Urinary albumin to osmolality ratio predicts 24-hour urine albumin excretion in diabetes mellitus

Reference Intervals of Spot Urine Creatinine-to-Osmolality Ratio as a Surrogate of Urinary Creatinine Excretion Rate

A spot urine creatinine-to-osmolality ratio (sUCr/Osm) is proposed as a surrogate of the urinary excretion rate of creatinine (Cr) and convenient for forecasting serum Cr (SCr) trends. The lower the sUCr/Osm, the lower the excreted Cr amount accompanied by per unit of osmoles, the higher the risk of Cr accumulation. For exploring the reference intervals of sUCr/Osm in general adults, a cross-sectional analysis was performed on a subset of data from the National Health and Nutrition Examination Survey (NHANES) 2011–2012. Of the eligible 3,316 adults aged 18.0 to 79.9 years, the age (mean ± SD) was 45.2 ± 17.2 years old, women was 45.02%, body weight (BW) was 76.1 ± 14.5 kg, and African Americans was 23.6%. Blood urea nitrogen (BUN) was 12.6 ± 4.7 mg/dL; SCr was 0.89 ± 0.34 mg/dL. As spot urine Cr and osmolality were 127.1 ± 84.0 mg/dl and 649 ± 266 mOsm/kg, respectively, sUCr/Osm was 0.19 ± 0.08. With adjustment of factors related to personal urinary excretion of Cr and osmoles by multivariable regression analysis, the estimated sUCr/Osm (esUCr/Osm) for an individual was 0.153 × (age in year)^−0.070 × (BW in kg)^0.283 × 1.244 [if African American] × (BUN in mg/dL)^−0.310 × (SCr in mg/dL)^0.681. The index of sUCr/Osm to personalized esUCr/Osm was 1.05 ± 0.39. When only low urinary excretion of Cr is likely to be of clinical concern, further analysis showed 157 individuals (4.7%, outside the 5th percentile) had their original sUCr/Osm < 0.08; 157 had the sUCr/Osm indexed for personalized esUCr/Osm < 0.50.

Longitudinal progression trajectory of random urine creatinine as a novel predictor of ESRD among patients with CKD

BACKGROUND

The clinical importance of random urine creatinine concentration in CKD population remains undetermined. Earlier studies found that lower 24-h urine creatinine excretion was associated with the risk of ESRD and all-cause mortality among CKD patients.

METHODS

We modeled the longitudinal trajectories of serial random urine creatinine among 4689 CKD patients enrolled in a national registry-based pre-ESRD program between 2003 and 2015 at a tertiary medical center. Other biochemical parameters including kidney function and serum albumin were regularly evaluated. Primary study outcomes were ESRD requiring maintenance dialysis and all-cause mortality.

RESULTS

By group-based trajectory modeling, the urine creatinine trajectories were characterized into three patterns: (1) stable low; (2) medium; and (3) high-declining. The adjusted hazard ratio of incident ESRD and all-cause mortality increased by 6% (95% CI: 1-12%) and 9% (95% CI: 2-17%), respectively, for each 20 mg/dL reduction in baseline random urine creatinine concentration. Consistently, there was a significant inverse linear dose-response relationship between baseline random urine creatinine and incident ESRD, but not all-cause mortality. Compared to patients with "medium" and "high-declining" urine creatinine trajectories combined, the adjusted hazard ratio for incidental ESRD among patients with a "stable-low" trajectory who had serial random urine creatinine concentrations stably below 100 mg/dL was 1.46 (95% CI: 1.00-2.12) after considering the competing risk of death.

CONCLUSIONS

Random urine creatinine not only serves as a common urinary concentration corrector but has its own clinical significance in risk stratification and outcome prediction in patients with advanced CKD.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Sample normalization methods in quantitative metabolomics

To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts.

A matrix-induced ion suppression method to normalize concentration in urinary metabolomics studies using flow injection analysis electrospray ionization mass spectrometry

Normalizing the total urine concentration is important for minimizing bias in urinary metabolomics analysis comparisons. In this study, we report a matrix-induced ion suppression (MIIS)-based method to normalize concentration using flow injection analysis coupled with electrospray ionization mass spectrometry (FIA-ESI-MS). An ion suppression indicator (ISI) was spiked into urine samples, and the intensity of the extracted ion chromatogram (EIC) for ISI in a urine matrix was subtracted by the EIC for a blank solution and used to calculate the extent to which the signal was reduced by the urine matrix. A series dilution of pooled urine samples was used to correlate the urine concentration and level of ion suppression for ISI. A regression equation was used to estimate the relative concentration of unknown urine samples. The MIIS method was validated for linearity, precision and accuracy. We obtained a good correlation using a quadratic regression model for 1- to 32-fold urine dilutions (R(2)=0.998). The reproducibility (n=4) and intermediate precision (n=3) were below 5% RSD, and the accuracy ranged from 97.15% to 102.10%. The established method was used to estimate the relative concentrations of 16 urine samples, and the results were compared with commonly used normalization methods. Pearson's correlation test was used to demonstrate that the MIIS method correlated highly with the creatinine and osmolarity methods; the correlation coefficients were 0.93 and 0.99, respectively. We successfully applied this method to a urinary metabolomics study on breast cancer. This study demonstrated the MIIS method is simple, accurate and can contribute to data integrity in urinary metabolomics studies.

Urine osmolality in the US population: implications for environmental biomonitoring

BACKGROUND

For many environmental chemicals, concentrations in spot urine samples are considered valid surrogates of exposure and internal dose. To correct for urine dilution, spot urine concentrations are commonly adjusted for urinary creatinine. There are, however, several concerns about the use of urine creatinine. While urine osmolality is an attractive alternative; its characteristics and determinants in the general population remain unknown. Our objective was to describe the determinants of urine osmolality and to contrast the difference between osmolality and creatinine in urine.

METHODS

From the National Health and Nutrition Examination Survey (NHANES) (2009-2010), 10,769 participants aged 16 years or older with measured urine osmolality and creatinine were used in the analysis. Very dilute and very concentrated urine was defined as urine creatinine lower than 0.3g/l and higher than 3g/l, respectively. Linear and logistic regression analyses were performed to investigate the associations of interest.

RESULTS

Urine osmolality and creatinine were highly correlated (Pearson correlation coefficient=0.75) and their respective median values were 648 mOsm/kg and 1.07 g/l. The prevalence of very dilute and very concentrated urine samples was 8.1% and 3.1%, respectively. Factors associated in the same direction with both urine osmolality and urine creatinine included age, sex, race, body mass index (BMI), hypertension, water intake, and blood osmolality. The magnitude of associations expressed as percent change was significantly stronger with creatinine than osmolality. Compared to urine creatinine, urine osmolality did not vary by diabetes status but was affected by daily total protein intake. Participants with chronic kidney disease (CKD) had significantly higher urine creatinine concentrations but lower urine osmolality. Both very dilute and concentrated urine were associated with a diverse array of sociodemographic, medical conditions, and dietary factors. For instance, females were approximately 3.3 times more likely to have urine over-dilution than male [the adjusted odds ratios (95% CI)=3.27 (2.10-5.10)].

CONCLUSION

Although the determinants of urine osmolality were generally similar to those of urine creatinine, the relative influence of socio-demographic and medical conditions was less on urine osmolality than on urine creatinine. Protocols for spot urine sample collection could recommend avoiding excessive and insufficient water intake before urine sampling to improve urine adequacy. The feasibility of adopting urine osmolality adjustment and water intake recommendations before providing spot urine samples for environmental biomonitoring merits further investigation.

Determination of total concentration of chemically labeled metabolites as a means of metabolome sample normalization and sample loading optimization in mass spectrometry-based metabolomics

For mass spectrometry (MS)-based metabolomics, it is important to use the same amount of starting materials from each sample to compare the metabolome changes in two or more comparative samples. Unfortunately, for biological samples, the total amount or concentration of metabolites is difficult to determine. In this work, we report a general approach of determining the total concentration of metabolites based on the use of chemical labeling to attach a UV absorbent to the metabolites to be analyzed, followed by rapid step-gradient liquid chromatography (LC) UV detection of the labeled metabolites. It is shown that quantification of the total labeled analytes in a biological sample facilitates the preparation of an appropriate amount of starting materials for MS analysis as well as the optimization of the sample loading amount to a mass spectrometer for achieving optimal detectability. As an example, dansylation chemistry was used to label the amine- and phenol-containing metabolites in human urine samples. LC-UV quantification of the labeled metabolites could be optimally performed at the detection wavelength of 338 nm. A calibration curve established from the analysis of a mixture of 17 labeled amino acid standards was found to have the same slope as that from the analysis of the labeled urinary metabolites, suggesting that the labeled amino acid standard calibration curve could be used to determine the total concentration of the labeled urinary metabolites. A workflow incorporating this LC-UV metabolite quantification strategy was then developed in which all individual urine samples were first labeled with (12)C-dansylation and the concentration of each sample was determined by LC-UV. The volumes of urine samples taken for producing the pooled urine standard were adjusted to ensure an equal amount of labeled urine metabolites from each sample was used for the pooling. The pooled urine standard was then labeled with (13)C-dansylation. Equal amounts of the (12)C-labeled individual sample and the (13)C-labeled pooled urine standard were mixed for LC-MS analysis. This way of concentration normalization among different samples with varying concentrations of total metabolites was found to be critical for generating reliable metabolome profiles for comparison.