Background
Frailty is a medical syndrome resulting from age-associated impairments in several physiological systems. This syndrome is characterized by a high vulnerability to even minor environmental stressors (e.g., a minor infection), which leads to increased risk of disability, dependency, need for long-term care, and mortality [
1]. Given the elevated prevalence of this syndrome [
2,
3] and its serious consequences, there is substantial interest in the identification of the risk factors for frailty, as well as in the development of interventions to avoid or delay its onset [
4,
5].
Emerging evidence suggests that low intake of certain micronutrients and protein could be a risk factor for frailty [
6,
7]. However, people do not eat nutrients; rather, they eat meals consisting of foods with complex combinations of nutrients that may interact. Moreover, intake of several nutrients is usually correlated (e.g., meat protein and saturated fat, vitamin A and C). Therefore, it might be difficult to assess the independent health effect of nutrients. Additionally, the effect of a single nutrient might be too small to be detected, but the joint effect of many nutrients within a dietary pattern could be large enough to be detectable. Accordingly, the investigation of dietary patterns can complement the study of individual nutrients and overcome its potential limitations [
8].
Two main approaches have been used to describe dietary patterns [
8,
9]. The first approach formulates indices or scales representing the degree of adherence to
a priori-defined dietary patterns that are deemed to be healthy according to the prevailing scientific evidence. A good example frequently used in the literature is the Trichopoulou index, which represents the traditional Mediterranean diet [
10]. The second approach uses factor or cluster analysis to empirically define a few food patterns that reflect different dietary compositions. These patterns derived
a posteriori have the advantage of reflecting existing food habits in the study population. These two approaches address different research questions. For instance, the traditional Mediterranean diet, defined
a priori, represents the diet consumed by populations in the Mediterranean basin during the 1960s, whereas a pattern defined
a posteriori represents the present-day dietary intake. This is important because, in recent decades, diet in Mediterranean countries has undergone a process of westernization, which includes a large increase in the consumption of red meat, saturated fats and simple carbohydrates, and reduced consumption of whole-grain cereals and legumes [
11].
In a recent paper using information from the Seniors-ENRICA cohort, we have found that adherence to the Mediterranean diet, as assessed by
a priori scales including the Trichopoulou index, was associated with a lower risk of frailty [
12]. Further, in previous research, an index of global diet quality [
13] and several
a priori scales of Mediterranean diet adherence [
14-
17] have been linked to lower frequency of frailty or some of its components. Herein, we have performed a factor analysis with food consumption data from the Seniors-ENRICA cohort to identify existing dietary patterns in the population. To our knowledge, no previous study has yet examined the association between
a posteriori-defined dietary patterns and the risk of frailty in older adults.
Results
Compared to individuals in the first (lowest) tertile of the PP, those in the third tertile were younger, had a higher energy intake, and there was a lower percentage of smokers, persons diagnosed with diabetes or depression, and with limitations in IADL; however, there was a higher percentage of individuals with BMI ≥30 kg/m
2 and reporting osteomuscular disease. However, it should be noted that for depression, IADL limitation, and BMI, the data did not suggest a linear association with the PP. As regards the WP, those in the third tertile were younger and showed a higher energy intake, there was a greater percentage of smokers and individuals with limitation in IADL, and a lower percentage of subjects with university education and osteomuscular disease (Table
2). Nevertheless, for IADL limitation and university education, we found no indication of a linear association with the WP.
Table 2
Characteristics of the non-frail population at baseline, by adherence to the dietary patterns
n | 620 | 627 | 625 | | 624 | 625 | 623 | |
Number of frailty components, mean (SE) | 0.3 (0.1) | 0.3 (0.1) | 0.2 (0.1) | <0.01 | 0.2 (0.1) | 0.3 (0.1) | 0.3 (0.1) | <0.01 |
Sex, women , % | 51.1 | 51.8 | 51.8 | 0.53 | 51.6 | 51.5 | 51.5 | 0.55 |
Age, years, mean (SE) | 70.1 (0.3) | 68.4 (0.3) | 67.6 (0.3) | <0.01 | 69.2 (0.3) | 68.7 (0.3) | 68.2 (0.3) | 0.02 |
Educational level, % | | | | | | | | |
≤Primary | 56.7 | 48.4 | 56.4 | <0.01 | 51.7 | 56.3 | 53.6 | <0.01 |
Secondary | 22.9 | 27.4 | 24.8 | | 24.2 | 25.2 | 25.7 | |
University | 20.4 | 24.2 | 18.8 | | 24.2 | 18.5 | 20.7 | |
Occupational social class, % | | | | | | | | |
I (Professionals, managers, proprietors and clerical workers) | 61.9 | 66.0 | 63.0 | 0.04 | 66.7 | 60.5 | 63.7 | 0.07 |
II (Self-employed farm workers) | 2.1 | 2.7 | 4.4 | | 2.7 | 3.4 | 3.1 | |
III (Skilled and unskilled manual workers) | 34.5 | 30.2 | 31.5 | | 29.7 | 35.0 | 31.4 | |
IV (Paid farm workers) | 1.5 | 1.1 | 1.1 | | 0.9 | 1.1 | 1.8 | |
Tobacco, % | | | | | | | | |
Never smoker | 61.6 | 59.1 | 60.3 | <0.01 | 63.9 | 60.9 | 56.1 | <0.01 |
Former smoker | 25.5 | 29.4 | 30.4 | | 29.6 | 26.4 | 29.2 | |
Current smoker | 12.9 | 11.5 | 9.3 | | 6.5 | 12.7 | 14.6 | |
Body mass index, kg/m2, % | | | | | | | | |
<25 | 19.5 | 18.6 | 20.1 | 0.002 | 20.8 | 18.8 | 18.5 | 0.01 |
25–29.9 | 49.6 | 53.4 | 46.5 | | 49.6 | 50.6 | 49.4 | |
≥30 | 30.8 | 28.0 | 33.4 | | 29.6 | 30.5 | 32.1 | |
Energy intake, kcal/d, mean (SE) | 1731 (18) | 2009 (18) | 2358 (18) | <0.01 | 1854 (19) | 1922 (19) | 2324 (19) | <0.01 |
Mini-Mental State Examination, mean (SE) | 27.8 (0.1) | 28.2 (0.1) | 28.2 (0.1) | <0.01 | 28.2 (0.1) | 28.1 (0.1) | 27.9 (0.1) | <0.01 |
Eating difficulties, % | 3.3 | 2.9 | 2.6 | 0.11 | 2.5 | 2.3 | 4.0 | 0.03 |
Diagnosed diseases, % | | | | | | | | |
Cardiovascular diseasea | 4.9 | 4.4 | 5.1 | 0.005 | 5.7 | 4.2 | 4.5 | 0.002 |
Diabetes | 13.0 | 11.9 | 9.7 | <0.01 | 12.2 | 10.1 | 12.2 | <0.01 |
Cancer | 1.2 | 2.1 | 2.2 | 0.19 | 2.0 | 1.8 | 1.7 | 0.42 |
Asthma or chronic bronchitis | 7.5 | 7.8 | 7.1 | 0.15 | 7.5 | 8.2 | 6.8 | 0.12 |
Osteomuscular diseaseb | 43.5 | 47.6 | 49.8 | <0.01 | 48.1 | 47.9 | 44.9 | <0.01 |
Depression needing treatment | 7.4 | 4.6 | 4.8 | <0.01 | 5.2 | 5.3 | 6.2 | <0.01 |
Limitation in IADL, % | 9.0 | 6.0 | 7.5 | <0.01 | 6.8 | 6.7 | 8.9 | <0.01 |
Number of treatments, mean (SE) | 2.1 (0.1) | 1.9 (0.1) | 1.9 (0.1) | <0.01 | 2.2 (0.1) | 1.8 (0.1) | 1.8 (0.1) | <0.01 |
During a mean follow-up of 3.5 years, we ascertained 96 cases of incident frailty. Table
3 shows the association between adherence to the dietary patterns and the risk of frailty. Results from models with partial (model 1) and full (model 2) adjustment were rather similar, and showed that the PP was inversely associated with the risk of frailty while the WP had a non-statistically significant tendency to a higher risk. In model 2, the OR (95% CI) of frailty among those in the first, second, and third tertile of adherence to the PP were 1, 0.64 (0.37–1.12), and 0.40 (0.2–0.81), respectively;
P-trend = 0.009. The corresponding values for the WP were 1, 1.53 (0.85–2.75), and 1.61 (0.85–3.03);
P-trend = 0.14. For both patterns, these results were similar to those obtained in all the sensitivity analyses.
Table 3
Association between dietary patterns and risk of frailty during a 3.5-year follow-up of older adults
Main analysis
| | | | | | | | |
Number of frailty events | 48 | 27 | 21 | | 24 | 35 | 37 | |
Model 1 | Ref. | 0.69 (0.41–1.17) | 0.59 (0.33–1.04) | 0.05 | Ref. | 1.53 (0.87–2.72) | 1.61 (0.91–2.84) | 0.11 |
Model 2 | Ref. | 0.64 (0.37–1.12) | 0.40 (0.20–0.81)* | 0.009 | Ref. | 1.53 (0.85–2.75) | 1.61 (0.85–3.03) | 0.14 |
Sensitivity analyses
| | | | | | | | |
Excluding weight loss from the definition of frailty | | | | | | | | |
Number of frailty events | 102 | 78 | 62 | | 71 | 81 | 90 | |
Model 1 | Ref. | 0.80 (0.57–1.13) | 0.72 (0.50–1.03) | 0.07 | Ref. | 1.23 (0.86–1.76) | 1.43 (1.00–2.03)* | 0.04 |
Model 2 | Ref. | 0.83 (0.58–1.18) | 0.67 (0.44–1.02) | 0.06 | Ref. | 1.27 (0.88–1.83) | 1.53 (1.03–2.26)* | 0.03 |
Excluding 58 individuals with eating difficulty | | | | | | | | |
Number of frailty events | 40 | 23 | 13 | | 20 | 31 | 31 | |
Model 1 | Ref. | 0.70 (0.40–1.23) | 0.63 (0.34–1.15) | 0.11 | Ref. | 1.69 (0.92–3.13) | 1.61 (0.87–2.98) | 0.14 |
Model 2 | Ref. | 0.64 (0.35–1.18) | 0.40 (0.19–0.84)* | 0.01 | Ref. | 1.64 (0.87–3.11) | 1.45 (0.73–2.90) | 0.28 |
Excluding 543 individuals with diagnosed severe diseases | | | | | | | | |
Number of frailty events | 29 | 12 | 10 | | 14 | 19 | 18 | |
Model 1 | Ref. | 0.54 (0.26–1.13) | 0.48 (0.22–1.05) | 0.05 | Ref. | 1.30 (0.61–2.76) | 1.34 (0.62–2.87) | 0.46 |
Model 2a | Ref. | 0.52 (0.24–1.16) | 0.34 (0.13–0.89)* | 0.02 | Ref. | 1.23 (0.57–2.67) | 1.44 (0.63–3.29) | 0.38 |
Excluding 182 individuals with limitation in IADL
b
| | | | | | | | |
Number of frailty events | 32 | 17 | 10 | | 15 | 22 | 22 | |
Model 1 | Ref. | 0.55 (0.29–1.03) | 0.40 (0.19–0.85)* | 0.01 | Ref. | 1.56 (0.78–3.10) | 1.39 (0.69–2.80) | 0.37 |
Model 2 | Ref. | 0.49 (0.25–0.98)* | 0.26 (0.11–0.63)** | 0.002 | Ref. | 1.70 (0.84–3.44) | 1.36 (0.63–2.94) | 0.40 |
Finally, a greater adherence to the PP showed a non-statistically significant tendency to a lower risk of exhaustion and of slow walking speed (Table
4). However, the WP did show an association with an increasing risk of slow walking speed and weight loss. Specifically, the OR (95% CI) of slow walking speed across tertiles of the WP were 1, 1.15 (0.74–1.76), and 1.85 (1.19–2.87);
P-trend = 0.007. For weight loss, the corresponding values were 1, 1.37 (0.77–2.41), and 2.12 (1.22–3.70);
P-trend = 0.007. Results for the association between the WP and exhaustion and low physical activity were in the same direction but did not reach statistical significance (Table
4).
Table 4
Association between dietary patterns and risk of each frailty criterion during a 3.5-year follow-up of robust older adults at baseline
Frailty criteria
| | | | | | | | |
Exhaustion, n events | 57 | 42 | 39 | | 40 | 50 | 48 | |
Odds ratio (95% CI) | Ref. | 0.83 (0.53–1.30) | 0.75 (0.44–1.26) | 0.27 | Ref. | 1.40 (0.89–2.21) | 1.58 (0.96–2.58) | 0.07 |
Low physical activity, n events | 53 | 80 | 68 | | 52 | 70 | 79 | |
Odds ratio (95% CI) | Ref. | 1.46 (0.99–2.17) | 0.95 (0.61–1.50) | 0.77 | Ref. | 1.38 (0.98–2.04) | 1.46 (0.96–2.21) | 0.08 |
Slow walking speed, n events | 71 | 64 | 56 | | 56 | 55 | 80 | |
Odds ratio (95% CI) | Ref. | 0.83 (0.55–1.25) | 0.74 (0.46–1.18) | 0.20 | Ref. | 1.15 (0.74–1.76) | 1.85 (1.19–2.87)* | 0.007 |
Weight loss, n events | 34 | 29 | 40 | | 24 | 30 | 49 | |
Odds ratio (95% CI) | Ref. | 0.76 (0.44–1.30) | 0.80 (0.46–1.41) | 0.46 | Ref. | 1.37 (0.77–2.41) | 2.12 (1.22–3.70)* | 0.007 |
Muscle weakness, n events | 95 | 75 | 68 | | 90 | 77 | 71 | |
Odds ratio (95% CI) | Ref. | 0.93 (0.64–1.35) | 0.95 (0.62–1.46) | 0.81 | Ref. | 1.01 (0.70–1.45) | 1.09 (0.73–1.63) | 0.68 |
Discussion
Our results show that adherence to a PP, characterized by high intake of olive oil and vegetables, had an inverse dose-response relationship with frailty; in contrast, an increasing adherence to the WP, characterized by high intake of refined cereals, whole dairy, and red and processed meat, was associated with increased the risk of slow walking speed and weight loss.
Previous population-based surveys and cohort studies with selected samples have found dietary patterns consistent with the PP, which has also been called the ‘healthy’ or ‘whole food’ pattern, and with the WP, also called the ‘processed food’ pattern. As in our study, the PP has usually been associated with a healthier lifestyle, while the WP has been linked to less healthy behaviors [
31,
32].
In cohort studies, the PP has been associated with lower risk of coronary disease [
31,
33] and diabetes [
34]. It has also shown an inverse cross-sectional association with the metabolic syndrome and insulin resistance [
35], as well as with levels of numerous biomarkers of inflammation and cardiovascular risk [
32,
36]. Moreover, some studies have found the PP to be associated with lower risk of cognitive impairment [
37,
38] and depression [
39,
40]; however, a cross-sectional study found no association with cognitive function [
41] and in one prospective investigation the protection against depression, was restricted to individuals older than 60 years [
42]. As regards the WP, it has been linked to higher risk of coronary disease [
31,
33], diabetes [
34], metabolic syndrome, and insulin resistance [
35]. There is also evidence that the WP is associated with higher levels of cardiometabolic biomarkers, including insulin, C-peptide, leptin, homocysteine, and inflammation mediators (sTNFR2, IL-6, CRP, E-selectin) [
34,
36,
43]. Finally, the WP has been related to an increased risk of poor cognition [
37,
41], while some [
39], but not all studies [
40,
42], have reported a higher risk of depression. Given that obesity [
44], systemic inflammation [
45-
47], cardiovascular disease [
2], poor cognitive function [
48,
49], and depression [
50] are all well-known risk factors of frailty, all these health disorders could contribute to the association between the PP, the WP, and frailty.
There is evidence that healthy diets, which are consistent with the PP in our study, may reduce the risk of several components of frailty. Specifically, a diet rich in vegetables, whole grains, and blue fish has been associated with higher grip strength in older adults [
51]. Further, a healthy diet has been shown to protect against slow walking speed [
13-
17], unintentional weight loss [
14,
15], low physical activity [
13,
14], and muscle weakness [
6]. Despite the clear inverse association between the PP and frailty in our study, the PP did not evidence a statistically significant association with any of its components, though it showed some tendency to reduce the risk of exhaustion and slow walking speed. This suggests that the protective effect of the PP on frailty might result from synergic benefits on each component of frailty, which are nevertheless too small to be detectable when assessed separately. We are not aware of previous investigations on the effect of the WP on frailty or its components; in our study, this pattern was directly associated with several frailty components, in particular slow walking speed and weight loss.
Although the Mediterranean diet, as assessed by
a priori scales such as the Trichopoulou index [
10,
12], and the PP empirically identified in this study share a high consumption of olive oil and vegetables, there are substantial differences between these two dietary patterns. Specifically, some foods which are typical of the Mediterranean diet are not part of the PP. This is the case for fruits and alcoholic beverages (e.g., wine). The reverse situation is also true, so that consumption of white and red meat contributes to the PP, while consumption of any type of meat scores negatively in the Trichopoulou index; moreover, consumption of potatoes is a component of the PP but not of the traditional Mediterranean diet. As a result, the correlation between the PP and the Trichopoulou index was modest (Pearson r = 0.37) in our study sample.
Our results are unique in showing that, despite changes in the traditional diet in Spain derived from the socioeconomic development and modern living arrangements during the last decades [
11], a PP characterized by intake of olive oil and vegetables, and also potatoes and meat, protects from the frailty syndrome in older adults. Of particular note is that the PP does not include alcoholic beverages; this is important because, in some studies, alcohol intake has been identified as one of the main contributors to the health benefits of the Mediterranean diet [
52,
53]. Given that older adults frequently consume alcohol-interacting medications and are particularly vulnerable to the effects of alcohol [
54], they can be reassured that a PP which does not contain wine or any other alcoholic beverage is a healthy dietary option. Lastly, the PP includes all types of meat (white, red, and processed), which is an important source of protein. This is consistent with the emerging evidence of the protective effect of protein intake on frailty [
1,
7].
This work has several strengths and limitations. Among the former was the prospective design with a sufficient duration to allow for ascertaining a good number of frailty cases. Indeed, there is evidence from clinical trials that the Mediterranean diet lowers the risk of cardiovascular disease in the first few months post-intervention [
55,
56]; also, previous studies on frailty found a protective effect of the Mediterranean diet after only a few years of follow-up [
14,
17]. Other strengths were that diet was assessed with a validated instrument and that the results were robust in the sensitivity analyses.
The main limitation was that diet was self-reported. Although we used a validated diet history, we cannot rule out some recall bias. This bias may particularly affect individuals with severe diseases and poor cognition, but the results did not change substantially after excluding these subjects from the analysis. Moreover, recall bias usually leads to underestimation of the study association; nevertheless, it did not impede observation of clear associations between the PP, the WP, and frailty. A further limitation was that factor analysis necessarily involves several arbitrary decisions, including the consolidation of food items into food groups, the number of factors to extract, the method of rotation, and the labeling of the dietary patterns [
57]. However, the observed patterns are consistent with those found in studies in other countries [
8,
31]. Finally, although the analyses accounted for a good number of potential confounders, some residual confounding cannot be ruled out because we only used rather crude measures of depression (diagnosed disease requiring treatment) and cognitive functioning (the Mini-Mental State Examination).
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
LLM and FRA had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: LLM, EGE, ELG, JRB, and FRA. Acquisition of data: LLM, ELG, JRB, and FRA. Statistical analysis: LLM, EGE and FRA. Interpretation of data: LLM, EGG, ELG, JRB, and FRA. Drafting the manuscript: LLM and FRA. Critical revision of the manuscript for important intellectual content: LLM, EGG, ELG, JRB, and FRA. Obtained funding: ELG, JRB, and FRA. Study supervision: FRA. All authors have read and approved the final manuscript.