Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later

Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 ...

511KB Sizes 0 Downloads 134 Views

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

YCLNU3865_proof ■ 22 April 2019 ■ 1/8

Clinical Nutrition xxx (xxxx) xxx

Contents lists available at ScienceDirect

Clinical Nutrition journal homepage: http://www.elsevier.com/locate/clnu

Original article

Q6

Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men

Q5

€lsson b, Tommy Cederholm a, Per Sjo € gren a Mikael Karlsson a, *, Wulf Becker a, Karl Michae a b

Q1

Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Husargatan 3, BMC, SE-751 22, Uppsala, Sweden Department of Surgical Sciences, Section of Orthopedics, Uppsala University, Uppsala University Hospital, Uppsala, Sweden

a r t i c l e i n f o

s u m m a r y

Article history: Received 25 October 2018 Accepted 11 April 2019

Background & aims: The growing recognition of the significance of sarcopenia has highlighted the need to understand etiologic factors, where food intake likely plays a role. The aim was to investigate the association between dietary patterns at mean age 71 and the prevalence of sarcopenia at mean age 87 in a Swedish cohort of community dwelling men. Methods: Dietary habits were assessed using a 7-day food record. Adherences to official dietary guidelines, defined by the World Health Organization (WHO) by using the Healthy Diet Indicator, and Mediterranean-like dietary habits by using the Mediterranean Diet Score, were calculated. Sarcopenia was determined using the definition from the European Working Group on Sarcopenia in Older People (EWGSOP) and associations to each dietary pattern were analyzed using logistic regression, adjusted for potential confounders. Results: Our study population included 254 men, mean age 71 at baseline, and 53 (21%) were defined as sarcopenic 16 years later. There was no linear relationship between increased adherence to WHO dietary guidelines and future prevalence of sarcopenia, although those with medium adherence seemed to be protected (crude OR ¼ 0.41, 95% CI 0.19e0.92). On the other hand, an inverse relationship to sarcopenia was found for each SD increment in the Mediterranean diet score (crude OR ¼ 0.68, 95% CI 0.46e0.99), which remained after adjusting for potential confounders. Sensitivity analysis indicated relationships to be independent of changes in physical activity and dietary misreporting. Conclusions: In this prospective study of elderly men, using a single measure of diet at age 71 as a reflection of habitual dietary habits, healthy dietary patterns tended to protect against the development of sarcopenia over 16 years. In particular, we found indications that increased adherence to a Mediterranean dietary pattern might be advantageous. © 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Keywords: Sarcopenia Dietary pattern Mediterranean diet Healthy diet indicator Cohort Longitudinal

1. Introduction Sarcopenia is a geriatric syndrome consisting of age-related decline in both muscle mass and muscle function. Accumulating evidence suggests this condition to be associated with many adverse health outcomes, such as disability [1], falls [2], physical and cognitive impairment [3], poor quality of life [1] and mortality [4], with significant impact on social care costs [5,6]. Although the loss of muscle mass is a physiological part of aging, the term sarcopenia is used if muscle mass and muscle function fall below defined thresholds. Sarcopenia has been defined by the

* Corresponding author. E-mail address: [email protected] (M. Karlsson).

European Working Group on Sarcopenia in Older People (EWGSOP) as a progressive and general loss of muscle mass and muscle function (either low muscle strength or low physical performance) [7]. The prevalence of sarcopenia increases steeply with age, especially after the age of 75, with a prevalence of 1e6% reported among men <74 years, and 17e75% among men >80 years, depending on age stratum and choice of definition [8e10]. The growing recognition of sarcopenia as a geriatric syndrome has highlighted the need to understand its etiology. As of today, established risk factors include hormonal changes, neurodegeneration, low-grade inflammation and physical inactivity (including bed rest) [7]. Physical inactivity is clearly linked to the loss of muscle mass and muscle strength. Several intervention studies have shown that physical activity has positive effects on muscle mass and muscle strength improving overall physical

https://doi.org/10.1016/j.clnu.2019.04.009 0261-5614/© 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

YCLNU3865_proof ■ 22 April 2019 ■ 2/8

2

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

function [11,12]. The role of diet is less clear, and studies examining the role of diet in sarcopenia have predominantly focused on single food components. A growing body of evidence indicate that sufficient intakes of protein, vitamin D, antioxidant nutrients, and longchain n-3 polyunsaturated fatty acids, may improve or maintain muscle function [13]. Furthermore, several studies suggest that an overall healthy diet contributes to maintained physical performance, but there is limited evidence when it comes to healthy diets in relation to muscle mass and muscle strength, as reviewed [14]. To our knowledge, only four studies have assessed dietary patterns in relation to sarcopenia, defined as low muscle mass with either low muscle strength and/or low muscle performance [15e18]. These four studies used different methodologies to assess the role of dietary patterns, but they all indicate a relationship between diet and the development of sarcopenia. The aim of the present study was to evaluate the association between the adherence of two pre-defined dietary patterns at mean age 71 years and the prevalence of sarcopenia at mean age 87 in a Swedish cohort of community dwelling men. The dietary patterns assessed were: 1) official dietary guidelines, as defined by WHO by using the Healthy Diet Indicator (HDI) and 2) Mediterranean-like dietary habits by using the Mediterranean Diet Score (MDS).

2. Material and methods 2.1. Study population The present study was performed using the Uppsala Longitudinal Study of Adult Men (ULSAM), a population-based cohort study that started in 1970 and invited all men at age 50, born 192024 and living in Uppsala County (described in detail at http://www. pubcare.uu.se/ulsam). At the first investigation cycle a total of 2322 men agreed to participate, which corresponds to a participation rate of 82%. Several re-examinations have been carried out, see Fig. 1. Baseline in this study is the third investigation cycle conducted 1991e1995 at mean age 71 (1681 men were invited and 1221 (73%) participated) when detailed dietary intake was reported for the first time, using a validated 7-day food record (n ¼ 1138). At the fifth investigation cycle (2003e2005), at mean age 82, 530 men (56%) out of 952 participated in the investigation. At the sixth investigation cycle (2008e2009), at mean age 87, 354 men (58%) out of 613 participated, which included a test battery of physical function tests. In addition, 290 of the participants completed body composition measurement by whole body Dual-energy X-ray absorptiometry (DXA). Inclusion criterion for this study (Fig. 1) was availability of data on body composition at age 87 (n ¼ 290). Exclusion criteria were either the absence of data on dietary habits at baseline (n ¼ 31), extreme values of reported energy intake (<800 or >4000 kcal/day, n ¼ 2), missing data on both physical function and muscle strength at mean age 87 (n ¼ 2), or weight loss exceeding 10% between 60 and 70 years of age (n ¼ 1). These criteria rendered 254 men eligible for inclusion in the analyses. The ethics committee at Uppsala University approved the study and all participants gave their informed consent to participate.

2.2. Examinations All examinations, including anthropometric measurements and questionnaires regarding medical history, smoking habits, education and level of physical activity, were made under standardized conditions, as previously described [19].

2.3. Assessment of body composition Lean muscle mass was determined by DXA (DPX Prodigy, Lunar corp., Madison, WI, USA), and the precision errors of the DXA measurements in our laboratory were 1.5% for total fat mass and 1.0% for total lean mass. Skeletal muscle index (SMI) was calculated as the appendicular lean muscle mass (legs and arms) divided by height in meters squared [20]. 2.4. Assessment of physical function The handgrip strength was measured, in kg, using an adjustable hydraulic hand dynamometer (Fabrication Enterprises, White Plains NY, USA). Participants were sitting on a chair with the wrist in a neutral position, elbow at an angle of 90 , and feet on the floor, starting with the dominant hand. The highest of three measured values for each hand was used. The self-chosen walking speed was measured by using the intermediate six meters of a distance of 10 m. 2.5. Diagnosis of sarcopenia We applied the definition recommended by the European Working Group on Sarcopenia in Older People (EWGSOP) [7], and participants was categorized as sarcopenic, i.e. having low muscle mass together with either low muscle strength and/or low physical performance, or otherwise as non-sarcopenic. Low muscle mass was defined as a skeletal muscle mass index (SMI) less than 7.26 kg/m [2,20]. Muscle strength was defined as low if handgrip strength was less than 30 kg [21], using the strongest hand. Physical performance was defined as low if gait speed was <0.8 m/s [21]. 2.6. Assessment of covariates At each clinical visit, body height was measured to the nearest centimeter and body weight (BW) to the nearest 0.1 kg. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. High sensitivity C-reactive protein (CRP) measurements were performed by latex enhanced reagent (Dade Behring, Deerfield, IL) using a Behring BN ProSpec analyzer (Dade Behring) and categorized as normal (<3 mg/L), slightly elevated (3e10 mg/L) or elevated (>10 mg/L). Protein intake was categorized in three subgroups: low (<0.8 g/kg BW), medium (0.8e1.0 g/kg BW), and high (>1.0 g/kg BW), based on nutritional intake at baseline (see below). A validated questionnaire [22] was used at baseline (mean age 71) and at the fifth investigation cycle (mean age 82). The questionnaire was used to define leisure-time physical activity (PA) according to four categories: sedentary, moderate, regular or athletic [23]. PA was dichotomized into regular physical activity (athletic and regular) or not (moderate and sedentary). Information on educational level, stratified as low (elementary school only, 6e7 years), medium (high school), or high (college or university) was obtained from the questionnaire at baseline. Detailed questions on civil status at baseline were used to categorize participants as living alone or not (i.e. living with spouse, cohabitant, own children, grandchildren or other relatives). Smoking habits, stratified as never, former or current, were obtained from the questionnaire at follow-up (mean age 87). Charlson unweighted Comorbidity Index [24,25] was calculated based on in-patient diagnoses from patient records before the examination at follow-up. ICD codes for ICD 7 through ICD 10 were used, and when necessary, adapted to the Swedish ICD system. The co-morbidity score was categorized in three subgroups according

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

YCLNU3865_proof ■ 22 April 2019 ■ 3/8

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

3

66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 Q7 Fig. 1. Flow chart of invited, excluded and included participants in the study population. 97 98 99 cultural component. Higher scores indicate greater adherence to to score (0, 1 and 2). Information from the National Patient Reg100 the dietary pattern and participants were categorized in groups ister was used to define hospital stay, from age 70 to the date of 101 (low-, medium-, or high adherence) for each dietary pattern score DXA measurement. Hospital stay was defined as the number of care 102 [30]. These a priori determined categories were chosen to reflect a periods, lasting at least three days, dichotomized as yes (at least 1 103 large divergence between the different groups in both dietary care period) or no (no care period). 104 patterns, but the cut off values are similar to those used in previous 105 studies [30]. Dietary components included in each diet score and 2.7. Dietary assessments and dietary patterns 106 respective cut offs, as well as the modifications applied, are pre107 sented in Supplemental Table 1 (Tab S1). 2.7.1. Dietary assessments 108 Information on dietary intake at baseline (mean age 71) was 109 2.7.3. Modified Healthy Diet Indicator (mHDI) collected during 7 consecutive days using a menu book. The menu 110 The Healthy Diet Indicator [28] is based on WHO dietary recbook was an optically readable, pre-coded, food record prepared 111 ommendations for the prevention of chronic diseases [31], and was and used by the Swedish National Food Agency and validated 112 modified prior to application in our study. A dichotomous variable against a 7 day open-ended weighed record [26]. 113 was generated for each food or nutrient included in mHDI (see Tab A dietician or a nurse instructed all participants on how to fill 114 S1). If the participant's intake was below median (SFA and cholesout the menu book and instructions were also included in the menu 115 terol) or above median (PUFA, protein, carbohydrates, fish and fruit book. Food intake was reported in household measures or as pre116 and vegetables) the nutrient or food was coded as 1 point (p), defined portion sizes. Pictures of portions were included to ease 117 otherwise coded as 0 p, except for sucrose which was coded as the specification of portion sizes. 118 0 or 1 p. In total, mHDI could take a value between 1 and 8 p and Dietary data was analyzed with commercial software using a 119 food composition database from the Swedish National Food Agency each participant's adherence was categorized as low (1 to 1 p), 120 (SLV version 1990). Dietary variables were adjusted for energy medium (2e4 p) or high (5e8 p). 121 intake either by the residual method [27] (variables in g/day) or as 122 nutrient densities (energy percent or g/MJ), depending on the na123 2.7.4. Modified Mediterranean Diet Score (mMDS) ture of components included in each dietary pattern score. 124 The Mediterranean Diet Score is based on foods typical of the 125 traditional Mediterranean diet, and a modified version of the score 126 2.7.2. Dietary patterns defined by Trichopoulou et al. [29] was applied. The median of each 127 Adherence to two predefined dietary patterns, the Healthy Diet included food group or nutrient (fat quality, vegetables, fruits, ce128 Indicator (HDI) [28] and the Mediterranean Diet Score (MDS) [29], reals, fish, meat, dairy, and alcohol) in the population served as cut 129 was calculated for each participant. Both HDI and MDS were offs, identifying the participants with the most Mediterranean-like 130 modified to reflect a Swedish diet [30] as dietary intake has a strong dietary intake (see Tab S1). Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

YCLNU3865_proof ■ 22 April 2019 ■ 4/8

4

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

When calculating the mMDS score, participants were assigned a value of 0 p for unfavorable food intake (below median) or 1 p for favorable food intake (above median). The mMDS could take a total value between 0 and 8 points and each participant's adherence was categorized as low (2 p), medium (3e5 p) or high (6 p) [30]. 2.8. Statistical analysis Data were checked visually for normality and by using ShapiroeWilk's normality test. Differences in continuous variables were assessed with analysis of variance (ANOVA) or Student's t-test if normally distributed and with Kruskal Wallis-test or Mann Whitney U-test if skewed. Categorical variables were assessed with chi2test or Fisher's exact test. Participants were divided into groups, categorized as low, medium or high adherent, based on their dietary pattern scores. The association between each dietary pattern score and the presence of sarcopenia was analyzed using logistic regression models. The first model was unadjusted, and the second model was adjusted for BMI at baseline (continuous), education (categorical), civil status (categorical), regular physical activity at baseline (categorical), hospital stay (categorical) and age at follow-up (continuous). The third model was further adjusted for inflammation (CRP) at baseline (categorical), smoking (categorical), morbidity (categorical) at follow-up and protein intake at baseline (categorical). Analyzes were repeated using dietary pattern scores as continuous variables, applied as an SD increment (equivalent to a 2p increase in the score) in each diet score. Restricted cubic splines (using 3 knots placed at the 10th, 50th and 90th percentiles of the distribution and with the lowest adherence as reference point) were used to investigate potential non-linear relationships between a continuous increment in the dietary patterns and the prevalence of sarcopenia. Further, the individual relations between dietary patterns and the three components included in the definition of sarcopenia (i.e. muscle mass, handgrip strength and gait speed) were analyzed using regression analysis adjusted for the potential confounders included in Model 2 (see above). To analyze the potential influence of changes in physical activity on the relation between dietary patterns and sarcopenia, a sensitivity analysis was made in a subgroup (n ¼ 147) with stable adherence to physical activity pattern (either stable in regular physical activity or stable in no regular physical activity, as defined above). Another subgroup (mHDI n ¼ 64 & mMDS n ¼ 78), with stable adherence to dietary pattern over time, was used to assess whether potential associations for dietary patterns were changed. For this, we added data from a dietary assessment (using a similar 7-day dietary record as baseline) performed when participants were 82years old. Finally, a third sensitivity analysis was carried out excluding dietary mis-reporters of energy intake (n ¼ 38), based on the Goldberg equation [32], taking the level of physical activity and calculated basal metabolic rate into consideration. All analyzes were performed in STATA (Stata Corp, College Station; TX, USA) with P-values less than 0.05 considered statistically significant. 3. Results Among the 254 participants included in this study, 53 men (21%) were defined as sarcopenic at age 87. As depicted in Table 1, participants defined as sarcopenic had a lower body weight (73.1 ± 9.9 SD vs. 81.3 ± 9.2 SD) and BMI (24.4 ± 2.9 SD vs. 26.3 ± 2.7 SD) at baseline compared to non-sarcopenic individuals (both p < 0.001). There were no corresponding differences at baseline in the

reported intakes of energy (kcal/d) or protein (g/d), self-reported education or level of physical activity. Determining dietary patterns among the 254 men revealed 16% (n ¼ 40) to have low adherence to mHDI, whilst 30% (n ¼ 75) had high adherence to this dietary pattern. A low adherence to mMDS was found in 20% (n ¼ 52), whilst 13% (n ¼ 34) had a high adherence to this dietary pattern. The reported consumption of food items according to adherence to each dietary pattern is shown in Supplemental Table 2 (Tab S2), and the corresponding relations for baseline characteristics in Supplemental Table 3 (Tab S3). 3.1. Modified Healthy Diet Indicator (mHDI) As presented in Table 2, the crude odds of having sarcopenia at mean age 87 for individuals with high adherence to mHDI at age 71, compared to low adherence, was 0.61 (95% CI: 0.26e1.43). The corresponding odds ratio for individuals with medium adherence to this dietary pattern was 0.41 (95% CI: 0.19e0.92). There were no relationships between mHDI applied as a continuous variable and sarcopenia 16 years later, and all relationships remained essentially unchanged after controlling for potential confounders. The nonlinear relationship between mHDI and sarcopenia is further displayed in Supplemental Fig. 1 (Fig S1). Finally, there were no significant associations between mHDI and the individual components of sarcopenia, i.e. muscle mass, handgrip strength and gait-speed (all p > 0.20, linear regression, data not shown). 3.2. Modified Mediterranean Diet Score (mMDS) As shown in Table 3, the crude odds ratio of having sarcopenia at mean age 87, as compared to low adherent individuals was 0.47 (95% CI: 0.15e1.45) for high adherent, and 0.69 (95% CI: 0.34e1.41) for medium adherent individuals. These relationships remained in adjusted models (Model 2 and 3). When mMDS was applied as a continuous variable an inverse relationship to sarcopenia was found (OR ¼ 0.68, 95% CI: 0.46e0.99), which remained after controlling for potential confounders (OR ¼ 0.57, 95% CI: 0.37e0.87, Model 2). The inverse linear relationship between mMDS and sarcopenia is further illustrated in restricted cubic spline analysis, as depicted in Supplemental Fig. 1 (Fig S1). Finally, linear regression analysis between mMDS and the individual components of sarcopenia revealed no relations to hand grip strength or gait speed (both p > 0.20, data not shown), but a positive relationship to SMI (r ¼ 0.12, p ¼ 0.02, adjusted according to Model 2). 3.3. Sensitivity analyses Sensitivity analyses were performed to consider the potential influence of certain changes related to aging, such as a decline in physical activity. There was a change in physical activity pattern over time, evaluated in a subgroup (n ¼ 217) by comparing the activity pattern at baseline and at mean age 82. For example, among men with regular physical activity at mean age 71 (n ¼ 143), 69% (n ¼ 99) were still physically active on a regular basis at mean age 82. However, the associations between dietary patterns and sarcopenia found in the larger study group remained essentially unchanged in this subgroup of individuals with stable physical activity patterns over time (see Supplemental Table 4, Tab S4). For example, the crude OR for medium adherence to mHDI was 0.24 (95% CI: 0.08e0.70), compared with low adherence. Further adjustments for potential confounders did not affect the risk relation. In turn, the ORs for mMDS, applied as a continuous variable were almost identical to those in the larger study group, but with wider confidence intervals (e.g. OR 0.68, 95% CI: 0.40e1.18, crude). The inverse relationships to sarcopenia for medium adherence to mHDI and for

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

YCLNU3865_proof ■ 22 April 2019 ■ 5/8

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

5

Table 1 Characteristics at mean age 71 (baseline) and at mean age 87 (follow-up) of all participants and grouped as sarcopenic or non-sarcopenic.

Age (years)

Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Baseline Baseline Baseline

Weight (kg) BMI (kg/m2) CRP (mg/l)a Reported energy intake (kcal) Reported protein intake, total (g) Reported protein intake, g/kg (%) Low (<0.8 g/kg) Mid (0.8e1.0 g/kg) High (>1.0 g/kg) Education (%) Low Medium High Living alone (%) Regular physical activity (%) Smoking (%) Currentb Former Never Charlson Comorbidity Index (%) 0 1 2 Hospital stay (%) 1 care period 3 days

Total

Sarcopenic

Non-sarcopenic

(n ¼ 254)

(n ¼ 53)

(n ¼ 201)

70.9 [0.6] 86.6 [1.0] 79.6 [9.9] 76.3 [11.4] 25.9 [2.9] 25.6 [3.5] 2.4 [2.5] 1855 [441] 69.1 [16.1]

71.0 [0.6] 86.6 [1.0] 73.1 [9.9] 67.8 [9.1] 24.4 [2.9] 23.4 [2.6] 2.3 [2.1] 1795 [447] 67.8 [17.3]

70.9 [0.6] 86.6 [1.0] 81.3 [9.2]** 78.6 [10.9]** 26.3 [2.7]** 26.2 [3.4]** 2.5 [2.6] 1870 [439] 69.5 [15.8]

41.7 33.5 24.8

34.0 32.1 34.0

43.8 33.8 22.4

47.6 31.1 21.3 13.0 68.1

47.2 37.7 15.1 7.5 60.4

47.8 29.4 22.9 13.9 70.1

3.1 55.9 40.9

1.9 56.6 41.5

3.5 55.7 40.8

39.8 35.4 24.8

34.0 39.6 34.3

41.3 34.3 24.4

67.7

81.1

64.2*

Baseline

Baseline Baseline Follow-up

Follow-up

Follow-up

Values are expressed as means [standard deviation] or percentage. Body mass index (BMI), C-reactive protein (CRP). High education: college or university. Regular physical activity: PAL 1.6. Energy and protein intakes are expressed as mean daily intake [95% CI]. P-value calculated, between Sarcopenic and Non-sarcopenic, using Student t-test (unpaired) or chi2-test, **p ¼ <0.0001, *p ¼ <0.05. a Total n ¼ 245, sarcopenic n ¼ 52, non-sarcopenic n ¼ 193. b Total n ¼ 8, sarcopenic n ¼ 1, non-sarcopenic n ¼ 7.

Table 2 Logistic regression analysis between adherence to modified Healthy Diet Indicator at mean age 71 and prevalence of sarcopenia at mean age 87. modified Healthy Diet Indicator Low adherence n ¼ 40

Crude Model 2 Model 3f

b

Medium adherence n ¼ 139

c

Continuous (SD increment)a

High adherence n ¼ 75

d

n ¼ 254e

OR

OR

95% CI

OR

95% CI

OR

95% CI

1.00 1.00 1.00

0.41 0.44 0.38

0.19e0.92 0.19e1.05 0.15e0.97

0.61 0.56 0.47

0.26e1.43 0.22e1.42 0.17e1.28

0.99 0.92 0.90

0.73e1.35 0.66e1.28 0.63e1.27

Model 2: adjusted for BMI at baseline (continuous), education (categorical), living alone (categorical), regular physical activity at baseline (categorical), hospital stay and age at follow-up (continuous). Model 3: further adjusted for inflammation (CRP) at baseline (categorical), smoking (categorical), morbidity at follow-up (categorical) and protein intake at baseline (categorical). a Equivalent to a 2p increase in the dietary score. b,c,d,e Individuals defined as being sarcopenic at age 88: a n ¼ 13, b n ¼ 23, c n ¼ 17 and d n ¼ 5. f Total n ¼ 243.

each SD increment in mMDS also remained in the sub-group of men identified as adequate dietary reporters, according to the Goldberg cut-off (see Supplemental Table 5, Tab S5). For example, adjusted odds ratios (Model 2) were 0.38 (95% CI: 0.16e0.93) for medium adherence to mHDI and 0.58 (95% CI: 0.36e0.94) for the continuous variable of mMDS. Finally, we also made an attempt to perform sensitivity analysis in individuals with stable dietary patterns overtime, in a subgroup of individuals (n ¼ 135) who also reported their habitual dietary intake at mean age 82. The agreement in dietary scores between the two dietary assessments was

limited, but few individuals and insufficient power (data not shown) prevented more detailed analysis. 4. Discussion In this population of 254 71-year-old men, adherence to healthy dietary patterns was associated with lower prevalence of sarcopenia 16 years later. The odds of having sarcopenia at mean age 87 decreased by >30% with a continuous increase in the Mediterranean Diet Score. There were also indications that the odds of having

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

YCLNU3865_proof ■ 22 April 2019 ■ 6/8

6

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

Table 3 Logistic regression analysis between adherence to modified Mediterranean Diet Score at mean age 71 and prevalence of sarcopenia at mean age 87. modified Mediterranean Diet Score Low adherence n ¼ 52

Crude Model 2 Model 3

f

b

Medium adherence n ¼ 168

c

Continuous (SD increment)a

High adherence n ¼ 34

d

n ¼ 254e

OR

OR

95% CI

OR

95% CI

OR

95% CI

1.00 1.00 1.00

0.69 0.53 0.50

0.34e1.41 0.24e1.17 0.22e1.16

0.47 0.36 0.33

0.15e1.45 0.10e1.22 0.09e1.23

0.68 0.57 0.49

0.46e0.99 0.37e0.87 0.31e0.80

Model 2: adjusted for BMI at baseline (continuous), education (categorical), living alone (categorical), regular physical activity at baseline (categorical), hospital stay (categorical) and age at follow-up (continuous). Model 3: further adjusted for inflammation (CRP) at baseline (categorical), smoking (categorical), morbidity at follow-up (categorical) and protein intake at baseline (categorical). a Equivalent to a 2p increase in the dietary score. b,c,d,e Individuals defined as being sarcopenic at age 88: a n ¼ 14, b n ¼ 34, c n ¼ 5 and d n ¼ 53. f Total n ¼ 243.

sarcopenia at mean age 87 was more than halved in individuals with medium, but not high, adherence to the WHO dietary guidelines (mHDI). These relationships seemed to be independent of changes in physical activity. To our knowledge, only four previous studies [15e18] have examined dietary patterns in relation to sarcopenia. Only two of these were performed in a population with mean age 65 years [17,18], which is likely to be of importance considering the nature of sarcopenia. The study by Chan et al. [18] found no associations between high adherence to Mediterranean dietary pattern and sarcopenia. On the other hand, both Hashemi et al. [17] and Mosheni et al. [16] found those with the highest adherence to a Mediterranean dietary pattern to have lower OR of being sarcopenic. These two studies were cross-sectional using data driven approaches to define dietary patterns, whilst the study by Chan et al. [18] was a prospective cohort study (4-year followup) with pre-defined dietary patterns. We also applied a predefined approach, but our follow-up was longer and covered the life span when most cases of sarcopenia develop. Still, all studies including ours, despite using slightly different criteria for sarcopenia, suggest that a healthy dietary pattern has a favorable role in the prevention of sarcopenia. Few studies have evaluated dietary patterns in relation to sarcopenia, but several studies have analyzed the corresponding relations to individual components of sarcopenia. As recently reviewed [14], individuals with overall healthy diets are more likely to maintain physical performance, compared to those with less healthy diets. For example, high adherence to a Mediterranean-like diet has been linked to faster walking speed [33e35], better physical function [36], and slower decline of mobility over time [37]. Furthermore, Zbeida et al. [36] found the Mediterranean dietary pattern to predict functional outcomes in both a Mediterranean and a non-Mediterranean population, despite differences in absolute levels of intake. Other healthy diets have also been identified as beneficial for mobility, e.g. Parsons et al. [38], who used a healthy diet defined by the WHO, and Perala et al. [39], who assessed a healthy Nordic diet. The evidence for a beneficial effect from healthy diets on muscle mass and muscle strength is, however, less clear [14]. This uncertainty is strongly driven by the lack of longitudinal studies confirming results from studies with cross-sectional design. For example, Yokoyama et al. [40] found no significant relation over time between lean body mass and a healthy diet based on a strong variation in foods consumed, and Leon-Munoz et al. [41] found no association between a prudent diet and future handgrip strength. Still, several cross-sectional studies have detected relationships between various healthy diets and both muscle mass and muscle strength. For example, one study found a highly varied diet to be

associated with higher mean arm skeletal muscle mass [42]. Other publications have found that a healthy eating pattern was related to higher grip strength [43] and greater knee extension power [44], and a Mediterranean-like diet was related to higher percentage of fat-free mass [45] and higher appendicular lean mass [46] in women, but not in men. Although a healthy dietary pattern likely has a favorable role in the prevention of sarcopenia, the outcome of dietary patterns in relation to sarcopenia and features of sarcopenia varies between studies. Chan et al. [18] found no association between the Mediterranean dietary pattern and sarcopenia, but found high adherence to the Dietary Quality Index-International (DQI-I) to be associated with a lower likelihood of sarcopenia in men, but not in women. Similar discrepancies are present in studies comparing healthy dietary patterns in relation to the individual components of sarcopenia. For example, Smee [47] found an association between the HDI score and lean body mass and physical function in women but did not find any association using the Healthy Eating Index (HEI). Also in our study, the association between dietary patterns and the prevalence of sarcopenia differed to some extent. The dietary patterns evaluated in our material were population-based, with high adherence reflecting healthy dietary habits. They do share the same consumption pattern for some, but not all, food components. However, their associations with sarcopenia were not identical. When applied as a continuous variable, adherence to the Mediterranean dietary pattern was inversely related to sarcopenia, but not when applied as categories of low, medium and high adherence. Still, the trend in risk estimates for medium and high adherent individuals to the Mediterranean diet indicates that the absent association could be explained by inherent study limitations. Of note, the 30e50% lower (depending on adjustment) odds ratio of being sarcopenic at mean age 87 with each SD increment in the mMDS should be of clinical importance and further support a beneficial role of the Mediterranean dietary pattern in public health communication. The corresponding results for mHDI in our study are more unclear and difficult to interpret. Compared to low adherent individuals, those with medium, but not high adherence to mHDI had a significantly lower risk (>50%) of being sarcopenic at mean age 87, and no relation was found between sarcopenia and a continuous increase in mHDI. We found no obvious reason why only medium, but not high, adherence to this healthy dietary pattern was related to sarcopenia. The difference in risk relation seemed not to be explained by misreporting of dietary intake, differences in dietary habits or differences in other phenotypic or life-style variables available in this cohort. Overall, such ambiguities, as present in our and others results, highlights the need for a better understanding of

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

YCLNU3865_proof ■ 22 April 2019 ■ 7/8

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

Q2

what nutrients and food groups, in specific amounts and combinations that may be involved in preventing, or delaying, features of sarcopenia. It is possible that none of the established pre-defined dietary indexes applied in the present study represent a healthy diet optimal for the prevention of sarcopenia and data driven approaches might be advantageous in this context. This study has several strengths, one being that anthropometric measures are based on DXA, which is considered a referencemethod, and defining sarcopenia with established criteria (EWGSOP) is another. Other strengths relate to the use of a validated 7day food record, the application of well-known pre-defined dietary patterns, and the ability to adjust for relevant covariates, such as energy intake, BMI, smoking, physical activity, morbidity, and hospital stay. The long follow-up period in our study can be considered a strength (enough time to develop sarcopenia), but also a limitation (possible changes in dietary pattern). The stability of dietary patterns over longer periods such as 15 years is, to our knowledge, neither described nor examined previously. However, the dietary intake was relatively stable during the first 10 years of the Seven countries study [48] and there is support that dietary patterns are reasonably stable over a period of 8 [49] and 10 years [50]. Nevertheless, the internal stability to dietary patterns in our study was limited, which may lead to a failure to detect associations or attenuations of observed effects. Furthermore, and as pointed out by UNESCO [51], the original Mediterranean dietary pattern includes more than the nutritional aspect and should be seen as a set of skills, such as knowledge, rituals and traditions that includes harvesting, cooking and in particular, sharing and consuming food together. In this study, as in most studies, the Mediterranean dietary pattern is solely based on the nutritional aspect. Another limitation in this study is the lack of measures of muscle strength and physical performance at baseline, making it impossible to exclude participants having sarcopenia already when entering the study. However, the prevalence of sarcopenia at age 71 is generally considered low [8e10] and the population at hand was rather physically active at baseline. Still, to minimize the risk of including participants with loss of muscle mass due to illness before baseline (mean age 71), participants with a weight loss of 10% or more, between age 60 and baseline were excluded. In addition, the fact that participants included in our study were quite healthy, both at mean age 71 and at mean age 87, limits the possibility to apply these findings to a frailer population. Finally, we only studied men and according to the literature, genderdifferences for the impact of diet on both muscle strength and physical performance might be present [14]. In this study we determined adherence to predefined dietary patterns in a population of 71-year-old men, by using a single measure of diet as a reflection of habitual dietary habits, and sarcopenia was defined according to EWGSOP criteria 16 years later. Overall, our data indicate healthy dietary patterns to be associated with lower future prevalence of sarcopenia, relationships that were independent of changes in physical activity. In particular, increased adherence to Mediterranean-like dietary habits was associated with >30% lower odds of having sarcopenia at mean age 87. The corresponding relations for the adherence to WHO dietary guidelines were less clear, although pointing in the same direction, which might mirror the current uncertainty on what kind of diet is optimal for the prevention of sarcopenia.

CRediT author statement Mikael Karlsson: Conceptualization, Methodology, Formal Analysis, Data Curation, Writing e Original draft.

7

Wulf Becker: Conceptualization, Methodology, Writing e Review & Editing, Supervision. €lsson: Conceptualization, Methodology, Writing e Karl Michae Review & Editing. Tommy Cederholm: Conceptualization, Methodology, Writing e Review & Editing, Supervision, Funding Acquisition. € gren: Conceptualization, Methodology, Writing e OrigPer Sjo inal draft, Review & Editing, Supervision, Project Administration. All authors read and approved the final version of the paper.

66 67 68 69 70 71 72 73 74 75 76 Conflict of interest Q3 77 78 None declared. 79 80 Appendix A. Supplementary data 81 82 Supplementary data to this article can be found online at 83 https://doi.org/10.1016/j.clnu.2019.04.009. 84 85 86 References 87 88 [1] Janssen I. Influence of sarcopenia on the development of physical disability: the Cardiovascular Health Study. J Am Geriatr Soc 2006;54(1):56e62. 89 [2] Landi F, Liperoti R, Russo A, Giovannini S, Tosato M, Capoluongo E, et al. 90 Sarcopenia as a risk factor for falls in elderly individuals: results from the 91 ilSIRENTE study. Clin Nutr 2012;31(5):652e8. [3] Tolea MI, Galvin JE. Sarcopenia and impairment in cognitive and physical 92 performance. Clin Interv Aging 2015;10:663e71. 93 [4] Liu P, Hao Q, Hai S, Wang H, Cao L, Dong B. Sarcopenia as a predictor of all94 cause mortality among community-dwelling older people: a systematic review and meta-analysis. Maturitas 2017;103:16e22. 95 [5] Janssen I, Shepard DS, Katzmarzyk PT, Roubenoff R. The healthcare costs of 96 sarcopenia in the United States. J Am Geriatr Soc 2004;52(1):80e5. 97 [6] Sousa AS, Guerra RS, Fonseca I, Pichel F, Ferreira S, Amaral TF. Financial impact 98 of sarcopenia on hospitalization costs. Eur J Clin Nutr 2016;70(9):1046e51. [7] Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. 99 Sarcopenia: European consensus on definition and diagnosis: report of the 100 European working group on sarcopenia in older people. Age Ageing 101 2010;39(4):412e23. [8] Akune T, Muraki S, Oka H, Tanaka S, Kawaguchi H, Nakamura K, et al. Exercise 102 habits during middle age are associated with lower prevalence of sarcopenia: 103 the ROAD study. Osteoporos Int 2014;25(3):1081e8. 104 [9] Yamada M, Nishiguchi S, Fukutani N, Tanigawa T, Yukutake T, Kayama H, et al. Prevalence of sarcopenia in community-dwelling Japanese older adults. J Am 105 Med Dir Assoc 2013;14(12):911e5. 106 [10] Volpato S, Bianchi L, Cherubini A, Landi F, Maggio M, Savino E, et al. Preva107 lence and clinical correlates of sarcopenia in community-dwelling older people: application of the EWGSOP definition and diagnostic algorithm. 108 J Gerontol A Biol Sci Med Sci 2014;69(4):438e46. 109 [11] Denison HJ, Cooper C, Sayer AA, Robinson SM. Prevention and optimal man110 agement of sarcopenia: a review of combined exercise and nutrition interventions to improve muscle outcomes in older people. Clin Interv Aging 111 2015;10:859e69. 112 [12] Beaudart C, Dawson A, Shaw SC, Harvey NC, Kanis JA, Binkley N, et al. 113 Nutrition and physical activity in the prevention and treatment of sarcopenia: 114 systematic review. Osteoporos Int 2017;28(6):1817e33. [13] Robinson S, Cooper C, Aihie Sayer A. Nutrition and sarcopenia: a review of the 115 evidence and implications for preventive strategies. J Aging Res 2012;2012: 116 510801. 117 [14] Bloom I, Shand C, Cooper C, Robinson S, Baird J. Diet quality and sarcopenia in older adults: a systematic review. Nutrients 2018;10(3). 118 [15] Fanelli Kuczmarski M, Mason MA, Beydoun MA, Allegro D, Zonderman AB, 119 Evans MK. Dietary patterns and sarcopenia in an urban African American and 120 White population in the United States. J Nutr Gerontol Geriatr 2013;32(4): 291e316. 121 [16] Mohseni R, Aliakbar S, Abdollahi A, Yekaninejad MS, Maghbooli Z, Mirzaei K. 122 Relationship between major dietary patterns and sarcopenia among menopausal women. Aging Clin Exp Res 2017. Q4 123 [17] Hashemi R, Motlagh AD, Heshmat R, Esmaillzadeh A, Payab M, Yousefinia M, 124 et al. Diet and its relationship to sarcopenia in community dwelling Iranian 125 elderly: a cross sectional study. Nutrition 2015;31(1):97e104. 126 [18] Chan R, Leung J, Woo J. A prospective cohort study to examine the association between dietary patterns and sarcopenia in Chinese community-dwelling 127 older people in Hong Kong. J Am Med Dir Assoc 2016;17(4):336e42. 128 [19] Vessby B, Tengblad S, Lithell H. Insulin sensitivity is related to the fatty acid 129 composition of serum lipids and skeletal muscle phospholipids in 70-year-old 130 men. Diabetologia 1994;37(10):1044e50.

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

YCLNU3865_proof ■ 22 April 2019 ■ 8/8

8

M. Karlsson et al. / Clinical Nutrition xxx (xxxx) xxx

[20] Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 1998;147(8):755e63. [21] Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A, et al. Ageassociated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol (1985) 2003;95(5): 1851e60. [22] Lochen ML, Rasmussen K. The Tromso study: physical fitness, self reported physical activity, and their relationship to other coronary risk factors. J Epidemiol Community Health 1992;46(2):103e7. [23] Byberg L, Zethelius B, McKeigue PM, Lithell HO. Changes in physical activity are associated with changes in metabolic cardiovascular risk factors. Diabetologia 2001;44(12):2134e9. [24] Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43(11):1130e9. [25] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373e83. [26] Nydahl M, Gustafsson IB, Mohsen R, Becker W. Comparison between optical readable and open-ended weighed food records. Food Nutr Res 2009;53. [27] Willett W. Nutritional epidemiology. 3rd ed. Oxford: Oxford University Press; 2013. [28] Huijbregts P, Feskens E, Rasanen L, Fidanza F, Nissinen A, Menotti A, et al. Dietary pattern and 20 year mortality in elderly men in Finland, Italy, and The Netherlands: longitudinal cohort study. BMJ 1997;315(7099):13e7. [29] Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med 2003;348(26): 2599e608. [30] Sjogren P, Becker W, Warensjo E, Olsson E, Byberg L, Gustafsson IB, et al. Mediterranean and carbohydrate-restricted diets and mortality among elderly men: a cohort study in Sweden. Am J Clin Nutr 2010;92(4):967e74. [31] WHO. Diet, nutrition, and the prevention of chronic diseases. Report of a WHO Study Group. World Health Organ Tech Rep Ser 1990;797:1e204. [32] Black AE. 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 2000;24(9):1119e30. [33] Shahar DR, Houston DK, Hue TF, Lee JS, Sahyoun NR, Tylavsky FA, et al. Adherence to mediterranean diet and decline in walking speed over 8 years in community-dwelling older adults. J Am Geriatr Soc 2012;60(10):1881e8. [34] Talegawkar SA, Bandinelli S, Bandeen-Roche K, Chen P, Milaneschi Y, Tanaka T, et al. A higher adherence to a Mediterranean-style diet is inversely associated with the development of frailty in community-dwelling elderly men and women. J Nutr 2012;142(12):2161e6. [35] Bollwein J, Diekmann R, Kaiser MJ, Bauer JM, Uter W, Sieber CC, et al. Dietary quality is related to frailty in community-dwelling older adults. J Gerontol A Biol Sci Med Sci 2013;68(4):483e9. [36] Zbeida M, Goldsmith R, Shimony T, Vardi H, Naggan L, Shahar DR. Mediterranean diet and functional indicators among older adults in nonMediterranean and Mediterranean countries. J Nutr Health Aging 2014;18(4):411e8.

[37] Milaneschi Y, Bandinelli S, Corsi AM, Lauretani F, Paolisso G, Dominguez LJ, et al. Mediterranean diet and mobility decline in older persons. Exp Gerontol 2011;46(4):303e8. [38] Parsons TJ, Papachristou E, Atkins JL, Papacosta O, Ash S, Lennon LT, et al. Healthier diet quality and dietary patterns are associated with lower risk of mobility limitation in older men. Eur J Nutr 2018. [39] Perala MM, von Bonsdorff MB, Mannisto S, Salonen MK, Simonen M, Pohjolainen P, et al. The healthy nordic diet and mediterranean diet and incidence of disability 10 years later in home-dwelling old adults. J Am Med Dir Assoc 2018. [40] Yokoyama Y, Nishi M, Murayama H, Amano H, Taniguchi Y, Nofuji Y, et al. Dietary variety and decline in lean mass and physical performance in community-dwelling older Japanese: a 4-year follow-up study. J Nutr Health Aging 2017;21(1):11e6. [41] Leon-Munoz LM, Garcia-Esquinas E, Lopez-Garcia E, Banegas JR, RodriguezArtalejo F. Major dietary patterns and risk of frailty in older adults: a prospective cohort study. BMC Med 2015;13:11. [42] Bernstein MA, Tucker KL, Ryan ND, O'Neill EF, Clements KM, Nelson ME, et al. Higher dietary variety is associated with better nutritional status in frail elderly people. J Am Diet Assoc 2002;102(8):1096e104. [43] Robinson SM, Jameson KA, Batelaan SF, Martin HJ, Syddall HE, Dennison EM, et al. Diet and its relationship with grip strength in community-dwelling older men and women: the Hertfordshire cohort study. J Am Geriatr Soc 2008;56(1):84e90. [44] Xu B, Houston DK, Locher JL, Ellison KJ, Gropper S, Buys DR, et al. Higher Healthy Eating Index-2005 scores are associated with better physical performance. J Gerontol A Biol Sci Med Sci 2012;67(1):93e9. [45] Kelaiditi E, Jennings A, Steves CJ, Skinner J, Cassidy A, MacGregor AJ, et al. Measurements of skeletal muscle mass and power are positively related to a Mediterranean dietary pattern in women. Osteoporos Int 2016;27(11): 3251e60. [46] Nikolov J, Spira D, Aleksandrova K, Otten L, Meyer A, Demuth I, et al. Adherence to a mediterranean-style diet and appendicular lean mass in community-dwelling older people: results from the berlin aging study II. J Gerontol A Biol Sci Med Sci 2016;71(10):1315e21. [47] Smee D, Pumpa K, Falchi M, Lithander FE. The relationship between diet quality and falls risk, physical function and body composition in older adults. J Nutr Health Aging 2015;19(10):1037e42. [48] Keys A. Seven countries. A multivariate analysis of death and coronary heart disease. Harvard University Press; 1980. [49] Kimokoti RW, Newby PK, Gona P, Zhu L, Campbell WR, D'Agostino RB, et al. Stability of the Framingham nutritional risk score and its component nutrients over 8 years: the Framingham nutrition studies. Eur J Clin Nutr 2012;66(3): 336e44. [50] Winkvist A, Klingberg S, Nilsson LM, Wennberg M, Renstrom F, Hallmans G, et al. Longitudinal 10-year changes in dietary intake and associations with cardio-metabolic risk factors in the Northern Sweden Health and Disease Study. Nutr J 2017;16(1):20. [51] UNESCO. Representative list of the intangible cultural heritage of humanity. 2013.

Please cite this article as: Karlsson M et al., Associations between dietary patterns at age 71 and the prevalence of sarcopenia 16 years later in older Swedish men, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.009

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82