All articles are permanently available online to the public without restrictions or subscription fees. They are free to be used, cited, and distributed, provided that appropriate acknowledgment is included. Authors retain the copyright of their original contributions and grant the Canadian Online Publication Group (COPG) a license to publish the article and identify itself as the original publisher. CPOJ articles are licensed under the Creative Commons Attribution 4.0 International License. CPOJ Website: https://jps.library.utoronto.ca/index.php/cpoj/index Editorial Office: cpoj@online-publication.com ISSN: 2561-987X CPOJ is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE). CPOJ articles are freely accessible on PubMed Central® (PMC). 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 https://jps.library.utoronto.ca/index.php/cpoj/index mailto:cpoj@online-publication.com https://publicationethics.org/about/our-organisation https://pmc.ncbi.nlm.nih.gov/journals/?term=%22Canadian+Prosthetics+%26+Orthotics+Journal%22 https://publicationethics.org/members/canadian-prosthetics-orthotics-journal https://doi.org/10.33137/cpoj.v8i1.43717 https://pmc.ncbi.nlm.nih.gov/journals/?term=%22Canadian+Prosthetics+%26+Orthotics+Journal%22 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 https://doi.org/10.33137/cpoj.v8i1.43717 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43715/33312 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43716/33400 3 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 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43715/33312 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43715/33312 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43715/33312 4 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 https://doi.org/10.33137/cpoj.v8i1.43717 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43715/33312 5 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) https://doi.org/10.33137/cpoj.v8i1.43717 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. https://doi.org/10.33137/cpoj.v8i1.43717 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 https://doi.org/10.33137/cpoj.v8i1.43717 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 q R T -P C R T a q M a n A s s a y s R o u ti n e B lo o d T e s t E L IS A K it M u lt ip le x Im m u n o a s s a y E L IS A K it W e s te rn B lo t Im m u n o s ta in in g G e la ti n Z y m o g ra p h y M u lt ip le x Im m u n o a s s a y L u m in o l- B a s e d B io lu m in e s c e n c e Im a g in g 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% https://doi.org/10.33137/cpoj.v8i1.43717 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 ✓ https://doi.org/10.33137/cpoj.v8i1.43717 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 https://doi.org/10.33137/cpoj.v8i1.43717 11 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 https://doi.org/10.33137/cpoj.v8i1.43717 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43716/33400 12 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 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 https://doi.org/10.33137/cpoj.v8i1.43717 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43715/33312 https://jps.library.utoronto.ca/index.php/cpoj/article/view/43716/33400 13 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. 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DOI:10.1111/jebm.12266 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/