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BackgroundMeasures of kidney tubule health are risk markers for acute kidney injury (AKI) in persons with chronic kidney disease (CKD) during hypertension treatment, but their associations with other adverse events (AEs) are unknown.MethodsAmong 2377 Systolic Blood Pressure Intervention Trial (SPRINT) participants with CKD, we measured at baseline eight urine biomarkers of kidney tubule health and two serum biomarkers of mineral metabolism pathways that act on the kidney tubules. Cox proportional hazards models were used to evaluate biomarker associations with risk of a composite of pre-specified serious AEs (hypotension, syncope, electrolyte abnormalities, AKI, bradycardia and injurious falls) and outpatient AEs (hyperkalemia and hypokalemia).ResultsAt baseline, the mean age was 73 ± 9 years and mean estimated glomerular filtration rate (eGFR) was 46 ± 11 mL/min/1.73 m2. During a median follow-up of 3.8 years, 716 (30%) participants experienced the composite AE. Higher urine interleukin-18, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin (NGAL) and monocyte chemoattractant protein-1 (MCP-1), lower urine uromodulin (UMOD) and higher serum fibroblast growth factor-23 were individually associated with higher risk of the composite AE outcome in multivariable-adjusted models including eGFR and albuminuria. When modeling biomarkers in combination, higher NGAL [hazard ratio (HR) = 1.08 per 2-fold higher biomarker level, 95% confidence interval (CI) 1.03-1.13], higher MCP-1 (HR = 1.11, 95% CI 1.03-1.19) and lower UMOD (HR = 0.91, 95% CI 0.85-0.97) were each associated with higher composite AE risk. Biomarker associations did not vary by intervention arm (P > 0.10 for all interactions).ConclusionsAmong persons with CKD, several kidney tubule biomarkers are associated with higher risk of AEs during hypertension treatment, independent of eGFR and albuminuria.

Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide range of operating conditions. Gradient boosting regression has been found to outperform the other model types with a coefficient of determination (R

The viability and the relative merits of competing biomass and waste gasification schemes depends on a complex mix of interacting factors. Conventional analytical methods that are used to aid decision making rely on a plethora of poorly defined parameters. Here we develop a method that eschews the uncertainty in process representation by using a machine learning, data driven, approach to predicting a set of 10 key measures of gasification technology’s performance. We develop an artificial neural network that is novel in its use of both categorical and continuous data inputs, which makes it flexible and broadly applicable in assessing gasification process designs. It is the first model applicable to a wide range of feedstock types, gasifying agents, and reactor options. A strong predictive performance, quantified by a coefficient of determination (R2) of 0.9310, was confirmed. The approach has the potential to generate accurate input data for e.g., cost-benefit analysis (CBA) and life cycle sustainability assessment (LCSA) and thus allow for more transparency in the decisions made by policy makers and investors.

Rationale & objectiveSingle measurements of urinary biomarkers reflecting kidney tubule health are associated with chronic kidney disease (CKD) risk in HIV infection, but the prognostic value of repeat measurements over time is unknown.Study designCohort study.Setting & participants647 women living with HIV infection enrolled in the Women's Interagency Health Study.Exposures14 urinary biomarkers of kidney tubule health measured at 2 visits over a 3-year period.OutcomeIncident CKD, defined as estimated glomerular filtration rate (eGFR)&nbsp

Additional file 1: Supplemental Table 1. Simultaneous multivariable adjusted associations of baseline and follow-up CKD risk factors with longitudinal changes in urine biomarker levels among HIV-positive women.

ObjectiveTo evaluate the effects of HIV preexposure prophylaxis (PrEP) with tenofovir disoproxial fumurate (TDF)/emtricitabine (FTC) on kidney function and kidney tubular health.DesignThe Iniciativa Profilaxis Pre-Exposicion open-label extension (iPrEx-OLE) study enrolled former PrEP trial participants to receive open-label TDF/FTC. This study included 123 iPrEx-OLE participants who demonstrated PrEP adherence.MethodsWe compared estimated glomerular filtration rate calculated using serum creatinine (eGFRcr), serum cystatin C (eGFRcys), and in combination (eGFRcr-cys), and a panel of 14 urine biomarkers reflecting kidney tubular health before and 6 months after PrEP initiation.ResultsAt baseline, mean eGFRcr, eGFRcys, and eGFRcr-cys were 108.3, 107.0, and 111.1 ml/min per 1.73 m, respectively. Six months after PrEP initiation, eGFRcr declined by -4% (95% CI: -5.7 to -2.4%), eGFRcys declined by -3.3% (95% CI: -8.3 to 1.9%), and eGFRcr-cys declined by -4.1% (95% CI: -7.5 to -0.7%). From the urine biomarker panel, α1-microglobulin and β2-microglobulin increased by 22.7% (95% CI: 11.8--34.7%) and 14.1% (95% CI: -6.1 to 38.6%), whereas chitinase-3-like 1 protein and monocyte chemoattractant protein-1 decreased by -37.7% (95% CI: -53.0 to -17.3%) and -15.6% (95% CI: -31.6 to 4.2%), respectively. Ten of the 14 urine biomarkers, including albumin, had estimated changes of less than 12% with wide confidence intervals.ConclusionSix months of PrEP with TDF/FTC was associated with decreases in eGFRcr and eGFRcys. We also observed for the first time changes in flour of 14 urine biomarkers reflecting kidney tubular health. These findings demonstrate that PrEP has direct effects on eGFR and the proximal tubule.

BACKGROUND:HIV-infected (HIV+) persons are at increased risk of chronic kidney disease, but serum creatinine does not detect early losses in kidney function. We hypothesized that urine biomarkers of kidney damage would be associated with subsequent changes in kidney function in a contemporary cohort of HIV+ and HIV-uninfected (HIV-) men. METHODS:In the Multicenter AIDS Cohort Study, we measured baseline urine concentrations of 5 biomarkers from 2009 to 2011 in 860 HIV+ and 337 HIV- men: albumin, alpha-1-microglobulin (α1m), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), and procollagen type III N-terminal propeptide (PIIINP). We evaluated associations of urine biomarker concentrations with annual changes in estimated glomerular filtration rate (eGFR) using multivariable linear mixed models adjusted for demographics, traditional kidney disease risk factors, HIV-related risk factors, and baseline eGFR. RESULTS:Over a median follow-up of 4.8 years, the average annual eGFR decline was 1.42 mL/min/1.73 m2/year in HIV+ men and 1.22 mL/min/1.73 m2/year in HIV- men. Among HIV+ men, the highest vs. lowest tertiles of albumin (-1.78mL/min/1.73 m2/year, 95% CI -3.47 to -0.09) and α1m (-2.43 mL/min/1.73 m2/year, 95% CI -4.14 to -0.73) were each associated with faster annual eGFR declines after multivariable adjustment. Among HIV- men, the highest vs. lowest tertile of α1m (-2.49 mL/min/1.73 m2/year, 95% CI -4.48 to -0.50) was independently associated with faster annual eGFR decline. Urine IL-18, KIM-1, and PIIINP showed no independent associations with eGFR decline, regardless of HIV serostatus. CONCLUSIONS:Among HIV+ men, higher urine albumin and α1m are associated with subsequent declines in kidney function, independent of eGFR.

Subclinical kidney disease is associated with developing hypertension in the general population, but data are lacking among HIV-infected people. We examined associations of kidney function and injury with incident hypertension in 823 HIV-infected and 267 HIV-uninfected women in the Women's Interagency HIV Study, a multicenter, prospective cohort of HIV-infected and uninfected women in the United States. Baseline kidney biomarkers included estimated glomerular filtration rate using cystatin C, urine albumin-to-creatinine ratio, and 7 urine biomarkers of tubular injury: α-1-microglobulin, interleukin-18, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, liver fatty acid-binding protein, N-acetyl-β-d-glucosaminidase, and α1-acid-glycoprotein. We used multivariable Poisson regression to evaluate associations of kidney biomarkers with incident hypertension, defined as 2 consecutive visits of antihypertensive medication use. During a median follow-up of 9.6 years, 288 HIV-infected women (35%) developed hypertension. Among the HIV-infected women, higher urine albumin-to-creatinine ratio was independently associated with incident hypertension (relative risk =1.13 per urine albumin-to-creatinine ratio doubling, 95% confidence interval, 1.07-1.20), as was lower estimated glomerular filtration rate (relative risk =1.10 per 10 mL/min/1.73 m2 lower estimated glomerular filtration rate; 95% confidence interval, 1.04-1.17). No tubular injury and dysfunction biomarkers were independently associated with incident hypertension in HIV-infected women. In contrast, among the HIV-uninfected women, urine albumin-to-creatinine ratio was not associated with incident hypertension, whereas higher urine interleukin-18, α1-acid-glycoprotein, and N-acetyl-β-d-glucosaminidase levels were significantly associated with incident hypertension. These findings suggest that early glomerular injury and kidney dysfunction may be involved in the pathogenesis of hypertension in HIV-infected people. The associations of tubular markers with hypertension in HIV-uninfected women should be validated in other studies.

Table S1. Urine biomarker Assay information. Table showing each biomarker assay information including intra-assay coefficients of variation for all biomarkers used in this analysis. (DOCX 16 kb)

Table S1. Urine biomarker Assay information. Table showing each biomarker assay information including intra-assay coefficients of variation for all biomarkers used in this analysis. (DOCX 16 kb)