Sleep,Fatigue,and Inflammatory Biomarkers in Older Adults with Chronic Venous Leg Ulcers Receiving Intensive Outpatient Wound Care

Abstract

Objective:

Chronic venous ulcers are a relatively common and distressing condition that disproportionately affects older individuals. Along with multiple concomitant issues such as wound drainage, pain, and mobility impairments, individuals with chronic venous leg ulcers (CVLUs) commonly report sleep disturbances and fatigue; however, limited research has examined these symptoms in relation to inflammatory biomarkers in this population over the intensive wound care treatment trajectory. This study aimed at describing the symptoms of sleep and fatigue in older adults with CVLUs receiving intensive wound treatment with weekly debridement and exploring the relationships between these symptoms and tumor necrosis factor-alpha (TNF-α), c-reactive protein (CRP), and interleukin (IL)-6.

Approach:

Demographics, clinical characteristics, Pittsburgh Sleep Quality Index (PSQI) scores, Brief Fatigue Inventory (BFI), TNF-α, CRP, and IL-6 levels were collected from 84 older adults with CVLUs at three time points (baseline, week 4, and week 8). Data analysis included descriptive statistics and Bayesian estimation of associations.

Results:

Findings showed a consistent pattern of poor sleep quality and mild fatigue among these individuals. Lower IL-6 levels at week 4 and higher CRP levels at week 8 were linked to poor sleep quality. Higher CRP levels were linked to greater fatigue at baseline and week 8. Sleep and fatigue were correlated at all time points.

Innovation and Conclusion:

This study highlights the importance of clinicians evaluating sleep and fatigue in those with CVLUs. Further research is needed to validate circulating inflammatory biomarkers to enhance our understanding of sleep and fatigue's role in wound healing.

Samantha Winders, PhD, RN

INTRODUCTION

Venous leg ulcers (VLUs) are open skin lesions of the lower extremities and are commonly a result of venous insufficiency.^1,2 Approximately two million individuals are affected by chronic venous leg ulcers (CVLUs) annually, as they are the most common lower leg ulcers.³

Older age is one of several risk factors for developing chronic wounds, as age increases one's risk for developing chronic disease and slower healing.^4,5 With aging, there is a gradual decline of tissue integrity, leading to fragile, dry skin with atrophic features and diminished elasticity.⁶ The wound-healing process in aged skin is characterized by sustained inflammation and dysregulation of the immune cells signaling pathways, in addition to an increase in cell senescence.

These changes lead to impaired angiogenesis, proliferation, and migration of cells, with a delay in granulation tissue development and ultimate wound closure.^6,7 This is of particular concern as it is projected that the number of older adults aged 60 and older will increase to 1.4 billion by 2030, and this number will continue to grow as the population ages.⁸

Previous studies have found that those with CVLUs commonly experience pain, sleep disturbances, and fatigue, with limited treatments available for these symptoms.^2,9
–11 These individuals may also experience psychological and socioeconomic burdens.^2,5,12 Recently, there has also been growing recognition of the vital links between sleep and health outcomes. A loss of sleep can adversely affect cognitive performance, mental health, metabolism, and the immune system.

Moreover, it has been linked to increased risk for inflammation, mortality, obesity, type 2 diabetes, cardiovascular disease, hypertension, cancer, and traffic accidents.^13,14 Sleep plays a significant role in supporting tissue healing and immune function.^15,16 Sleep loss can lead to oxidative stress, cytokine profile changes, elevated cortisol levels, and reduced growth hormone, all of which may impact the wound-healing process, which consists of four stages: hemostasis, inflammation, proliferation, and remodeling.^15,17
–19

A recent systematic review found weak evidence for elevated levels of interleukin (IL)-1α, IL-6, IL-8, tumor necrosis factor-alpha (TNF-α), and vascular endothelial growth factor in non-healing VLUs, declining with healing.²⁰ Among various chronic illnesses, IL-6, TNF-α, and c-reactive protein (CRP) were the most frequently used and consistently correlated biomarkers of sleep disturbance.²¹ However, studies on these biomarkers in the context of sleep disturbance and wound healing have thus far only been conducted on animal wound models.^18,22

Another consequence of poor sleep is increased fatigue, a feeling of physical or mental exhaustion.²³ Fatigue has been found to be a predictor of quality of life in individuals with VLUs.⁹ The demands of the wound-healing process require additional energy, including inflammation, tissue regeneration, and collagen synthesis, which can contribute to fatigue.^17,24 Biomarkers released during the inflammatory response have also been associated with fatigue.²⁴

Studies in chronic fatigue syndrome and cancer populations have reported associations between fatigue and inflammatory biomarkers such as TNF-α, soluble tumor necrosis factor receptor 1 (sTNFR1), IL-2, IL-4, IL-6, IL-1RA, IL-1α, IL-1β, transforming growth factor-beta (TGF-β), CRP, monocyte chemoattractant protein-1 (MCP-1), and macrophage migration inhibitory (MIF).³ However, research on biomarkers associated with fatigue in individuals with CVLUs is limited.³

The National Institutes of Health Symptom Science Model (NIH-SSM) is a useful guide to describe the relationship between sleep, fatigue, and inflammatory biomarkers (TNF-α, CRP, and IL-6) in older adults with CVLUs undergoing intensive wound treatment. Intensive wound treatment consisted of weekly outpatient clinic visits for wound sharp debridement, wound assessment including wound measurements, and infection monitoring.

This model involves identifying and phenotyping symptoms and testing biomarkers, which may lead to personalized interventions to address the identified symptom.²⁵ This study aimed at (1) describing the symptoms of sleep and fatigue in older adults with CVLUs receiving intensive wound treatment with weekly debridement and (2) exploring the relationship between these symptoms and inflammatory biomarkers (TNF-α, IL-6, and CRP).

CLINICAL PROBLEM ADDRESSED

This study addresses the clinical challenge of effectively managing older adults with CVLUs who are undergoing intensive outpatient wound care. CVLUs often lead to pain, sleep disturbances, fatigue, and psychological and socioeconomic burdens. Recognizing the link between sleep, fatigue, and inflammatory biomarkers, we explored these relationships aiming at identifying biomarkers that can enhance our understanding of these symptoms and ultimately improving wound healing in individuals with CVLUs.

MATERIALS AND METHODS

Design

This pilot study used demographics, clinical characteristics, Pittsburgh Sleep Quality Index (PSQI) global scores, Brief Fatigue Inventory (BFI) scores, and inflammatory biomarker levels obtained from participants in an ongoing R01 study (NR016986). The parent study aims at testing proposed associations among biobehavioral factors, symptoms, and wound healing in older adults with CVLUs. This current analysis used data from the parent study from three time points (baseline, week 4 and week 8) collected from November 2019 to August 2022.

Setting and sample

The participants in the parent study were recruited from a local wound care clinic using a convenience sampling method. Participants in the parent study had to meet the following inclusion criteria: (1) at least 55 years of age; (2) had a VLU confirmed by ultrasound Doppler or clinical diagnosis; (3) had adequate arterial blood perfusion (ankle-brachial index [ABI] between 0.7 and 1.3, inclusive); (4) a chronic venous ulceration duration of greater than 30 days in duration; (5) had scheduled weekly sharp debridement at a wound care clinic; (6) fluent in English, and (7) cognitively intact as determined by a minimum score of 24 on the Mini-Mental State Examination (MMSE).

They were excluded if they (1) were undergoing kidney dialysis for renal failure; (2) receiving immunosuppressant treatment (including systemic steroids or topical steroids in the wound within 4 weeks before the study); (3) had a systemic infection; (4) had participated within the past 30 days in another clinical research trial related to wounds or an intervention that could interfere with the integrity of this study; (5) were on chemotherapy, including antimetabolites, alkylating agents, platinum-containing agents, or other cancer drugs known to be significant immunomodulators within 4 weeks before study entry; (6) had a concomitant condition that would make the participant unlikely to complete all visits as scheduled (e.g., unstable chronic obstructive pulmonary disease with frequent hospital admissions).

Further, they were excluded if they (7) had immune suppression (human immunodeficiency virus [HIV], transplant status) or autoimmune disorders, including lupus erythematosus, Crohn's disease, rheumatoid arthritis, or other autoimmune diseases that alter the systemic inflammatory environment; (8) had a recent (within 6 months) history of Clostridium difficile; (9) had severe or significant hypoalbuminemia (albuminemia <30 g/L, and/or pre-albumin <5 mg/dL), or hypoproteinemia (proteinemia <55 g/L); (10) did not have an ABI between 0.7 and 1.3; and/or (11) had a history of non-adherence to scheduled clinic appointments.

The data collected from the parent study included patient demographics, clinical characteristics, symptom measures, plasma, and wound fluid samples. Institutional Review Board (IRB No. 201700566) approval was obtained for the parent study.

Procedures

Participants had a weekly sharp debridement at a wound care clinic using a standardized intensive approach to treating CVLUS. Sharp debridement consisted of the physician performing wound tissue acquisition with a dermal curette, scissors, forceps, or a scalpel as needed. The wound edges were debrided first, followed by the wound base in layers until viable tissue was reached. Debridement was then based on the judgment of the physician to allow for proper wound healing.

Participants in the parent study completed surveys during their scheduled visits to the wound clinic. Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at the University of Florida.²⁶ Demographics, clinical characteristics, symptom measures, and cytokine levels from the 84 participants included in this analysis were obtained from the parent study REDCap database.

Variables and measures

Demographics and clinical characteristics

Participant demographics included age, race, ethnicity, biological sex, marital status, education level, household income, and smoking status and were obtained from a demographic questionnaire and electronic health records. Clinical characteristics included body mass index (BMI) and comorbidities. BMI [weight, in kilograms, divided by the square of height in meters (kg/m²)] was used to approximate an individual's body adiposity, which may relate to health risks.²⁷

The Charlson Comorbidity Index (CCI) uses International Classification of Disease (ICD) codes to categorize patient comorbidities to predict disease burden and mortality rate.²⁸ Each category can be weighted from 1 to 6; a total score of 0 indicates no comorbidities, whereas a higher score indicates a higher risk for disease burden and mortality risk. It is valid, reliable, and highly sensitive.²⁸ Wound factors are also described based on collected information on baseline wound diameter (mm) and wound duration (days).

Sleep

The PSQI was used to measure sleep quality and disturbance within the past month. The PSQI is a 19-item self-assessment questionnaire that contains seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleeping medication use, and daytime dysfunction. Questions ask about the amount of sleep and factors that may have interfered with sleep and are scored using a four-point Likert scale ranging from 0 (not at all) to 3 (more than three times/week).

Those component scores are summed to give a global score ranging from 0 to 21, where ≥5 indicates poor quality sleep.²⁹ In addition to being multifaceted, the PSQI is a widely used tool, easily and quickly administered, has good reliability (Cronbach's α = 0.83), and studies have reported high validity and reliability in older adults.³⁰

Fatigue

The BFI is a 9-item self-assessment questionnaire used to measure fatigue over the past 24 h. Questions ask about fatigue severity and its interference with daily activities and are scored on an 11-point rating scale ranging from 0 (no fatigue or does not interfere) to 10 (as bad as you can imagine or completely interferes). Higher scores indicate greater fatigue.³¹ The BFI is easily and quickly administered, and studies have reported good reliability with Cronbach's α ranging from 0.82 to 0.96³¹ and high concurrent validity.³²

Inflammatory biomarkers

CRP, TNF-α, and IL-6 were collected at baseline, week 4, and week 8. Blood samples (3 mL) were collected by venipuncture at the clinic and placed in a biohazard container. They were then transported to the laboratory where they were processed (within 4 h) and stored at −80°C in preparation for batch processing, and serum was used for biomarker analysis.

Data analysis

Descriptive statistics (means, standard deviations [SD], frequencies, percentages) were obtained for the participants' demographics, clinical variables, cytokine levels, and symptom characteristics using R version 4.3.³³ Distributions for PSQI, BFI values, and inflammatory biomarkers were examined for normality, outlying, and patterns of missing. Pairwise available samples varied between weeks, as patients were dropped from the study on ulcer healing.

Bayesian estimates for robust correlation coefficients (i.e., using rank-transformed values) between the PSQI, biomarker and BFI values were estimated using the “correlation” package in R.³⁴ Robust correlations were employed due to the non-normal distribution of the raw values. To evaluate the resulting correlation coefficients, 95% highest density interval credibility intervals (HDI), probability of direction (PD), percent of distribution in the region of practical equivalence (ROPE), and Bayes factor (BF) were used.³⁵

RESULTS

Demographics and clinical characteristics

This sample included 84 participants. Fifty-one (61%) had unhealed CVLUs at the last collection time point. The total sample was, on average, 73 (SD 9.6) years of age, 46 (55%) were male, 67 (80%) were white, and 82 (98%) were non-Hispanic or Latino. The average BMI was 33.1 (SD 9.5), and the average CCI was 6.0 (SD 2.0). At baseline, participant wounds were on average 35.8 (SD 34.8) mm in diameter and present for 303 (SD 482) days before entering the study. The demographics and clinical characteristics of the total sample are reported in Table 1.

Table 1.

Descriptive statistics, including N, mean (STD) or frequency (%), and range

Variable	N	Mean (STD) or Frequency (%)	Range
Age	84	72.6 (9.6)	55–92
BMI	84	33.1 (9.5)	15–67
CCI	84	6.0 (2.0)	3–11
Baseline wound diameter (mm)	84	35.84 (34.78)	2–250
Baseline wound duration (days)	84	303.3 (482.2)	20–2,944
Race	84		N/A
White		67 (79.8)
African-American		17 (20.2)
Hispanic	84		N/A
No		82 (97.6)
Yes		2 (2.4)
Biological sex	84		N/A
Male		46 (54.8)
Female		38 (45.2)
Marital status	84		N/A
Married		40 (47.6)
Divorced		17 (20.2)
Widowed		16 (19.0)
Never married		10 (11.9)
Unmarried couple		1 (1.2)
Highest grade completed	84		N/A
Elementary		3 (3.6)
Some high school		9 (10.7)
High school graduate/GED		22 (26.2)
Some college or technical school		21 (25.0)
College graduate		27 (32.1)
Prefer not to answer		2 (2.4)
Current employment	84		N/A
Retired		59 (70.2)
Employed		12 (14.3)
Unable to work		9 (10.7)
Out of work 12+ months		1 (1.19)
Homemaker		2 (2.4)
Student		1 (1.2)
Annual household income	84		N/A
<$25,000		19 (22.6)
$25,000 to $34,999		9 (10.7)
$35,000 to $44,999		3 (3.6)
$45,000 to $54,999		4 (4.8)
$55,000 to $64,999		5 (6.0)
$65,000 to $74,999		4 (4.8)
$75,000 to $84,999		2 (2.4)
$85,000 to $94,999		3 (3.6)
>$95,000		7 (8.3)
Prefer not to answer		28 (33.3)
Smoking history	84		N/A
Never smoked		41 (48.8)
Former smoker		36 (42.9)
Current smoker		7 (8.3)
Wound status during study	84		N/A
Healed week 2		7 (8.3)
Healed week 4		5 (6.0)
Healed week 6		10 (11.9)
Healed week 8		11 (13.1)
Not healed		51 (60.7)
BFI
Baseline	84	2.6 (2.6)	0–9
Week 4	66	2.6 (2.6)	0 - 9
Week 8	36	2.6 (2.7)	0 - 9
PSQI global score
Baseline	84	6.3 (4.0)	0 - 17
Week 4	66	6.3 (3.4)	0 - 15
Week 8	36	5.6 (4.1)	0 - 18
Poor quality sleep (PSQI ≥5)			N/A
Baseline	84	51 (60.7)
Week 4	66	44 (66.7)
Week 8	36	18 (50.0)
CRP
Baseline	83	10.9 (22.8)	0.2–157
Week 4	66	9.0 (10.6)	0.5–55
Week 8	35	7.4 (7.8)	0.4–33
TNF-α
Baseline	41	9.1 (3.7)	3–22
Week 4	33	9.2 (3.8)	2– 24
Week 8	12	8.2 (2.4)	4–12
IL-6
Baseline	41	2.1 (2.5)	0.4–17
Week 4	33	2.0 (1.0)	0.6–4
Week 8	12	1.8 (0.8)	0.6–3.5

BFI, Brief Fatigue Inventory; BMI, body mass index; CCI, Charlson Comorbidity Index; CRP, c-reactive protein; PSQI, Pittsburgh Sleep Quality Index; IL, interleukin; STD, standard deviation; TNF-α, tumor necrosis factor-alpha.

Sleep and fatigue characteristics

The total number of participants included at each time point varied because participants with wounds healing before the 8-week time point were not followed after healing. Several others did not complete the study for other reasons (e.g., Coronavirus disease [COVID-19] infection, starting a steroid as treatment, or being admitted to the hospital) or were not included due to missing data. Table 1 displays BFI and PSQI global scores for the total sample. At baseline, week 4, and week 8, the average BFI scores were 2.6 (SD 2.6), 2.6 (SD 2.6), and 2.6 (SD 2.7), respectively; whereas the PSQI global scores were 6.3 (SD 4.0), 6.2 (SD 3.4), and 5.6 (4.1).

Inflammatory biomarkers

Table 1 also displays inflammatory biomarker levels for the total sample at baseline, week 4, and week 8. CRP levels (normal range: <5.0 mg/dL)³⁶ were, on average, 10.9 (SD 22.8), 9.0 (SD 10.6), and 7.4 (SD 7.8). TNF-α levels (normal range: <1.4 pg/mL)³⁷ were 9.1 (SD 3.7), 9.2 (SD 3.8), and 8.2 (SD 2.4). IL-6 levels (normal range: <6.4 pg/mL)³⁸ were 2.1 (SD 2.5), 2.0 (SD 1.0), and 1.8 (SD 0.8).

Relationship between sleep, fatigue, and inflammatory biomarkers at baseline

Table 2 presents the findings from the Bayesian analysis for sleep, fatigue, and inflammatory biomarkers at baseline. According to Jeffreys' interpretation of BFs,³⁹ there is anecdotal evidence for an association between fatigue and CRP (r_s = 0.23; 95% Credibility Interval [CI] = [0.04–0.42]; PD = 98.88%; ROPE = 10.93%; BF = 2.63). There is extreme evidence for the association between sleep and fatigue (r_s = 0.40; 95% CI = [0.22–0.57]; PD = 100.00%; ROPE = 0.03%; BF = 531).

Table 2.

Baseline robust correlation coefficient, 95% credibility interval, probability of direction, % in region of practical equivalence, and Bayes factor

Variables	r_s	95% CI	PD (%)	% ROPE	BF
PSQI and CRP	0.07	[−0.13 to 0.28]	72.70	56.88	0.31
PSQI and TNF-α	−0.08	[−0.35 to 0.23]	69.53	43.30	0.41
PSQI and IL6	0.08	[−0.21 to 0.35]	73.05	43.33	0.41
BFI and CRP	0.23	[0.04 to 0.42]	98.88	10.93	2.63
BFI and TNF-α	0.12	[−0.15 to 0.39]	79.65	37.70	0.49
BFI and IL-6	0.19	[−0.10 to 0.46]	89.05	25.35	0.79
PSQI and BFI	0.40	[0.22 to 0.57]	100.00	0.03	531

BF, Bayes factor; CI, Credibility Interval; PD, probability of direction; ROPE, region of practical equivalence; r_s , robust correlation coefficient.

Relationship between sleep, fatigue, and inflammatory biomarkers at week 4

Table 3 presents the findings from the Bayesian analysis for sleep, fatigue, and inflammatory biomarkers at week 4. There is anecdotal evidence for the relationship between sleep and IL-6 (r_s = −0.31; 95% CI = [−0.57 to −0.01]; PD = 97.67; ROPE = 9.38%; BF = 2.59). There is extreme evidence for the relationship between sleep and fatigue (r_s = 0.44; 95% CI = [0.23–0.62]; PD = 100.00%; ROPE = 0.02%; BF = 572).

Table 3.

Week 4 robust correlation coefficient, 95% credibility interval, probability of direction, % in region of practical equivalence, and Bayes factor

Variables	r_s	95% CI	PD (%)	% ROPE	BF
PSQI and CRP	0.07	[−0.14 to 0.31]	73.45	51.75	0.34
PSQI and TNF-α	−0.14	[−0.45 to 0.15]	80.70	32.90	0.54
PSQI and IL-6	−0.31	[−0.57 to −0.01]	97.67	9.38	2.59
BFI and CRP	0.12	[−0.11 to 0.34]	84.00	39.85	0.47
BFI and TNF-α	0.18	[−0.13 to 0.47]	86.60	26.15	0.71
BFI and IL-6	−0.03	[−0.33 to 0.28]	56.53	46.60	0.39
PSQI and BFI	0.44	[0.23 to 0.62]	100.00	0.02	572

Relationship between sleep, fatigue, and inflammatory biomarkers at week 8

Table 4 presents the findings from the Bayesian analysis for sleep, fatigue, and inflammatory biomarkers at week 8. There is anecdotal evidence for the association between sleep and CRP (r_s = 0.25; 95% CI = [−0.06 to 0.52]; PD = 94.23%; ROPE = 14.72%; BF = 1.44). There is moderate evidence for the relationship between fatigue and CRP (r_s = 0.36; 95% CI = [0.11–0.63]; PD = 99.40%; ROPE = 2.90%; BF = 6.19). There is extreme evidence for the relationship between sleep and fatigue (r_s = 0.60; 95% CI = [0.37–0.79]; PD = 100.00%; ROPE = 0.00%; BF = >1,000).

Table 4.

Week 8 robust correlation coefficient, 95% credibility interval, probability of direction, % in region of practical equivalence, and Bayes factor

Variables	r_s	95% CI	PD (%)	% ROPE	BF
PSQI and CRP	0.25	[−0.06 to 0.52]	94.23	14.72	1.44
PSQI and TNF-α	−0.18	[−0.61 to 0.27]	76.85	24.90	0.78
PSQI and IL6	−0.17	[−0.60 to 0.27]	74.95	25.67	0.73
BFI and CRP	0.36	[0.11 to 0.63]	99.40	2.90	6.19
BFI and TNF-α	0.07	[−0.38 to 0.50]	61.95	30.63	0.61
BFI and IL-6	0.01	[−0.41 to 0.46]	51.62	31.15	0.58
PSQI and BFI	0.60	[0.37 to 0.79]	100.00	0.00	>1,000

DISCUSSION

Sleep and fatigue

The primary purpose of this study was to describe sleep and fatigue in individuals with CVLUs over time and their relationship with inflammatory biomarkers. Our results revealed that a majority of participants experienced poor quality sleep at baseline, whereas participants' global scores averaged 5.7–6.3 over 8 weeks. The average scores for fatigue stayed consistent at 2.6 over 8 weeks, which is categorized as mild fatigue on the BFI.

These findings are consistent with previous studies that have also identified sleep disturbance and fatigue as common symptoms among those with leg ulcers.^9
–11 In comparison to previous research, our study examined these symptoms longitudinally, providing insights into their changes over time.

Inflammatory biomarkers

CRP is a protein produced by the liver and is released into the bloodstream in response to inflammation. Our sample's CRP levels averaged 7.1–10.9 mg/dL across 8 weeks. CRP levels above <5.0 mg/dL indicate elevation that could be due to systemic inflammation from chronic disease or infection.³⁶ TNF-α is a protein produced by white blood cells in response to our natural immune system. Our sample's TNF-α levels averaged 7.8–9.3 pg/mL across 8 weeks. Levels above 1.4 pg/mL indicate elevation.³⁷ IL-6 is a protein released by macrophages and monocytes in response to other inflammatory cytokines.

Our sample's IL-6 levels averaged 1.9–2.5 pg/mL. Levels above 6.4 pg/mL indicate elevation in healthy individuals.³⁸ During the wound-healing process, the activation of immune cells and factors initiates the inflammatory process, facilitates wound cleansing, and promotes subsequent tissue healing. Chronic wounds are characterized by high quantities of pro-inflammatory macrophages.⁴⁰ Our results are consistent with other study findings for elevated serum levels of CRP and TNF-α in those with VLUs.^20,41

However, our sample did not have elevated IL-6, which is consistent with recent systematic review findings showing no elevation in IL-6 serum samples in those with VLUs. However, they found elevations in wound fluid and biopsy samples.²⁰

Sleep and inflammatory biomarkers

There was anecdotal evidence of an inverse correlation between PSQI global scores and IL-6 levels at week 4. In other words, those with worse sleep quality tend to have lower levels of IL-6. Previous research has indicated that IL-6 is driven by circadian processes, peaking twice throughout the night at 1900 and 0500 h.⁴² Sleep deprivation may delay this nocturnal increase of IL-6.

In addition, our results found anecdotal evidence for a positive correlation between sleep and CRP at week 8. Further, those with worse sleep quality tend to have increased levels of CRP. Current literature has reported evidence of short sleep duration leading to elevated CRP levels.⁴³ Sleep disturbance could have immediate effects on IL-6, whereas an increase in CRP may be found when sleep is prolonged or more severe.⁴²

In a recent review encompassing chronic illnesses, CRP and IL-6 were common measures of sleep disturbance. Most studies reported positive correlations between sleep and IL-6, except for one study involving individuals with head and neck squamous cell carcinomas, where sleep measured by the Medical Outcomes Sleep (MOS) scale demonstrated a negative correlation with IL-6.²¹

Results regarding CRP were varied, with positive correlations seen in cases where sleep was measured using the PSQI in conditions such as psoriatic arthritis and familial Mediterranean fever.²¹ Our study was the first to examine the interplay of sleep, CRP, and IL-6 in older adults with CVLUs, so our results may differ due to the characteristics of our sample. Our findings also showed extreme evidence for correlations between sleep and fatigue at all time points. This emphasizes the need for clinicians to assess symptoms of both sleep and fatigue in this vulnerable population.

Fatigue and inflammatory biomarkers

Further, our study revealed anecdotal-moderate evidence for associations between fatigue and CRP at baseline and week 8. All correlations showed positive relationships, indicating that higher levels of fatigue were associated with elevated CRP. Similar associations have been reported in previous research in cancer populations, likely attributed to these biomarkers triggering the body's immune response and signaling the central nervous system, resulting in fatigue.⁴⁴

Notably, several of our results have >80% of correlation coefficients in a particular direction, and given other values (BF, % ROPE), this suggests a need for larger studies to further explore these relationships.

Age-related changes in wound healing and sleep quality

CVLUs are prevalent among older individuals, exemplified by our sample, where the mean age was 73 years. Aging induces significant changes in the skin morphology, marked by collagen depletion, resulting in skin thinning. This impacts the functionality of both the epidermis and dermis, contributing to heightened skin vulnerability, impaired blood vessel support, and delayed wound healing.^7,45

Specifically, dysregulation of the skin mechanical properties results in collagen fibril fragmentation and disorganization, an upsurge of cellular senescence, as well as sustained levels of inflammatory biomarkers.^7,45 The accumulation of senescent cells in both the epidermis and dermis can lead to elevated levels of IL-6, IL-1, TNF-α, and matrix metalloproteinases, thereby contributing to the persistence of an inflammatory state.^6,46,47

However, in our study, the levels of TNF-α were elevated, as expected, but the levels of IL-6 were not. Further, the chronicity of a wound itself affects the micro-environment of the dermis and epidermis, leading to sustained inflammation, dysregulation of angiogenesis and matrix deposition, an increase in cell senescence populations, as well biofilm formation.⁴⁶ Therefore, incorporating age-related skin changes into an already complex context of chronic wounds, combined with the specific attributes of CVLUs, further compounds the intricacies of the wound-healing process.

Aging was also identified as being negatively correlated with sleep quality in a survey conducted in 2018, which included 223,334 individuals. The study applied the PSQI within the adult and elderly population and concluded that sleep quality decreases with age.⁴⁸ As part of the natural aging process, there is a noticeable decline in sleep quality, potentially attributed to reductions in total sleep duration and alterations in the ratio of slow wave to rapid eye movement sleep.

Further, the authors also emphasized that diminished sleep quality can also be influenced by the presence of multiple comorbidities.⁴⁸ Our sample included older individuals with multiple comorbidities, with a mean CCI of 6 (SD 2.0), which may also contribute to the high incidence of poor sleep quality scores in our sample.

Limitations

The repeated measures design of our study was a strength, allowing us to capture the variability in sleep, fatigue, and inflammatory biomarkers over 8 weeks. However, there are several limitations to consider. First, some participants could not finish the course of the study due to the COVID-19 pandemic, starting a steroid as treatment, or being admitted to the hospital. Second, the initial wound size or treatment may impact the healing process.

Third, the symptom surveys were subjective and may lead to recall bias. Fourth, participants were selected through convenience sampling, so participants who received treatment at this wound clinic may not represent those with CVLUs who do not receive a similar treatment protocol by other providers. Further, additional factors that may affect these symptoms and inflammatory biomarkers, such as diet, BMI, medications, overall physical and mental health, and sleep disorders were not accounted for in this analysis.

In addition, the possible diurnal rhythm of the cytokines could not be accounted for due to the naturalistic nature of the study that followed clinic patients and collected data during regularly scheduled visits. There is some evidence that samples taken in the afternoon contain higher concentrations of cytokines and chemokines than morning, particularly in plasma.

In this study, we measured serum cytokines, and most patients had consistent schedules and came to the clinic for either morning or afternoon appointments so that within-persons comparisons of cytokine levels should not have been affected by diurnal responses and between-persons variability should have been only minimally affected.

INNOVATION

This study addresses the gap in understanding sleep, fatigue, and inflammatory biomarkers in older adults with CVLUs receiving intensive wound care treatment trajectory. Using an 8-week repeated measures design, we observed poor sleep, mild fatigue, and correlations between these symptoms and inflammatory biomarkers (IL-6, CRP). This emphasizes the importance of assessing sleep and fatigue in these individuals.

Our study provides insights into the use of the NIH-SSM for symptom and biomarker characterization in personalized intervention research. Further validation studies are needed to better understand the role that sleep and fatigue play in wound healing in this vulnerable population.

KEY FINDINGS

Poor sleep quality and mild fatigue were consistently found among older adults with CVLUs.

Poor sleep quality was associated with lower IL-6 levels at week 4 and higher CRP levels at week 8. Greater fatigue at baseline and week 8 was associated with higher CRP levels. Sleep and fatigue were correlated at all time points.

Clinicians should prioritize assessing sleep and fatigue in those with CVLUs, and further research should validate inflammatory biomarkers to better understand their impact on wound healing.

ACKNOWLEDGMENTS AND FUNDING SOURCES

The authors thank the clinic staff and participants for participating in this study. This work was supported, in part, by the National Institutes of Health (NIH), the National Institute of Nursing Research (NINR) at the University of Florida (Grant No. R01:NR016986), and by the National Institutes of Health, the National Institute of Nursing Research Interdisciplinary Nurse Scientist Training in Multilevel Approaches: Biology to Society Training Program at the University of Washington (Grant No. T32NR016913). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

AUTHORs' CONTRIBUTIONS

S.W.: Conceptualization, methodology, investigation, and writing. D.E.L. and D.L.K.: Conceptualization, methodology, and writing. M.T.W.: Formal analysis, visualization, and writing. F.Y.: Formal analysis, visualization. M.R.d.C.: Writing. J.K.S.: Conceptualization, methodology, and supervision.

AUTHOR DISCLOSURE AND GHOSTWRITING

All authors declared no conflict of interest. No ghostwriters were used in the writing of this manuscript.

ABOUT THE AUTHORS

Samantha Winders, PhD, RN, is Postdoctoral Scholar-Fellow, University of Washington School of Nursing, Seattle, WA, USA. At the time this research was completed, she was PhD Candidate, University of Florida College of Nursing, Gainesville, FL, USA. Debra E. Lyon, PhD, RN, FNP-BC, FNAP, FAAN, is Executive Associate Dean and Kirbo Endowed Chair, University of Florida College of Nursing, Gainesville, FL, USA. Debra Lynch Kelly, PhD, RN, OCN, CNE, FAAN, is Associate Dean for Academic Affairs Undergraduate Education and Associate Professor, University of Florida College of Nursing, Gainesville, FL, USA.

Michael T. Weaver, PhD, RN, FAAN, is Professor, University of Florida College of Nursing, Gainesville, FL, USA. Fan Yi, is Assistant Professor, University of Idaho Department of Mathematics and Statistical Science, Moscow, ID, USA. At the time this research was completed, she was Research Assistant, University of Florida College of Nursing, Gainesville, FL, USA. Magali Rezende de Carvalho, MSN, APRN, CWOCN is PhD Student, University of Florida College of Nursing, Gainesville, FL, USA. Joyce K. Stechmiller, PhD, APRN, FAAN, is Professor Emeritus, University of Florida College of Nursing, Gainesville, FL, USA.

Abbreviations and Acronyms

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