Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events After Hospitalization: Findings From the ASSESS-AKI and ARID Studies.

RATIONALE & OBJECTIVE: The role of plasma soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 in the prognosis of clinical events after hospitalization with or without acute kidney injury (AKI) is unknown. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: Hospital survivors from the ASSESS-AKI (Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury) and ARID (AKI Risk in Derby) studies with and without AKI during the index hospitalization who had baseline serum samples for biomarker measurements. PREDICTORS: We measured sTNFR1 and sTNFR2 from plasma samples obtained 3 months after discharge. OUTCOMES: The associations of biomarkers with longitudinal kidney disease incidence and progression, heart failure, and death were evaluated. ANALYTICAL APPROACH: Cox proportional hazard models. RESULTS: Among 1,474 participants with plasma biomarker measurements, 19% had kidney disease progression, 14% had later heart failure, and 21% died during a median follow-up of 4.4 years. For the kidney outcome, the adjusted HRs (AHRs) per doubling in concentration were 2.9 (95% CI, 2.2-3.9) for sTNFR1 and 1.9 (95% CI, 1.5-2.5) for sTNFR2. AKI during the index hospitalization did not modify the association between biomarkers and kidney events. For heart failure, the AHRs per doubling in concentration were 1.9 (95% CI, 1.4-2.5) for sTNFR1 and 1.5 (95% CI, 1.2-2.0) for sTNFR2. For mortality, the AHRs were 3.3 (95% CI, 2.5-4.3) for sTNFR1 and 2.5 (95% CI, 2.0-3.1) for sTNFR2. The findings in ARID were qualitatively similar in terms of the magnitude of association between biomarkers and outcomes. LIMITATIONS: Different biomarker platforms and AKI definitions; limited generalizability to other ethnic groups. CONCLUSIONS: Plasma sTNFR1 and sTNFR2 measured 3 months after hospital discharge were independently associated with clinical events regardless of AKI status during the index admission. sTNFR1 and sTNFR2 may assist with the risk stratification of patients during follow-up.

Biomarkers During Recovery From AKI and Prediction of Long-term Reductions in Estimated GFR.

RATIONALE & OBJECTIVE: The effects of acute kidney injury (AKI) on long-term kidney function, cardiovascular disease, and mortality are well documented. We aimed to identify biomarkers for the estimation of risk of new or worsening chronic kidney disease (CKD) following AKI. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Adults from a single clinical center who experienced AKI between May 2013 and May 2016 and survived until 3 years after the hospitalization during which AKI occurred. Participants included those with and without preexisting CKD. PREDICTORS: Panel of 11 plasma biomarkers measured 3 months after hospitalization. OUTCOME: Kidney disease progression, defined as a≥25% decrease in estimated glomerular filtration rate (eGFR) combined with worsening CKD stage, assessed 3 years after the occurrence of AKI. ANALYTICAL APPROACH: Associations between biomarkers and kidney disease progression were evaluated in multivariable logistic regression models. Importance of predictor variables was assessed by constructing multiple decision trees, with penalized least absolute shrinkage and selection operator logistic regression for variable selection used to produce multivariable models. RESULTS: A total of 500 patients were studied. Soluble tumor necrosis factor receptor (sTNFR) 1, sTNFR2, cystatin C, neutrophil gelatinase-associated lipocalin, 3-month eGFR, and urinary albumin-creatinine ratio were independently associated with kidney disease progression and were more important than AKI severity or duration. A multivariable model containing sTNFR1, sTNFR2, cystatin C, and eGFR discriminated between those with and without kidney disease progression (area under the curve, 0.79 [95% CI, 0.70-0.83]). Optimizing the cutoff point to maximize utility as a "rule-out" test to identify those at low risk increased the sensitivity of the model to 95% and its negative predictive value to 92%. LIMITATIONS: Lack of external validation cohort. Analyses limited to patients who survived for 3 years after AKI. Mixed population of patients with and without baseline CKD. CONCLUSIONS: A panel of plasma biomarkers measured 3 months after discharge from a hospitalization complicated by AKI provides a potential opportunity to identify patients who are at very low risk of incident or worsening CKD. Further study is required to determine its clinical utility through independent prospective validation.

Blood and urinary cytokine balance and renal outcomes at cardiac surgery.

BACKGROUND: Increased perioperative pro-inflammatory biomarkers, renal hypoperfusion and ischemia reperfusion injury (IRI) heighten cardiac surgery acute kidney injury (CS-AKI) risk. Increased urinary anti-inflammatory cytokines attenuate risk. We evaluated whether blood and urinary anti-inflammatory biomarkers, when expressed as ratios with biomarkers of inflammation, hypoperfusion and IRI are increased in CS-AKI patients. METHODS: Preoperative and 24-h postoperative blood and urinary pro-inflammatory and anti-inflammatory cytokines, blood VEGF and H-FABP (hypoperfusion biomarkers), and MK, a biomarker for IRI, were measured in 401 cardiac surgery patients. Pre- and postoperative concentrations of biomarkers and selected ratios thereof, were compared between non-CS-AKI and CS-AKI patients. RESULTS: Compared with non-CS-AKI, blood pro-inflammatory (pre- and post-op TNFα, IP-10, IL-12p40, MIP-1α, NGAL; pre-op IL-6; post-op IL-8, MK) and anti-inflammatory (pre- and post-op sTNFsr1, sTNFsr2, IL-1RA) biomarkers together with urinary pro-inflammatory (pre- and post-op uIL-12p40; post-op uIP-10, uNGAL) and anti-inflammatory (pre- and post-op usTNFsr1, usTNFsr2, uIL-1RA) biomarkers, were significantly higher in CS-AKI patients. Urinary anti-inflammatory biomarkers, when expressed as ratios with biomarkers of inflammation (blood and urine), hypoperfusion (blood H-FABP and VEGF) and IRI (blood MK) were decreased in CS-AKI. In contrast, blood anti-inflammatory biomarkers expressed as similar ratios with blood biomarkers were increased in CS-AKI. CONCLUSIONS: The urinary anti-inflammatory response may protect against the injurious effects of perioperative inflammation, hypoperfusion and IRI. These finding may have clinical utility in bioprediction and earlier diagnosis of CS-AKI and informing future therapeutic strategies for CS-AKI patients.

Feto-maternal indicators of cardiac dysfunction as a justification for the cardiac origins for pre-eclampsia.

While the pathophysiology of pre-eclampsia has been postulated as being secondary to placental dysfunction, a cardiac origin has more recently been proposed. Although an association between fetal congenital cardiovascular disease and pre-eclampsia has been demonstrated, no precise pathophysiologic mechanism for this association has been described. This review highlights the current biophysical (including echocardiography and Doppler indices) and biochemical (including proteomic, metabolomic and genetic/transcriptomic) markers of cardiac dysfunction that have been investigated in maternal and fetal cardiac disease and their overlap with predictors of pre-eclampsia.   Common pathways of inflammatory and anti-angiogenesis imbalance, endothelial damage, and oxidative stress have been demonstrated in both cardiovascular disease and pre-eclampsia and further investigation into these pathways could help to elucidate the common pathophysiologic mechanisms linking these disorders.

Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics.

Background: Detailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones. Materials and methods: We applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes. Results: Results showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16. Conclusions: CACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals.

Plasma Amino Acids in NAFLD Patients with Obesity Are Associated with Steatosis and Fibrosis: Results from the MAST4HEALTH Study.

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) have been linked to changes in amino acid (AA) levels. The objective of the current study was to examine the relationship between MRI parameters that reflect inflammation and fibrosis and plasma AA concentrations in NAFLD patients. Plasma AA levels of 97 NAFLD patients from the MAST4HEALTH study were quantified with liquid chromatography. Medical, anthropometric and lifestyle characteristics were collected and biochemical parameters, as well as inflammatory and oxidative stress biomarkers, were measured. In total, subjects with a higher MRI-proton density fat fraction (MRI-PDFF) exhibited higher plasma AA levels compared to subjects with lower PDFF. The concentrations of BCAAs (p-Value: 0.03), AAAs (p-Value: 0.039), L-valine (p-Value: 0.029), L-tyrosine (p-Value: 0.039) and L-isoleucine (p-Value: 0.032) were found to be significantly higher in the higher PDFF group compared to lower group. Plasma AA levels varied according to MRI-PDFF. Significant associations were also demonstrated between AAs and MRI-PDFF and MRI-cT1, showing the potential utility of circulating AAs as diagnostic markers of NAFLD.

Diagnostic accuracy of heart-type fatty acid-binding protein for the early diagnosis of acute myocardial infarction.

OBJECTIVE: The aim of this study was to evaluate the diagnostic efficacy of multiple tests-heart-type fatty acid-binding protein (H-FABP), cardiac troponin I (cTnI), creatine kinase-MB, and myoglobin-for the early detection of acute myocardial infarction among patients who present to the emergency department with chest pain. METHODS: A total of 1128 patients provided a total of 2924 venous blood samples. Patients with chest pain were nonselected and treated according to hospital guidelines. Additional cardiac biomarkers were assayed simultaneously at serial time points using the Cardiac Array (Randox Laboratories Ltd, Crumlin, United Kingdom). RESULTS: Heart-type fatty acid-binding protein had the greatest sensitivity at 0 to 3 hours (64.3%) and 3 to 6 hours (85.3%) after chest pain onset. The combination of cTnI measurement with H-FABP increased sensitivity to 71.4% at 3 to 6 hours and 88.2% at 3 to 6 hours. Receiver operating characteristic curves demonstrated that H-FABP had the greatest diagnostic ability with area under the curve at 0 to 3 hours of 0.841 and 3 to 6 hours of 0.894. The specificity was also high for the combination of H-FABP with cTnI at these time points. Heart-type fatty acid-binding protein had the highest negative predictive values of all the individual markers: 0 to 3 hours (93%) and 3 to 6 hours (97%). Again, the combined measurement of cTnI with H-FABP increased the negative predictive values to 94% at 0 to 3 hours, 98% at 3 to 6 hours, and 99% at 6 to 12 hours. CONCLUSION: Testing both H-FABP and cTnI using the Cardiac Array proved to be both a reliable diagnostic tool for the early diagnosis of myocardial infarction/acute coronary syndrome and also a valuable rule-out test for patients presenting at 3 to 6 hours after chest pain onset.

Non-alcoholic fatty liver disease-A pilot study investigating early inflammatory and fibrotic biomarkers of NAFLD with alcoholic liver disease.

Introduction: Non-alcoholic fatty liver disease (NAFLD) is a condition where excess fat accumulates in the liver (hepatic steatosis) and there is no history of alcohol abuse or other secondary causes of chronic liver disease. NAFLD is a very common disorder, occurring in 25% of the global population. NAFLD is now the most common chronic liver disorder in Western countries. Liver biopsy is the gold standard for NAFLD diagnosis and staging; however, this is invasive, costly and not without risk. Biomarkers that could diagnose and stage disease would reduce the need for biopsy and allow stratification of patients at risk of progression to non-alcoholic steatohepatitis (NASH). Methods: One hundred and thirty-five patients were involved in the study [N = 135: n = 34 controls; n = 26 simple steatosis; n = 61 NAFLD/NASH, and n = 14 alcoholic liver disease (ALD)]. Clinically diagnosed (ICD-10) patient serum samples were obtained from Discovery Life Sciences (US) along with clinical history. Samples were run in duplicate using high-sensitivity cytokine array I, immunoassays and ELISAs. In total, n = 20 individual biomarkers were investigated in this pilot study. Results: Thirteen/20 (65%) biomarkers were identified as significantly different between groups; IFNγ, EGF, IL-1ß, IL-6, IL-8, IL-10, TNFα, FABP-1, PIIINP, ST2/IL-33R, albumin, AST and ALT. Five/20 (25%) biomarker candidates were identified for further investigation; namely, three biomarkers of inflammation, IL-6, IL-8, and TNFα, and two biomarkers of fibrosis, PIIINP and ST2/IL-33R. Discussion: Single biomarkers are unlikely to be diagnostic or predictive at staging NAFLD due to the complex heterogeneity of the disease. However, biomarker combinations may help stratify risk and stage disease where patients are averse to biopsy. Further studies comparing the 5 biomarkers identified in this study with current diagnostic tests and fibrotic deposition in liver tissue are warranted.

A Novel Combination of Serum Markers in a Multivariate Model to Help Triage Patients Into "Low-" and "High-Risk" Categories for Prostate Cancer.

Background: Almost 50,000 men in the United Kingdom (UK) are diagnosed each year with prostate cancer (PCa). Secondary referrals for investigations rely on serum prostate-specific antigen (PSA) levels and digital rectal examination. However, both tests lack sensitivity and specificity, resulting in unnecessary referrals to secondary care for costly and invasive biopsies. Materials and Methods: Serum samples and clinical information were collected from N = 125 age-matched patients (n = 61 non-PCa and n = 64 PCa) and analyzed using Biochip Array Technology on high-sensitivity cytokine array I (IL-2, IL-4, IL-6, IL-8, IL-10, IL-1α, IL-1ß, TNFα, MCP-1, INFγ, EGF, and VEGF), cerebral array II (CRP, D-dimer, neuron-specific enolase, and sTNFR1), and tumor PSA oncology array (fPSA, tPSA, and CEA). Results: The data showed that 11/19 (68.8%) markers were significantly different between the non-PCa and the PCa patients. A combination of EGF, log10 IL-8, log10 MCP-1, and log10 tPSA significantly improved the predictive potential of tPSA alone to identify patients with PCa (DeLong, p < 0.001). This marker combination had an increased area under the receiver operator characteristic (0.860 vs. 0.700), sensitivity (78.7 vs. 68.9%), specificity (76.5 vs. 67.2%), PPV (76.2 vs. 66.7%), and NPV (79.0 vs. 69.4%) compared with tPSA. Conclusions: The novel combination of serum markers identified in this study could be employed to help triage patients into "low-" and "high-risk" categories, allowing general practitioners to improve the management of patients in primary care settings and potentially reducing the number of referrals for unnecessary, invasive, and costly treatments.

Biomarkers to assess the risk of bladder cancer in patients presenting with haematuria are gender-specific.

Introduction: Haematuria is a common red flag symptom of urinary tract cancer. Bladder cancer (BC) is the most common cancer to present with haematuria. Women presenting with haematuria are often underdiagnosed. Currently, no gender-specific tests are utilized in clinical practice. Considerable healthcare resources are needed to investigate causes of haematuria and this study was set up to help identify markers of BC. The aim of the study was to define biomarker algorithms in haematuria patients using an expanded panel of biomarkers to diagnose BC and investigate if the algorithms are gender-specific. Materials and Methods: A total of n=675 patients with a history of haematuria were recruited from Northern Ireland hospitals. Patients were collected on a 2:1 ratio, non-BC (control) n=474: BC n=201. A detailed clinical history, urine and blood samples were collected. Biomarkers, known to be involved in the pathobiology underlying bladder carcinogenesis were investigated. Biomarkers differentially expressed between groups were investigated using Wilcoxon rank sum and linear regression. Results: Biomarkers were gender specific. Two biomarker-algorithms were identified to triage haematuria patients; male - u_NSE, s_PAI-1/tPA, u_midkine, u_NGAL, u_MMP-9/TIMP-1 and s_prolactin (u=urine; s=serum); sensitivity 71.8%, specificity 72.8%; AUROC 0.795; and female urine biomarkers - IL-12p70, IL-13, midkine and clusterin; sensitivity 83.7%, specificity 79.7%; AUROC 0.865. Addition of the clinical variable infection to both algorithms increased both AUROC to 0.822 (DeLong p=0.014) and to 0.923 (DeLong p=0.004) for males and females, respectively. Combining clinical risk factors with biomarker algorithms would enable application of the algorithms to triage haematuria patients. Conclusion: Using gender-specific biomarker algorithms in combination with clinical risks that are associated with BC would allow clinicians to better manage haematuria patients and potentially reduce underdiagnosis in females. In this study, we demonstrate, for the first time, that blood and urine biomarkers are gender-specific when assessing risk of BC in patients who present with blood in their urine. Combining biomarker data with clinical factors could improve triage when referring patients for further investigations.

Analysis of reactive aldehydes in urine and plasma of type-2 diabetes mellitus patients through liquid chromatography-mass spectrometry: Reactive aldehydes as potential markers of diabetic nephropathy.

Introduction: Diabetes is a major public health issue that is approaching epidemic proportions globally. Diabetes mortality is increasing in all ethnic groups, irrespective of socio-economic class. Obesity is often seen as the main contributor to an increasing prevalence of diabetes. Oxidative stress has been shown to trigger obesity by stimulating the deposition of white adipose tissue. In this study, we measured reactive aldehydes by liquid chromatography-mass spectrometry (LC-MS), in the urine and plasma of type-2 diabetic mellitus (T2DM) patients, as potential surrogates of oxidative stress. Our hypothesis was that reactive aldehydes play a significant role in the pathophysiology of diabetes, and these reactive species, may present potential drug targets for patient treatment. Materials and methods: Study participants [N = 86; control n = 26; T2DM n = 32, and diabetic nephropathy (DN) n = 28] were recruited between 2019 and 2020. Urine and blood samples were collected from all participants, including a detailed clinical history, to include patient behaviours, medications, and co-morbidities. Reactive aldehyde concentrations in urine and plasma were measured using pre-column derivatisation and LC-MS, for control, T2DM and DN patients. Results: Reactive aldehydes were measured in the urine and plasma of control subjects and patients with T2DM and DN. In all cases, the reactive aldehydes under investigation; 4-HNE, 4-ONE, 4-HHE, pentanal, methylglyoxal, and glyoxal, were significantly elevated in the urine and serum of the patients with T2DM and DN, compared to controls (p < 0.001) (Kruskal-Wallis). Urine and serum reactive aldehydes were significantly correlated (≥0.7) (p < 0.001) (Spearman rho). The concentrations of the reactive aldehydes were significantly higher in plasma samples, when compared to urine, suggesting that plasma is the optimal matrix for screening T2DM and DN patients for oxidative stress. Conclusion: Reactive aldehydes are elevated in the urine and plasma of T2DM and DN patients. Reactive aldehydes have been implicated in the pathobiology of T2DM. Therefore, if reactive aldehydes are surrogates of oxidative stress, these reactive aldehyde species could be therapeutic targets for potential drug development.

Biomarkers for Detecting Kidney Dysfunction in Type-2 Diabetics and Diabetic Nephropathy Subjects: A Case-Control Study to Identify Potential Biomarkers of DN to Stratify Risk of Progression in T2D Patients.

Introduction: Currently there are no biomarkers that are predictive of when patients with type-2 diabetes (T2D) will progress to more serious kidney disease i.e., diabetic nephropathy (DN). Biomarkers that could identify patients at risk of progression would allow earlier, more aggressive treatment intervention and management, reducing patient morbidity and mortality. Materials and Methods: Study participants (N=88; control n=26; T2D n=32; DN n=30) were recruited from the renal unit at Antrim Area Hospital, Antrim, UK; Whiteabbey Hospital Diabetic Clinic, Newtownabbey, UK; Ulster University (UU), Belfast, UK; and the University of the Third Age (U3A), Belfast, UK; between 2019 and 2020. Venous blood and urine were collected with a detailed clinical history for each study participant. Results: In total, 13/25 (52.0%) biomarkers measured in urine and 25/34 (73.5%) biomarkers measured in serum were identified as significantly different between control, T2D and DN participants. DN patients, were older, smoked more, had higher systolic blood pressure and higher serum creatinine levels and lower eGFR function. Serum biomarkers significantly inversely correlated with eGFR. Conclusion: This pilot-study identified several serum biomarkers that could be used to predict progression of T2D to more serious kidney disease: namely, midkine, sTNFR1 and 2, H-FABP and Cystatin C. Our results warrant confirmation in a longitudinal study using a larger patient cohort.

Association of Dietary Patterns with MRI Markers of Hepatic Inflammation and Fibrosis in the MAST4HEALTH Study.

Whereas the etiology of non-alcoholic fatty liver disease (NAFLD) is complex, the role of nutrition as a causing and preventive factor is not fully explored. The aim of this study is to associate dietary patterns with magnetic resonance imaging (MRI) parameters in a European population (Greece, Italy, and Serbia) affected by NAFLD. For the first time, iron-corrected T1 (cT1), proton density fat fraction (PDFF), and the liver inflammation fibrosis score (LIF) were examined in relation to diet. A total of 97 obese patients with NAFLD from the MAST4HEALTH study were included in the analysis. A validated semi-quantitative food frequency questionnaire (FFQ) was used to assess the quality of diet and food combinations. Other variables investigated include anthropometric measurements, total type 2 diabetes risk, physical activity level (PAL), and smoking status. Principal component analysis (PCA) was performed to identify dietary patterns. Six dietary patterns were identified, namely "High-Sugar", "Prudent", "Western", "High-Fat and Salt", "Plant-Based", and "Low-Fat Dairy and Poultry". The "Western" pattern was positively associated with cT1 in the unadjusted model (beta: 0.020, p-value: 0.025) and even after adjusting for age, sex, body mass index (BMI), PAL, smoking, the center of the study, and the other five dietary patterns (beta: 0.024, p-value: 0.020). On the contrary, compared with low-intake patients, those with medium intake of the "Low-Fat Dairy and Poultry" pattern were associated with lower values of cT1, PDFF, and LIF. However, patients with a "Low-Fat Dairy and Poultry" dietary pattern were negatively associated with MRI parameters (cT1: beta: -0.052, p-value: 0.046, PDFF: beta: -0.448, p-value: 0.030, LIF: beta: -0.408, p-value: 0.025). Our findings indicate several associations between MRI parameters and dietary patterns in NAFLD patients, highlighting the importance of diet in NAFLD.

A novel algorithm for cardiovascular screening using conjunctival microcirculatory parameters and blood biomarkers.

Microvascular haemodynamic alterations are associated with coronary artery disease (CAD). The conjunctival microcirculation can easily be assessed non-invasively. However, the microcirculation of the conjunctiva has not been previously explored in clinical algorithms aimed at identifying patients with CAD. This case-control study involved 66 patients with post-myocardial infarction and 66 gender-matched healthy controls. Haemodynamic properties of the conjunctival microcirculation were assessed with a validated iPhone and slit lamp-based imaging tool. Haemodynamic properties were extracted with semi-automated software and compared between groups. Biomarkers implicated in the development of CAD were assessed in combination with conjunctival microcirculatory parameters. The conjunctival blood vessel parameters and biomarkers were used to derive an algorithm to aid in the screening of patients for CAD. Conjunctival blood velocity measured in combination with the blood biomarkers (N-terminal pro-brain natriuretic peptide and adiponectin) had an area under receiver operator characteristic curve (AUROC) of 0.967, sensitivity 93.0%, specificity 91.5% for CAD. This study demonstrated that the novel algorithm which included a combination of conjunctival blood vessel haemodynamic properties, and blood-based biomarkers could be used as a potential screening tool for CAD and should be validated for potential utility in asymptomatic individuals.

Nutrigenetic Interactions Might Modulate the Antioxidant and Anti-Inflammatory Status in Mastiha-Supplemented Patients With NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease with no therapeutic consensus. Oxidation and inflammation are hallmarks in the progression of this complex disease, which also involves interactions between the genetic background and the environment. Mastiha is a natural nutritional supplement known to possess antioxidant and anti-inflammatory properties. This study investigated how a 6-month Mastiha supplementation (2.1 g/day) could impact the antioxidant and inflammatory status of patients with NAFLD, and whether genetic variants significantly mediate these effects. We recruited 98 patients with obesity (BMI ≥ 30 kg/m2) and NAFLD and randomly allocated them to either the Mastiha or the placebo group for 6 months. The anti-oxidative and inflammatory status was assessed at baseline and post-treatment. Genome-wide genetic data was also obtained from all participants, to investigate gene-by-Mastiha interactions. NAFLD patients with severe obesity (BMI > 35kg/m2) taking the Mastiha had significantly higher total antioxidant status (TAS) compared to the corresponding placebo group (P value=0.008). We did not observe any other significant change in the investigated biomarkers as a result of Mastiha supplementation alone. We identified several novel gene-by-Mastiha interaction associations with levels of cytokines and antioxidant biomarkers. Some of the identified genetic loci are implicated in the pathological pathways of NAFLD, including the lanosterol synthase gene (LSS) associated with glutathione peroxidase activity (Gpx) levels, the mitochondrial pyruvate carrier-1 gene (MPC1) and the sphingolipid transporter-1 gene (SPNS1) associated with hemoglobin levels, the transforming growth factor-beta-induced gene (TGFBI) and the micro-RNA 129-1 (MIR129-1) associated with IL-6 and the granzyme B gene (GZMB) associated with IL-10 levels. Within the MAST4HEALTH randomized clinical trial (NCT03135873, www.clinicaltrials.gov) Mastiha supplementation improved the TAS levels among NAFLD patients with severe obesity. We identified several novel genome-wide significant nutrigenetic interactions, influencing the antioxidant and inflammatory status in NAFLD. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT03135873.

Acute kidney injury risk in orthopaedic trauma patients pre and post surgery using a biomarker algorithm and clinical risk score.

Acute kidney injury (AKI) after major trauma is associated with increased mortality. The aim of this study was to assess if measurement of blood biomarkers in combination with clinical characteristics could be used to develop a tool to assist clinicians in identifying which orthopaedic trauma patients are at risk of AKI. This is a prospective study of 237 orthopaedic trauma patients who were consecutively scheduled for open reduction and internal fixation of their fracture between May 2012 and August 2013. Clinical characteristics were recorded, and 28 biomarkers were analysed in patient blood samples. Post operatively a combination of H-FABP, sTNFR1 and MK had the highest predictive ability to identify patients at risk of developing AKI (AUROC 0.885). Three clinical characteristics; age, dementia and hypertension were identified in the orthopaedic trauma patients as potential risks for the development of AKI. Combining biomarker data with clinical characteristics allowed us to develop a proactive AKI clinical tool, which grouped patients into four risk categories that were associated with a clinical management regime that impacted patient care, management, length of hospital stay, and efficient use of hospital resources.

Stratifying risk of acute kidney injury in pre and post cardiac surgery patients using a novel biomarker-based algorithm and clinical risk score.

Acute kidney injury (AKI) following cardiac surgery significantly increases morbidity and mortality risks. Improving existing clinical methods of identifying patients at risk of perioperative AKI may advance management and treatment options. This study investigated whether a combination of biomarkers and clinical factors pre and post cardiac surgery could stratify patients at risk of developing AKI. Patients (n = 401) consecutively scheduled for elective cardiac surgery were prospectively studied. Clinical data was recorded and blood samples were tested for 31 biomarkers. Areas under receiver operating characteristic (AUROCs) were generated for biomarkers pre and postoperatively to stratify patients at risk of AKI. Preoperatively sTNFR1 had the highest predictive ability to identify risk of developing AKI postoperatively (AUROC 0.748). Postoperatively a combination of H-FABP, midkine and sTNFR2 had the highest predictive ability to identify AKI risk (AUROC 0.836). Preoperative clinical risk factors included patient age, body mass index and diabetes. Perioperative factors included cardio pulmonary bypass, cross-clamp and operation times, intra-aortic balloon pump, blood products and resternotomy. Combining biomarker risk score (BRS) with clinical risk score (CRS) enabled pre and postoperative assignment of patients to AKI risk categories. Combining BRS with CRS will allow better management of cardiac patients at risk of developing AKI.

SRC family kinases and receptors: analysis of three activation mechanisms by dynamic systems modeling.

Src family kinases (SFKs) interact with a number of cellular receptors. They participate in diverse signaling pathways and cellular functions. Most of the receptors involved in SFK signaling are characterized by similar modes of regulation. This computational study discusses a general kinetic model of SFK-receptor interaction. The analysis of the model reveals three major ways of SFK activation: release of inhibition by C-terminal Src kinase, weakening of the inhibitory intramolecular phosphotyrosine-SH2 interaction, and amplification of a stimulating kinase activity. The SFK model was then extended to simulate interaction with growth factor and T-cell receptors. The modular SFK signaling system was shown to adapt to the requirements of specific signaling contexts and yield qualitatively different responses in the different simulated environments. The model also provides a systematic overview of the major interactions between SFKs and various cellular signaling systems and identifies their common properties.

Deactivation of Src family kinases: hypothesis testing using a Monte Carlo sensitivity analysis of systems-level properties.

Src family tyrosine kinases play a key role in many cellular signalling networks, but due to the high complexity of these networks their precise function remains elusive. Many factors involved in Src regulation, such as specific kinases and phosphatases, are still unknown. Mathematical models have been constructed to improve the understanding of the system and its dynamic behavior. Using a computational random parameter search, we characterized and compared the dynamics of three alternative models in order to assess their likelihoods. For this, we investigated how systems-level properties such as bistability and excitable behavior relate to kinetic and physiological parameters and how robust these properties were. Our results suggest the existence of a putative negative feedback loop in the Src system. A previously suggested role for PTPalpha in the deactivation of Src was not supported by the model.