Abstract
Sarcopenia is a progressive skeletal muscle disease in which oxidative stress has been proposed as one of the primary markers. The oxidative balance score (OBS) represents the oxidative balance of a person's dietary pattern using the merged intake of anti-oxidants and pro-oxidants. Therefore, the present study assessed the association between OBS and sarcopenia in Iranian older adults. In the current study, 80 people with sarcopenia and 80 without it were considered the case and control groups, respectively. All controls were matched by sex with cases. To confirm sarcopenia, skeletal muscle mass index (SMI), handgrip strength (HGS) measurement, and gait speed were used. Also, body composition was measured by bioelectrical impedance analysis (BIA). A valid and reliable food frequency questionnaire (FFQ) was used to assess all participants' dietary intake of pro-oxidants and anti-oxidants. Conditional logistic regression was applied to assess the association between OBS and sarcopenia. In the bivariate model, we observed lower odds of sarcopenia in the second and last tertile of OBS in comparison to the first tertile (T) (T2 – odds ratio (OR) = 0.414, 95% confidence interval (CI) : 0.186–0.918 and T3 – OR = 0.101, 95% CI: 0.041–0.248). After adjusting for potential confounders, the association was not significant in second and last tertile of OBS in comparision to the first one. The present study's findings demonstrated that overcoming exposure to anti-oxidants over pro-oxidants, as illustrated by a higher OBS, is not related to lower odds of sarcopenia in older adults.
Keywords: Oxidative balance score, Sarcopenia, Older adults, Elderly, Iranian
Subject terms: Biomarkers, Diseases, Health care, Medical research
Introduction
Sarcopenia is a progressive skeletal muscle disease in which muscle function is disrupted due to the loss of muscle mass. Adverse consequences of this disease include decreased performance, frailty, falls, and death1. The prevalence of this disease is estimated between 10 and 27% in people over 60 years old2.
Sarcopenia is more common in older adults3, but muscle mass decreases by age 404. Sarcopenia has a complex pathophysiology and can occur as a result of an increase in apoptotic activity of myofibrils, a reduction in the number of α-motor neurons, hormonal imbalance (a decrease in anabolic hormones), an increase in proinflammatory cytokines, biological changes, changes in mitochondrial function, increased oxidative stress, and factors such as energy deficiency5,6. One of the primary markers proposed for sarcopenia is oxidative stress7. Oxidative stress is an imbalance between the oxidant and anti-oxidant systems of the body8. Oxidative stress can be the main cause of sarcopenia as a result of the excessive production of mitochondrial radicals due to the reduction of anti-oxidant enzymes in muscle cells9.
The oxidative balance score (OBS) represents the oxidative balance of a person's dietary pattern using the merged intake of anti-oxidants and pro-oxidants10. So far, studies on the role of oxidative stress in diseases such as colorectal adenoma11, colorectal cancer12, prostate cancer13, hypertension14, and osteoporosis15 have been conducted using this index. However, to our knowledge, no study has examined the association between OBS and sarcopenia. But, some studies have investigated the parameters of oxidative stress in sarcopenia. A cross-sectional study showed that glutathione peroxidase in diabetic patients with sarcopenia was significantly lower than in the control group16. Also, xanthine oxidase was significantly higher in people with sarcopenia than in the control group16. In contrast, a study revealed no significant difference in the serum levels of thioredoxin-1 (a protein protecting the cell against oxidative stress) in people with sarcopenia compared to non-sarcopenia17.
Despite the role of oxidative stress in the pathogenesis of sarcopenia, no studies have been conducted on the association between OBS and the risk of sarcopenia in older adults. Therefore, the present study aimed to assess the association between OBS and sarcopenia in Iranian older adults.
Methods
Study population
We conducted a case-control study, a subset of a previous cross-sectional study, on the older adult population referring to healthcare centers in Shiraz, Iran, from August 2017 to February 201818. In summary, in the case group, there were 80 people with sarcopenia confirmed in the previous cross-sectional study, and in the control group, there were 80 people without sarcopenia. All controls were matched by sex with cases. Also, all participants without cognitive problems (including history of Alzheimer's disease and dementia) were included in the present study. However, incompleteness of the food-frequency questionnaire (FFQ) or unwillingness to participate in the study were exclusion criteria. All participants completed the written informed consent before their inclusion in the study. The ethics committee of Shiraz University of Medical Sciences approved the current study, and it was carried out in line with the principles of the Declaration of Helsinki (Code: 27983).
Demographic data about sex, age, education, and smoking status for all participants were collected through a checklist. Also, using a digital scale, participants’ weight was measured according to standard methods. Also, height was measured by a tap meter according to standard methods. Body mass index (BMI) was determined as weight (kg) divided by the square of height (meters). Also, the International Physical Activity Questionnaire (IPAQ) was used for physical activity assessment19.
Sarcopenia diagnosis
A diagnosis of low muscle mass, low muscle strength, and/or low muscle function was necessary to confirm the presence of sarcopenia, according to the Asian Working Group on Sarcopenia (AWGS) guidelines18,20. Based on AWGS guidelines, individuals with low skeletal muscle mass and low muscle strength or low physical function were considered to have sarcopenia, and individuals with low skeletal muscle mass and both low muscle strength and low physical function were considered to have severe sarcopenia18,20.
Body composition was determined by bioelectrical impedance analysis (BIA) using an InBody S10 analyzer (BioSpace Co., Ltd., South Korea). Then, the skeletal muscle mass index (SMI) was calculated by dividing appendicular skeletal muscle mass21 by the squared height (meters). SMI of less than 7 kg/m2 for men and less than 5.7 kg/m2 for women was considered the first step to confirm sarcopenia18,20. Muscle strength was determined by measuring handgrip strength (HGS) using a hydraulic hand dynamometer (model MSD, Sihan, Korea). The participants squeezed a hand dynamometer in both hands three times with 15-s pauses in a seated position and 90-degree elbow flexion. The maximum value was used for further analyses. Muscle strength of less than 18 kg for women and less than 26 kg for men was considered a low HGS18,20. The participants' muscle function was evaluated using the usual Gait Speed (GS) at a distance of four meters. Each participant was asked to walk the distance without any assistance, and then the time was recorded in seconds by a chronometer. A GS of less than 0.8 m/s was determined as a low physical function indicator18,20. All measurements were performed in the morning for all participants, and they were asked not to change their usual dietary pattern and daily physical activity and to refrain from vigorous activities the day before the test.
Dietary assessment and food grouping
A valid and reliable 168-item FFQ with standard and common serving sizes used by Iranians22 was administered to all participants by trained interviewers. Participants were asked to report their daily, weekly, monthly, or yearly intake of food or food items during the past year. Then, all food items were changed to grams based on the methods of Ghaffarpour et al.23. Finally, energy intake and all nutrients were extracted by Nutritionist IV software24.
Also, OBS was calculated using the following: intake of dietary pro-oxidants such as iron, polyunsaturated fatty acids (PUFAs), and saturated fatty acids (SFAs), non-dietary pro-oxidants including smoking and obesity, dietary anti-oxidant intakes such as fibers, folate, vitamin C, vitamin E, beta-cryptoxanthin, lycopene, lutein/zeaxanthin, alpha-carotene, beta-carotene, selenium, and zinc, and non-dietary anti-oxidants such as physical activity 25–28. This method was suggested by Goodman et al. 29, and at first, the dietary intake of each item was changed to tertile, and the score was based on Table 1. The final score of OBS was between 0.0 and 34.030,31. The lowest score (0.0) is related to the greater level of exposure to pro-oxidants, and the higher score (34.0) is considered to have a greater level of exposure to anti-oxidants.
Table 1.
Scoring of OBS components.
| OBS components | Score | ||
|---|---|---|---|
| Non-dietary antioxidant components | |||
| Physical activity (MET-min/d) | 0 = low (1st tertile), 1 = medium (2nd tertile), and 2 = high (last tertile) | ||
| Non-dietary pro-oxidant components | |||
| Obesity | 0 = BMI ≥ 30 kg/m2 AND WC ≥ 0.88 m in females | ||
| 1 = BMI ≥ 30 kg/m2 OR WC ≥ 0.88 m in females | |||
| 2 = BMI < 30 kg/m2 AND WC < 0.88 m in females | |||
| Smoking | 0 = current, 1 = former, and 2 = never | ||
| Dietary anti-oxidant components | |||
| 1st tertile | 2nd tertile | last tertile | |
| Vitamin E (mg) | 0 = low | 1 = medium | 2 = high |
| Vitamin C (mg) | 0 = low | 1 = medium | 2 = high |
| Alpha-carotene (µg) | 0 = low | 1 = medium | 2 = high |
| Beta-carotene (µg) | 0 = low | 1 = medium | 2 = high |
| Beta-cryptoxanthin | 0 = low | 1 = medium | 2 = high |
| Lutein (µg) | 0 = low | 1 = medium | 2 = high |
| Lycopene (µg) | 0 = low | 1 = medium | 2 = high |
| Vitamin B9 (µg) | 0 = low | 1 = medium | 2 = high |
| Zinc (mg) | 0 = low | 1 = medium | 2 = high |
| Selenium (µg) | 0 = low | 1 = medium | 2 = high |
| Fiber (g) | 0 = low | 1 = medium | 2 = high |
| Dietary pro-oxidant components | |||
| SFA (g) | 2 = low | 1 = medium | 0 = high |
| PUFA (g) | 2 = low | 1 = medium | 0 = high |
| Iron (mg) | 2 = low | 1 = medium | 0 = high |
OBS oxidative balance score, MET metabolic equivalent of task, BMI body mass index, WC waist circumference, SFA, saturated fatty acid, PUFA polyunsaturated fatty acid.
Statistical analysis
SPSS (version 24) was used for statistical analysis in the present study. A two-sided p-value ˂ 0.05 was considered as a significance level. The Kolmogorov–Smirnov test was used to evaluate the normality of the data. Continuous parameters were reported as mean ± standard deviation (SD) or median (interquartile range (IQR)), and categorical parameters were reported as frequency or percentage. Paired sample T-test or Wilcoxon U test and McNemar test were used to analyze the continuous and categorical variables of the study population between the case and control groups, respectively. Also, analysis of covariance (ANCOVA) t-test was used to adjust the role of age. The consumption of macronutrient intake based on OBS tertile was assessed by the Kruskal–Wallis U-test. Also, conditional logistic regression models were used to evaluate the association between OBS and sarcopenia in bivariate and multivariable models. In addition, variables with p-value < 0.25 were entered in the multivariable-adjusted model and finally, age, energy, and protein intake were included in the multivariable model.
Ethics approval and consent to participate
This study was approved by the medical research and ethics committee of Shiraz University of Medical Sciences, and the informed consents were completed by all participants. Also, we confirmed all the methods included in this study were in accordance with the Declaration of Helsinki.
Results
According to the basic features of the study population in Table 2, there was a significant difference in the median age between the case and control groups (70.0 years in the case and 68.0 in the control group) (P = 0.001). Also, weight, height, BMI, muscle strength, GS, and SMI significantly differed between the case and control groups (P˂0.001 for all). Furthermore, energy intake, OBS, and all components (SFA, PUFA, iron, fiber, vitamin E, folate, vitamin C, alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein, lycopene, zinc, and selenium) were significantly different between both groups and all of them were higher in the control group (P˂0.001 for all, expect selenium).
Table 2.
The basic characteristics of the study population.
| Variables | Case (n = 80) | Control (n = 80) | P-value | P-value* |
|---|---|---|---|---|
| Age (year)a | 70.0 (8.0) | 68.0 (6.0) | 0.001 | – |
| Sex, %a | 1.000 | – | ||
| Male | 55.0 | 55.0 | ||
| Female | 45.0 | 45.0 | ||
| Education, %a | 0.719 | − | ||
| Under diploma | 65.0 | 61.3 | ||
| Diploma and higher | 35.0 | 38.8 | ||
| Income (Rials) per month, %a | 0.127 | − | ||
| Less than 3 million | 38.8 | 36.3 | ||
| 3–6 million | 47.5 | 37.5 | ||
| More than 6 million | 13.7 | 26.2 | ||
| Smoking, %a | 0.718 | – | ||
| Yes | 23.7 | 27.5 | ||
| No | 76.3 | 72.5 | ||
| Weight (kg)b | 59.6 ± 9.2 | 78.1 ± 9.1 | ˂0.001 | ˂0.001 |
| Height (cm)b | 157.2 ± 9.8 | 163.6 ± 8.9 | ˂0.001 | ˂0.001 |
| BMI (kg/m2)b | 24.5 ± 4.1 | 29.2 ± 3.8 | ˂0.001 | ˂0.001 |
| Muscle strength (kg)a | 16.0 (10.7) | 50.8 (24.2) | ˂0.001 | ˂0.001 |
| Skeletal muscle index (kg/m2)a | 6.1 (1.4) | 7.9 (0.9) | ˂0.001 | ˂0.001 |
| Gait speed (m/second)b | 0.70 ± 0.10 | 1.00 ± 0.95 | ˂0.001 | 0.014 |
| Physical activity (MET-min/week)a | 429.0 (1020.7) | 462.0 (1386.0) | 0.315 | 0.097 |
| Total OBS1 | 15.0 (8.7) | 21.0 (7.7) | ˂0.001 | ˂0.001 |
| Energy (kcal/day)b | 1329.3 ± 472.9 | 1861.9 ± 450.4 | ˂0.001 | ˂0.001 |
| SFA (g/day)a | 10.1 (8.3) | 16.3 (4.9) | ˂0.001 | ˂0.001 |
| PUFA (g/day)a | 8.7 (4.0) | 10.4 (3.9) | ˂0.001 | 0.001 |
| Iron (mg/day)b | 8.8 ± 3.2 | 12.9 ± 3.9 | ˂0.001 | ˂0.001 |
| Fiber (g/day)b | 26.1 ± 12.5 | 36.1 ± 11.2 | ˂0.001 | ˂0.001 |
| Vitamin E (mg/day)b | 10.8 ± 4.1 | 13.9 ± 3.5 | ˂0.001 | ˂0.001 |
| Folate (µg/day)b | 347.8 ± 138.1 | 558.8 ± 163.4 | ˂0.001 | ˂0.001 |
| Vitamin C (mg/day)a | 122.5 (213.5) | 251.5 (231.8) | ˂0.001 | ˂0.001 |
| Alpha-carotene (µg/day)a | 180.4 (289.2) | 587.7 (626.6) | ˂0.001 | ˂0.001 |
| Beta-carotene (µg/day)a | 1482.3 (3334.3) | 6007.3 (7929.2) | ˂0.001 | ˂0.001 |
| Beta-cryptoxantine (µg/day)a | 297.1 (587.2) | 636.9 (543.4) | ˂0.001 | ˂0.001 |
| Lutein (µg/day)a | 768.1 (569.4) | 1452.4 (940.9) | ˂0.001 | ˂0.001 |
| Lycopene (µg/day)a | 3530.3 (6242.2) | 12,546.5 (6126.3) | ˂0.001 | ˂0.001 |
| Zinc (mg/day)b | 6.1 ± 2.5 | 9.0 ± 2.7 | ˂0.001 | ˂0.001 |
| Selenium (mg/day)a | 56.4 (33.1) | 67.1 (30.6) | 0.006 | 0.002 |
BMI body mass index, OBS oxidative balance score, SFA saturated fatty acids, MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids, MET metabolic equivalent of task.
Values are median (IQR), mean ± SD, or percentage.
P-value less than 0.05 was considered significant.
aWilcoxon U-test has been used.
bPaired sample T-test has been used.
cMcNemar test has been used.
*Adjusted for age by ANCOVA test.
The study population nutrient intakes are shown in Table 3. Nutrient intakes of the total population were compared between different tertiles of OBS. Based on the table, participants in the last tertile of OBS had significantly higher intakes of carbohydrates and lower intakes of SFA, monounsaturated fatty acids (MUFA), and PUFA compared to the first tertile (P˂0.001 for all except SFA).
Table 3.
Consumption of macronutrient intake based on OBS tertile.
| Variables | T1 (n = 56) | T2 (n = 54) | T3 (n = 50) | P-valuea |
|---|---|---|---|---|
| Carbohydrate (% energy) | 57.11 (10.87) | 64.81 (6.39) | 67.20 (9.12) | ˂0.001 |
| Protein (% energy) | 13.82 (4.12) | 13.86 (2.70) | 13.53 (2.45) | 0.979 |
| SFA (% energy) | 8.65 (4.01) | 7.60 (4.03) | 7.13 (2.48) | 0.001 |
| MUFA (% energy) | 10.32 (3.58) | 8.17 (1.86) | 7.42 (2.16) | ˂0.001 |
| PUFA (% energy) | 7.06 (2.90) | 5.36 (1.40) | 5.10 (1.33) | ˂0.001 |
OBS oxidative balance score, T tertile, SFA saturated fatty acids, MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids.
Values are median (IQR).
P-value less than 0.05 was considered significant.
aKruskal–Wallis U-test has been used.
Bivariate and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for OBS with sarcopenia are shown in Table 4. In the bivariate model, we observed lower odds of sarcopenia in the second and last tertile of OBS in comparison to the first tertile (T) (T2—OR = 0.414, 95% CI: 0.186–0.918 and T3—OR = 0.101, 95% CI: 0.041–0.248). After adjusting for potential confounders, the association was not significant in second and last tertile of OBS in comparision to the first one.
Table 4.
Association between oxidative balance score and sarcopenia.
| Variables | Bivariate | Multivariable | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P-value | OR | 95% CI | P-value | |
| Oxidative balance score | ||||||
| T1 (≤ 15) | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
| T2 (16–21) | 0.414 | 0.186–0.918 | 0.030 | 0.885 | 0.322–2.482 | 0.813 |
| T3 (≥ 22) | 0.101 | 0.041–0.248 | ˂0.001 | 0.387 | 0.099–1.514 | 0.173 |
| Age (year) | 1.155 | 1.066–1.252 | 0.001 | 1.201 | 1.086–1.329 | ˂0.001 |
| Energy intake (kcal/day) | 0.997 | 0.996–0.998 | ˂0.001 | 0.999 | 0.998–1.001 | 0.979 |
| Protein intake (g/day) | 0.928 | 0.905–0.952 | ˂0.001 | 0.934 | 0.889–0.981 | 0.007 |
| Income (Rials), % | ||||||
| Less than 3 million | Ref | Ref | Ref | Ref | Ref | Ref |
| 3–6 million | 1.185 | 0.592–2.369 | 0.631 | – | – | – |
| More than 6 million | 0.492 | 0.203–1.191 | 0.116 | – | – | – |
| Smoking, % | ||||||
| Yes | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
| No | 1.257 | 0.538–2.708 | 0.558 | – | – | – |
Obtained from conditional logistic regression.
OR odds ratio, CI confidence interval.
Adjusted for variables with p-value < 0.25 in multivariable analysis (Age, energy, and protein intake were included in the multivariable model).
These values are odds ratio (95% CIs).
Significant values are shown in bold.
Discussion
The present study investigated the association between OBS and sarcopenia in older adults. There was no significant association between OBS and the odds of sarcopenia after adjusted for potential confounders in backward conditional method. This finding is inconsistent with the hypothesis that the redox balance between exposure to anti-oxidants and pro-oxidants is protective against the possibility of sarcopenia.
In the new guidelines, muscle strength is considered one of the main characteristics of sarcopenia to better identify sarcopenia in clinical practice32. In fact, sarcopenia, accompanied by the loss of muscle strength and skeletal mass, has adverse clinical consequences3,33. In hospitalized patients, this disease is associated with complications such as loss of independence, infection, low quality of life, pressure ulcers, and increased mortality34.
The association between OBS and oxidative stress in some diseases, such as some cancers, has already been studied35,36. These studies reported a relationship between OBS and oxidative stress in the occurrence of cancers. Diet accounts for 75% of the overall score of OBS25,37,38. Therefore, changing the diet, including reducing the consumption of hydrogenated fats, processed meats, and red meat and increasing the consumption of legumes, whole grains, vegetables, fruits, and nuts can help to increase OBS and increase the anti-oxidant status of the body15. Therefore, increasing the consumption of the mentioned foods and reducing some others may prevent diseases related to the body's oxidative imbalance. However, the current study could not find an association between sarcopenia and OBS. The reason for the difference in the results of our study with other studies can be due to the different etiology of cancer and sarcopenia and the different way of scoring in the calculation of OBS.
Many factors are involved in the pathogenesis of sarcopenia, including inflammation, malnutrition, inactivity, endocrine changes, and oxidative stress, many of which do not act in isolation39. Although it is believed that oxidative stress plays an important role in many diseases, there is no conclusive evidence regarding the association between anti-oxidants and pro-oxidants with particular health outcomes40. This discrepancy in the lack of relationship between these diseases and oxidative stress can be caused by inadequate methods of assessing oxidative stress in humans11. In epidemiological studies, OBS is a relatively simple tool to investigate oxidative status and its relationship with diseases41.
The OBS undoubtedly needs to be modified and discussed further. One of the limitations of the scoring method in this index is that it considers the same weights for exposure to anti- and pro-oxidants, while they do not have the same effect. For example, α-tocopherol has been shown to have a higher redox potential than vitamin C42. Thus, the contribution of vitamin E is different compared to vitamin C42. Although OBS is a relatively simple index that evaluates the individual's oxidative balance, more anti-oxidant components can be considered to better investigate the relationship between anti- and pro-oxidants41.
The study’s results showed an inverse relationship between protein intake and the risk of sarcopenia. A systematic review and meta-analysis study indicated that people with sarcopenia consumed less protein compared to people without sarcopenia43. Inadequate dietary protein intake has also been demonstrated to be associated with reduced muscle mass in the elderly due to lower muscle protein synthesis44. Furthermore, we found a positive association between age and the chance of sarcopenia. A cross-sectional study also showed that total skeletal muscle mass and total lean body mass decrease linearly with age45. In addition, the decrease in muscle function and muscle mass with age has been shown in other studies46.
The present study had some limitations. FFQ does not consider the bioavailability of nutrients or may not include all sources of each nutrient, and it may also have recall bias47. However, in epidemiological studies, the FFQ is the most commonly used dietary assessment tool and an easy and effective tool for collecting dietary data. Also, OBS considers factors such as lifestyle and diet and does not include the endogenous measures of the cell's anti-oxidant function. In addition, the relatively small sample size and the study's cross-sectional design can be considered limitations of the present research. However, the current study has some strengths. This study is the first to examine the association between OBS and sarcopenia in older adults. Also, considering more dietary anti-oxidants, including beta-cryptoxanthin, lutein, zeaxanthin, lycopene, etc., in the OBS was another strength of this study.
Conclusions
The present study's findings demonstrated that overcoming exposure to anti-oxidants over pro-oxidants, as illustrated by a higher OBS, is not related to lower odds of sarcopenia in older adults. Examining the relationship between OBS and inflammatory markers in older adults with sarcopenia would be interesting. More studies are needed to confirm the findings.
Author contributions
M.M., Z.S., S.A.R.N., and M.N.; Contributed to writing the first draft. M.N.; Contributed to all data, statistical analysis, and interpretation of data. N.N: Contributed to data collection. Z.S and M.H.D.; Contributed to the research concept, supervised the work, and revised the manuscript. All authors read and approved the final manuscript.
Data availability
Data are available through a reasonable request from the corresponding author.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Marzieh Mahmoodi and Zainab Shateri.
Contributor Information
Mehran Nouri, Email: mehran_nouri71@yahoo.com.
Mohammad Hossein Dabbaghmanesh, Email: dabbaghm@sums.ac.ir.
References
- 1.Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393(10191):2636–2646. doi: 10.1016/S0140-6736(19)31138-9. [DOI] [PubMed] [Google Scholar]
- 2.Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, Celis-Morales C. Global prevalence of sarcopenia and severe sarcopenia: A systematic review and meta-analysis. J. Cachexia Sarcopenia Muscle. 2022;13(1):86–99. doi: 10.1002/jcsm.12783. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.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 diagnosis report of the European working group on Sarcopenia in older people. Age Ageing. 2010;39(4):412–423. doi: 10.1093/ageing/afq034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.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(1):16–31. doi: 10.1093/ageing/afy169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Morley JE. Anorexia, sarcopenia, and aging. Nutrition. 2001;17(7–8):660–663. doi: 10.1016/S0899-9007(01)00574-3. [DOI] [PubMed] [Google Scholar]
- 6.Muscaritoli M, Anker S, Argilés J, Aversa Z, Bauer J, Biolo G, Boirie Y, Bosaeus I, Cederholm T, Costelli P. Consensus definition of sarcopenia, cachexia and pre-cachexia: Joint document elaborated by Special Interest Groups (SIG)“cachexia-anorexia in chronic wasting diseases” and “nutrition in geriatrics”. Clin. Nutr. 2010;29(2):154–159. doi: 10.1016/j.clnu.2009.12.004. [DOI] [PubMed] [Google Scholar]
- 7.Montes AC, Boga JA, Millo CB, González AR, Ochoa YP, Naredo IV, Reig MM, Rizos LR, Jurado PMS, Solano JJ. Potential early biomarkers of sarcopenia among independent older adults. Maturitas. 2017;104:117–122. doi: 10.1016/j.maturitas.2017.08.009. [DOI] [PubMed] [Google Scholar]
- 8.Betteridge DJ. What is oxidative stress? Metabolism. 2000;49(2):3–8. doi: 10.1016/S0026-0495(00)80077-3. [DOI] [PubMed] [Google Scholar]
- 9.Sullivan-Gunn MJ, Lewandowski PA. Elevated hydrogen peroxide and decreased catalase and glutathione peroxidase protection are associated with aging sarcopenia. BMC Geriatr. 2013;13(1):1–9. doi: 10.1186/1471-2318-13-104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Van Hoydonck PG, Temme EH, Schouten EG. A dietary oxidative balance score of vitamin C, β-carotene and iron intakes and mortality risk in male smoking Belgians. J. Nutr. 2002;132(4):756–761. doi: 10.1093/jn/132.4.756. [DOI] [PubMed] [Google Scholar]
- 11.Kong SYJ, Bostick RM, Flanders WD, McClellan WM, Thyagarajan B, Gross MD, Judd S, Goodman M. Oxidative balance score, colorectal adenoma, and markers of oxidative stress and inflammationoxidative balance score and markers of oxidative stress. Cancer Epidemiol. Biomark. prev. 2014;23(3):545–554. doi: 10.1158/1055-9965.EPI-13-0619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Mao Z, Prizment AE, Lazovich D, Gibbs DC, Bostick RM. Dietary and lifestyle oxidative balance scores and incident colorectal cancer risk among older women; The Iowa women’s health study. Nutr. Cancer. 2021;73(11–12):2323–2335. doi: 10.1080/01635581.2020.1821904. [DOI] [PubMed] [Google Scholar]
- 13.Agalliu I, Kirsh VA, Kreiger N, Soskolne CL, Rohan TE. Oxidative balance score and risk of prostate cancer: Results from a case-cohort study. Cancer Epidemiol. 2011;35(4):353–361. doi: 10.1016/j.canep.2010.11.002. [DOI] [PubMed] [Google Scholar]
- 14.Annor FB, Goodman M, Okosun IS, Wilmot DW, Il'yasova D, Ndirangu M, Lakkur S. Oxidative stress, oxidative balance score, and hypertension among a racially diverse population. J. Am. Soc. Hypertens. 2015;9(8):592–599. doi: 10.1016/j.jash.2015.05.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Shahriarpour Z, Nasrabadi B, Hejri-Zarifi S, Shariati-Bafghi S-E, Yousefian-Sanny M, Karamati M, Rashidkhani B. Oxidative balance score and risk of osteoporosis among postmenopausal Iranian women. Arch. Osteoporos. 2021;16:1–10. doi: 10.1007/s11657-021-00886-w. [DOI] [PubMed] [Google Scholar]
- 16.Küçükdiler A, Varli M, Yavuz Ö, Yalçin A, Selvi Öztorun H, Devrim E, Aras S. Evaluation of oxidative stress parameters and antioxidant status in plasma and erythrocytes of elderly diabetic patients with sarcopenia. J. Nutr., Health Aging. 2019;23:239–245. doi: 10.1007/s12603-018-1137-y. [DOI] [PubMed] [Google Scholar]
- 17.Can B, Kara O, Kizilarslanoglu MC, Arik G, Aycicek GS, Sumer F, Civelek R, Demirtas C, Ulger Z. Serum markers of inflammation and oxidative stress in sarcopenia. Aging Clin. Exp. Res. 2017;29:745–752. doi: 10.1007/s40520-016-0626-2. [DOI] [PubMed] [Google Scholar]
- 18.Nasimi N, Dabbaghmanesh MH, Sohrabi Z. Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults. Exp. Gerontol. 2019;122:67–73. doi: 10.1016/j.exger.2019.04.009. [DOI] [PubMed] [Google Scholar]
- 19.Moghaddam MB, Aghdam FB, Jafarabadi MA, Allahverdipour H, Nikookheslat SD, Safarpour S. The Iranian version of international physical activity questionnaire (IPAQ) in Iran: Content and construct validity, factor structure, internal consistency and stability. World Appl. Sci. J. 2012;18(8):1073–1080. [Google Scholar]
- 20.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(2):95–101. doi: 10.1016/j.jamda.2013.11.025. [DOI] [PubMed] [Google Scholar]
- 21.Roager HM, Vogt JK, Kristensen M, Hansen LBS, Ibrügger S, Mærkedahl RB, Bahl MI, Lind MV, Nielsen RL, Frøkiær H. Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: A randomised cross-over trial. Gut. 2019;68(1):83–93. doi: 10.1136/gutjnl-2017-314786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.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(5):654–662. doi: 10.1017/S1368980009991698. [DOI] [PubMed] [Google Scholar]
- 23.Ghaffarpour M, Houshiar-Rad A, Kianfar H. The manual for household measures, cooking yields factors and edible portion of foods. Tehran Nashre Olume Keshavarzy. 1999;7(213):42–58. [Google Scholar]
- 24.Nutritionist I: N-squared computing. Silverton: Nutritionist IV 1998.
- 25.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Lewinger JP, Stern MC, Hines L, Baumgartner KB, Giuliano AR, Wolff RK. Angiogenesis genes, dietary oxidative balance and breast cancer risk and progression: The breast cancer health disparities study. Int. J. Cancer. 2014;134(3):629–644. doi: 10.1002/ijc.28377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Slattery ML, Lundgreen A, Torres-Mejia G, Wolff RK, Hines L, Baumgartner K, John EM. Diet and lifestyle factors modify immune/inflammation response genes to alter breast cancer risk and prognosis: The breast cancer health disparities study. Mutat. Res./Fundam. Mol. Mech. Mutagen. 2014;770:19–28. doi: 10.1016/j.mrfmmm.2014.08.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Dash C, Bostick RM, Goodman M, Flanders WD, Patel R, Shah R, Campbell PT, McCullough ML. Oxidative balance scores and risk of incident colorectal cancer in a US prospective cohort study. Am. J. Epidemiol. 2015;181(8):584–594. doi: 10.1093/aje/kwu318. [DOI] [PubMed] [Google Scholar]
- 28.Lakkur S, Goodman M, Bostick RM, Citronberg J, McClellan W, Flanders WD, Judd S, Stevens VL. Oxidative balance score and risk for incident prostate cancer in a prospective US cohort study. Ann. Epidemiol. 2014;24(6):475–478. doi: 10.1016/j.annepidem.2014.02.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Goodman M, Bostick RM, Dash C, Terry P, Flanders WD, Mandel J. A summary measure of pro-and anti-oxidant exposures and risk of incident, sporadic, colorectal adenomas. Cancer Causes Control. 2008;19:1051–1064. doi: 10.1007/s10552-008-9169-y. [DOI] [PubMed] [Google Scholar]
- 30.Lakkur S, Bostick RM, Roblin D, Ndirangu M, Okosun I, Annor F, Judd S, Dana Flanders W, Stevens VL, Goodman M. Oxidative balance score and oxidative stress biomarkers in a study of whites, African Americans, and African immigrants. Biomarkers. 2014;19(6):471–480. doi: 10.3109/1354750X.2014.937361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Cho A-R, Kwon Y-J, Lim H-J, Lee HS, Kim S, Shim J-Y, Lee H-R, Lee Y-J. Oxidative balance score and serum γ-glutamyltransferase level among Korean adults: A nationwide population-based study. Eur. J. Nutr. 2018;57:1237–1244. doi: 10.1007/s00394-017-1407-1. [DOI] [PubMed] [Google Scholar]
- 32.Robinson S, Granic A, Sayer AA. Nutrition and muscle strength, as the key component of sarcopenia: An overview of current evidence. Nutrients. 2019;11(12):2942. doi: 10.3390/nu11122942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Shachar SS, Williams GR, Muss HB, Nishijima TF. Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur. J. Cancer. 2016;57:58–67. doi: 10.1016/j.ejca.2015.12.030. [DOI] [PubMed] [Google Scholar]
- 34.Malafarina V, Úriz-Otano F, Iniesta R, Gil-Guerrero L. Sarcopenia in the elderly: Diagnosis, physiopathology and treatment. Maturitas. 2012;71(2):109–114. doi: 10.1016/j.maturitas.2011.11.012. [DOI] [PubMed] [Google Scholar]
- 35.Dash C, Goodman M, Flanders WD, Mink PJ, McCullough ML, Bostick RM. Using pathway-specific comprehensive exposure scores in epidemiology: Application to oxidative balance in a pooled case-control study of incident, sporadic colorectal adenomas. Am. J. Epidemiol. 2013;178(4):610–624. doi: 10.1093/aje/kwt007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Kong SYJ, Bostick RM, Flanders WD, McClellan WM, Thyagarajan B, Gross MD, Judd S, Goodman M. Oxidative balance score, colorectal adenoma, and markers of oxidative stress and inflammation. Cancer Epidemiol. Biomark. Prev. 2014;23(3):545–554. doi: 10.1158/1055-9965.EPI-13-0619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Goodman M, Bostick RM, Dash C, Flanders WD, Mandel JS. Hypothesis: oxidative stress score as a combined measure of pro-oxidant and antioxidant exposures. Ann. Epidemiol. 2007;17(5):394–399. doi: 10.1016/j.annepidem.2007.01.034. [DOI] [PubMed] [Google Scholar]
- 38.Ilori TO, Wang X, Huang M, Gutierrez OM, Narayan KV, Goodman M, McClellan W, Plantinga L, Ojo AO. Oxidative balance score and the risk of end-stage renal disease and cardiovascular disease. Am. J. Nephrol. 2017;45(4):338–345. doi: 10.1159/000464257. [DOI] [PubMed] [Google Scholar]
- 39.Meng S-J, Yu L-J. Oxidative stress, molecular inflammation and sarcopenia. Int. J. Mol. Sci. 2010;11(4):1509–1526. doi: 10.3390/ijms11041509. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Halliwell B, Grootveld M. The measurement of free radical reactions in humans: Some thoughts for future experimentation. FEBS Lett. 1987;213(1):9–14. doi: 10.1016/0014-5793(87)81455-2. [DOI] [PubMed] [Google Scholar]
- 41.Hernández-Ruiz Á, García-Villanova B, Guerra-Hernández E, Amiano P, Ruiz-Canela M, Molina-Montes E. A review of a priori defined oxidative balance scores relative to their components and impact on health outcomes. Nutrients. 2019;11(4):774. doi: 10.3390/nu11040774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Thomas SR, Neuzil J, Mohr D, Stocker R. Coantioxidants make alpha-tocopherol an efficient antioxidant for low-density lipoprotein. Am. J. Clin. Nutr. 1995;62(6):1357S–1364S. doi: 10.1093/ajcn/62.6.1357S. [DOI] [PubMed] [Google Scholar]
- 43.Coelho-Junior HJ, Calvani R, Azzolino D, Picca A, Tosato M, Landi F, Cesari M, Marzetti E. Protein intake and sarcopenia in older adults: A systematic review and meta-analysis. Int. J. Environ. Res. Public Health. 2022;19(14):8718. doi: 10.3390/ijerph19148718. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Scott D, Blizzard L, Fell J, Giles G, Jones G. Associations between dietary nutrient intake and muscle mass and strength in community-dwelling older adults: The tasmanian older adult cohort study. J. Am. Geriatr. Soc. 2010;58(11):2129–2134. doi: 10.1111/j.1532-5415.2010.03147.x. [DOI] [PubMed] [Google Scholar]
- 45.Ljm III, Khosla S, Crowson CS, O'Connor MK, O'Fallon WM, Riggs BL. Epidemiology of sarcopenia. J. Am.Geriatr. Soc. 2000;48(6):625–630. doi: 10.1111/j.1532-5415.2000.tb04719.x. [DOI] [PubMed] [Google Scholar]
- 46.Frontera WR, Hughes VA, Fielding RA, Fiatarone MA, Evans WJ, Roubenoff R. Aging of skeletal muscle: A 12-yr longitudinal study. J. Appl. Physiol. 2000;88(4):1321–1326. doi: 10.1152/jappl.2000.88.4.1321. [DOI] [PubMed] [Google Scholar]
- 47.Schatzkin A, Kipnis V. Could exposure assessment problems give us wrong answers to nutrition and cancer questions? J. Natl. Cancer Inst. 2004;96(21):1564–1565. doi: 10.1093/jnci/djh329. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available through a reasonable request from the corresponding author.