Sarcopenia is recognised as a significant health concern in older individuals(Reference Rosenberg1), including physical impairment, risk of falls and fractures, disability(Reference Roubenoff2), reduced quality of life(Reference Beaudart, Biver and Reginster3,Reference Beaudart, Locquet and Reginster4) and higher in-hospital and 1-year mortality(Reference Roubenoff2). Sarcopenia was initially defined as an age-related decline in muscle mass. Later, muscle function has been included in most sarcopenia definitions(Reference Muscaritoli, Anker and Argilés5–Reference Studenski, Peters and Alley9), since muscle strength, muscle power and physical performance are observed as stronger predictors of clinically relevant outcomes, e.g. functional status, falls and mortality(Reference Goodpaster, Park and Harris10–Reference Visser and Schaap13). Even if it may arise in mid-life, it arises generally as an age-related progression(Reference Cruz-Jentoft and Sayer14).
Since sarcopenia influences the individual’s health and well-being, it is of great importance to identify contributing modifiable causes. Most studies have focused on the components in the definition as an outcome, rather than sarcopenia itself. Increased physical activity has been reported to have positive effects on muscle mass and improves overall physical function(Reference Denison, Cooper and Sayer15,Reference Beaudart, Dawson and Shaw16) . However, the effect of diet is less clear, and data are mostly limited to cross-sectional studies(Reference Bloom, Shand and Cooper17,Reference Granic, Sayer and Robinson18) . Studies examining the role of diet in sarcopenia have predominantly focused on single nutrients or food items(Reference Granic, Sayer and Robinson18). However, this might not be the best approach when examining effects on chronic diseases in the non-deficient state(Reference Jacobs, Gross and Tapsell19,Reference Jacobs and Tapsell20) . From a public health perspective, it is difficult to provide dietary advice based solely on nutrients, as the nutrient intake is associated with dietary patterns (DP)(Reference Hu21). It is therefore difficult to examine effects of single nutrients or food items without potential influences of both foods and DP(Reference Randall, Marshall and Graham22). Cross-sectional and longitudinal studies suggest that so-called healthy DP contribute to maintain physical performance in ageing. However, there is limited evidence concerning the relation to muscle mass and muscle strength(Reference Bloom, Shand and Cooper17).
In general, a higher adherence to predefined ‘healthy DP’ have been associated with lower prevalence of sarcopenia, reported both by us(Reference Karlsson, Becker and Michaëlsson23) and others(Reference Chan, Leung and Woo24–Reference Hashemi, Motlagh and Heshmat26), although some ‘healthy DP’ were not associated with sarcopenia in the same study populations(Reference Karlsson, Becker and Michaëlsson23,Reference Chan, Leung and Woo24) . Established predefined dietary indexes are often based on current knowledge of the association between diet and the outcome. As the knowledge of associations between diet and sarcopenia is limited, a more exploratory technique, such as data driven so-called a posteriori approaches, might be advantageous. Therefore, we assessed the associations between adherences to a posteriori DP, among 71-year-old Swedish men, and the prevalence of sarcopenia, and its components, 16 years later.
Materials and methods
Ethical Approval
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Regional Ethical Review Board at Uppsala University; Dnr 251/90 & 2007/338. Written informed consent was obtained from all subjects.
Study population
This study is based on the Uppsala Longitudinal Study of Adult Men (ULSAM)(27), an ongoing population-based cohort study that started in 1970 when all men born 1920–1924 and living in Uppsala County were invited to a health examination; 2322 men participated (participation rate 82 %), outlined in Supplementary Fig. 1. Reexaminations of these men have been carried out at the approximate ages of 60, 70, 77, 82, 88 and 93 years.
Baseline for the present study was the third investigation cycle conducted in 1991–1995 (ULSAM3), at the average age of 71 years, when 1221 men participated (participation rate 73 %) in the reinvestigation. In order to define the DP, availability of dietary data at baseline (n 1138) and a reported energy intake between 800 and 4000 kcal/d(Reference Willett28) (n 1133) were required. Follow-up was the sixth investigation cycle conducted in 2008–2009 (ULSAM6), at the average age of 87, when 354 men participated (participation rate 58 %). Of these 354 men, 39 had missing data on dietary intake in ULSAM3 and were excluded. The examination at ULSAM6 included physical function tests and 290 of the participants completed body composition measurement using whole-body dual-energy X-ray absorptiometry, as described below. In total, 315 men were included in the analyses of DP and the constituent variables in the definition of sarcopenia. In 257 men, data were available to define sarcopenia according to the latest EWGSOP definition(Reference Cruz-Jentoft, Bahat and Bauer29), and none of these participants had missing values in covariates.
Dietary assessment, food grouping and a posteriori dietary pattern
Information on dietary intake was collected during seven consecutive days using a pre-coded menu book (online Supplementary Material 1) from the Swedish National Food Agency, giving preprinted alternatives for food items, dishes and when the meals are consumed. Food intake was reported in household measures or in predefined portion sizes. Consumptions not pre-coded in the menu book were to be reported in free-text(Reference Rosell, Hellénius and de Faire30). A dietician or a trained nurse instructed the participant on how to use the menu book, which also included written instructions and pictures of portions. Dietary data were analysed with commercial software using a food composition database from the Swedish National Food Agency (SLV version 1990). The menu book was validated in a subgroup of the study population(Reference Nydahl, Gustafsson and Mohsen31), by comparison with open-ended weighed food records, displaying a larger proportion of participants under-reporting energy intake compared to weighed food records, but with moderate to high agreements between the methods regarding intake of macro and micro-nutrients.
DP were defined based on the 1133 men (ULSAM3) having a reported dietary intake during 7 d. Mean intake of the seven recoding days of reported food items was categorised into twenty-six food groups, based on the food item’s nutrient composition and how the food item is used in cooking or eaten in the population studied (online Supplementary Table 1). For example, milk and fermented milk products were divided into different groups because fermented milk is commonly used together with cereals at breakfast or as a light meal, and milk has been used as a beverage in Sweden since the 1930s(Reference Martiin32,Reference Scander, Monteagudo and Nilsen33) . Cold meats include food items often used on bread at breakfast or as light meals. In Sweden, potatoes are used in a similar way to rice and pasta in other countries and are therefore excluded from the food group Vegetables. Tea, coffee and tap water were excluded as they did not significantly contribute neither to energy intake nor to DP separation. Another reason for excluding tap water was due to probable misreporting. Cream (385 men had a reported intake, mean intake < 6 g/d) and vegetable oils (three men had a reported intake, mean intake < 5 g/d) were excluded due to infrequent consumption. Butter and margarine were excluded as they may be misclassified (in Sweden margarine is often called ‘butter’ in everyday language), and fat used for cooking was included in dishes. The exclusion of these food items rendered a greater proportion of variance explained and more distinct DP, but did not affect the characteristic features of the DP. In order to preserve a wider variation in dietary intake, the dietary intake was not adjusted for the total energy intake when DP were defined(Reference Northstone, Ness and Emmett34). However, total energy intake was included in the multivariable adjusted analyses of DP in relation to the outcomes.
DP were defined using principal component analysis, which reduces larger amounts of observed variables to a smaller number of principal components while maximising the variance and identifying structures in the observed data. Bartlett’s test of sphericity (< 0·001), indicating that the variables are related, and Kaiser-Meyer-Olkin measure of sampling (0·557), indicating the proportion of variances that might be caused by underlying factors, together indicated that the factor analysis was suitable for structure detection and useful with our data. We applied a commonly used threshold(Reference Northstone, Ness and Emmett34,Reference Varraso, Garcia-Aymerich and Monier35) (principal components loadings > 0·30 and < –0·30 for each food group) for interpretation of the DP (online Supplementary Table 2); however, DP were not labelled on the basis of this interpretation.
The top four principal components were selected based on a combined assessment of a break point (elbow) in the scree plot, eigenvalues (> 1·5) and domestic-cultural knowledge. The identified principal components were rotated with varimax, creating orthogonal (uncorrelated) factors that form the defined DP for this study. The four chosen DP (DP1, DP2, DP3 and DP4) accounted for 28 % of the total variance.
Using the postestimation command predict, each participant was given a factor score, based on the retained factors, to reflect the agreement with each DP. Hence, a high factor score indicates a high intake from food groups that loaded positively and low intake of food groups loading negatively on the given DP. Based on the factor scores’ tertile percentiles in the total population of 1133 men at baseline, participants were categorised as having low, medium or high adherence to a given DP.
Definition of sarcopenia
The European Working Group on Sarcopenia in Older People (EWGSOP) proposed diagnostic criteria for sarcopenia in 2010(Reference Cruz-Jentoft, Baeyens and Bauer36), denoted as EWGSOP1. These criteria were revised in 2019(Reference Cruz-Jentoft, Bahat and Bauer29) and denoted as EWGSOP2. Sarcopenia defined according to EWGSOP2 was the main outcome of this study and defined as the combination of low muscle strength (reduced hand grip strength, < 27 kg(Reference Dodds, Syddall and Cooper37) and/or five-times chair stand test, > 15 s)(Reference Cesari, Kritchevsky and Newman38) and low muscle mass (appendicular lean mass index, lean mass index, < 7·0 kg/m2)(Reference Gould, Brennan and Kotowicz39). Severity grading of sarcopenia was not done.
Sarcopenia defined according to EWGSOP1 was used in sensitivity analysis, to allow comparison with previous published studies. Participants were categorised as having sarcopenia, i.e. having low muscle mass together with either low hand grip strength and/or low gait speed or otherwise as not having sarcopenia. The chosen cut-off values were for appendicular lean mass index < 7·26 kg/m2 (low muscle mass)(Reference Baumgartner, Koehler and Gallagher40), hand grip strength < 30 kg (low muscle strength)(Reference Lauretani, Russo and Bandinelli41) and gait speed < 0·8 m/s (low physical performance)(Reference Lauretani, Russo and Bandinelli41).
Muscle strength, muscle power and physical performance
Handgrip strength was measured using an adjustable hydraulic hand dynamometer (Fabrication Enterprises, White Plains), considered to measure grip strength with the same precision as the Jamar hydraulic hand dynamometer(Reference Mathiowetz, Vizenor and Melander42). Participants were sitting on a chair with the arm supported, shoulder relaxed, elbow at an angle of 90°, the wrist in a neutral position and feet on the floor. Measurement started with the dominant hand and the highest of three results for each hand was recorded, whereof the highest value, regardless if dominant hand or not, was used. In total, 301 participants performed the test at follow-up; two participants were unable to perform the test due to pain or disease and 12 due to unknown reasons.
The chair stand test included five chair rises, as fast as possible in a safe manner, with arms crossed over the chest. The duration was measured, to the nearest 0·5 s, from the first rise until seated ageing after the fifth rise. A total of 241 participants performed five rises, sixty-six participants tried but were unable to perform the test, four participants declined to perform the test or were unable due to medical reasons and four participants had missing value due to unknown reasons. Participants who failed the chair stand test were handled as having a result above the cut-off (> 15 s) in the sarcopenia definition but were excluded in analyses using chair stand test as outcome.
A self-chosen comfortable walking speed was measured using the intermediate 6 m of a distance of 10 m with no obstacles. Participants were allowed to use walking aid if they preferred to. In total, 257 participants performed the test at follow-up. Reasons for missing data included home visit and therefore not tested (n 50), unable to perform the test due to disease or lack of strength (n 7) or due to unknown reason (n 1). The fifty-eight participants with missing data were excluded from analyses using gait speed as outcome.
Anthropometry and body composition measurements
Body height was measured to the nearest centimeter and body weight to the nearest 0·1 kg. BMI was calculated as the ratio of the weight to the squared height (kg/m2).
Lean muscle mass was measured using dual-energy X-ray absorptiometry (DPX Prodigy, Lunar corp.). Precision errors of dual-energy X-ray absorptiometry measurements in the present laboratory have been calculated to be 1·5 % for total fat mass and 1·0 % for total lean mass(Reference Melhus, Snellman and Gedeborg43). Appendicular lean mass index was calculated as the ratio of appendicular muscle mass (lean mass of legs and arms) to the squared height (kg/m2)(Reference Baumgartner, Koehler and Gallagher40).
Covariates used for regression analyses
Questionnaires on education, physical activity and smoking habits were completed at baseline (ULSAM3) under standardised conditions, as earlier described(Reference Vessby, Tengblad and Lithell44). Information on educational level was categorised by years in school (6–7 years, 8–13 years or > 13 years). The leisure-time physical activity was defined as sedentary, moderate, regular or athletic(Reference Byberg, Zethelius and McKeigue45) using a validated questionnaire(Reference Lochen and Rasmussen46). Physical activity was then categorised in three subgroups: low (sedentary and moderate), medium (regular) or high (athletic). Smoking status was categorised as never, former or current smoker.
The follow-up period was defined as the period from the date of examination at baseline to the date of dual-energy X-ray absorptiometry measurement at follow-up.
Charlson’s unweighted Comorbidity Index(Reference Quan, Sundararajan and Halfon47,Reference Charlson, Pompei and Ales48) was calculated based on in-patient diagnoses from patient records recorded before baseline. The comorbidity score was then dichotomised according to score (0 and ≥ 1).
Age, follow-up period, reported energy intake and BMI were used as continuous variables in statistical analyses. Education, physical activity, smoking and the Charlson Comorbidity Index were used as categorical variables.
Statistical analyses
The association between each DP and the prevalence of sarcopenia was analysed using logistic regression models estimating OR and their 95 % CI.
Further, we assessed the associations between DP and the four components in the definition of sarcopenia (i.e. handgrip strength, chair stand test, appendicular lean mass index and gait speed) using linear regression analyses estimating beta coefficients and their 95 % CI.
For each of these analyses, linear and non-linear associations were explored. In the linear model, each DP was entered as a standardised continuous variable with mean 0 and 1 sd increments. Non-linear associations were first explored with each DP as a categorical variable, according to low, medium or high adherence, based on their factor scores and tertile limits determined in the total baseline population of 1133 men. A joint Wald test was performed to evaluate whether factor indicators are equal to 0. Next, each DP was entered as a standardised continuous variable with 1 sd increments using restricted cubic splines with three knots placed at the 10th, 50th and 90th percentiles of the DP distribution, as recommended by Harrell(Reference Harrell49), and with the median used as reference point, illustrating potential nonlinear associations; results are presented as graphs.
A directed acyclic graph approach was applied using DAGitty(Reference Textor, Hardt and Knüppel50) (online Supplementary Fig. 2) to identify potential confounders to be included in the multivariable models(Reference Hernán, Hernández-Díaz and Werler51). The directed acyclic graph is a graphical presentation of the assumed causal relationships between factors relevant for the current research question. Based on these assumptions and assuming no residual confounding, DAGitty provides information about minimal sufficient adjustment sets for estimating the total effect of an exposure on the outcome. We applied three models. Model 1 was unadjusted for potential confounders. Model 2 was adjusted for age at baseline, follow-up period, reported energy intake at baseline, education, physical activity level at baseline, smoking and morbidity at baseline. Model 3 was further adjusted for BMI at baseline. Model 3 is in agreement with the minimal sufficient adjustment set suggested by DAGitty.
All analyses were performed in Stata version 15.1 (Stata Corp).
Results
Characteristics of the studied population
There were some differences in baseline characteristics between the main study population of 257 men with follow-up information and the total group of 1133 men with dietary information at baseline (online Supplementary Table 3). The group of 257 men had a lower BMI, a higher reported energy intake, a smaller proportion were smokers and had more years of education, higher reported physical activity level and a lower Charlson Comorbidity Index. Differences in average dietary intake were small and not clinically relevant (Table 1 and online Supplementary Table 4).
Dietary intake among men included in the main analysis, displayed as food groups at baseline by adherence to each dietary pattern at baseline
(Mean values and standard deviations).
Values are presented as mean daily intake in grams (sd).
* Food groups are defined and exemplified in Supplementary Table 1.
† Food groups with principal components loadings > 0·30 and < –0·30.
Characteristics of the 257 men in our main study population at baseline and at follow-up are displayed by DP adherence in Table 2 and in Supplementary Table 5. According to EWGSOP2 (total n 257), 50 (19 %) had confirmed sarcopenia. When using the definition EWGSOP1 (total n 255) 54 (21 %) had confirmed sarcopenia. Thirty-three participants were defined as having sarcopenia by both definitions. Compared with those without sarcopenia, participants with confirmed sarcopenia (EWGSOP2) had a lower body weight and BMI both at baseline (73·3 kg v. 81·0 kg, 24·1 v. 26·3 kg/m2) and at follow-up (68·6 kg v. 78·1 kg, 23·2 v. 26·2 kg/m2). There were no mean differences between participants with or without sarcopenia in terms of reported intake of energy (kcal/d) or protein (g/d), years of education, reported physical activity level, prevalence of smoking or comorbidity at baseline.
Characteristics at baseline (mean age 71) and follow-up (mean age 87) of men included in the main analysis and grouped according to low, respectively, high adherence to each dietary pattern at baseline
(Mean values and standard deviations; numbers and percentages).
Values are presented as mean (standard deviation) for continuous measures and number (percentage) for categorical measures.
LMI, lean mass index.
Dietary patterns
Factor loadings of food groups in the DP (online Supplementary Table 2) correspond to differences in the mean dietary intake, displayed in Supplementary Table 4. The four DP explained 28 % of the total variance. DP1 (7·9 % of total variance) was mainly characterised by high positive loadings for milk and cereals, indicating that participants with high adherence to DP1 consumed greater amounts of these foods, and high negative loadings of alcohol. Thus, participants with high adherence to DP1 consumed, on average, more than three times as much milk, but three times less alcohol as compared with those with low adherence. DP2 (6·9 % of total variance) mainly represents a DP with high consumption of vegetables, green salad and fruit as well as poultry, rice and pasta. For example, those with high adherence to DP2 consumed on average more than one fruit (180 g) per day, and their consumption of fruits and vegetables was three times higher, compared with those with low adherence. DP3 (6·7 % of total variance) was mainly characterised by high consumption of bread, cheese and marmalade, jam and sugar. The intake of these foods among participants with high adherence to DP3 was twice as high compared with those with low adherence. Positive loadings for potato, meat and egg and negative loading for fermented milk mainly characterised DP4 (6·1 % of total variance). Participants with high adherence to DP4 reported a mean intake of 763 g meat per week, 300 g more than those with low adherence (455 g). On average, there were only small differences in consumption of fruit and vegetables regardless of adherence to DP4.
The mean intake of selected nutrients is displayed in Supplementary Table 6, based on adherence to the respective DP.
Dietary patterns and sarcopenia
The associations between the DP and sarcopenia (EWGSOP2) are displayed in Table 3. When the DP were used as continuous variables (estimates expressed per 1 sd increment), DP1, DP3 and DP4 had an adjusted OR (model 3) above 1·0 (1·04, 1·19 and 1·08, respectively), with wide CIs. The adjusted OR (model 3) for DP2 and sarcopenia was 0·70 (95 % CI: 0·48, 1·03).
Logistic regression analysis between adherence to each dietary pattern at baseline (mean age 71) and prevalence of sarcopenia defined according to EWGSOP2 at follow-up (mean age 87)
(Odd ratio and 95 % confidence intervals).
DP, dietary pattern.
Participants were categorised as low, medium or high adherent to each DP based on factor scores and according to tertiles limits determined based on adherence to dietary patterns at baseline in the total population of 1133 men.
* Model 1: unadjusted for potential confounders.
† Model 2: adjusted for age at baseline (continuous), follow-up period (continuous), reported energy intake at baseline (continuous), education (categorical), physical activity at baseline (categorical), smoking (categorical) and morbidity at baseline (categorical).
‡ Model 3: further adjusted for BMI at baseline (continuous).
In analyses with the DP as categorical variables medium and high adherence v. low adherence; Table 3), the p-values from the Wald tests were 0·91, 0·06, 0·40 and 0·47 for DP1, DP2, DP3 and DP4, respectively, indicating that the association between DP2 and sarcopenia may be nonlinear. Compared with low adherence to DP2, both medium and high adherence was associated with lower OR of sarcopenia, adjusted OR (model 3): 0·41 (95 % CI: 0·17, 0·98) and 0·40 (95 % CI: 0·17, 0·94), respectively. No clear patterns were observed for DP1, DP3 or DP4 in relation to sarcopenia and confidence intervals were wide (Table 3, online Supplementary Fig. 3). Discrepancies between linear and categorical OR for DP3, where the linear OR was 1·19/sd (95 % CI: 0·71, 2·00) and medium and high adherence (v. low adherence) had adjusted OR of 0·50 (95 % CI: 0·18, 1·40) and 0·53 (95 % CI: 0·17, 1·70) can be explained by the cut-off limits indicated in Supplementary Fig. 3. A higher adherence to DP4 was associated with higher odds ratio of sarcopenia although CI were wide (OR: 1·61, 95 % CI: 0·67, 3·87, model 3).
No clear associations were observed between DP and sarcopenia, defined according to EWGSOP1 (online Supplementary Table 7).
Dietary patterns and the variables used in the definition of sarcopenia
The analyses of DP in relation to the individual variables used in the definition of sarcopenia displayed no clear associations (Fig. 1 and online Supplementary Table 8). However, higher adherence to DP3 was associated with higher handgrip strength, which was statistically significant if unadjusted, but not if adjusted according to model 2 or 3. Medium adherence to DP4 was associated with lower handgrip strength and slower gait speed, which was statistically significant both unadjusted and adjusted. Medium adherence to DP4 was also associated with longer time to perform chair stand test, which was statistically significant if unadjusted, but not if adjusted according to model 2 or 3.
Associations between dietary pattern (DP) 1–4 with the variables used in the definition of sarcopenia: A. hand grip strength (kg), n 301, B. chair stand test (seconds), n 241, C. appendicular lean mass index (kg/m2), n 257 and D. gait speed (m/s), n 257. Beta estimates (on each Y axis) were modelled using restricted cubic splines (three knots placed at the 10th, 50th and 90th percentiles) and were adjusted for age at baseline (continuous), follow-up period (continuous), reported energy intake at baseline (continuous), education (categorical), physical activity at baseline (categorical), smoking (categorical), morbidity at baseline (categorical) and BMI at baseline (continuous), i.e. Model 3. The solid line represents the beta coefficient and the shaded area represents its 95 % CI.
Sensitivity analyses
In a subgroup analysis (n 247), we excluded participants (n 10) with self-reported body weight loss in the past year before the baseline measurement to limit potential influences of recent change of dietary intake due to, for example, undiagnosed disease before baseline. This exclusion did not affect the overall associations between the DP and sarcopenia (EWGSOP2) (online Supplementary Table 9).
In another subgroup analysis (n 237), we excluded participants with BMI below 22 at baseline (n 20) to limit the potential risk of participants having low muscle mass at baseline. This exclusion did not significantly affect the association between DP and sarcopenia (EWGSOP2), with the exception of wider CI (online Supplementary Table 10).
In addition, we derived DP, using the same method and thresholds as in the main study group (n 1133), in a subgroup of adequate energy reporters (n 855), according to the Goldberg cut-offs(Reference Black52), based on individual specific energy requirements. The derived DP and the associations between the DP and sarcopenia (EWGSOP2) were largely similar to our main analysis, however, with larger CI (online Supplementary Material 2).
Discussion
In this cohort of 71-year-old Swedish men, adherence to a dietary pattern mainly characterised by a high consumption of fruit, vegetables, poultry, rice and pasta (DP2) was associated with lower prevalence of sarcopenia defined according to EWGSOP2, 16 years later. In contrast, a higher adherence to a dietary pattern mainly characterised by a high consumption of potato, meat and egg and low consumption of fermented milk (DP4) was associated with higher prevalence of sarcopenia, although with low precision. These results add potentially important knowledge to the limited literature using dietary patterns rather than nutrients or single foods, to investigate the association between dietary intake and sarcopenia.
Methods used when defining DP are often subdivided into a priori and a posteriori. Several a priori DP are linked to dietary guidelines and thus based on the current state of knowledge, with the limitation that they do not necessarily capture the complexity of the total diet. A posteriori defined DP are, in contrast, statistically derived patterns from the dietary data at hand without prior hypotheses about the association between diet and outcome and are to a larger extent population specific. A posteriori approaches can, thus, be useful when knowledge of the association between diet and outcome is limited, as with diet and sarcopenia. Factor analysis, including PCA used in the present study, calculates a score for each individual’s adherence to each DP, reflecting that most participants eat all types of foods, albeit in different amounts and frequencies. In cluster analysis, each participant contributes to one single derived cluster. Given that, from a statistical point of view, our study population is small, factor analysis may be preferable.
In PCA-derived DP, all included food groups contribute to each DP and explain the total diet; low or no intake of foods may be as important as foods consumed in larger amounts. We, therefore, label the DP in our study as DP1, DP2, DP3 and DP4, rather than label them as ‘Western’ or ‘Healthy’. As none of the previously published studies includes DP similar to DP1 or DP3, and we do not display any clear associations, these are not discussed further. The DP explained 28 % of the total variance, leaving 72 % of the variance unexplained. This is similar to what has been reported from other studies and may be considered as fairly high explanation rate due to the diversity of dietary habits within a population.
Previous longitudinal studies are few and reported no associations between a posteriori DP and incident sarcopenia in community-dwelling men and women in Hong Kong(Reference Chan, Leung and Woo24) (age ≥ 65 years, 4-year follow-up) and UK(Reference Granic, Mendonça and Sayer53) (age ≥ 85 years; 3-year follow-up). However, cross-sectional associations were observed in these, and other, studies. A DP characterised by a high consumption of butter, red meat, gravy, vegetables, sweets/dessert and potatoes was associated with higher prevalence of sarcopenia(Reference Granic, Mendonça and Sayer53). DP characterised by a high intake of poultry(Reference Fanelli Kuczmarski, Mason and Beydoun54); vegetables, fruit, fish, nuts and vegetable oil(Reference Mohseni, Aliakbar and Abdollahi25,Reference Hashemi, Motlagh and Heshmat26) ; vegetables and fruits(Reference Chan, Leung and Woo24); snacks, coffee, milk and fast food(Reference Chan, Leung and Woo24); vegetables, fruit, fish and tofu(Reference Suthuvoravut, Takahashi and Murayama55) were all associated with a lower prevalence of sarcopenia. Even though most of previous studies report that high adherence to DP characterised by high intake of vegetables and fruit is associated with lower prevalence of sarcopenia, the total diet seems important, and a high intake of vegetables alone may not be enough as dissonant results have been reported(Reference Karlsson, Becker and Michaëlsson23,Reference Chan, Leung and Woo24,Reference Granic, Mendonça and Sayer53) . Dietary patterns add context to the single food items. Dissimilar findings may be explained by regional or cultural differences in how foods are used in cooking or combined in meals, how foods are combined into food groups by the researchers, the definition of sarcopenia (e.g. EWGSOP1 vs. EWGSOP2), and follow-up period.
Based on the same cohort as in this study, we displayed that a Mediterranean-like diet was associated with lower prevalence of sarcopenia (EWGSOP1)(Reference Karlsson, Becker and Michaëlsson23). Despite the similarities of the Mediterranean diet with DP2 in the present study, we did not see an association of DP2 with sarcopenia according to EWGSOP1 in the present study. Although several aspects are similar between DP2 in the current study and the Mediterranean-like diet used in our previous study(Reference Karlsson, Becker and Michaëlsson23), the factor loading of alcohol in DP2 is low but is included as one item in the Mediterranean-like diet. However, the significance of this is difficult to estimate. The sarcopenia definitions partly capture different individuals and differ mainly in the decisive variables used; the chair stand test in EWGSOP2 (main outcome in the present study) and handgrip strength in EWGSOP1. In the present study, DP2 was associated with sarcopenia defined as the combination of muscle function and muscle mass, but not with the components of sarcopenia. This could be an indication that the combination of an impaired physical function, low muscle mass and poor strength may be more detrimental than each component on its own, similar to the complexity of dietary intake where DP represent a more complete picture than single foods or nutrients. However, a high adherence to DP4 seemed to have an inverse association with muscle function and muscle mass. A similar DP characterised by, among other food groups, high intake of red meat and potatoes was inversely associated with muscle strength and physical performance(Reference Granic, Mendonça and Sayer53).
In the present study, DP were calculated based on the actual intake of each food group in grams(Reference Hu21) and may represent different energy densities. To ensure that any associations between the DP and the outcome are not due to differences in energy intake, we include energy intake as a covariate in the multivariable analyses and excluded extreme outliers of energy intake. This allows for ranking of individuals’ dietary intakes, which is essential when studying associations(Reference Willett28). Additional exclusion of inadequate energy reporters(Reference Black52) in a sensitivity analysis is described in Supplementary Material 2. To limit misreporting of dietary intake, we used a valid 7-d menu book (online Supplementary Material 1) that was filled in prospectively and allowed for adequate calculation of total energy intake, however, with potential selection of what is eaten during this specific period. An FFQ would cover a larger period and might provide a broader picture but is on the other hand recorded retrospectively.
Several studies, examining single food groups, have reported a high intake of fruit and vegetables being associated with lower prevalence of sarcopenia(Reference Granic, Dismore and Hurst56), in line with our and previous studies on dietary patterns. Mechanisms may involve anti-oxidative(Reference Dobrowolny, Barbiera and Sica57) and anti-inflammatory(Reference Pascual-Fernández, Fernández-Montero and Córdova-Martínez58) effects on skeletal muscle and ageing, potentially involving the phytochemical content of fruits and vegetables(Reference Granic, Dismore and Hurst56) or the acid–base balance of the diet, as net acid load has been reported to be associated with loss of muscle mass(Reference Cruz-Jentoft, Dawson Hughes and Scott59). A high intake of protein has been associated with muscle mass(Reference Houston, Nicklas and Ding60), and a higher intake of protein per kg/body weight has been recommended(Reference Bauer, Biolo and Cederholm61). In this context, it is interesting to note that a higher adherence to our DP2 was associated with a higher absolute protein intake (online Supplementary Table 6). However, Granic et al. have reported that a DP (high in butter, red meat, gravy and potato) was associated with an increased risk of sarcopenia, even when the intake of protein was high(Reference Granic, Mendonça and Sayer53).
Strengths of the present study include the longitudinal approach with a long follow-up period, a well-characterised population, and that we adjust for a large number of potential confounders including BMI and physical activity that are associated with both energy requirement and lifestyle. The relatively small study population was partly compensated for by an age-standardised set-up and a follow-up period well adapted to the period when sarcopenia begins to become more common(Reference Guillamón-Escudero, Diago-Galmés and Tenías-Burillo62–Reference Akune, Muraki and Oka65). As the age-related decline of muscle mass and function is slow, and the sarcopenia definition is based on thresholds, a long follow-up period is crucial. We created the DP among the full population at baseline, and not only among those attending the follow-up, to better reflect the distribution of the DP at that time. However, it should be borne in mind that the diet among 71-year-old men in the 1990s probably does not correspond to a typical diet in that age group in the 2020s. Dietary patterns are influenced by cultural eating habits and may therefore not be easily transferable to other populations(Reference Hu21). This is somewhat counteracted by the fact that we are ranking individuals according to DP adherence and do not focus on specific amounts of foods or nutrients.
Using a single measure of dietary intake as a reflection of habitual dietary habits may be seen as a limitation as the dietary intake may change over time although adherence to dietary patterns has been reported to be reasonably stable during 10 years in Swedish and other populations(Reference Winkvist, Klingberg and Nilsson66–Reference Kimokoti, Newby and Gona68). However, if dietary habits changed during the follow-up period, it would probably affect our estimates towards the null.
The main limitation of the present study is that we were not able to define sarcopenia at baseline. The prevalence of sarcopenia at an approximate age of 70 in this population is usually considered as low(Reference Volpato, Bianchi and Cherubini64). We did not see a major impact on our estimates by exclusion of those with a low BMI at baseline, being at higher risk of having sarcopenia. Nonetheless, we cannot preclude the possibility of reverse causation. There may be an influence of residual or unmeasured confounding, and we cannot exclude that any statistically significant associations may have occurred by chance. Because sarcopenia was defined on average 16 years after baseline at a follow-up examination, participants included in our study had a higher physical activity level and fewer comorbidities at baseline, compared to excluded participants, a possible result of a healthy survivor effect or natural selection by increasing age, introducing potential selection bias. However, since many confounders in our adjusted model are linked with reasons for nonparticipation, the influence of such selection bias is limited(Reference Nohr and Liew69). The participants in our study probably had a normal nutritional status at baseline, even though we lack specific information on nutritional status. Our results may not be generalisable to a more sedentary population, to a population with more comorbidities or with great cultural differences in eating habits, to other age groups, or to women(Reference Bloom, Shand and Cooper17,Reference Yamada, Nishiguchi and Fukutani63-Reference Akune, Muraki and Oka65) .
Conclusions
In summary, using an a posteriori approach and reported dietary intake in a cohort of community-dwelling 71-year-old Swedish men, we defined four DP. A higher adherence to DP2, mainly characterised by high consumption of vegetables, green salad and fruit as well as poultry, rice and pasta, was associated with a lower prevalence of sarcopenia defined according to EWGSOP2 16 years later. Contradicting results when comparing studies, using different methods, stress the need to use equivalent definitions and methodology when defining sarcopenia. Thus, it could be of value to consider reanalysis of some previous studies, using EWGSOP1 and EWGSOP2 for the diagnosis of sarcopenia.
Acknowledgements
We acknowledge the contribution of Käthe Ström, research nurse and Vilmantas Giedraitis, ULSAM data manager. We acknowledge Inga-Britt Gustafsson and Lars Berglund (Department of Public Health and Caring Sciences, Uppsala University) for the work they invested in the development of the menu book.
The Uppsala Geriatric Foundation and the Region Örebro County supported this work. These funding sources had no role in the design, conduct of research or decision of publication.
Conceptualisation, M. K., W. B., T. C. and L. B.; methodology, M. K., W. B., T. C. and L. B.; formal analysis, M. K.; data curation, M. K.; writing – original draft preparation, M. K.; writing – review and editing, M. K., W. B., T. C. and L. B.; supervision, W. B., T. C. and L. B. All authors have read and agreed to the published version of the manuscript.
None. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114521003901
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