Proteomic Biomarkers Research Articles

Systemic proinflammatory−profibrotic response in aortic stenosis patients with diabetes and its relationship with myocardial remodeling and clinical outcome

BackgroundPrevious studies have mainly focused more on how diabetes affects the valve than the myocardium in aortic stenosis (AS). In the pressure-overloaded heart, myocardial fibrosis is an important driver of the progression from compensated hypertrophy to heart failure. Using comprehensive noninvasive imaging and plasma proteomics, we investigated whether and how diabetes aggravates the remodeling of the myocardium and its relation with prognosis in AS patients.MethodsSevere AS patients were enrolled in two prospective cohorts for imaging and biomarker analysis. The imaging cohort (n = 253) underwent echocardiography and cardiac magnetic resonance, and the biomarker cohort (n = 100) blood sampling with multiplex proximity extension assay for 92 proteomic biomarkers. The composite outcome of hospitalization for heart failure admissions and death was assessed in the imaging cohort.ResultsDiabetic patients were older (70.4 ± 6.8 versus 66.7 ± 10.1 years) with more advanced ventricular diastolic dysfunction and increased replacement and diffuse interstitial fibrosis (late gadolinium enhancement % 0.3 [0.0–1.6] versus 0.0 [0.0–0.5], p = 0.009; extracellular volume fraction % 27.9 [25.7–30.1] versus 26.7 [24.9–28.5], p = 0.025) in the imaging cohort. Plasma proteomics analysis of the biomarker cohort revealed that 9 proteins (E-selectin, interleukin-1 receptor type 1, interleukin-1 receptor type 2, galectin-4, intercellular adhesion molecule 2, integrin beta-2, galectin-3, growth differentiation factor 15, and cathepsin D) were significantly elevated and that pathways related to inflammatory response and extracellular matrix components were enriched in diabetic AS patients. During follow-up (median 6.3 years), there were 53 unexpected heart failure admissions or death in the imaging cohort. Diabetes was a significant predictor of heart failure and death, independent of clinical covariates and aortic valve replacement (HR 1.88, 95% CI 1.06−3.31, p = 0.030).ConclusionsPlasma proteomic analyses indicate that diabetes potentiates the systemic proinflammatory−profibrotic milieu in AS patients. These systemic biological changes underlie the increase of myocardial fibrosis, diastolic dysfunction, and worse clinical outcomes in severe AS patients with concomitant diabetes.

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.

Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologicmethods. Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.

Enrichr in silico analysis of MS-based extracted candidate proteomic biomarkers highlights pathogenic pathways in systemic sclerosis

Systemic sclerosis (SSc) is a rheumatic disease characterised by vasculopathy, inflammation and fibrosis. Its aetiopathogenesis is still unknown, and the pathways/mechanisms of the disease are not clarified. This study aimed to perform in silico analysis of the already Mass Spectrometry (MS)-based discovered biomarkers of SSc to extract possible pathways/mechanisms implicated in the disease. We recorded all published candidate MS-based found biomarkers related to SSc. We then selected a number of the candidate biomarkers using specific criteria and performed pathway and cellular component analyses using Enrichr. We used PANTHER and STRING to assess the biological processes and the interactions of the recorded proteins, respectively. Pathway analysis extracted several pathways that are associated with the three different stages of SSc pathogenesis. Some of these pathways are also related to other diseases, including autoimmune diseases. We observe that these biomarkers are located in several cellular components and implicated in many biological processes. STRING analysis showed that some proteins interact, creating significant clusters, while others do not display any evidence of an interaction. All these data highlight the complexity of SSc, and further investigation of the extracted pathways/biological processes and interactions may help study the disease from a different angle.

Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.

The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability. We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios. In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC. Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.

Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67+ regulatory Tcells (Tregs) and Ki-67+ CD8+ Tcells is also likely to be associated with increased risk of irAEs. We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen.

Mass spectrometry analysis of gut tissue in acute SIV-infection in rhesus macaques identifies early proteome alterations preceding the interferon inflammatory response

HIV infection damages the gut mucosa leading to chronic immune activation, increased morbidities and mortality, and antiretroviral therapies, do not completely ameliorate mucosal dysfunction. Understanding early molecular changes in acute infection may identify new biomarkers underlying gut dysfunction. Here we utilized a proteomics approach, coupled with flow cytometry, to characterize early molecular and immunological alterations during acute SIV infection in gut tissue of rhesus macaques. Gut tissue biopsies were obtained at 2 times pre-infection and 4 times post-infection from 6 macaques. The tissue proteome was analyzed by mass spectrometry, and immune cell populations in tissue and blood by flow cytometry. Significant proteome changes (p < 0.05) occurred at 3 days post-infection (dpi) (13.0%), 14 dpi (13.7%), 28 dpi (16.9%) and 63 dpi (14.8%). At 3 dpi, proteome changes included cellular structural activity, barrier integrity, and activation of epithelial to mesenchymal transition (EMT) (FDR < 0.0001) prior to the antiviral response at 14 dpi (IFNa/g pathways, p < 0.001). Novel EMT proteomic biomarkers (keratins 2, 6A and 20, collagen 12A1, desmoplakin) and inflammatory biomarkers (PSMB9, FGL2) were associated with early infection and barrier dysfunction. These findings identify new biomarkers preceding inflammation in SIV infection involved with EMT activation. This warrants further investigation of the role of these biomarkers in chronic infection, mucosal inflammation, and disease pathogenesis of HIV.

Foreseeing Alcohol-Associated Liver Disease through Proteomic Biomarkers.

Cirrhosis is the 11th leading cause of death and 15th leading cause of morbidity worldwide (1). Liver disease due to viral hepatitis is declining; however, nonalcoholic fatty liver disease (NAFLD) and alcohol-associated liver disease (AALD) are both surging global concerns, often coexisting, with a significant contribution from alcohol as primary etiology or a cofactor. It is estimated that there are 1.5 billion individuals with chronic liver disease, of whom many remain asymptomatic and undiagnosed until advanced stages of disease (1, 2). Obesity is recognized as the predominant risk factor for NAFLD and alcohol consumption for AALD; minimally invasive screening tests in at-risk populations that inform on clinically significant parameters, such as fibrosis, could guide preventive strategies and assessment of prognosis in patients. Timely identification of at-risk patients is especially important due to the reversible nature of early stages of both diseases and the lack of approved effective therapies for more advanced disease.

Saliva based diagnostic methodologies for a fast track detection of autism spectrum disorder: A mini-review.

Autism spectrum disorder (ASD) is considered a complicated neurodevelopment disorder with rising prevalence globally. ASD is characterized by a series of events including varying degrees of defects in communication, learning, and social interaction which is accompanied by stereotypical behavioral patterns. Despite extensive research, the current diagnosis for ASD is complex and almost solely based on the behavioral assessments of the suspected individuals. The multifactorial etiopathology of this disease along with the diversity of symptoms among different individuals adds to the current intricacies for accurate prognosis of ASD. Hence, there exists a dire need for biologically relevant biomarkers for an early diagnosis and for tracking the efficacy of therapeutic interventions. Until recently, among various biofluids, saliva has gained increasing interest for biomarker identification, the advantages include the non-invasive nature and ease of sample handling. This mini-review aims to provide a succinct summary of recent literature on saliva-based diagnostic modalities for ASD, examine various studies that highlight the potential use of proteomic and/or RNA-based biomarkers. Finally, some conclusive perspectives of using the salivary system for ASD mechanistic details and diagnosis are also discussed.

Prognostic circulating proteomic biomarkers in colorectal liver metastases

The liver is the most common site of metastasis in colorectal cancer. Multimodal treatment, including liver resection, is potentially curative and prolongs survival for selected patients with colorectal liver metastases (CRLM). However, the treatment of CRLM remains challenging because recurrence is common, and prognosis varies widely between patients despite curative-intent treatment. Clinicopathological features and tissue-based molecular biomarkers, either alone or in combination, are insufficient for accurate prognostication. As most of the functional information in cells resides in the proteome, circulating proteomic biomarkers may be useful for rationalising the molecular complexities of CRLM and identifying potentially prognostic molecular subtypes. High-throughput proteomics has accelerated a range of applications including protein profiling of liquid biopsies for biomarker discovery. Moreover, these proteomic biomarkers may provide non-invasive prognostic information even before CRLM resection. This review evaluates recently discovered circulating proteomic biomarkers in CRLM. We also highlight some of the challenges and opportunities with translating these discoveries into clinical applications.

The Latest Developments in Using Proteomic Biomarkers from Urine and Serum for Non-Invasive Disease Diagnosis and Prognosis.

Due to diagnostic improvements, medical diagnostics is demanding non-invasive or minimally invasive methods. Non-invasively obtained body fluids (eg., Urine, serum) can replace cerebral fluid, amniotic fluid, synovial fluid, bronchoalveolar lavage fluid, and others for diagnostic reasons. Many illnesses are induced by perturbations of cellular signaling pathways and associated pathway networks as a result of genetic abnormalities. These disturbances are represented by a shift in the protein composition of the fluids surrounding the tissues and organs that is, tissue interstitial fluid (TIF). These variant proteins may serve as diagnostic "signatures" for a variety of disorders. This review provides a concise summary of urine and serum biomarkers that may be used for the diagnosis and prognosis of a variety of disorders, including cancer, brain diseases, kidney diseases, and other system diseases. The studies reviewed in this article suggest that serum and urine biomarkers of various illnesses may be therapeutically useful for future diagnostics. Correct illness management is crucial for disease prognosis, hence non-invasive serum and urine biomarkers have been extensively studied for diagnosis, subclassification, monitoring disease activity, and predicting treatment results and consequences.

Genomic and proteomic biomarker landscape in clinical trials

The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.

Proteomic investigation and biomarker identification of lung and spleen deficiency syndrome in HIV/AIDS immunological nonresponders

Yi-Ting Wang, Shao-Xiu Ji, Xin-Yi Xia, Ting-Jun Wan, Jia-Xi Zou, Bai-Xue Li, Qian-Nan Hu, Xin Chen, Jie Mu, Quan-Sheng Feng, Li Wen

Proteomic Applications and Considerations: From Research to Patient Care.

Despite technological advancements in the field of proteomics, the rate at which serum and plasma biomarkers identified using proteomic approaches are translated into clinical use remains extremely low. In this chapter, we describe recent technological advancements and analytical strategies in proteomic methods. We also describe the progress of proteomic blood-based biomarkers to date and discuss what the future of proteomics might entail with the use of multi-omic approaches and implementing machine learning on large proteomic datasets. Lastly, we provide several key considerations for biomarker studies, ranging from sample type to the use of reference samples, in order to achieve progress from bench to bedside, ultimately improving patient diagnosis, disease, and/or therapeutic monitoring and care.

Clinical Ageing.

Ageing is generally characterised by the declining ability to respond to stress, increasing homeostatic imbalance, and increased risk of ageing-associated diseases . Mechanistically, the lifelong accumulation of a wide range of molecular and cellular impairments leads to organismal senescence. The aging population poses a severe medical concern due to the burden it places on healthcare systems and the general public as well as the prevalence of diseases and impairments associated with old age.In this chapter, we discuss organ failure during ageing as well as ageing of the hypothalamic-pituitary-adrenal axis and drugs that can regulate it. A much-debated subject is about ageing and regeneration. With age, there is a gradual decline in the regenerative properties of most tissues. The goal of regenerative medicine is to restore cells, tissues, and structures that are lost or damaged after disease, injury, or ageing. The question arises as to whether this is due to the intrinsic ageing of stem cells or, rather, to the impairment of stem-cell function in the aged tissue environment. The risk of having a stroke event doubles each decade after the age of 55. Therefore, it is of great interest to develop neurorestorative therapies for stroke which occurs mostly in elderly people. Initial enthusiasm for stimulating restorative processes in the ischaemic brain with cell-based therapies has meanwhile converted into a more balanced view, recognising impediments related to survival, migration, differentiation, and integration of therapeutic cells in the hostile aged brain environment. Therefore, a current lack of understanding of the fate of transplanted cells means that the safety of cell therapy in stroke patients is still unproven. Another issue associated with ischaemic stroke is that patients at risk for these sequels of stroke are not duly diagnosed and treated due to the lack of reliable biomarkers. However, recently neurovascular unit-derived exosomes in response to Stroke and released into serum are new plasma genetic and proteomic biomarkers associated with ischaemic stroke. The second valid option, which is also more economical, is to invest in prevention.

Intraoperative Complications With Vitreous Biopsy for Molecular Proteomics.

To study the incidence of intraoperative complications while collecting a vitreous sample for proteomic biomarker analyses during small-gauge pars plana vitrectomy (PPV). A retrospective case series was assembled from the surgical logs and charts of patients who underwent 23-, 25-, and 27-gauge PPV along with an undiluted vitreous biopsy. Primary surgical indication and detailed operative reports were reviewed. Complications specific to vitreous biopsy were assessed while complications related to vitrectomy in general without biopsy were not tabulated. In 1190 eyes that underwent vitreous biopsy, the most common indications for PPV were rhegmatogenous retinal detachment (24.2%), epiretinal membrane (ERM) (21.7%), vitreous hemorrhage (11.0%), uveitis (8.3%), and macular hole (7.5%). An adequate sample of 0.5 cc to 1.0 cc was obtained in all cases. There was one sclerotomy break associated with biopsy, but no instances of lens touch, retinal contusion, retinal detachment, or intraocular hemorrhage. Undiluted vitreous biopsy obtained at the time of small-gauge vitrectomy is a generally safe procedure and may be considered for collection of samples for proteomic analysis. [Ophthalmic Surg Lasers Imaging Retina 2023;54:32-36.].

Proteome Analysis of Whole Blood Collected by Volumetric Absorptive Microsampling.

Proteomic biomarker discovery and analysis from human biofluids using liquid chromatography-mass spectrometry (LC-MS) is an area of intense biomedical research. There is a growing interest to analyze microsampled patient blood specimens as this is potentially more patient-friendly enabling at-home and bedside self-collection of small blood volumes. However, there are limited studies applying LC-MS proteomic analysis of whole blood as it is dominated by red blood cell proteins such as hemoglobin which suppresses the detection of other less abundant proteins. Volumetric absorptive microsampling (VAMS) devices overcome this issue in part by providing a trapping matrix which allows depletion of abundant blood cell proteins through washing, prior to proteolysis and LC-MS. This approach allows the analysis of proteins from erythrocytes, leukocytes, and plasma and leads to deeper proteomic coverage compared to conventional plasma proteomics, increasing the prospects to discover novel biomarker proteins.

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection.

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

Identification of a possible proteomic biomarker in Parkinson's disease: discovery and replication in blood, brain and cerebrospinal fluid.

Biomarkers to aid diagnosis and delineate the progression of Parkinson's disease are vital for targeting treatment in the early phases of the disease. Here, we aim to discover a multi-protein panel representative of Parkinson's and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 cerebrospinal fluid samples and 37 brain tissue samples collected from two observational longitudinal cohorts (the Oxford Parkinson's Disease Centre and Tracking Parkinson's) and the Parkinson's Disease Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis were performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities [cerebrospinal fluid (area under curve) = 0.74, P = 0.0009; brain area under curve = 0.75, P = 0.006; serum area under curve = 0.66, P = 0.0002]. Focusing on serum samples and using only those with severe disease compared with controls increased the area under curve to 0.72 (P = 1.0 × 10-4). In the validation data set, we showed that the same classifiers were significantly related to disease status (P < 0.001). Differential expression analysis and weighted gene correlation network analysis highlighted key proteins and pathways with known relationships to Parkinson's. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provide mechanistic insights into the disease as well as nominate a protein signature classifier that deserves further biomarker evaluation.

PLASMA PROTEOMIC SIGNATURE OF DECLINE IN GAIT SPEED AND GRIP STRENGTH

Abstract Physical function predicts health-related quality of life. The biological mechanisms underlying declines in physical function with age remain unclear. We examined the plasma proteomic profile associated with longitudinal changes of physical functions measured by gait speed and grip strength in community-dwelling adults. We applied aptamer-based platform to assay 1,161 plasma proteins on 2,871 participants (60% women, aged 76 years) in Cardiovascular Health Study (CHS) in 1992/1993 and 1,550 participants (55% women, aged 54 years) in Framingham Offspring Study (FOS) in 1991-1995. Gait speed and grip strength were measured annually for 6 years in CHS and at cycles 7 (1998-2001) and 8 (2005-2008) in FOS. The associations of individual protein levels (log-transformed and standardized) with longitudinal changes of gait speed and grip strength in two populations were examined separately by linear mixed effect models. Meta-analyses were implemented using random effect models with a Bonferroni correction for multiple testing. We found that plasma levels of 18 and 12 proteins were associated with changes in gait speed and grip strength, respectively (Bonferroni-corrected p &amp;lt; .05). The proteins most strongly associated with gait speed decline were growth/differentiation factor 15 (GDF-15) (uncorrected Meta-analytic p = 1.60E-15), pleiotrophin (PTN) (1.29E-08), and metalloproteinase inhibitor 1 (TIMP-1) (2.02E-08). For grip strength decline, the strongest associations were for GDF-15 (1.39E-07), carbonic anhydrase III (6.60E-07), and TIMP-1 (3.21E-06). Several statistically significant proteins are involved in the alternative complement pathway, extracellular matrix remodeling or immune function. These novel proteomic biomarkers may inform our understanding of the pathophysiology of functional decline.