Skip to main content Accessibility help
×
Home

Dietary protein intake is associated with better physical function and muscle strength among elderly women

Published online by Cambridge University Press:  09 February 2016

Masoud Isanejad
Affiliation:
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, PO Box 1627, FI70211 Kuopio, Finland
Jaakko Mursu
Affiliation:
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, PO Box 1627, FI70211 Kuopio, Finland
Joonas Sirola
Affiliation:
Department of Orthopaedics and Traumatology, Kuopio University Hospital, Building 3, PO Box 100, FIN-70290, Kuopio, Finland Kuopio Musculoskeletal Research Unit, University of Eastern Finland, FIN-70211 Kuopio, Finland
Heikki Kröger
Affiliation:
Department of Orthopaedics and Traumatology, Kuopio University Hospital, Building 3, PO Box 100, FIN-70290, Kuopio, Finland Kuopio Musculoskeletal Research Unit, University of Eastern Finland, FIN-70211 Kuopio, Finland
Toni Rikkonen
Affiliation:
Kuopio Musculoskeletal Research Unit, University of Eastern Finland, FIN-70211 Kuopio, Finland
Marjo Tuppurainen
Affiliation:
Department of Obstetrics and Gynaecology, Kuopio University Hospital, PO Box 1777, FIN-70211, Kuopio, Finland
Arja T. Erkkilä
Affiliation:
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, PO Box 1627, FI70211 Kuopio, Finland
Corresponding
E-mail address:
Rights & Permissions[Opens in a new window]

Abstract

Dietary protein intake might be beneficial to physical function (PF) in the elderly. We examined the cross-sectional and prospective associations of protein intake of g/kg body weight (BW), fat mass (FM) and lean mass (LM) with PF in 554 women aged 65·3–71·6 years belonging to the Osteoporosis Risk Factor and Prevention Fracture Prevention Study. Participants filled a questionnaire on lifestyle factors and 3-d food record in 2002. Body composition was measured by dual-energy X-ray absorptiometry, and PF measures were performed at baseline and at 3-year follow-up. Sarcopaenia was defined using European Working Group on Sarcopenia in Older People criteria. At the baseline, women with higher protein intake (≥1·2 g/kg BW) had better performance in hand-grip strength/body mass (GS/BM) (P=0·001), knee extension/BM (P=0·003), one-leg stance (P=0·047), chair rise (P=0·043), squat (P=0·019), squat to the ground (P=0·001), faster walking speed for 10 m (P=0·005) and higher short physical performance battery score (P=0·004) compared with those with moderate and lower intakes (0·81–1·19 and ≤0·8 g/kg BW, respectively). In follow-up results, higher protein intake was associated with less decline in GS/BM, one-leg stance and tandem walk for 6 m over 3 years. Overall, results were no longer significant after controlling for FM. Associations were detected between protein intake and PF in non-sarcopaenic women but not in sarcopaenic women, except for change of GS (P=0·037). Further, FM but not LM was negatively associated with PF measures (P<0·050). This study suggests that higher protein intake and lower FM might be positively associated with PF in elderly women.

Type
Full Papers
Copyright
Copyright © The Authors 2016 

The aetiology of sarcopaenia is multifactorial. The European Working Group on Sarcopenia in Older People (EWGSOP) has provided a working definition of sarcopaenia( Reference Cruz-Jentoft, Baeyens and Bauer 1 , Reference Cruz-Jentoft, Landi and Schneider 2 ). EWGSOP proposed that sarcopaenia is diagnosed using the criteria of low lean mass (LM) and low physical performance, either low muscle strength (MS) and/or low physical function (PF) in elderly( Reference Cruz-Jentoft, Baeyens and Bauer 1 , Reference Cruz-Jentoft, Landi and Schneider 2 ). It is known that decline in MS and PF is an important contributing factor of the quality of life, and it increases the risk of frailty, fracture and falls in older individuals( Reference Cruz-Jentoft, Landi and Schneider 2 Reference Rikkonen, Sirola and Salovaara 4 ). Although the aetiology of the decline in physical performance is not fully understood, poor nutrition may contribute to its development and progression( Reference Volpi, Campbell and Dwyer 5 ). Therefore, measurement of MS and PF as indicators of physical performance status, as well as nutritional status, gained considerable attention in the past years( Reference Norman, Stobaus and Gonzalez 6 ).

Indeed, new evidence shows that adequate dietary protein is beneficial to support good health, promote recovery from illness and maintain LM in older adults( Reference Pedersen, Kondrup and Borsheim 7 Reference Bauer, Biolo and Cederholm 11 ). It also has positive association with MS and PF( Reference Beasley, Wertheim and LaCroix 12 Reference Robinson, Jameson and Batelaan 15 ). However, the adequacy of current RDA( 16 ) for protein of 0·8 g/kg body weight (BW) has been questioned recently regarding that it might not be enough to maintain the LM and to prevent functional decline among the elderly( Reference Volpi, Campbell and Dwyer 5 , Reference Fulgoni 17 , Reference Beasley, Shikany and Thomson 18 ). To this end, recent reviews and consensus statements have suggested that a protein intake between 1·0 and 1·5 g/kg per d may confer health benefits beyond those afforded by simply meeting the minimum( Reference Volpi, Campbell and Dwyer 5 , Reference Paddon-Jones and Leidy 19 ). It might be inappropriate also to generalise the protein intake requirements based on healthy young men to older adults( Reference Beasley, Shikany and Thomson 18 ). International Study Group, and including 11 other members, to review dietary protein needs with aging (PROT-AGE Study Group) recommendations for dietary protein intake in healthy older adults is an average in the range of 1·0–1·2 g/kg BW( Reference Bauer, Biolo and Cederholm 11 ). Further, Nordic Nutrition Recommendation 2012 (NNR) for elderly also suggested protein intake in the range of 1·1–1·3 g/kg BW (1·2 g/kg BW for planning purposes at the population level)( 20 Reference Hickson 22 ).

Ageing is accompanied with changes in body composition with a gradual increase in the proportion of fat mass (FM) and decline in LM( Reference Baumgartner 23 ). LM is the main reservoir of protein in the human body, and it has a significant role in movement and posture, regulation of metabolism, and storage of energy and N( Reference Timmerman and Volpi 24 ). Previous studies supported the correlation between decreased LM and impaired physical performance( Reference Wolfe 25 ). In a study by Pedrero-Chamizo et al.( Reference Pedrero-Chamizo, Gomez-Cabello and Melendez 26 ), elderly men and women with sarcopaenic obesity showed lower physical fitness levels compared with non-sarcopaenic subjects( Reference Rennie, Wackerhage and Spangenburg 27 ). Notably, older individuals have an attenuated muscle protein synthetic response after the ingestion of dietary protein and amino acids. This resistance to the usually anabolic effect of protein on myofibrillar protein synthesis (MPS) may partially contribute to the age-related decline in LM( Reference Moore, Churchward-Venne and Witard 28 ). Because of metabolic changes associated with ageing, elderly persons may produce less LM than younger people from the same amount of ingested protein( Reference Campbell 29 ). It is recommended, therefore, that in cases of acute illness or psychological stress or sarcopaenia higher protein intake is required( Reference Suominen, Jyvakorpi and Pitkala 30 ).

The primary aim of this study was to evaluate the differences in MS and PF in elderly women with higher protein intake than current daily allowance as compared with those with lower intake at the baseline and over the 3-year follow-up. We hypothesised that a positive association of protein intake with PF measures is more pronounced in non-sarcopaenic women as compared with those with diagnosed sarcopaenia based on EWGSOP criteria( Reference Cruz-Jentoft, Landi and Schneider 2 ). Further, the associations of total body FM and LM with PF and MS measures were examined at the baseline and at 3 years of follow-up.

Methods

Study design and participants

Data of the present study were collected from the Osteoporosis Risk Factor and Prevention Fracture Prevention Study (OSTPRE-FPS), which was a 3-year intervention to investigate the effect of Ca and vitamin D supplementation on the incidence of falls and fractures among elderly women( Reference Karkkainen, Tuppurainen and Salovaara 31 ). Inclusion criteria were being older than 65 years of age by the end of November 2002, residing in Kuopio region and no previous participation in OSTPRE bone densitometry sample( Reference Karkkainen, Tuppurainen and Salovaara 31 ). Supplementation group received daily 800 IU (20μg) of cholecalciferol and 1000 mg of Ca for 3 years, whereas the control group received neither supplementation nor placebo with the aim to study the effects of vitamin D and Ca supplementation on bone mineral density. In total, 750 women were randomly taken into this subsample for participating in detailed examinations including measurement of body composition, physical performance tests and food records( Reference Jarvinen, Tuppurainen and Erkkila 32 ). Out of those, 554 women returned valid food record and had valid body composition and physical performance measurements for both at the baseline and at the 3-year follow-up. All clinical measurements were performed in the Kuopio Musculoskeletal Research Unit of the Clinical Research Center of the University of Kuopio. All participants provided written permission for participation. The study was approved in October 2001 by the ethical committee of Kuopio University Hospital. The study was registered in Clinical trials.gov by the identification no. NCT00592917.

Body composition measurements

The height and weight of participants were measured in light indoor clothing without shoes, and BMI was calculated as weight (kg) divided by height squared (m2). FM and LM were measured by dual-energy X-ray absorptiometry (DXA) by specially trained nurses. The DXA measurements were carried out using the same Lunar Prodigy adhering to the imaging and analysis protocols provided by the manufacturer (Lunar Co.)( Reference Jarvinen, Tuppurainen and Erkkila 32 ). DXA is currently a common tool suitable for estimation of body composition in terms of evaluating the ratio between fat, muscle and bone in different parts of the body( Reference Lohman, Tallroth and Kettunen 33 ). DXA also has been showed to be superior to bioimpedance for estimation of the body composition( Reference Aandstad, Holtberget and Hageberg 34 ).

Physical performance measurements

Physical performance measures were assessed by trained nurses at baseline and at year 3, consisting of three main domains – (1) MS: hand-grip strength (GS, kPa), number of chair rises in 30 s, ability to squat, ability to squat to the ground and knee extension (kPa); (2) mobility test: walking speed (WS) for 10 m (m/s) and tandem walk for 6 m (m/s); and (3) balance ability: standing with closed eyes for 10 s and one-leg stance performance for 30 s. GS was measured in a controlled sitting position with a pneumatic hand-held dynamometer (Martin Vigorimeter; Gebruder Martin GmbH & Co., KG) by calculating the mean of three successive measurements from the dominant hand. To standardise, GS and knee extension were further expressed as a ratio to body mass (BM) (FM+LM), which have been suggested to be better predictors of GS and knee extension alone( Reference Shilland and McCarthy 35 , Reference Martien, Delecluse and Boen 36 ). The chair rise test was conducted if the participant was able to stand at least once without using arms from a straight-backed, non-padded, armless chair. Any measurement errors were excluded from the statistical analysis( Reference Sjoblom, Suuronen and Rikkonen 37 ). The follow-up variable of knee extension was excluded from analysis because of an unexpected increase in measured extension force and/or possible data entry errors. Further, based on EWGSOP definition, short physical performance battery (SPPB) score was calculated using three individual measures of physical performance including WS for 10 m (m/s), chair rises in 30 s and one-leg stance performance categorised in quartiles( Reference Kwon, Perera and Pahor 38 ). Each quartile was scored on a scale of 1–4 points, with the total score ranging to 12; higher scores of SPPB indicate better performance. Further, absolute changes in PF and MS measures were calculated by subtracting the baseline measures from those measured at year 3. The magnitude of meaningful changes in physical performance measures, as well as SPPB, have been evaluated previously, and these measures are consistently used as preferred indicators of physical performance in older adults( Reference Cruz-Jentoft, Landi and Schneider 2 , Reference Kwon, Perera and Pahor 38 , 39 ).

Diagnosis of sarcopaenia

Relative skeletal muscle index (RSMI) was calculated as the sum of the non-fat, non-bone skeletal muscle in arms and legs divided by height squared (m2). Women were subdivided into quartiles according to their RSMI values: (1) 5·3–6·3 kg/m2, (2) 6·3–6·7 kg/m2, (3) 6·7–7·2 kg/m2 and (4) 7·2–9·3 kg/m2. Baumgartner( Reference Baumgartner 23 ) reported that the sarcopaenia cutoff point was 5·45 kg/m2, which was calculated as 2 sd below the mean in the young reference population. However, in our study, there were only six women whose RSMI was <5·45 kg/m2. Accordingly, we decided to use the lowest quartile below 6·3 kg/m2 as cutoff in the present study( Reference Sjoblom, Suuronen and Rikkonen 37 ). The study population was divided into quartiles also for their GS: (1) <22·3 kPa, (2) 22·3–25·7 kPa, (3) 25·7–28·7 kPa and (4) 28·7–40 kPa. Physical performance test was assessed by measuring WS by a 10-m WS test in a controlled situation and the WS was divided into quartiles: (1) <0·51 m/s, (2) 1·42–1·63 m/s, (3) 1·64–1·85 m/s and (4) >1·85 m/s. The women who were not able to walk were allocated into the group of the lowest quartile. A woman was classified as sarcopaenic if she belonged to the lowest quartile of RSMI and the lowest quartile of either GS or WS or both. A non-sarcopaenic woman did not belong to the lowest quartile of any measurement (RSMI, GS or WS), whereas pre-sarcopaenic women were in the lowest quartile of RSMI but not in the lowest quartile of any other outcome measure. Non-classified women belonged to the lowest quartile of either GS or WS or both, but not to that of RSMI.

Dietary intakes

Dietary intake was collected by using 3-d food record at baseline. A questionnaire and instructions were sent to the participants beforehand, and they were returned on the visiting day. Participants were advised to fill the questionnaire for 3 consecutive days, including 2 d during the week and 1 d in the weekend (Saturday or Sunday). Participants were instructed to write down everything they ate and drank and to evaluate the amount of food consumed using household measures. In case of uncertainties in the food record, a nutritionist called the participant for additional information( Reference Erkkila, Jarvinen and Karvonen 40 ). To assess the under-reporting, the ratio of energy intake:estimated BMR was calculated based on BW according to equations given by the Department of Health( 41 ) in the UK. The ratio of energy intake:BMR cutoff value for under-reporting was chosen to be 1·49, as derived from Goldberg et al.( Reference Goldberg, Black and Jebb 42 ) and Black( Reference Black 43 ), and none of the participants was excluded from the analyses. Nutritional intake from food was calculated using Nutrica program (version 2.5; Finnish Social Insurance Institute). Collected data provided calculations of animal and plant sources of protein in addition to total protein intake.

Potential confounders

All lifestyle-related information was gathered by the self-administered questionnaire. The questionnaire included questions on age, hormone therapy use (never used or used), time since menopause (years), smoking status (never, former and current), self-reported Ca and vitamin D supplementation, and alcohol consumption (portions/week). Total physical activity was based on self-reported amounts of sports, recreation and miscellaneous activities, including walking, jogging, skiing, cycling, swimming, aerobic exercise, ball sports and other more strenuous activities. Women were asked how many days they performed each activity per month. The sum of each activity days during all 12 months was divided by 12 in order to obtain the mean activity level per month. Furthermore, the mean activity level was multiplied by self-reported strenuousness of the exercise (the scale was 1 (low) to 4 (strenuous))( Reference Sjoblom, Suuronen and Rikkonen 37 ).

Statistical analysis

Protein intake was reported as crude protein intake per BW (g/kg BW). Protein intake was categorised based on three different nutrition recommendations, RDA( 16 ) (≤0·8 g/kg BW), PROT-AGE Study Group recommendation( Reference Bauer, Biolo and Cederholm 11 ) (0·81–1·19 g/kg BW) and NNR(≥1·2 g/kg BW)( 20 ). For the purpose of this study, these three categories were referred to as lower, moderate and higher intake, respectively. Continuous variables were compared across the protein intake categories using ANOVA and ANCOVA, and categorical variables were compared using χ 2 test.

Mean values and standard deviations of PF and MS measures at the baseline and absolute changes in them were tested in the ANCOVA across the categories of protein intake. Multiple linear regression or logistic regression models were used to calculate β-coefficients and 95 % CI of PF and MS measures at the baseline and changes in them across categories of protein intake. Tests for a linear trend across categories of protein intake were conducted by using the median value in each category as a continuous variable in the linear and logistic regression models. Pairwise comparisons of the group means were performed with Tukey’s post hoc test. Linear and logistic regression analyses evaluated the association of FM and LM with PF and MS measures at baseline and over the 3-year follow-up. We examined further the association of protein intake of g/kg BW with PF measures at baseline and over the 3-year follow-up according to sarcopaenia status. To achieve balanced numbers of participants in the stratified analysis and to evaluate our secondary hypothesis, women were classified as sarcopaenic if they belonged to the pre-sarcopaenia, sarcopaenia and severe sarcopaenia (lowest quartile of RSMI) group, and non-sarcopaenic group was compiled from normal and non-classified groups (normal RSMI).

We initially assessed known covariates of frailty, including age, total energy intake, smoking status, alcohol consumption (portions/week), physical activity (h/week), hormone therapy use, osteoporosis and self-reported history of medical conditions (fall in the past 12 months, depression, diabetes mellitus, hypertension and rheumatoid arthritis) and also for baseline height, FM and LM. Further, covariates were selected on the basis of their multicollinearity and their predictive values alone, which led to selection of the following models. Model 1 presents the unadjusted results controlling only for age and energy intake. Model 2 was adjusted for variables in model 1 plus smoking status, alcohol consumption, physical activity, hormone therapy use, osteoporosis, LM and height. Model 3 was adjusted for variables in model 2, but LM was replaced by FM. Longitudinal analyses were adjusted for vitamin D and Ca supplementation (study group) to control for plausible vitamin D effect on physical performance, as well as PF and MS baseline measures to account for differential subsequent changes in physical performance depending on the initial physical performance measures. Comparing model 2 and 3 provided the opportunity to evaluate whether LM and FM differently associate with PF and MS, as suggested by previous studies( Reference Rikkonen, Sirola and Salovaara 4 , Reference Delmonico, Harris and Visser 44 , Reference Lemieux, Filion and Barbat-Artigas 45 ).

All statistical analyses were executed using the SPSS software version 21 for Windows (IBM Corp.). The result was considered significant if a P value was <0·05.

Results

The participants were 65·3–71·6 years old (mean age was 68 (sd 1·9) years), and mean energy intake was 6560 (sd 1556) kJ/d (Table 1). Total protein intake was 68·2 g/d, which constituted to 17 % of total energy intake and corresponded to 0·96 g/kg BW. The minimum protein intake reported was 0·24 g/kg BW and the maximum was 2·25 g/kg BW. In addition, 30 % of women had protein intake ≤0·8 g/kg BW, 48 % were in the moderate range of 0·8–1·19 g/kg BW and 22 % consumed protein ≥1·2 g/kg BW. Higher protein intake was significantly associated with higher energy intake and lower carbohydrate intake as percentage of energy, but higher carbohydrate intake as g/d.

Table 1 Baseline characteristics of the participants in different protein intake categories (Mean values and standard deviations)

a Mean values with unlike superscript letters of the lowest category was significantly different from the middle and highest categories after Tukey’s post hoc test.

b Mean values with unlike superscript letters of the middle category was significantly different than from the highest category after Tukey’s post hoc test.

* ANCOVA and χ2 tests were used to evaluate the differences between participants’ characteristics and dietary intake with protein intake categories as expressed per body weight according to different recommendations.

Includes walking, gardening, cycling, cross-country skiing and other more strenuous activities (times/month×strenuousness).

In total, 8 % of women had osteoporosis, 42 % had hypertension, 3 % had diabetes, 6 % had rheumatoid arthritis, 3 % had depression, 12 % had hip arthrosis, 28 % had knee arthrosis and 21·8 % reported fall accident in the past 12 months. However, no significant associations between reported diseases and protein intake of g/kg BW were observed. Mean duration of hormone therapy was 11 years, and time passed after menopause was 18 years. Women with higher protein intake reported more frequent use of hormone therapy, weighed less and had lower BMI as compared with moderate and lower intake. Among body composition measurements, FM index, LM index and LM index (LMI) were significantly lower for higher protein intake. Women with higher protein intake had significantly higher RSMI than those with lower protein intake.

In Table 2, differences of baseline characteristics between non-sarcopaenic and sarcopaenic participants are presented. The sarcopaenic group (n 127) had significantly lower mean weight (−13·2 %), BMI (−12·7 %), FM (−16·0 %) and LM (−12·0 %) as compared with the non-sarcopaenic group (n 369). Average protein intake was similar in the sarcopaenic and non-sarcopaenic groups: 17·6 (sd 2·9) and 17·9 (sd 3·1) % of energy, respectively.

Table 2 Baseline characteristics of the participants according to sarcopaenia status (Mean values and standard deviations)

* Independent sample t test and χ2 test were used to evaluate the differences between participant’s characteristics according to sarcopaenia status.

Includes walking, gardening, cycling, cross-country skiing and other more strenuous activities (times/month×strenuousness).

Significant differences in physical performance measures between women with higher protein intake and those with lower protein intake at the baseline and over the 3-year follow-up were detected (Table 3). At the baseline after adjustment for selected factors previously described as associated with physical performance (model 2), those with higher protein intake as compared with those with moderate and lower intake had greater GS/BM (P=0·001), knee extension/BM (P=0·003), longer one-leg stance performance (P=0·047), better chair rise performance (P=0·043), faster WS for 10 m (P=0·005), squat completion (P=0·019), squat to the ground completion (P=0·001) and higher SPPB score (P=0·004). Overall results were no longer significant after controlling for FM (model 3). Results for the prospective analysis showed that those with higher protein intake had less decline in GS/BM (P=0·027), one-leg stance performance duration (P=0·024) and had increased tandem walk speed (P=0·024), which were no longer significant after controlling for FM.

Table 3 Physical performance measures in protein intake categories at the baseline and over the 3-year follow-upFootnote * (Mean values and standard deviations)

BW, body weight.

a Mean values with unlike superscript letters of the lowest category was significantly different from the middle and highest categories after Tukey’s post hoc test.

b Mean values with unlike superscript letters of the middle category was significantly different from the highest category after Tukey’s post hoc test.

* Tests for a linear trend across categories of protein intake were conducted by using the median value in each category as a continuous variable in the linear and logistic regression models. Median total protein intake for each category was 0·66, 0·98 and 1·34 g/kg BW, respectively.

Model 1 was adjusted for age and total energy intake.

Model 2 was adjusted for variables in model 1 plus smoking status, alcohol consumption (portions/week), physical activity level, hormone therapy use, osteoporosis, baseline height and lean mass.

§ Model 3 was adjusted for variables in model 2, but lean mass was replaced by fat mass.

|| Longitudinal analyses were adjusted also for physical performance baseline variables and Ca and vitamin D intervention.

In linear regression analyses with physical performance measures and SPPB as the dependent measures, results from models including energy-adjusted fat intake (g/d) or energy-adjusted carbohydrate intake (g/d) as determinant instead of protein showed no significant contribution for fat (g/d) and carbohydrate (g/d) (data not shown).

Further, we examined the association of protein intake with physical performance measures according to sarcopaenia status (Table 4). Results of model 2 showed that among non-sarcopaenic women protein intake was positively associated with GS/BM (β=0·35 and P=0·001), knee extension/BM (β=0·25 and P=0·008), one-leg stance performance (β=0·26 and P=0·001), chair rises (β=0·15 and P=0·039), WS for 10 m (β=0·30 and P<0·001), ability to squat (β=0·18 and P=0·003), ability to squat to the ground (β=0·29 and P=0·001) and also with SPPB score (β=0·32 and P<0·001) at the baseline. However, significant associations were lost after controlling for FM. Results of the prospective analysis indicated that higher protein intake in non-sarcopaenic women was in positive relationship with changes of one-leg stance performance (β=0·14 and P=0·037) and standing with eyes closed (β=0·23 and P=0·001). No significant associations between protein intake and physical performance measures were observed among sarcopaenic women, except for GS/BM change (β=0·23 and P=0·037) and a non-significant relation with chair rise change (β=0·27 and P=0·064), which were lost after controlling for selected confounders and FM.

Table 4 ·Effect of protein intake (g/kg body weight) and physical performance measures according to sarcopaenia status (Regression coefficients and 95 % confidence intervals)

* Model 1 was adjusted for age and total energy intake.

Model 2 was adjusted for variables in model 1 plus smoking status, alcohol consumption (portions/week), physical activity level, hormone therapy use, osteoporosis, study group and baseline height.

Model 3 was adjusted for variables in model 2 plus fat mass.

§ Longitudinal analyses were adjusted also for physical performance baseline variables and Ca and vitamin D intervention.

The associations between total body FM and LM with physical performance measures and changes in them are shown in Table 5. After adjustment for LM and factors previously described as associated with physical performance, FM was negatively correlated with GS/BM, GS, knee extension/BM (only at the baseline), one-leg stance, chair rises, WS for 10 m, squat, squat to the ground and SPPB score at the baseline and over the 3-year follow-up (β≥−0·07 and P≤0·050). FM was also negatively associated with change of standing with closed eyes for 10 s (β=−0·22 and P<0·001). Further, LM was positively associated with GS, knee extension and one-leg stance performance at the baseline, as well as with GS changes over the 3-year follow-up (β≥0·06 and P≤0·025). Results remained significant after controlling for FM.

Table 5 Association of total body fat mass and lean mass with physical performance measures at the baseline and over the 3-year follow-up (β-Coefficients with their standard errors)

* Model 1 was adjusted for age, total energy intake, smoking status, alcohol consumption (portions/week), physical activity level, hormone therapy use, osteoporosis and height.

Model 2 adjusted for variables in model 1, and lean mass and fat mass were adjusted for each other.

Longitudinal analyses were adjusted also for physical performance baseline variables and Ca and vitamin D intervention.

Discussion

This study examined cross-sectional and prospective associations of protein intake (g/kg BW) and body composition (FM and LM) with different PF and MS measures in 554 elderly women belonging to the OSTPRE-FPS study. Associations of protein intake with PF and MS were also evaluated according to sarcopaenia status. However, the significant associations were lost in the final models because of high collinearity of FM with physical performance. Our findings supported the hypothesis that protein intake higher than the current RDA (0·8 g/kg BW) might be associated with better PF and MS among elderly women. Further, the present study showed that the total body FM was negatively associated with physical performance tests, whereas total body LM was positively associated with GS, knee extension and one-leg stance.

In recent years, there has been increased support for the contention that the current daily allowance (0·8 g/kg BW) for protein is insufficient to promote optimal health and preserve physical performance in the elderly( Reference Volpi, Campbell and Dwyer 5 , Reference Beasley, Wertheim and LaCroix 12 , Reference Gregorio, Brindisi and Kleppinger 13 , Reference Beasley, Shikany and Thomson 18 , Reference Lemieux, Filion and Barbat-Artigas 45 Reference Deutz, Bauer and Barazzoni 47 ). Consistently, in our cross-sectional findings, those women with higher protein intake performed better in many of the physical performance measures as compared with those who had moderate and lower protein intakes. The higher protein intake category had greater GS/BM, knee extension/BM, longer one-leg stance, better chair rise performance, faster WS for 10 m, better squat and squat to the ground ability, and higher SPPB score. The prospective results also showed that women in the higher protein intake group had less decline in GS/BM and one-leg stance performance, and had the highest increased chair rise performance over the 3-year follow-up. No significant differences were observed between protein intake categories and WS for 10 m and tandem walk speed for 6 m prospectively. Thus, it might be that higher protein intake (g/kg BW) can be more related to preserving MS rather than mobility, which may partially explain the protein–frailty association. However, these associations were no longer significant after adjustment for FM.

Findings of the study by Gregorio et al.( Reference Gregorio, Brindisi and Kleppinger 13 ) among 387 healthy women aged 60–90 years showed that those in the lower protein intake <0·8 g/kg BW category performed less well in the single-leg stance test than those in the higher protein intake ≥0·8 g/kg BW category. They also walked 8 feet at a slower pace, and their SPPB score was lower than in women in the higher protein category. Further, Lemieux et al.( Reference Lemieux, Filion and Barbat-Artigas 45 ) indicated that among seventy-two postmenopausal women higher protein intake ≥1·2 g/kg BW was positively correlated to GS and knee extension. Women’s Health Initiative clinical and observational study( Reference Beasley, Wertheim and LaCroix 12 ) was conducted in 134 961 participants aged 50–79 years for an average of 7 years of follow-up. Results showed that mean GS at baseline was slightly higher among women with higher calibrated daily protein intake (using urinary N protocol to estimate protein consumption over 24-h period), and these women experienced a smaller decline in GS over time than those with low calibrated protein intake. In addition, women in the highest quintile of calibrated protein intake completed on average 0·5 more chair rises at baseline than women in the lowest quintile. In contrast, there was no significant association between calibrated protein intake and the timed 6-m walk in either cross-sectional or prospective analyses. Furthermore, the same results were shown when protein intake was expressed as g/kg BW.

A new finding was that among non-sarcopaenic women at the baseline protein intake (g/kg BW) was in positive relationship with GS/BM, knee extension/BM, one-leg stance ability, chair rise performance, WS for 10 m, ability to squat and squat to the ground, and SPPB. Protein intake in these women was also associated with preserving physical performance over the 3-year follow-up, including one-leg stance and standing with eyes closed for 10 s. No such association was observed in sarcopaenic women, except a positive relationship between protein intake and GS change. Thus, consistent with our hypothesis, the positive association of protein intake (g/kg BW) with PF was more pronounced in non-sarcopaenic than in sarcopaenic women. It has been suggested that older individuals suffering from illness, physiological stress or sarcopaenia are required to consume higher protein intake (1·2–1·5 g/kg BW) as compared with healthy older people (1–1·2 g/kg BW)( Reference Suominen, Jyvakorpi and Pitkala 30 ). However, we could not explore this because of the threshold of protein intake in these data between sarcopaenic and non-sarcopaenic women.

A preponderance of evidence now suggests that ageing might result in the stimulation of MPS becoming resistant to the anabolic effect of hyperaminoacidemia, particularly at lower protein intakes( Reference Timmerman and Volpi 24 , Reference Suominen, Jyvakorpi and Pitkala 30 , Reference Carbone, McClung and Pasiakos 48 Reference Paddon-Jones and Rasmussen 50 ). It was shown in the study by Moore et al.( Reference Moore, Churchward-Venne and Witard 28 ) that the relative quantity of ingested protein required to maximise MPS is greater in older as compared with younger men( Reference Beasley, Shikany and Thomson 18 ). However, it is unestablished whether elderly individuals with greater LM have higher capacity of MPS as compared with those with lower LM. Besides, previous research indicates that protein from different sources (animal and plant protein) may have different effects on physical performance( Reference Gilbert, Bendsen and Tremblay 51 , Reference Sahni, Mangano and Hannan 52 ). However, this study did not find any significant association between animal and plant protein intake with PF and MS measures.

Declines in LM might predict a reduction in muscle force and performance( Reference Cruz-Jentoft, Baeyens and Bauer 1 , Reference Carbone, McClung and Pasiakos 48 ). It has also been shown that FM is associated with functional decline and muscle weakness in elderly individuals( Reference Shilland and McCarthy 35 , Reference Delmonico, Harris and Visser 44 , Reference Schaap, Koster and Visser 53 ). In this study, total body FM was in strong negative correlation with all PF and MS measures at baseline and changes in them at 3 years, except for knee extension, tandem walk and standing with eyes closed at the baseline, whereas LM was positively correlated with GS and the change in it, knee extension and one-leg stance. Therefore, these findings accompanied with the loss of significant associations between protein intake and physical performance measures after controlling for FM but not LM suggest that FM and LM may have opposite association with PF and MS in elderly women. There are different pathways through which fatness might be related to LM and MS( Reference Koster, Ding and Stenholm 54 ). However, more studies are needed to disentangle the relationship between FM and physical performance.

It is well known that adequate energy intake is required to optimally utilise dietary protein to maintain physical performance rather than as an energy source( Reference Gregorio, Brindisi and Kleppinger 13 ). It was to our surprise that those with higher energy and protein intake had a lower weight. The actual cause is uncertain, but this might be because of the higher physical activity level in the higher protein category, and also because of the possible under-reporting of total energy and fat intake in those with higher BMI( Reference Mendez, Popkin and Buckland 55 ). Worthy of note is that LMI (LM/height (m2)) and RSMI are both used as indicators of muscle mass in the diagnosis of sarcopaenia( Reference Cruz-Jentoft, Landi and Schneider 2 ). However, in this study, protein intake showed the same association with LMI and RSMI, and thus we used RSMI as a clinical indicator of sarcopaenia, as adapted by EWGSOP( Reference Cruz-Jentoft, Landi and Schneider 2 ).

A limitation of this study was that the study population consists of only elderly women, and therefore caution should be taken when generalising the findings to elderly men. The 3-d food records method has been described as a suitable instrument for assessing energy and protein intake in elderly people( Reference Luhrmann, Herbert and Gaster 56 , Reference Paddon-Jones, Sheffield-Moore and Katsanos 57 ). The latter study has also validated protein intake against urinary N studies in both community-dwelling and institutionalised elderly people( Reference Paddon-Jones, Sheffield-Moore and Katsanos 57 ). However, a single 3-d dietary record at the baseline might not be an appropriate method to capture the long-term effect of protein intake. Albeit we covered a wide selection for several known confounders that might influence physical performance, other factors such as health status, habitual physical activity level and/or dietary habits in participants in different protein intake categories might have affected the observed results. Last, based on the observational nature of our study, we cannot establish a causal association.

An additional analysis in the present data showed no significant effect of vitamin D (800 IU; 20μg) and Ca supplementation (1000 mg) on MS and PF, and longitudinal analysis was controlled for the study group receiving those. The availability of multiple standardised physical performance measures at baseline and over a 3-year period added significant strength to our study. Dynamometric measures of GS as a physical marker of lower limb strength and knee extension for a variety of functional tasks, such as walking, chair rising and stair climbing, particularly are predominate for the quantification of physical performance in older adults( Reference Martien, Delecluse and Boen 36 , Reference Bohannon and Magasi 58 ). The introduced protein intake categories in the present study took into account the newer intake recommendations for elderly, which have not been used in the previous studies.

Conclusion

It is appropriate to focus on the relationship between protein intake and MS and PF in the elderly, because this group is most vulnerable to nutritional deficiencies. This cohort study suggests that higher protein intake and lower FM might be positively associated with MS and PF in elderly women. However, further research is required to establish causal association.

Acknowledgements

The OSTPRE-FPS was supported by the Finnish Cultural Foundation (Hulda Tossavainen Foundation; Matti Kärkkäinen), Sigrid Juselius Foundation (H. K. and T. R.), Academy of Finland (M. T.) and Kuopio University Hospital EVO grant. This study was supported by the Päivikki ja Sakari Sohlbergin foundation (M. I.): 2543.

H. K. and M. T. designed the original OSTPRE-FPS study. M. I., A. T. E., J. S. and J. M. planned the present study together and collaborated on drafting the manuscript. M. I. carried out the statistical analysis and summarised the results in tables. H. K., M. T. and T. R. critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

The authors have no relevant conflicts of interest to declare.

References

1. Cruz-Jentoft, AJ, Baeyens, JP, Bauer, JM, et al. (2010) Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing 39, 412423.CrossRefGoogle ScholarPubMed
2. Cruz-Jentoft, AJ, Landi, F, Schneider, SM, et al. (2014) Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing 43, 748759.CrossRefGoogle Scholar
3. Mithal, A, Bonjour, JP, Boonen, S, et al. (2013) Impact of nutrition on muscle mass, strength, and performance in older adults. Osteoporos Int 24, 15551566.CrossRefGoogle ScholarPubMed
4. Rikkonen, T, Sirola, J, Salovaara, K, et al. (2012) Muscle strength and body composition are clinical indicators of osteoporosis. Calcif Tissue Int 91, 131138.CrossRefGoogle Scholar
5. Volpi, E, Campbell, WW, Dwyer, JT, et al. (2013) Is the optimal level of protein intake for older adults greater than the recommended dietary allowance? J Gerontol A Biol Sci Med Sci 68, 677681.CrossRefGoogle Scholar
6. Norman, K, Stobaus, N, Gonzalez, MC, et al. (2011) Hand grip strength: outcome predictor and marker of nutritional status. Clin Nutr 30, 135142.CrossRefGoogle ScholarPubMed
7. Pedersen, AN, Kondrup, J & Borsheim, E (2013) Health effects of protein intake in healthy adults: a systematic literature review. Food Nutr Res 57, 10.3402/fnr.v57i0.21245.CrossRefGoogle ScholarPubMed
8. Muller, MJ, Geisler, C, Pourhassan, M, et al. (2014) Assessment and definition of lean body mass deficiency in the elderly. Eur J Clin Nutr 68, 12201227.CrossRefGoogle ScholarPubMed
9. Houston, DK, Nicklas, BJ, Ding, J, et al. (2008) Dietary protein intake is associated with lean mass change in older, community-dwelling adults: the Health, Aging, and Body Composition (Health ABC) Study. Am J Clin Nutr 87, 150155.Google ScholarPubMed
10. Meng, X, Zhu, K, Devine, A, et al. (2009) A 5-year cohort study of the effects of high protein intake on lean mass and BMC in elderly postmenopausal women. J Bone Miner Res 24, 18271834.CrossRefGoogle ScholarPubMed
11. Bauer, J, Biolo, G, Cederholm, T, et al. (2013) Evidence-based recommendations for optimal dietary protein intake in older people: a position paper from the PROT-AGE Study Group. J Am Med Dir Assoc 14, 542559.CrossRefGoogle ScholarPubMed
12. Beasley, JM, Wertheim, BC, LaCroix, AZ, et al. (2013) Biomarker-calibrated protein intake and physical function in the Women’s Health Initiative. J Am Geriatr Soc 61, 18631871.CrossRefGoogle ScholarPubMed
13. Gregorio, L, Brindisi, J, Kleppinger, A, et al. (2014) Adequate dietary protein is associated with better physical performance among post-menopausal women 60-90 years. J Nutr Health Aging 18, 155160.CrossRefGoogle ScholarPubMed
14. Bartali, B, Frongillo, EA, Stipanuk, MH, et al. (2012) Protein intake and muscle strength in older persons: does inflammation matter? J Am Geriatr Soc 60, 480484.CrossRefGoogle ScholarPubMed
15. Robinson, SM, Jameson, KA, Batelaan, SF, et al. (2008) Diet and its relationship with grip strength in community-dwelling older men and women: the Hertfordshire cohort study. J Am Geriatr Soc 56, 8490.CrossRefGoogle ScholarPubMed
16. Institute of Medicine of the National Academy of Sciences (editors) (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington, DC: National Academies Press.Google Scholar
17. Fulgoni, VL 3rd (2008) Current protein intake in America: analysis of the National Health and Nutrition Examination Survey, 2003–2004. Am J Clin Nutr 87, 1554S1557S.Google ScholarPubMed
18. Beasley, JM, Shikany, JM & Thomson, CA (2013) The role of dietary protein intake in the prevention of sarcopenia of aging. Nutr Clin Pract 28, 684690.CrossRefGoogle Scholar
19. Paddon-Jones, D & Leidy, H (2014) Dietary protein and muscle in older persons. Curr Opin Clin Nutr Metab Care 17, 511.CrossRefGoogle ScholarPubMed
20. Nordic Council of Ministers 2014 editor (2013) Nordic Nutrition Recommendations 2012. Integrating Nutrition and Physical Activity, Nord 2014:002, 5th ed. Copenhagen: Nordic Council of Ministers 2014.Google Scholar
21. Gaillard, C, Alix, E, Salle, A, et al. (2007) Energy requirements in frail elderly people: a review of the literature. Clin Nutr 26, 1624.CrossRefGoogle ScholarPubMed
22. Hickson, M (2015) Nutritional interventions in sarcopenia: a critical review. Proc Nutr Soc 74, 378386.CrossRefGoogle ScholarPubMed
23. Baumgartner, RN (2000) Body composition in healthy aging. Ann N Y Acad Sci 904, 437448.CrossRefGoogle ScholarPubMed
24. Timmerman, KL & Volpi, E (2008) Amino acid metabolism and regulatory effects in aging. Curr Opin Clin Nutr Metab Care 11, 4549.CrossRefGoogle Scholar
25. Wolfe, RR (2013) Perspective: optimal protein intake in the elderly. J Am Med Dir Assoc 14, 6566.CrossRefGoogle ScholarPubMed
26. Pedrero-Chamizo, R, Gomez-Cabello, A, Melendez, A, et al. (2015) Higher levels of physical fitness are associated with a reduced risk of suffering sarcopenic obesity and better perceived health among the elderly: the EXERNET multi-center study. J Nutr Health Aging 19, 211217.CrossRefGoogle ScholarPubMed
27. Rennie, MJ, Wackerhage, H, Spangenburg, EE, et al. (2004) Control of the size of the human muscle mass. Annu Rev Physiol 66, 799828.CrossRefGoogle ScholarPubMed
28. Moore, DR, Churchward-Venne, TA, Witard, O, et al. (2015) Protein ingestion to stimulate myofibrillar protein synthesis requires greater relative protein intakes in healthy older versus younger men. J Gerontol A Biol Sci Med Sci 70, 5762.CrossRefGoogle ScholarPubMed
29. Campbell, WW (2007) Synergistic use of higher-protein diets or nutritional supplements with resistance training to counter sarcopenia. Nutr Rev 65, 416422.CrossRefGoogle ScholarPubMed
30. Suominen, MH, Jyvakorpi, SK, Pitkala, KH, et al. (2014) Nutritional guidelines for older people in Finland. J Nutr Health Aging 18, 861867.CrossRefGoogle ScholarPubMed
31. Karkkainen, M, Tuppurainen, M, Salovaara, K, et al. (2010) Effect of calcium and vitamin D supplementation on bone mineral density in women aged 65-71 years: a 3-year randomized population-based trial (OSTPRE-FPS). Osteoporos Int 21, 20472055.CrossRefGoogle Scholar
32. Jarvinen, R, Tuppurainen, M, Erkkila, AT, et al. (2012) Associations of dietary polyunsaturated fatty acids with bone mineral density in elderly women. Eur J Clin Nutr 66, 496503.CrossRefGoogle ScholarPubMed
33. Lohman, M, Tallroth, K, Kettunen, JA, et al. (2009) Reproducibility of dual-energy X-ray absorptiometry total and regional body composition measurements using different scanning positions and definitions of regions. Metabolism 58, 16631668.CrossRefGoogle ScholarPubMed
34. Aandstad, A, Holtberget, K, Hageberg, R, et al. (2014) Validity and reliability of bioelectrical impedance analysis and skinfold thickness in predicting body fat in military personnel. Mil Med 179, 208217.CrossRefGoogle ScholarPubMed
35. Shilland, K & McCarthy, HD (2015) The impact of body composition on skeletal muscle strength in Caucasian women: implications for sarcopenia screening. Proc Nutr Soc 74, E152.CrossRefGoogle Scholar
36. Martien, S, Delecluse, C, Boen, F, et al. (2015) Is knee extension strength a better predictor of functional performance than handgrip strength among older adults in three different settings? Arch Gerontol Geriatr 60, 252258.CrossRefGoogle ScholarPubMed
37. Sjoblom, S, Suuronen, J, Rikkonen, T, et al. (2013) Relationship between postmenopausal osteoporosis and the components of clinical sarcopenia. Maturitas 75, 175180.CrossRefGoogle ScholarPubMed
38. Kwon, S, Perera, S, Pahor, M, et al. (2009) What is a meaningful change in physical performance? Findings from a clinical trial in older adults (the LIFE-P study). J Nutr Health Aging 13, 538544.CrossRefGoogle Scholar
39. Working Group on Functional Outcome Measures for Clinical Trials (2008) Functional outcomes for clinical trials in frail older persons: time to be moving. J Gerontol A Biol Sci Med Sci 63, 160164.CrossRefGoogle ScholarPubMed
40. Erkkila, AT, Jarvinen, R, Karvonen, H, et al. (2012) Validation of a semi-quantitative FFQ using food records as a reference in older women in the Kuopio Fracture Prevention Study (OSTPRE-FPS). Public Health Nutr 15, 635639.CrossRefGoogle Scholar
41. Department of Health editor (1991) Dietary Reference Values for Food Energy and Nutrients for the United Kingdom. London: HMSO.Google ScholarPubMed
42. Goldberg, GR, Black, AE, Jebb, SA, et al. (1991) Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 45, 569581.Google ScholarPubMed
43. Black, AE (2000) Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes Relat Metab Disord 24, 11191130.CrossRefGoogle ScholarPubMed
44. Delmonico, MJ, Harris, TB, Visser, M, et al. (2009) Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr 90, 15791585.CrossRefGoogle ScholarPubMed
45. Lemieux, FC, Filion, ME, Barbat-Artigas, S, et al. (2014) Relationship between different protein intake recommendations with muscle mass and muscle strength. Climacteric 17, 294300.CrossRefGoogle ScholarPubMed
46. Beasley, JM, LaCroix, AZ, Neuhouser, ML, et al. (2010) Protein intake and incident frailty in the Women’s Health Initiative observational study. J Am Geriatr Soc 58, 10631071.CrossRefGoogle ScholarPubMed
47. Deutz, NE, Bauer, JM, Barazzoni, R, et al. (2014) Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN Expert Group. Clin Nutr 33, 929936.CrossRefGoogle ScholarPubMed
48. Carbone, JW, McClung, JP & Pasiakos, SM (2012) Skeletal muscle responses to negative energy balance: effects of dietary protein. Adv Nutr 3, 119126.CrossRefGoogle ScholarPubMed
49. Shilland, K & McCarthy, HD (2015) The impact of diet and physical activity on skeletal muscle mass and strength in Caucasian women: implications for sarcopenia prevention. Proc Nutr Soc 74, E158.CrossRefGoogle Scholar
50. Paddon-Jones, D & Rasmussen, BB (2009) Dietary protein recommendations and the prevention of sarcopenia. Curr Opin Clin Nutr Metab Care 12, 8690.CrossRefGoogle ScholarPubMed
51. Gilbert, JA, Bendsen, NT, Tremblay, A, et al. (2011) Effect of proteins from different sources on body composition. Nutr Metab Cardiovasc Dis 21, Suppl. 2, B16B31.CrossRefGoogle ScholarPubMed
52. Sahni, S, Mangano, KM, Hannan, MT, et al. (2015) Higher protein intake is associated with higher lean mass and quadriceps muscle strength in adult men and women. J Nutr 145, 15691575.CrossRefGoogle ScholarPubMed
53. Schaap, LA, Koster, A & Visser, M (2013) Adiposity, muscle mass, and muscle strength in relation to functional decline in older persons. Epidemiol Rev 35, 5165.CrossRefGoogle ScholarPubMed
54. Koster, A, Ding, J, Stenholm, S, et al. (2011) Does the amount of fat mass predict age-related loss of lean mass, muscle strength, and muscle quality in older adults? J Gerontol A Biol Sci Med Sci 66, 888895.CrossRefGoogle ScholarPubMed
55. Mendez, MA, Popkin, BM, Buckland, G, et al. (2011) Alternative methods of accounting for underreporting and overreporting when measuring dietary intake-obesity relations. Am J Epidemiol 173, 448458.CrossRefGoogle ScholarPubMed
56. Luhrmann, PM, Herbert, BM, Gaster, C, et al. (1999) Validation of a self-administered 3-day estimated dietary record for use in the elderly. Eur J Nutr 38, 235240.Google ScholarPubMed
57. Paddon-Jones, D, Sheffield-Moore, M, Katsanos, CS, et al. (2006) Differential stimulation of muscle protein synthesis in elderly humans following isocaloric ingestion of amino acids or whey protein. Exp Gerontol 41, 215219.CrossRefGoogle ScholarPubMed
58. Bohannon, RW & Magasi, S (2015) Identification of dynapenia in older adults through the use of grip strength t-scores. Muscle Nerve 51, 102105.CrossRefGoogle Scholar

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 466
Total number of PDF views: 1759 *
View data table for this chart

* Views captured on Cambridge Core between September 2016 - 18th January 2021. This data will be updated every 24 hours.

Access
Hostname: page-component-77fc7d77f9-cctwg Total loading time: 0.656 Render date: 2021-01-18T23:41:25.137Z Query parameters: { "hasAccess": "1", "openAccess": "0", "isLogged": "0", "lang": "en" } Feature Flags last update: Mon Jan 18 2021 22:56:20 GMT+0000 (Coordinated Universal Time) Feature Flags: { "metrics": true, "metricsAbstractViews": false, "peerReview": true, "crossMark": true, "comments": true, "relatedCommentaries": true, "subject": true, "clr": true, "languageSwitch": true, "figures": false, "newCiteModal": false, "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true }

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Dietary protein intake is associated with better physical function and muscle strength among elderly women
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Dietary protein intake is associated with better physical function and muscle strength among elderly women
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Dietary protein intake is associated with better physical function and muscle strength among elderly women
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *