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VOLUME 8, ISSUE 1 

 2025 
 

REVIEW ARTICLE 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of 

chemical biomarkers. Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

 

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1 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

 

 

REVIEW ARTICLE 

 

WOUND MANAGEMENT, HEALING, AND EARLY PROSTHETIC REHABILITATION: PART 3 - 

A SCOPING REVIEW OF CHEMICAL BIOMARKERS 

Williams-Reid H1, Johannesson A2, Buis A1*  

1 Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, Scotland. 
2 Össur Clinics EMEA, Stockholm, Sweden.   

 
  
 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

INTRODUCTION   

 

1: OVERALL RATIONALE, AIMS, AND OBJECTIVES 

A wound is defined as damage to biological tissue,1 

encompassing various forms, including deep tissue injuries  

 

 

 

 

 

associated with prolonged prosthesis use and the surgical 

site resulting from amputation. The wound healing process 

is a complex biological process involving four interlinked 

phases: hemostasis, inflammation, proliferation, and tissue 

remodeling.2-4 This process requires intricate cellular 

coordination, rendering it vulnerable to impairment that can 

result in a stalled (also known as chronic or non-healing) 

wound.5 Amputation surgical sites, however, do not always 

heal optimally, instead experiencing complications such as 

infection, pain, wound dehiscence, stitch abscesses, tissue 

necrosis, and poor residual limb formation.6,7 These 

 
OPEN  ACCESS 

ABSTRACT 

BACKGROUND: Poor post-amputation healing delays prosthetic fitting, adversely affecting mortality, quality 

of life, and cardiovascular health. Current residual limb assessments are subjective and lack standardized 

guidelines, emphasizing the need for objective biomarkers to improve healing and prosthesis readiness 

assessments.  

OBJECTIVE(S): This review aimed to identify predictive, diagnostic, and indicative chemical biomarkers of 

healing of the tissues and structures found in the residual limbs of adults with amputation. 

METHODOLOGY: This scoping review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. 

Searches using the terms “biomarkers,” “wound healing,” and “amputation” were performed across Web of 

Science, Ovid Medline, Ovid Embase, Scopus, Cochrane, PubMed, and CINAHL databases. Inclusion 

criteria were: 1) References to chemical biomarkers and healing; 2) Residuum tissue healing; 3) Repeatable 

methodology with ethical approval. Included articles were evaluated for quality of evidence (QualSyst tool) 

and level of evidence (JBI classification). Sources were categorized by study (e.g., randomized controlled 

trial or bench research), wound (diabetic, amputation, other), and model (human, murine, other) type. 

Chemical biomarkers repeated across study categories, and quantification methods were reported on.  

FINDINGS: From 3,306 titles and abstracts screened, 646 underwent full-text review, and 203 met the criteria 

for data extraction, with 76% classified as strong quality. 38 chemical biomarkers were identified across 4 to 

50 sources, with interleukins (predictive, indicative, and diagnostic) and HbA1c (predictive) most prevalent, 

appearing in 50 and 48 sources, respectively. Other biomarkers included predictive blood markers (e.g., 

cholesterol, white blood cell counts), indicative growth factors, bacteria presence (predictive), proteins 

(predictive, indicative, and diagnostic, e.g., matrix metalloproteinases), and cellular markers (indicative and 

diagnostic, e.g., Ki-67, alpha-smooth muscle actin [α-SMA]).  

CONCLUSION: Predictive biomarkers identify comorbidities that may hinder healing, aiding in pre-

amputation risk assessment for poor recovery. Indicative biomarkers monitor key biological healing 

processes, such as angiogenesis (the formation of new blood vessels), wound contraction, and inflammation. 

Diagnostic biomarkers provide direct insights into tissue composition and cellular-level healing. Integrating 

these biomarkers into post-amputation assessments enables continuous monitoring of the healing process 

while accounting for comorbidities, enhancing the objectivity of post-surgical healing management and 

ensuring more effective, personalized rehabilitation strategies. 

 

 

 

ARTICLE INFO 

Received: July 12, 2024 

Accepted: February 12, 2025 

Published: February 21, 2025 

 

CITATION 

 

Williams-Reid H, Johannesson A, 

Buis A. Wound management, 

healing, and early prosthetic 

rehabilitation: Part 3 - A scoping 

review of chemical biomarkers. 

Canadian Prosthetics & Orthotics 

Journal. 2025; Volume 8, Issue 1, 

No.1. 

Https://doi.org/10.33137/cpoj.v8i1

.43717 

KEYWORDS 

Amputation; Scoping Review; 

Wound Healing; Surgical Site 

Healing; Chemical Biomarkers; 

Chemical Markers of Healing; 

Residuum Healing; Residual Limb 

Healing; Wound Management; 

Early Prosthetic Rehabilitation  

Please refer to the end of the 

article for a list of Abbreviations 

& Acronyms. 

 

* CORRESPONDING AUTHOR: 

Professor Arjan Buis, PhD 

Department of Biomedical Engineering, Faculty of Engineering, University 
of Strathclyde, Glasgow, Scotland. 

E-Mail: arjan.buis@strath.ac.uk 

ORCID ID: https://orcid.org/0000-0003-3947-293X 

 

Journal Homepage: https://jps.library.utoronto.ca/index.php/cpoj/index 

Volume 8, Issue 1, Article No.1. 2025 

 

 

https://doi.org/10.33137/cpoj.v8i1.43717
https://doi.org/10.33137/cpoj.v8i1.43717
https://doi.org/10.33137/cpoj.v8i1.43717
mailto:arjan.buis@strath.ac.uk
https://orcid.org/0000-0003-3947-293X
https://jps.library.utoronto.ca/index.php/cpoj/index


 

2 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

complications stall healing and, in severe cases, 

necessitate revision surgeries or re-amputation.6  

Despite their critical role in preventing complications like re-

amputation, wound healing assessments remain 

subjective.8 This is particularly significant for individuals with 

major lower limb amputation, defined as amputation through 

or proximal to the ankle, whose readiness for prosthetic 

rehabilitation depends on the health and healing of their 

residual limb. Early prosthetic fitting improves mobility, 

ambulation, and daily functioning,9-11 whilst increased costs 

and elevated three-year post-amputation mortality rates are 

associated with delays or failure to provide timely prosthetic 

interventions.10,11 However, current evaluations of the 

residual limb post-amputation rely on clinical judgement, 

lacking standardized guidelines or objective metrics.8,12,13 

While factors like wound healing, pain management, and 

limb volume are considered, they are not consistently 

quantified. Additionally, debates over rehabilitation 

practices promoting residual limb healing, such as the use 

of rigid versus soft immediate post-operative dressings,14,15 

further highlight inconsistencies in clinical approaches.  

There is a need for objective measures, such as 

biomarkers, to evaluate wound healing and thus readiness 

for prosthetic fitting. Biomarkers, as defined by the United 

States Food and Drug Administration (U.S. FDA) as 

measurable indicators of biological processes or responses 

to treatment,16 provide a means to minimize the subjectivity 

of current practices. However, their application in early-

stage post-amputation healing remains largely 

unexplored.8,17,18 To address this research need, a scoping 

review was developed and implemented with the following 

aim: 

Identify predictive, diagnostic, and/or indicative biomarkers 

(physical, chemical, or other) of healing of the tissues and 

structures found in the residual limbs of adults with 

amputation. 

To meet this aim, the following objectives were compiled: 

1) Collate and synthesize the reported definitions of healing 

and non-healing in the literature investigating healing of the 

tissues and structures found in the residual limbs of adults 

with amputation. 

2) Identify and collate physical biomarkers predictive, 

diagnostic, and/or indicative of healing repeated in sources 

investigating healing of the tissues and structures found in 

the residual limbs of adults with amputation.  

3) Identify and collate chemical biomarkers predictive, 

diagnostic, and/or indicative of healing repeated in sources 

investigating healing of the tissues and structures found in 

the residual limbs of adults with amputation. 

4) Assess the quality and levels of evidence from sources 
investigating the healing of the tissues and structures found 

in the residual limbs of adults with amputation.   

In the aim, biomarkers are classified by their nature and 

function. Physical biomarkers are measurable attributes of 

the wound or tissue itself, such as wound pH or 

temperature, whilst chemical biomarkers are molecules 

found in biological tissue or fluids (e.g., sweat, sebum, 

saliva, and blood) that signal biological processes such as 

cytokines. Functionally, predictive biomarkers assess the 

likelihood of a healing state or treatment response, while 

diagnostic biomarkers definitively confirm healing 

progression or status. Indicative biomarkers suggest the 

presence of a condition or physiological state but are not 

definitive.  

2: PART 3 RATIONALE, AIMS, AND OBJECTIVES 

This article (Part 3) addresses Objective 3 and is the final 

instalment in a three-part series examining Objectives 1 

through 3. Part 1 highlighted significant gaps in defining 

healing and non-healing, emphasizing the need for an 

amputation-specific wound healing assessment scale 

incorporating objective measures like biomarkers.17 

Part 2 focused on physical biomarkers quantifying macro-

level physiological properties.18 While useful and easily non-

invasively measured, these biomarkers, such as 

hemodynamic and oxygenation measures, often indicate 

changes resulting from cellular healing processes rather 

than directly representing the healing process itself. For 

example, wound temperature changes (a physical 

biomarker) may reflect inflammation, immune responses, 

vasodilation, and tissue metabolism.19-21 In contrast, 

chemical biomarkers like interleukins and C-reactive protein 

directly signal inflammatory responses22 and serve as more 

precise diagnostic indicators of healing mechanisms. 

Currently, poor healing is defined by clinical endpoints like 

wound dehiscence or necrotic tissue formation.17 Chemical 

biomarkers provide earlier insights into the healing process, 

allowing evaluation of treatments and rehabilitation 

programs. For instance, serum levels of matrix 

metalloproteinase 2 (MMP-2) and MMP-7 can predict 

wound dehiscence,23,24 as these MMPs support 

extracellular matrix remodeling, which is essential for tensile 

skin strength.25 

Chemical biomarkers provide diagnostic insights into 

healing because they are intrinsic components of the 

healing process, with their levels directly reflecting specific 

healing mechanisms. For instance, the Ki-67 protein 

functions as a marker of cellular proliferation in human 

cells.26,27 The proliferation of fibroblasts, endothelial cells, 

and keratinocytes is vital for cutaneous wound healing, as it 

constitutes the third stage of the four-step healing 

process.28 To demonstrate the indicative and diagnostic 

power of Ki-67 in healing, Escuin-Ordinas et al.29 observed 

that diabetic wounds with higher wound closure scores 

exhibited significantly greater numbers of Ki-67-positive 

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Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

cells. Similarly, collagen, another chemical biomarker, is 

embedded in essential healing mechanisms. It aids healing 

by attracting fibroblasts and promoting new collagen 

formation within the wound bed.30 Thus, chemical 

biomarkers offer sensitive, specific measures of healing, 

improving understanding of post-amputation recovery and 

guiding rehabilitation. Therefore, the aim of this review was 

to: 

Identify predictive, diagnostic, and/or indicative chemical 

biomarkers of healing in the tissues and structures found in 

the residual limbs of adults with amputations. 

To achieve this aim, the following objectives were 

established: 

1) Identify and compile chemical biomarkers that are 

predictive, diagnostic, and/or indicative of healing as 

reported in sources investigating the tissues and structures 

of residual limbs in adults with amputations. 

2) Identify and summarize the techniques used to quantify 

these chemical biomarkers in studies focused on the 

healing of tissues and structures in residual limbs of adults 

with amputations. 

3) Assess the quality and levels of evidence in sources 

investigating the healing of tissues and structures found in 

the residual limbs of adults with amputations. 

METHODOLOGY 

The detailed methodology and rationale for this review have 

been outlined previously in Parts 117 and 2.18 Briefly, the 

review adhered to the Preferred Reporting Items for 

Systematic Reviews and Meta-Analyses extension for 

Scoping Reviews (PRISMA-ScR) checklist31,32 and the 

Joanna Briggs Institute (JBI) guidelines.33-36 Data were 

managed using Excel Version 2303 (Microsoft, Washington, 

USA) on Windows 11 Version 22H2 (Microsoft, 

Washington, USA).  

1: INCLUSION CRITERIA AND SEARCH STRATEGY 

Finalized search terms, based on the terms “biomarker”, 

“amputation”, and “wound healing”, were applied to Web of 

Science, MEDLINE (Ovid), Embase (Ovid), Scopus, 

Cochrane, PubMed, and CINAHL databases. In stage one 

of screening titles and abstracts were screened using 

primary inclusion criteria: references to biomarkers of 

healing and publications from 2017 onward. Given the 

limited exploration of chemical biomarkers in early-stage 

post-amputation healing,8,17,18 the inclusion criteria were 

broadened to cover tissues and structures biologically 

comparable to those in a residuum (e.g., skin, muscles, 

tendons, ligaments, bone, peripheral nervous system, and 

vasculature). In the second screening phase of full-texts, 

additional criteria were introduced, including reproducible 

methodologies, clear ethical approval (where applicable), 

and participants aged 18 years or older for human studies. 

Bench research using in vitro, in silico, or murine models 

was considered for inclusion to capture biomarkers 

requiring cell or tissue samples which are ethically easier to 

obtain in these contexts. Murine models were considered 

suitable due to sufficient genetic similarities to humans and 

common use in biological research.37 Studies from all 

contexts and regions were considered if available in 

English. Search results were managed in EndNote 20 

(Version 20.2.1, Clarivate, 2021), where duplicates were 

removed.  

2: DATA EXTRACTION, ANALYSIS AND PRESENTATION 

Using a pre-defined data extraction tool (Part 1, Appendix 

A17), data (including chemical biomarkers and study 

characteristics) was extracted from sources that passed 

both screening rounds. Study quality and evidence levels 

were evaluated using the QualSyst tool38 and JBI levels of 

evidence39 respectively. All extracted data are openly 

accessible in the review’s dataset.40  

Included sources were categorized by study (randomized 

controlled trial, case-controlled, observational, or bench 

research), wound (diabetic, amputation, or other), and 

model (human, murine, or other) type. Chemical biomarkers 

that were observed more than once within and across study 

categories are reported on. These repeated chemical 

biomarkers are represented in tabular form and analyzed in 

comparison with existing literature for their indicative, 

predictive, and/or diagnostic potential in healing 

assessment. The review emphasizes recurring biomarkers, 

assuming their repeated observation indicates a stronger 

evidence base for the biomarker’s use, thus supporting 

future research. Descriptive results section (section 3: 

Measurement Techniques of The Repeated Chemical 

Biomarkers) and discussion section (section 2.2: 

Quantification Techniques) summarize biomarker 

quantification methodologies, offering additional context for 

the future use of the biomarkers in residual limb healing 

management.  

RESULTS 

1: OVERALL RESULTS  

1.1: Search Strategy Results 

As disseminated in Part 1,17 the search strategy identified 

7,041 sources for screening. Of these, 3,735 were 

duplicates and were subsequently removed (Part 1 - 

PRISMA diagram). From the remaining 3,306 articles 

screened at the title and abstract level, 646 met the criteria 

for full-text screening. 219 articles satisfied the inclusion 

criteria and were selected for data extraction. Primary 

reasons for exclusion included unclear methodologies, lack 

of ethical approval, and review article study type. Of the 219 

included sources, 203 reported on chemical biomarkers and 

are therefore the focus of this Part 3 review. 

https://doi.org/10.33137/cpoj.v8i1.43717
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Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

1.2: Quality and Levels of Evidence 

The quality assessment of the included sources revealed a 

strong emphasis on high-quality quantitative research. The 

majority of included sources (155 out of 203, or 76%)29,41-194 

were classified as being of strong quality. An additional 40 

sources195-234 were rated as good quality, while only 8 

sources235-242 fell into the adequate quality category. None 

of the sources were categorized as having limited quality.  

Contrastingly levels of evidence of the included sources 

demonstrated greater variability. In the Prognosis category, 

29 studies were classified as level 1.b, representing the 

second-highest evidence level, while only 3 studies64,68,173 

fell into level 5.c, the lowest evidence tier (Table 1). 

Conversely, within the Effectiveness category, a minimal 

number of studies were rated at the higher evidence levels, 

with 1 study207 classified as 1.b and 12 

studies75,109,170,176,182,199,204,205,210,221,222,241 as 1.c (Table 1). 

However, the majority of studies in this category, (96 

studies) were assigned to level 5.c. This prevalence of level 

5.c can be attributed to the significant number of bench 

research studies (Study Categories 9 to 13 in Table 2), which 

are considered the lowest evidence level.  

For a comprehensive discussion of the quality and evidence 

levels of all 219 sources that satisfied the inclusion criteria 

for the overall review aim, refer to Part 1.17 

1.3 Study Types and Characteristics 

Of the 203 included sources, observational and bench 

research studies accounted for the largest proportions, 

compromising 89 and 97 sources, respectively (Table 2). In 

contrast, only 13 sources were randomized controlled trials 

(RCTs), and 6 were case-controlled studies.  

In Categories 1 to 8 (Table 3), studies involving human 

participants featured sample sizes ranging from just 1 

participant in a case-controlled study145 to 11,943,000 

participants in an observational retrospective study.140 This 

large sample size is attributed to the examination of annual 

rates of hemoglobin A1c (HbA1c) testing and major leg 

amputations among Medicare patients with diabetes 

spanning from 2003 to 2012 across 306 hospital referral 

regions in the USA.140 Of the 106 sources, 95 provided 

gender information, with median male representation within 

each category ranging from 50% to 71% of participants 

(Table 3).  

Median mean participant ages exceeded 58 years across 

all study categories, with reported means ranging from 

28.8204 to 77.3220 years. Among the human participant 

studies, 68 investigated diabetic wounds, 27 focused on 

amputations (some resulting from diabetic wounds), and 20 

explored other wound types (Table 3). Examples of the latter 

included skin wounds,50,175 lower limb mangled 

extremities,154 and infected wounds.128  

Similarly, to the human participant studies, bench research 

predominantly used male subjects and focused on diabetic 

wounds. Of the murine models employed in 81% (79 

sources) of the bench research studies, 56 sources utilized 

all male rats/mice, 6 sources used all female, and the 

remaining 17 sources used both or did not specify gender 

(Table 2). In place of murine models, the remaining bench 

research studies utilized cell lines and tissue samples (15 

sources41,42,45,53,95,98,108,132,135,139,151,166,196,237,242), a mathe-

matical model (1 source76), and a gene expression dataset 

(1 source156). Categories 9 to 11 (80 sources; Table 2) 

specifically investigated diabetic wounds, while 

7,55,101,141,148,184,211,214 4,64,161,163,173 2,87,155 and 4,53,139,166,242 

sources explored skin wounds, traumatic injuries, sciatic 

nerve injuries, and wound/scratch assays (a type of cell-

based wound model), respectively.  

2: REPEATED CHEMICAL BIOMARKERS 

Of 38 identified repeated chemical biomarkers (Table 4), 

interleukins (ILs) were the most frequently reported, 

appearing in 50 sources (25% of 203 included sources). 

This was followed by glycated hemoglobin (HbA1c) and 

vascular endothelial growth factor (VEGF), which were 

utilized in 48 and 39 sources, respectively. Other notable 

biomarkers included C-reactive protein (CRP) and tumor 

necrosis factor (TNF), each reported in 34 studies, and 

albumin, which was employed in 31 sources. Biomarkers 

such as matrix metalloproteinases (MMPs), collagen, and 

creatinine were observed in 10% to 14% of sources, 

whereas less frequently reported biomarkers, including zinc 

and myeloperoxidase (MPO), were present in only 2% to 

5% of studies. 

The distribution of repeated biomarkers across study 

categories (Table 2 and Table 4) underscores the 

relationship between study design and biomarker 

prevalence. For instance, 27 of the 38 biomarkers were 

identified in bench research studies (Study Categories 9 to 

13) which primarily use murine models (79 of 97 bench 

research included sources), indicating that such study types 

provide more detailed chemical biomarker data. In contrast, 

biomarkers exclusively observed in human participant 

studies only (Study Categories 1 to 8) include HbA1c, CRP, 

white blood cells (WBC), hemoglobin (Hb), cholesterol, 

erythrocyte sedimentation rate (ESR), fasting blood sugar, 

neutrophils and lymphocytes, platelets, zinc, and 

hemoglobin, all of which are typically routine blood 

biomarkers used to assess participants’ general health 

status. Additionally, the high prevalence of diabetic wound 

studies (explored in Study Categories 1 to 3, 6, and 9 to 11) 

is reflected in the extensive use of HbA1c, which is clinically 

utilized for diabetes diagnosis.243  

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Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

 

Table 1: Levels of evidence of the 203 included articles ranked using the JBI (Joanna Briggs Institute) Levels of Evidence (44) (NA = not applicable). 

Evidence 
Level 

JBI Evidence Level Study Categories 

Effectiveness Diagnosis Prognosis 

1.a 0 0 0 

1.b 1 (207) 
7 (67, 73, 85, 
130, 154, 217, 

239) 

29 (47, 61, 69, 72-74, 85, 86, 90, 115, 116, 120, 123, 130, 138, 
144, 149, 154, 157, 159, 162, 169, 175, 197, 217, 225, 234, 

239, 240) 

1.c 12 (75, 109, 170, 176, 182, 199, 204, 205, 210, 221, 222, 241) NA NA 

1.d 0 NA NA 

2.a 0 0 0 

2.b 0 0 0 

2.c 0 NA NA 

2.d 0 NA NA 

3.a 0 0 0 

3.b 1 (104) 0 
42 (43, 44, 46, 58, 60, 62, 63, 65, 67, 77-79, 81-84, 92, 94, 100, 

102, 106, 128, 129, 131, 133, 134, 140, 142, 147, 150, 160, 
168, 171, 172, 179, 181, 185, 200, 220, 228, 230, 235) 

3.c 3 (89, 203, 217) NA NA 

3.d 3 (97, 127, 195) NA NA 

3.e 
30 (43, 46, 48, 50, 52, 57, 59, 62, 67, 73, 74, 85, 90, 94, 99, 129, 
131, 143, 144, 146, 149, 164, 175, 185, 189, 198, 224, 236, 238, 

240) 
NA NA 

4.a 0 0 0 

4.b 0 0 2 (97, 152) 

4.c 0 NA NA 

4.d 1 (145) NA NA 

5.a 0 0 0 

5.b 0 0 0 

5.c 

96 (29, 41, 42, 45, 49, 51, 53-56, 64, 66, 68, 70, 71, 76, 80, 87, 
88, 91, 93, 95, 96, 98, 101, 103, 105, 107, 108, 110-114, 117-

119, 121, 122, 124-126, 132, 135-137, 139, 141, 148, 151, 153, 
155, 156, 158, 161, 163, 165-167, 174, 177, 178, 180, 183, 184, 
186-188, 190-194, 196, 201, 202, 206, 208, 209, 211-216, 218, 

219, 223, 226, 227, 229, 231-233, 237, 242) 

3 (68, 126, 194) 3 (64, 68, 173) 

 

Table 2: Summary of the study types of all 203 included sources utilizing chemical biomarkers. The sources are categorized by study type, wound 

type, and model type, with reference numbers provided for each category as used throughout the review. 

Study Type 
Category 
Reference 
Number 

Number (%) of 
Included 
Sources 

Included Source References 

Randomized Controlled Trial 1 13 (6%) 
(75, 109, 170, 176, 182, 199, 204, 205, 207, 210, 

221, 222, 241) 

Case-Controlled Study 2 6 (3%) (97, 127, 131, 145, 152, 195) 

Observational 

Prospective 

Diabetic Wounds 3 29 (14%) 
(47, 48, 59, 61, 62, 69, 72-74, 89, 90, 115, 120, 

123, 138, 143, 144, 146, 157, 197, 198, 203, 217, 
224, 225, 234, 236, 238, 239) 

Amputation 4 8 (4%) (57, 85, 130, 149, 159, 162, 169, 240) 

Other Wounds 5 6 (3%) (50, 52, 86, 99, 154, 175) 

Retrospective 

Diabetic Wounds 6 18 (9%) 
(58, 63, 67, 81, 84, 92, 94, 100, 133, 134, 164, 

171, 172, 179, 181, 185, 230, 235) 

Amputation 7 14 (7%) 
(43, 44, 46, 60, 79, 83, 129, 140, 142, 150, 160, 

189, 200, 220) 

Other Wounds 8 12 (6%) 
(65, 77, 78, 82, 102, 104, 106, 116, 128, 147, 168, 

228) 

Bench Research 

Diabetic 
Wounds 

Rat Models 9 25 (12%) 
(51, 54, 70, 88, 96, 110, 111, 113, 117, 121, 122, 
125, 136, 137, 165, 167, 174, 186, 191, 218, 223, 

229, 231-233) 

Mouse 
Models 

10 41 (20%) 

(29, 49, 56, 66, 71, 80, 91, 93, 103, 105, 107, 112, 
114, 118, 119, 124, 126, 153, 158, 177, 178, 180, 
183, 187, 188, 190, 192-194, 201, 202, 206, 208, 

209, 212, 213, 215, 216, 219, 226, 227) 

Other Models 11 14 (7%) 
(41, 42, 45, 68, 76, 95, 98, 108, 132, 135, 151, 

156, 196, 237) 

Other 
Wounds 

Rat/Mouse 
Models 

12 13 (6%) 
(55, 64, 87, 101, 141, 148, 155, 161, 163, 173, 

184, 211, 214) 

Other Models 13 4 (2%) (53, 139, 166, 242) 

 

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6 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

 Table 3: The characteristics (wound type, sample size, gender distribution, and age) of the included sources involving human participants (Study 

Categories 1 to 8; Table 2). Note that for wound type some sources fall under more than one wound type. For example, Norvell et al.142 (a Category 7 

source) investigated wound healing of lower limb amputation due to diabetes or peripheral arterial disease. The notation “No. (%) of references” 

indicates the number and percentage of sources that provide characteristic information relative to the total number of sources within that category (T.G. 

= treatment groups; C.G. = control groups; No. = number; NA = not applicable).  

 
Study Category 

1 2 3 4 5 6 7 8 

Wound Type Totals 

Diabetic 

10 (75, 170, 
176, 182, 
199, 205, 
207, 221, 
222, 241) 

5 (127, 131, 
145, 152, 

195) 
29* 1 (159) 0 18* 

5 (83, 129, 
140, 142, 

200) 
0 

Amputation 1 (109) 
4 (97, 131, 
145, 152) 

0 8* 0 0 14* 0 

Other 2 (204, 210) 0 0 0 6* 0 0 12* 

Sample Size Totals 

Range (Min-Max) 15-200 1-120 4-684 10-556 5-735 48-1032 46-11943000 45-637 

Median 33 20 57 21 18 148 205 125 

No. (%) of References 13 (100%) 6 (100%) 29 (100%) 8 (100%) 6 (100%) 18 (100%) 14 (100%) 12 (100%) 

Sample Gender (% Male) Totals 

Range (Min-Max) 40%-82% 0%-100% 33%-91% 55%-100% 20%-79% 44%-85% 45%-99% 54%-82% 

Median 63% 50% 63% 64% 62% 64% 71% 67% 

No. (%) of References 

11 (85%) 
(109, 170, 
176, 182, 
199, 204, 
205, 207, 
210, 222, 

241) 

6 (100%) 

26 (90%) (47, 
48, 59, 61, 

62, 72-74, 89, 
90, 115, 120, 

123, 138, 
143, 144, 
146, 157, 
197, 198, 
203, 217, 
224, 225, 
234, 239) 

8 (100%) 
4 (67%) (52, 
86, 99, 175) 

15 (83%) (58, 
63, 67, 81, 
84, 92, 94, 
100, 164, 
171, 172, 
181, 185, 
230, 235) 

14 (100%) 

11 (92%) (65, 
77, 78, 82, 

102, 104, 106, 
116, 128, 147, 

168) 

Sample Mean Age (Years) Total 

Range (Min-Max) 
T.G.: 40.6-
69.0; C.G.: 
28.8-64.7 

60.2-65.0 47.4-73.4 49.0-74.0 NA 54.5-72.5 38.0-77.3 56.0-74.0 

Median 
T.G. 58.1; 
C.G.: 58.9 

61.5 59.5 65.2 NA 61.2 66.7 72.0 

No. (%) of References 

12 (92%) (75, 
109, 170, 
176, 182, 
199, 204, 
205, 207, 
210, 222, 

241) 

3 (50%) (97, 
127, 145) 

27 (93%) (47, 
48, 59, 61, 

62, 69, 72-74, 
89, 90, 115, 
120, 123, 
138, 143, 
144, 146, 
157, 197, 
198, 203, 
217, 224, 
225, 234, 

239) 

6 (75%) (85, 
149, 159, 
162, 169, 

240) 

NA 

16 (89%) (58, 
63, 67, 81, 
84, 92, 94, 
100, 133, 
134, 164, 
171, 181, 
185, 230, 

235) 

12 (86%) (43, 
46, 60, 83, 
129, 140, 
142, 150, 
160, 189, 
200, 220) 

8 (67%) (65, 
77, 78, 102, 

104, 106, 147, 
168) 

Sample Age Range (Years) Total 

Range (Min-Max) NA 35-94 20-89 23-87 28-88 23-100 26-96 22-96 

No. (%) of References NA 
3 (50%) (131, 

152, 195) 

11 (38%) (48, 
61, 69, 90, 
115, 138, 
143, 198, 
203, 217, 

225) 

3 (38%) (57, 
85, 159) 

4 (67%) (52, 
86, 99, 175) 

6 (33%) (63, 
84, 94, 133, 
134, 235) 

2 (14%) (43, 
129) 

1 (8%) (147) 

Sample Median Age (Years) Totals 

Range (Min-Max) NA NA NA NA NA NA 47.0-62.0 31.0-71.2 

Median NA NA NA NA NA 72.5 54.5 68.4 

No. (%) of References NA NA NA NA NA 1 (6%) (172) 
2 (14%) (44, 

79) 
3 (25%) (82, 

116, 128) 

*References are provided in Table 2. 

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7 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

 Table 4: A comprehensive breakdown of the repeated chemical biomarkers. A biomarker was considered “repeated” if it was used in more than one 
source within a study category and appeared in more than one study category. The occurrence of these biomarkers in the 203 included sources is 
presented, along with their representation across the various study categories (Table 2; freq. = frequency; ILs = interleukins; HbA1c = glycated 
hemoglobin; VEGF = vascular endothelial growth factor; CRP = C-reactive protein; TNF = tumor necrosis factor; TGF = transforming growth factor; 
WBC = white blood cells; CD = cluster of differentiation; Hb = hemoglobin; α-SMA = alpha-smooth muscle actin; MMPs = matrix metalloproteinases; 
FGF = fibroblast growth factor; ESR = erythrocyte sedimentation rate; PDGF = platelet-derived growth factor; CCLs = chemokine (C-C motif) ligands; 
MCPs = monocyte chemoattractant proteins; EGF = endothelial growth factor; IFN = interferon; iNOS = inducible nitric oxide synthase; eNOS = 
endothelial nitric oxide synthase; HIF-1α = hypoxia-inducible factor 1 alpha; Ki-67 = Kiel 67; NF-κB = nuclear factor kappa B; MPO = myeloperoxidase; 
TIMPs = tissue inhibitors of metalloproteinases; ROS = reactive oxygen species; p-ERK = phosphorylated Extracellular Signal-Regulated Kinase; IGF 
= Insulin-like growth factor). 

Repeated 
Chemical 

Biomarkers 

Sources Study Categories 

Freq. 
% 

Included 
Sources 

References Freq. 
% of 

Categories 
Categories 
Included 

ILs 50 25% 

(42, 45, 48, 54, 57, 75, 80, 87, 91, 101, 103, 105, 107, 108, 110, 111, 
117, 121, 124, 130, 135, 137, 138, 153, 156, 158, 161, 167, 170, 173, 
176, 177, 180, 183, 187, 191, 194, 196, 198, 205, 206, 209, 212, 213, 

215, 216, 223, 226, 227, 236) 

7 54% 
1, 3, 4, 9, 10, 

11, 12 

HbA1c 48 24% 
(46, 58, 59, 61, 63, 67, 69, 72-74, 81, 83, 84, 90, 92, 94, 102, 109, 123, 
133, 134, 140, 143-147, 152, 157, 159, 160, 162, 169, 170, 172, 181, 

189, 195, 197, 198, 200, 203, 207, 222, 224, 228, 230, 234) 
7 54% 

1, 2, 3, 4, 6, 7, 
8 

VEGF 39 19% 
(42, 48, 51, 52, 54, 56, 59, 70, 75, 76, 99, 103, 108, 112-115, 119, 121, 
124, 125, 143, 148, 153, 158, 161, 165, 167, 177, 184, 186, 190, 191, 

193, 204, 205, 209, 223, 236) 
7 54% 

1, 3, 5, 9, 10, 
11, 12 

CRP 34 17% 
(46, 47, 65, 69, 74, 78, 82-84, 90, 92, 94, 102, 106, 123, 129, 133, 134, 
145, 150, 152, 157, 160, 164, 168, 170, 172, 185, 198, 199, 209, 220, 

230, 235) 
6 46% 1, 2, 6, 7, 8 

TNF 34 17% 
(42, 45, 54, 59, 71, 75, 76, 101, 103, 117, 121, 124, 135, 137, 153, 156, 
158, 161, 167, 170, 173, 180, 191, 196, 198, 209, 212, 215, 216, 222, 

223, 227, 232, 236) 
6 46% 

1, 3, 9, 10, 11, 
12 

Albumin 31 15% 
(43, 44, 46, 58, 60, 63, 65, 77-79, 83, 84, 90, 102, 104, 106, 116, 133, 

134, 157, 160, 161, 163, 172, 173, 179, 189, 198, 220, 234, 235) 
5 38% 3, 6, 7, 8, 12 

TGF 29 14% 
(42, 51, 59, 70, 75, 76, 103, 113, 117, 121, 124, 132, 135, 137, 148, 158, 

161, 165, 167, 170, 182, 183, 190, 191, 213, 215, 233, 236, 238) 
6 46% 

1, 3, 9, 10, 11, 
12 

WBC 27 13% 
(44, 47, 59, 63, 77, 78, 82-84, 90, 94, 116, 123, 133, 134, 142, 152, 157, 

159, 169, 172, 195, 220, 224, 230, 234, 235) 
6 46% 2, 3, 4, 6, 7, 8 

CD31 27 13% 
(51, 55, 66, 71, 80, 93, 96, 103, 105, 107, 114, 119, 148, 165, 174, 177, 

187, 190, 191, 194, 206, 208, 209, 211, 218, 219, 233) 
3 23% 9, 10, 12 

Hb 26 13% 
(46, 47, 58, 61, 63, 69, 72, 78, 83, 84, 90, 94, 106, 116, 120, 133, 134, 

145, 160, 168, 172, 189, 195, 197, 198, 217) 
5 38% 2, 3, 6, 7, 8 

α-SMA 23 11% 
(54, 55, 71, 87, 88, 103, 113, 117, 119, 136, 148, 153, 174, 177, 178, 

180, 190-192, 213, 215, 216, 233) 
3 23% 9, 10, 12 

MMPs 22 11% 
(41, 45, 54, 59, 101, 113, 115, 167, 177, 191, 193, 202, 203, 211, 215, 

216, 223, 226, 227, 232, 236, 239) 
5 38% 3, 9, 10, 11, 12 

Collagen 21 10% 
(41, 54, 55, 59, 71, 75, 88, 105, 108, 113, 117, 137, 153, 191, 212-214, 

216, 233, 236, 241) 
6 46% 

1, 3, 9, 10, 11, 
12 

Creatinine 20 10% 
(77, 78, 84, 94, 116, 133, 134, 144, 157, 159, 161-163, 170, 173, 198, 

199, 228, 234, 235) 
6 46% 1, 3, 4, 6, 8, 12 

Cholesterol 16 8% (67, 69, 72, 74, 81, 84, 90, 92, 109, 133, 134, 170, 172, 224, 230, 234) 3 23% 1, 3, 6 

FGF 16 8% 
(41, 42, 49, 51, 98, 105, 107, 125, 136, 139, 148, 156, 166, 184, 191, 

215) 
5 38% 

9, 10, 11, 12, 
13 

ESR 14 7% (47, 59, 84, 94, 123, 133, 134, 157, 164, 198, 199, 207, 230, 235) 3 23% 1, 3, 6 

CCLs/MCPs 12 6% (42, 45, 108, 135, 148, 161, 177, 196, 206, 209, 212, 215) 3 23% 10, 11, 12 

PDGF 11 5% (42, 56, 76, 124, 125, 137, 141, 143, 161, 186, 236) 5 38% 3, 9, 10, 11, 12 

Fasting Blood 
Sugar 

11 5% (61, 63, 67, 74, 84, 100, 133, 134, 143, 171, 230) 2 15% 3, 6 

Neutrophils and 
Lymphocytes 

11 5% (47, 61, 63, 90, 129, 157, 171, 172, 179, 200, 224) 3 23% 3, 6, 7 

EGF 10 5% (41, 42, 56, 139, 143, 151, 166, 193, 198, 238) 4 31% 3, 10, 11, 13 

Total Proteins 10 5% (54, 92, 94, 117, 133, 134, 137, 185, 198, 234) 3 23% 3, 6, 9 

Platelets 9 4% (44, 47, 69, 79, 84, 133, 134, 142, 200) 3 23% 3, 6, 7 

IFN 9 4% (41, 45, 48, 91, 161, 173, 183, 198, 227) 4 31% 3, 10, 11, 12 

iNOS and eNOS 9 4% (71, 124, 167, 180, 191, 194, 206, 209, 226) 2 15% 9, 10 

HIF-1α 7 3% (48, 114, 119, 186, 191, 194, 236) 3 23% 3, 9, 10 

Bacteria 7 3% (61, 91, 115, 138, 201, 225, 227) 2 15% 3, 10 

Ki-67 7 3% (29, 51, 165, 177, 191, 219, 231) 2 15% 9, 10 

NF-κB 6 3% (48, 54, 117, 121, 167, 236) 2 15% 3, 9 

MPO 6 3% (121, 213, 216, 219, 227, 233) 2 15% 9, 10 

Zinc 5 2% (58, 104, 106, 133, 134) 2 15% 6, 8 

TIMPs 5 2% (45, 108, 115, 203, 236) 2 15% 3, 11 

CD68 5 2% (51, 117, 148, 155, 233) 2 15% 9, 12 

ROS 5 2% (49, 118, 165, 202, 218) 2 15% 9, 10 

Hematocrit 4 2% (44, 142, 152, 195) 2 15% 2, 7 

ERK, p-ERK, and 
p-ERK1/2 

4 2% (29, 54, 113, 192) 2 15% 9, 10 

IGF 4 2% (48, 124, 177, 236) 2 15% 3, 10 

 

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8 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

3: MEASUREMENT TECHNIQUES OF THE REPEATED 

CHEMICAL BIOMARKERS 

Gene expression was analyzed for 15 of the repeated 

chemical biomarkers using qRT-PCR (quantitative real-time 

polymerase chain reaction), including TaqMan assays 

(Table 5 and Table 6). Immunostaining, to quantify biomarker 

expression in wound tissue samples, was similarly used for 

15 of the 37 repeated biomarkers, such that qRT-PCR and 

immunostaining were the most frequently used 

quantification techniques. Interestingly, quantifying wound 

tissue biomarker expression used the greatest array of 

measurement techniques, including ELISA (enzyme-linked 

immunosorbent assay) kits, Western Blot, immunostaining, 

gelatine zymography, and multiplex immunoassays.  

The number of measurement techniques for each 

biomarker varied. MMPs, for example, were assessed using 

7 techniques, whereas markers found in the blood such as 

HbA1c (glycated hemoglobin), Hb (hemoglobin), WBC 

(white blood cells), and platelets were analyzed using only 

one method, a routine blood test. 

Table 5: Measurement techniques reported in included sources used to quantify gene expression, serum expression, and/or wound tissue 

expression of the identified repeated chemical biomarkers (ELISA = enzyme-linked immunosorbent assay; qRT-PCR = quantitative real-time 

polymerase chain reaction; biomarker abbreviations are defined in the Table 4 caption).  

 Biomarker Measurement Techniques 

 Gene Expression Serum Expression Wound Tissue Expression 

Repeated Chemical 
Biomarkers 

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Albumin   ✓         

α-SMA        ✓    

CCLs/MCPs ✓ ✓  ✓  ✓  ✓    

CD31        ✓    

CD68        ✓    

Cholesterol   ✓         

Collagen ✓           

Creatinine   ✓         

CRP   ✓         

EGF      ✓ ✓ ✓    

ERK, p-ERK, and p-
ERK1/2 

✓      ✓ ✓    

ESR   ✓         

Fasting blood sugar   ✓         

FGF ✓     ✓  ✓    

Hematocrit   ✓         

Hb   ✓         

HbA1c   ✓         

HIF-1α ✓   ✓   ✓ ✓    

IFN ✓   ✓ ✓     ✓  

IGF ✓           

ILs ✓   ✓  ✓ ✓     

iNOS and eNOS       ✓ ✓    

Ki-67        ✓    

MMPs ✓ ✓  ✓  ✓ ✓ ✓ ✓   

MPO      ✓  ✓    

Neutrophils and 
Lymphocytes 

  ✓         

NF-κB ✓     ✓ ✓     

PDGF ✓     ✓  ✓    

Platelets   ✓         

ROS        ✓   ✓ 

TGF ✓   ✓  ✓      

TIMPs ✓ ✓    ✓ ✓     

TNF ✓   ✓  ✓      

Total proteins   ✓         

VEGF ✓   ✓  ✓ ✓ ✓    

WBC   ✓         

Zinc   ✓         

Totals 15 3 14 8 1 12 9 16 1 1 1 

% of 37 Biomarkers 41% 8% 3% 22% 3% 32% 24% 43% 3% 3% 3% 

 

 

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9 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

 

Table 6: Overview of the measurement techniques utilized in the included sources to quantify the chemical biomarkers referenced (Table 4 

(biomarkers); Table 5 (corresponding quantification techniques); ELISA = enzyme-linked immunosorbent assay; MMP = matrix metalloproteinase; 

qRT-PCR = quantitative Real-Time Polymerase Chain Reaction; TaqDNA = Taq Deoxyribonucleic Acid Polymerase; miRNA = micro ribonucleic 

acid; BLI = bioluminescence imaging; NADPH = nicotinamide adenine dinucleotide phosphate; ROS = reactive oxygens species). 

Chemical Biomarker 
Measurement Technique 

Brief Description of Principle 

Gelatin Zymography 
Method to detect proteolytic enzymes capable of degrading gelatin from biological sources such as the gelatinases MMP-2 and 
MMP-9 (244). 

ELISA  
Employs the catalytic properties of enzymes to detect and quantify immunologic reactions (245). It is a solid-phase test generating 
a color reaction and is therefore easy to interpret (246). 

Multiplex Immunoassay  
It utilizes traditional immunoassay methods working on the principle of exploiting binder molecules (antibodies, proteins, or 
peptides) to capture circulating proteins or antibodies (246). Unlike ELISA, multiplex immunoassays enable the simultaneous 
measurement of multiple analytes in a single biological sample (246). 

TaqMan Assay 
This is a specific form of qRT-PCR and one of the earliest methods introduced for real-time PCR monitoring (247). It exploits the 
5' endonuclease activity of TaqDNA polymerase (an enzyme) to cleave an oligonucleotide probe during PCR, thereby generating 
a detectable signal (247). 

Western Blot 

Method to detect protein molecules among a mixture (248). The key steps include cell lysis (makes protein unfold into linear 
chains coated with a negative charge), gel electrophoresis (sorts proteins by size), blocking (prevents nonspecific reactions from 
occurring), incubating the sample with a primary antibody (binds specifically to the protein of interest), and finally incubating with 
a secondary antibody which binds to the primary and produces some signal (such as color or light) (248). 

qRT-PCR 
This is considered the gold standard for quantifying miRNAs with high sensitivity and specificity (249). It utilizes fluorescence 
generated during PCR to reflect the amount of DNA amplicons in a sample at a specific time (250). 

Immunostaining 
Requires incubating a tissue sample with antibodies specific to the protein of interest, which can then be visualized with a 
fluorescence (immunofluorescence) or chromogen (immunohistochemistry) which is bound to or binds to the antibody (251). 

Luminol-Based 
Bioluminescence Imaging 
(BLI) 

As demonstrated by Nguyen et al. (202), superoxide derived from NADPH oxidase can be detected through bioluminescence 
imaging by intraperitoneally injecting an animal with L-012. L-012 is a luminol-based chemiluminescent probe that emits light 
upon reacting with ROS (252). The intensity of the luminescent signal, measured in photons per second per centimeter squared, 
correlates with the amount of superoxide present, where a higher signal indicates greater superoxide levels, the most abundant 
ROS (253). 

 

Table 7: Classification of the repeated chemical biomarkers used in included sources as predictive, indicative, or diagnostic when considering 

their influence on the healing process and their behavior in the reviewed sources. 

Biomarker Predictive Indicative Diagnostic 

Routine Blood Profile 
Biomarkers 

Cholesterol (includes high and low-density lipoproteins and triglycerides) ✓   

Erythrocyte Sedimentation Rate (ESR) ✓ ✓  

Fasting Blood Sugar (or Fasting Plasma Glucose) ✓   

Glycated Hemoglobin (HbA1c) ✓   

Hematocrit (HCT) ✓ ✓  

Hemoglobin (Hb) ✓   

Neutrophils and Lymphocytes ✓   

Platelets ✓   

Total Proteins ✓ ✓  

White Blood Cell (WBC) Counts ✓   

Growth Factors 

Epidermal Growth Factor (EGF)  ✓  

Fibroblast Growth Factor (FGF)  ✓  

Insulin-Like Growth Factor (IGF)  ✓  

Platelet-Derived Growth Factor (PDGF)  ✓  

Transforming Growth Factor (TGF)  ✓  

Tumor Necrosis Factor (TNF)  ✓  

Vascular Endothelial Growth Factor (VEGF)  ✓  

Albumin ✓   

Alpha-Smooth Muscle Actin (α-SMA)  ✓ ✓ 

Bacteria (includes Colony-forming units, bacterial counts, and bacterial RNA assessment) ✓   

CC Chemokines (also known as Monocyte Chemoattractant [MCPs])  ✓  

Clusters of Differentiation (CD68 and CD31)   ✓ 

Collagen  ✓ ✓ 

C-Reactive Protein (CRP) ✓ ✓  

Creatinine ✓   

Endothelial and Inducible Nitric Oxide Synthase (eNOS and iNOS)  ✓  

Extracellular Signal-Regulated Kinases (ERKs)  ✓ ✓ 

Hypoxia Inducible Factor-1 (HIF-1)  ✓  

Interferon (IFN) ✓ ✓  

Interleukins (ILs) ✓ ✓ ✓ 

Kiel-67 (Ki-67)  ✓  

Matrix Metalloproteinases (MMPs) and Tissue Inhibitors of Metalloproteinases (TIMPs)  ✓ ✓ 

Myeloperoxidase (MPO)  ✓  

Nuclear Factor Kappa-Light-Chain-Enhancer of Activated B Cells (NF-κB)  ✓  

Reactive Oxygen Species (ROS)  ✓  

Zinc ✓   

 

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10 

Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

Bacteria were assessed somewhat differently, as presented 

in the following list, and are therefore excluded from Table 

5: 

• Biofilms were detected using the tissue culture plate 

method. 

• Antimicrobial susceptibility testing was performed using 

the Kirby-Bauer disc diffusion method. 

• Molecular characterization of biofilm-forming resistant 

isolates was done by PCR. 

DISCUSSION 

1: KEY FINDINGS  

This scoping review identifies chemical biomarkers 

associated with the healing of tissues and structures in the 

residual limbs of adults with amputation. These biomarkers 

serve predictive, indicative, and diagnostic purposes, 

offering a foundation for improved prosthesis readiness and 

residuum health assessments.  

Predictive biomarkers such as bacterial counts, nutritional 

markers (e.g., zinc, albumin), and routine blood markers 

(e.g., glycated hemoglobin [HbA1c], white blood cells 

[WBC], C-reactive protein [CRP]) indicate health status and 

help anticipate healing outcomes. For instance, elevated 

HbA1c is predictive of impaired healing due to 

hyperglycemia. Indicative biomarkers like growth factors, 

ILs, and reactive oxygen species (ROS) reflect critical 

healing processes, enabling monitoring of healing 

progression. Diagnostic biomarkers, such as alpha-smooth 

muscle actin (α-SMA), offer clear insights into wound 

healing at the cellular level.  

Despite identifying 38 biomarkers in research, only routine 

blood markers are used clinically. Limited application stems 

from reliance on experimental methods (e.g., 

immunohistochemical staining) often restricted to animal 

studies. Bridging this gap requires advancements in 

measurement techniques that negate the need for wound 

tissue samples. 

Population-specific factors (e.g., age, gender, 

comorbidities) and measurement differences (e.g., timing, 

location) influence healing and biomarker behavior. Thus, to 

improve healing assessment objectivity, a combination of 

biomarkers is required.  

2: REPEATED CHEMICAL BIOMARKERS 

2.1: Chemical Biomarkers 

To classify a biomarker as predictive, indicative, or 

diagnostic (Table 7), its role in the healing process and 

observed behavior in the reviewed sources must be 

considered. For example, interleukins (ILs), a class of 

cytokines predominantly expressed by leukocytes, are 

integral to inflammatory and immune responses254 and 

critical for wound healing.  For instance, IL-2 receptors are 

present on macrophages, lymphocytes, keratinocytes, 

fibroblasts, vascular endothelial cells, and T-cells; cells that 

influence the entire healing process.255 Additionally, 

research on the treatment of diabetic foot ulcers (DFUs) with 

Therapeutic Magnetic Resonance (TMR®) devices revealed 

increased IL-10 expression and improved healing.75 

Similarly, elevated IL-1RL2 and IL-33 gene expression is 

linked to inflammation and bone remodeling, suggesting 

predictive potential for healing post-percutaneous 

osseointegrated prosthesis implantation.130 Thus, ILs can 

be predictive, indicative, and diagnostic of healing.  

Predictive biomarkers, such as bacterial counts, nutritional 

markers (like zinc and albumin), and routine blood profile 

markers like glycated hemoglobin (HbA1c), white blood cell 

counts (WBCs), and C-reactive protein (CRP), indicate an 

individual’s health status, enabling the anticipation of 

healing outcomes. For example, bacterial counts reflect the 

wound microbiome and potential infection, which impairs 

healing.256 Similarly, HbA1c indicates glycemic control243 

and predicts healing, as hyperglycemia inhibits keratinocyte 

migration and promotes oxidative stress through reactive 

oxygen species (ROS) production.257 Zinc is a marker of 

nutritional status,258 with deficiency negatively impacting 

healing,259 and supplementation accelerating it.260 

Predictive biomarkers primarily identify comorbidities or 

conditions, such as infection or poor nutritional status, that 

contribute to impaired healing rather than diagnosing 

specific healing mechanisms. This makes them appropriate 

for pre-amputation risk assessments, given the high 

prevalence of comorbidities, such as diabetes, among 

individuals undergoing amputation. For example, the 

Scottish Physiotherapy Amputee Research Group 

(SPARG) reported in 2019 that 56% of lower limb amputees 

recorded had the etiology of diabetes.261  

Indicative biomarkers, including growth factors (Table 7), 

ILs, and signaling molecules like ROS and nuclear factor 

kappa-light-chain-enhancer of activated B cells (NF-κB), 

reflect biological processes essential for healing (e.g., 

tissue remodeling and cellular proliferation), enabling 

monitoring and quantification of progress. For example, 

vascular endothelial growth factor (VEGF) promotes 

angiogenesis by influencing vascular endothelial cells, 

keratinocytes, and macrophages.262 Supporting this, Kim et 

al.103 demonstrated that increased VEGF levels correlated 

with near-complete epithelial coverage in a diabetic wound 

mouse model treated with Substance P, indicating healing.  

The 6 identified diagnostic biomarkers (Table 7) provide 

insights into tissue structure and composition, enabling 

precise healing assessments. For example, alpha-smooth 

muscle actin (α-SMA), expressed temporarily by 

myofibroblasts during their differentiation from granulation 

tissue fibroblasts,263 serves as a marker of smooth muscle 

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Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

differentiation and wound contraction,264 diagnosing healing 

progression during the epithelialization phase. By directly 

reflecting healing at a cellular level, diagnostic biomarkers 

offer the most objective insights into healing progression 

and hold high objective value for inclusion in a post-

amputation healing assessment scale.  

While numerous biomarkers show promise for enhancing 

post-amputation healing, their clinical application requires 

careful categorization and validation. Indicative biomarkers, 

such as growth factors, also diagnose specific molecular 

mechanisms, suggesting potential as diagnostic tools. For 

example, Ki-67, a marker of cellular proliferation,26,27 plays 

a diagnostic role by identifying fibroblast, endothelial cell, 

and keratinocyte proliferation (key processes in cutaneous 

wound healing).28 However, further research in amputee-

specific populations is needed to validate such biomarkers 

for clinical use, facilitating their transition from bench 

research to diagnostic applications. 

Many identified biomarkers, including ILs and WBCs, are 

integral to immune and inflammatory responses. Elevated 

WBC counts (leukocytosis), for instance, are linked to 

higher risks of re-amputation, longer post-amputation 

healing times, and greater chance of amputation due to 

DFUs.44,84,235 However, leukocytosis may stem from factors 

unrelated to wound healing, such as infections elsewhere, 

medications, stress, or serious conditions like leukemia.265 

To address this variability in biomarker causation, a broader 

array of biomarkers is needed to capture all phases of 

healing and account for patient-specific factors known to 

affect healing like stress,266 poor nutrition,259 renal 

disease,267 smoking,268,269 and alcohol use.270 

The limitations of predictive biomarkers are evident in 

conflicting findings. To illustrate, Adams et al.43 reported 

higher mortality rates after transmetatarsal amputation 

(TMA) in patients with preoperative albumin levels below 

3.5 g/dL (p < 0.05). Similarly, Brookes et al.58 observed 

significantly lower albumin levels in amputees compared to 

non-amputees (p = 0.03). However, Ahn et al.44 found no 

significant correlation between serum albumin and TMA re-

amputation rates (p = 0.644). Although the sources differ in 

participant populations and follow-up durations, the 

contrasting conclusions highlight the need for a biomarker 

profile rather than relying on a single predictive biomarker. 

This will enhance predictive accuracy whilst acknowledging 

a biomarker’s limitations.  

Future research must clarify the impact of quantification 

timing and location on biomarker levels during healing, to 

optimize their clinical application. For example, Anguiano-

Hernandez et al.48 demonstrated that NF-κB expression and 

localization are indicative of healing progression. In DFU 

patients treated with hyperbaric oxygen therapy, NF-κB 

expression decreased, and its localization shifted from 

nuclear to cytoplasmic in endothelial cells and fibroblasts, 

correlating with complete healing.48 These findings stress 

the importance of not only measuring biomarker levels but 

also assessing their localization to fully understand their role 

in the healing process. 

A comprehensive biomarker profile that spans predictive, 

indicative, and diagnostic categories is essential to enhance 

the assessment and management of post-amputation 

healing. Such profiles would account for comorbidities, 

capture all healing stages, and improve clinical decision-

making, particularly in early prosthetic rehabilitation. 

2.2: Quantification Techniques 

The method by which a biomarker is quantified dictates its 

applicability in research and clinical settings. Diagnostic 

markers like Ki-67, CDs (clusters of differentiation), α-SMA, 

and ERKs (extracellular signal-regulated kinases) rely on 

techniques such as immunohistochemical staining, 

immunofluorescence, or RT-qPCR (Table 5), which require 

tissue samples, making them time-consuming, costly, and 

ethically challenging in human studies. Consequently, their 

use is largely confined to murine models emphasizing the 

need for advancements in quantification techniques. For 

example, ROS can be quantified via non-invasive in vivo 

chemiluminescence imaging,202 validated in animal 

models271 but untested clinically.  

Conversely, biomarkers like CRP and routine blood 

markers, measurable through peripheral blood draws,272 

are more feasible for human studies and already employed 

in clinical settings.273 Unfortunately, such biomarkers are 

typically predictive or indicative, whereas markers like Ki-67 

and α-SMA are diagnostic and thus hold greater clinical 

value.  

Developing accessible, cost-effective, and ethically viable 

quantification techniques will facilitate the integration of 

chemical biomarkers into research and clinical practice, 

ultimately optimizing post-amputation healing and 

prosthetic rehabilitation outcomes. 

3: OVERALL SEARCH RESULTS AND STUDY 

CHARACTERISTICS 

Trends in study characteristics align with the findings from 

the Part 2 review;18 for full details, refer to Part 2. Diabetic 

wounds dominated the reviewed sources, reflecting the 

global diabetes burden,274 with DFUs being a major diabetic 

complication275 and a risk factor for amputation,276,277 

reinforcing the importance of pre-amputation biomarker 

assessments to identify comorbidities predictive of non-

healing like diabetes.8  

Aging further complicates the healing process, with the 

median mean age of participants in Study Categories 1 to 8 

ranging from 58.1 to 72.0 years. Non-healing wounds are 

often linked to vascular disease,278 venous insufficiency,279 

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Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

areas of high unrelieved pressure,280 diabetes,281 and 

disability;282 conditions that are increasingly prevalent as 

the population ages.281 Aging contributes to prolonged 

inflammation and increased ROS production,283 

necessitating objective measures to monitor wound healing, 

particularly for older adults requiring prosthetic fitting.261  

Gender differences were evident, with male participants at 

higher risk for DFU development,284 poorer DFU healing,285 

increased post-surgery infection rates,286 and higher in-

hospital immortality rates after trauma.287 This highlights the 

need for gender-specific research288 and biomarkers 

unaffected by hormonal or gender-related factors. 

Most included sources investigated wound healing in 

populations similar to individuals with amputation rather 

than residual limb healing specifically, highlighting the lack 

of standardized approaches and the need for a foundational 

database of biomarkers for residual limb recovery, 

particularly for lower limbs, which have unique health 

requirements due to weight-bearing during ambulation. 

4: METHODOLOGICAL DISCUSSION 

4.1: Methodological Strengths 

This review’s methodology aligns with Part 1 and Part 2; 

detailed discussions of methodological strengths, 

limitations, and ethical considerations can be found there.  

This review broadly explored chemical biomarkers 

associated with post-amputation healing, serving as a 

foundation for future systematic reviews on specific 

biomarkers supported by high-quality evidence. A key 

strength is its focus on diagnostic, predictive, and indicative 

biomarkers with the potential to improve the prevention and 

treatment of non-healing surgical sites and to enhance post-

amputation healing assessment, enabling timely prosthetic 

interventions.289 

4.2: Methodological Limitations 

Limitations include the unreliability of animal studies due to 

biological differences290 and the oversimplification of human 

biology in mathematical models,291,293 requiring cautious 

interpretation of biomarker behavior reported in these 

source types. While the review included wound types 

relevant to the residuum, future research should 

differentiate between the healing of secondary intention 

wounds (e.g., DFUs) and primary intention wounds (e.g., 

surgical sites). Additionally, prioritizing only repeatedly 

studied biomarkers risks oversimplification.  

5: ETHICAL CONSIDERATIONS 

Ethical rigor was prioritized over strict adherence to 

evidence hierarchies, such that only studies with clear 

ethical approval and informed consent from participants 

aged 18 or older were included. Grey literature was 

reviewed to reduce bias,294 but none met the inclusion 

criteria due to methodological shortcomings and lack of 

ethical transparency.  

CONCLUSION 

This scoping review identified 38 repeated chemical 

biomarkers relevant to healing in the tissues and structures 

in residual limbs of adults with amputation, classified as 

predictive, indicative, or diagnostic based on their function 

and behavior in the 203 reviewed sources. Predictive 

biomarkers, such as blood markers (e.g., glycated 

hemoglobin [HbA1c], white blood cells [WBC]), assess 

health and healing potential, aiding pre-amputation risk 

assessments and identifying conditions impairing healing, 

like infection or poor nutrition. Indicative biomarkers, 

including growth factors and interleukins (ILs), reflect 

biological processes like cell proliferation and tissue 

remodeling, essential for post-amputation healing. For 

instance, vascular endothelial growth factor (VEGF) 

supports angiogenesis (blood vessel formation), a key 

healing component. Diagnostic biomarkers, such as alpha-

smooth muscle actin (α-SMA), reveal tissue structure and 

healing progress at the cellular level.   

While many biomarkers show potential for improving post-

amputation healing, their clinical application requires careful 

validation in amputee populations. Biomarkers like WBCs 

play a key role in immune responses, but elevated WBC 

counts can be influenced by factors unrelated to wound 

healing, such as infections or stress. Using a biomarker 

array could better capture all healing stages and account for 

comorbidities, population differences, and lifestyle factors 

(e.g. infection, poor nutrition, smoking, alcohol use, gender, 

and age) known to affect healing. Understanding the impact 

of biomarker quantification, timing and location (e.g. wound 

fluid or serum) is crucial for clinical optimization.  

Integrating diagnostic biomarkers into clinical practice is 

challenged by the invasive and complex nature of current 

measurement techniques. Most biomarkers, apart from 

routine blood markers like cholesterol and WBC counts 

(which are predictive of healing), remain confined to 

research due to reliance on techniques like 

immunohistochemistry requiring tissue samples, raising 

ethical and logistical barriers. Further research must 

develop accessible, non-invasive diagnostic tools. Bridging 

the gap between experimental research and clinical 

application is essential to standardize post-amputation 

healing assessments, reduce subjectivity, and ultimately 

enhance patient rehabilitation outcomes.  

ACKNOWLEDGEMENTS 

The author of this article would like to express appreciation to the 

Strathclyde Body Device Interface Mechanobiology Research 

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Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. 
Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 

CANADIAN PROSTHETICS & ORTHOTICS JOURNAL 

ISSN: 2561-987X WOUND MANAGEMENT: CHEMICAL BIOMARKERS 

Williams-Reid et al., 2025 

Group for their assistance in the discussion of the review’s 

methodology. 

DECLARATION OF CONFLICTING INTERESTS 

The author has no conflicts of interest to declare. 

AUTHORS’ CONTRIBUTION 

• Hannelore Williams-Reid: the primary author of the 

manuscript, undertook the scoping review and prepared the 

final manuscript as part of a 4-year PhD program.  

• Arjan Buis: the primary PhD supervisor, assisted in developing 

the scoping review methodology and preparing the manuscript 

for publication. 

• Anton Johannesson: the secondary PhD supervisor, assisted 

in developing the scoping review methodology and preparing 

the manuscript for publication. 

 

All authors have read and approved the final version of the 

manuscript. 

 

SOURCES OF SUPPORT 

The PhD project under which this scoping review/manuscript falls 

is funded by the UKRI EPSRC as part of the Centre of Doctoral 

Training (CDT) in Prosthetics and Orthotics (P&O) (studentship 

2755854 "Wound management and early prosthetic rehabilitation" 

within project EP/S02249X/1) and by Össur. 

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Abbreviations & Acronyms: 

 

Abbreviations 

& Acronyms 
Definition 

BLI Bioluminescence Imaging 

CCL Chemokine (C-C motif) Ligand 

CD Cluster of Differentiation 

CRP C-Reactive Protein 

DFU Diabetic Foot Ulcer 

DNA Deoxyribonucleic Acid 

EGF Epidermal Growth Factor 

ELISA Enzyme-Linked Immunosorbent Assay 

eNOS Endothelial Nitric Oxide Synthase 

ERK Extracellular signal-Regulated Kinase 

ESR Erythrocyte Sedimentation Rate 

FBS Fasting Blood Sugar 

Freq. Frequency 

FGF Fibroblast Growth Factor 

Hb Hemoglobin  

HbA1c Hemoglobin A1C (glycated hemoglobin)  

HCT Hematocrit 

HDL High-Density Lipoprotein 

HIF1-α Hypoxia-Inducible Factor 1 Alpha 

IFN Interferon 

IGF Insulin-like Growth Factor 

IL Interleukin 

iNOS Inducible Nitric Oxide Synthase 

JBI Joanna Briggs Institute 

Ki-67 Antigen Kiel 67 

LDL Low-Density Lipoprotein 

MCP Monocyte Chemoattractant Protein 

MMP Matrix Metalloproteinases 

MPO Myeloperoxidase 

NA Not Applicable 

NADPH Nicotinamide Adenine Dinucleotide Phosphate 

NF-κB 
Nuclear Factor kappa-light-chain-enhancer of 

activated B cells 

No. Number 

PDGF Platelet-Derived Growth Factor 

PRISMA-ScR 
Preferred Reporting Items for Systematic Reviews 

and Meta-Analyses extension for Scoping Reviews 

qRT-PCR 
Quantitative Reverse Transcription Polymerase 

Chain Reaction 

RCT Randomized Controlled Trial 

Refs. References 

RNA Ribonucleic Acid 

ROS Reactive Oxygen Species 

SPARG Scottish Physiotherapy Amputee Research Group 

TGF Transforming Growth Factor 

TIMPs Tissue Inhibitor of Metalloproteinase 

TMA Transmetatarsal amputation 

TMR® Therapeutic Magnetic Resonance 

TNF Tumor Necrosis Factor 

USA United States of America 

VEGF Vascular Endothelial Growth Factor  

WBC White Blood Cells 

α-SMA Alpha-Smooth Muscle Actin 

 
 

https://doi.org/10.33137/cpoj.v8i1.43717
https://assets.publishing.service.gov.uk/media/5a82c07340f0b6230269c82d/Diabetesprevalencemodelbriefing.pdf
https://assets.publishing.service.gov.uk/media/5a82c07340f0b6230269c82d/Diabetesprevalencemodelbriefing.pdf
https://www.ukri.org/news/use-of-both-sexes-to-be-default-in-laboratory-experimental-design/
https://www.ukri.org/news/use-of-both-sexes-to-be-default-in-laboratory-experimental-design/

