Introduction

Muscle strength and physical performance are important indicators of the health status of older adults [1–4]. As average life expectancy continues to increase, many older adults will experience functional impairment especially in very old age (aged 85 and over) [5,6]. Progressive decline in muscle strength and physical performance quantified by objective measures such as hand grip strength (HGS) [7] and the Timed Up-and-Go test (TUG) [8] is associated with poor health outcomes, disability, cognitive decline, frailty, malnutrition, risk of falls, and mortality in adults aged 65 and over [1,2,4,9–16]. However less is known about the determinants of decline, particularly diet, in the very old (aged 85+) [17,18].

The majority of observational and nutritional intervention studies have investigated the impact of single macro- (e.g. protein) and micro-nutrients (e.g. vitamin D, B₁₂, antioxidants) on skeletal muscle function but have yielded inconclusive results (reviewed in [19,20]) possibly because such strategies fail to account for the cumulative effects of (synergies between) the complex mixture of different foods within most diets. To understand the role of the whole diet on health and functioning outcomes, dietary pattern (DP) analysis became the methodology of choice. DP analysis utilises either a priori (or hypothesis-driven) or a posteriori (exploratory or data-driven) approach to derive patterns [21,22]. A priori developed DP are based on pre-defined dietary scores for a specific diet or dietary guidelines, in which higher scores indicate heathier DP and a higher intake of beneficial foods (e.g. whole grains, fruits and vegetables, and fish). This approach is limited because it does not necessarily capture the complexity of whole diet, and because it relies on the current state of knowledge of what represents a healthy diet. The posteriori approach uses factor or cluster analysis to derive DP and takes into account a total diet without prior hypotheses about the diet-health relationship. Cluster analysis has advantages over other procedures because it groups individuals into unique, non-overlapping groups based on similarities of food/nutrient consumption. Individuals within each cluster have a similar diet, which may be based on mean intake, frequency or absence/presence of particular food/nutrient [23].

Studies examining the effect of whole diet or DP on decline in muscle strength and physical performance in the very old are scarce. Utilising data from the Newcastle 85+ Study, we therefore aimed to determine the association between DP derived by cluster analysis and change in HGS and TUG over 5 years.

Methods

Statistical analysis

SPSS Two-Step clustering was used to derive DP as described previously [26]. Briefly, the best cluster solution was determined with 30 food groups by automatic selection and the Bayesian Information Criterion (BIC), and the robustness and stability of the three-DP solution was re-evaluated by random ordering of cases and by comparing DP characteristics [26].

Muscle strength and physical performance by DP.

The differences between DP in raw HGS and TUG times were compared using ANOVA for normally distributed and Kruskal-Wallis test for non-normally distributed continuous variables (Table 1).

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Table 1. Hand grip strength and Timed Up-and-Go raw scores by dietary patterns at baseline and follow-ups.

https://doi.org/10.1371/journal.pone.0149699.t001

Decline in muscle strength and physical performance by DP.

HGS data (kg_F) were normally distributed, whereas TUG times were positively skewed at each assessment (wave), and thus the latter were log₁₀ transformed, and used as a continuous variable. Lower log₁₀-s indicated quicker (better) TUG performance.

We utilised multilevel linear modeling [32] to examine: (a) the association between DP and HGS and TUG times at baseline, 1.5, 3 and 5-year follow-up; and (b) to identify baseline variables associated with the initial level and the rate of change in both HGS and TUG over the study period. DP2 was deemed to be the healthiest dietary pattern ([26]; S1 Table), and used as a referent in all analyses.

For HGS we fitted a series of linear growth curve models as follows: (a) with ‘time’ in study (coded as: baseline (0), 1.5 years (1), 3 years (2), and 5 years (3)) at within-person level (Level 1; to examine the linear trend of time), and DP at between-person levels (Level 2; to test whether intercept (initial status) varied by DP), and with an interaction of DP and time, to test for varying slopes (rate of change) by DP (Model 1); and (c) with additional adjustment for confounders associated with HGS and diet as identified in the literature (Model 2 (sociodemographic, health and functioning-related variables), and Model 3 (lifestyle variables)) (Table 2). Random effects terms included both intercept and slope of HGS over time.

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Table 2. Parameter estimates^* (β coefficients) of growth curve models for hand grip strength (HGS) over 5 years by dietary patterns^†.

https://doi.org/10.1371/journal.pone.0149699.t002

For TUG we examined both linear and quadratic effects of time (i.e. to account for nonlinear change over time) and their interaction terms over 5 years, and adjusted for a similar set of covariates including use of walking aids (Table 3). All predictors were time-invariant (baseline) except for walking aids use (Table 1). Random effects included both intercept and slope (linear) of TUG over time.

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Table 3. Parameter estimates^* (β coefficients) of growth curve models for Timed up-and-go (TUG) test over 5 years by dietary patterns^†.

https://doi.org/10.1371/journal.pone.0149699.t003

The analyses for change in HGS and TUG by DP were also stratified by sex. Negative β estimates for HGS, and positive (increasing) β estimates for TUG indicated worse performance.

We used SPSS MIXED procedure (SPSS, 2002) with ‘REPEATED’ command, restricted maximum likelihood (RML), and diagonal and unstructured covariance matrix at Level 1 and Level 2, respectively to generate parameter estimates (β) for both outcomes.

All analyses were conducted using IBM SPSS (V.21; IBM Corporation, Armonk, NY, USA), and all statistics were 2-sided at α = 0.05.

Results

We examined the association between previously identified dietary patterns (DP1 (‘High Red Meat’), DP2 (‘Low Meat’), and DP3 (‘High Butter’)) [26] and change in muscle strength (HGS) and physical performance (TUG) across four measurement occasions (Table 1). Participants within each DP differed with respect to consumption of several food groups (including butter, unsaturated fats spreads and oils, gravy, potato/potato dishes and red meats/meat dishes), and to key sociodemographic and health measures (for details see [26]; S1 Table). Briefly, DP1 (‘High Red Meat’) had the highest proportion of participants consuming red meats/meat dishes, gravy, potato/potato dishes, and unsaturated fats spreads and oils, but the lowest proportion of participants eating butter compared with DP2 and DP3. DP2 (‘Low Meat’) had the lowest proportion of participants consuming meats (i.e. red meats/meat dishes, processed meats, and poultry), gravy and potato/potato dishes, and the highest proportion of those eating fruits, fish/seafood, whole grains/cereal product, dairy, and soups compared with others. DP3 (‘High Butter’) had the highest proportion of participants eating butter, and the lowest proportion of those consuming unsaturated fats spreads and oils compared with DP1 and DP2. Participants in DP2 were healthier (had less disability, depression and dementia, and better lipid profile), more physically active, and wealthier (more educated, belonged to higher social class, and lived in affluent areas), but were less likely to be married compared with others.

Discussion

In the present study we investigated the relationship between previously defined DP [26] and muscle strength and physical performance (characterised by HGS and TUG, respectively) in very old participants from the Newcastle 85+ Study. To the best of our knowledge, this is the first such prospective evaluation of the impact of DP on HGS and TUG in this age group. DP1 (‘High Red Meat’), a dietary pattern high (i.e. the highest proportion of people) in red meats/meat dishes, gravy and potato [26] was associated with overall worse HGS in the entire cohort and in men, but men in DP3 (‘High Butter’), a dietary pattern high in butter and low in unsaturated fats had a steeper rate of decline in HGS (by -0.63 kg_F per wave) over 5 years compared with DP2 (‘Low Meat’), a dietary pattern low (i.e. the lowest proportion of people) in red/processed meats, gravy and potato. In the entire cohort, participants in DP3 had overall poorer TUG performance and required longer time to complete the TUG over 5 years compared with those in DP2 after adjustment for important covariates. Men in DP1 and women in DP3 had worse overall TUG times, but the rate of decline did not differ by DP. Excluding participants diagnosed with dementia/AD at baseline or adjusting for cognitive status (i.e. SMMSE) did not change the results, which infers that the age-related decline which we observed is not due to cognitive impairment. Participants in DP2 had reached very old age more physically fit compared with their peers in the other DPs, irrespective of their cognitive health.

Existing studies of physical performance and muscle function in older adults (aged 60+) have investigated a limited number of nutrients (reviewed in [19,20]), such as protein [20], vitamin D (serum and dietary) (e.g. [33,34]), and magnesium [35]. However, despite the recognised utility of a single nutrient approach for testing muscle heath-diet hypotheses [36], focusing on consumption of a single nutrient or food group may be of limited value from a public health perspective because it ignores the likely cumulative, synergistic/antagonistic effect of foods/nutrients within complex diets on muscle strength and physical performance. Some observational studies have investigated the cross-sectional associations between DP (derived a posteriori using Principal Component Analysis), muscle strength and physical performance in older adults (e.g. [37–39]) but none in the very old. For example, in the Hertfordshire Cohort Study of ~3000 older men and women (aged 59–73), a higher ‘prudent’ dietary score (characterised by high intake of fruits, vegetables, fatty fish and breakfast cereal) was associated with stronger hand grip [37]. Additional analysis of those aged 63–73 from this cohort [38] revealed a faster 3-m walk times in women consuming diets with the higher ‘prudent’ score. Similarly, we found that our healthiest dietary pattern (DP2) which was high in fruits, fish/sea food, eggs, nuts and whole grains but low in red/processed meats and potato/potato dishes (DP2) [26] was associated with stronger hand grip and faster TUG performance compared with those with the less healthy dietary patterns characterised by a higher intake of red meats (including processed meats, ham and bacon) and butter independently of several important covariates (S1 and S2 Figs). The benefits of DP2 on muscle strength/performance were independent of the effects of other health-related behaviours (physical activity, smoking, BMI), disease burden, and vitamin D and cognitive status—factors reported to be associated with grip strength and physical performance decline in older adults [2,34,35,40]. Furthermore, in the 1999–2002 National Health and Nutrition Examination Survey, higher overall diet quality (determined by the Healthy Eating Index-2005 using a priori approach) was associated with faster gait speed and stronger knee extensor power in a nationally representative sample of U.S. older adults aged 60 and over [39].

There have been some longitudinal studies in young old adults linking DP to subsequent decline in physical performance [41,42]. In the InCHIANTI study, higher adherence to Mediterranean-style diet was associated with less decline in lower extremity function (mobility) over 9 years in participants aged 65+ [41], and with slower decline over 8 years in usual and rapid 20-m walking speed among 2000 older adults aged 70+ in the Health, Aging, and Body Composition study [42].

Several biological mechanisms may have played a role in the poorer physical performance and muscle strength in participants belonging to DP1 (‘High Red Meat’) and DP3 (‘High Butter’). At a cellular level, older adults experience a progressive loss of lean muscle tissue, especially in very old adulthood [43], a concomitant increase in intramyocellular lipids [44], diminished muscle protein synthesis from the available amino acids pool (i.e. ‘anabolic resistance’) [45], and declining oxidative capacity, quality, and number of mitochondria [46]. Participants in DP3 (‘High Butter’), the DP highest in total fat (and percent energy (%E) from fat), cholesterol, saturated fatty acids (SFA) (and %E from SFA), and monounsaturated fatty acids (MUFA), but lowest in polyunsaturated fatty acids (PUFA) and MUFA/SFA ratio and %E from carbohydrates [26] may have compromised aged muscle fibers through potentially increased lipid deposition, insulin resistance, and inflammation, and thus diminished overall muscle quality and composition [47]. Indeed, experimental studies in older rodents have shown that a high-fat diet induces insulin resistance and a strong increase in ectopic fat deposition in muscle cells, and reduced muscle mitochondrial activity and muscle protein anabolism [48,49]. In contrast, animals fed a diet high in ω-6 PUFA showed improved insulin sensitivity and reduced intramyocellular lipid accumulation compared to those consuming a high-SFA diet [50,51].

Several studies have suggested a role for muscle quality in functional decline in older adults as possible explanation for the apparent disconnect between age-related changes in muscle mass and in muscle strength—the latter being more profound with ageing [47]. Although we did not observe statistically significant differences in fat mass across DP (i.e. total body adiposity estimated by bioelectrical impedance), participants in DP3 had a lower lean muscle mass compared with those in DP1 and DP2 (p = 0.003; details not shown). Thus, a combination of lower lean mass and poorer muscle quality in participants consuming DP3 may have contributed to a faster rate of decline in TUG and a steeper loss of HGS in men over 5 years compared with those in DP2 (‘Low Meat’). Similarly, despite having higher lean muscle mass, the quality of muscle may have been compromised in participants belonging to DP1.

Of course, moderate consumption of lean red meat (an excellent source of protein, leucine, creatine, vitamins (e.g. B12) and minerals (iron and zinc)) may be beneficial in maintenance of muscle mass in older adults [52,53]. Indeed, a recent randomized control trial of 100 women aged 70–90 has shown that consumption of cooked lean red meat (~160 g/day 6 days a week for 4 months) in combination with resistance training improved total lean muscle mass and function [54]. Also, leg lean muscle mass and leg extension increased in older women (aged 65–70) consuming a healthy diet (characterised by ω-6/ω-3 PUFA < 2 ratio; MUFA and PUFA from plant oils as main source of fat; nuts and seeds, and 20%E of protein from lean meat, low fat dairy, and fish/sea food) for 6 months in combination with resistant training [55]. The latter diet has similarities with DP2 (‘Low Meat’), which had the highest percentage of participants consuming potentially more healthy foods for muscle quality, mass and strength (fish/sea food, dairy, nuts, whole grains/cereal, fruits) and overall health ([26]; S1 Table).

Another possible mechanism may be related to the effects of an unfavourable acid-base balance for muscle health in DP1, as a greater dietary acidity has been implicated in muscle loss and decline in physical performance [56,57]. Based on estimated net endogenous acid production (calculated using the Frassetto et al [58] algorithm), DP2 (‘Low Meat’), the DP high in alkalizing fruits and vegetables and low in acid-producing meats, had a lower acidity compared with DP1 (p = 0.01; details not shown), which may have resulted in more balanced diet for maintenance of lean muscle mass. Additionally, although we adjusted for disease burden (multi-morbidity), the presence of greater vascular pathology among participants in DP1 [26] may have contributed to reduced physical activity and, therefore muscle function, through vascular pathways. Participants following DP2 had better overall, including cardiovascular, health [26], which may have contributed to their better physical functioning.

Finally, it is important not to assume that the label (title) given to a DP represents the only, or even the most biologically, important aspect/component of that DP. All DP are complex and contain constellations of foods and nutrients with multiple effects on metabolism and health. When interpreting the potential causal relationships between DP and functional outcomes, it is also important to consider the foods/nutrients which are missing from (or present in low amounts in) a given DP as those which characterise a particular dietary pattern. In this case, the lower consumption of fruits, nuts, fish and sea food, eggs, soups and dairy among DP1 participants may be mechanistically important.

Taken together, these and the findings from our study indicate that a healthy dietary pattern characterised by higher consumption of fish, fruits, vegetables, nuts, cereal, dairy and oils rich in unsaturated fatty acids may ameliorate age-related functional decline and associated adverse health outcomes [2–4] independently of other health-related factors. If replicated in other populations, these findings may be an important foundation for public health policies and interventions to facilitate healthy ageing. In particular, we have shown that diets high in red/processed meats, gravy or butter but low in beneficial foods (e.g. fish and plant-based foods) may have negative effects on muscle strength and physical performance, and exacerbate age-related decline [59] at very old age especially in men. Conversely adherence to a healthy dietary pattern may have a long-lasting effect on physical function well into late adulthood, irrespective of cognitive status, disease burden and physical activity.

Supporting Information

Acknowledgments

We acknowledge the operational support of the North of England Commissioning Support Unit and of the local general practitioners and their staff. We thank the research, management and clerical team for outstanding work throughout, as well as many colleagues for their expert advice. Thanks are due especially to the study participants and, where appropriate, their families and carers.