Abstract
Background
Sarcopenia may be related to increases in reactive oxygen species formation and inflammation, both of which are associated with elevations in serum uric acid.
Objective
To test the hypothesis that a reduced skeletal muscle mass index, indicative of sarcopenia, is related to elevations in uric acid.
Design
Cross-sectional analysis of nationally representative data.
Setting
Third National Health and Nutrition Examination Survey, 1988–1994.
Patients
7544 men and women 40 years of age and older who had uric acid, skeletal muscle mass, and select covariate information.
Measurements
Skeletal muscle mass assessment was based on a previously published equation including height, BIA-resistance, gender, and age. Absolute skeletal muscle mass was calculated for all study population individuals and compared against the sex-specific mean for younger adults. Serum uric acid data were gathered from the NHANES laboratory file.
Results
A logistic regression analysis revealed that elevations in serum uric acid are significantly related to sarcopenia status. For every unit (mg/dL) increase in uric acid, the odds ratio of manifesting a skeletal muscle mass index at least one standard deviation below the reference mean was 1.12. Participants in the highest grouping (>8 mg/dL) of serum uric acid concentration had 2.0 times the odds of manifesting sarcopenia compared to the lowest grouping (<6 mg/dL) (p<0.01) after adjusting for the additional covariates.
Limitations
This study design was limited in its cross-sectional nature. Potential selection, measurement, and recall bias may have occurred, and methodology used to classify sarcopenia status based on skeletal muscle mass index is not validated.
Conclusion
This observation provides support for the theory that elevations in uric acid may lead to sarcopenia, although the proposed mechanism needs further experimental support.
Key words: Uric acid, sarcopenia, NHANES III, aging, reactive oxygen species
Introduction
The aging population of the world is increasing at an accelerated rate. Declining fertility rates, combined with steady improvements in life expectancy over the latter half of the 20th century, have produced dramatic growth in the world’s elderly population (1). As of 2000, the proportion of the population 65 years of age and older in the United States was 12.4%, and this number is projected to increase to 19.6% by 2030 (2). The process of senescence is associated with increasing inflammation and oxidative stress; both of which can exert negative health effects at local and systemic levels. Locally, it is well established that oxidative stress causes oxidative modification and damage to protein, lipid, and DNA in skeletal muscle (3). This invariably leads to cellular dysfunction and muscle protein degradation, as well as a decline in muscle mass and function. Loss of muscle strength has been associated with increased risk of frailty, disability, and mortality (4), and is implicated in the pathogenesis of sarcopenia (5); a process that can begin as early as the fourth decade of life (6).
Reactive oxygen species (ROS) can be generated by several mechanisms. One such process involves the reaction of xanthine oxidase with xanthine to generate a superoxide anion and uric acid (UA) during purine metabolism, as illustrated in the following equation:
| xanthine + O2 + H2O ↔ UA + H2O2 |
While elevated levels of UA have been implicated in certain chronic disease states, such as chronic kidney disease (7), congestive heart failure (8), and metabolic syndrome (9), limited data exist examining the potential link between UA production and sarcopenia. Serum UA data have been collected in the Third National Health and Nutrition Examination Survey (NHANES III), and using known methodology (10), skeletal muscle mass also can be extrapolated from NHANES III and used as a surrogate for sarcopenia classification. Therefore, the objective of the present investigation was to test the hypothesis that a reduced skeletal muscle mass index, indicative of sarcopenia, is related to elevations in UA.
Methods
Study Population
NHANES III is a nationally representative, cross-sectional study examining the prevalence of major diseases and risk factors for these diseases in the United States. The sampling period for NHANES III occurred from 1988 to 1994, and included a total of 33,199 persons. Briefly, study procedures in NHANES III consisted of an in-home interview followed by a medical evaluation and blood sample collection in a mobile examination center. Full details of the study design can be accessed from the U.S. Department of Health and Human Services (11). Institutional Review Board (IRB) approval was not sought for this type study in accordance with the IRB exemption policy of Baylor University.
Relevant to the current analysis, the NHANES III dataset includes validated bioelectric impedance analysis (BΙA) measures (used in the classification of sarcopenia) and UA measures on 13,294 adults aged 18 years and older (Non-Hispanic White n= 5,838; Non-Hispanic Black n = 3,707; Mexican American n= 3,749). Data from young adults (18-39 years; 2,781 men and 2,969 women) in this sample served as the reference population for the classification of sarcopenia based on the skeletal muscle mass index score initially used by Janssen and colleagues (10). Skeletal muscle mass then was determined for 3,663 men and 3,881 women 40 years of age and older (6) who had UA (mg/dL) and all potential confounding covariate measurements.
Sarcopenia Assessment & Classification
Based on Janssen et al. 2002, the following equation was used to calculate skeletal muscle mass measurements (10):
| Skeletal muscle mass (kg) = [(height2 / BΙA-resistance Χ 0.401) + (gender X 3.825) + (age X -0.071)] + 5.102 |
where height is in cm; BΙA-resistance is in ohms; for gender, men=1 and women=0; and age is in years. Absolute skeletal muscle mass (kg) was calculated for all study population individuals and compared against the sex-specific mean for young adults (age 18-39). Subjects were considered to have Class I sarcopenia if they fell within negative one to negative two standard deviations of young adult values, and Class II sarcopenia was classified in subjects who fell below negative two standard deviations of young adult values (10).
Potential Confounders
We included age, ethnicity, smoking status, body mass index (BMI), physical activity level, protein intake, and presence of other chronic disease as potential confounding variables in the multivariate analysis. Age in years is a continuous covariate, and race is coded as a three-level class variable indicating Non-Hispanic White, Non-Hispanic Black, and Mexican-American. We classified survey participants who affirmed that they presently smoke cigarettes as current smokers; participants who claimed to not currently smoke, but have consumed 100+ cigarettes in their lifetime as past smokers; and participants who were not current smokers and have never smoked 100+ cigarettes as never-smokers. BMI is defined as weight in kilograms (kg) divided by the square of the height in meters (m), and participants were stratified into underweight, normal weight, overweight, and obese categories based on the World Health Organization classification (12). We defined physical activity level by totaling the number of times per month participants engaged in various leisure exercise modalities including walking, jogging, swimming, cycling, dancing, calisthenics, aerobics, weightlifting, and other sports. Participants’ activity levels were classified as inactive, low, moderate, or high depending on whether participants report frequencies of the given activities as less than 4 times per month, between 4 and 11 times per month, 12 to 20 times per month, or greater than 20 times per month, respectively. This stratification is based on the American College of Sports Medicine guidelines, suggesting that persons engage in physical activity three to five times per week for health (13). Protein intake was assessed as a continuous covariate by dividing reported grams (g) of protein eaten per day by weight in kg. Other major chronic illnesses were assessed in the home interview and were considered present if subjects reported ever being told that they had the given condition by their physician. The conditions of interest were diabetes, cardiovascular disease [(CVD); indicated by myocardial infarction, congestive heart failure, and/or stroke], cancer, lung disease (as indicated by chronic bronchitis and/or emphysema), and arthritis. NHANES III participants with any missing covariate information were excluded from the analysis.
Statistical Analysis
We performed all analyses using SAS version 9.1.3 (14). To account for the complex survey sampling design of NHANES III, we produced unbiased population estimates of demographic characteristics using PROC SURVEYMEANS and PROC SURVEYFREQ. Continuous measures are presented as means and 95% confidence intervals, while categorical measures are presented as percentages and 95% confidence intervals. All demographic characteristics are reported by gender and sarcopenia status. Additionally, we performed a logistic regression analysis to relate the odds of having sarcopenia (Class I or II) versus UA concentration adjusting for the appropriate potential confounding variables using PROC SURVEYLOGISTIC. Results are presented as odds ratios and 95% confidence intervals. We assume a Type I error rate of 5% for all statistical comparisons.
Role of the Funding Source
This study had no external funding source.
Results
Demographic characteristics
Among the 18,867 NHANES III participants aged 18 or older, the records of 13,294 adults contained the minimal necessary information for this analysis. Table 1 contains a descriptive summary for participants aged 40 and older as well as the unadjusted probability of observing sarcopenia separately for men and women. Generally, the probability of observing a low skeletal muscle mass index, indicative of sarcopenia, increases as UA concentration increases across both genders. The unadjusted odds ratio of observing sarcopenia (Class I or II) per unit (mg/dL) increase in serum UA is 1.40 (p<0.001) for males and 1.77 (p<0.001) for females. In Figure 1 we explore the relationship between serum UA concentration and sarcopenia pictorially; however, it is of interest to control for known confounding variables to determine whether this relationship can be independently attributed to serum UA.
Table 1.
Descriptive Summary of Risk Factors for Sarcopenia Among Individuals Age 40 and Older
| Male | Female | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Risk Factor | No Sarcopenia | Class I Sarcopenia | Class II Sarcopenia | No Sarcopenia | Class I Sarcopenia | Class II Sarcopenia | ||||||
| (n=2050) | (95% CI) | (n=1330) | (95% CI) | (n=283) | (95% CI) | (n=1578) | (95% CI) | (n=1918 | (95% CI) | (n=385) | (95% CI) | |
| Age (years) | 54.1 | (53.4-54.9) | 58.9 | (57.8-59.9) | 60.4 | (58.5-62.2) | 54.6 | (53.8-55.4) | 61.0 | (60.2-61.8) | 63.3 | (61.6-65.0) |
| % 40-50 | 46.5 | (43.0-50.0) | 27.3 | (23.0-31.7) | 21.2 | (13.4-29.0) | 46.7 | (43.2-50.2) | 22.5 | (19.5-25.5) | 15.1 | (9.6-20.7) |
| % 50-60 | 23.2 | (20.3-26.2) | 24.7 | (20.9-28.5) | 26.4 | (17.9-35.0) | 21.9 | (19.2-24.6) | 22.8 | (20.2-25.5) | 24.0 | (17.9-30.1) |
| % 60-70 | 16.0 | (13.9-18.1) | 26.8 | (23.3-30.4) | 29.8 | (22.4-37.3) | 15.0 | (12.7-17.3) | 27.8 | (25.0-30.7) | 27.0 | (20.9-33.1) |
| % 70-80 | 10.8 | (9.2-12.4) | 15.8 | (13.3-18.2) | 16.9 | (11.7-22.2) | 11.6 | (9.8-13.4) | 18.3 | (16.1-20.5) | 23.2 | (17.5-28.9) |
| %80+ | 3.4 | (2.9-4.0) | 5.3 | (4.3-6.3) | 5.6 | (3.7-7.6) | 4.8 | (3.9-5.7) | 8.6 | (7.4-9.9) | 10.6 | (7.2-14.0) |
| BMI (kg/m2) | 25.4 | (25.2-25.7) | 29.5 | (29.2-29.8) | 34.3 | (33.1-35.4) | 23.9 | (23.7-24.2) | 29.6 | (29.2-30.0) | 36.3 | (35.4-37.3) |
| % Underweight (<18.49) | 1.6 | (0.8-2.4) | 0.0 | - | 0.0 | - | 4.4 | (3.0-5.7) | 0.2 | (0.0-0.5) | 0.0 | - |
| % Normal (18.5-24.9) | 44.5 | (41.1-47.9) | 8.3 | (6.6-10.0) | 0.8 | (0.1-1.5) | 63.5 | (60.2-66.7) | 17.6 | (15.3-19.9) | 4.8 | (1.5-8.1) |
| % Overweight (25-29.9) | 44.8 | (41.3-48.3) | 49.4 | (45.2-53.7) | 24.5 | (17.9-31.0) | 24.6 | (21.7-27.5) | 40.6 | (37.5-43.7) | 13.5 | (9.4-17.7) |
| % Obese (>30) | 9.1 | (6.9-11.4) | 42.3 | (38.0-46.6) | 74.7 | (68.1-81.4) | 7.5 | (5.9-9.1) | 41.6 | (38.5-44.8) | 81.7 | (76.6-86.8) |
| Ethnicity | ||||||||||||
| % Non-Hispanic White | 87.7 | (86.5-88.9) | 87.6 | (86.1-89.1) | 87.5 | (84.0-91.0) | 90.3 | (89.3-91.4) | 85.0 | (83.6-86.4) | 79.9 | (76.0-83.7) |
| % Non-Hispanic Black | 8.5 | (7.5-9.5) | 8.4 | (7.2-9.6) | 9.5 | (6.3-12.7) | 6.9 | (6.0-7.8) | 11.2 | (9.9-12.4) | 16.6 | (13.1-20.2) |
| % Mexican American | 3.8 | (3.3.-4.3) | 4.0 | (3.4-4.7) | 3.0 | (1.9-4.0) | 2.7 | (2.3-3.2) | 3.8 | (3.4-4.3) | 3.5 | (2.5-4.5) |
| Physical Activity (days) | 19.9 | (17.9-21.8) | 15.7 | (13.9-17.6) | 13.1 | (10.1-16.1) | 17.7 | (16.3-19.2) | 13.1 | (11.9-14.4) | 10.0 | (7.3-12.8) |
| % Sedentary | 34.6 | (31.4-37.8) | 41.7 | (37.6-45.8) | 46.4 | (37.8-55.0) | 34.5 | (31.4-37.7) | 48.0 | (44.8-51.2) | 60.1 | (53.1-67.1) |
| % Low | 18.4 | (15.7-21.2) | 15.7 | (12.7-18.8) | 18.6 | (11.9-25.3) | 17.4 | (14.8-20.1) | 16.2 | (13.8-18.6) | 13.5 | (8.2-18.7) |
| % Moderate | 11.2 | (9.2-13.3) | 11.8 | (9.0-14.6) | 9.8 | (3.9-15.7) | 13.5 | (11.1-16.0) | 11.3 | (9.2-13.5) | 7.9 | (4.2-11.7) |
| % High | 35.7 | (32.3-39.1) | 30.8 | (26.7-34.8) | 25.2 | (18.1-32.3) | 34.5 | (31.1-37.9) | 24.5 | (21.8-27.2) | 18.5 | (12.7-24.3) |
| Presence of Chronic Disease | ||||||||||||
| % Cardiovascular | 9.1 | (7.4-10.8) | 13.5 | (10.9-16.1) | 17.0 | (11.6-22.5) | 3.6 | (2.6-4.6) | 10.3 | (8.6-12.1) | 14.5 | (9.6-19.4) |
| % Cancer | 10.2 | (8.4-11.9) | 12.9 | (10.3-15.5) | 18.8 | (12.5-25.0) | 13.3 | (11.0-15.5) | 15.6 | (13.3-17.9) | 10.6 | (5.6-15.6) |
| % Lung | 7.3 | (5.7-8.8) | 10.2 | (7.9-12.5) | 13.0 | (7.5-18.4) | 8.9 | (7.0-10.8) | 12.7 | (10.5-14.9) | 15.4 | (9.7-21.1) |
| % Diabetes | 5.9 | (4.5-7.2) | 11.0 | (8.4-13.6) | 17.5 | (10.3-24.6) | 7.0 | (5.4-8.6) | 8.0 | (6.5-9.6) | 15.9 | (11.1-20.8) |
| % Arthritis | 19.6 | (17.0-22.2) | 27.4 | (23.9-30.9) | 35.4 | (27.4-43.4) | 25.0 | (22.3-27.8) | 42.2 | (39.1-45.3) | 55.9 | (49.0-62.8) |
| Protein Intake (g/kg) | 1.2 | (1.2-1.2) | 1.0 | (1.0-1.0) | 0.8 | (0.7-0.9) | 1.0 | (1.0-1.1) | 0.8 | (0.8-0.8) | 0.7 | (0.6-0.7) |
| Skeletal Muscle Index (%) | 41.2 | (41.0-41.5) | 34.8 | (34.6-34.9) | 29.7 | (29.4-29.9) | 31.4 | (31.2-31.6) | 25.3 | (25.2-25.4) | 20.8 | (20.6-21.0) |
| Smoking Status | ||||||||||||
| % Never | 28.0 | (24.9-31.1) | 25.5 | (21.8-29.2) | 26.6 | (18.7-34.4) | 53.1 | (49.6-56.5) | 53.5 | (50.3-56.7) | 53.9 | (46.9-60.9) |
| % Former | 41.0 | (37.6-44.5) | 52.6 | (48.4-56.9) | 59.9 | (51.4-68.5) | 23.5 | (20.4-26.6) | 28.4 | (25.4-31.4) | 36.7 | (29.7-43.7) |
| % Current | 31.0 | (27.8-34.2) | 21.9 | (18.5-25.3) | 13.5 | (7.7-19.2) | 23.4 | (20.5-26.4) | 18.2 | (15.6-20.7) | 9.4 | (5.6-13.3) |
| Uric Acid (mg/dL) | 5.8 | (5.7-5.9) | 6.4 | (6.3-6.5) | 6.4 | (6.1-6.7) | 4.4 | (4.3-4.5) | 5.3 | (5.2-5.4) | 5.8 | (5.6-6.0) |
| % <6 | 56.9 | (53.5-60.3) | 36.5 | (32.5-40.6) | 36.6 | (27.9-45.3) | 89.8 | (88.0-91.6) | 73.1 | (70.2-75.9) | 58.4 | (51.5-65.3) |
| % 6-7 | 25.9 | (23.0-28.9) | 33.5 | (29.4-37.6) | 28.7 | (21.1-36.4) | 6.0 | (4.7-7.3) | 15.8 | (13.5-18.1) | 21.2 | (15.6-26.7) |
| % 7-8 | 12.0 | (9.7-14.3) | 17.2 | (14.2-20.2) | 20.5 | (13.7-27.2) | 3.6 | (2.4-4.8) | 6.5 | (4.7-8.4) | 13.4 | (8.6-18.2) |
| % 8+ |
5.2 |
(3.8-6.6) |
12.8 |
(9.8-15.7) |
14.2 |
(8.9-19.5) |
0.6 |
(0.3-0.9) |
4.6 |
(3.4-5.7) |
7.0 |
(2.9-11.1) |
Data presented as weighted means/percentages (95% confidence interval)
Figure 1.
The Prevalence of Normal Skeletal Muscle Index, Class I sarcopenia, Class II Sarcopenia in Men and Women 40 and Older Stratified by Uric Acid Concentration
Logistic Regression Model
To adjust for potential confounding variables, we modeled the probability of sarcopenia by continuous and discrete covariates. We included in the model the following continuous covariates: age (years), BMI (kg/m2), protein intake (g/kg), and serum UA (mg/dL); and the following categorical covariates: gender, ethnicity, physical activity level, smoking status, and indicators of the following chronic diseases: CVD, lung disease, cancer, diabetes, and arthritis.
Initially we modeled the data using a multinomial response (no sarcopenia, Class I sarcopenia, or Class II sarcopenia) via the generalized logit model, but parameter estimates were found to be similar across the two sarcopenia classifications. Thus, we collapsed the sarcopenia classes and modeled the simpler binary response of sarcopenia status as our primary outcome measure. Table 2 presents the resulting odds ratios and 95% confidence intervals from the logistic regression model. We observe significance among the following covariates: age, BMI, ethnicity, gender, smoking status, diseases of the lung, diabetes, physical activity level, and serum UA. Within the ethnicity variable, Non-Hispanic blacks were at a significantly increased risk of sarcopenia compared to Non-Hispanic whites, but the difference between Mexican Americans and Non-Hispanic whites was not significant (p=0.32). Furthermore, the difference between Mexican Americans and Non-Hispanic blacks also was not significant (p=0.15). Smoking status was significant, with former smokers at an elevated odds (p=0.01) compared to persons who had never smoked; however, current smokers were not at significantly higher odds than never smokers (p=0.30). Furthermore, high, moderate, and low physical activity categories had significantly lower odds of sarcopenia (p=0.03, p=0.04, and p=0.03, respectively) compared to those persons falling into the non-active stratum.
Table 2.
Adjusted Sarcopenia Odds Ratio Estimates Among Individuals Age 40 and Older
| Odds Ratio Estimates | |||
|---|---|---|---|
| Covariate | Point Estimate | 95% Wald Confidence Limits | |
| Age (years) | 1.1* | (1.1-1.1) | |
| Gender | Female vs. Male | 2.4* | (2.0-3.0) |
| Ethnicity | Mexican American vs. Non-Hispanic White | 1.1 | (0.9-1.4) |
| Non-Hispanic Black vs. Non-Hispanic White | 1.31* | (1.1-1.6) | |
| BMI (kg/m2) | 1.4* | (1.4-1.5) | |
| Physical Activity | High vs. None | 0.8* | (0.7-1.0) |
| Level | Med vs. None | 0.7* | (0.6-1.0) |
| Low vs. None | 0.8* | (0.6-1.0) | |
| Chronic Disease | Cardiovascular Disease | 1.2 | (0.9-1.5) |
| Status | Lung Disease | 1.8* | (1.3-2.3) |
| Cancer | 1.0 | (0.8-1.3) | |
| Diabetes | 2.0* | (1.4-2.8) | |
| Arthritis | 0.9 | (0.7-1.1) | |
| Smoking Status | Current vs. Never | 1.1 | (0.9-1.5) |
| Former vs. Never | 1.3 * | (1.1-1.6) | |
| Protein Intake (g/kg) | 0.9 | (0.7-1.0) | |
| Uric Acid (mg/dL) |
1.1 * |
(1.1-1.2) |
|
*p<0.05
Serum UA concentration significantly contributed to our logistic regression model after adjustment for potential confounding covariates. For every unit increase in UA, the odds ratio of manifesting a skeletal muscle mass index at least one standard deviation below the reference mean was 1.12. When using the UA categories presented in Table 1 in the logistic regression model, participants in the fourth (>8 mg/dL) category of serum UA concentration had 2.0 times the odds of manifesting sarcopenia compared to the first (<6 mg/dL) category (p<0.01) after adjusting for the additional covariates.
To ensure the validity of these results, we performed a backward variable selection procedure removing the least significant covariate and refitting the model until the only remaining covariates in the model are significant at the 0.05 level. This procedure resulted in the elimination of the nonsignificant effects shown in Table 2 with no major changes in the estimates or significance status of the displayed covariates.
Discussion
Data from this study crudely reaffirm previously published studies that link age (15, 16), ethnicity (17), physical activity (15, 16, 18), BMI (19, 20), chronic disease (20, 21), dietary protein intake (21), and smoking status (22) with sarcopenia. Furthermore, we note that elevations in serum UA are significantly related to the presence of low skeletal muscle mass index, clinically defined as sarcopenia.
A considerable public health problem in the United States, sarcopenia disproportionately affects older adults (23), and significantly contributes to the health care burden. In 2000, the estimated direct healthcare costs attributable to sarcopenia in the United States was $18.5 billion, and according to a recent report by Janssen, a 10% reduction in the prevalence of sarcopenia could result in a savings of $1.1 billion per year (24). While the exact mechanisms responsible for this reduction in muscle mass remain to be clearly defined, our study suggests that elevations in UA may be involved.
Indirectly, serum UA serves as a neutral proxy for increased ROS production via xanthine oxidase (25). Increases in ROS may indirectly explain the relationship we observed in our study. It has long been suspected that increases in ROS may contribute to the aging process. In 1956, Harman proposed that ROS formed during normal oxygen metabolism induce macromolecular damage (26), and accumulation of such products accounts for the deleterious physical changes we experience during senescence. This hypothesis, named the Free Radical Theory of Aging, has recently been applied to the development of sarcopenia as mitochondria obtained from aged muscle fibers display several functional abnormalities including increased proteolysis, ROS overproduction, and vulnerability to apoptosis (27).
It is widely accepted that the ubiquitin-proteasome pathway is the primary mechanism by which proteins are degraded during muscle atrophy (28). During this process proteins targeted for degradation are identified by ubiquitin and subsequently proteolyzed by the 26S proteasome (29). In physiological states, three major classes of molecules have been shown to up-regulate this system, namely inflammatory cytokines, hormones, and ROS (30, 31). To illustrate the ability of ROS to up-regulate this process, a recent study by Li et al. reported that exposure of skeletal muscle myotubes to hydrogen peroxide (a surrogate for oxidative stress) increased the expression of important components of the proteasome-proteolytic system, leading to increased protein breakdown and decreased myosin expression (32). With specific reference to UA, soluble UA has been shown to induce NADPH oxidase-dependent ROS in adipocytes, eventually leading to the activation of the deleterious MAP kinases: p38 and ERK1/2 (33). Interestingly, this finding by Sautin and colleagues calls into question whether UA is a neutral bystander or causative agent in the formation of ROS.
Additionally, ROS production is known to be associated with calpain activation (34) and reductions in protein synthesis (35, 36, 37). In 2002, Patel and colleagues examined the effects of hydrogen peroxide on protein synthesis in hamster ovary cells. In this model, oxidative stress (i.e. hydrogen peroxide administration) was shown to decrease the activity of translational regulators including ribosomal S6 kinase and eukaryotic initiation factor eIF-4E (37). In so doing, the authors demonstrated the ability of ROS to reduce translational activity and subsequent protein synthesis. Therefore, the above mentioned studies affirm the suggestion that ROS production can cause significant muscle wasting by increasing cellular mechanisms of apoptosis, as well as prevention of protein synthesis, and that elevations in serum UA can indicate elevations of ROS in the body.
Hyperuricemia itself also may increase sarcopenic risk by stimulating systemic inflammatory conditions. Like ROS, inflammatory biomarkers are known to up-regulate the ubiquitin-proteasome pathway. The main mechanisms by which UA causes activation of systemic inflammatory conditions involves an inhibition of endothelial nitric oxide (NO) bioavailability, activation of the renin-angiotensin system, and direct actions on endothelial cells and vascular smooth muscle cells (38).
A recent mouse model study (39) found that when hyperuricemia was induced by uricase inhibitor, it caused a decrease in serum NO which was reversed by lowering UA levels. In addition, the authors found that soluble UA impaired NO generation in cultured endothelial cells. This indicates that hyperuricemia may induce endothelial dysfunction, possibly resulting in vascular conditions. Mazzali et al. performed a similar study design with the aim of examining blood pressure effects of hyperuricemia in a rodent model (40). While the blood pressure of the control rats remained normotensive during the intervention, hyperuricemic rats developed elevated blood pressure. Moreover, an increase in renin and a decrease in NO synthase also were also found in the hyperuricemic rats, important findings as both are characteristic findings in many models of hypertension (40). In humans, current research has found that childhood serum UA concentrations are associated with an increased blood pressure in adults, which may contribute to a premature onset of hypertension (41).
UA-induced inflammation also could be related to C-reactive protein (CRP) expression (42). CRP is a systemic inflammatory marker, which may contribute directly to atherosclerosis by causing leukocyte activation and endothelial dysfunction (42). In cell cultures UA has been shown to induce expression of CRP in vascular endothelial and smooth muscle cells, as well as inhibit endothelial cell proliferation and impair NO production. Although the exact mechanism for the impairment of NO production will require further study, authors state that they believe it could relate to the expression of CRP (42). While studies supporting these inflammatory conditions exist (39, 40, 42), at present data are lacking to confirm the role UA as an inflammatory agent in the progression of sarcopenia.
Finally, the suggestion that increases in UA may play a protective role, attempting to thwart further progression of disease, also is worthy of note. UA itself is a well known antioxidant (43) and in some disease states, increasing UA levels have been proposed as a possible medicinal therapy (44). Moreover, low concentrations of UA are suggested to be associated with the development and/or progression of a variety of diseases, including multiple sclerosis (MS), Parkinson’s disease, Alzheimer’s disease, and optic neuritis, likely due to the lack of antioxidant activity available (44). In 2006, Rentzos et al. found that serum UA concentrations were significantly lower in patients with MS when compared with healthy controls and patients with various inflammatory conditions (45). Although it could not be concluded if the low values of UA were a cause or a consequence of the disease, the possibility remains that low UA concentrations are associated with the development of MS. A similar mechanism could be applied here; that is, the cause-and-effect mechanism that we have previously discussed could be reversed, with elevations in UA serving as a consequence of sarcopenia.
Regardless of a neutral, protective, or causative role, our study suggests that elevations in serum UA are related to the presence of reduced skeletal muscle mass, even after adjusting for known confounding variables. Thus, there remains a strong possibility that UA could be implicated in the progression of sarcopenia, and there is a need for longitudinal, randomized, controlled trials to elucidate a causal relationship between UA and sarcopenia.
There are some limitations of the current study worthy of note. First, the cross-sectional nature of the NHANES III dataset ascertains outcomes and exposures at the same time, thereby allowing only for correlational inferences. Further, because involvement in the NHANES III study required participants to travel to the mobile testing centers, a selection bias is introduced. Relative to this study, it is probable that some participants with a low skeletal muscle index may have been physically unable to attend the testing sessions. Additionally, sarcopenia was estimated using a statistical approach proposed by Janssen and colleagues (10). Authors state that their criterion used for classifying subjects as sarcopenic was “chosen arbitrarily and at present there is insufficient data to determine the exact point at which declines skeletal muscle mass index translate into decreased functional capacity” (10). Further, the BΙA technology used to ascertain skeletal muscle mass in this model has a standard error of 9% (46), thus introducing the potential of subject misclassification in using this method. Finally, many of the measures used in this analysis, including protein intake, disease status, smoking status, and physical activity level, were based on self-report and subject to recall bias.
In summary, our findings support the conclusion that hyperuricemia is a potential nontraditional risk factor independently associated with sarcopenia in the United States population 40 yeras of age and older. These results support the importance of UA screening by clinicians in the aging population and prompts additional research from prospective studies to assess a causal inference between UA and sarcopenia.
Acknowledgements: We would like to thank Dr. John J.B. Anderson for thoughtfully reviewing this manuscript and significantly contributing to its completion.
Financial disclosure: None of the authors had any financial interest or support for this paper.
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