distributed variables (HDDS, MEDAS)
Different superscript letters indicate statistically significant differences:
- For DQI-I: letters (a,b) indicate significant differences at p < 0.05 by Tukey's post-hoc test following one-way ANOVA (F = 3.375, p = 0.019); DQI: Underweight vs. overweight, p = 0.050
- For HDDS: Kruskal–Wallis test showed a significant overall difference (χ2 = 8.015, p = 0.046). However, post-hoc Mann–Whitney U tests with Bonferroni correction revealed no significant pairwise comparisons (all adjusted p > 0.05). Therefore, no superscript letters are used to denote group differences
No post-hoc analyses were performed for HEI and MEDAS as the overall ANOVA/Kruskal–Wallis tests were not significant (p > 0.05)
Multinomial and binary logistic regression models were used to examine the relationship between dietary quality indices according to anthropometric measurements in Table 3. In the crude multinomial model, no significant association was observed between underweight and normal-weight participants for any of the diet quality indices. However, in Model 1, higher DQI scores were significantly associated with a reduced likelihood of being underweight, as each unit increase was associated with a 7.9% decrease in the odds of underweight status. In Model 2, this association remained significant (p = 0.012, OR = 0.894, 95% CI:0.819–0.976), indicating that each unit increase in DQI-I was associated with a 10.6% decrease in the odds of being underweight. Additionally, a higher HDDS score was significantly associated with a reduced risk of underweight status (p = 0.040, OR = 0.638, 95% CI:0.415–0.980), suggesting that dietary diversity may play a protective role against underweight. The crude multinomial model found no significant association between individuals with overweight and individuals with normal-weight for HEI-2015, DQI-I and MEDAS. However, higher HDDS was protective against being overweight, with each unit increase associated with 21% reduced odds in the crude model. This association remained significant in model 1 as well as in model 2. In model 2, a higher HDDS was significantly associated with lower odds of having overweight (p = 0.028, OR = 0.765, 95% CI:0.602–0.971), suggesting that individuals with overweight consumed a less diverse diet. The crude multinomial model showed no significant association between DQI-I or MEDAS and obesity compared to normal weight. Higher HEI scores were found to slightly increase the risk of obesity in the crude model (p = 0.036, OR = 1.042, 95%CI: 1.003–1.083), although this finding was unexpected; after adjustment, this association was attenuated (p > 0.05). In contrast, higher HDDS was found to be a strong protective factor against obesity. Specifically, higher HDDS significantly reduced the risk of obesity (p = 0.005, OR = 0.613) in model 2. This suggests that individuals with obesity had a significantly lower dietary diversity than individuals with normal-weight. For waist circumference, participants with a larger waist circumference (men > 110 cm; women > 98 cm) indicating obesity had significantly lower HDDS scores in all models (crude OR = 0.754, 95% CI: 0.615–0.924, p = 0.006; adjusted OR = 0.717, 95% CI: 0.579–0.889, p = 0.002), suggesting a negative association between waist circumference and dietary diversity. No significant associations were observed for HEI-2015, DQI-I or MEDAS. Finally, no significant associations with dietary quality indices were found in either the crude or adjusted models for body fat percentage. However, in MEDAS, there was a marginal trend indicating that participants with higher body fat were less likely to adhere to the Mediterranean diet (OR = 0.831, 95% CI: 0.697–0.989, p = 0.038).
Table 3.
Logistic Regression analysis examining the association between dietary quality indices according to anthropometric measurements
| Anthropometric measurements | Dietary quality | Crude Model | Model 1 | Model 2 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio (Exp(B)) | 95% CI for OR | p for trend | Odds Ratio (Exp(B)) | 95% CI for OR | p for trend | Odds Ratio (Exp(B)) | 95% CI for OR | p for trend | ||
| Body Mass Index | ||||||||||
| Underweight vs. Normal | HEI-2015 | 1.030 | 0.982–1.080 | 0.230 | 1.033 | 0.983–1.084 | 0.197 | 1.044 | 0.989–1.101 | 0.118 |
| DQI-I | 0.924 | 0.853–1.001 | 0.054 | 0.921 | 0.851–0.998 | 0.045 | 0.894 | 0.819–0.976 | 0.012 | |
| HDDS | 0.724 | 0.490–1.069 | 0.105 | 0.706 | 0.475–1.047 | 0.084 | 0.638 | 0.415–0.980 | 0.040 | |
| MEDAS | 1.142 | 0.863–1.509 | 0.353 | 1.159 | 0.872–1.539 | 0.309 | 1.268 | 0.928–1.732 | 0.136 | |
| Overweight vs. Normal | HEI-2015 | 1.017 | 0.991–1.045 | 0.202 | 1.017 | 0.990–1.045 | 0.224 | 1.019 | 0.991–1.048 | 0.188 |
| DQI-I | 1.025 | 0.979–1.074 | 0.295 | 1.025 | 0.977–1.075 | 0.311 | 1.022 | 0.973–1.073 | 0.381 | |
| HDDS | 0.791 | 0.628–0.996 | 0.046 | 0.777 | 0.615–0.982 | 0.035 | 0.765 | 0.602–0.971 | 0.028 | |
| MEDAS | 0.996 | 0.853–1.164 | 0.965 | 1.004 | 0.859–1.173 | 0.964 | 1.013 | 0.860–1.194 | 0.874 | |
| Obesity vs. Normal | HEI-2015 | 1.042 | 1.003–1.083 | 0.036 | 1.037 | 0.997–1.079 | 0.072 | 1.033 | 0.990–1.077 | 0.135 |
| DQI-I | 0.957 | 0.895–1.024 | 0.202 | 0.960 | 0.896–1.030 | 0.255 | 0.953 | 0.884–1.027 | 0.208 | |
| HDDS | 0.607 | 0.441–0.837 | 0.002 | 0.588 | 0.424–0.815 | 0.001 | 0.613 | 0.436–0.860 | 0.005 | |
| MEDAS | 0.947 | 0.759–1.181 | 0.628 | 0.967 | 0.771–1.214 | 0.773 | 0.936 | 0.732–1.198 | 0.599 | |
| Waist Circumference | ||||||||||
| Men ≥ 110 cm; Women ≥ 98cm | HEI-2015 | 1.005 | 0.982–1.029 | 0.671 | 1.008 | 0.983–1.033 | 0.551 | 1.007 | 0.981–1.034 | 0.602 |
| DQI-I | 1.022 | 0.981–1.064 | 0.298 | 1.016 | 0.974–1.060 | 0.468 | 1.017 | 0.973–1.064 | 0.454 | |
| HDDS | 0.754 | 0.615–0.924 | 0.006 | 0.717 | 0.579–0.889 | 0.002 | 0.731 | 0.584–0.914 | 0.006 | |
| MEDAS | 0.977 | 0.848–1.125 | 0.745 | 0.989 | 0.851–1.149 | 0.882 | 0.978 | 0.836–1.144 | 0.779 | |
| Body Fat % | ||||||||||
| Men ≥ 30%; Women ≥ 42% | HEI-2015 | 1.003 | 0.975–1.031 | 0.851 | 1.002 | 0.974–1.031 | 0.900 | 0.997 | 0.968–1.027 | 0.855 |
| DQI-I | 0.999 | 0.951–1.049 | 0.972 | 1.001 | 0.952–1.053 | 0.962 | 0.999 | 0.948–1.051 | 0.957 | |
| HDDS | 0.928 | 0.735–1.173 | 0.533 | 0.935 | 0.739–1.182 | 0.574 | 0.973 | 0.763–1.241 | 0.827 | |
| MEDAS | 0.853 | 0.723–1.007 | 0.060 | 0.853 | 0.723–1.007 | 0.061 | 0.831 | 0.697–0.989 | 0.038 | |
Normal Body Mass Index (23–26.9 kg/m2—Reference); Waist Circumference (Men < 110 cm; Women < 98 cm—Reference); Body Fat %: (Men < 30%; Women < 42% -Reference) HEI-2015: Healthy eating index-2015, DQI-I: Dietary Quality Index International, HDDS: Household Dietary Diversity Scale, MEDAS: Mediterranean Diet Adherence Scale
Crude Model
Model 1 (with age, gender)
Model 2 (with age, gender, marital status, education level, income, smoking status, presence of chronic illness, nutrition education)
Discussion
Published studies have investigated the dietary quality of older individuals and anthropometric measures. However, little is known about the dietary quality of participants in the University of the Third Age (U3A), representing a significant knowledge gap in Türkiye and worldwide. This study examined associations between dietary quality indices (HEI-2105, DQI-I, HDDS and MEDAS) and anthropometric measures in this population. Our findings revealed significant associations between these indices and body composition parameters (BMI, WC, and BF%). Higher DQI-I and HDDS scores were associated with lower BMI and WC, suggesting that greater dietary diversity and overall diet quality may promote healthier weight regulation. In addition, greater adherence to the Mediterranean diet was inversely associated with body fat percentage, which is consistent with its potential role in managing adiposity in an ageing population. Collectively, these results point to the important role of diet quality in the context of obesity-related risks in the U3A members and underscore the importance of dietary interventions in promoting healthy ageing.
A key finding to emerge from this study is the apparent dissociation between dietary diversity and overall diet quality. It is particularly noteworthy that, whereas dietary variety, as measured by the HDDS, was high among most participants (95.2%), this stands in marked contrast to the results from the more comprehensive dietary indices. Specifically, the data indicate that only 5.9% of participants achieved ‘good’ diet quality classification according to the HEI-2015, while a mere 23.9% exhibited high adherence to the MEDAS (see Table 1). This discrepancy suggests that a diet encompassing a wide range of food groups does not automatically equate to a nutritionally balanced or high-quality diet [6, 25, 26]. A possible explanation for this may be the fact that the primary motivation for U3A participation is centered on cognitive stimulation, social integration, and personal fulfillment, rather than the direct intention of improving physical health and healthy dietary habits [27]. This interpretation implies that proactive behaviors in cognitive and social domains may not extend seamlessly to nutritional practices, thereby pointing to a compartmentalization of health-related behaviors in later life. These findings have important implications for health promotion strategies. The U3A setting can be seen as a potentially valuable platform for integrating targeted nutritional education. Leveraging the existing ethos of lifelong learning could provide an effective means to bridge this gap and encourage the adoption of dietary practices that support holistic healthy ageing [28, 29].
When examining the relationships between diet quality indices and anthropometric measurements, the results showed a significant association between the DQI-I and BMI categories. Lower DQI-I scores were observed in individuals with underweight and individuals with obesity compared to those with normal weight (see Table 2). These results are consistent with previous research. One study found that individuals in the older age range with malnutrition had significantly lower DQI-I scores than those without malnutrition [30]. Similarly, another study found that people with a BMI < 18.5 kg/m2 had lower DQI-I scores than those in other BMI categories, confirming our findings [31]. The association between higher DQI-I scores and higher intakes of dietary fiber, vitamins, minerals and antioxidants, and lower intakes of dietary fat, sodium and simple sugars suggests that maintenance of an optimal DQI-I may be associated with better nutritional support for older individuals who are underweight. Age-related physiological changes—such as delayed gastric emptying, tooth loss, and altered taste and olfactory perception—combined with socioeconomic constraints may contribute to inadequate dietary intake and increase the risk of underweight status in the older population [31, 32]. In addition, HDDS values differed significantly by BMI category. Individuals in the older age range classified as normal weight exhibited higher HDDS values compared to individuals with overweight or obesity (see Table 2). This finding is consistent with the study by Ishikawa et al.'s [33] and Rezaei et al.’s [34] study. However, the existing literature provides mixed evidence on the relationship between dietary diversity and body weight. While some studies suggest that greater dietary diversity is associated with obesity [35, 36], differences in gender, cultural dietary patterns, different DDS calculation methods and study populations may account for these discrepancies.
Higher DQI-I and HDDS scores were associated with lower BMI and waist circumference, suggesting dietary quality and diversity may support healthy weight management and be associated with a reduced risk of central obesity in older individuals (see Table 3). Conversely, the association between HEI-2015 and obesity was less straightforward. A preliminary, unadjusted analysis suggested a slight positive association; however, this association was attenuated after adjusting for socio-demographic and lifestyle confounders. This pattern can be understood by considering the distinct focus of the HEI-2015, which evaluates dietary composition independently of total energy intake [37]. Consequently, it is possible to consume a high-quality diet that is also energy-dense, which may contribute to weight gain in the context of a positive energy balance. This contrasts with the DQI-I and HDDS, which more directly capture dietary variety and nutritional adequacy [6, 25]. The initial association may also reflect reverse causality. As noted in a systematic review on diet indices and obesity, it is plausible that “overweight individuals adopt a healthier diet to manage their weight,” which could mask or alter the true association between diet quality and obesity status in cross-sectional analyses [6]. This finding highlights that diet quality and energy balance are distinct yet complementary aspects of weight regulation.
The inverse relationships observed for DQI-I and HDDS with obesity risk can be explained by several mechanisms. A higher DQI-I score reflects a balanced diet that helps ensure adequate nutrient and energy intake, and limits exposure to unhealthy foods. Similarly, a higher HDDS indicates greater dietary diversity, which is often associated with increased consumption of nutrient-dense foods such as fruits, vegetables, and whole grains. These dietary components are thought to contribute to improved satiety, enhanced metabolic regulation, and reduced energy overconsumption, which together could reduce the risk of obesity. Furthermore, both indices have been linked to enhanced better metabolic health, potentially through improved insulin sensitivity and lower consumption of ultra-processed foods, thereby supporting long-term weight management [7, 38, 39].
Waist circumference, an important indicator of central adiposity and cardiometabolic risk [9, 11], was inversely associated with HDDS, even after adjusting for confounding factors (see Table 3). This finding aligns with previous research proposing that a more diverse diet, characterized by a higher intake of nutrient-dense foods such as fruits, vegetables, whole grains, fish, and white meat, may be protective against abdominal adiposity [6, 39]. However, the absence of significant associations between waist circumference and other dietary indices (including HEI-2015, DQI-I, and MEDAS) could indicate that overall dietary patterns exert a stronger influence on general adiposity than on specific fat distribution patterns. The literature on dietary diversity and abdominal obesity presents inconsistent findings. While some studies report no significant association [6, 34], other describes a significant inverse relationship [40], and some report a positive association [35]. Our results are in line with the study that have described an inverse relationship between dietary diversity scores (DDS) and abdominal obesity. These discrepancies in the literature are likely influenced by variations in dietary diversity scoring systems, given that higher DDS scores do not invariably reflect a healthier dietary pattern. For instance, greater dietary diversity can sometimes include an increased intake of energy-dense, high-fat, and starchy foods, which have been associated with a higher risk of obesity [7, 39]. These nuances underscore the importance of evaluating what constitutes dietary diversity when examining its relationship with central adiposity and metabolic health outcomes.
Body fat, a key factor influencing metabolic health in the older population [10, 11, 41], demonstrated a significantly negative association with MEDAS scores (see Table 3). This finding indicates that greater adherence to a Mediterranean-style diet is associated with lower body fat levels in this population. The Mediterranean diet, with its particular emphasis on healthy fats, fiber and antioxidants, has been widely linked to improved metabolic outcomes and a reduced risk of obesity in previous studies [41–43]. However, the lack of significant associations for HEI-2015, DQI-I, and HDDS suggest that the specific dietary pattern characterized by the Mediterranean diet may offer distinct advantages for body composition, over and above the influence of general diet quality.
Strengths, limitations and future directions
This study has several limitations that should be considered. Firstly, the generalizability of our findings is limited by the study population. U3A members, and are therefore likely to be more educated, health-conscious and socially active than the general older population. Secondly, the use of a single 24-h dietary recall, while a practical tool for ranking participants by diet quality in cross-sectional studies, does not capture habitual intake at the individual level, and is subject to day-to-day variation. Furthermore, the cross-sectional design prevents the establishment of causal relationships and dietary assessment is subject to potential recall bias. A further limitation is the lack of evaluation for sarcopenic obesity, which is crucial for a comprehensive understanding of body composition in the older population [11].
Despite these limitations, this study has notable strengths and holds significant relevance. It is one of the first studies to focus specifically on the U3A population in the context of diet quality and anthropometric measurements, offering valuable insights into this growing population. Using age-specific cut-off values for BMI, waist circumference and body fat percentage enhance the accuracy of obesity assessment in this age group. Moreover, the comprehensive evaluation of dietary assessment using four different indices offers a nuanced perspective that can inform the development of tailored dietary approaches.
Consequently, our findings suggest valuable implications. The observed associations imply that integrating education on dietary diversity and Mediterranean-style dietary patterns into U3A programs could be an effective approach to encouraging healthy ageing within this unique population engaged in lifelong learning and active ageing. Further research should seek to validate these findings in more diverse populations, employing longitudinal designs to establish causality and explore the underlying mechanisms, including the role of physical activity and sarcopenic obesity assessment.
Conclusion
This study examined the associations between dietary quality indices and anthropometric measurements in U3A members. The observed relationships between BMI, waist circumference, and body fat percentage and dietary indices (DQI-I, HDDS and MEDAS) indicate the potential value of dietary strategies promoting diversity, nutrient-dense foods and adherence to a Mediterranean diet. In view of the cross-sectional design and the specific characteristics of the sample (individuals in the older age range enrolled in U3As promoting lifelong learning and active ageing), causal inferences cannot be made, and the generalizability of these findings to broader older populations may be limited. Therefore, incorporating education on the Mediterranean diet and dietary diversity into U3A’s programs could be useful, initial step to potentially improve dietary quality and manage obesity-related parameters in this specific population. Future research should aim to replicate these findings in more diverse populations, using longitudinal designs to better explore the underlying mechanisms of these associations.
Supplementary Information
Acknowledgements
We extend our sincere gratitude to Prof. Dr. İsmail Tufan for his invaluable support in facilitating this study and for his pioneering role in establishing the “Tazelenme” University at the Antalya Campus—the first exemplary social responsibility initiative in Türkiye aimed at promoting lifelong learning and active engagement among individuals in the older age range. We also thank the students of the “Tazelenme” University for their voluntary participation in this study.
Abbreviations
- BF%
Body Fat Percentage
- BMI
Body Mass Index
- DQI-I
Diet Quality Index-International
- HDDS
Household Dietary Diversity Score
- HEI-2015
Healthy Eating Index-2015
- MEDAS
Mediterranean Diet Adherence Screener
- TAU
“Tazelenme” University
- U3As
Universities of the Third Age
- WC
Waist Circumference
Authors’ contributions
Conceptualization, H.K.A., G.S., S.Ç.; Methodology, H.K.A. and G.S.; Data curation, G.S., and S.Ç.; Formal analysis and interpretation, G.S. and S.Ç.; Literature review, H.K.A. and G.S.; Writing original draft preparation, G.S. and S.Ç.; Critical review and editing, H.K.A., and S.Ç.; All authors had read and approved the final version of this manuscript.
Funding
The authors have not received any specific funding from any public, commercial or not-for-profit organization for the research, the writing or the publication of this article.
Data availability
The data used to support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved and conducted in accordance with the ethical principles of the Declaration of Helsinki by the Clinical Research Ethics Committee of Antalya Training and Research Hospital, University of Health Sciences, Türkiye (Decision No: 3/16, Date: 03.02.2022). Written informed consent was obtained from all participants prior to enrolling them in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon reasonable request.