Impact of Psychological Distress on Physiological Indicators of Healing Prognosis in Patients with Chronic Diabetic Foot Ulcers: A Longitudinal Study

Abstract

Objective:

Diabetic foot ulcers (DFUs) are devastating complications of diabetes, responsible for a high number of amputations worldwide. Due to its impact on chronic inflammation, psychological distress may negatively impact the healing process. Thus, this study evaluated the influence of psychological distress on physiological indicators of healing prognosis and the potential of stress-reducing therapies for DFU healing.

Approach:

Patients with chronic DFU were recruited and assessed at enrollment and 2 months later. According to psychological scores at enrollment, participants were allocated into groups without (group 1) or with (group 3) psychological distress. Participants who reported clinical distress were then randomly allocated into a control (no stress-reducing intervention—group 4) or experimental (with stress-reducing interventions—group 5) group. Subsequently, indicators of healing prognosis were measured.

Results:

Groups 1 and 3 presented no differences in the Perfusion, Extent, Depth, Infection and Sensation score, glycated hemoglobin, or inflammatory and angiogenic markers. However, the immune cell ratio was increased by more than twofold in group 3, compared with group 1. Importantly, the expression of circulating microRNAs was significantly increased in group 3 (miR-21-5p, miR-155-5p, miR-146a-5p, and miR-221-3p [p < 0.05]), compared with group 1. Two months later, group 5 displayed a significant improvement in the Perceived Stress Scale and Hospital Anxiety and Depression Scale scores (p < 0.01), and the immune cell ratio was decreased by more than 2.5-fold.

Innovation:

This study helped to identify which variables and psychological interventions are more successful in promoting DFU healing.

Conclusion:

Psychological distress influenced clinical and physiological parameters, leading to compromised DFU healing and consequently underlining the potential of adjuvant stress-reducing approaches.

Eugénia Carvalho, PhD

INTRODUCTION

Are complex lesions of the lower extremities that significantly reduce the patient's life expectancy¹ and quality of life.^2,3 Diabetic foot ulcers (DFUs) develop from peripheral neuropathy, microangiopathy, and chronic inflammation,^4,5 which commonly occur in patients with diabetes mellitus (DM), especially those with poor glycemic control.

From 19% to 34% of patients with DM will, eventually, develop a DFU during their life time,⁶ a leading cause of prolonged hospitalizations.^5,7 In fact, the success rate of DFU treatment is limited and a consequent amputation is common, especially in patients with severe infection.^8,9 Moreover, DFU has a recurrence rate of 40% within 1 year and 65% within 5 years.^6,10

In addition, due to the enormous costs related to DFU management,^1,3 prevention and self-care are as important as the development of adequate treatment strategies. So far, the best treatment results arise in the context of multidisciplinary approaches, where key aspects of diabetic wound care are tackled, including glycemic control, local wound management, vascular disease, and infection prevention.^10,11

INNOVATION

Current DFU therapeutic approaches arise in the context of multidisciplinary approaches to increment the quality of the diabetic foot care system. Here, a randomized longitudinal trial was conducted to assess the contribution of psychological variables and the effectiveness of psychological interventions to foster DFU management.

Through the identification of clinical, physiologic, and psychological variables influencing DFU healing, and the integration of psychological interventions, this study helped to identify which variables and interventions are more successful in decreasing the time of healing, and consequent amputation rates and associated economic costs, while promoting a higher quality of life of patients with DFUs.

CLINICAL PROBLEM ADDRESSED

There are risk factors that are negatively associated with wound-healing outcomes in patients with DM.⁸ The microbial load, microbial diversity, and pathogenicity are considered the most important.^12,13 Recent evidence has demonstrated that chronic inflammation may play a leading role in DFU.⁴

Single-cell gene expression characterization of diabetic and non-diabetic ulcers has revealed an accumulation of effector T cells in non-healing DFUs,¹⁴ whereas previous reports have also shown a higher number of circulating effector T cells in the blood of patients with non-healing DFUs.^15,16 In parallel, the relevance of microRNAs as disease biomarkers has increased, especially due to their easy and reliable measurement in body fluid.¹⁷

Moreover, microRNAs have been described as important mediators of chronic inflammation and impaired wound healing in rodents and patients with DM.^18

–22

Psychological distress has also emerged as a key, sometimes silent, player in wound healing.²³ Indeed, it is known that psychological distress can increase wound healing time in healthy individuals.²⁴ In fact, it can increase the release of proinflammatory cytokines during tissue repair and decrease tissue oxygen levels, consequently delaying the wound-healing process.^23,25

Negative emotions contributed to prolonged infections, delayed wound healing, and poor quality of life, underlining the association between DFUs and psychological distress. ^26
–28 Moreover, increased anxiety and depression levels have been associated with delayed wound healing.²⁹

Therefore, the aims of this study were to: (1) compare wound-healing physiological indicators between patients with chronic DFUs with and without clinical levels of psychological distress; (2) understand the underlying mechanism of how psychological distress relates to DFU healing progression; and (3) assess the effects of relaxation/hypnosis interventions, designed as stress-reducing tools, on DFU healing physiological indicators.

MATERIALS AND METHODS

Participants and procedure

This study is a longitudinal randomized controlled trial on the effectiveness of two stress-reducing interventions in clinically distressed patients with chronic DFUs. The protocol following the CONSORT 2010 standards, including the rationale for defining the study sample size, has been previously published³⁰ and is briefly described next.

The study was approved by the Ethical Committees of the Centro Hospitalar do Tâmega e Sousa, E.P.E. and the Centro Hospitalar e Universitário de Santo António, E.P.E. (references 198/2018 and 2018.205 [180-DEFI/179-CES], respectively). Participants were attending the Multidisciplinary Diabetic Foot Consultation and provided written informed consent (ClinicalTrials.gov number: NCT04698720).

Inclusion criteria included: age ≥18 years; and ≤2 chronic DFUs at baseline assessment (DFUs with ≥6 weeks but <12 weeks of duration at patient enrollment). In patients with two DFUs, only the largest was considered. Exclusion criteria included: DFUs that, at baseline, were considered a relapse; selected pathologies or treatments that could interfere with the proposed analysis; or receiving psychological counseling during the study period.

On providing written informed consent, patients were included in the study and assessed in two moments: at enrollment (baseline) and 2 months later (follow-up), as per Fig. 1. Not all participants who are part of the follow-up group had blood samples collected at baseline. All participants received standard-of-care treatment.

Figure 1.

Study design with critical time points and corresponding tasks. CHTS, Centro Hospitalar do Tâmega e Sousa, E.P.E.; CHUdSA, Centro Hospitalar e Universitário de Santo António, E.P.E.; DFU, diabetic foot ulcer; DM, diabetes mellitus; HADS, Hospital Anxiety and Depression Scale; HbA1c, hemoglobin A1c or glycated hemoglobin; PEDIS, Perfusion, Extent, Depth, Infection, and Sensation classification system; PSS, Perceived Stress Scale.

Participants with no significant clinical distress, according to the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS) scores at baseline, were categorized without psychological distress (w/o) (group 1, n = 25—at enrollment, and group 2, n = 19—2 months later). The discrepancy of six participants w/o psychological distress observed between baseline and follow-up was due to drop-outs at follow-up (n = 10) or foot amputations (n = 2), despite six participants being only included at follow-up.

The detailed reasons of drop-outs include: participant withdrawal (n = 1), participant unavailability to attend the scheduled consultations (n = 2), COVID-19 pandemic confinement (n = 4), compromised sample collection/processing (n = 2), and follow-up assessment date exceeding the project ending (n = 1).

Participants who reported clinical distress were categorized with psychological distress (w/) (group 3, n = 42—at enrollment, and groups 4 and 5—2 months later). Participants w/psychological distress were randomly allocated into one of two groups after enrollment: control (group 4) or experimental (group 5). The discrepancy of 12 participants w/psychological distress observed between baseline and follow-up assessments was due to drop-outs at follow-up (n = 19), despite 7 participants being only included at follow-up.

The detailed reasons of drop-outs include: participant withdrawal (n = 1), participant unavailability to attend the scheduled consultations (n = 4), COVID-19 pandemic confinement (n = 10), compromised sample collection/processing (n = 3), and hospital discharge of the participant (n = 1).

Participants allocated to the control group (group 4, n = 14) received no stress-reducing intervention, but some participants (n = 6) received sessions of neutral guided imagery to mitigate possible placebo effects.³⁰ Participants receiving no stress-reducing interventions and those receiving neutral guided imagery were combined in the control group, due to the limited number of participants.

Neutral guided imagery was conducted by the same health psychologist responsible for the muscle relaxation intervention, and included four sessions, each one with a duration of 45 min, delivered every 2 weeks. On the other hand, those allocated to the experimental group (group 5, n = 16) receiving either muscle relaxation intervention with guided imagery (n = 8) or hypnosis with guided imagery (n = 8) were combined in the stress-reducing experimental group, due to the limited number of participants.³⁰

Muscle relaxation with guided imagery sessions initiated with diaphragmatic breathing was followed by Jacobson's progressive muscle relaxation, a technique that consisted of tensing and relaxing 16 muscle groups of the body. Sessions of hypnosis followed a hypnotic protocol that involved the following stages: pre-talk, absorption, ratification, alitiation, dissociation, and awakening. In both interventions, guided imagery was focused on the DFU healing process.

The protocol for both interventions included four sessions, each one of ∼45 min, delivered every 2 weeks. A health psychologist conducted the muscle relaxation sessions, and a qualified hypnotherapist conducted the hypnosis sessions. Only participants who completed at least 75% of the intervention and placebo sessions were considered for further analyses at follow-up.

Sociodemographic, clinical, and psychological variables

Sociodemographic and clinical variables

Sociodemographic data (e.g., gender, age) were collected through the sociodemographic questionnaire, at enrollment. Clinical data were gathered by the clinical team at both assessment moments (enrollment and 2 months later).^30,31 Electronic laboratory notebook was not used.

Psychological variables

Psychological distress was defined according to the HADS³² and the PSS³³ scores, at baseline. Participants' evaluation and scoring followed the protocol previously published.³⁰

Biochemical variables

Peripheral blood (PB) samples were collected in ethylenediaminetetraacetic acid VACUETTE^® tubes. While PB was freshly used, plasma samples were stored at −80°C after PB centrifugation for 10 min at 1,000 g.

Quantification of blood immune cell populations

Immune cell analyses were performed on fresh PB samples. Leukocyte populations were quantified using a Coulter LH 780 hematology analyzer (Beckman Coulter, CA). Immunophenotypic analysis of surface antigen expression on PB cells was performed using a stain-and-then-lyse eight color direct immunofluorescence technique.¹⁵

The monoclonal antibodies (mAbs) indicated in Supplementary Table S1 were used (BioLegend, CA). The different subpopulations were selected using anti-CD4 or anti-CD8 together with anti-CD27 and/or anti-CD28, and anti-CD45RO mAbs. This strategy, proposed by Appay et al.,³⁴ allowed for a clear identification of three major blood T-cell populations: naive (CD27⁺CD28⁺HLA-DR⁻CD45RO⁻), activated/memory (CD27⁺CD28⁺HLA-DR⁺ CD45RO⁺), and effector (CD27⁻CD28⁻). Naturally occurring regulatory T cells (CD4⁺CD28⁺CD38⁻CD25⁺⁺) were also quantified.

The immune cell ratio between effector CD4⁺ and CD8⁺ T cells and naive CD4⁺ and CD8⁺ T-cells, which is associated with poor healing,¹⁵ was determined. Flow cytometry data were collected on an FACSCanto II flow cytometer, using FACSDiva software for sample acquisition and FlowJo for data analysis (BD Biosciences, CA).

Expression of microRNAs

Total RNA was extracted from 250 μL of plasma using TRIzol-like serum reagent, according to the manufacturer's protocol, with the addition of a synthetic microRNA from Arabidopsis thaliana (ath-miR-159a-3p) as a spike-in control, and glycogen (10 μg/μL) (ThermoFisher Scientific) as a carrier. RNA concentration and purity were evaluated through Optical Density at 260/280 nm using the NanoDrop™ Spectrophotometer 2,000c (Wilmington, DE).

Approximately 100 ng of total RNA was converted to cDNA using microRNA specific loop reverse (RT)—primers in a 2-step reverse transcription method by the activity of the SuperScript^® III RT (U/200 μL) enzyme, in a Doppio Thermal Cycler (VWR, Carnaxide, Portugal). Diluted (1:10) cDNA samples were then mixed with the universe reverse primer primer, forward sequences, and SYBR^® Green Mix^® detector (Qiagen, Hilden, Germany) and the quantitative real-time polymerase chain reaction was performed in duplicate, in a thermocycler (Applied Biosystems, MA).

Relative microRNA expression values were normalized against the geometric mean of small nuclear RNA U6 and ath-miR-159a-3p expression. Primer sequences are described in Supplementary Table S2. Reagents used were from Invitrogen^® and microRNA sequences from TAG Copenhagen A/S.

Quantification of plasma cytokines

Plasma levels of interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α, angiopoietin-2, endothelial growth factor, fibroblast growth factor, platelet endothelial cell adhesion molecule-1, placental growth factor, and vascular endothelial growth factor were quantified using a LEGENDplex™ Human Angiogenesis Panel 1 Mix and Match (9-plex). Samples were read on a BD FACSCalibur™ Flow Cytometer (MA).

Statistical analysis

For normal distribution, the Shapiro–Wilk test was used. For variance homogeneity, the Levenes' test was used. To assess effects of psychological distress, groups were compared using an independent sample t-tests or the Mann–Whitney U non-parametric test, depending on sample distribution.

Comparisons between groups to assess the effect of stress-reducing interventions were made using a one-way analysis of variance (ANOVA) followed by Tukey's multiple-comparison test or Kruskal–Wallis with pairwise comparisons, depending on sample distribution. Correlation between continuous variables was assessed using the Spearman's correlation.

Categorical variables were tested using a χ ² test. Data are presented as mean ± standard deviation (SD) for parametric data and median (interquartile range, Q1–Q3) for non-parametric data. All analyses were performed using IBM SPSS version 28 (SPSS, Inc., Chicago, IL). GraphPad Prism version 8 (GraphPad, Inc., La Jolla, CA) was used for graphical representation. A p-value of ≤0.05 was considered statistically significant.

RESULTS

Effect of psychological distress on physiological indicators of healing prognosis in patients with chronic DFU

The effects of psychological distress on physiological indicators of healing at enrollment and 2 months later were assessed in participants without (w/o) versus with (w/) psychological distress (Table 1, Fig. 2, and Supplementary Table S3).

Figure 2.

Circulating microRNA levels in participants without versus those with psychological distress, at enrollment (groups 1 and 3, respectively) and 2 months later (groups 2 and 4, respectively). Relative expression of (A) miR-21-5p, (B) miR-155-5p, (C) miR-146a-5p, (D) miR-221-3p, and (E) miR-29b-3p, normalized to miR-159a-3p and U6. Data are presented as median with IQR, Q1–Q3. *p ≤ 0.05, **p ≤ 0.01, ^† p ≤ 0.06. IQR, interquartile range.

Table 1.

Sociodemographic, clinical, psychological, and biochemical characteristics of participants without versus those with psychological distress, at enrollment (groups 1 and 3, respectively) and 2 months later (groups 2 and 4, respectively)

	n	Group 1	n	Group 2	n	Group 3	n	Group 4	^a p	^b p	^c p	^d p
Sociodemographic variables
Gender (male/female)	25	22/3	19	16/3	42	30/12	14	13/1	0.12	0.72	0.10	0.45
Age (years)	25	67 ± 11	19	65 ± 11	42	63 ± 10	14	65 ± 7	0.14	0.55	0.57	0.96
Clinical variables
DM duration (years)	25	20 (12–30)	19	20 (14–30)	42	20 (12–26)	14	20 (11–30)	0.49	0.80	0.83	0.93
Health literacy score	23	54 (49–58)	19	53 (48–57)	42	54 (50–56)	14	53 (50–54)	0.70	0.53	0.47	0.76
First DFU (yes)	25	13	19	8	42	13	14	5	0.09	0.63	0.74	0.71
Diabetic foot type	25		19		42		14
Neuropathic		14		10		24		9	0.93	0.52	0.64	0.50
Neuroischemic		11		9		15		5	0.50	0.82	1.00	0.50
Ischemic		0		0		3		0	—	—	—	—
PEDIS score	25	6 (6–8)	19	4 (1–7)	42	7 (5–8)	14	5 (1–6)	0.72	0.009	0.016	0.73
Area (cm²)	25	1.00 (0.25–6.00)	19	0.04 (0.00–0.80)	42	1.00 (0.25–4.44)	14	0.12 (0.00–1.41)	0.80	0.004	0.012	0.53
Depth (0/1/2/3)	25	2/14/9	19	8/4/3/4	42	18/13/11	14	4/4/4/2	—	—	—	—
Perfusion (0/1/2)	25	14/9/2	19	12/6/1	42	24/13/5	14	7/5/2	—	—	—	—
Infection (0/1/2/3)	25	16/5/4	19	8/9/0/2	42	29/7/6	14	4/4/6/0	—	—	—	—
Sensibility (0/1)	25	3/22	19	1/18	42	3/39	14	0/14	—	—	—	—
Psychological variables
Stress (PSS)	25	6 (4–9)	18	4 (3–11)	42	21 (18–24)	14	17 (15–21)	≤0.001	0.45	0.027	≤0.001
Anxiety (HADS)>	25	2 (1–5)	18	3 (0–4)	42	11 (7–13)	14	7 (3–10)	≤0.001	0.31	0.006	≤0.001
Depression (HADS)>	25	2 (1–4)	18	3 (1–5)	42	7 (5–11)	14	9 (5–10)	≤0.001	0.85	0.99	0.004
Biochemical variables
HbA1c (%)	25	7.0 (6.1–9.2)	11	7.1 (6.5–8.7)	40	8.1 (6.6–9.1)	7	8.9 (7.7–9.1)	0.49	0.79	0.22	0.10
Immune cells
B cells (n/μL)	19	140.07 (104.78–158.93)	10	118.49 (93.22–253.22)	12	230.84 (101.88–293.61)	6	371.88 (145.26–396.49)	0.12	0.80	0.21	0.053
CD4⁺ T cells
Total (n/mL)	19	737.29 ± 262.03	10	715.49 ± 407.90	12	846.25 ± 444.00	6	1,120.42 ± 314.41	0.40	0.86	0.20	0.06
Naive (n/mL)	19	442.53 ± 178.63	10	411.64 ± 267.87	12	518.73 ± 356.99	6	576.06 ± 243.94	0.44	0.71	0.73	0.24
Activated (n/mL)	19	191.61 (124.69–290.10)	10	216.67 (87.37–495.48)	12	276.31 (119.95–349.75)	6	288.34 (174.61–574.04)	0.73	1.00	0.39	0.71
Effector (n/mL)	19	25.74 (6.35–67.82)	10	9.95 (5.93–29.29)	12	71.61 (28.58–108.96)	6	28.39 (21.01–127.62)	0.028	0.23	0.44	0.18
CD8⁺ T cells
Total (n/mL)	19	551.28 (295.41–737.10)	10	378.98 (253.64–603.97)	12	386.73 (347.49–966.84)	6	921.85 (541.61–1,193.50)	0.95	0.29	0.21	0.12
Naive (n/mL)	19	149.36 ± 126.84	10	127.88 ± 109.10	12	108.76 ± 74.33	6	87.96 ± 49.94	0.33	0.65	0.69	0.42
Activated (n/mL)	19	108.12 (55.38–179.72	10	97.87 (37.49–138.18)	12	144.30 (51.73–263.13)	6	365.23 (262.49–454.99)	0.73	0.35	0.041	≤0.001
Effector (n/mL)	19	104.00 (69.09–192.42)	10	106.85 (53.77–134.74)	12	183.20 (120.21–432.98)	6	253.22 (187.96–374.21)	0.10	0.46	0.18	0.002
Ratio between effector and naive T cells	19	0.26 (0.12–0.58)	10	0.27 (0.12–0.69)	12	0.57 (0.26–1.49)	6	0.46 (0.25–1.27)	0.06	0.95	0.62	0.26
Treg (n/μL)	19	38.72 (27.95–50.96)	10	27.34 (17.65–44.89)	12	48.83 (23.81–57.63)	6	48.26 (41.34–61.51)	0.98	0.09	0.59	0.031

Significant p-values (p ≤ 0.05) are highlighted in bold.

Data are presented as mean ± SD for parametric data and median (IQR) for non-parametric data. Categorical data were analyzed by χ ² test (two sided).

p value: group 1 versus group 3.

p value: group 1 versus group 2.

p value: group 3 versus group 4.

p value: group 2 versus group 4.

DFU, diabetic foot ulcer; DM, diabetes mellitus; HADS, Hospital Anxiety and Depression Scale; HbA1c, hemoglobin A1c or glycated hemoglobin; IQR, interquartile range; PEDIS, Perfusion, Extent, Depth, Infection and Sensation; PSS, Perceived Stress Scale; SD, standard deviation; Treg, regulatory T cells.

From a total of 88 study participants, 42 exhibited psychological distress (w/) (group 3) at enrollment, whereas 25 did not reveal psychological distress (w/o) (group 1), according to the PSS and HADS scores (p ≤ 0.001), with missing or drop-out of 12 and 9 participants for each group, respectively (Table 1). The two groups were gender and age matched and presented no differences at enrollment in variables such as: DM duration, health literacy score, first DFU, diabetic foot type, Perfusion, Extent, Depth, Infection and Sensation (PEDIS) score, glycated hemoglobin (HbA1c), inflammatory or angiogenic markers (Table 1 and Supplementary Table S3).

Despite no statistical significance, participants from group 3 presented a higher PEDIS score of 7 (5–8) at enrollment, in contrast to participants from group 1 who presented a PEDIS score of 6 (6–8). Moreover, the number of effector CD4⁺ T cells was significantly increased in participants from group 3 (p = 0.028), when compared with group 1 (Table 1).

Likewise, the immune cell ratio increased by more than twofold in participants from group 3, when compared with group 1 (Table 1). Importantly, the expression levels for specific microRNAs were significantly different between participants w/o and w/psychological distress, at enrollment.

A significant increase in the circulating levels of miR-21-5p (p = 0.006), miR-155-5p (p = 0.006), miR-146a-5p (p = 0.022), and miR-221-3p (p = 0.013) was observed in participants from group 3 (w/distress), when compared with group 1 (w/o distress) (Fig. 2A–D, respectively). A similar tendency was also observed for miR-29b-3p (p = 0.056) (Fig. 2E).

As depicted in Fig. 1, participants from group 1 (w/o distress) and group 3 (w/distress) were followed up for 2 months. We first compared the variables measured at enrollment versus 2 months later in participants w/o psychological distress (groups 1 and 2, respectively). Although results do not correspond to paired comparisons due to the limitations described earlier, results showed no gender or age effects, in the present cohort (Table 1).

Yet the PEDIS score (p = 0.009) and subsequent DFU area (p = 0.004) were reduced in participants from group 2, as expected, when compared with group 1 (Table 1). No further differences were observed between the groups w/o psychological distress either in the psychological variables, or HbA1c, immune cell population distributions, microRNAs, or inflammatory and angiogenic markers (Table 1, Fig. 2, and Supplementary Table S3).

Subsequently, we compared the variables measured at enrollment versus 2 months later in participants w/psychological distress (groups 3 and 4, respectively). Results do not correspond to paired comparisons due to the limitations described earlier. No differences were observed in gender or age across these groups. However, a significant decrease in the PEDIS score (p = 0.016) and subsequent DFU area (p = 0.012) was observed, in participants from group 4, when compared with group 3 (Table 1).

In addition, participants from group 4 displayed a significant improvement in perceived stress and anxiety (p < 0.03), as well as an increase in activated CD8⁺ T cells (p = 0.041), a tendency for a decrease by more than 1.2-fold in the immune cell ratio, and a decrease in miR-146a-5p levels (p = 0.046), when compared with group 3 (Table 1, and Fig. 3C). An increase in red blood cell levels (p = 0.016) was also observed in group 4, compared with group 3 (Supplementary Table S3). No further differences were observed in the remaining variables (Table 1, Fig. 2, and Supplementary Table S3).

Figure 3.

Circulating microRNA levels in participants with psychological distress at enrollment (group 3) and 2 months after control (group 4) or experimental (group 5) interventions. Relative expression of (A) miR-21-5p, (B) miR-155-5p, (C) miR-146a-5p, (D) miR-221-3p, and (E) miR-29b-3p, normalized to miR-159a-3p and U6. Data are presented as median with IQR, Q1–Q3. ^† p ≤ 0.06.

Finally, variables measured 2 months after enrollment in participants w/o psychological distress and in those w/psychological distress from groups 2 and 4, respectively, were compared. Both groups were age and gender matched and presented no differences in the PEDIS scores (Table 1). Despite a significant improvement in perceived stress and anxiety in participants from group 4, participants from group 2 presented even lower scores for all psychological variables (p < 0.005) (Table 1).

In addition, no differences were observed in HbA1c, inflammatory or angiogenic markers, or microRNAs levels, between groups 2 and 4 (Table 1, Fig. 2, and Supplementary Table S3). However, activated and effector CD8⁺ T-cell levels (p ≤ 0.001 and p = 0.002, respectively), as well as regulatory T-cell levels (p = 0.031), were higher in participants from group 4, when compared with group 2 (Table 1).

Effect of stress-reducing interventions on physiological indicators of healing prognosis in participants with psychological distress

The effects of stress-reducing interventions were evaluated in participants w/psychological distress at enrollment and 2 months later (Table 2, Fig. 3, and Supplementary Table S4). Results do not correspond to paired comparisons, due to sample collection missing at either time point, drop-out or foot amputation of 12 participants.

Table 2.

Sociodemographic, clinical, psychological, and biochemical characteristics of participants with psychological distress at enrollment (group 3) and 2 months after control (group 4) or experimental (group 5) interventions

	n	Group 3	n	Group 4	n	Group 5	^a p	^b p
Sociodemographic variables
Gender (male/female)	42	30/12	14	13/1	16	12/4	0.79	0.19
Age (years)	42	63 ± 10	14	65 ± 7	16	57 ± 10	0.12	0.10
Clinical variables
DM duration (years)	42	20 ± 9	14	21 ± 12	16	17 ± 8	0.65	0.58
Health literacy score	42	54 (50–56)	14	53 (50–54)	16	53 (50–58)	1.00	1.00
First DFU (yes)	42	13	14	5	16	6	0.64	0.63
Diabetic foot type	42		14		16
Neuropathic		24		9		10	0.71	0.98
Neuroischemic		15		5		6	0.90	0.98
Ischemic		3		0		0	—	—
PEDIS score	42	7 (5–8)	14	5 (1–6)	15	5 (1–7)	0.15	1.00
Area (cm²)	42	1.00 (0.25–4.44)	14	0.12 (0.00–1.41)	15	0.24 (0.00–1.00)	0.035	1.00
Depth (0/1/2/3)	42	18/13/11	14	4/4/4/2	15	3/2/6/4	—	—
Perfusion (0/1/2)	42	24/13/5	14	7/5/2	15	11/2/2	—	—
Infection (0/1/2/3)	42	29/7/6	14	4/4/6/0	15	3/10/2/0	—	—
Sensibility (0/1)	42	3/39	14	0/14	15	1/14	—	—
Psychological variables
Stress (PSS)	42	21 ± 6	14	17 ± 4	16	16 ± 7	0.005	0.66
Anxiety (HADS)	42	10 ± 4	14	7 ± 4	16	6 ± 4	0.004	0.98
Depression (HADS)	42	8 ± 4	14	8 ± 4	16	5 ± 3	0.96	0.30
Biochemical variables
HbA1c (%)	40	7.9 ± 1.7	7	8.7 ± 1.2	9	8.0 ± 1.6	0.71	0.71
Immune cells
B cells (n/μL)	12	223.39 ± 122.37	6	304.21 ± 145.18	5	242.07 ± 80.99	0.67	0.24
CD4⁺ T cells
Total (n/mL)	12	846.25 ± 444.00	6	1,120.42 ± 314.41	5	1,364.42 ± 435.28	0.07	0.60
Naive (n/mL)	12	518.73 ± 356.99	6	576.06 ± 243.94	5	776.91 ± 319.60	0.32	0.57
Activated (n/mL)	12	242.59 ± 143.71	6	371.82 ± 273.80	5	491.08 ± 330.09	0.13	0.67
Effector (n/mL)	12	71.61 (28.58–108.96)	6	28.39 (21.01–127.62)	5	27.17 (8.32–200.61)	1.00	1.00
CD8⁺ T cells
Total (n/mL)	12	386.73 (347.49–966.84)	6	921.85 (541.61–1,193.50)	5	640.30 (475.25–1,081.67)	0.92	1.00
Naive (n/mL)	12	128.76 (21.91–155.96)	6	77.61 (56.48–124.74)	5	183.44 (115.42–189.75)	0.38	0.32
Activated (n/mL)	12	175.61 ± 153.43	6	357.12 ± 117.02	5	230.12 ± 120.33	0.92	0.33
Effector (n/mL)	12	183.20 (120.21–432.98)	6	253.22 (187.96–374.21)	5	104.36 (50.63–614.45)	1.00	0.50
Ratio between effector and naive T cells	12	0.57 (0.26–1.49)	6	0.46 (0.25–1.27)	5	0.21 (0.06–1.47)	0.35	0.99
Treg (n/μL)	12	48.83 (23.81–57.63)	6	48.26 (41.34–61.51)	5	32.65 (29.69–72.95)	1.00	1.00

Significant p-values (p ≤ 0.05) are highlighted in bold.

Data are presented as mean ± SD for parametric data and median (IQR) for non-parametric data. Categorical data were analyzed by χ ² test (two sided).

p value: group 3 versus group 5.

p value: group 4 versus group.

First, variables measured in participants w/psychological distress who received stress-reducing interventions, at enrollment (group 3, n = 42) versus 2 months later (group 5, n = 16), were compared. No differences were observed in gender or age. The PEDIS score was decreased by more than 1.2-fold in group 5, with a significant decrease in the DFU area (p = 0.035), when compared with group 3 (Table 2).

Participants from group 5 displayed a significant improvement overtime in the perceived stress and anxiety (p < 0.01), representing a better psychological outcome when compared with group 4 (Table 2). Moreover, a decrease, by more than 2.5-fold, in the immune cell ratio was observed in group 5, when compared with group 3 (Table 2). No further differences were observed in the remaining variables (Table 2, Fig. 3, and Supplementary Table S4).

Subsequently, we compared the variables measured in participants w/psychological distress from group 4 with those from group 5. Both groups were gender and sex matched. In addition, no differences were observed in the PEDIS score, psychological variables, HbA1c, immune cell population distributions, nor inflammatory and angiogenic markers (Table 2, and Supplementary Table S4). However, a near significant increase in the circulating miR-146a-5p levels (p = 0.051) was observed in group 5, when compared with group 4 (Fig. 3C).

DISCUSSION

Wound healing is a complex process, susceptible to interruption or delay, due to alteration in local and systemic key factors indispensable for healing.³⁵ Psychological distress has emerged as a silent but crucial player of the healing process.²³ In addition, microRNAs have been widely recognized as key regulators of the different phases of wound healing.¹⁸

Data showed that PEDIS scores were similar in participants w/o and w/psychological distress at enrollment (groups 1 and 3). Despite no significant differences, participants w/psychological distress presented a higher PEDIS score of 7 (5–8), when compared with participants w/o psychological distress. PEDIS scores ≥7 are associated with a greater risk of adverse outcomes,³¹ observed in the participants w/psychological distress.

In addition, the HbA1c percentage was similar in both participant groups, w/o and w/psychological distress, at enrollment. Nonetheless, despite not statistically significant, participants w/psychological distress presented a higher HbA1c percentage of 8.1 (6.6–9.1), when compared with participants w/o psychological distress, which may be linked to poor clinical management of glucose levels.³⁶

Moreover, the immune cell ratio in participants w/psychological distress was more than twofold higher at enrollment, when compared with those w/o psychological distress. The increased immune cell ratio is associated with an accumulation of effector T cells in participants w/psychological distress.

These findings are supported by studies that showed a reduction of naive T cells and T-cell receptor repertoire diversity, while observing an accumulation of effector T cells, in patients with non-healing DFUs compared with those with healed DFUs.^15,16 The accumulation of effector T cells will lead to the secretion of large quantities of inflammatory cytokines, fostering a chronic inflammatory environment and, consequently, worsening healing.^15,16

Further, participants w/psychological distress presented higher expression of specific microRNAs, important mediators of chronic inflammation and impaired healing,^18

–22 at enrollment, when compared with those identified w/o psychological distress. Importantly, the circulating levels of miR-21-5p, miR-155-5p, miR-146a-5p, and miR-221-3p were increased in participants w/psychological distress.

These outcomes are in agreement with data showing that plasma levels of miR-155 were increased in patients with obesity,³⁷ as well as in patients with depression.³⁸ Increased expression of miR-155 is related to the synthesis of pro-inflammatory cytokines, such as TNF-α and IL-6.³⁹

Others reported that the expression of miR-146 is decreased in PB mononuclear cells of patients with newly diagnosed type I DM,⁴⁰ as well as in patients with higher recent stressful life event scores.⁴¹ It has been shown that miR-146a owns anti-inflammatory properties and may be a good alternative for negative regulation of inflammation.⁴²

In addition, more than half of DFUs in the group of participants w/o psychological distress were first ulcers, whereas only one-third of DFUs from the group w/psychological distress were reported as first ulcers. Higher DFU recurrence rate in participants w/psychological distress indicates that the risk factors for the development of a new DFU do not decrease with the resolution of the first ulcer.⁶ Together, these data demonstrated that psychological distress influences DFU outcomes, leading to impaired wound healing.

Data further demonstrated that, although the PEDIS score was reduced after 2 months, as expected, in participants w/o psychological distress, the psychological variables, HbA1c, immune cell ratio, circulating levels of microRNAs, as well as inflammatory and angiogenic markers, were not altered. Similar results were observed in participants w/psychological distress with no stress-reducing intervention.

Despite a similar decrease in the PEDIS score and subsequent DFU area, participants w/psychological distress presented a worse healing profile, when compared with those w/o psychological distress.

Lastly, data suggested that stress-reducing interventions led to a decreased DFU area, despite a small decrease in the overall PEDIS score, in participants w/psychological distress. Moreover, a decrease by more than 2.5-fold in the immune cell ratio between effector CD4⁺ and CD8⁺ T cells and naive CD4⁺ and CD8⁺ T cells was shown in these participants.

In addition, only miR-146a-5p indicated a tendency for an increase after 2 months, in participants who underwent stress-reducing interventions. This result is supported by others, indicating that the restoration of circulating miR-146a levels may improve diabetic wound healing.⁴² Overall, these results may support the potential of stress-reducing interventions, to improve DFU healing in participants w/psychological distress.

These findings are in line with studies that show benefits of psychological stress-reducing interventions, such as relaxation, guided imagery, and mindfulness-based stress reduction approaches, on wound healing.^43,44

Despite no differences in miR-29b-3p levels between the participants w/o and those w/psychological distress, a negative correlation was observed with the immune cell ratio (Supplementary Table S5). miR-29b-3p is an enhancer of the transition from type III to type I collagen and of the activity of metalloproteinase 9, both essential for appropriate tissue remodeling.¹⁸

In addition, microRNAs levels were strongly positively correlated between themselves (Supplementary Table S5). These data further underlined the association between key factors for DFU prognosis, which if dysregulated may lead to a scenario of chronic inflammation and poor wound healing.

Further, several studies have shown that anxiety and depression levels are associated with increased serum⁴⁵ or salivary²⁹ cortisol levels and poor wound healing.²⁹ Nonetheless, circadian rhythm (sample collection time), insulin administration, the presence of stress before sample collection (debridement and dressing placement), and the presence of acute pain and infections are factors that influence cortisol levels.^29,46,47 In this study, these important factors were not controlled for, therefore, precluding adequate measurements of cortisol levels in this study.

To note, our study was started in July 2019. It took place during the COVID-19 pandemic, which had a huge impact on the power of our stress-reducing interventions and on the follow-up studies, as well as on patient well-being and subsequent patient recruitment. Since DM and COVID-19 have a bidirectional adverse impact on each other,^48,49 DFU outcomes may have been greatly affected.

Therefore, the power of the study was not enough to show strong differences between variables. Indeed, the sample size of the study was limited and, therefore, the results need to be interpreted cautiously.

In addition, the inclusion criteria of DFUs with ≥6 weeks but <12 weeks of duration, at the time of participant enrollment may also be a limitation of the study. Future longitudinal studies with larger sample size are needed to validate these results and to emphasize the importance of multidisciplinary treatment approaches.

Despite all this, the main results show a significant increase in microRNA expression, at enrollment, in participants w/psychological distress, compared with those w/o psychological distress.

KEY FINDINGS

Despite no differences in the PEDIS score, HbA1c or inflammatory and angiogenic markers, the immune cell ratio between effector CD4⁺ and CD8⁺ T-cells and naive CD4⁺ and CD8⁺ T cells, associated with poor healing, showed a tendency for an increase by more than twofold in participants w/psychological distress (group 3), compared with those w/o psychological distress (group 1).

Moreover, microRNA levels (miR-21-5p, miR-155-5p, miR-146a-5p, and miR-221-3p [p < 0.05]) were increased in participants w/psychological distress (group 3), compared with those w/o psychological distress (group 1).

Two months later, participants w/psychological distress who underwent stress-reducing interventions (group 5) displayed a significant improvement in the PSS and HADS scores (p < 0.01), and the immune cell ratio was decreased by more than 2.5-fold.

Higher powered studies are needed to validate these results and to further emphasize the importance of stress-reducing approaches as adjuvant therapies to improve DFU management.

ACKNOWLEDGMENTS AND FUNDING SOURCES

The authors thank Dr. Margarida Lima and Dr. João Moura for expertise and assistance in immunophenotyping and cellular analysis studies at the Cytometry Laboratory from the Centro Hospitalar do Porto, E.P.E. The authors also thank André Louro and Gabriela Ferreira for their valuable participation for data collection, as well as Aryane Pinho and Isadora Pombeiro for sample transportation.

The Graphical Abstract was created in the web application BioRender.com (Biorender.com, accessed on March 27, 2023).

This study was supported by FCT—Fundação para a Ciência e Tecnologia, through the Portuguese State Budget UIDB/01662/2020 assigned to PI (M.G.P.); COMPETE 2020—Operational Programme for Competitiveness and Internationalization (POCI-01-0145-FEDER-028163) under the project PTDC/PSI-GER/28163/2017 assigned to PI (M.G.P.) and co-PI (E.C.), and by the SPD—Sociedade Portuguesa de Diabetologia, through the awards Bolsa Luís Marques 2019 and 2020. This study was also financed by ERDF—European Regional Development Fund, through Centro 2020 Regional Operational Programme under the project CENTRO-01-0145-FEDER-000012-HealthyAging2020 and through COMPETE 2020—Operational Programme for Competitiveness and Internationalization, Portuguese national funds via FCT—Fundação para a Ciência e a Tecnologia, I.P., under projects POCI-01-0145-FEDER-007440, UIDB/04539/2020, UIDP/04539/2020, and LA/P/0058/2020, and Ph.D. grants 2020.04990.BD (J.D.S.) and SFRH/BD/144199/2019 (D.S.).

Footnotes

AUTHORs' CONTRIBUTIONS

Conceptualization: E.C., M.G.P., and M.V.; recruitment of study participants: A.C., M.J.D., and R.C.; formal analysis: D.S. and J.D.S.; writing—original draft preparation: D.S. and J.D.S.; writing—review and editing: A.C., D.S., E.C., J.D.S., M.G.P., M.J.D., M.V., and R.C.; project administration: E.C. and M.G.P.; funding acquisition: E.C., and M.G.P.

AUTHOR DISCLOSURE AND GHOSTWRITING

All authors have read and agreed to the published final version of the article, and declare no conflict of interest, financial or otherwise. No ghostwriter was involved in the writing of the article.

ABOUT THE AUTHORS

Jessica Da Silva, PhD candidate of the PDBEB—PhD Programme in Experimental Biology and Biomedicine (Institute for Interdisciplinary Research, University of Coimbra), is an undergraduate researcher of the Obesity, Diabetes and Complications Group (CNC—Center for Neuroscience and Cell Biology, University of Coimbra), where she studies novel therapeutic approaches for DFU management.

Diana Santos, PhD candidate of the PDBEB, is also a laboratory member of the Obesity, Diabetes and Complications Group, while Margarida Vilaça, PhD, is a junior researcher at CIPsi—Psychology Research Centre (School of Psychology, University of Minho).

André Carvalho, MD, and Rui Carvalho, MD, are physicians at ChudSA—Centro Hospitalar e Universitário de Santo António, E.P.E., while Maria de Jesus Dantas, MD, is a physician at CHTS—Centro Hospitalar do Tâmega e Sousa, E.P.E.

Maria G. Pereira, PhD, is an associate professor with aggregation at the University of Minho and the coordinator of the Health, Wellbeing and Performance Research Lab at CIPsi.

Eugénia Carvalho, PhD, is the leader of the Obesity, Diabetes and Complications Group at CNC, where she studies the mechanisms of insulin resistance and related complications, including DFUs. Moreover, she investigates insulin action/metabolic dysfunction with emphasis on biomarkers of disease across the lifespan.

SUPPLEMENTARY MATERIAL

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

Supplementary Table S5

Abbreviations and Acronyms

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