OBJECTIVE: The aim of this review is to examine studies that focused on the different types of samples which may serve as a good and promising biomarker for early diagnosis of CKD or to detect rapidly declining renal function among CKD patient.
METHOD: The review of international literature was made on paper and electronic databases Nature, PubMed, Springer Link and Science Direct. The Scopus index was used to verify the scientific relevance of the papers. Publications were selected based on the inclusion and exclusion criteria.
RESULT: 63 publications were found to be compatible with the study objectives. Several biomarkers of interest with different sample types were taken for comparison.
CONCLUSION: Biomarkers from urine samples yield more significant outcome as compare to biomarkers from blood samples. But, validation and confirmation with a different type of study designed on a larger population is needed. More comparison studies on different types of samples are needed to further illuminate which biomarker is the better tool for the diagnosis and prognosis of CKD.
PURPOSE: This study provides new insights on the changes of endogenous metabolites caused by I. aquatica ethanolic extract and improves the understanding on the therapeutic efficacy and mechanism of I. aquatica ethanolic extract.
METHODS: By using a combination of 1H nuclear magnetic resonance (NMR) with multivariate analysis (MVDA), the changes of metabolites due to I. aquatica ethanolic extract administration in obese diabetic-induced Sprague Dawley rats (OB+STZ+IA) were identified.
RESULTS: The results suggested 19 potential biomarkers with variable importance projections (VIP) above 0.5, which include creatine/creatinine, glucose, creatinine, citrate, carnitine, 2-oxoglutarate, succinate, hippurate, leucine, 1-methylnicotinamice (MNA), taurine, 3-hydroxybutyrate (3-HB), tryptophan, lysine, trigonelline, allantoin, formiate, acetoacetate (AcAc) and dimethylamine. From the changes in the metabolites, the affected pathways and aspects of metabolism were identified.
CONCLUSION: I. aquatica ethanolic extract increases metabolite levels such as creatinine/creatine, carnitine, MNA, trigonelline, leucine, lysine, 3-HB and decreases metabolite levels, including glucose and tricarboxylic acid (TCA) intermediates. This implies capabilities of I. aquatica ethanolic extract promoting glycolysis, gut microbiota and nicotinate/nicotinamide metabolism, improving the glomerular filtration rate (GFR) and reducing the β-oxidation rate. However, the administration of I. aquatica ethanolic extract has several drawbacks, such as unimproved changes in amino acid metabolism, especially in reducing branched chain amino acid (BCAA) synthesis pathways and lipid metabolism.
RESEARCH DESIGN AND METHODS: NIDDM patients of Chinese, Indian, and Malay origin attending a diabetic clinic in Kuala Lumpur, Malaysia, were matched for age, sex, diabetes duration, and glycemic control (n = 34 in each group). Urinary albumin-to-creatinine ratio was measured in an early morning urine sample. Biochemical measurements included markers of the acute-phase response: serum sialic acid, triglyceride, and (lowered) HDL cholesterol.
RESULTS: The frequency of microalbuminuria did not differ among the Chinese, Indian, and Malay patients (44, 41, and 47%, respectively). In Chinese patients, those with microalbuminuria had evidence of an augmented acute-phase response, with higher serum sialic acid and triglyceride and lower HDL cholesterol levels; and urinary albumin-to-creatinine ratio was correlated with serum sialic acid and triglyceride. The acute-phase response markers were not different in Indians, with microalbuminuria being high in even the normoalbuminuric Indians; only the mean arterial blood pressure was correlated with urinary albumin-to-creatinine ratio in the Indians. Malay NIDDM subjects had an association of microalbuminuria with acute-phase markers, but this was weaker than in the Chinese subjects.
CONCLUSIONS: Microalbuminuria is associated with an acute-phase response in Chinese NIDDM patients in Malaysia, as previously found in Caucasian NIDDM subjects. Elevated urinary albumin excretion has different correlates in other racial groups, such as those originating from the Indian subcontinent. The acute-phase response may have an etiological role in microalbuminuria.
SUBJECTS/METHODS: A taste database including 467 foods' sweet, sour, bitter, salt, umami and fat sensation values was combined with food intake data to assess dietary taste patterns: the contribution to energy intake of 6 taste clusters. The FFQ's reliability was assessed against 3-d 24hR and urinary biomarkers for sodium (Na) and protein intake (N) in Dutch men (n = 449) and women (n = 397) from the NQplus validation study (mean age 53 ± 11 y, BMI 26 ± 4 kg/m2).
RESULTS: Correlations of dietary taste patterns ranged from 0.39-0.68 between FFQ and 24hR (p
METHODS: Urine samples were collected from pesticide applicators in Malaysia, Uganda, and the UK during mixing/application days (and also during non-application days in Uganda). Samples were collected pre- and post-activity on the same day and analysed for biomarkers of active ingredients (AIs), including synthetic pyrethroids (via the metabolite 3-phenoxybenzoic acid [3-PBA]) and glyphosate, as well as creatinine. We performed multilevel Tobit regression models for each study to assess the relationship between exposure modifying factors (e.g., mixing/application of AI, duration of activity, personal protective equipment [PPE]) and urinary biomarkers of exposure.
RESULTS: From the Malaysia, Uganda, and UK studies, 81, 84, and 106 study participants provided 162, 384 and 212 urine samples, respectively. Pyrethroid use on the sampling day was most common in Malaysia (n = 38; 47%), and glyphosate use was most prevalent in the UK (n = 93; 88%). Median pre- and post-activity 3-PBA concentrations were similar, with higher median concentrations post-compared to pre-activity for glyphosate samples in the UK (1.7 to 0.5 μg/L) and Uganda (7.6 to 0.8 μg/L) (glyphosate was not used in the Malaysia study). There was evidence from individual studies that higher urinary biomarker concentrations were associated with mixing/application of the AI on the day of urine sampling, longer duration of mixing/application, lower PPE protection, and less education/literacy, but no factor was consistently associated with exposure across biomarkers in the three studies.
CONCLUSIONS: Our results suggest a need for AI-specific interpretation of exposure modifying factors as the relevance of exposure routes, levels of detection, and farming systems/practices may be very context and AI-specific.
CONCLUSION: The diagnosis of LPI is usually not suspected by clinical findings alone, and specific laboratory investigations and molecular analysis are important to get a definitive diagnosis.
OBJECTIVES: To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics.
METHOD: Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC).
RESULTS: The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSION: This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy.
STUDY DESIGN: Literature-based meta-analysis and individual-study-data meta-analysis of diagnostic studies following PRISMA-IPD guidelines.
SETTING & STUDY POPULATIONS: Studies of adults investigating AKI, severe AKI, and AKI-D in the setting of cardiac surgery, intensive care, or emergency department care using either urinary or plasma NGAL measured on clinical laboratory platforms.
SELECTION CRITERIA FOR STUDIES: PubMed, Web of Science, Cochrane Library, Scopus, and congress abstracts ever published through February 2020 reporting diagnostic test studies of NGAL measured on clinical laboratory platforms to predict AKI.
DATA EXTRACTION: Individual-study-data meta-analysis was accomplished by giving authors data specifications tailored to their studies and requesting standardized patient-level data analysis.
ANALYTICAL APPROACH: Individual-study-data meta-analysis used a bivariate time-to-event model for interval-censored data from which discriminative ability (AUC) was characterized. NGAL cutoff concentrations at 95% sensitivity, 95% specificity, and optimal sensitivity and specificity were also estimated. Models incorporated as confounders the clinical setting and use versus nonuse of urine output as a criterion for AKI. A literature-based meta-analysis was also performed for all published studies including those for which the authors were unable to provide individual-study data analyses.
RESULTS: We included 52 observational studies involving 13,040 patients. We analyzed 30 data sets for the individual-study-data meta-analysis. For AKI, severe AKI, and AKI-D, numbers of events were 837, 304, and 103 for analyses of urinary NGAL, respectively; these values were 705, 271, and 178 for analyses of plasma NGAL. Discriminative performance was similar in both meta-analyses. Individual-study-data meta-analysis AUCs for urinary NGAL were 0.75 (95% CI, 0.73-0.76) and 0.80 (95% CI, 0.79-0.81) for severe AKI and AKI-D, respectively; for plasma NGAL, the corresponding AUCs were 0.80 (95% CI, 0.79-0.81) and 0.86 (95% CI, 0.84-0.86). Cutoff concentrations at 95% specificity for urinary NGAL were>580ng/mL with 27% sensitivity for severe AKI and>589ng/mL with 24% sensitivity for AKI-D. Corresponding cutoffs for plasma NGAL were>364ng/mL with 44% sensitivity and>546ng/mL with 26% sensitivity, respectively.
LIMITATIONS: Practice variability in initiation of dialysis. Imperfect harmonization of data across studies.
CONCLUSIONS: Urinary and plasma NGAL concentrations may identify patients at high risk for AKI in clinical research and practice. The cutoff concentrations reported in this study require prospective evaluation.