Background
Frailty is an age-associated clinical syndrome, characterized by a decline in physiological compensation and increased risk to stressors [
1,
2]. Although frailty is usually examined in older populations, it commences at mid-life and is a risk for mortality at younger ages [
3,
4]. Frailty prevalence increases with age but is also influenced by social determinants of health (SDOH). For example, frailty is prevalent for individuals living below poverty [
3,
5]. Frailty is more prevalent in older (≥65 years, mean age = 73.6 years) African American adults than in White adults [
6]. However, at specific ages in midlife (45–54 years), White adults have a higher prevalence of frailty than African American adults [
3]. Frailty prevalence is also higher in women, compared to men; however, despite higher chronic disease burden, women have a longer lifespan compared to men [
7‐
10]. Thus, the frailty phenotype is influenced by biological factors such as sex and age and SDOH such as race and poverty. Therefore, it is important to decipher underlying biological mechanisms that may drive frailty and identify biomarkers that may assist in early screening and diagnosis, especially to distinguish individuals at risk for premature mortality.
Frailty is a multisystem and complex condition that likely is the result of dysregulation of several pathophysiological processes. Chronic low grade sterile inflammation contributes to the frailty phenotype, through interfering with homeostatic tissue repair mechanisms leading to the accumulation of tissue damage [
1,
2]. Frailty is also associated with higher levels of several proinflammatory cytokines, including IL-6 [
11‐
13]. Furthermore, this chronic sterile inflammation which can begin at midlife is a risk for frailty later in life [
14]. Sterile inflammation resulting from cellular stress and damage, ischemia, trauma, or environmental conditions causes the extrusion of cellular components and debris including damage associated molecular pattern (DAMP) molecules [
15‐
17]. These DAMPs include nuclear and mitochondrial DNA (mtDNA), specific proteins, reactive oxygen species as well as other molecules [
15‐
17]. DAMPs can then bind to specific pattern recognition receptors that can initiate a cascade of cellular signals that promote an inflammatory state. Therefore, tissue damage can spur a chronic feedback loop where DAMPs are released leading to a proinflammatory state and inflammation can then further inhibit maintenance and repair of tissue [
15,
16]. However, we are only beginning to understand the relationship between DAMPs, inflammatory proteins, and frailty in humans and how this relationship may potentiate the frailty phenotype.
Circulating levels of specific DAMP molecules, circulating cell-free mitochondrial DNA (ccf-mtDNA), and circulating cell-free DNA (ccf-DNA), have been explored in the context of frailty. One study found that total ccf-DNA was associated with the inflammatory markers CRP and IL-6 and with frailty, while ccf-mtDNA copy number was correlated with frailty, but not CRP and IL-6 in individuals older than 90 years of age [
18]. A study conducted by Ampo et al. found that ccf-mtDNA was significantly elevated in individuals who self-identified as pre-frail/frail having late-life depression compared to healthy, never-depressed individuals [
19]. Therefore, we have yet to fully grasp the relationship between ccf-mtDNA, inflammation, and frailty.
Ccf-mtDNA in plasma can be encapsulated in extracellular vesicles (EVs) [
20‐
22]. EVs are small, membrane bound vesicles that are important mediators of intracellular communication between cells [
23,
24]. EVs can carry various cargo, including nucleic acids (DNA, various RNAs), lipids, and proteins [
24‐
27]. There are several types of EVs that are released from cells (i.e. exosomes, microvesicles, and apoptotic bodies), but due to the difficulty in distinguishing the biogenesis pathway for these vesicles, the general term EV is used [
28]. EVs can be isolated from biofluids making them attractive biomarkers for various conditions and diseases [
23,
29‐
32].
Few studies have explored EVs in frailty. A small study of elderly adults (79–92 years) found no significant difference in serum EV concentration comparing frail (
n = 5) and robust (
n = 7) participants [
33]. Another study explored microRNAs (miRNAs) in EVs as candidate biomarkers of frailty [
34]. In this cohort (
n = 14) of White individuals, eight miRNAs were enriched in frail individuals compared to either young individuals or robust elderly individuals: miR-10a-3p, miR-92a-3p, miR-185-3p, miR-194-5p, miR-326, miR-532-5p, miR-576-5p, and miR-760 [
34]. Using immunoblotting as a semi-quantitative method for EV protein levels, levels of three mitochondrial specific proteins, adenosine triphosphate 5A (ATP5A; complex V), nicotinamide adenine dinucleotide reduced form (NADH): ubiquinone oxidoreductase subunit S3 (NDUFS3; complex I), and succinate dehydrogenase complex iron sulfur subunit B (SDHB; complex II), were lower in individuals with frailty and sarcopenia (
n = 11) compared to individuals without sarcopenia and frailty (
n = 10) [
35]. These data indicate that there may be differences in mitochondrial components with frailty, but warrants follow up in a larger population using quantitative methods.
Thus far, there is limited information on EVs in the context of frailty, especially with relation to race, sex, and poverty status. This is important as there are disparities in frailty prevalence across all three demographic variables. Previously, our laboratory has shown that EVs can carry various DAMP molecules, inflammatory proteins, and ccf-mtDNA [
20,
36,
37]. In this exploratory study, we examined whether EVs and their associated cargo including mtDNA and inflammatory proteins are altered with frailty in a middle-aged cohort of African American and White adults living above and below poverty.
Discussion
We examined EVs in the context of frailty by performing a large-scale study of EVs from frail and non-frail middle-aged individuals in the context of race, sex, and poverty. EV concentration was higher in frail White individuals, and the DAMP, ccf-mtDNA, was higher in EVs from frail individuals. Inflammatory proteins were also more often present in EVs from frail individuals (FGF-21, HGF, IL-12B, PD-L1, PRDX3, and STAMBP). Notably, sex and the social determinants of health race and poverty status influenced the presence of inflammatory proteins in both African American and White adults.
Our findings with EV concentration are similar to another study that found higher levels of EV protein abundance, as a proxy for EV levels, in elderly frail (mean age = 78 years;
n = 11) compared to non-frail (mean age = 74 years;
n = 10) individuals in the BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons (BIOSPHERE) cohort [
35]. It should be noted that in this study frail individuals were classified as having sarcopenia as well. Immunoblotting was used to assess the levels of EV markers and mitochondrial proteins in these EVs. Using this semi-quantitative method, the authors reported lower levels of the mitochondrial proteins ATP5A, NDUFS3, and SDHB in participants with frailty and sarcopenia [
35]. Another study found no differences in EV concentration in a small cohort of elderly (79–92 years) non-frail (
n = 7) and frail (
n = 5) individuals from Spain [
33].
Mitochondrial components as EV cargo are an emerging topic of interest [
21,
47]. Previously, we reported that EV mtDNA levels decline with advancing age [
20], but are not associated with mortality [
36]. Here we found that EV mtDNA levels were higher in frail participants versus non-frail participants. Although the four different primer sets were all significantly correlated with each other, only two primer sets against the NADH dehydrogenase 2 (
MT-ND2) gene region (Mito_4625) and Cytochrome c oxidase subunit 2 (
COX2) gene region (Mito_7878) were significantly associated with frailty. It is not known whether these differences are due to possible fragmentation of the mitochondrial genome, resistance of some gene regions to degradation, or variability in other regions. Nevertheless, these gene regions are adjacent on the mitochondrial genome, and only separated by the
COX1 gene, leading to the idea that there may be a biological explanation for the association of these mtDNA regions with frailty. Consistent with our data in EVs, whole plasma levels of ccf-mtDNA have been reported to be higher in frail individuals with late-life depression (> 65 years) [
19]. In addition, ccf-mtDNA copy number was associated with frailty in individuals > 90 years [
18].
Ccf-mtDNA can act as a DAMP and elicit a sterile immune response triggering inflammation [
15]. Here we report that EV-associated inflammatory proteins are highly correlated with EV mtDNA levels. These data support the hypothesis that higher ccf-mtDNA in frail individuals may contribute to chronic systemic inflammation that may increase the vulnerability of frail individuals to environmental and endogenous stressors and may promote or accelerate the development of age associated disease.
Although we assayed 92 proteins only 14 met our stringent criteria for further analysis. Of these proteins, we found that protein levels had varying relationships with frailty and race (Fig.
3A), frailty, poverty, and race (Fig.
3B), frailty and sex (Fig.
3C), sex and poverty status (Fig.
4A), and poverty status (Fig.
4B). These complex relationships suggest that various factors can influence the inflammatory protein levels in EVs and that these factors should be considered when examining EV cargo [
30]. For additional clarity we have compiled our data into a Table (Supplementary Table
3) and cross referenced with previous studies analyzing EV inflammatory protein levels in HANDLS sub-cohorts in the context of diabetes mellitus and mortality [
36,
37]. Twelve proteins (8 in linear regression; 4 in presence/absence analysis) in our analysis were also significant in our various comparisons of individuals with or without diabetes or measures of disease severity [
37]. As the FRAIL scale includes information about comorbidities, including diabetes mellitus, this may also indicate that diabetes mellitus may contribute to altered inflammatory protein content in EVs in frail individuals. Comparing to individuals with early mortality, only one protein, STAMBP, was significantly associated with frailty and mortality [
36]. There is little known about this intracellular protein as EV cargo. However, it does play a role in regulating intracellular trafficking by deubiquitinating the
endosomal
sorting
complexes
required for
transport (ESCRT) proteins. Future work lies in elucidating the biological mechanisms that contribute to STAMBP in plasma EVs.
There were higher levels of CD5, CD8A, CD244, CXCL1, CXCL6, CXCL11, LAP-TGF-beta-1, and MCP-4 in frail White individuals compared to frail/non-frail African American and non-frail White participants (Fig.
3A). Previously we reported genome-wide transcriptome changes in peripheral blood mononuclear cells with frailty and that these differences were race-dependent [
43]. Biological pathways related to inflammatory and immune responses were differentially altered with frailty in African American and White adults. Like our data, CXCL1 was also higher in White individuals compared to African American individuals. Frailty prevalence in middle-age also varies by race. For example, frailty prevalence is higher in White adults aged 45–55 years compared to African American adults [
3]. However, frailty prevalence is higher in African American adults compared to White adults when examined at older ages in the Cardiovascular Health Study (65–74 years) [
48,
49] and in the Women’s Health and Aging Studies (70–79 years) [
5]. Although differences in frailty prevalence may occur over the lifespan, these data point to race as an important SDOH that may differentially influence frailty.
Men living below poverty have higher levels of EV-associated CD5, CD8A, CXCL1, LAP-TGF-beta-1, and uPA compared to men living above poverty and women living below or above poverty. These data are intriguing given that living in poverty can be a lifelong stressor that can lead to “weathering” and the accelerated aging phenotype [
50]. Living in poverty can have cumulative effects over the lifespan leading to adverse health outcomes, health disparities, and shortened lifespan. This chronic environmental stressor can drive biological transduction pathways that can affect transcriptional changes, inflammation, immune response as well as other pathways, for review [
51]. In line with this idea, African American men living below poverty are particularly vulnerable to early mortality in the HANDLS study [
52]. In a large meta-analysis of 1.7 million people, low socioeconomic status was a major risk factor for premature mortality [
53]. Therefore, our finding that men living below poverty have higher levels of EV-associated inflammatory proteins provides a clue for how adversity can manifest resulting in heightened inflammation. This is especially important since inflammation drives many age-associated diseases.
In addition to examining levels of EV-associated inflammatory proteins, we also analyzed whether the presence of inflammatory proteins were different between frail and non-frail participants. We found that FGF-21, HGF, IL-12B, PD-L1, PRDX3, and STAMBP were more likely to be present in EVs from frail individuals compared to non-frail individuals. Interestingly, FGF-21 is a pleiotropic factor that plays complex roles in normal physiology and in pathological conditions. Elevated serum FGF-21 is associated with metabolic disorders, such as obesity and diabetes mellitus, as well as with mitochondrial diseases [
54]. Plasma/serum FGF-21 has also been proposed as a potential biomarker of frailty [
11]. However, current data are limited, and this hypothesis should be investigated further. PD-L1 is a ligand for the PD-1 immune checkpoint regulator and is an important mediator of immune escape of cancer cells [
55]. Importantly, PD-L1 on circulating exosomes can be detected in healthy donors but the levels are significantly higher in patients with metastatic melanoma [
56]. Exosomal PD-L1 may be important for the response and affect clinical outcomes to anti-PD-1 therapies [
56]. Thus far, we do not fully understand the consequences of the presence of cancer-associated proteins in EVs and if the presence reflects normal physiology or a pathological process.
Our study has several limitations. Here we categorized frailty using the FRAIL scale, which was developed for utilization in community-based clinics [
57,
58]
. This measure is a broad construct that considers categories of physical fitness and health. Nevertheless, this construct has shown validity across comparative studies with other frailty measures for prediction of adverse outcomes and mortality [
58,
59]. EV isolation remains a challenge in the field. Here we have used SEC to isolate EVs from plasma, which effectively removes soluble plasma proteins. The technological advancement of using the AFC for SEC for EV isolation allowed us to process samples in a high throughput manner and to date this is one of the largest scale studies using SEC. With this technique, we cannot exclude that there may be non-vesicular material that may co-precipitate during the isolation process. To circumvent this issue, we have implemented strict criteria for including proteins in our analysis and have included a DNase treatment step in our DNA isolation procedure to remove any DNA on the outside of the EVs. Our study is exploratory in nature warranting follow-up and validation in future studies.
Methods
Clinical study participants
The study cohort was selected from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study performed by the National Institute on Aging (NIA) Intramural Research Program (IRP), National Institutes of Health (NIH) [
60]. HANDLS has been approved by the Institutional Review Board of the NIH and all participants provided written informed consent. HANDLS is a longitudinal, epidemiologic study comprised of community-dwelling adults in Baltimore, Maryland. The study is focused on examining the interaction of social, biological, and environmental factors on health disparities in aging and in the development and progression of age-associated illnesses. In this study, race was self-reported as either African American or White. Participants’ poverty status (above or below poverty) was based on household income at enrollment as defined by 125% of the 2004 U.S. Health and Human Services Poverty Guidelines [
61].
For this study, we selected frail participants with available fasting blood samples between the ages of 45–55 years and randomly selected non-frail controls matched on race and sex. Due to sample availability, this final sub-cohort consisted of 177 participants (87 frail, 90 non-frail). Participants had a physical exam and were free from human immunodeficiency virus infection. The cohort information is listed in Table
1. Frailty was determined using a modified FRAIL scale, as previously reported [
3]. Briefly, the FRAIL scale includes five categories, including fatigue, resistance (ability to climb stairs), ambulation (ability to walk a certain distance), number of illnesses, and loss of weight. Illness was assessed to be positive if participants reported a physician’s diagnosis for five or more of the following conditions: hypertension, diabetes, cancer, chronic lung disease, heart attack, congestive heart failure, angina, asthma, arthritis, stroke, and kidney disease. FRAIL scores are the number of components present and range from 0 (all components absent) to 5 (all components present). Participants were required to have data on at least three of the five components to be included in the sample, similar to the criteria used for other frailty studies [
3,
48]. FRAIL scores are generally categorized into three frailty groups: frail (frail score 3–5), pre-frail (1-2), or non-frail (0) [
57]. This study only included those either frail or non-frail.
Blood samples were collected in the morning after overnight fasting into sodium heparin collection tubes. For plasma isolation, Histopaque®-1077 (Sigma Aldrich, Cat: 10771) was slowly added to blood samples in 15 ml conical tubes and centrifuged for 20 min at 610 g with a slow deceleration. Upon successful separation, the top plasma layer was aliquoted and stored at − 80 °C.
SEC EV isolation
Plasma (0.5 ml) samples were fractionated through size exclusion chromatography (SEC) with the AFC (IZON, Cat: AFC-V1) fitted with a qEVoriginal 70 nm column (IZON; Cat: SP1). Isolation specifications were followed using the default Collection Schedule for the qEV column. Briefly, the count/number of fractions was 10, size of fractions was 0.5 ml, and the buffer volume was left at the default collection volume. The columns were flushed with 15 ml 0.2 μm filtered phosphate buffered saline (PBS) prior to loading of the samples. The sample was loaded and run with 10 ml PBS. All 10 fractions were collected and the eluate from F1–3 were pooled together (1.5 ml), and collectively referred to as the EV-enriched fractions. F4–10 were kept separate. Additionally, 500 μl of the void volume was also collected separately. Columns were flushed with 15 ml PBS and were reused 5 times, according to the manufacturer’s recommendations. Samples were stored at − 80 °C for long-term storage.
Quantification of protein concentration
EVs were isolated as described above from non-frail (n = 3) and frail (n = 3) individuals. All fractions were lysed in a 10X lysis buffer (10X Tris-buffered saline (TBS), 10% TritonX-100, 20 mM Ethylenediaminetetraacetic acid (EDTA) with protease and phosphatase inhibitors). Final concentration of the lysis buffer was 1X. Protein concentration was calculated using the Bradford Assay using a standard curve of bovine serum albumin (BSA). Samples were run in duplicate and absorbance at 595 nm was read on a SpectraMax M2 Microplate Reader (Molecular Devices, LLC). For each sample, the fractions were normalized to the mean of the EV fractions (F1–3), respective of frailty status.
Immunoblotting
EV-enriched fractions (F1–3), as well as F4–10 and the void volume were lysed (1:10) in a 10X lysis buffer as described above. SEC fractions (~ 7 μg), and an equal volume of the void fraction were loaded compared to the EV-enriched fraction. Human umbilical vein endothelial cell (HUVEC) lysate was used as a positive control and thus more protein (~ 37 μg) was loaded to ensure that EV markers were visible. Samples were run on 4–12% NuPAGE Bis-Tris gel under sodium dodecyl sulfate (SDS)-denaturing conditions (Invitrogen ThermoFisher Scientific) and transferred onto polyvinylidene difluoride (PVDF). After blocking in 3% BSA in TBS with 0.1% Tween® 20 Detergent, the membrane was incubated with primary antibodies for 1 h at room temperature: CD9 (System Biosciences EXOAB-CD9A-1), CD81 (System Biosciences EXOAB-CD81A-1), Flotillin-1 (Abcam ab133497), GM130 (Abcam ab52649), and ApoA1 (Abcam ab64308). All primary antibodies were diluted 1:500. For detection, the membranes were incubated with the appropriate secondary horseradish peroxidase (HRP)-conjugated antibodies at 1:5000 dilutions for 32 min. These blots were visualized with the KwikQuant Ultra HRP Substrate Solution and imaging system according to the manufacturer’s protocol (Kindle Biosciences, LLC; Cat: R1002).
Exo-check™ exosome antibody array
We analyzed EV markers using Exo-Check™ Exosome Antibody Array (System Biosciences; Cat #: EXORAY200A-4). SEC-isolated EV-enriched fractions (F1–3) were lysed following the manufacturer’s procedure. The blot was visualized with the KwikQuant Ultra HRP Substrate Solution and imaging system were used according to the manufacturer’s protocol (Kindle Biosciences, LLC; Cat: R1002).
Nanoparticle tracking analysis
SEC-isolated EVs were diluted into 0.2 μm filtered PBS. Different dilutions were used due to variation in concentration and the dilution factor was adjusted for when calculating the concentration per sample. Size distribution and concentration were analyzed using nanoparticle tracking analysis (NTA) on a NanoSight NS500 (Malvern Panalytical, software version NTA 3.4 Build 3.4.4). Samples were recorded in five videos of 20 sec at camera level 16 and detection level 4. Samples were analyzed on the same machine by one user. Total EV concentration from plasma was calculated as previously described [
62]. EV concentration values were log
2 transformed as they were positively skewed.
Electron microscopy
Electron microscopy was performed by the Johns Hopkins University School of Medicine Microscope Facility. SEC-isolated EVs were absorbed to freshly ionized 400 mesh formvar/carbon coated grids (Electron Microscopy Sciences, Cat: CF400-Cu-UL) and then washed with TBS (3 drops) and negatively stained in 1% aqueous uranyl acetate. Images were then taken with a transmission electron microscope (ThermoFisher Talos L120C) at 120 kV using a ThermoFisher Ceta 16mP 16bit CMOS camera. Original image was zoomed in retaining all information including scale using Adobe Photoshop.
EV DNA isolation
Previously, we established an experimental pipeline for analyzing EV mtDNA levels [
20]. Here we have optimized this protocol for use of SEC-isolated EVs and details of the experimental workflow are in Supplementary Fig.
1. For DNA isolation from the SEC EVs, 155 μl of each sample was DNase treated (Lucigen, Cat: DB0715K; 5 U) to remove any DNA on the surface of the EVs at 37 °C for 30 min. The reaction was stopped by the addition of 20 μl DNase Stop Solution at 65 °C for 10 min. DNA was isolated following the DNeasy Blood and Tissue kit protocol (Qiagen, Cat: 69506). An additional spin was conducted at 20,000
g for 1 min after adding Buffer AW2, and new waste collection tubes were used in between each spin. Additionally, a 5 min incubation at room temperature of 50 μl AE Buffer in the spin column was added before the final 1 min 8000
g spin for DNA elution. The eluted DNA (~ 50 μl) was further diluted in an additional 50 μl of AE Buffer (Qiagen) and stored at − 20 °C.
Quantitative real time-PCR
Quantitative real-time PCR (qPCR) analysis was performed as previously reported [
20]. Briefly, each reaction was a total of 13 μl and included mitochondrial gene-specific primers (2.5 μl/rxn), TaqMan™ Fast Advanced Master Mix (7.5 μl/rxn), and DNA isolated from the EVs (3 μl/rxn). The primer design is described in [
20], and primer sequences are listed in Supplemental Table
2. A 7900HT Fast Real-Time PCR System was used to run the samples (Applied Biosystems, software version SDS 2.4.1). The thermal profile used is as follows: 50 °C for 2 min, 95 °C for 10 min, followed by 40 cycles of 15 sec at 95 °C, and 1 min at 60 °C. mtDNA levels were calculated as previously reported [
20]. EV mtDNA values were log
2 transformed as they were positively skewed.
Multiplex proximity extension assay
SEC-isolated EVs were lysed in 10X lysis solution (described above) with protease and phosphatase inhibitors. Protein concentration was calculated as above and processed similarly as previously described [
37]. Here, 60 μg of protein in 40 μl (f.c 1.5 μg/μl) of each EV lysate were added to 96-well plates, and then analyzed with Olink® Proteomics biomarker Inflammation Panel using Proximity Extension Assay (PEA) technology (Olink® Proteomics). Experiments were performed blind of group status. Internal controls were used in each step, including a negative control that accounted for background levels and an inter-plate control that accounted for different plates. Protein data were normalized to inter- and intra-assay controls and represented as normalized protein expression (NPX) units on a log
2 scale, referred to here as normalized protein level (NPL). In total, 92 proteins were tested using the Olink® Inflammation panel. Out of the 92 proteins, 14 proteins met our threshold for being less than or equal to 35% at the lower limit of detection, meaning that each of those proteins was present in more than 65% of all the EV samples. Any missing protein values were excluded from the analysis.
Statistics
Statistical analysis was performed using R software (software version R 4.2.0) [
63]. Student’s t-test was used to analyze differences between groups for age. Pearson’s chi-squared test was used to test for differences across sex, race, and poverty status. Correlations between EV mtDNA levels were assessed by Pearson correlation (pairwise complete observation). EV concentration, EV mtDNA levels, and EV inflammatory protein levels were analyzed using linear regression, which was modeled to the study design of frailty status, sex, race, and poverty status. Backward stepwise regression was used for all linear regression models starting with a full model considering all possible three-way interactions and statistical significance based on the relevant coefficient in the model. Non-significant interactions were eliminated until a final model, or base model of all main effects, was achieved. All models included frailty status, sex, race, and poverty status as main effects. Presence of EV proteins were analyzed using threshold of detection data via logistic regression, including the study design of frailty status, sex, race, and poverty status. Statistical significance was defined as a
p-value < 0.05.
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