Introduction

Ageing may be broadly defined as ‘the time-dependent functional decline that affects most living organisms’ [1]. Nevertheless, the trajectory each individual follows in the last decades of life depends on the interplay between several genetic, biologic and environmental factors [2]. Nutrition seems to influence both the ageing process itself and the course of several age-related diseases, thus affecting functional status and health [3, 4]. Malnutrition, in particular, is a frequent condition that can undermine healthy ageing, because it is associated with several co-morbidities and higher mortality [5, 6]. According to the ESPEN definition [7], the concept of malnutrition related to ageing is currently considered a synonym of undernutrition [8]. Numerous risk factors for malnutrition have been identified, such as functional decline and social isolation [9].

Whereas it may be relevant to study the impact of nutrition on healthy ageing at the level of individual foods or nutrients, it may be more meaningful to examine diet as a whole, i.e. the dietary pattern (DP), exploring foods and nutrients’ interactions [10, 11]. Overall, the ‘dietary pattern’ approach may better capture the effect of diet on health and may be easier to translate into public health messages [10]. In the last decades, a growing body of literature has been published on the association between DPs and health [12, 13]. Higher scores in healthy eating indexes [12] and greater adherence to the Mediterranean diet [13] have been associated with a significant decrease in the risk of cardiovascular diseases, cancer, type 2 diabetes and neurodegenerative diseases. Higher diet quality has also been associated with lower mortality risk, including among the oldest old (≥80 years of age) [12, 14]. Evidence specifically regarding older adults indicates that worse DPs are associated with poorer general health, less active days [15] and frailty [16]. On the contrary, a high adherence to the Mediterranean diet has been associated with a slower decline of mobility over time [17].

Despite the aforementioned, evidence is still scarce on the relationship between DPs and health among older subjects, particularly in the oldest old, as dietary assessment may be even more difficult in these age groups [18]. Specifically, and regardless of the known interactions between ageing, malnutrition and diet [19], there is a gap in the literature regarding which DPs derived a posteriori from population-based samples could be independently associated with nutritional status at older age [20, 21]. Therefore, the main aim of the present study was to examine the associations between a posteriori derived DPs and malnutrition and body mass index (BMI) in the Portuguese population aged 65 or over living in the community.

Methods

Study design and subjects

Data included in the present study derived from a larger project, the PEN-3S (acronym for ‘Portuguese Elderly Nutritional Status Surveillance System’), a cross-sectional study aimed at characterising the nutritional status of the Portuguese older population, as detailed elsewhere [22]. Data regarding the community sub-sample used here resulted from a collaboration between PEN-3S and the National Food, Nutrition and Physical Activity Survey (IAN-AF 2015–2016) [23]. A multistage approach sampling, weighed for sex and age was followed to ensure a national representative sample from all seven Portuguese NUTS II regions, when compared to data from the National Statistics Institute regarding the Portuguese population [23, 24]. Briefly, there was a random selection of individuals aged 65 or over registered in the randomly selected primary healthcare units, stratified by NUTS II, and weighted by the number of individuals registered in each unit.

Subjects that were bedridden; not able to understand and/or answer the questionnaire (e.g. due to dementia, severe hearing or visual impairment; non-Portuguese speakers); living in collective residences or institutions; or living in Portugal for <1 year were considered non-eligible. Eligible individuals were invited to participate by telephone and post mail. The total participation rate (accepted/invited to enrol) was 23.4%. Dietary assessment was not performed among those categorised as cognitively impaired by the Mini Mental State Examination [25] (score below the cut-off studied for the Portuguese population [26]) due to possible unreliable reporting. Therefore, from the total sample of 1120 participants, 849 had complete dietary data (2 × 24-h recalls) and were included in the present study.

Data collection procedures and instruments

From October 2015 to September 2016, interviews were scheduled with subjects who accepted to participate. After signing the written informed consent, structured interviews were conducted face-to-face in the primary healthcare unit or, less often, at the participant’s home by trained nutritionists assisted by electronic platforms [22, 23]. Collected data included sociodemographic characteristics (sex, age, educational level), cognitive function, health status, physical activity, nutritional status (anthropometry and the Mini Nutritional Assessment) and dietary intake.

Regarding health status, participants were asked to rate their own general health in a five-point scale ranging from ‘1—excellent’ to ‘5—very poor’ [27], and to indicate (‘yes’/’no’ answer) if they had any diseases requiring regular healthcare (e.g. exams, medical appointments). Functional status to conduct instrumental activities of daily living was assessed through the Lawton Scale, and a total score <8 denoted functional limitations [28]. The UCLA Loneliness Scale was used to assess subjective feelings of loneliness and social isolation [29], and a total score >32 indicated presence of loneliness feelings [30]. Physical activity level was categorised into ‘Low’ or ‘Moderate or High’ based on the International Physical Activity Questionnaire’s cut-offs [31].

Anthropometric measurements were performed according to standard procedures, as detailed elsewhere [22]. BMI was used to classify individuals into World Health Organization’s categories: <25.0 kg/m2; 25.0–29.9 kg/m2, ≥30.0 kg/m2 [32]. Nutritional status was also assessed by the 18-item Mini Nutritional Assessment (MNA® full form) [33,34,35]. If weight was missing, the MNA version that uses calf circumference instead of BMI was applied [36]. Participants’ nutritional status was classified as normal (24–30 points), at risk of malnutrition (17–23.5 points), or malnutrition (<17 points).

Dietary assessment was assisted by the ‘You eAT & Move’ electronic platform developed by the IAN-AF team according to the EU Menu methodology, which follows the European Food Safety Agency Guidance [37]. This methodology requires two non-consecutive 24-h dietary recalls, 8–15 days apart, randomly assigned to include all days of the week and distributed over 12 months. Following the Automated Multiple-Pass Method for 24 h (five steps) [38], participants were asked to report all foods (including beverages) they had consumed in the previous 24 h. Recipes were disaggregated into ingredients that were then individually characterised [39]. The software converted foods into nutrients using a database grounded on the Portuguese composition table [40] that was constantly updated.

Statistical methods

In this study, from 71 previously defined food items, seven were excluded due to a percentage of consumption under 5%, and the remaining were organised into 27 food groups. To minimise the impact of zero inflations and non-continuous variables from the 24-h recalls, each food group was divided into three categories of consumption: nil; equal or below the sample median; and above the sample median (among consumers). Afterwards, DPs were derived a posteriori by a latent class transition model including sex and age as concomitant variables. Models with two and three latent classes were identified. For each day of report, each subject had an estimated probability (%, continuous variable) to belong to each DP (membership). The number of selected classes (patterns) was decided based on the Bayesian information criteria (BIC) and Akaike information criteria, the log-likelihood value, relative entropy and substantive interpretation. Selected DPs were then characterised using the 27 food groups based on the estimated posterior probability.

Except for sample characterisation purposes, complex sample analyses were used to take into account both weighting for the distribution of the Portuguese population and the design effect. Estimates (mean or frequency) with the corresponding 95% confidence intervals (95% CI) are presented. Considering the skewed distribution of vitamins C, B6 and B12 intakes, these variables were log-transformed, and the geometric means are presented. For analysing differences between DPs in health and nutritional variables, individuals were classified into one DP (categorical) according to the most likely class membership; and the Rao–Scott adjusted chi-square statistic (for prevalence) or the Wald F test (for means) were used.

Linear regression models were performed to analyse the associations between DP membership and the following outcomes (a) MNA score, and (b) BMI, as continuous variable. For this purpose, MNA score was reversed and log-transformed [log(31-MNA)], considering its left skewed distribution. As a consequence, regression coefficients are presented as exponential of the coefficients [EXP(β)]. For outcome (b) BMI, results are presented as regression coefficients (β). Logistic regressions were used to study the association between DP membership and outcome (c) BMI categories (BMI ≥ 30 kg/m2 vs. BMI < 30 kg/m2). Results are presented as odds ratios and 95% CI. Model 1A (outcome a) was adjusted for sociodemographic variables (sex, age, educational level), energy intake and the interaction term ‘energy intake × dietary pattern’, because a significant interaction effect was detected. Models 1B (outcomes b and c) are adjusted for sociodemographic variables. Models 2 (outcomes a, b and c) are further adjusted for variables that were significantly associated with DPs in the univariate analyses (i.e. self-reported health status and loneliness feelings).

A significance level of 5% was used. DPs were derived using MPLUS (V5.2; Muthén & Muthén, Los Angeles, CA, USA), and the remaining statistical analysis were performed using SPSS® software, version 24.0.

Results

Sample characteristics

A total of 849 community dwellers aged ≥65 were included in the present study; 48.5% were women, and 58.7% belonged to the ‘65–74’ age group. Furthermore, 63.1% were married or living together, and a vast majority (68.0%) were either illiterate or attended school for a maximum of 4 years (primary school).

Dietary patterns

Two DPs (classes) were extracted using a latent class transition model with sex and age as concomitant variables (BIC: 65254). Table 1 shows the posterior probability of class membership that defines dietary pattern 1 (DP1) and 2 (DP2) by food group. DP1 had a higher percentage of individuals with the highest consumption (≥median) of legumes, white, red and processed meats and sweets, and thus was labelled ‘Protein-based foods’ pattern. On the other hand, DP2 was characterised by those having the highest consumption of vegetables, fruits, vegetable soup, dairy, cereals, tubers and bread, fishery and olive oil, and was labelled ‘Mediterranean’ pattern. In addition, 18.9% of the studied population presented different DPs in the 2 days of report: 23.1% of those that were on DP1 in the first day of report changed to DP2; and 14.6% of those in DP2 changed to DP1. Thus, three DPs were considered in the analyses: DP1 (‘Protein-based foods’ DP), followed by 22.0% of the Portuguese population aged ≥65; DP2 (‘Mediterranean’ DP) followed by 59.1%; and a pattern in between those two (named ‘In-between’ DP), characterising 18.9% of the population who alternated between DP1 and DP2.

Table 1 Dietary patterns (DPs) derived by the latent transition model with age and sex as concomitant variables assuming the posterior probability of class membershipa,b.
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Nutritional intake, socioeconomic characteristics, physical activity level, health and nutritional status according to dietary patterns

The nutritional intake of older adults belonging to each of the three DPs (adjusted for age and sex) is described in Table 2. Individuals in the ‘Protein-based foods’ pattern reported a significantly higher energy, fat (total, saturated, monounsaturated and trans), iron, vitamins B6, B12 and sodium intake, compared to those in the other patterns (Table 2). The ‘In-between’ DP followers showed an intermediate intake between the other two DPs for almost all studied nutrients.

Table 2 Nutritional intake according to dietary patterns (DPs) derived by latent transition model with age and sex as concomitant variables.
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The socioeconomic characteristics, physical activity level, health and nutritional status of older adults according to their DPs are described in Table 3. The prevalence of women, age ≥85, illiteracy, ‘poor’ or ‘very poor’ self-perceived health status and loneliness feelings were significantly higher among followers of the ‘Mediterranean’ DP, compared to the ‘Protein-based foods’ DP. Although no significant differences were found, followers of the ‘In-between’ and ‘Protein-based foods’ DPs were more physically active and had a lower prevalence of BMI ≥30.0 kg/m2. According to MNA cut-offs, a much lower prevalence of individuals at risk of malnutrition or malnourished was found among those following the ‘Protein-based foods’ DP (4.3%, 95% CI 1.9, 9.4), compared to the ‘In-between’ (10.2%, 95% CI 5.8, 17.1), and the ‘Mediterranean’ DP followers (13.3%, 95% CI 9.2, 19.0, p = 0.034).

Table 3 Socioeconomic characteristics, physical activity level, health and nutritional status according to dietary patterns derived by latent transition model with age and sex as concomitant variables.
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Dietary patterns, MNA and BMI

Predominantly following a ‘Protein-based foods’ DP was significantly associated with a better MNA score [EXP(β) = 0.588, 95% CI 0.413, 0.837], compared to a ‘Mediterranean’ pattern, after adjusting for sex, age, education and energy intake (Table 4). This association remained statistically significant after further adjustment for self-reported health and loneliness feelings [EXP(β) = 0.716, 95% CI 0.533, 0.962]. No significant association was observed between the derived DPs and BMI.

Table 4 Association of dietary patterns (exposure) with the Mini Nutritional Assessment scorea and body mass index (outcomes) among community-dwelling adults aged >=65 in Portugal.
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As a significant interaction effect of energy intake on the relationship between DPs and MNA was detected in the regression models, this result was further explored, and is represented in Fig. 1. It was observed that the ‘Protein-based foods’ DP was associated with a better nutritional status (higher MNA score). However, when energy intake reached 2193 kcal, followers of the ‘In-between’ DP started showing a better nutritional status than those presenting a ‘Protein-based foods’ pattern. ‘Mediterranean’ DP followers had the lowest MNA scores (worse nutritional status) across the whole energy intake range observed in this study (809–2320 kcal).

Fig. 1
figure 1

Interaction effect of energy intake (kcal) on the relationship between dietary patterns (‘Protein-based foods’, ‘In-between’ and ‘Mediterranean’ DPs) and the MNA score as a nutritional status indicator.

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Discussion

The present study aimed to examine the association between a posteriori derived DPs and nutritional status in a national representative sample of community-dwelling adults aged ≥65, without cognitive impairment. Three DPs emerged from a latent class transition model with sex and age as concomitant variables: ‘Protein-based foods’ DP, ‘Mediterranean’ DP and ‘In-between’ DP. Considering the age group studied here, the overall diet is in line with the Portuguese cultural heritage (moderate-to-high consumption of fruit, vegetables, legumes, soup, cereals, bread, olive oil and wine; and very low consumption of sugary drinks and salty snacks).

In the present study, older adults predominantly following a ‘Protein-based foods’ pattern had better nutritional status (given by MNA score) than ‘Mediterranean’ pattern followers. This association remained significant after adjusting for energy, sociodemographic characteristics, self-perceived health (general health status indicator) and loneliness feelings (psychological health indicator). Although these analyses were based on DPs and not individual food groups or nutrients, one may hypothesise that the higher consumption of meat and legumes in the ‘Protein-based foods’ group is necessary to meet protein and micronutrient requirements, and to maintain a good nutritional status in advanced age [41]. As a matter of fact, in the present study, there was a trend of higher protein intake among ‘Protein-based foods’ DP followers, who also had significantly higher intakes of iron, vitamin B6 and B12. Moreover, the ‘Protein-based foods’ DP still had a substantial contribution of vegetables and fruits, excellent sources of micronutrients and phytochemicals that are also relevant for healthy ageing [3].

Despite the role of specific nutrients on nutritional status, the significant interaction effect of energy intake observed on the relationship between DPs and MNA emphasises that total energy intake must be considered. From an energy intake of ~2200 kcal/day, the MNA score of those classified as having a ‘In-between’ DP surpassed that of ‘Protein-based foods’ followers, indicating better status for the former. This result points out that shifting between a ‘Protein-based foods’ and a ‘Mediterranean’ type of diet may be beneficial in terms of nutritional status, provided that adequate energy intake is guaranteed.

As already stated, studies examining the relationship between elders’ DPs and MNA in the community setting are scarce, supporting the novelty of the present study. The few published studies did not find significant associations between DPs, either defined a priori or a posteriori, and malnutrition. Data from the Health ABC cohort of 2234 community dwellers aged 70–79 years revealed that a poor Healthy Eating Index was not associated with malnutrition [42]. The authors hypothesised that the index might not be adequate for older adults’ requirements. Also, a cross-sectional study conducted in Greece (n = 207) showed similar adherence to the Mediterranean Diet among well-nourished subjects and those at risk of malnutrition, as defined by MNA [43]. A study conducted in Spain including independently living individuals aged ≥70 showed no association between a posteriori DPs and malnutrition, probably because the frequency of individuals at risk of malnutrition (3.3%) based on MNA short-form was too low to detect differences among DPs [16].

Contrary to the findings using MNA as nutritional status indicator, no significant association between DPs and BMI was found in the current study. Likewise, a 2011 review concluded that there was an inconsistent relationship between obesity, as measured by BMI and/or waist circumference, and food patterns [20]. A study conducted in Spain observed that a higher adherence to the Mediterranean DP was significantly associated with higher odds to present abdominal obesity (assessed by waist circumference), but not with BMI [44]. In fact, a growing body of evidence suggests that BMI alone may not be a good indicator regarding older persons’ health, due to a variety of age-related changes in body composition [41]. In this age group, weight trajectories and fat-free mass seem more relevant to health outcomes than weight/height [45, 46].

The current work has some limitations that should be pointed out. First, its cross-sectional design does not guarantee the temporal sequence of the studied associations. Moreover, reverse causality can be a concern in cross-sectional studies, but is less probable in what refers to the outcome ‘malnutrition risk’ than the outcome ‘BMI’. Second, 24-h recalls may be subject to recall bias and depend on self-reported information. To minimise bias, individuals classified as cognitively impaired did not complete the dietary assessment and were excluded from the analyses. Overall, refusals and those that were not included in this study due to missing dietary information (n = 271) were older, less educated and had a lower BMI [24]. Moreover, disabled, demented and severely impaired individuals were excluded a priori, as to guarantee reliable answers to the applied instruments. Institutionalised individuals were also excluded, but, considering that in Portugal the proportion of older adults living in nursing homes is relatively low (~4%), the present findings concern the vast majority of the Portuguese older adults [47]. Altogether, one may hypothesise that results obtained here concern a somehow healthier sample. Nevertheless, another Portuguese study including a national representative sample of community-dwelling older adults found a similar overall prevalence of risk of malnutrition and malnutrition [48]. Lastly, deriving DPs a posteriori relies on a number of decisions taken by researchers (e.g. number of patterns, patterns’ labelling) that may affect the end result, and DPs are population-specific because they are subject to cultural differences in food habits [20]. In addition, a minority (19%) of the studied population changed from one pattern to the other between the 2 days of report. Reasons for these observations were not explored in this study. They may correspond to real differences in food intake (e.g. weekdays vs. weekends, eating at home vs. at relatives’), but may also be partly explained by a systematic error (e.g. participants reporting a more Mediterranean-like diet in the second 24-h recall).

Despite the aforementioned shortcomings, the present study is original in defining DPs, and their association with nutritional status within a national sample of community dwellers aged ≥65 (no upper age limit). This study adds to ageing-related evidence that DPs are associated with malnutrition risk, but not with BMI. Following a more ‘Protein-based foods’ DP was associated with better nutritional status as assessed by MNA, at least up until 2200 kcal a day. From that energy intake onwards, a ‘In-between’ pattern (i.e. shifting between ‘Protein-based foods’ and ‘Mediterranean’ DP) could lead to a more diverse diet that seems to be beneficial. This possibility of transition between DPs derives from the latent class transition model used here. This model was chosen because it considers the intra-individual variability and the possibility of a subject changing between patterns from 1 day to the other, thus using all the information provided by the two 24-h recalls. It constitutes one of the novelties of the present work, and should be further explored in future studies. Overall, these findings should be considered when disseminating dietary recommendations for an adequate nutritional status in advanced age. This topic is highly relevant as the number of people aged ≥65 continues to increase, and national-level dietary guidelines specific for healthy ageing are both urgently needed and expected.