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. 2015 Dec 23;38(1):1. doi: 10.1007/s11357-015-9864-z

Biomarkers of oxidative stress are associated with frailty: the Framingham Offspring Study

Christine K Liu 1,2,✉, Asya Lyass 3,4, Martin G Larson 3,4,5, Joseph M Massaro 3,5, Na Wang 4, Ralph B D’Agostino Sr 3,4, Emelia J Benjamin 3,6,7, Joanne M Murabito 3,8,✉
PMCID: PMC5005887  PMID: 26695510

Abstract

Cardiovascular disease and frailty frequently occur together. Both are associated with inflammation, which may be partially triggered by oxidative stress, especially in cardiovascular disease. We investigated whether inflammatory and oxidative stress biomarkers linked to cardiovascular disease were associated with frailty and the related outcome of gait speed. We report cross-sectional associations of biomarkers and frailty assessed at Framingham Offspring Study cycle eight. Participants ≥60 years were eligible if they had information on frailty and at least one of the following: C-reactive protein, interleukin-6, tumor necrosis factor receptor 2, 8-epi-FGFα isoprostanes (isoprostanes), lipoprotein phospholipase A2 (LpPLA2) mass or activity, osteoprotegerin, intracellular adhesion molecule-1, monocyte chemoattractant protein-1 or P-selectin. Stepwise logistic models were utilized for frailty and stepwise linear models for gait speed. Covariates included age, sex, body mass index, smoking, and co-morbidities. Odds ratios (ORs) and slope estimates (B) are reported per standard deviation increase of loge-transformed biomarker. Of the 1919 participants, 142 (7 %) were frail. In a stepwise model, frailty odds increased with higher interleukin-6 (OR 1.90, 95 % CI 1.51, 2.38), isoprostanes (OR 1.46, 95 % CI 1.12, 1.92), and LpPLA2 mass (OR 1.29, 95 % CI 1.00, 1.65). Stepwise regression found that slower gait speeds were associated with interleukin-6 (B = −0.025 m/s, 95 % CI 0.04, −0.01), isoprostanes (B = −0.019, 95 % CI −0.03, −0.008), LpPLA2 mass (B = −0.016, 95 % CI −0.03, −0.004), and osteoprotegerin (B = −0.015, 95 % CI −0.03, −0.002, all p < 0.05). Interleukin-6, isoprostanes, and LpPLA2 mass were associated with greater frailty odds and slower gait speeds. Oxidative stress may be a mechanism contributing to frailty.

Electronic supplementary material

The online version of this article (doi:10.1007/s11357-015-9864-z) contains supplementary material, which is available to authorized users.

Keywords: Frailty, Inflammation, Oxidative stress, Gait speed

Introduction

Frailty describes individuals with minimal injury resilience and little physiological reserve. In the definition of frailty by Fried et al., frail persons display three or more of the following characteristics: (1) unintentional weight loss, (2) weakness, (3) exhaustion, (4) low physical activity, and (5) slowness in motor performance (Fried et al. 2001). Prevalent in older adults, epidemiological cohorts have demonstrated that frail individuals are extremely vulnerable to disability (Xue et al. 2011; Ensrud et al. 2008), hospitalization (Fried et al. 2001), and death (Fried et al. 2001; Woods et al. 2005; Bandeen-Roche et al. 2006; Gill et al. 2010).

Excess inflammation has been postulated as a potential driver of frailty pathogenesis. Observational studies have shown that frailty is associated with several pro-inflammatory biomarkers, including interleukin-6 (Leng et al. 2007; Bandeen-Roche et al. 2009; Hubbard et al. 2009), C-reactive protein (CRP) (Hubbard et al. 2009; Walston et al. 2002; Puts et al. 2005), and tumor necrosis factor-α (Hubbard et al. 2009). While these studies have established a relation between frailty and systemic inflammation, the majority have only examined non-specific biomarkers of inflammation. The specific inflammatory pathways that are activated in frailty have not yet been well defined.

In animal models, mice displaying a frailty phenotype have greater vascular stiffness and cardiac dysfunction (Sikka et al. 2013). Epidemiologic studies have shown that cardiovascular disease is associated with higher rates of incident frailty (Woods et al. 2005). Recent work demonstrated that incident cardiovascular disease is predicted by hand grip strength, one of the components of frailty (Leong et al. 2015). Cardiovascular disease is caused by atherosclerosis, which has been shown to also increase inflammation (Legein et al. 2013; El Assar et al. 2013). One of the possible drivers of the relation between inflammation and cardiovascular disease is oxidative stress (Chen and Keaney 2012), which has also been thought to play a role in frailty (Mulero et al. 2011).

Given this evidence, we hypothesized that frailty and cardiovascular disease may share common biologic mechanisms. Using the Fried frailty criteria, we examined the cross-sectional associations of frailty with ten pro-inflammatory and oxidative stress biomarkers associated with cardiovascular disease. We chose biomarkers representing different phases of the inflammatory process. As a secondary aim, we also examined the cross-sectional associations of gait speed with our panel of inflammatory and oxidative stress biomarkers. Slow gait speeds have also been associated with inflammation (Verghese et al. 2012; Kositsawat et al. 2013; Ferrucci et al. 2002) and cardiovascular disease (Matsuzawa et al. 2013); additionally, slow gait speeds have been linked with greater morbidity and mortality (Studenski et al. 2011).

Methods

Study participants

The Framingham Offspring Study is a prospective, observational cohort study following the Offspring and Offspring spouses of the Framingham Heart Study Original cohort participants. The study’s methods and sampling design details have been published previously (Feinleib et al. 1975). Established in 1971, the Framingham Offspring Study enrolled 5124 participants who have been examined every 4 to 8 years. At each examination, participants underwent a medical history, physical examination, and laboratory tests. Participants aged 60 years and older who attended examination cycle eight (2005–2008) were eligible (n = 2328) for this analysis. Participants were excluded if they had no biomarker information or had missing data regarding frailty or covariates. The final sample consisted of 1919 participants.

Inflammatory and oxidative stress biomarkers

Fasting blood and urine samples were centrifuged, frozen at −80 °C, and then thawed for assay. CRP was assayed using particle-enhanced immunonephelometry. Interleukin-6, tumor necrosis factor receptor 2 (TNFR2), lipoprotein-associated phospholipase A2 (LpPLA2) activity and mass, osteoprotegerin, intracellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), and P-selectin were assayed using quantitative ELISA. Frozen urine was assayed for 8-epi-PGFα isoprostanes (isoprostanes) using quantitative ELISA. Details of assays have been published (Schnabel et al. 2008) or available in further detail online (Fontes et al. 2011). Mean intra-assay coefficients of variation were ≤7 %.

Frailty

We defined frailty as outlined by Fried et al. (Supplemental Table S1) (Fried et al. 2001). Participants were classified with unintentional weight loss if they answered “yes” to the question of unintentional loss greater than 10 lb in the past year. Exhaustion was defined as an answer of “Most or all of the time” to “Could not get going” or “I felt everything was an effort” from the Center for Epidemiologic Studies Depression Scale (Orme et al. 1986). Using the Framingham Physical Activity Index (Kannel et al. 1979), participants reported daily routine activities. Activities were assigned a value based on estimated oxygen consumption and multiplied by the average time spent on the activity within a 24-h period. The activity score was the sum of these values, ranging from 24 to 120. Scores in the lowest quintile for each sex were classified as low physical activity. To assess slowness, participants walked 4 meters at usual pace with time measured to the nearest hundredth of a second. The faster of the two trials was used in the analysis. Participants were classified with slowness using the gait speed criteria per Fried and colleagues. For weakness, participants were asked to squeeze a Jamar dynamometer (Lafayette Instrument Co, Lafayette, IN) as hard as possible, three times in each hand. Force generated was recorded in kilograms, and the highest value was used. Weakness was defined using the hand grip strength cutoffs previously defined (Fried et al. 2001). Individuals were considered frail if they fulfilled three or more criteria or pre-frail if they fulfilled one or two criteria.

Covariates

Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. History of cardiovascular disease was defined as coronary heart disease (myocardial infarction, angina pectoris, or coronary insufficiency), stroke or transient ischemic attack, intermittent claudication, or heart failure (Feinleib et al. 1975). Diabetes was defined as a fasting glucose more than 125 mg/dL, use of insulin, or use of oral hypoglycemic medications. Current smoking was smoking one or more cigarette per day in the prior year. History of cancer was defined using the World Health Organization International Classification of Diseases-O (Driver et al. 2012), excluding non-malignant neoplasms and non-melanoma skin cancers. Chronic kidney disease was defined by estimated glomerular filtration rate <60 mL/min/1.73 m2 based on creatinine (Levey et al. 2009).

Statistical analyses

Biomarker values are reported as median value and 25th and 75th percentiles. For subsequent analyses, biomarkers were loge-transformed and standardized (mean 0, SD 1). Descriptive statistics are represented as mean and SD for continuous variables and frequencies for categorical variables.

For the frailty analyses, the comparison group was non-frail participants; pre-frail participants were excluded. Multivariate-adjusted logistic regression models were used to relate frailty (dependent variable) with individual biomarkers, adjusting for age, sex, body mass index, smoking, diabetes, cardiovascular disease, cancer, and chronic kidney disease. Since CRP and interleukin-6 were correlated (r = 0.5), CRP was withheld from the multi-biomarker models. Odds ratios for frailty are presented per one standard deviation increase in loge-transformed biomarker. Biomarkers individually associated with frailty (p < 0.10) were included in a backward elimination model (p < 0.10 to stay) with covariates forced in. Analyses were repeated for (i) participants 70 years or older and (ii) those free of cancer, as age and malignancy are potential modifiers of the association between inflammation and frailty (Singh and Newman 2011).

A similar set of analyses was performed for pre-frailty. The comparison group was non-frail participants only; frail participants were excluded from the analyses.

Multivariable-adjusted linear regression models were utilized to analyze gait speed with individual biomarkers, adjusting for the covariates previously listed. Biomarkers individually associated with gait speed (p < 0.10) were included in a backward elimination (p < 0.10 to stay) with covariates forced into the model. Biomarker quartiles were calculated; multivariable-adjusted linear models were used to assess the relations between gait speed and biomarker quartiles. The highest quartile was used as the reference group. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). A two-sided p < 0.05 was considered statistically significant.

Results

Characteristics of study participants

Descriptive statistics are provided in Table 1, stratified by frailty category. The proportion of women was similar in the frail, pre-frail, and non-frail groups. Compared to non-frail, the frail group was older, had a higher mean body mass index, and has a greater burden of comorbidities. Of those who were frail, 31 % fulfilled four or five criteria. Among those who were pre-frail, 611 persons (71 %) fulfilled only one frailty criteria, while 253 (29 %) fulfilled two criteria. A total of 409 participants were excluded for missing data on frailty (n = 121), biomarkers (n = 278), or covariates (n = 10); excluded participants were similar in age and BMI to the rest of the sample (all p > 0.25) but were more likely to be smokers (10 % vs. 7 %, p = 0.05) and to have a history of diabetes (22 % vs. 15 %, p = 0.002).

Table 1.

Characteristics of study sample (n = 1919)

Frail (n = 142) Pre-frail (n = 864) Non-frail (n = 913)
Characteristic, mean ± SD or n (%)
 Age, years 77 ± 6 72 ± 7 69 ± 6
 Women 77 (54) 495 (57) 463 (51)
 Body mass index (kg/m2) 29.6 ± 7.0 28.8 ± 5.3 27.3 ± 4.5
 Current smoking 14 (9) 64 (7) 50 (5)
 Diabetes 34 (24) 154 (18) 101 (11)
 Cardiovascular disease 61 (43) 184 (21) 130 (14)
 Cancer 34 (24) 140 (16) 129 (14)
 Chronic kidney disease 45 (33) 132 (15) 103 (11)
 Framingham Physical Activity Index score 29.9 ± 2.7 34.1 ± 4.6 37.0 ± 5.0
 Gait speed (m/s) 0.80 ± 0.20 1.09 ± 0.26 1.28 ± 0.21
 Hand grip strength (kg) 21.8 ± 8.50 27.5 ± 10.60 33.8 ± 10.70
Frailty criteria
 Slow (gait speed) 127 (89) 356 (41) 0
 Weak (hand grip strength) 114 (80) 330 (38) 0
 Low physical activity 113 (80) 227 (26) 0
 Exhaustion 66 (46) 98 (11) 0
 Weight loss 55 (39) 106 (12) 0
Biomarkers, median (25th percentile, 75th percentile)
 CRP (mg/L) 2.81 (1.26,5.93) 1.94 (0.90,3.83) 1.29 (0.72,2.65)
 Interleukin-6 (pg/mL) 3.42 (2.15,6.48) 2.21 (1.46,3.28) 1.64 (1.17,2.56)
 TNFR2 (pg/mL) 3151 (2506,4447) 2669 (2149,3443) 2385 (1963,2877)
 Isoprostanes (mg/L)a 11.5 (8.50,15.40) 10.2 (7.60,14.30) 9.5 (7.1,12.8)
 LpPLA2 mass (ng/mL) 210 (183,237) 199 (172,229) 199 (168,228)
 LpPLA2 activity (nm/mL/min) 139 (119,166) 137 (115,160) 136 (114,159)
 Osteoprotegerin (pm/L) 5.88 (4.82,7.41) 5.13 (4.23,6.13) 4.81 (4.01,5.59)
 ICAM-1 (ng/mL) 307 (250,381) 293 (244,359) 270 (233,334)
 MCP-1 (pg/mL) 415 (345,501) 384 (324,462) 364 (301,441)
 P-selectin (ng/mL) 41 (32,51) 41 (33,49) 39 (32,47)
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CRP C-reactive protein, TNFR2 tumor necrosis factor receptor 2, isoprostanes 8-epi-FGFα isoprostanes, LpPLA2 lipoprotein phospholipase A2, ICAM-1 intracellular adhesion molecule-1, MCP-1 monocyte chemoattractant protein-1

aIndexed to urinary creatinine

Inflammatory and oxidative stress biomarkers with frailty: associations of biomarkers singly and as a group

Frailty was individually associated with elevated CRP, interleukin-6, isoprostanes, LpPLA2 mass, ICAM-1, and MCP-1 in single-marker models (all p < 0.05, Supplemental Table S2). In the stepwise model, three biomarkers were retained: interleukin-6, isoprostanes, and LpPLA2 mass (Table 2). One standard deviation of loge interleukin-6 was associated with a 90 % increase in frailty odds (p < 0.001), and loge isoprostanes were associated with a 46 % increase in frailty odds (p = 0.006). Loge LpPLA2 mass was also associated with a higher odds of frailty (OR 1.29, p = 0.05). Subsequently, analysis was limited to those aged 70 years of age or older. While the relation between isoprostanes and frailty became insignificant, the relation between interleukin-6 and frailty remained, although somewhat attenuated (OR 1.65, p < 0.001). The association of LpPLA2 mass with frailty in this subgroup remained significant and essentially unchanged (OR 1.42, p = 0.02). The associations of frailty with interleukin-6, isoprostanes, and LpPLA2 mass were unchanged and remained significant in individuals without cancer (all p < 0.05).

Table 2.

Odds ratios of frailty for multiple biomarkers in specified samples of participants

Odds ratio (95 % CI)
Interleukin-6 p value Isoprostanes p value LpPLA2 mass p value
Primary sample: ≥60 years (n = 937) 1.90 (1.51, 2.38) <0.001 1.46 (1.12, 1.92) 0.006 1.29 (1.00, 1.65) 0.05
Secondary sample: ≥70 years (n = 418) 1.65 (1.26, 2.17) <0.001 1.33 (0.96, 1.84) 0.090 1.42 (1.07, 1.89) 0.02
Secondary sample: free of cancer (n = 796) 1.91 (1.48, 2.47) <0.001 1.50 (1.11, 2.03) 0.008 1.35 (1.02, 1.79) 0.04
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Per one standard deviation increase of loge-transformed biomarker. Mean ± SD of loge-transformed biomarkers for all persons included in primary sample are the following: IL-6 0.75 ± 0.73 pg/mL, isoprostanes 2.32 ± 0.48 mg/L, and LpPLA2 mass 5.27 ± 0.2 ng/mL. Comparison group is non-frail persons (n = 913, pre-frail individuals excluded). Biomarkers initially included in stepwise models if individual association was p ≤ 0.10. Final models used backward selection with cutoff p value of 0.10. All models adjusted for age, sex, body mass index, current smoking, diabetes, cardiovascular disease, cancer, and chronic kidney disease

Isoprostanes 8-epi-FGFα isoprostanes, LpPLA2 lipoprotein phospholipase A2

Inflammatory and oxidative stress biomarkers with pre-frailty: associations of biomarkers singly and as a group

Pre-frailty was individually associated with elevated levels of CRP, interleukin-6, TNFR2, LpPLA2 activity, ICAM-1, and MCP-1 (all p < 0.05, Supplemental Table S2). In the stepwise model where the biomarkers were examined as a group, the associations with interleukin-6, ICAM-1, and MCP-1 remained significant (all p < 0.05, Supplemental Table S3). There was little change in the strength of these associations in the subgroup of individuals free of cancer (all p < 0.05). In those 70 years or older, the association between pre-frailty and MCP-1 became non-significant but remained unchanged with interleukin-6 and ICAM-1 (both p = 0.04).

Associations of biomarkers with gait speed

Slower gait speeds were associated with higher levels of CRP, interleukin-6, isoprostanes, LpPLA2 mass and activity, and osteoprotegerin (all p < 0.05, Supplemental Table S4). In the stepwise model, interleukin-6, isoprostanes, LpPLA2 mass, and osteoprotegerin remained associated with gait speed (all p < 0.05, Table 3). Figure 1 displays the associations of gait speed with quartiles of interleukin-6, isoprostanes, LpPLA2 mass, and osteoprotegerin. Slower gait speeds were associated with the highest quartile of each biomarker.

Table 3.

Associations of gait speed with biomarkers

Biomarker Estimated beta (95 % CI) p value
Gait speed in meters per second (n = 1710)a
 Interleukin-6 −0.025 (0.04, −0.010) <0.001
 Isoprostanes −0.019 (−0.03, −0.008) 0.001
 LpPLA2 mass −0.016 (−0.03, −0.004) 0.008
 Osteoprotegerin −0.015 (−0.03, −0.002) 0.020
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Regression estimates are per 1 SD of loge-transformed biomarker. Biomarkers initially included in stepwise models if individual association was p ≤ 0.10. Final stepwise models used backward selection with cutoff p value of 0.10. All models adjusted for age, sex, body mass index, current smoking, diabetes, cardiovascular disease, cancer, and chronic kidney disease

Isoprostanes 8-epi-FGFα isoprostanes, LpPLA2 lipoprotein phospholipase A2

aSample includes all frail, pre-frail, and non-frail persons who had data on all ten biomarkers of interest

Fig. 1.

Fig. 1

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Associations of quartiles of individual biomarkers with gait speed in covariate-adjusted regression models (n = 1710), adjusted for age, sex, body mass index, smoking, diabetes, cardiovascular disease, cancer, and chronic kidney disease; bars represent standard error. Biomarker values of 25th percentile, 50th percentile, and 75th percentile: IL-6 in picograms per milliliter (0.15, 1.3, 2.0), isoprostanes in milligrams per liter (10, 567, 927), and LpPLA2 mass in nanograms per milliliter (38, 171, 200)

Discussion

Principal findings

Our cross-sectional study of over a thousand community-dwelling older adults demonstrated several findings regarding the association of biomarkers of inflammation and oxidative stress with frailty and gait speed. We found that increased levels of isoprostanes and LpPLA2 mass were associated with greater odds of frailty. Higher mean concentrations of both these biomarkers and osteoprotegerin were associated with slower gait speeds. Our study confirmed that elevated levels of interleukin-6 were associated with frailty (Leng et al. 2007; Hubbard et al. 2009; Bandeen-Roche et al. 2009) and gait speed (Jenny et al. 2012). All associations were present in analyses that accounted for multiple biomarkers and potential confounders, including cardiovascular disease.

In the context of the current literature

Isoprostanes, LpPLA2 mass, and osteoprotegerin are each associated with increased oxidative stress. Other studies with smaller sample sizes have shown that oxidative stress is associated with frailty. Malondialdehyde and protein carbonyls, which are upregulated during oxidative stress, were both cross-sectionally associated with frailty in 742 older adults (Ingles et al. 2014). Oxidized glutathione and 8-hydroxy-2′-deoxyguanosine, oxidative damage byproducts, were associated with frailty in two studies of elders (Wu et al. 2009; Serviddio et al. 2009), but these studies each had less than a hundred persons. While a cross-sectional study of 552 older adults reported that isoprostanes were not associated with frailty (Collerton et al. 2012), the disparity in findings with our results may be secondary to differences in sample size and isoprostane assessment. Our results showing that frailty is associated with isoprostanes and LpPLA2 in a larger sample support that there is a relation between oxidative stress and frailty.

Prior studies examining the relation between oxidative stress and gait speed have been conflicting. One study found that indicators of oxidative stress, such as carbonylated proteins, lipid peroxidation, and total antioxidant activity, were not associated with lower limb function (Caballero et al. 2014). In contrast, another study demonstrated that slower gait speeds in older women were associated with protein carbonyl levels (Semba et al. 2007). In persons with gait speeds less than 0.8 m/s, a gait speed associated with greater mortality (Studenski et al. 2011), in vitro induction of oxidative stress in whole blood cells generated more free radicals (Baptista et al. 2012). Of note, the prior two studies examined only a single oxidative stress biomarker, whereas we demonstrated that two oxidative stress biomarkers were simultaneously elevated in persons with slower gait speeds. Our results provide additional evidence for the relation between oxidative stress and gait speed.

We also demonstrated an association between osteoprotegerin and slower gait speeds. Osteoprotegerin was originally described as a biomarker of bone remodeling, but emerging evidence shows it plays a role in the neuromuscular and cardiovascular systems as well. Regarding the neuromuscular system, in vivo experiments have shown that myoblasts produce osteoprotegerin; in a murine model of muscular dystrophy, exogenous osteoprotegerin increases skeletal muscle force (Cesari et al. 2015). A study of ten healthy young adult men found that quadriceps muscle damage increased osteoprotegerin production (Philippou et al. 2009). In terms of cardiovascular disease, osteoprotegerin is thought to contribute to atherosclerosis (Montecucco et al. 2007) and has been associated with multiple cardiovascular diseases (Montagnana et al. 2013), including peripheral arterial disease (Poulsen et al. 2011). Notably, peripheral arterial disease is also associated with slow gait speeds (McDermott et al. 2004).

Our study confirmed the associations between interleukin-6, frailty, and gait speed. The cross-sectional relation between elevated interleukin-6 and frailty has been demonstrated in multiple cohorts of older adults (Leng et al. 2007; Bandeen-Roche et al. 2009; Hubbard et al. 2009). In regard to gait speed, our study confirmed investigators’ findings that interleukin-6 is associated with slower gait speeds (Ferrucci et al. 2002; Boxer et al. 2008; Jenny et al. 2012).

Potential mechanisms

Oxidative stress likely causes musculoskeletal system damage, which facilitates the development of frailty and slow gait speeds. Potential pathways include increased intracellular calcium, which is associated with oxidative stress; studies in mice have shown that intracellular calcium promotes proteasomal activity and accelerates muscle breakdown (Boittin et al. 2006). Reactive oxygen species have been reported to trigger apoptosis of murine skeletal muscle (Adhihetty et al. 2007). In elderly rats, chronic-free radical exposure decreases myoblast proliferation (Derbre et al. 2014). In addition, the remaining muscle is likely impaired; murine models have demonstrated that changes in LpPLA2 affect muscle contractility and endurance (Gong et al. 2006). In a murine model of frailty, adenosine triphosphate production in skeletal muscle, which fuels muscle contraction, is reduced in comparison to skeletal muscle from wild-type mice (Akki et al. 2014). The product of all these mechanisms is dysfunctional muscle tissue in reduced amounts.

Strengths and limitations of study

Strengths of our study include the large sample size. We investigated multiple biomarkers representing diverse inflammatory pathways; our analyses accounted for simultaneous activation of multiple pathways through stepwise models of multiple biomarkers. Additionally, we examined the association of these biomarkers with gait speed, reinforcing our primary findings. Several limitations also merit comment. The lack of ethnic diversity in our study sample may limit the generalizability of the results. We suspect that the relation between oxidative stress and frailty is bi-directional, with the presence of one condition increasing the risk of the other condition. However, the study design was cross-sectional, limiting our ability to determine causality or temporality of this association. There are multiple definitions of frailty; while the Fried criteria are utilized the most frequently, it does limit the relevance of our findings to other frailty definitions. Finally, while the panel of biomarkers examined was relatively large, it was not comprehensive and may have excluded the contribution of pathways such as those related to autoimmunity (Leng et al. 2011).

Clinical implications

Reduction of oxidative stress may be a potential target for frailty treatment; one promising intervention is physical activity (Liu and Fielding 2011). In pilot studies, regimens of regular physical activity decreased biomarkers of oxidative stress (de Oliveira et al. 2012) and reduced the expression of oxidative stress genes (Gano et al. 2011). In clinical trials, frailty prevalence was reduced by 14.7 % using a year-long multidisciplinary intervention that included physical activity (Cameron et al. 2013); similarly, frailty prevalence was reduced from 23 to 10 % with a program of just physical activity (Cesari et al. 2015). Studies have shown that physical activity interventions are feasible even in those who are functionally limited (Pahor et al. 2014). Future studies should continue to investigate the effects of physical activity on oxidative stress, inflammation, and frailty.

Conclusions

In community-dwelling older adults, biomarkers of oxidative stress were associated with frailty and gait speed. Elevated levels of isoprostanes and LpPLA2 mass were cross-sectionally associated with increased odds of frailty and slower gait speeds. Osteoprotegerin was associated with slower gait speed. Our findings with these biomarkers add to the growing body of evidence that oxidative stress contributes to frailty in older adults. Reduction of oxidative stress may be a potential target for frailty treatment. Future research should explore whether frailty can be prevented or treated by interventions that reduce oxidative stress.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Table S1 (29.5KB, doc)

(DOC 29 kb)

Table S2 (40.5KB, doc)

(DOC 40 kb)

Table S3 (33.5KB, doc)

(DOC 33 kb)

Table S4 (34KB, doc)

(DOC 34 kb)

Acknowledgments

We thank Emily Abrams for her assistance. The investigators are indebted to Framingham Offspring Study participants and staff. Lipoprotein-associated phospholipase A2 activity measurements were provided by GlaxoSmithKline and mass measurements by diaDexus at no cost. This was presented at the Annual Meeting of the American Geriatrics Society in Grapevine, TX, May 3–5, 2013. Correspondence should be addressed to Dr. Christine Liu, Boston Medical Center, 88 East Newton Street, Robinson 2, Boston, MA, 02118 or Christine.Liu@bmc.org.

Compliance with ethical standards

Funding

This work was supported by the National Institute on Aging (grant number R01-AG029451 to JM Murabito, 1R01-AG028321 to EJB) and by the National Heart, Lung and Blood Institute (grant number 1RO1-HL64753, R01-HL076784 to EJB), both at the National Institutes of Health. The Framingham Heart Study is supported by the National Heart, Lung, and Blood Institute in collaboration with Boston University (contract number N01-HC-25195). CKL was supported by the American Federation for Aging Research, the John A. Hartford Foundation, and the U.S. Department of Agriculture, under Agreement no. 1950-51000-068-01S.

Contributor Information

Christine K. Liu, Phone: (617) 638-6100, Email: Christine.Liu@bmc.org

Joanne M. Murabito, Phone: (508) 935-3461, Email: murabito@bu.edu

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