Abstract
Background
Data on the association of Alternative Healthy Eating Index-2010 (AHEI-2010) with sarcopenia are scarce. We aimed to evaluate the association between adherence to AHEI-2010 and sarcopenia and its components including low muscle mass, low muscle strength, and low muscle performance among elderly people.
Methods
In this cross-sectional study, which was conducted on 300 older people (150 men and 150 women) aged ≥55 years. Dietary information was done using a valid 117-item food frequency questionnaire (FFQ). To construct AHEI-2010 score, earlier studies were used. Sarcopenia and its components were described based on both former and new European Working Group on Sarcopenia in Older People (EWGSOP) guidelines.
Results
We found no significant association between AHEI-2010 score and odds of EWGSOP2-sarcopenia, either before (OR for the highest vs. lowest tertiles: 0.55; 95% CI: 0.19, 1.55) or after (OR: 0.44; 95% CI: 0.14, 1.34) adjustment for confounders. In gender-stratified analyses, we found a significant protective association between adherence to the AHEI-2010 score and odds of EWGSOP2-sarcopenia among women after controlling for confounders (0.20; 95%CI: 0.04, 0.91).
Conclusions
In conclusion, healthy eating was inversely associated with odds of sarcopenia among women, but not in men. Further studies with a large sample size and prospective design are needed to examine this association.
Keywords: Alternative healthy eating index, Sarcopenia, Diet, Cross-sectional, Muscle mass, Muscle strength
Introduction
Sarcopenia is a progressive skeletal muscle disorder affecting muscle strength and function [1, 2]. This disorder is often linked to aging, however, in some cases it occurs at earlier age [1]. In addition to muscle strength, sarcopenia affects muscle quantity/quality and physical performance [1]. It is associated with fall, fracture and mortality [3, 4]. The prevalence of sarcopenia in western societies [5] and Asian countries [3] is estimated at 1 to 29% and 2 to 46%, respectively. Overall prevalence among Iranians is 16.5 to 32.5% [6].
Along with several non-dietary contributors including telomere length, dietary intake of macro- and micro-nutrients [1, 7, 8] and several food groups including fruits [9] and nuts [10] has been linked with this condition. However, earlier studies have mostly focused on individual nutrients or foods and limited attention has been given to the whole diet approach. To examine adherence of people to whole dietary recommendations, several indicators, including Healthy Eating Index (HEI), have been developed. The Alternative Healthy Eating Index-2010 (AHEI-2010) is an amended version of the Healthy Eating Index (HEI), which measures dietary compliance with suggested healthy eating habits [11–13]. AHEI is different from HEI in terms of components. The AHEI-2010 focuses on fat quality (i.e., omega-3 and polyunsaturated fat intake), highlights nuts and legumes intake, and considers moderate alcohol consumption as being healthful regardless of disease status (i.e., diabetes). Furthermore, it recommends restricting the intake of red and processed meats and added sugars (i.e., sugar-sweetened beverages and fruit juice) [14]. The majority of earlier studies on chronic conditions have revealed that healthy eating, as measured by HEI and AHEI, were protectively associated with risk of these conditions [15, 16]. In addition, in a nationally representative elderly cohort of US population, greater HEI score was associated with longevity [17]. Furthermore, in a cross-sectional study, HEI scores were associated with handgrip strength (HGS) [18]. Several components of AHEI-2010 has been assessed in relation to sarcopenia [9, 19, 20], but the holistic approach of total diet was rarely examined. In particular, we are aware of no study examining the relationship between AHEI and risk of sarcopenia.
This is particularly important in the Middle East, where the application of other dietary patterns, like Mediterranean diet, might be less relevant. In addition, the growing number of elderly people in this region highlights the need for finding preventive measures, including dietary measures, for sarcopenia [21, 22]. Given the lack of evidence on the association between AHEI and sarcopenia in this region, we conducted this observational study to investigate the association between adherence to AHEI-2010 and prevalence of sarcopenia among Iranian elderly people.
Meterials and Methodes
Participants
The present work was done based on the dataset of a previous population-based, cross-sectional study conducted from May to October 2011 in Tehran, Iran. Several analyses from that dataset has earlier been published [23–25], in which detailed information were given about the sampling and data collection [26]. In brief, using cluster random sampling method in district 6 of Tehran, we enrolled 300 elderly people (150 men and 150 women) over the age of 55 y. A ten-digit postal code determined the head of each cluster, based on which we enrolled individuals aged ≥55 years, with the ability to move without crutches, walker or assistive devices and those without any active cancers (based on self-reported data). We did not include people who were susceptible to sarcopenia including individuals with artificial limbs or limb prosthesis and those with a history of debilitating disease including Congestive Heart Failure (CHF), Chronic Obstructive Pulmonary Disorder (COPD), Chronic Renal Failure (CRF), cirrhosis and liver failure (based on self-reported data). All participants signed an informed written consent form. Written informed consent forms were received from all participants.
Dietary intake assessment
A pretested valid food frequency questionnaire (FFQ), containing 117 food items, was used to assess usual dietary intake of participants [26, 27]. In this questionnaire, each food item had its own pre-specified portion size. All the questionnaires were filled by a trained dietitian. The questionnaire consisted of a list of foods with a specific portion size. Study participants were asked to determine the frequency of their consumption of a certain food item considering its consumption in a single day, week or month. After completing the FFQ, the frequency of each food item was converted to grams per day, taking into account the household measurements of portion sizes. Then, we used Nutritionist IV software, whom database was based on the US Department of Agriculture food composition table, was used to calculated daily energy, macro- and micro- nutrients intakes of each participant.
Construction of alternative healthy eating index
To assess the adherence to the healthy eating guidelines, AHEI-2010 can be a good option [14]. AHEI-2010 contains 11 components including fruit, vegetables, whole grains, nuts and legumes, long-chain n-3 fats (DHA, EPA and ALA), PUFA, alcohol consumption, sugar-sweetened drinks and fruit juice, red and processed meats, trans-fat and salt. Due to lack of data on trans-fat intake in the current dataset, we did not consider it in the calculation. Also, because of restrictions in Iran, the amount of alcohol consumption is not so high. As a result, we used AHEI-2010 with 9 components in the current study. Participants were first classified based on decile categories of intakes of these components. Participants in the highest deciles of fruits, vegetables, nuts and legumes, whole grains, PUFA, and long-chain n-3 fats were given a score of 10, and those in the lowest deciles were given a score of 1. Other deciles received the corresponding scores. With regard to sugar-sweetened drinks and fruit juice, red and processed meat and sodium intake, we did vice versa; such that the lowest intakes received the greatest score (i.e. 10) and the highest intakes received the score of 1. Finally, total AHEI-2010 was obtained by summing up the scores of all these 9 components for each individual.
Assessment of sarcopenia
Sarcopenia was determined based on the “European Working Group on Sarcopenia (EWGSOP)” definition which was a combination of low muscle mass and low muscle function (either strength or performance) [28]. Based on EWGSOP-2, low muscle strength is a key component of sarcopenia. In addition, this definition uses low muscle quantity and quality to confirm the diagnosis, and considers poor physical performance as indicative of severe sarcopenia [1]. The muscle mass was assessed as the proportion of an individual’s total lean mass of legs and arms (also named Appendicular Skeletal Muscle or ASM) [29] to their squared height (ASM/height2). An DXA scanner (Discovery W S/N 84430) was used to calculate ASM. Based on EWGSOP, low muscle mass was determined as the quantity of muscle mass less than 5.45 kg/m2 and 7.26 kg/m2 for women and men, respectively [28]. In case of muscle strength, the handgrip test was used. The handgrip test was evaluated by a pneumatic instrument that is a squeeze bulb dynamometer (c7489–02 Rolyan) calibrated in pound per square inch (psi). Handgrip strength (maximum voluntary contractions) was measured three times for each individual, in the right and left hands, with a 30-second rest between each time. Finally, the average of the measurements for each hand was reported as muscle strength. Merkies et.al recommended sex- and age-specific cut-off points to identify low muscle strength [30]. A 4-Meter walk gait speed test was used to measure muscle performance [28]. Low muscle performance was determined according to gait speeds less than 0.8 m/s [28].
Assessment of other variables
A primary demographic questionnaire was used to get general information including age, sex, socio-economic status, medical history, medication use, smoking habits, and alcohol consumption. The International Physical Activity Questionnaire (IPAQ)- short form-was used to assess the subjects’ physical activity levels. This questionnaire was filled by a trained interviewer. Previous studies have confirmed the validity of IPAQ [27]. Based on the IPAQ guidelines [31], each participant’s physical activity measures were expressed in metabolic equivalent-hours per week (MET-h/week). A digital scale was used to measure weight, while participants were minimally clothed. Height was measured by a wall tape meter in a standing position without shoes. Waist circumference was measured in the middle of the lower rib margin and iliac crest. Body mass index (BMI) was calculated using Weight (kg) divided by height squared (m2).
Statistical analysis
Subjects were categorized across tertiles of AHEI-2010 score. General characteristics of study participants across tertiles of AHEI-2010 were compared using Chi-square and one-way analysis of variance (ANOVA) for categorical and continuous variables, respectively. Dietary intakes of subjects were compared using analysis of covariance (ANCOVA) analysis after adjustment for age, sex and energy intakes of participants. Multivariable logistic regression was performed to find the association between AHEI-2010 and odds of sarcopenia. In the first statistical model, the association was controlled for age (years), sex (male/female) and energy intake (kcal/d). Further controlling was conducted for physical activity (MET-h/week), smoking (yes/no), medication use (yes/no), alcohol consumption (yes/no), and positive history of disease (yes/no) in the second model. To assess the trends of odds ratio across tertiles of the AHEI-2010, the tertile categories were considered as an ordinal variable in the logistic regression models. To assess the strength of associations between variables, we used Pearson’s correlation r and the Spearman rank correlation coefficient (non-parametric testing with Spearman rho) for selected analysis. All analyses were performed using SPSS, version 26 (SPSS Inc., Chicago, IL, USA). P-values were considered significant at < 0.05.
Results
General characteristics of study participants across tertile categories of AHEI-2010 are presented in Table 1. People with EWGSOP2-sarcopenia were less likely to be obese and diabetic and more likely to be sexual hormone and corticosteroids user. The mean ± SD of AHEI-2010 in our study was 49.5 ± 9.9. When we examined across tertiles of AHEI-2010 score, individuals in the highest tertile were more likely to be smoker. No other significant differences were seen.
Table 1.
General characteristics of study participants across tertiles of Alternative Healthy Eating Index and individuals with and without sarcopenia a
| Sarcopenia c | Tertiles of Alternative Healthy Eating Index | P-valueb | ||||||
|---|---|---|---|---|---|---|---|---|
| Total (n = 300) | Yes (n = 31) | No (n = 269) | Pb | T1 (n = 106) | T2 (n = 94) | T3 (n = 100) | ||
| Age (year) | 66.79 ± 7.70 | 64.5 ± 6.3 | 67.0 ± 7.8 | 0.08 | 66.1 ± 7.4 | 67.7 ± 7.5 | 67.2 ± 8.0 | 0.51 |
| BMI (kg/m2) d | 27.38 ± 4.20 | 23.4 ± 2.7 | 27.8 ± 4.1 | < 0.001 | 27.0 ± 3.9 | 27.7 ± 4.4 | 27.3 ± 4.2 | 0.55 |
| Physical activity (MET-h/wk) e | 1294.52 ± 1429.57 | 1223.4 ± 1122.3 | 1302.7 ± 1462.3 | 0.77 | 1286.8 ± 1563.3 | 1076.0 ± 1013.6 | 1508.0 ± 1588.5 | 0.10 |
| Female (%) | 51 | 64.5 | 49.4 | 0.11 | 44.3 | 58.5 | 51.0 | 0.13 |
| Alcohol use (%) f | 13.3 | 9.7 | 13.8 | 0.52 | 15.1 | 16.0 | 9.0 | 0.29 |
| Smoking (%) | 12.7 | 12.9 | 12.6 | 0.96 | 19.8 | 7.4 | 10.0 | 0.02 |
| Medical history | ||||||||
| Diabetes (%) | 20.7 | 6.5 | 22.3 | 0.03 | 17.9 | 20.2 | 24.0 | 0.55 |
| MI (%) g | 12 | 6.5 | 12.6 | 0.31 | 13.2 | 9.6 | 13.0 | 0.68 |
| CVA (%) h | 2.7 | 6.5 | 2.2 | 0.16 | 1.9 | 5.3 | 1.0 | 0.14 |
| Arthritis (%) | 1.7 | 3.2 | 1.5 | 0.47 | 0.9 | 4.3 | 0 | 0.05 |
| Asthma (%) | 2 | 3.2 | 1.9 | 0.60 | 2.8 | 3.2 | 2.0 | 0.21 |
| Drug history | ||||||||
| Sexual hormone use (%) | 3 | 9.7 | 2.2 | 0.02 | 2.8 | 4.3 | 2.0 | 0.64 |
| Statin use (%) | 36.7 | 35.5 | 36.8 | 0.88 | 31.1 | 40.4 | 39.0 | 0.33 |
| ACEI use (%) i | 7.7 | 3.2 | 8.2 | 0.32 | 6.6 | 9.6 | 7.0 | 0.69 |
| Corticosteroid use (%) | 2.7 | 12.9 | 1.5 | < 0.001 | 1.9 | 5.3 | 1.0 | 0.14 |
aContinuous variables are expressed as means±SD and categorical variables are expressed as percentages
bObtained from ANOVA or chi-square test, where appropriate
cSarcopenia was defined based on European Working Group on Sarcopenia in Older People 2 (EWGSOP2) definition
dBody mass index
eMetabolic equivalent of task
fHistory of alcohol use in the past 6 months
gMyocardial infarction
hCerebrovascular accident
iAngiotensin-converting enzyme inhibitor
Table 2 provides the multivariate-adjusted dietary intakes of study population across tertiles of AHEI-2010. Compared to the bottom tertile, individuals in the top tertile of AHEI-2010 had higher intakes of fruits, vegetables, nuts, legumes and soy, dairy products, grains, fiber, calcium, pyridoxine and folate and lower intake of energy, sugar-sweetened beverages and sweets, red and processed meats and sodium.
Table 2.
Dietary intakes of participants by tertiles of Alternative Healthy Eating Index a
| Tertiles of Alternative Healthy Eating Index | P-valueb | |||
|---|---|---|---|---|
| T1 (n = 106) | T2 (n = 94) | T3 (n = 100) | ||
| AHEI range | < 45 | 45–53.66 | > 53.66 | |
| Food groups (g/day) | ||||
| Fruits | 527.3 ± 23.9 | 657.8 ± 25.3 | 776.1 ± 24.7 | < 0.001 |
| Vegetables | 431.5 ± 21.7 | 514.9 ± 23.0 | 717.5 ± 22.4 | < 0.001 |
| Nuts, legumes and soy | 49.2 ± 3.4 | 56.1 ± 3.6 | 72.2 ± 3.5 | < 0.001 |
| Dairy products | 342.2 ± 29.3 | 627.4 ± 31.1 | 513.3 ± 30.3 | 0.027 |
| Grains | 387.1 ± 17.4 | 331.0 ± 18.5 | 307.2 ± 18.0 | 0.005 |
| Sugar-sweetened beverages and sweets | 128.2 ± 11.1 | 84.9 ± 11.8 | 53.0 ± 11.5 | < 0.001 |
| Red and processed meats | 50.7 ± 2.7 | 33.8 ± 2.9 | 27.3 ± 2.8 | < 0.001 |
| Nutrients | ||||
| Sodium | 3856.1 ± 124.0 | 3296.8 ± 131.8 | 3148.5 ± 128.4 | < 0.001 |
| Energy (kcal/d) | 2156.6 ± 88.1 | 2119.2 ± 93.5 | 2510.7 ± 90.3 | 0.004 |
| Carbohydrate (g/day) | 373.3 ± 5.3 | 364.4 ± 5.6 | 359.7 ± 5.5 | 0.200 |
| Protein (g/day) | 84.7 ± 1.7 | 85.4 ± 1.8 | 88.0 ± 1.8 | 0.411 |
| Fat (g/day) | 55.9 ± 1.8 | 60.4 ± 1.9 | 61.7 ± 1.8 | 0.074 |
| Fiber (g/day) | 27.0 ± 0.7 | 29.6 ± 0.8 | 33.3 ± 0.8 | < 0.001 |
| Calcium (mg/d) | 1287.7 ± 42.0 | 1433.3 ± 44.6 | 1304.7 ± 43.5 | 0.039 |
| Pyridoxine (mg/d) | 2.3 ± 0.1 | 2.8 ± 0.1 | 2.6 ± 0.1 | 0.023 |
| Folate (mcg/d) | 499.4 ± 10.6 | 537.9 ± 11.2 | 595.5 ± 10.9 | < 0.001 |
aAll values are mean ± SE; energy intake is adjusted for age and sex, all other values are adjusted for age, sex and energy intake
bANCOVA for all variables
Means of sarcopenia components as well as the prevalence of EWGSOP2-sarcopenia and its components across different AHEI-2010 categories are shown in Table 3. In the general population, mean gait speed and hand grip strength were significantly lower among cases in the highest tertile of AHEI-2010 compared with those in the lowest tertile. Stratified analysis by gender revealed no significant difference in means of muscle mass, hand grip strength and gait speed across different AHEI-2010 categories. Examining the prevalence of EWGSOP2-sarcopenia across categories of AHEI-2010, we found a high prevalence of slower gait speed among those in the highest category of AHEI-2010 compared with those in the lowest tertile (36% vs. 34%, P < 0.05). In terms of other components, we did not find a significant difference across tertiles of AHEI-2010 score. This was the case when we assessed by gender.
Table 3.
Means of sarcopenia components and their prevalence across categories of Alternative Healthy Eating Index
| Tertiles of Alternative Healthy Eating Index | P-valuea | |||
|---|---|---|---|---|
| T1 | T2 | T3 | ||
| AHEI range | < 45 | 45–53.66 | > 53.66 | |
| Whole population | ||||
| n | 106 | 94 | 100 | |
| Muscle mass [ASM/h2] (kg) b | 6.6 ± 1.0 | 6.5 ± 0.9 | 6.6 ± 1.0 | 0.701 |
| Hand grip strength (psi) | 11.5 ± 3.9 | 10.2 ± 3.4 | 11.3 ± 3.2 | 0.026 |
| Gait speed (m/s) | 0.8 ± 0.2 | 0.7 ± 0.2 | 0.8 ± 0.2 | 0.019 |
| Lower muscle mass, n (%) c | 48 (45.3) | 34 (36.2) | 35 (35) | 0.253 |
| Lower hand grip strength, n (%) d | 31 (29.2) | 38 (40.4) | 27 (27) | 0.101 |
| Slower gait speed (m/s), n (%) e | 36 (34) | 50 (53.2) | 36 (36) | 0.011 |
| Sarcopenia, n (%) f | 11 (10.4) | 14 (14.9) | 6 (6) | 0.126 |
| Men | ||||
| n | 59 | 39 | 49 | |
| Muscle mass [ASM/h2] (kg) | 7.2 ± 0.7 | 7.1 ± 0.7 | 7.2 ± 0.7 | 0.814 |
| Hand grip strength (psi) | 14.0 ± 3.0 | 12.8 ± 3.1 | 13.6 ± 2.6 | 0.153 |
| Gait speed (m/s) | 0.9 ± 0.2 | 0.8 ± 0.2 | 0.8 ± 0.2 | 0.067 |
| Lower muscle mass, n (%) c | 33 (55.9) | 22 (56.4) | 25 (51) | 0.842 |
| Lower hand grip strength, n (%) d | 9 (15.3) | 10 (25.6) | 6 (12.2) | 0.226 |
| Slower gait speed (m/s), n (%) e | 15 (25.4) | 18 (46.2) | 13 (26.5) | 0.065 |
| Sarcopenia, n (%) f | 4 (6.8) | 6 (15.4) | 1 (2) | 0.059 |
| Women | ||||
| n | 47 | 55 | 51 | |
| Muscle mass [ASM/h2] (kg) | 5.8 ± 0.8 | 6.1 ± 0.8 | 6.1 ± 0.9 | 0.305 |
| Hand grip strength (psi) | 8.3 ± 2.1 | 8.3 ± 2.1 | 9.0 ± 2.0 | 0.156 |
| Gait speed (m/s) | 0.8 ± 0.2 | 0.7 ± 0.1 | 0.8 ± 0.2 | 0.449 |
| Lower muscle mass, n (%) c | 15 (31.9) | 12 (21.8) | 10 (19.6) | 0.319 |
| Lower hand grip strength, n (%) d | 22 (46.8) | 28 (50.9) | 21 (41.2) | 0.603 |
| Slower gait speed (m/s), n (%) e | 21 (44.7) | 32 (58.2) | 23 (45.1) | 0.288 |
| Sarcopenia, n (%) f | 7 (14.9) | 8 (14.5) | 5 (9.8) | 0.697 |
aObtained from ANOVA for quantitative variables and chi-square for qualitative variables (P < 0.05 significant)
bAppendicular skeletal muscle
cMuscle mass lower than 5.5 (kg/m2) for women and 7.0 (kg/m2) for men
dLower muscle strength were defined according previous study
eGait speeds equal or slower than 0.8 m/s
fSarcopenia was defined based on European Working Group on Sarcopenia in Older People 2 (EWGSOP2) definition
Crude and multivariable-adjusted ORs for sarcopenia across tertiles of AHEI-2010 score are provided in Table 4. In the crude model, AHEI-2010 was not significantly associated with the risk of EWGSOP2-sarcopenia [OR: 0.55; 95% confidence interval (CI): 0.19, 1.55]. When the analysis was controlled for potential confounders, this association remained non-significant (OR: 0.44; 95% CI: 0.14, 1.34). Even further adjustment for BMI did not alter the associations (OR: 0.39; 95% CI: 0.10, 1.51). The same findings were observed in the sex-stratified analyses either before (men: OR: 0.30; 95% CI: 0.03, 3.14 and women: 0.37; 95% CI: 0.09, 1.52) or after controlling for BMI (men: OR: 0.35; 95% CI: 0.02, 4.82 and women: 0.52; 95% CI: 0.10, 2.53). The same findings were seen when we applied EWGSOP-sarcopenia definition, either before (OR: 0.58; 95% CI: 0.27, 1.27) or after adjusting for potential confounders (OR: 0.58; 95% CI: 0.26, 1.31) or BMI (OR: 0.55; 95% CI: 0.22, 1.37). In the gender-stratified analyses, no significant relationship was found among men, either before (OR 0.76; 95% CI: 0.26, 2.21) or after controlling for BMI (OR 0.83; 95% CI: 0.26, 2.64). However, women in the top tertile of AHEI-2010 score, were 80% less likely to have EWGSOP-sarcopenia compared to those in the bottom tertile before considering BMI (OR: 0.20; 95% CI: 0.04, 0.91). Additional adjustment for BMI made this association non-significant (OR: 0.23; 95% CI: 0.04, 1.38).
Table 4.
Multivariable-adjusted odds ratios (95% CIs) for sarcopenia across tertile categories of Alternative Healthy Eating Index, stratified by gender
| Tertiles of Alternative Healthy Eating Index | P trend | |||
|---|---|---|---|---|
| T1 | T2 | T3 | ||
| Sarcopenia a | ||||
| n | 106 | 94 | 100 | |
| Crude | 1.00 | 1.51 (0.65–3.51) | 0.55 (0.19–1.55) | 0.318 |
| Model 1b | 1.00 | 1.47 (0.61–3.49) | 0.44 (0.15–1.32) | 0.189 |
| Model 2 c | 1.00 | 1.43 (0.57–3.60) | 0.44 (0.14–1.34) | 0.187 |
| Model 3 f | 1.00 | 1.60 (0.54–4.75) | 0.39 (0.10–1.51) | 0.259 |
| Men | ||||
| n | 59 | 39 | 49 | |
| Crude | 1.00 | 2.50 (0.65–9.51) | 0.28 (0.03–2.65) | 0.413 |
| Model 1 e | 1.00 | 2.75 (0.70–10.82) | 0.30 (0.03–2.79) | 0.457 |
| Model 2 c | 1.00 | 2.98 (0.57–15.40) | 0.30 (0.03–3.14) | 0.461 |
| Model 3 f | 1.00 | 1.10 (0.11–10.39) | 0.35 (0.02–4.82) | 0.476 |
| Women | ||||
| n | 47 | 55 | 51 | |
| Crude | 1.00 | 0.97 (0.32–2.91) | 0.62 (0.18–2.11) | 0.451 |
| Model 1 e | 1.00 | 0.92 (0.30–2.83) | 0.42 (0.10–1.63) | 0.218 |
| Model 2 c | 1.00 | 0.82 (0.24–2.78) | 0.37 (0.09–1.52) | 0.168 |
| Model 3 f | 1.00 | 1.58 (0.37–6.69) | 0.52 (0.10–2.53) | 0.347 |
| Sarcopenia d | ||||
| n | 106 | 94 | 100 | |
| Crude | 1.00 | 1.31 (0.66–2.59) | 0.58 (0.27–1.27) | 0.211 |
| Model 1 b | 1.00 | 1.39 (0.69–2.81) | 0.55 (0.25–1.22) | 0.180 |
| Model 2 c | 1.00 | 1.47 (0.71–3.04) | 0.58 (0.26–1.31) | 0.236 |
| Model 3 f | 1.00 | 1.74 (0.75–4.03) | 0.55 (0.22–1.37) | 0.264 |
| Men | ||||
| n | 59 | 39 | 49 | |
| Crude | 1.00 | 2.18 (0.85–5.55) | 0.85 (0.31–2.31) | 0.847 |
| Model 1 e | 1.00 | 2.01 (0.77–5.29) | 0.58 (0.74–2.26) | 0.676 |
| Model 2 c | 1.00 | 1.94 (0.69–5.42) | 0.76 (0.26–2.21) | 0.693 |
| Model 3 f | 1.00 | 1.92 (0.60–6.09) | 0.83 (0.26–2.64) | 0.834 |
| Women | ||||
| n | 47 | 55 | 51 | |
| Crude | 1.00 | 0.82 (0.29–2.28) | 0.35 (0.10–1.25) | 0.113 |
| Model 1 e | 1.00 | 0.72 (0.25–2.04) | 0.03 (0.22–0.05) | 0.037 |
| Model 2 c | 1.00 | 0.78 (0.25–2.36) | 0.20 (0.04–0.91) | 0.039 |
| Model 3 f | 1.00 | 1.34 (0.31–5.73) | 0.23 (0.04–1.38) | 0.552 |
aSarcopenia was defined based on European Working Group on Sarcopenia in Older People 2 (EWGSOP2) definition. b Model 1: Adjusted for age, sex and energy intake. c Model 2: Further adjusted for physical activity, smoking, alcohol consumption, medication use (statin, ACEI, estrogen, testosterone), corticosteroid use and positive history of disease (asthma, arthritis, myocardial infarction, cerebrovascular accident, diabetes). e Model 1: Adjusted for age and energy intake. d Sarcopenia was defined based on European Working Group on Sarcopenia in Older People (EWGSOP) definition. f Additionally adjusted for BMI
Regression coefficients for the association between AHEI-2010 score and components of sarcopenia are presented in Table 5. Both in crude and in adjusted models, we failed to find a significant association between adherence to AHEI-2010 and muscle mass, hand grip strength and gait speed.
Table 5.
Linear regression analysis of the association between Alternative Healthy Eating Index and components of sarcopenia
| AHEI diet score | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Crude | Model 1 a | Model 2 b | Model 3 d | ||||||||||||||||
| β | 95% CI | P | R2 | β | 95% CI | P | R2 | β | 95% CI | P | R2 | β | 95% CI | P | R2 | ||||
| Muscle mass [ASM/h2] C | 0.019 | −0.118, 0.155 | 0.78 | 0.016 | 0.069 | −0.044, 0.182 | 0.23 | 0.586 | 0.061 | −0.053, 0.175 | 0.29 | 0.603 | 0.050 | − 0.022, 0.123 | 0.173 | 0.862 | |||
| Hand grip strength | −0.119 | − 0.610, 0.372 | 0.63 | 0.028 | 0.149 | −0.180, 0.477 | 0.37 | 0.756 | 0.174 | −0.155, 0.503 | 0.29 | 0.767 | −0.023 | −0.085, 0.040 | 0.475 | 0.379 | |||
| Gait speed | −0.018 | −0.048, 0.013 | 0.25 | 0.066 | −0.011 | −0.041, 0.018 | 0.45 | 0.337 | −0.013 | −0.042, 0.016 | 0.371 | 0.421 | 0.001 | −0.065, 0.067 | 0.982 | 0.365 | |||
aModel 1: Adjusted for age, sex and energy intake
bModel 2: Further adjusted for physical activity, smoking, alcohol consumption, medication use (statin, ACEi, estrogen, testosterone), corticosteroid use and positive history of disease (asthma, arthritis, myocardial infarction, cerebrovascular accident, diabetes)
CAppendicular skeletal muscle
dAdditionally adjusted for BMI
Table 6 shows Pearson correlation coefficients (r) AHEI score and components of sarcopenia. No significant correlation was found between AHEI and Muscle mass, Hand grip strength and Gait speed.
Table 6.
Association between Alternative Healthy Eating Index and components of sarcopenia a
| Variables | r | P-value |
|---|---|---|
| Muscle mass [ASM/h2] b | −0.078 | 0.17 |
| Hand grip strength | 0.008 | 0.89 |
| Gait speed | 0.01 | 0.86 |
aObtained from Pearson correlation coefficient
bAppendicular skeletal muscle
Discussion
In this cross-sectional study, we failed to find a significant association between adherence to AHEI-2010 and odds of sarcopenia in older adults, even after adjustment for potential confounders. Following sex-stratified analysis, we found a significant relationship between AHEI-2010 and odds of EWGSOP-sarcopenia among women, such that; women in the top tertile of AHEI-2010 score have lower risk of EWGSOP-sarcopenia compared to those in the bottom tertile. To the best of our knowledge, this is the first study investigating the association between AHEI-2010 and sarcopenia.
Sarcopenia is linked to adverse health related outcomes, including fall, fracture and mortality [3, 4]. Sarcopenia and its components were strongly linked to nutritional status [32]. In addition, body composition, for instance high body fat percentage, was significantly associated with poor health-related quality of life that can in turn result in sarcopenia [33, 34]. Based on observational studies, adhering to AHEI-2010 is associated with a lower risk of all-cause mortality, cardiovascular disease, cancer and type 2 diabetes mellitus [15]. However, there is no evidence on the link between AHEI-2010 and risk of sarcopenia risk. A cross-sectional study on 14,585 individuals aged ≥65 years indicated that increased consumption of fruits was associated with a reduced risk of sarcopenia in women, but not in men. However, vegetables consumption was related to a lower risk in either gender [9]. Dietary salt intake [35] and sugar-sweetened drinks [36] were also associated with elevated risk of sarcopenia in earlier investigations. Examining the relationship between healthy dietary patterns, similar to what AHEI-2010 reflects, and risk of sarcopenia demonstrated that such dietary patterns were associated with a lower risk of sarcopenia. For instance, Chan et al. reported that higher Diet Quality Index-International (DQI-I), “vegetables-fruits” dietary pattern score, and also greater scores of “snacks-drinks-milk products” dietary pattern [37] and Mediterranean diet were associated with a lower risk of sarcopenia [38]. Esmaeily et al. [18] reported that adhering to the HEI-2015 might improve muscle strength in aging individuals. It must be taken into account that muscle strength is only one component of sarcopenia and the association with that index might not indicate the relationship of HEI with sarcopenia. In addition to having a larger sample size in the current study compared with that in Esmaeily et al. (300 vs. 201), we used DXA scanner to calculate ASM. In addition, lack of controlling for BMI in that study as one of the key variables affecting sarcopenia might further confine the interpretation of their findings. In opposite to above-mentioned studies, in the Newcastle cohort study performed on 757 individuals, a positive association was seen between a “traditional British” dietary pattern- high in butter, red meat, gravy and potato intake -and sarcopenia [39]. In addition, no significant association was found between adherence to the DASH-style diet, a healthy dietary pattern, and odds of sarcopenia in our previous analysis on this population [24]. Different findings between studies might be arisen from differences in scoring methods as well as components of dietary patterns they used. For instance, when we applied AHEI-2010, rather than DASH scoring in our previous study [24], in the same population, we found a significant protective association among women. We believe that accurate assessment between healthy eating and risk of sarcopenia should be done by the application of locally-designed scoring methods to reflect healthy eating in each region separately. Developing such local indices based on diet-disease associations in different parts of the world should be a priority for nutrition investigators.
The gender-discrepant findings in the current study may be related to differences in physical activity levels, testosterone and body fat levels between men and women [34, 40]. However, this finding became non-significant after additionally adjusting for BMI, which can further highlight the importance of body composition across genders in this association. Previous investigations showed that testosterone may augment the benefit of low-intensity physical training on skeletal muscle mitochondrial function in elderly male mice [41].
Although the exact mechanisms through which healthy diets might affect the risk of sarcopenia is poorly understood, there are some hypotheses in this regard. First is lowered levels of oxidative stress through consumption of these diets [42, 43]. Oxidative stress can in turn increase the gene expression of inflammatory cytokines such as interleukin-1 (IL-1), tumor necrosis factor (TNF), and IL-6 and consequently damaged muscle tissue [44, 45]. Also, reduction of SFAs was associated with a lower risk of sarcopenia [43]. Second is the low content of salt in healthy eating patterns, which is inversely associated with fat accumulation and muscle weakness and consequently sarcopenia [35]. However, in our previous study on this population about DASH diet [24], which is a sodium restricted diet, we did not find a significant relationship with sarcopenia. Therefore, it seems that other mechanisms might play a role in this regard. Some possible reasons might explain lack of clear association between AHEI-2010 and risk of sarcopenia in the current analysis. In addition to the cross-sectional design of the study which might help explaining this, alcohol intake and trans-fat intake as two main components of AHEI-2010 were not included in this study. These components may have concealed the protective association between AHEI-2010 and risk of sarcopenia. Furthermore, the protective association between dairy consumption and risk of sarcopenia was reported in earlier studies [46], while dairy intake was not included in the scoring of AHEI-2010. In the current population, compared to those in the bottom tertile, individuals in the top tertile of AHEI-2010 had higher intakes of dairy products (P = 0.02). This might also affect our findings.
This study has several major strengths. Although the links between healthy eating and sarcopenia were investigated previously, to our knowledge, this is the first study evaluating the association between AHEI-2010 and odds of sarcopenia and its components. Several potential confounders were adjusted in the current analysis. Additionally, a validated FFQ was used to evaluate dietary intakes. However, some limitations should also be considered. Causality cannot be inferred due to cross-sectional design. Some components of AHEI-2010 such as trans-fats and alcohol consumption were not included in the AHEI-2010 score due to lack of data. Also, the index does not evaluate animal protein sources (such as fish) except red meat. Furthermore, dairy consumption was not included in the scoring method of AHEI-2010, which might further affect the associations. The study was performed on a small sample from a limited area where the DEXA device was located, because of financial limitations and insufficient access to the DEXA device in Tehran (maximum 300 cases). Accordingly, the generalizability of these results should be done cautiously. It should be kept in mind that our study population were relatively young (300 subjects aged > 55 years old) which might further explain lack of finding a significant association.
Conclusion
In conclusion, findings from this cross-sectional study revealed that no significant association between adherence to AHEI-2010 and risk of sarcopenia, even after adjustment for potential confounders. However, in the gender-stratified analysis, we found a significant inverse relationship between AHEI-2010 and odds of sarcopenia among women. Further prospective studies are required to confirm the results of present study.
Acknowledgments
We wish to thank all individuals who kindly participated in our study.
Authors’ contributions
SMG, SEK, RH, RH, ADM and AE contributed to the conception, design, data collection, statistical analyses, data interpretation, manuscript drafting, approval of the final version of the manuscript and agreed for all aspects of the work.
Funding
The financial support for this study comes from the Tehran Endocrine and Metabolism Research Center and the Tehran University of Medical Science.
Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
All study protocols were conducted according to the Declaration of Helsinki, and results were reported based on the strengthening the reporting of observational studies in epidemiology (STROBE) statement for cross-sectional studies. The study protocol was approved by the Tehran University of Medical Sciences ethics committee.
Consent for publication
The data provided to the researchers did not include any personal information, and all participants were adults. Not applicable.
Competing interests
The authors declared no conflicts of interest.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Rezvan Hashemi, Email: r_hashemi@tums.ac.ir.
Ahmad Esmaillzadeh, Email: a-esmaillzadeh@sina.tums.ac.ir.
References
- 1.Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:16–31. doi: 10.1093/ageing/afy169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Chen L-K, Lee W-J, Peng L-N, Liu L-K, Arai H, Akishita M, for Sarcopenia AWG Recent advances in sarcopenia research in Asia: 2016 update from the Asian working Group for Sarcopenia. J Am Med Dir Assoc. 2016;17:767. e761–767. e767. doi: 10.1016/j.jamda.2016.05.016. [DOI] [PubMed] [Google Scholar]
- 3.Chen L-K, Liu L-K, Woo J, Assantachai P, Auyeung T-W, Bahyah KS, Chou M-Y, Chen L-Y, Hsu P-S, Krairit O. Sarcopenia in Asia: consensus report of the Asian working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15:95–101. doi: 10.1016/j.jamda.2013.11.025. [DOI] [PubMed] [Google Scholar]
- 4.Bloom I, Shand C, Cooper C, Robinson S, Baird J. Diet quality and sarcopenia in older adults: a systematic review. Nutrients. 2018;10:308. doi: 10.3390/nu10030308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Cruz-Jentoft AJ, Landi F, Schneider SM, Zúñiga C, Arai H, Boirie Y, Chen L-K, Fielding RA, Martin FC, Michel J-P. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the international sarcopenia initiative (EWGSOP and IWGS) Age Ageing. 2014;43:748–759. doi: 10.1093/ageing/afu115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Shafiee G, Heshmat R, Ostovar A, Nabipour I, Larijani B. Sarcopenia disease in Iran: an overview. J Diabetes Metab Disord. 2019;18(2):665-74. [DOI] [PMC free article] [PubMed]
- 7.Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393:2636–2646. doi: 10.1016/S0140-6736(19)31138-9. [DOI] [PubMed] [Google Scholar]
- 8.Lorenzi M, Bonassi S, Lorenzi T, Giovannini S, Bernabei R, Onder G. A review of telomere length in sarcopenia and frailty. Biogerontology. 2018;19:209–221. doi: 10.1007/s10522-018-9749-5. [DOI] [PubMed] [Google Scholar]
- 9.Koyanagi A, Veronese N, Solmi M, Oh H, Shin JI, Jacob L, Yang L, Haro JM, Smith L. Fruit and vegetable consumption and sarcopenia among older adults in low-and middle-income countries. Nutrients. 2020;12:706. doi: 10.3390/nu12030706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Hai S, Wang H, Cao L, Liu P, Zhou J, Yang Y, Dong B. Association between sarcopenia with lifestyle and family function among community-dwelling Chinese aged 60 years and older. BMC Geriatr. 2017;17:187. doi: 10.1186/s12877-017-0587-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.T Kennedy E, Ohls J, Carlson S, Fleming K. The healthy eating index: design and applications. J Am Diet Assoc. 1995;95:1103–1108. doi: 10.1016/S0002-8223(95)00300-2. [DOI] [PubMed] [Google Scholar]
- 12.Guenther PM, Reedy J, Krebs-Smith SM, Reeve BB. Evaluation of the healthy eating index-2005. J Am Diet Assoc. 2008;108:1854–1864. doi: 10.1016/j.jada.2008.08.011. [DOI] [PubMed] [Google Scholar]
- 13.Guenther PM, Kirkpatrick SI, Reedy J, Krebs-Smith SM, Buckman DW, Dodd KW, Casavale KO, Carroll RJ. The healthy eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 dietary guidelines for Americans. J Nutr. 2014;144:399–407. doi: 10.3945/jn.113.183079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, Stampfer MJ, Willett WC. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr. 2012;142:1009–1018. doi: 10.3945/jn.111.157222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Schwingshackl L, Hoffmann G. Diet quality as assessed by the healthy eating index, the alternate healthy eating index, the dietary approaches to stop hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies. J Acad Nutr Diet. 2015;115:780–800. e785. doi: 10.1016/j.jand.2014.12.009. [DOI] [PubMed] [Google Scholar]
- 16.Onvani S, Haghighatdoost F, Surkan P, Larijani B, Azadbakht L. Adherence to the healthy eating index and alternative healthy eating index dietary patterns and mortality from all causes, cardiovascular disease and cancer: a meta-analysis of observational studies. J Hum Nutr Diet. 2017;30:216–226. doi: 10.1111/jhn.12415. [DOI] [PubMed] [Google Scholar]
- 17.Rathod AD, Bharadwaj AS, Badheka AO, Kizilbash M, Afonso L. Healthy eating index and mortality in a nationally representative elderly cohort. Arch Intern Med. 2012;172:275–277. doi: 10.1001/archinternmed.2011.1031. [DOI] [PubMed] [Google Scholar]
- 18.Esmaeily Z, Tajary Z, Daei S, Rezaei M, Eyvazkhani A, Motlagh ARD, Palmowski A. Association between Healthy Eating Index-2015 scores and probable sarcopenia in community-dwelling Iranian older adults: A cross-sectional study. J Nutr Sci. 2021;10:151-60. [DOI] [PMC free article] [PubMed]
- 19.Hai S, Wang H, Cao L, Liu P, Zhou J, Yang Y, Dong B. Association between sarcopenia with lifestyle and family function among community-dwelling Chinese aged 60 years and older. BMC Geriatr. 2017;17:1–7. doi: 10.1186/s12877-017-0587-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Okamura T, Hashimoto Y, Miki A, Kaji A, Sakai R, Iwai K, Osaka T, Ushigome E, Hamaguchi M, Yamazaki M. Reduced dietary omega-3 fatty acids intake is associated with sarcopenia in elderly patients with type 2 diabetes: a cross-sectional study of KAMOGAWA-DM cohort study. J Clin Biochem Nutr. 2020;26:19-85. [DOI] [PMC free article] [PubMed]
- 21.Da Silva R, Bach-Faig A, Quintana BR, Buckland G, de Almeida MDV, Serra-Majem L. Worldwide variation of adherence to the Mediterranean diet, in 1961–1965 and 2000–2003. Public Health Nutr. 2009;12:1676–1684. doi: 10.1017/S1368980009990541. [DOI] [PubMed] [Google Scholar]
- 22.Fuster V. Changing demographics: a new approach to global health care due to the aging population. American College of Cardiology Foundation Washington, DC; 2017. [DOI] [PubMed] [Google Scholar]
- 23.Bagheri A, Soltani S, Hashemi R, Heshmat R, Motlagh AD, Esmaillzadeh A. Inflammatory potential of the diet and risk of sarcopenia and its components. Nutr J. 2020;19:1–8. doi: 10.1186/s12937-020-00649-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Soltani S, Hashemi R, Heshmat R, Motlagh AD, Esmaillzadeh A. Association of dietary approaches to stop hypertension eating style and risk of sarcopenia. Sci Rep. 2020;10:1–9. doi: 10.1038/s41598-020-76452-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hashemi R, Motlagh AD, Heshmat R, Esmaillzadeh A, Payab M, Yousefinia M, Siassi F, Pasalar P, Baygi F. Diet and its relationship to sarcopenia in community dwelling Iranian elderly: a cross sectional study. Nutrition. 2015;31:97–104. doi: 10.1016/j.nut.2014.05.003. [DOI] [PubMed] [Google Scholar]
- 26.Hashemi R, Heshmat R, Motlagh AD, Payab M, Esmaillzadeh A, Baigy F, Pasalar P, Siassi F. Sarcopenia and its determinants among Iranian elderly (SARIR): study protocol. J Diabetes Metab Disord. 2012;11:1–6. doi: 10.1186/2251-6581-11-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13:654–662. doi: 10.1017/S1368980009991698. [DOI] [PubMed] [Google Scholar]
- 28.Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel J-P, Rolland Y, Schneider SM. Sarcopenia: European consensus on definition and diagnosisReport of the European working group on sarcopenia in older PeopleA. J. Cruz-Gentoft et al. Age Ageing. 2010;39:412–423. doi: 10.1093/ageing/afq034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Heymsfield SB, Smith R, Aulet M, Bensen B, Lichtman S, Wang J, Pierson R., Jr Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr. 1990;52:214–218. doi: 10.1093/ajcn/52.2.214. [DOI] [PubMed] [Google Scholar]
- 30.Merkies I, Schmitz P, Samijn J, Meché FVD, Toyka K, Van Doorn P. Assessing grip strength in healthy individuals and patients with immune-mediated polyneuropathies. Muscle Nerve. 2000;23:1393–1401. doi: 10.1002/1097-4598(200009)23:9<1393::AID-MUS10>3.0.CO;2-O. [DOI] [PubMed] [Google Scholar]
- 31.IR C . Guidelines for data processing and analysis of the international physical activity questionnaire (IPAQ)-short and long forms. 2005. [Google Scholar]
- 32.Flood A, Chung A, Parker H, Kearns V, O’Sullivan TA. The use of hand grip strength as a predictor of nutrition status in hospital patients. Clin Nutr. 2014;33:106–114. doi: 10.1016/j.clnu.2013.03.003. [DOI] [PubMed] [Google Scholar]
- 33.Giovannini S, Macchi C, Liperoti R, Laudisio A, Coraci D, Loreti C, Vannetti F, Onder G, Padua L, Bonaccorsi G. Association of Body fat with Health-Related Quality of life and depression in nonagenarians: the Mugello study. J Am Med Dir Assoc. 2019;20:564–568. doi: 10.1016/j.jamda.2019.01.128. [DOI] [PubMed] [Google Scholar]
- 34.Fukuoka Y, Narita T, Fujita H, Morii T, Sato T, Sassa MH, Yamada Y. Importance of physical evaluation using skeletal muscle mass index and body fat percentage to prevent sarcopenia in elderly Japanese diabetes patients. J Diabetes Investig. 2019;10:322–330. doi: 10.1111/jdi.12908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Yoshida Y, Kosaki K, Sugasawa T, Matsui M, Yoshioka M, Aoki K, Kuji T, Mizuno R, Kuro-o M, Yamagata K, et al. High salt diet impacts the risk of sarcopenia associated with reduction of skeletal muscle performance in the Japanese population. Nutrients. 2020;12:3474. doi: 10.3390/nu12113474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Wu H, Li X, Zhang Q, Liu L, Meng G, Gu Y, Zhang S, Sun S, Zhou M, Jia Q. Association between soft drink consumption and handgrip strength in middle aged and older adults: the TCLSIH cohort study. Int J Food Sci Nutr. 2020;71:856–862. doi: 10.1080/09637486.2020.1734544. [DOI] [PubMed] [Google Scholar]
- 37.Chan R, Leung J, Woo J. A prospective cohort study to examine the association between dietary patterns and sarcopenia in Chinese community-dwelling older people in Hong Kong. J Am Med Dir Assoc. 2016;17:336–342. doi: 10.1016/j.jamda.2015.12.004. [DOI] [PubMed] [Google Scholar]
- 38.Mohseni R, Aliakbar S, Abdollahi A, Yekaninejad MS, Maghbooli Z, Mirzaei K. Relationship between major dietary patterns and sarcopenia among menopausal women. Aging Clin Exp Res. 2017;29:1241–1248. doi: 10.1007/s40520-016-0721-4. [DOI] [PubMed] [Google Scholar]
- 39.Granic A, Mendonça N, Sayer AA, Hill TR, Davies K, Siervo M, Mathers JC, Jagger C. Effects of dietary patterns and low protein intake on sarcopenia risk in the very old: the Newcastle 85+ study. Clin Nutr. 2020;39:166–173. doi: 10.1016/j.clnu.2019.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Yang L, Smith L, Hamer M. Gender-specific risk factors for incident sarcopenia: 8-year follow-up of the English longitudinal study of ageing. J Epidemiol Community Health. 2019;73:86–88. doi: 10.1136/jech-2018-211258. [DOI] [PubMed] [Google Scholar]
- 41.Guo W, Wong S, Li M, Liang W, Liesa M, Serra C, Jasuja R, Bartke A, Kirkland JL, Shirihai O. Testosterone plus low-intensity physical training in late life improves functional performance, skeletal muscle mitochondrial biogenesis, and mitochondrial quality control in male mice. PLoS One. 2012;7:e51180. doi: 10.1371/journal.pone.0051180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Chun OK, Kim DO, Smith N, Schroeder D, Han JT, Lee CY. Daily consumption of phenolics and total antioxidant capacity from fruit and vegetables in the American diet. J Sci Food Agric. 2005;85:1715–1724. doi: 10.1002/jsfa.2176. [DOI] [Google Scholar]
- 43.Montiel-Rojas D, Santoro A, Nilsson A, Franceschi C, Capri M, Bazzocchi A, Battista G, de Groot LCPGM, Feskens EJM, Berendsen AAM, et al. Beneficial role of replacing dietary saturated fatty acids with polyunsaturated fatty acids in the prevention of sarcopenia: findings from the NU-AGE cohort. Nutrients. 2020;12:3079. doi: 10.3390/nu12103079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Schaap LA, Pluijm SM, Deeg DJ, Visser M. Inflammatory markers and loss of muscle mass (sarcopenia) and strength. Am J Med. 2006;119:526. e529–526. e517. doi: 10.1016/j.amjmed.2005.10.049. [DOI] [PubMed] [Google Scholar]
- 45.Mahan LK, Raymond JL. Krause's Food & the Nutrition Care Process, Iranian Edition E-Book: Elsevier Health Sciences; 2016.
- 46.Cuesta-Triana F, Verdejo-Bravo C, Fernández-Pérez C, Martín-Sánchez FJ. Effect of Milk and other dairy products on the risk of frailty, sarcopenia, and cognitive performance decline in the elderly: a systematic review. Adv Nutr. 2019;10:S105–S119. doi: 10.1093/advances/nmy105. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.