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PubMed2025

Baseline metabolomic profile as potential biomarker for weight change after Roux-en-Y gastric bypass (RYGB) surgery.

Metabolic and bariatric surgery (MBS) is the most effective intervention for sustained weight loss and cardiometabolic improvement in individuals with severe obesity. However, long-term outcomes vary, with many patients experiencing weight regain. The biological determinants of this variability remain incompletely understood. Given the integrative nature of the metabolome-capturing interactions among host genetics, diet, microbiota, and environmental exposures-we hypothesized that baseline circulating metabolites could stratify individuals into distinct long-term weight trajectory groups. We profiled untargeted fasting plasma metabolites in a nested case-control study within the Longitudinal Assessment of Bariatric Surgery (LABS-2) cohort. From these metabolites, a 13-metabolite risk score (MetRS) predictive of weight regain five years after Roux-en-Y gastric bypass was derived. The MetRS, which captures pathways including fatty acid oxidation, bile acid conjugation, and microbial-host co-metabolism, outperformed clinical variables in predicting long-term weight outcomes. Its performance was evaluated in two independent cohorts, including one assessed a median of seven years post-surgery. Genomic analyses identified common variants in loci including AGXT2 and SLC7A5 associated with key MetRS metabolites, suggesting a heritable component to the observed metabolic signature. Together, these findings lay the groundwork for a clinically actionable framework to identify individuals at risk for weight recidivism and support the integration of metabolic profiling into preoperative assessment for personalized obesity care.

Journal Article↗
PubMed2026

Plasma Proteomic Profiling Identifies Candidate Biomarkers for Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that is often diagnosed after curative treatment is no longer feasible. Existing biomarkers, particularly CA19-9, have limited sensitivity and specificity. Plasma proteins that capture tumor-associated biological alterations may therefore provide useful signals for earlier detection. METHODS: Plasma samples from 99 patients with PDAC and 30 healthy controls were analyzed using data-independent acquisition (DIA) proteomics. Differentially expressed proteins were identified using predefined statistical thresholds and further examined by functional enrichment analysis. Selected candidate biomarkers were validated by ELISA in an independent subset. RESULTS: Among 565 quantified plasma proteins, 52 were differentially expressed between PDAC and controls. These proteins were enriched in extracellular processes, cholesterol metabolism, complement and coagulation cascades, and pancreatic secretion pathways. ELISA validation confirmed higher plasma levels of Cathepsin S, CTRB2, MARCO, PIGR, PRDX6, REG1A, Trypsin-2, and PEP-FAP in patients with PDAC compared with healthy controls. ROC analyses showed moderate-to-good discriminatory performance for several candidates, and the MARCO + PEP-FAP model improved classification compared with either marker alone. CONCLUSION: These findings reveal circulating proteins linked to key PDAC-related biological processes and identify eight candidates for further evaluation in multi-protein diagnostic panels. Larger validation studies incorporating clinically relevant disease control groups are warranted to determine their diagnostic specificity and clinical utility.

Humans↗
PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗
PubMed2004

Activity-based protein profiling: applications to biomarker discovery, in vivo imaging and drug discovery.

The genomic revolution has created a wealth of information regarding the fundamental genetic code that defines the inner workings of a cell. However, it has become clear that analyzing genome sequences alone will not lead to new therapies to fight human disease. Rather, an understanding of protein function within the context of complex cellular networks will be required to facilitate the discovery of novel drug targets and, subsequently, new therapies directed against them. The past ten years has seen a dramatic increase in technologies that allow large-scale, systems-based methods for analysis of global biological processes and disease states. In the field of proteomics, several well-established methods persist as a means to resolve and analyze complex mixtures of proteins derived from cells and tissues. However, the resolving power of these methods is often challenged by the diverse and dynamic nature of the proteome. The field of activity-based proteomics, or chemical proteomics, has been established in an attempt to focus proteomic efforts on subsets of physiologically important protein targets. This new approach to proteomics is centered around the use of small molecules termed activity-based probes (ABPs) as a means to tag, enrich, and isolate, distinct sets of proteins based on their enzymatic activity. Chemical probes can be 'tuned' to react with defined enzymatic targets through the use of chemically reactive warhead groups, fused to selective binding elements that control their overall specificity. As a result, ABPs function as highly specific, mechanism-based reagents that provide a direct readout of enzymatic activity within complex proteomes. Modification of protein targets by an ABP facilitates their purification and isolation, thereby eliminating many of the confounding issues of dynamic range in protein abundance. In this review, we outline recent advances in the field of chemical proteomics. Specifically, we highlight how this technology can be applied to advance the fields of biomarker discovery, in vivo imaging, and small molecule screening and drug target discovery.

Animals↗
PubMed2004

Clinical and biomarker correlates of androgen-independent, locally aggressive prostate cancer with limited metastatic potential.

PURPOSE: We have identified a subset of patients exhibiting extended survival with metastases from androgenindependent prostate cancer of which the principal site of progression was the tumor primary. The purpose of this study was to evaluate the expression of selected biomarkers to characterize this subset of prostate cancer patients. EXPERIMENTAL DESIGN: A 105 core tissue microarray was constructed from primary tumor samples from 16 patients, with matched lymph node metastases in 5 cases. Immunohistochemistry was used to evaluate selected biomarkers associated with prostate cancer progression. Standard statistical methodologies were used to compute the distribution of time to progression and overall survival associations between pairs of biomarkers. Hierarchical clustering was done between groups of biomarkers, and we devised new methods to assess homogeneity of biomarker expression. RESULTS: The median interval from diagnosis to salvage surgery was 65 months. The profile of biomarker expression was notable for virtual absence of neuroendocrine features, high CD10, low matrix metalloproteinase (MMP)-9, high E-cadherin expression, and high membranous beta-catenin. The mean proliferative index was 12.1 +/- 10.1%, and the mean apoptotic index was 3.48 +/- 2.22%, and there was a significant correlation between these indices. Expression of the epidermal growth factor receptor was associated with phospho-AKT and proliferative index but inversely associated with phospho-STAT3. CONCLUSIONS: The cohort of prostate cancer patients, characterized by locally aggressive disease rather than lethal metastatic progression, was associated with a distinctive biomarker signature. The biomarker profile was, in general, more consistent with low-grade prostate cancer exhibiting local growth rather than metastatic progression. Ongoing studies will establish whether this unique subset of patients can be identified prospectively.

Aged↗
PubMedDate not supplied

Proteomics for the identification of new prostate cancer biomarkers.

Molecular profiling studies of human prostate cancer provide great opportunities to identify new prostate cancer biomarkers to improve prostate cancer detection and treatment. Proteomics has distinct advantages over genomic and ribonucleic acid expression studies because it is the proteins that are ultimately responsible for the malignant phenotype. The goal of traditional proteomic studies is to identify disease-specific biomarkers. Two-dimensional (2-D) gel electrophoresis (polyacrylamide gel electrophoresis; PAGE) coupled with mass spectrometry is the most widely used experimental strategy and, to date, has yielded several potentially relevant prostate cancer biomarkers. A promising prostate cancer biomarker identified by 2-D PAGE and mass spectrometry is annexin I. Studies have already confirmed that annexin I is underexpressed in a majority of early stage prostate cancers. Other non-gel-based proteomic technologies that may have improved sensitivity as compared to 2-D PAGE have recently been developed. An example of this is the ProteomeLab PF 2-D (Beckman Coulter, Inc., Fullerton, CA). The goal of most proteomic studies is to identify biomarkers that can be measured by enzyme-linked immunosorbent assay or immunohistochemistry. Improvements in proteomic technology may be changing this paradigm because there are now efforts to develop proteomic technologies directly into clinical diagnostic tests. An example of this technology is surface-enhanced laser desorption ionization time-of-flight mass spectrometry. Using this technology combined with a pattern recognition based bioinformatics tool, discriminatory spectrum proteomic profiles were generated that could help discriminate men with prostate cancer from those with benign prostates. If several technologic hurdles can be overcome, it is possible that methodology will improve the specificity and sensitivity of prostate cancer detection.

Biomarkers, Tumor↗
PubMed2002

Assessment of histologic features and expression of biomarkers in predicting pathologic response to anthracycline-based neoadjuvant chemotherapy in patients with breast carcinoma.

BACKGROUND: There is significant variability in the response of tumors to neoadjuvant chemotherapy, and the underlying mechanism for this variability is unknown. In this study, the authors investigated the roles of tumor nuclear grade, mitotic activity, and biomarker expression profiles in predicting the pathologic response of breast tumors to preoperative chemotherapy. METHODS: Eighty-two patients with breast carcinoma participated in two clinical trials and were treated with neoadjuvant chemotherapy, which consisted of either a conventional dose of fluorouracil, doxorubicin, and cyclophosphamide (FAC) or dose-escalated FAC. The mean age of the patients was 46 years (range, 24-69 years). Nuclear grade, mitotic activity, and biomarker profile (Her2-neu and mitosin expression patterns) in pretreatment tumors were correlated with the postchemotherapy pathologic response. RESULTS: Twelve patients (15%) had a complete pathologic response (CPR), 23 patients (28%) had a near complete response (NCR), and 47 patients (57%) had significant residual disease present either at the primary site or in the axillary lymph nodes. The authors found that the nuclear grade and mitotic activity of pretreatment tumors were correlated significantly with CPR and NCR (P = 0.002 and P = 0.004). Mitosin also was correlated significantly with CPR and NCR (P = 0.028). A higher percentage of patients with Her2-neu-positive tumors had a CPR or an NCR (P = 0.152). CPR and NCR were not correlated significantly with disease stage (P = 0.186) or lymph node positivity (P = 0.498). CONCLUSIONS: The current results indicate that tumor nuclear grade and tumor proliferative activity (mitotic activity and mitosin immunostaining) of pretreatment tumors in patients with breast carcinoma may serve as important indicators for the pathologic responsiveness of tumors to neoadjuvant, anthracycline-based chemotherapy.

Adult↗
PubMedDate not supplied

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray.

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems' blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.

Animals↗
PubMed2004

Diagnosis of intra-amniotic infection by proteomic profiling and identification of novel biomarkers.

CONTEXT: Intra-amniotic infection (IAI) is commonly associated with preterm birth and adverse neonatal sequelae. Early diagnosis of IAI, however, has been hindered by insensitive or nonspecific tests. OBJECTIVE: To identify unique protein signatures in rhesus monkeys with experimental IAI, a proteomics-based analysis of amniotic fluid was used to develop diagnostic biomarkers for subclinical IAI in amniotic fluid and blood of women with preterm labor. DESIGN, SETTING, AND PARTICIPANTS: Surface-enhanced laser desorption-ionization/time-of-flight mass spectrometry, gel electrophoresis, and tandem mass spectrometry were used to characterize amniotic fluid peptides in 19 chronically instrumented pregnant rhesus monkeys before and after experimental IAI. Candidate biomarkers were determined by liquid chromatography-tandem mass spectrometry. Polyclonal antibodies were generated from synthetic peptides for validation of biomarkers of IAI. Amniotic fluid peptide profiles identified in experimental IAI were subsequently tested in a cohort of 33 women admitted to Seattle, Wash, hospitals between June 25, 1991, and June 30, 1997, with preterm delivery at 35 weeks or earlier associated with subclinical IAI (n = 11), preterm delivery at 35 weeks or earlier without IAI (n = 11), and preterm contractions with subsequent term delivery at later than 35 weeks (n = 11). MAIN OUTCOME MEASURES: Identification of peptide biomarkers for occult IAI. RESULTS: Protein expression profiles in amniotic fluid showed unique signatures of overexpression of polypeptides in the 3- to 5-kDa and 10- to 12-kDa molecular weight ranges in all animals after infection and in no animal prior to infection. In women, the 10- to 12-kDa signature was identified in all 11 patients with subclinical IAI, in 2 of 11 with preterm delivery without IAI, and in 0 of 11 with preterm labor and term delivery without infection (P<.001). Peptide fragment analysis of the diagnostic peak in amniotic fluid identified calgranulin B and a unique fragment of insulinlike growth factor binding protein 1, which were also expressed in maternal serum. Mapping of other amniotic fluid proteins differentially expressed in IAI identified several immunoregulators not previously described in amniotic fluid. CONCLUSIONS: This proteomics-based characterization of the differential expression of amniotic fluid proteins in IAI identified a distinct proteomic profile in an experimental primate chorioamnionitis model that detected subclinical IAI in a human cohort with preterm labor. These diagnostic protein expression signatures, complemented by immunodetection of specific biomarkers in amniotic fluid and in maternal serum, might have application in the early detection of IAI.

Adult↗
PubMed2005

Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule-learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy-terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.

Adult↗
PubMedDate not supplied

Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery.

The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.

Biomarkers↗
PubMed2006

Potential biomarkers found by protein profiling may provide insight for the macrovascular pathogenesis of diabetes mellitus.

Diabetes mellitus (DM) is an alarming threat to health of mankind, yet its pathogenesis is unclear. The purpose of this study was to find potential biomarkers to serve as indicators for the pathogenesis of DM in a time course manner. Based on our previous findings that oxidative stress occurred at week 8, aorta lysate and sera of 102 streptozotocin (STZ)-induced diabetic and 85 control male Sprague-Dawley rats were obtained at the 4th, 8th and 12th week after STZ injection. The protein profiles were studied employing surface-enhanced laser desorption/ionization time-of-flight mass spectrometry technology in attomole sensitivity range. In the aorta, a multiple biomarker panel was discovered at the 4th week. At the 8th week, 4 biomarkers were found, while at the 12th week, 3 biomarkers were identified. In the sera, a triplet of 3 peaks and 2 biomarkers were all discovered to have 100% classification accuracy rate to differentiate the DM and control groups at all time intervals. Besides, 2 biomarkers were also found to have high classification value at week 12. Comparing the aorta and sera from DM and non-DM rats, a bundle of potential biomarkers with significant changes in peak intensities and high classification values were found. Two of the serum biomarkers matched with islet amyloid polypeptide and resistin in the SWISS-PROT knowledgebase. Validation has been conducted using immunoassay kits. These potential biomarkers may provide valuable insight on the pathogenesis of DM and macrovascular complications.

Animals↗
PubMed2005

Proteomic profiling of pancreatic cancer for biomarker discovery.

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.

Biomarkers, Tumor↗
PubMed2002

Molecular and biologic markers of premalignant lesions of human breast.

There is currently great interest in the detection and characterization of putative precursor breast cancer lesions because of the possibility of chemoprevention. Knowledge of the biologic features of premalignant lesions, although limited, is rapidly evolving. Premalignant breast lesions have been examined for the presence of genetic alterations and for the expression of biomarkers such as the estrogen receptor (ER), Ki67, p53, and HER2/neu. Data obtained from genetic studies of precursor breast lesions clearly support the contention that genetic alterations begin quite early in selected subsets of histologically benign lesions. Although the results of biomarker expression profiles have been contradictory, most studies agree that precursor lesions significantly overexpress ER and that progressive alterations in ER expression accompany the transition of normal cells to hyperplastic lesions and carcinoma in situ. So far, the collected evidence indicates that precursor lesions in the breast demonstrate biomarker expression profiles and genetic abnormalities that are distinct from those of terminal ductal lobular units but share some of these features with invasive tumors. Future research in this field is urgently needed to identify specific biomarkers of prognostic and predictive value, which can help not only in the selection of patients for chemopreventive therapy but in monitoring the progression of high-risk lesions.

Biomarkers, Tumor↗
PubMed2004

Proteomic signature of human cancer cells.

We assessed proteomic profiles as biomarkers for monitoring cell phenotypes. Protein expression profiles were obtained by fluorescence two-dimensional difference gel electrophoresis (2-D-DIGE), in which quantitative ability is improved by labeling proteins with fluorescent dyes prior to electrophoresis. Integrated protein spot intensities were analyzed by a statistical approach. The proteomic data of two groups of cell lines: (1) adenocarcinoma (AC) cell lines derived from lung, pancreas and colon tissues and (2) lung cancer cell lines with different histological backgrounds, including AC, squamous cell carcinoma and small cell carcinoma, were assessed on the basis of prior biological information. Hierarchical clustering analysis and principal component analysis were used to divide the cell lines into subgroups on the basis of similarities between their protein expression profiles. The majority of cell lines were grouped according to their organ of origin or histological background. A machine-learning algorithm selected 32 protein spots that were responsible for the classification. The results indicate that proteomic data generated by 2-D-DIGE can provide a signature of essential cell phenotypes, suggesting that it might be possible to apply this technique to developing tumor markers that could identify the organ of origin of metastatic tumors and contribute to the differential diagnosis of lung cancer.

Cell Line, Tumor↗
PubMed2026

Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women.

PURPOSE: To examine secondary lipid, kidney-related, and liver-enzyme responses to three protein intakes during resistance training (RT) alone or the same RT plus cycling (CT) in middle-aged to older women. METHODS: In this randomized 2&#xd7;3 factorial trial, 108 women aged 40-77 years were assigned to RT or CT and 0.8, 1.6, or 2.2 g kg-1 d-1 protein for 12 weeks. This complete-case secondary analysis included 83 participants. Linear mixed-effects models tested Time &#xd7; Training, Time &#xd7; Protein, and Time &#xd7; Training &#xd7; Protein effects, with false-discovery-rate-adjusted omnibus tests and Holm-adjusted contrasts. RESULTS: Triglycerides, total cholesterol, LDL-C, and apolipoprotein B decreased and HDL-C increased in all conditions. Lipid changes differed by protein condition, and several were more favorable with CT; however, CT comprised RT plus additional cycling and greater exercise exposure. Urea, blood urea nitrogen, creatinine, the blood urea nitrogen-to-creatinine ratio, and cystatin C increased, whereas three eGFR estimates decreased. Responses differed mainly between 0.8 and the two higher protein conditions, with little evidence of differences between 1.6 and 2.2 g kg-1 d-1. ALT, AST, and GGT differed by protein condition; AST and GGT also showed training-dependent responses. CONCLUSIONS: The dietary and exercise interventions modified lipid and clinical-chemistry responses. Because energy and food composition were not fully matched and CT added cycling to RT, the findings do not isolate protein dose or exercise modality. Changes in eGFR estimates and liver enzymes do not establish organ injury or long-term safety.

Humans↗
PubMedDate not supplied

Data-driven analysis approach for biomarker discovery using molecular-profiling technologies.

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.

Algorithms↗