Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Background  

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.     

恶性肿瘤化疗后血糖变化的蛋白质组指纹模型

目的:应用表面增强激光解析离子飞行时间质谱(Surface-enhanced laser desorption ionization time of flight mass spectrometry,SELDI-TOF-MS)技术筛选与恶性肿瘤化疗后血糖变化情况相关的血清蛋白质组指纹并建立模型.方法:应用CM10弱阳离子芯片结合SELDI-TOF-MS技术检测197例恶性肿瘤患者化疗后血清样本的蛋白质谱,2年后随访,按血糖标准分为血糖正常组(171例)、糖耐量异常组(16例)和糖尿病组(10例),利用Biomarker Wizard软件比较各组间的血清蛋白质指纹图谱,Biomarker Pattern软件建立模型.结果:M/Z为4276和4662的两个蛋白质组成的诊断模型可将糖尿痛组与糖耐量异常组准确分组,灵敏度、特异度和准确度分别为70%、81.25%和76.92%;M/Z为2818、7535和2633的三个蛋白质组成的诊断模型可将糖尿病组与血糖正常组准确分组,灵敏度、特异度和准确度分别为80%、79.53%和82.32%;M/Z为2818、7744、3187、2564、4175、5165和3374的七个蛋白质组成的诊断模型可将糖耐量异常组与血糖正常组准确分组,灵敏度、特异度和准确度分别为87.5%、87.72%和88.77%.结论:SELDI-TOF-MS技术筛选出恶性肿瘤化疗后三组血糖情况的蛋白质指纹,M/Z为4175、4276、4086、3158、3374、3316、2044、3441、4662和4290可作为预测化疗后糖尿病的指标,M/Z为2818、3374、3352、4276、2932、8817、4070、3187、7535和15525可作为预测化疗后糖耐量异常的指标,M/Z为6021、3187、2818、2932、3273、4070、7916、8817、8057和4387可作为预测化疗后可能不会发生糖尿病的指标,这为化疗副反应的防治提供了科学依据.     

Identification of Novel Biomarkers for Sepsis Prognosis via Urinary Proteomic Analysis Using iTRAQ Labeling and 2D-LC-MS/MS

Objectives

Sepsis is the major cause of death for critically ill patients. Recent progress in proteomics permits a thorough characterization of the mechanisms associated with critical illness. The purpose of this study was to screen potential biomarkers for early prognostic assessment of patients with sepsis.

Methods

For the discovery stage, 30 sepsis patients with different prognoses were selected. Urinary proteins were identified using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with LC-MS/MS. Mass spec instrument analysis were performed with Mascot software and the International Protein Index (IPI); bioinformatic analyses were used by the algorithm of set and the Gene Ontology (GO) Database. For the verification stage, the study involved another 54 sepsis-hospitalized patients, with equal numbers of patients in survivor and non-survivor groups based on 28-day survival. Differentially expressed proteins were verified by Western Blot.

Results

A total of 232 unique proteins were identified. Proteins that were differentially expressed were further analyzed based on the pathophysiology of sepsis and biomathematics. For sepsis prognosis, five proteins were significantly up-regulated: selenium binding protein-1, heparan sulfate proteoglycan-2, alpha-1-B glycoprotein, haptoglobin, and lipocalin; two proteins were significantly down-regulated: lysosome-associated membrane proteins-1 and dipeptidyl peptidase-4. Based on gene ontology clustering, these proteins were associated with the biological processes of lipid homeostasis, cartilage development, iron ion transport, and certain metabolic processes. Urinary LAMP-1 was down-regulated, consistent with the Western Blot validation.

Conclusion

This study provides the proteomic analysis of urine to identify prognostic biomarkers of sepsis. The seven identified proteins provide insight into the mechanism of sepsis. Low urinary LAMP-1 levels may be useful for early prognostic assessment of sepsis.

Trial Registration

ClinicalTrial.gov {"type":"clinical-trial","attrs":{"text":"NCT01493492","term_id":"NCT01493492"}}NCT01493492     

Two of Them Do It Better: Novel Serum Biomarkers Improve Autoimmune Hepatitis Diagnosis

Background

Autoimmune hepatitis (AIH) is a chronic liver disease of unknown aetiology and characterized by continuing hepatocellular inflammation and necrosis. Autoantibodies represent accessible markers to measure the adaptive immune responses in the clinical investigation. Protein microarrays have become an important tool to discriminate the disease state from control groups, even though there is no agreed-upon standard to analyze the results.

Results

In the present study 15 sera of patients with AIH and 78 healthy donors (HD) have been tested against 1626 proteins by an in house-developed array. Using a Partial Least Squares Discriminant Analysis (PLS-DA) the resulting data interpretation led to the identification of both new and previously identified proteins. Two new proteins AHPA9419 and Chondroadherin precursor (UNQ9419 and CHAD, respectively), and previously identified candidates as well, have been confirmed in a validation phase by DELFIA assay using a new cohort of AIH patients. A receiver operating characteristic analysis was used for the evaluation of biomarker candidates. The sensitivity of each autoantigen in AIH ranged from 65 to 88%; moreover, when the combination of the two new autoantigens was analyzed, the sensitivity increased to 95%.

Conclusions

Our findings demonstrate that the detection of autoantibodies against the two autoantigens could improve the performance in discriminating AIH patients from control classes and in combination with previously identified autoantigens and they could be used in diagnostic/prognostic markers.     

Heart Rate Dynamics after Combined Strength and Endurance Training in Middle-Aged Women: Heterogeneity of Responses

The loss of complexity in physiological systems may be a dynamical biomarker of aging and disease. In this study the effects of combined strength and endurance training compared with those of endurance training or strength training alone on heart rate (HR) complexity and traditional HR variability indices were examined in middle-aged women. 90 previously untrained female volunteers between the age of 40 and 65 years completed a 21 week progressive training period of either strength training, endurance training or their combination, or served as controls. Continuous HR time series were obtained during supine rest and submaximal steady state exercise. The complexity of HR dynamics was assessed using multiscale entropy analysis. In addition, standard time and frequency domain measures were also computed. Endurance training led to increases in HR complexity and selected time and frequency domain measures of HR variability (P<0.01) when measured during exercise. Combined strength and endurance training or strength training alone did not produce significant changes in HR dynamics. Inter-subject heterogeneity of responses was particularly noticeable in the combined training group. At supine rest, no training-induced changes in HR parameters were observed in any of the groups. The present findings emphasize the potential utility of endurance training in increasing the complex variability of HR in middle-aged women. Further studies are needed to explore the combined endurance and strength training adaptations and possible gender and age related factors, as well as other mechanisms, that may mediate the effects of different training regimens on HR dynamics.     

Regional and Temporal Changes in Proteomic Profile after Middle Cerebral Artery Occlusion with or without Reperfusion in Rats

Although DNA microarray studies showed up-regulation of various genes, failures of translation of many genes are expected to occur under ischemic conditions even in the penumbra with mild reduction in cerebral blood flow. We applied surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology to study proteomic profile at 6, 12, and 24 h after photothrombotic middle cerebral artery (MCA) occlusion with or without YAG laser-induced reperfusion in adult male spontaneously hypertensive rats. Of the 43 protein peaks that differed from the sham-operation group with a criterion (no overlap of peak intensities between the two groups), 36 peaks (84%) were down-regulated, and seven were up-regulated. All increased peaks showed greater than twofold increases (up to 8.1 fold) compared with those in the sham-operation group. Effects of reperfusion were observed mainly at 24 h after 1 h of MCA occlusion only in the penumbra, where 23 of 32 peaks returned toward the control values, whereas none of 33 peaks showed such attenuation in the ischemic core. Major ischemia-induced changes in protein peaks detected with SELDI-TOF-MS were down-regulations. The present study showed that dynamic changes of protein profile were associated with progression and recovery of the ischemic core and penumbra.     

A Group of Novel Serum Diagnostic Biomarkers for Multidrug-Resistant Tuberculosis by iTRAQ-2D LC-MS/MS and Solexa Sequencing

The epidemic of pulmonary tuberculosis (TB), especially multidrug-resistance tuberculosis (MDR-TB) presented a major challenge for TB treatment today. We performed iTRAQ labeling coupled with two-dimensional liquid chromatography-tandem mass spectrometry (2D LC-MS/MS) and Solexa sequencing among MDR-TB patients, drug-sensitive tuberculosis (DS-TB) patients, and healthy controls. A total of 50 differentially expressed proteins and 43 differentially expressed miRNAs (fold change >1.50 or <0.60, P<0.05) were identified in the MDR-TB patients compared to both DS-TB patients and healthy controls. We found that 22.00% of differentially expressed proteins and 32.56% of differentially expressed miRNAs were related, and could construct a network mainly in complement and coagulation cascades. Significant differences in CD44 antigen (CD44), coagulation factor XI (F11), kininogen-1 (KNG1), miR-4433b-5p, miR-424-5p, and miR-199b-5p were found among MDR-TB patients, DS-TB patients and healthy controls (P<0.05) by enzyme-linked immunosorbent assay (ELISA) and SYBR green qRT-PCR validation. A strong negative correlation, consistent with the target gene prediction, was found between miR-199b-5p and KNG1 (r=-0.232, P=0.017). Moreover, we established the MDR-TB diagnostic model based on five biomarkers (CD44, KNG1, miR-4433b-5p, miR-424-5p, and miR-199b-5p). Our study proposes potential biomarkers for MDR-TB diagnosis, and also provides a new experimental basis to understand the pathogenesis of MDR-TB.     

Assessment of Metabolomic and Proteomic Biomarkers in Detection and Prognosis of Progression of Renal Function in Chronic Kidney Disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m²; n = 10) or advanced (8.9±4.5 mL/min/1.73 m²; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.     

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to “rescue” the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes.The past decade has witnessed rapid progress in mass spectrometry (MS)-based quantitative proteomics with the development of software and data analysis tools to interrogate large amounts of MS data. Quantitative proteomic technologies have shown great potential in delineating dysregulated proteomes in diseases such as cancer (1–4). Quantitative schemes via either stable isotope labeling or label-free quantitation (LFQ)¹ are used widely to assist MS for quantitative assessments of the changes in protein expression, post-translational modifications (5), and protein-protein interactions (6) in many biological systems, including tumor samples (7–11). However, the integration of accuracy, sensitivity, and totality in the analysis of tumor-specific proteoforms from individual patients still remains challenging with the current quantitative platforms. For example, strategies to increase analytical throughput (12) for tumor analysis have utilized the multiplexing advantage of isobaric mass tags such as tandem mass tags or isotope tagging for relative and absolute quantitation (13, 14). However, for routine quantitative analysis of large scale peptides/proteins, tandem mass tags and isotope tagging for relative and absolute quantitation reagents are prohibitively expensive due to the requirement of large amounts of protein as input. The use of added internal peptide standards, derived from isotope-labeled cell lines, or ¹⁸O labeling to quantify peptides (15) allows for quantitation of proteome expression changes; however, these methods require high resolution in both LC separation and MS acquisition for accurate quantitation of overlapping isotopes. The metabolic incorporation of in-spectra quantitative markers through cell culture (16, 17), in vivo quantitation strategies involving amino acid-coded tags (AACT, also known as SILAC or stable isotope labeling by amino acids in cell culture (18)), is still considered the gold standard for accurate quantitation of relative changes in protein abundance across different biological states. However, for tissue proteomics, neither a single cell line as an add-in SILAC standard (19) nor a library of cell lines (a super-SILAC mix (20)) is close to being a universal standard due to peptides that are either missing or present at low levels. The missing internal standards that fail to cover tissue-peptide counterparts, referred to as orphan peptides, preclude quantitative estimation of tissue proteome differences, an issue that has been addressed recently by the addition of peptide standards (21). A more universal labeling strategy such as complete labeling of the equivalent tissue of the organism of interest via stable isotope labeling of mammals (SILAM) has found limited utility (22, 23). The relatively high cost and laborious procedures associated with animal feeding and labeling prevent widespread use of SILAM.Conversely, quantitation of tissue and tumor proteins is very amenable to LFQ and has gained traction recently as an alternative to spiked-in labeled standards (24, 25). Despite the inherent low precision and low throughput of LFQ methods (i.e. multiple separate or independent LC-MS runs as opposed to interdependent, multiplexed LC-MS runs), LFQ does offer some advantages. Running each sample separately provides a higher number of peptide identifications, whereas LFQ avoids issues inherent to multiplexing, such as low or discrepant labeling efficiencies, inaccuracies in sample mixing, and the need for scrambling/switching the isotope-labeled samples to test whether conversion of isotopically labeled arginine to proline impacts results (26). Also LFQ using MS1 peak intensities can significantly improve the sensitivity as much as 60% compared with label-based quantitative methods such as AACT that rely on the MS1 peak intensities (18).We therefore reasoned that the integration of multiple quantitative schemes would provide synergy, higher throughput, and effectiveness to more precisely determine the changes of protein expressions with a larger coverage of given tissue proteome across different tumor subtypes. Specifically, combining peptide abundance differences using both LFQ and AACT label-based, Ratio-of-Ratio (RoR) estimations would greatly increase the overall number of quantifiable peptide/protein changes to distinguish various tumors. When a common set of peptide features cannot be matched and quantitated between two independent LFQ LC-MS runs due to the frequently occurring issues of retention-time misalignment, the labeled-based quantification strategy could provide complementary peptide ratio estimation. Conversely, when LFQ provides quantitation ratios between samples after retention-time alignment of features, a situation may also exist wherein, at minimum, one of the samples lacks a labeled peptide counterpart, making the label-based estimate impossible. To achieve a complementary quantitative scheme, here we report our development of a unified quantitative approach, termed QuantFusion, that uses a multivariate mixed model to interrogate quantifiable peptide data derived from both LFQ and label-based AACT methods from a single MS experimental run. As stated above, LFQ and RoR measurements share complementary information and therefore can be integrated to reduce the number of replicates required for generating the statistically significant LFQ ratios.The complexity of combining dependent outcomes with heterogeneous error structures and varying sample sizes within each protein necessitated the use of a statistical model. We demonstrate the merit of the mixed model-based approach on the integration of the global-scale proteome characteristics implicated in two major breast cancer (BC) subtypes. QuantFusion increased by 65% the number of distinct peptide ratios to highlight BC-subtypic proteome differences. This increase of quantifiable peptide coverage, in turn, increased the number of measurable protein fold-changes by 8% and increased the average precision of quantitative peptide estimates by 181%. The Statistical Analysis Software code used to implement the statistical model along with a test data set used in this study are available to investigators who wish to perform QuantFusion experiments.     

Serum Collagen Type II Cleavage Epitope and Serum Hyaluronic Acid as Biomarkers for Treatment Monitoring of Dogs with Hip Osteoarthritis

The aim of this study was to evaluate the use of serum type II collagen cleavage epitope and serum hyaluronic acid as biomarkers for treatment monitoring in osteoarthritic dogs. For this purpose, a treatment model based on mesenchymal stem cells derived from adipose tissue combined with plasma rich in growth factors was used. This clinical study included 10 dogs with hip osteoarthritis. Both analytes were measured in serum at baseline, just before applying the treatment, and 1, 3, and 6 months after treatment. These results were compared with those obtained from force plate analysis using the same animals during the same study period. Levels of type II collagen cleavage epitope decreased and those of hyaluronic acid increased with clinical improvement objectively verified via force plate analysis, suggesting these two biomarkers could be effective as indicators of clinical development of joint disease in dogs.     

Plasma miR-601 and miR-760 Are Novel Biomarkers for the Early Detection of Colorectal Cancer

Background

Colorectal cancer (CRC) is a major cause of death worldwide. Sensitive, non-invasive diagnostic screen methods are urgently needed to improve its survival rates. Stable circulating microRNA offers unique opportunities for the early diagnosis of several diseases, including cancers. Our aim has been to find new plasma miRNAs that can be used as biomarkers for the detection of CRC.

Methodology/Principal Findings

According to the results of miRNA profiling performed on pooling plasma samples form 10 CRC patients or 10 healthy controls, a panel of miRNAs (hsa-miR-10a, -19a, -22*, -24, -92a, 125a-5p, -141, -150, -188-3p, -192, -210, -221, -224*, -376a, -425*, -495, -572, -601, -720, -760 and hsa-let-7a, -7e) were deregulated in CRC plasma with fold changes >5. After large scale validation by qRT-PCR performed on another 191 independent individuals (90 CRC, 43 advanced adenoma and 58 healthy participants), we found that the levels of plasma miR-601 and miR-760 were significantly decreased in colorectal neoplasia (carcinomas and advanced adenomas) compared with healthy controls. ROC curve analysis showed that plasma miR-601 and miR-760 were of significant diagnostic value for advanced neoplasia. These two miRNAs together yield an AUC of 0.792 with 83.3% sensitivity and 69.1% specificity for separating CRC from normal controls, and yield an AUC of 0.683 with 72.1% sensitivity and 62.1% specificity in discriminating advanced adenomas from normal controls.

Conclusions/Significance

Plasma miR-601 and miR-760 can potentially serve as promising non-invasive biomarkers for the early detection of CRC.     

Performance of Serum Biomarkers for the Early Detection of Invasive Aspergillosis in Febrile,Neutropenic Patients: A Multi-State Model

Background

The performance of serum biomarkers for the early detection of invasive aspergillosis expectedly depends on the timing of test results relative to the empirical administration of antifungal therapy during neutropenia, although a dynamic evaluation framework is lacking.

Methods

We developed a multi-state model describing simultaneously the likelihood of empirical antifungal therapy and the risk of invasive aspergillosis during neutropenia. We evaluated whether the first positive test result with a biomarker is an independent predictor of invasive aspergillosis when both diagnostic information used to treat and risk factors of developing invasive aspergillosis are taken into account over time. We applied the multi-state model to a homogeneous cohort of 185 high-risk patients with acute myeloid leukemia. Patients were prospectively screened for galactomannan antigenemia twice a week for immediate treatment decision; 2,214 serum samples were collected on the same days and blindly assessed for (1->3)- β-D-glucan antigenemia and a quantitative PCR assay targeting a mitochondrial locus.

Results

The usual evaluation framework of biomarker performance was unable to distinguish clinical benefits of β-glucan or PCR assays. The multi-state model evidenced that the risk of invasive aspergillosis is a complex time function of neutropenia duration and risk management. The quantitative PCR assay accelerated the early detection of invasive aspergillosis (P = .010), independently of other diagnostic information used to treat, while β-glucan assay did not (P = .53).

Conclusions

The performance of serum biomarkers for the early detection of invasive aspergillosis is better apprehended by the evaluation of time-varying predictors in a multi-state model. Our results provide strong rationale for prospective studies testing a preemptive antifungal therapy, guided by clinical, radiological, and bi-weekly blood screening with galactomannan antigenemia and a standardized quantitative PCR assay.     

表面增强激光解吸电离飞行时间质谱技术筛查肺癌血清特异性蛋白质及其临床意义

目的:探讨用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术筛查肺癌血清特异性蛋白质的临床意义。方法:应用SELDI-TOF-MS对35例正常对照组、43例治疗前肺癌病人的血清样品进行蛋白质指纹图谱测定,用BioMarker Wizard 3．01及BioMarker Parrern System 5．01分析软件对测得的数据进行处理及建立诊断模型。结果:共检测到251个蛋白质峰,筛选出差异蛋白质峰11个,以质荷比(m/z)分别为M2799_26,M3227_41,M5739_70和M8164_30的4个蛋白质峰为依据组合构建分类决策树模型,分出5个终节点。决策树模型的原始判别总准确率为91．0%(71/78),敏感性为88．4%(38/43),特异性为94．3%(33/35);交叉验证总准确率为85．9%(67/78),敏感性为88．4%(38/43),特异性为82．9%(29/35)。结论:SELDI-TOF-MS在肺癌血清特异性蛋白质的筛选及诊断模型的建立有一定的临床意义。     

Determinants of the Changes in Glycemic Control with Exercise Training in Type 2 Diabetes: A Randomized Trial

Aims

To assess the determinants of exercise training-induced improvements in glucose control (HbA_1C) including changes in serum total adiponectin and FFA concentrations, and skeletal muscle peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) protein content.

Methods

A sub-cohort (n = 35; 48% men; 74% Caucasian) from the HART-D study undertaking muscle biopsies before and after 9 months of aerobic (AT), resistance (RT), or combination training (ATRT).

Results

Changes in HbA_1C were associated with changes in adiponectin (r = −0.45, P = 0.007). Participants diagnosed with type 2 diabetes for a longer duration had the largest increase in PGC-1α (r = 0.44, P = 0.008). Statistical modeling examining changes in HbA_1C suggested that male sex (P = 0.05), non-Caucasian ethnicity (P = 0.02), duration of type 2 diabetes (r = 0.40; P<0.002) and changes in FFA (r = 0.36; P<0.004), adiponectin (r = −0.26; P<0.03), and PGC-1α (r = −0.28; P = 0.02) explain ∼65% of the variability in the changes in HbA_1C.

Conclusions

Decreases in HbA_1C after 9 months of exercise were associated with shorter duration of diabetes, lowering of serum FFA concentrations, increasing serum adiponectin concentrations and increasing skeletal muscle PGC-1α protein expression.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00458133","term_id":"NCT00458133"}}NCT00458133