Urinary excretion of polyols and sugars in children with chronic kidney disease

Vanlede, Koen; Kluijtmans, Leo A. J.; Monnens, L.A.H.; Levtchenko, Elena

doi:10.1007/s00467-015-3135-1

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Additionally, the predicted R 2 (0.9296) was also in reasonable agreement with the adjusted R 2 (0.9804), with a difference of less than 0.2 [39]. Therefore, the relationship between the predicted and experimental values of hydroxyl value (OHV) response was proven to have a high R 2 -value, which suggested that the experimental values fitted well with the predicted value [40]. In addition, the adequate precision demonstrated that a signal-to-noise ratio of greater than 4 is desirable [41].…”

Section: Optimisation Reaction Conditions By Response Surface Methodosupporting

confidence: 63%

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Optimisation of Epoxide Ring-Opening Reaction for the Synthesis of Bio-Polyol from Palm Oil Derivative Using Response Surface Methodology

Kamairudin¹,

Hoong

Abdullah

et al. 2021

Molecules

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The development of bio-polyol from vegetable oil and its derivatives is gaining much interest from polyurethane industries and academia. In view of this, the availability of methyl oleate derived from palm oil, which is aimed at biodiesel production, provides an excellent feedstock to produce bio-polyol for polyurethane applications. In this recent study, response surface methodology (RSM) with a combination of central composite rotatable design (CCRD) was used to optimise the reaction parameters in order to obtain a maximised hydroxyl value (OHV). Three reaction parameters were selected, namely the mole ratio of epoxidised methyl oleate (EMO) to glycerol (1:5–1:10), the amount of catalyst loading (0.15–0.55%) and reaction temperature (90–150 °C) on a response variable as the hydroxyl value (OHV). The analysis of variance (ANOVA) indicated that the quadratic model was significant at 98% confidence level with (p-value > 0.0001) with an insignificant lack of fit and the regression coefficient (R2) was 0.9897. The optimum reaction conditions established by the predicted model were: 1:10 mole ratio of EMO to glycerol, 0.18% of catalyst and 120 °C reaction temperature, giving a hydroxyl value (OHV) of 306.190 mg KOH/g for the experimental value and 301.248 mg KOH/g for the predicted value. This result proves that the RSM model is capable of forecasting the relevant response. FTIR analysis was employed to monitor the changes of functional group for each synthesis and the confirmation of this finding was analysed by NMR analysis. The viscosity and average molecular weight (MW) were 513.48 mPa and 491 Da, respectively.

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Section: Optimisation Reaction Conditions By Response Surface Methodosupporting

confidence: 63%

“…Polyol is known to be a very important and beneficial material in pharmaceutical, food science and polymer chemistry [1][2][3]. Polyol is commonly used as raw materials in polymer chemistry in order to manufacture polyurethane products (coatings, elastomers and foam) by reacting with isocyanates [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Epoxide Ring-Opening Reaction for the Synthesis of Bio-Polyol from Palm Oil Derivative Using Response Surface Methodology

Kamairudin¹,

Hoong

Abdullah

et al. 2021

Molecules

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“…In accordance with the present results, elevated levels of ribitol are associated with retinal cell apoptosis in DR. 36 Moreover, elevated levels of ribitol and myo-inositol in chronic kidney disease stages 3-5 have been reported. 37 …”

Section: Discussionmentioning

confidence: 99%

Precision diagnostic approach to predict 5-year risk for microvascular complications in type 1 diabetes

et al. 2022

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“…In accordance with the present results, elevated levels of ribitol are associated with retinal cell apoptosis in DR (35). Moreover, elevated levels of ribitol and myo-inositol in chronic kidney disease stages 3-5 have been reported (36).…”

Section: Discussionmentioning

confidence: 99%

Precision Diagnostic Approach to Predict 5-Year Risk for Microvascular Complications in Type 1 Diabetes

Al-Sari

Kutuzova

Suvitaival

et al. 2021

Preprint

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OBJECTIVE: Our aim was to apply state-of-the-art machine learning algorithms to predict the risk of future progression to diabetes complications, including diabetic kidney disease (≥30% decline in eGFR) and diabetic retinopathy (mild, moderate or severe). RESEARCH DESIGN AND METHODS: Using data in a cohort of 537 adults with type 1 diabetes we predicted diabetes complications emerging during a median follow-up of 5.4 years. Prediction models were computed first with clinical risk factors at baseline (17 measures) and then with clinical risk factors and blood-derived metabolomics and lipidomics data (965 molecular features) at baseline. Participants were first classified into two groups: type 1 diabetes stable (n=195) or type 1 diabetes with progression to diabetes complications (n=190). Furthermore, progression of diabetic kidney disease (≥30% decline in eGFR; n=79) and diabetic retinopathy (mild, moderate or severe; n=111) were predicted in two complication-specific models. Models were compared by 5-fold cross-validated area under the receiver operating characteristic (AUROC) curves. The Shapley additive explanations algorithm was used for feature selection and for interpreting the models. Accuracy, precision, recall, and F-score were used to evaluate clinical utility. RESULTS: During a median follow-up of 5.4 years, 79 (21 %) of the participants (mean+-SD: age 54.8 +- 13.7 years) progressed in diabetic kidney disease and 111 (29 %) of the participants progressed to diabetic retinopathy. The predictive models for diabetic kidney disease progression were highly accurate with clinical risk factors: the accuracy of 0.95 and AUROC of 0.92 (95% CI 0.857;0.995) was achieved, further improved to the accuracy of 0.98 and AUROC of 0.99 (95% CI 0.876;0.997) when omics-based predictors were included. The predictive panel composition was: albuminuria, retinopathy, estimated glomerular filtration rate, hemoglobin A1c, and six metabolites (five identified as ribitol, ribonic acid, myo-inositol, 2,4- and 3,4-dihydroxybutanoic acids). Models for diabetic retinopathy progression were less predictive with clinical risk predictors at, AUROC of 0.81 (95% CI 0.754;0.958) and with omics included at AUROC of 0.87 (95% CI 0.781;0.996) curve. The final retinopathy-panel included: hemoglobin A1c, albuminuria, mild degree of retinopathy, and seven metabolites, including one ceramide and the 3,4-dihydroxybutanoic acid). CONCLUSIONS: Here we demonstrate the application of machine learning to effectively predict five-year progression of complications, in particular diabetic kidney disease, using a panel of known clinical risk factors in combination with blood small molecules. Further replication of this machine learning tool in a real-world context or a clinical trial will facilitate its implementation in the clinic.

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Urinary excretion of polyols and sugars in children with chronic kidney disease

Abstract: We show that the excretion of polyols and sugars depends on eGFR, which warrants a cautious interpretation of the results in patients with CKD.

Cited by 7 publications

References 12 publications

Optimisation of Epoxide Ring-Opening Reaction for the Synthesis of Bio-Polyol from Palm Oil Derivative Using Response Surface Methodology

Optimisation of Epoxide Ring-Opening Reaction for the Synthesis of Bio-Polyol from Palm Oil Derivative Using Response Surface Methodology

Precision diagnostic approach to predict 5-year risk for microvascular complications in type 1 diabetes

Precision Diagnostic Approach to Predict 5-Year Risk for Microvascular Complications in Type 1 Diabetes

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