Serum and Urine Metabolites and Kidney Function.

BACKGROUND: Metabolites represent a read-out of cellular processes underlying states of health and disease. METHODS: We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with "X" in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities (ARIC) Study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and UACR in longitudinal analyses. RESULTS: At visit 5 of the ARIC study, the mean age of participants was 76 years (SD 6), 56% were women, mean eGFR was 62 ml/min/1.73m2 (SD 20), and median urine albumin-to-creatinine level (UACR) was 13 mg/g (IQR 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected p < 4.0E-5 for serum analyses and p < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including 7 shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate, N2,N2-dimethylguanosine, hydroxy-N6,N6,N6-trimethyllysine, X-13844, and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (HR 2.3 [1.6,3.2], p = 2.7E-5) and urine X-12117 (HR 1.7 [1.3,2.2], p = 1.9E-5) were risk factors for UACR doubling, whereas urine sebacate (HR 0.86 [0.80,0.92], p = 1.9E-5) was inversely associated. Compared to clinical characteristics alone, including the top 5 endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (AUCs for eGFR decline: clinical model = 0.79, clinical + metabolites model = 0.87, p = 8.1E-6; for UACR doubling: clinical model = 0.66, clinical + metabolites model = 0.73, p = 2.9E-5). CONCLUSIONS: Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging.

Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.

Associations of Urine and Plasma Metabolites with Kidney Failure and Death in a CKD Cohort.

RATIONALE & OBJECTIVE: Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease. STUDY DESIGN: Prospective metabolome-wide association study. SETTING & PARTICIPANTS: Persons with CKD enrolled in the German CKD Study (GCKD) with metabolite measurements; with external validation within the Atherosclerosis Risk in Communities Study. EXPOSURES: 1,513 urine and 1,416 plasma metabolites (Metabolon, Inc.) measured at study entry using untargeted mass spectrometry. OUTCOMES: Main endpoints were kidney failure (KF), and a composite endpoint of KF, eGFR <15 mL/min/1.73m2, or 40% decline in eGFR (CKE). Death from any cause was a secondary endpoint. After a median of 6.5 years follow-up, 500 persons experienced KF, 1,083 experienced CKE and 680 died. ANALYTICAL APPROACH: Time-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design, with external validation. RESULTS: 5,088 GCKD participants were included in analyses of urine metabolites and 5,144 in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (hazard ratio: 1.95, 95% confidence interval: 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, CKE, and death. External validation of the identified associations of metabolites with KF or CKE revealed direction-consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported. LIMITATIONS: Use of observational data and semi-quantitative metabolite measurements at a single time point. CONCLUSIONS: The observed associations between metabolites and KF, CKE or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts.

The effect of spatial sampling on the resolution of the magnetostatic inverse problem.

In magnetoencephalography, linear minimum norm inverse methods are commonly employed when a solution with minimal a priori assumptions is desirable. These methods typically produce spatially extended inverse solutions, even when the generating source is focal. Various reasons have been proposed for this effect, including intrisic properties of the minimum norm solution, effects of regularization, noise, and limitations of the sensor array. In this work, we express the lead field in terms of the magnetostatic multipole expansion and develop the minimum-norm inverse in the multipole domain. We demonstrate the close relationship between numerical regularization and explicit suppression of spatial frequencies of the magnetic field. We show that the spatial sampling capabilities of the sensor array and regularization together determine the resolution of the inverse solution. For the purposes of stabilizing the inverse estimate, we propose the multipole transformation of the lead field as an alternative or complementary means to purely numerical regularization.

Effects of head modeling errors on the spatial frequency representation of MEG.

Objectives.We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM).Approach.A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m-1was defined. The vertices were then randomly perturbed radially up to 2%, 4%, 6%, 8% and 10% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case.Main results.In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies.Significance.OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.

A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coast Population Regions.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.