Sphingolipids and Kidney Disease: Possible Role of Preeclampsia and Intrauterine Growth Restriction (IUGR)

Yokota, Rodrigo; Bhunu, Benjamin; Toba, Hiroe; Intapad, Suttira

doi:10.34067/kid.0006322020

“…In a sensitivity analysis within the glomerular CKD subcohort, these lipid metabolites remained associated with FSGS. Our findings corroborate lipid dysmetabolism in FSGS pathophysiology 26 – 29 . Sphingomyelin abnormalities have been previously associated with FSGS and nephrotic syndrome.…”

Section: Discussionsupporting

confidence: 90%

“…Our findings corroborate lipid dysmetabolism in FSGS pathophysiology. [26][27][28][29] Sphingomyelin abnormalities have been previously associated with FSGS and nephrotic syndrome. In one prior study, patients with FSGS who underwent kidney transplant and developed recurrent FSGS had decreases in serum sphingomyelin phosphodiesterase acid-like 3b (SMPDL-3b) protein and acid sphingomyelinase activity.…”

Section: Fsgs and Lipid Associationsmentioning

confidence: 99%

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

Lee

¹

,

Hu

²

,

Xu

³

et al. 2022

JASN

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BackgroundUntargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).MethodsUntargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.ResultsML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome–derived histidine metabolites.ConclusionML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome–derived histidine metabolites are associated with OU.

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“…The pregnant women with PE had specific organ damage before they showed significant hypertension and proteinuria in the early pregnancy. Due to individual differences and the compensatory phase of organ damage, imaging and test indicators may still be within the normal range and not detected easily [30][31][32][33]. The combined assessment of risk factors and blood indicators in early pregnancy may play an important role in identifying high-risk groups for PE.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of IGFBP-2 in Predicting Preeclampsia before 20 Weeks of Pregnancy: A Prospective Nested Case-Control Study

Gao

¹

,

Yin

²

,

Long

³

et al. 2022

Computational and Mathematical Methods in Medicine

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Backgroud. Preeclampsia (PE) is a critical type of hypertensive disorder of pregnancy, which seriously affects maternal and infant health. The etiology of PE is unclear, and there is no clear prediction model. In this study, new biomarkers were identified before 20 weeks of gestation to construct an early PE prediction model. Purpose. To identify novel biomarker insulin-like growth factor binding protein-2 (IGFBP-2) associated with preeclampsia (PE) before 20 weeks of gestation and to explore the predictive value of plasma IGFBP-2 in PE. Methods. A prospective nested case-control investigation involving 122 PE patients and 122 normal controls (NC) that were matched 1 : 1 in terms of age and week of pregnancy was carried out in Guangzhou Women and Children’s Medical Center (Guangzhou, China, 2018030306) from April 2016 to December 2019. At 8 to 20 weeks, blood samples from the mother were taken. To calculate the correlations, univariate conditional logistic regression was employed. Results. Herein, 12 clinical indices were significantly different between the PE and NC groups (uric acid (UA), cystatin C (Cys C), aspartate aminotransferase (AST), glutamyl transpeptidase (γ-GT), total bilirubin (TB), prothrombin time (PT), red blood cell (RBC), hematocrit (HCT), red cell distribution width (RDW), platelets (PLT), mean platelet volume (MPV), and thrombocytocrit (PCT)). Compared with the NC group ( 36.79 ± 19.91 pg/mL), the expression level of IGFBP2 in the PE group ( 19.76 ± 19.40 pg/mL) before 20 weeks of pregnancy was significantly decreased ( P < 0.01 ). Two high-risk factors were found to be significantly associated with PE independently of confounders: anemia 4.35 (2.20-8.45) ( P < 0.01 ) and cesarean section history 8.25 (2.67-26.67) ( P < 0.01 ). As a result of the univariate logistic regression analysis, the following three variables were included in the final logistic regression model.: Y = − 18.841 − 0.085 × IGFBP ‐ 2 + 0.630 × RDW + 0.165 × AST + 0.863 × MPV . In comparison to IGFBP-2 alone as an independent predictor of PE ( AUC = 0.897 , 95% CI 0.830–0.964), the model’s discriminatory power was considerably higher ( AUC = 0.953 , 95% CI 0.911–0.995). Conclusion. Plasma IGFBP-2 before 20 weeks of pregnancy combined with high-risk factors and routine blood indexes has a high early predictive value for PE.

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“…Several studies have shown that S1P is involved in modulating intracellular Ca 2+ levels via the mobilization of internal pools of stored calcium ( 9 , 46 ). Therefore, we analyzed whether S1P was regulating Ca 2+ release in our cellular models of human myometrium.…”

Section: Resultsmentioning

confidence: 99%

Sphingosine 1-Phosphate Activates S1PR3 to Induce a Proinflammatory Phenotype in Human Myometrial Cells

Saurabh

¹

,

Mbadhi

²

,

Prifti

³

et al. 2023

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One of the common mechanisms responsible for obstetric complications, affecting millions of women every year, is abnormal uterine contractility. Despite the critical importance of this process for women’s health, mechanisms of uterine contraction regulation remain poorly understood. The initiation of uterine smooth muscle (myometrial) contraction is an inflammatory process, accompanied by upregulation of pro-inflammatory genes and cytokine release. In this study, we show that sphingolipid metabolism is activated during human labor and that sphingosine 1-phosphate (S1P), the main bioactive sphingolipid, may modify the myometrial pro-inflammatory phenotype. Our data in both primary and immortalized human myometrial cells show that exogenous S1P induces a pro-inflammatory gene signature and upregulates the expression of known inflammatory markers of parturition, such as IL8 and COX2. Using expression of IL8 as a readout for S1P activity in myometrial cells, we established that these S1P effects are mediated through the activation of S1P receptor 3 (S1PR3) and downstream activation of ERK1/2 pathways. Inhibition of S1PR3 in human myometrial cells attenuates upregulation of IL8, COX2 and JUNB both at the mRNA and protein levels. Furthermore, activation of S1PR3 with a receptor-specific agonist recapitulated the effects seen after treatment with exogenous S1P. Collectively, these results suggest a signaling pathway activated by S1P in human myometrium during parturition and propose new targets for development of novel therapeutics to alter uterine contractility during management of preterm labor or labor dystocia.

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Sphingolipids and Kidney Disease: Possible Role of Preeclampsia and Intrauterine Growth Restriction (IUGR)

Cited by 9 publications

References 117 publications

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

Diagnostic Value of IGFBP-2 in Predicting Preeclampsia before 20 Weeks of Pregnancy: A Prospective Nested Case-Control Study

Sphingosine 1-Phosphate Activates S1PR3 to Induce a Proinflammatory Phenotype in Human Myometrial Cells

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