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Biomarkers ofDiabetic Foot Ulcers

https://t.me/med1917

andIts Healing Progress

MonikaA.Niewczas andHetalShah

Abstract

There are urgent, unmet needs in the eld of biomarkers of diabetic wound healing. Rapidly developing molecular technologies supported by the parallel, robust progress in biostatistical and machine learning approaches are offering more granular insights into the disease processes and unraveling potential biomarkers. These methodological and computational innovations are tremendously accelerating biomarker research. National Institute of Diabetes and Digestive and Kidney Diseases has recently funded the Diabetic Foot Consortium, the largest initiative to date aiming to develop and validate biomarkers of diabetic foot ulcers; whereas the Food and Drug Administration and the National Institutes of Health offer an important glossary of nomenclature to help researchers navigate through different types of biomarkers. Multi-molecule signatures rather than single biomarker measurements will likely guide the diabetic wound healing course in the near future.

Denition ofBiomarkers andUnmet Needs

The U.S. Food and Drug Administration and the National Institutes of Health (FDA-NIH) Working Group for Biomarkers, EndpointS and other Tools (BEST) denes a biomarker as “a dened characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Molecular, histologic, radiographic or physiologic characteristics are types of biomarkers. A biomarker is not an assessment of how an individual

M. A. Niewczas (*) · H. Shah Section on Genetics and Epidemiology, Joslin Diabetes Center, Boston, MA, USA

Department of Medicine, Harvard Medical School, Boston, MA, USA e-mail: Monika.Niewczas@joslin.harvard.edu

feels, functions, or survives.” FDA-NIH BEST denitions of categories of biomarkers are outlined in Table18.1 [1].

To set the stage for the discussion on biomarkers of diabetic foot ulceration (DFU), it is important to understand different types of biomarkers. In individuals without the clinical manifestations of the disease, a risk biomarker is associated with an increased (or sometimes decreased) likelihood of developing the disease, and is detected at a time preceding the onset of clinical signs and symptoms [1]. For example, certain vascular or neuropathic indices are clinically recognized risk factors or risk biomarkers of developing new foot ulcer [2–4], whereas the HLA DR3/DR4-DQ8 risk alleles identify people at high risk of developing type 1 diabetes [5]. By early identication of individuals at risk, these biomarkers can help guide effective preventive strategies against disease onset.

Prognostic markers, on the other hand, are associated with an increased likelihood of a future clinical event, disease progression, or recurrence among individuals who have already been diagnosed with the disease [1]. Prognostic biomarkers of the diabetic wound healing course and of the DFU recurrence are of particular interest to the DFU community [6, 7]. According to current clinical guidelines, initially all patients with DFU are uniformly treated with the standard of care. A limited improvement in wound healing after approximately 4weeks prompts more specialized care. This tier-based approach is ineffective and generates a nancial burden for the healthcare system. Thus, biomarkers prognostic for the diabetic wound healing course that could differentiate, early in the disease course, subjects whose ulcers will successfully heal from those whose ulcers will likely fail to heal are urgently needed [6]. Percent change in wound area at 4weeks is one traditional biomarker prognostic for the DFU course [8]. Robust prognostic biomarkers have the potential to optimize clinical algorithms and support clinical trials for DFU.Moreover, after the foot ulcer resolution, more than half of patients will suffer from the DFU recurrence within 5years. For more details on the natural history of the disease and current clinical guidelines,

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Table 18.1 Biomarker nomenclature

Category of biomarker FDA-NIH BEST denition Diagnostic A biomarker used to reveal or verify the presence of a disease or condition of interest or to identify those

with a disease subtype

Monitoring A biomarker assessed at regular intervals to determine the burden of disease or extent of the medical

condition; or one that is used to indicate exposure to an environmental agent or a medical product

Pharmacodynamic/response A biomarker that demonstrates occurrence of a biological response following exposure to a medical product

or environmental agent

Susceptibility/risk A biomarker that signals the likelihood of developing a disease or medical condition in a person that is

asymptomatic or without any clinical evidence of the disease or medical condition

Prognostic A biomarker that indicates the potential occurrence of a clinical event, progression or recurrence of disease

in individuals that already have the disease or medical condition

Predictive A biomarker that discerns individuals who are more likely (than those without the biomarker) to experience

a differential effect (favorable or unfavorable) of a medical product or environmental agent Safety A biomarker that assesses toxicity after exposure to a medical product or environmental agent Surrogate endpoint An endpoint that is used as a substitute for a direct measure of a clinical benet

M. A. Niewczas and H. Shah

please refer to the American Diabetes Association Standards of Foot Care, other chapters of this book and other publications [2–4].

It is important to acknowledge that both susceptibility/ risk and prognostic markers are unrelated to the effects of an intervention. In contrast, predictive biomarkers can identify those who may or may not respond to treatment or may experience either benecial or adverse effects from an intervention [1]. In other words, when comparing an experimental group to a control group in a randomized clinical trial, the effects of treatment are different among those who are positive for the predictive biomarker compared to those who are biomarker-negative. For example, variants in the glucose transporter gene SLC2A2 are associated with differential glycemic response to metformin treatment [9]. In the Action to Control Cardiovascular Disease Risk in Diabetes (ACCORD) clinical trial, a genetic risk score discriminated individuals who experienced cardiovascular benets from intensive glycemic treatment versus those who adversely experienced increased cardiac mortality from this regimen [10]. A randomized clinical trial (PRIORITY) evaluating a mineralocorticoid receptor antagonist (spironolactone) guided by a proteomics biomarker (CKD273 classier) for diabetic kidney endpoints is an excellent example of incorporating prognostic and predictive biomarkers into the study [11]. Statistically speaking, a formal test of interaction between the treatment group and predictive biomarker should be signicant when examined in relation to the outcome as the dependent variable. In contrast, if only the biomarker is signicant and not the interaction term, the biomarker is considered prognostic and not predictive. It is important to distinguish these terms from statistical nomenclature where predictive models are used, in order to determine if a predictor or independent variable (any biomarker in this case) has an association with the disease outcome or a dependent variable [12]. In fact, the nomenclature traditionally used in epidemiology and biostatistics will refer to the

predictive models in a number of scenarios, which, in light of the BEST criteria, would be considered risk, prognostic, or predictive biomarkers.

Diabetic Foot Consortium

To accelerate advances toward the development of validated biomarkers for DFUs, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) established a large, national, multi-site framework of multidisciplinary and renowned experts forming the Diabetic Foot Consortium (DFC) in 2018 [6, 7]. The Consortium is led by Dr. Rodica Pop-Busui (University of Michigan, Ann Arbor, MI) under the NIDDK guidance of Dr. Teresa Jones. It comprises clinical sites dedicated to patient recruitment, a Data Coordinating Center led by Dr. Catherine Spino (University of Michigan, Ann Arbor, MI), a biorepository committee spearheaded by Dr. Brian Schmidt (University of Michigan, Ann Arbor, MI), and relevantly, a few Biomarker Analyses Units. This rstin- kind national initiative focuses solely on foot ulcerations in diabetes. Top priorities of the initiative include efforts to develop: (1) biomarkers to identify subjects with DFU who are unlikely to respond to standard therapies; (2) biomarkers monitoring the disease course; (3) biomarkers representing molecular heterogeneity; or (4) biomarkers of DFU recurrence in subjects who had their DFU recently healed. The initiative supports human translational studies to validate candidate biomarkers detecting the therapeutic response, rening the eligibility criteria, monitoring the disease course, or offering surrogate endpoints for more long-term outcomes, ultimately accelerating drug development strategies.

Early DFC Biomarker Studies One of the two earliest studies of the Consortium is a project led by Dr. Marjana TomicCanic (University of Miami, Miami, FL), entitled “C-myc Biomarker Study for Diabetic Foot Ulcers”; a clinicaltrials.

18 Biomarkers ofDiabetic Foot Ulcers andIts Healing Progress

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329

gov identier: NCT04591691. This prospective study

includes subjects with DFU from whom the tissue specimen from the wound edge is obtained at baseline, and the subjects are subsequently followed for their disease course for up to 12weeks. C-myc and phosphorylated glucocorticoid receptor (p-GR) are being tested by immunostaining. The study is built upon a substantial knowledge base coming from functional studies that demonstrate an activation of the Wnt pathway in non-healing wounds, with consequential increases in its downstream molecules: c-myc and p-GR [13–16].

Another early study of the Consortium is a project led by Dr. Chandan Sen (recently: Indiana University, Indianapolis, IN; now University of Pittsburgh, Pittsburgh, PA), entitled “TEWL Biomarker Study for Diabetic Foot Ulcer Recurrence” (NCT04558775, also [6, 7]). This prospective study aims to evaluate a trans-epidermal water loss (TEWL) with a semiopen probe as a local biomarker of recurrent DFU within 16weeks of follow-up. The currently recognized clinical outcome is based on the reepithelization of the skin with no discharge, which may not speak well to DFU recidivism. TEWL is considered an early and sensitive measure of skin barrier function and integrity and has been employed in studies of multiple skin diseases [17, 18]. Promising preclinical, functional studies suggest that TEWL may also be a valuable biomarker of DFU recurrence [19, 20]. Results of these projects should be available in the near future.

R61-Supported Biomarker Studies of the DFC Currently,

ve ongoing ancillary DFC studies are funded in response to the NIDDK funding opportunity: RFA-DK-21-001 via NIH R61 award mechanism (Table 18.2). All are investigating candidate biomarkers prognostic for the diabetic wound

healing course. These projects are excellent examples of diverse molecular phenotyping approaches as they study proteins, metabolites, coding, and non-coding RNA in a range of biospecimens relevant for the clinical phenotype of DFU (local wound tissue and uid, serum and urine) to develop composite biomarker signatures.

R61 Project on Inammatory Transcriptomics The project entitled “Inammation-related gene biomarkers in human diabetic foot ulcer healing” led by Dr. Kara Spiller (Drexel University, Philadelphia, PA) proposes to evaluate changes over time in seven inammatory candidate gene expressions in the debrided wound tissue by using a ratio of early inammation phase to late phase of inammation resolution genes involved. Molecular phenotyping entails quantitative RT-PCR measurements in the debrided wound tissue. The candidate roster stems from the discovery functional studies on inammation involving macrophage subtypes M1 and M2, and small longitudinal data in humans built upon bulk transcriptomics data [21–27].

R61 Project on Proteomics The project entitled “Proteomic Biomarkers Prognostic for Diabetic Wound Healing” co-led by Drs. Aristidis Veves and Monika Niewczas (Harvard Medical School, Boston, MA) aims to rene and validate a multi-protein biomarker signature in serum prognostic for diabetic wound healing. The project is built upon the scientic premise of the importance of circulating biomarkers coming from the targeted protein studies in DFU [28–30] further enhanced by recently developed robust, highthroughput proteomics approaches that have already demonstrated their value in studies of other diabetic complications [31–34].

Table 18.2 Diabetic Foot Consortium studies provide an excellent example of diverse molecular approaches to DFU biomarker research

Biomarker category

Prognostic for DFU course

C-myc biomarker study for diabetic foot ulcers Dr. Marjana Tomic-Canic, Univ. of

Inammation-related gene biomarkers in human diabetic foot ulcer healing Proteomic biomarkers prognostic for diabetic wound healing Diabetic foot ulcer wound uid biomarker Dr. Sashawati Roy, Indiana Univ. Metabolite Focused Local—wound uid Microbiome-based biomarkers of wound healing Drs. Meghan Brennan and Lindsay

Circulating urinary microRNAs as systemic biomarkers of healing outcomes in diabetic foot ulcers

Prognostic for DFU recurrence

TEWL biomarker study for diabetic foot ulcer recurrence

Project title

Molecule

Miami Dr. Kara Spiller, Drexel Univ. mRNA Focused Local—debrided

Drs. Aristidis Veves and Monika Niewczas, Harvard Medical School

Kalan, Univ. of Wisconsin Drs. Rivka Stone and Marjana Tomic-Canic, Univ. of Miami

Dr. Chandan Sen, Univ. of Pittsburgh

type Method scale Specimen

Protein Focused Local—wound edge

wound tissue

Protein High

throughput

Microbiome High

throughput

microRNA High

throughput

Water loss Focused Local—skin

Biouid—serum

Local—wound swab

Biouid—urine

physiological property

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M. A. Niewczas and H. Shah

R61 Project on Metabolites The project entitled “Diabetic Foot Ulcer Wound Fluid Biomarker” led by Dr. Sashawati Roy (Indiana University, Indianapolis, IN) aims to validate a cysteine-to-cystine ratio (Cys/CysS) as a wound uid biomarker prognostic for non-healing diabetic foot ulcers. This candidate two-metabolite biomarker is derived from a metabolomics study of over 500 metabolites [6, 7]. A ratio of Cys to its disulde derivative, CysS, is a biomarker of the oxidative stress, which has been shown to be associated with diabetic complications including wound healing [35, 36].

R61 Project on Skin Microbiome Another project co-led by Drs. Meghan Brennan and Lindsay Kalan entitled “Microbiome Based Biomarkers of Wound Healing” (University of Wisconsin, Madison, WI) proposes to investigate the wound microbiome with metatranscriptomics tools with a particular focus on the anaerobic species. This novel, culture-independent method will utilize a wound swab. Existing evidence has already demonstrated that the microbiome of non-healing wounds comprises persistent mixed populations of bacteria with a marked proportion of anaerobic species [37–41].

R61 Project on miRNA The project entitled “Circulating urinary microRNAs as systemic biomarkers of healing outcomes in diabetic foot ulcers” led by Drs. Rivka Stone and Marjana Tomic-Canic (University of Miami, Miami, FL) aims to identify candidate miRNAs in the urine prognostic for the DFU course based on the initial high-throughput miRNA proling. Several functional studies have strongly implicated miRNAs involvement in the diabetic wound healing process [42–46].

Precision Medicine andDFU Biomarkers

Diabetic wound healing over time varies among individuals. The heterogeneity of the disease course justies precision medicine approaches to capture molecular phenotypes in a high-throughput manner to best inform on the disease course [47, 48].

Proteins asBiomarkers

Proteomics is one such approach that holds particular promise. Proteins are closer to the clinical phenotype than genes or transcripts, so they may impact the disease course more directly than other types of -omics [49, 50]. Proteomics correlate only moderately with transcriptomics [51] and, as such, can arguably offer additional information. Mass spec-

trometry (MS) proteomics remains an important tool to study proteins in tissue. Indeed, studies at the local site of injury (diabetic wound) have been done [52, 53]. An untargeted MS-based study of wound biopsies in a medium-size study group of subjects with DFU pointed to SERPINB3 as a potential biomarker of the DFU course, which was subsequently validated in the study group subset [52]; whereas another MS-based study of wound uid biospecimens pointed to inammatory S100 proteins or metalloproteinases, among others [53].

Proteins measured in biouids (serum, plasma or urine) are frequent biomarkers. Early targeted biomarker studies have utilized enzyme-linked immunosorbent assays (ELISAs) or low multiplex solutions. One such study of over 100 subjects with diabetes prospectively followed for 18months for their DFU incidence and subsequent DFU course identied that TNF, MCP1, MMP9, and FGF2 increased in subjects who failed to have their DFU healed from among 20 circulating cytokines examined [30]. Overall, a few targeted biomarker studies have demonstrated that select inammatory cytokines in circulation are associated with DFU development and course [28–30], with a closely related clinical phenotype of diabetic neuropathy [54], and other diabetic complications [55–57]. However, the predominance of classical plasma proteins (albumin, immunoglobulins), broad dynamic ranges of circulating proteins (several orders of magnitude), or a possible cross-reactivity limits the applicability of MS and/or ELISA and other low multiplex solutions in studies of the circulating proteome.

An unparalleled opportunity for proling of the circulating proteome has recently become feasible. Innovative afnity proteomics outperforms other technologies by a number of critical features. It incorporates components of the genomic technologies that allow for a broad dynamic range and high level of multiplexing (a few thousand proteins). The two main technologies mainly differ by the recognition system. One method (SOMAscan platform, Somalogic Inc.) utilizes aptamers—small nucleic acids capable of specic protein binding, and another method (proximity extension assay (PEA) by Olink Inc.) utilizes dual antibody recognition system [58, 59]. Note that PEA proteomics is used as a reference for one of the major Human Protein Atlas projects—the Secretome [60, 61]. Afnity proteomics has already been employed in a number of studies of the circulating proteome of diabetes [62], and diabetic complications other than foot ulceration, including neuropathy [33, 63, 64], kidney [31, 32, 65], non-diabetic wound healing [66], as well as other clinical phenotypes. Ongoing studies of the DFC as discussed above shall unravel more insights into the circulating proteomics signatures of the diabetic wound healing processes.

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Metabolites asBiomarkers

Metabolites are low molecular weight analytes (<1800Da) that stem from biological processes occurring in various cells or external exposures present in tissues. Furthermore, circulating metabolites released extracellularly into biouids (plasma, serum, or urine) can be measured non-invasively through various relatively accessible techniques, such as mass spectrometry or nuclear resonance imaging, further enhancing their value as biomarkers of disease. As end- products of regulatory processes, metabolites, similar to proteins, are among the closest molecules to the disease phenotype [50, 67, 68].

Metabolomics assesses a wide range of metabolite classes using high-throughput methods such as high-resolution tandem mass spectrometry, often preceded by liquid or gas chromatography. However, in these high-throughput approaches, several metabolites remain with only a partially known structure or metabolites that are unannotated in the pathway databases [69].

Given the metabolic nature of diabetes, it is not surprising that metabolic alterations are involved in the pathogenesis of diabetic complications [35]. However, there are only few metabolomics studies in DFU.These point to phospholipids and select amino acids as candidate metabolites involved in the process [70, 71]. Nonetheless, metabolomics studies have already advanced our understanding of other diabetic complications. For example, a targeted metabolomics study revealed an impairment of amino acid and tricarboxylic acid cycle metabolism, as reected by changes in circulating metabolites associated with the course of the cardiovascular autonomic neuropathy in subjects with diabetes [72]. Two global metabolomics proling studies reported that higher levels of uremic solutes and other modied metabolites were associated with risk of progression of diabetic kidney disease in both types of diabetes [73, 74], while circulating short and medium chain fatty acids were reported as protective factors [75]. Another study identied orotidine as a novel biomarker of cardiovascular disease risk [76]. An ongoing DFC study is evaluating two metabolites as a potential biomarker measure of an oxidative stress and the DFU course.

Coding RNA asaPotential Biomarker

Coding RNA is a plausible biomarker as it directly reects the biological processes of transcriptions within the tissue. Indeed, some data suggest the potential value of this molecule type and is supported by published data and an ongoing DFC study. A number of bulk transcriptomics studies identied genes activated during an early and late phase of the wound healing process and demonstrated that the initial inammatory response is crucial for wound healing, but its prolonged persistence contributes to unfavorable DFU outcomes [21–27].

Next-generation RNA sequencing comparing human skin and oral acute wounds (the latter as a model of an “ideal” tissue repair) points to the importance of the transcription factors: FOXM1 and STAT3in the healing processes [77].

Nevertheless, RNA expression may be subject to variability due to biology, sampling techniques, and data normalization schemes. In addition to bulk transcriptomics, novel technologies emerged to offer an even higher resolution at the single level and/or in the context of the tissue/ cellular location. Single-cell or single nucleotide RNA sequencing (scRNAseq or snRNAseq) and/or spatial transcriptomics tremendously accelerate, without any doubt, the discovery of relevant pathways and processes. Indeed, important advances have been made in many areas including the DFU eld. Two scRNAseq studies in DFU point to the importance of select broblast clusters, select metalloproteinases (MMPs), tumor necrosis factor (TNF) or interferon gamma (IFNG)-related molecules differentiating tissues between subjects who had their ulcers successfully healed vs. those who had not [28, 78]. More in-depth insights on this topic have been described in previous reviews [79, 80]. Further research is needed to evaluate whether these great tools for discovery research provide a satisfactory benchmark performance as biomarkers.

MicroRNAs asPotential Biomarkers

MicroRNAs are endogenously produced short non-coding RNAs (~22 nucleotides long) that may play critical roles in pathophysiological processes by affecting gene regulation at the posttranscriptional level. By inhibiting translation of proteins or inducing mRNA degradation, miRNAs can obstruct target gene expression. There are over 2500 known human miRNAs that regulate at least 60% of the protein-coding genes. Studies have suggested that miRNAs play a vital role in cell proliferation, differentiation, development and immune responses, as well as modulation of disease-related genes [13, 81–84]. MiRNAs are potentially attractive biomarkers, as they are protected from degradation, and amenable to be studied in stored biospecimens [85].

Substantial evidence exists showing that miRNAs are involved in the mechanisms underlying diabetic wound healing [13, 43, 45, 46, 86, 87]. In an integrated miRNA-mRNA genomic approach, an induction of select miRNAs, namely: miR-21-5p, miR-34a-5p, and miR-145-5p in dermal broblasts inhibited healing processes of the diabetic wound [43]. Bacterial pathogen, Staphylococcus aureus, has been shown to induce miR-15b-5p suppressing inammatory response and DNA repair resulting in an impaired DFU healing [46]. The ongoing DFC study evaluating miRNA proling in urine, as described above, will soon shed light on this potential biomarker application for DFUs.

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Skin Microbiome asPotential Biomarkers

The skin microbiome is recognized for its diversity and importance in health and disease states [40]. DFU infections develop in approximately 50% of patients [41]. Culturedependent techniques take time, thus delaying targeted antibiotic therapies. Emerging novel molecular techniques like metagenomics can now comprehensively examine the skin microbiome [39, 41].

Recent, high-throughput sequencing studies performed in prospective cohorts of subjects with DFU offered a much higher resolution of the DFU microbiome and revealed the importance of select strains of S. aureus, of other bacteria such as Corynebacterium, Enterobacteriaceae, anaerobes and of fungal communities associated with the diabetic wound healing outcomes [88–90]. This topic has been discussed in-depth in recent reviews [37, 39, 41]. Skin microbi- ome studies have the potential to deliver prognostic biomarkers for DFU outcomes, and also diagnostic biomarkers for DFU infections that cannot be detected clinically or with culture-dependent techniques. An ongoing study supported by DFC is discussed above.

Building Biomarker Signature

Single Biomarker or Biomarker Signature? The DFU course is multi-factorial, thus it is plausible that a multiplex biomarker signature, rather than a single biomarker, will offer the most optimal prognostication [91].

The recent boom of rapidly developing molecular technologies offers a plethora of methods ranging from untargeted or high-throughput semi-quantitative platforms to scaled-down, quantitative panels built with low multiplex solutions suitable for biomarker advances. Afnity proteomics platforms, which are great tools of discovery, were discussed earlier in this chapter. Low multiplex platforms are great for biomarker signature renement and nal development. Meso Scale Discovery platform is one example of a high-precision protein quantication for multiplex biomarker detection. This unique electrochemiluminescent technology offers high sensitivity while assuring high reproducibility across the assays thanks to a non-uidic detection system. This technology has already been used to translate a multi-biomarker panel into clinical practice [92].

How to Identify Candidate Biomarkers? There are multiple approaches to identify candidate biomarkers. They may be hypothesis-driven, often focused and well-invested in biology, for example, the c-myc biomarker study [15]. Another approach is an untargeted one, resulting from an

unbiased, comprehensive screen of certain types of molecules, like proteomics or metabolomics. They can conrm associations for molecules or pathways known to be involved, offering a “proof of concept”, but they often point to novel molecules or pathways that have not yet been investigated with hypothesis-driven approaches [31, 73,

93]. For example, the Joslin Kidney Study identied circu-

lating TNF receptors 1 and 2 robustly associated with the 10-year risk of kidney failure in both types of diabetes using a targeted approach based on the hypothesis that inammation is involved in the loss of kidney function [56,

57]. Subsequently, the two targeted biomarkers were vali-

dated for their associations with the kidney outcomes in diabetes in multiple cohorts totaling over 5000 subjects followed for 5–10years [55]. Impressively, a proteome-wide scan of circulating inammation in three prospective cohorts conrmed associations for TNF receptors with the kidney outcome (“proof of concept”), but it also unraveled several new molecules that have never been considered before [31]. Moreover, an integrated approach informed by more than one -omics can be an efcient tool of candidate biomarker discovery. Successful examples of integrated approaches were already reported in DFU [43, 94] and other diabetic complications [95].

Local Versus Systemic Biomarkers Mainstream DFU research has been focused on studying biomarkers at the wound site. Local biomarkers are of a particular interest as they reect molecular processes of diabetic wound healing at its core [13]. Specimens include: wound uid for highthroughput or targeted protein or metabolite studies; wound swabs for transcriptomics, metabolomics, or microbiome studies; tissue specimens for histology, immunostaining, or different types of -omics [13, 79]. Specic examples include ongoing biomarker studies of the Consortium [6]: C-myc protein staining in the edge of the wound tissue [14–16], inammatory transcriptomic signature of the debrided tissue [23–25], or microbiome study of wound swab [88, 89]. However, local specimens (e.g., tissue from the surgical debridement) are mostly invasive, and often affected by high variability of the specimen collection process [96]. Ongoing, non-invasive biomarker studies of the Consortium include TEWL measurements of the density gradient of water evaporation from the skin [19, 20], or other imaging studies currently under evaluation [97, 98].

The systemic, circulating biomarkers may include molecules (often proteins or metabolites) that are actively secreted into the bloodstream, some of which can be upstream regulators of the disease process, as well as downstream in the process proteins that leak into the blood as a result of cell damage (for example from the wound site) [61]. Speaking to

18 Biomarkers ofDiabetic Foot Ulcers andIts Healing Progress

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that, two studies of atopic dermatitis demonstrated at least a partial overlap between circulating and local protein changes [99, 100]. Nevertheless, to date, very few circulating molecules have been translated to clinical practice. Hemoglobin A1c (circulating protein) and creatinine (circulating metabolite) are two, well-established, clinical legacy measures of poor glycemic control and kidney function, respectively, that constitute risk factors of DFU [2]. In addition, there have been research advances in the search of relevant circulating molecules. A few targeted biomarker studies have demonstrated that select circulating proteins are associated with DFU development and course [28–30], with other diabetic complications [54–57], and other disorders of the skin [99–101].

Biomarker Signature Renement Forming a biomarker signature from the identied candidate biomarkers relies on several important components: (1) data-driven associations with the outcome of interest; (2) correlations among each other; (3) independence from clinical covariates; (4) reection of relevant biological processes and pathways; (5) analytical performance; and (6) choices possibly also informed by longitudinal trajectories within individual over time.

Single or Repeated Biomarker Measurement How stable the biomarker is over time, impacts the decision on the number of times the biomarker should be measured. Albuminuria, a well-established risk factor for diabetic complications, is known to be highly variable and clinical guidelines recommend its measurements multiple times to determine albuminuria status [2]. On the other hand, a number of omics studies have shown that a marked proportion of molecules (proteins or metabolites) track well within individual over time. These are desired features of risk or prognostic biomarkers [74, 102]. On the other hand, the diabetic wound healing is a dynamic process as it has been shown in targeted protein biomarker studies [29] as well as in transcriptomic studies [25, 27]. These longitudinal trajectories may offer additional insights serving as surrogate biomarkers or supporting the development of the prognostic biomarkers.

Biostatistical andMachine Learning Approaches

After various preprocessing steps of the molecular data that are often specic to the platform and include screening for outliers, ltering, normalization, and transformation or controlling for batch effect, the omics data can then be analyzed using biostatistical multivariate approaches. These processes

test the relationships of the relative abundance of each molecule one at a time with the outcome of interest using methods such as t-tests, fold-change analysis, analysis of variance, or Wilcoxon rank-sum tests [49, 69]. However, it is paramount to correct for multiple hypotheses testing given that hundreds or thousands of molecules are being assayed. This may be done via Bonferroni corrections for the number of independent tests or controlling for the false discovery rate by various methods to produce a q-value, the minimum threshold at which an analyte is considered signicant [103]. Volcano plot is a highly informative tool to visualize the results of the multivariate screen. Ratios of molecules between the two groups (x axis) are typically plotted against the strengths of the signicance (p value adjusted for multiple testing) on the y axis. This graph has been utilized multiple times in omics studies of diabetic complications [31, 74].

Classical biostatistical tools like logistic or Cox regression models are highly interpretable. Effect estimates are in clinically meaningful units. In addition, one can quantitatively evaluate independence from clinical covariates via confounding or effect modication. Nevertheless, biostatistical tools may fall short in studies with multiple and possibly correlated high-throughput omics-based biomarkers. Machine learning is a rapidly developing computer science eld that aims to address these challenges. Although machine learning methods have lower interpretability, they are better at overcoming the challenges of correlations or non-linearity, offering the potential for superior model accuracy [104–106].

Data redundancy among molecules measured in a highthroughput manner is a common phenomenon. Spearman or Pearson correlation matrices are biostatistical approaches to examine such data and continue to be relevant methods. Among machine learning models, principal component analysis is one of the early approaches to leverage correlated data in an unsupervised way, meaning disregarding the outcome. Principal component analysis uses orthogonal projection directions to capture the data’s maximal variance, clustering subjects (score plot) and molecules (loading plot) accordingly, grouping molecules in the loading vectors of the principal components. Other unsupervised methods include hierarchal clustering, or two-mode clustering [73, 76].

Among machine learning models that are supervised, meaning those that take the outcome into account, elastic net model is an example of penalized regression well-suited for a large number of continuous variables featuring correlated data and is an “elastic compromise” between ridge and lasso regressions. Ridge regression welcomes correlated data whereas lasso regression discriminates against them [107]. On the other hand, generalized boosting model and random forest belong to the decision tree family and are applicable

334

Symbols credit to Biorender.com

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M. A. Niewczas and H. Shah

for detecting threshold effects and interactions. Neural network, an example of deep learning, is primed for complex non-linear relationships. Novel computational methods to address the needs of omics-based biomarker research are rapidly developing.

Limitations

We shall mention some limitations in scope. This chapter focuses on research advances of molecular biomarkers derived from targeted or high-throughput approaches. We do not discuss in greater detail risk or prognostic values of clinical characteristics (indices of diabetes, neuropathy, or peripheral vascular disease). We also do not discuss other potential types of biomarkers (e.g., social determinants of health, or imaging biomarkers). We do not discuss in-depth methodological assay aspects of the biomarker performance (detectability, cross-reactivity) or model accuracy metrics (sensitivity, specicity, positive predictive value) as those gold standard methods for biomarker evaluation are readily available elsewhere.

Into theBright Future

These are truly exciting times in biomarker research (Fig. 18.1). Increased awareness and ongoing research advances combine to address the molecular heterogeneity of the disease course with an increasing appreciation for the molecular underpinnings of these processes. Rapidly developing technologies support these efforts by offering high- resolution phenotyping of a variety of molecules moving toward single-cell or spatial -omics data. The efforts have begun to integrate data across multiple -omics layers that will further enhance biomarker discovery. Technical advances team with rapid development of computational tools in responding to the needs of these big data [47, 108, 109].

Biomarker research is an extremely relevant and active area of investigation in the diabetic wound healing eld. An important NIH-funded initiative supports multiple projects under an umbrella of a multi-site consortium. These and other advances should soon result in biomarker solutions supporting the ultimate goal of improving patient outcomes.

Fig. 18.1 Biomarker research in DFU—past, current, and future. (Symbols credit to Biorender.com)

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