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.

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.

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.

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.

Clinical features and predictive biomarkers for bladder cancer in patients with type 2 diabetes presenting with haematuria.

AIMS: To identify clinical features and protein biomarkers associated with bladder cancer (BC) in individuals with type 2 diabetes mellitus presenting with haematuria. MATERIALS AND METHODS: Data collected from the Haematuria Biomarker (HaBio) study was used in this analysis. A matched sub-cohort of patients with type 2 diabetes and patients without diabetes was created based on age, sex, and BC diagnosis, using approximately a 1:2 fixed ratio. Randox Biochip Array Technology and ELISA were applied for measurement of 66 candidate serum and urine protein biomarkers. Hazard ratios and 95% confidence intervals were estimated by chi-squared and Wilcoxon rank sum test for clinical features and candidate protein biomarkers. Diagnostic protein biomarker models were identified using Lasso-based binominal regression analysis. RESULTS: There was no difference in BC grade, stage, and severity between individuals with type 2 diabetes and matched controls. Incidence of chronic kidney disease (CKD) was significantly higher in patients with type 2 diabetes (p = 0.008), and CKD was significantly associated with BC in patients with type 2 diabetes (p = 0.032). A biomarker model, incorporating two serum (monocyte chemoattractant protein 1 and vascular endothelial growth factor) and three urine (interleukin 6, cytokeratin 18, and cytokeratin 8) proteins, predicted incidence of BC with an Area Under the Curve (AUC) of 0.84 in individuals with type 2 diabetes. In people without diabetes, the AUC was 0.66. CONCLUSIONS: We demonstrate the potential clinical utility of a biomarker panel, which includes proteins related to BC pathogenesis and type 2 diabetes, for monitoring risk of BC in patients with type 2 diabetes. Earlier urology referral of patients with type 2 diabetes will improve outcomes for these patients. TRIAL REGISTRATION: http://www.isrctn.com/ISRCTN25823942.

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.

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.

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.

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.

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.

Thrombomodulin Expression in Bladder Cancer Tissue and Its Association with Prognosis and Patient Survival.

BACKGROUND: Decreased expression of thrombomodulin (TM) in bladder cancer tissue has been shown to be associated with cell proliferation, increased malignancy and a poor prognosis. The aim of this study was to investigate the immunoexpression of TM in bladder tissue cores by immunohistochemistry (IHC) and the relationship between TM score and patient survival for the following pathologies: transitional cell papillary carcinoma (TCPC), transitional cell carcinoma (non-papillary) (TCC), squamous cell carcinoma (SCC), adenocarcinoma, and sarcoma. TM immunoexpression was also evaluated in normal adjacent bladder tissue cores. METHODS: TM immunoexpression was assessed in n=185 formalin-fixed paraffin-embedded (FFPE) bladder tissue cores from n=98 patients by IHC. Tissue cores included TCPC (n=29), TCC (n=85), SCC (n=21), adenocarcinoma (n=12), sarcoma (n=4), and normal tissue cores (n=34). RESULTS: TM immunoexpression scores are stronger in TCPC, TCC and SCC bladder cancer tissue cores with respect to adenocarcinoma and sarcoma (mean TM immunoexpression scores: 3.04, 2.57, 2.55, 1.55 and 1.19, respectively) (Kruskal-Wallis p<0.001). TM immunoexpression scores significantly decreased in bladder cancer tissue cores across both stage (p<0.001) and grade (p<0.001) (Kruskal-Wallis). Survival data were available for n=45 bladder cancer patients (mean follow-up of 34 months). Applying a TM immunoexpression cut-off score of 3.0 demonstrated that patients with bladder cancer who had a TM immunoexpression score <3.0 had lower survival rates (median survival 23.5 months). In contrast, patients with TM immunoexpression scores ≥3.0 had longer survival rates (median survival 40 months) (log-rank; p=0.045). CONCLUSION: TM immunoexpression in bladder cancer tissue may be a clinically relevant predictor of tumor progression and survival. Low expression of TM in bladder cancer biopsies or in recurrent bladder cancer may be indicative of a poor prognosis. TM immunoexpression could be used to guide clinical decision making.

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.