A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity

Smith, Eve; Eleuteri, A.; Goilav, Béatrice; Lewandowski, Laura B.; Phuti, Angel; Rubinstein, Tamar; Wahezi, Dawn M.; Jones, Caroline; Marks, Stephen D.; Corkhill, Rachel; Pilkington, Clarissa; Tullus, Kjell; Putterman, Chaim; Scott, Christiaan; Fisher, A.C.; Beresford, Michael W.

doi:10.1016/j.clim.2018.10.021

Cited by 13 publications

(9 citation statements)

References 49 publications

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“…PGD 2 are considered as an inflammatory marker, while lipocalin-like-prostaglandin-D synthase (L-PGDS) is considered as a urinary biomarker for human active lupus nephritis [76]. One study of 184 longitudinal observations in 80 patients showed that lipocalin-like-prostaglandin-D synthase could predict the onset/remission of LN [77]. In patients with systemic lupus erythematosus (SLE), increased expression of PGD 2 receptors (PTGDRs) in blood basophils causes an increase of PGD 2 metabolites in the plasma [78].…”

Section: Mechanism Of Aa-induced Renal Inflammationmentioning

confidence: 99%

Arachidonic Acid Metabolism and Kidney Inflammation

Wang

Chen

et al. 2019

IJMS

245

171

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As a major component of cell membrane lipids, Arachidonic acid (AA), being a major component of the cell membrane lipid content, is mainly metabolized by three kinds of enzymes: cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP450) enzymes. Based on these three metabolic pathways, AA could be converted into various metabolites that trigger different inflammatory responses. In the kidney, prostaglandins (PG), thromboxane (Tx), leukotrienes (LTs) and hydroxyeicosatetraenoic acids (HETEs) are the major metabolites generated from AA. An increased level of prostaglandins (PGs), TxA2 and leukotriene B4 (LTB4) results in inflammatory damage to the kidney. Moreover, the LTB4-leukotriene B4 receptor 1 (BLT1) axis participates in the acute kidney injury via mediating the recruitment of renal neutrophils. In addition, AA can regulate renal ion transport through 19-hydroxystilbenetetraenoic acid (19-HETE) and 20-HETE, both of which are produced by cytochrome P450 monooxygenase. Epoxyeicosatrienoic acids (EETs) generated by the CYP450 enzyme also plays a paramount role in the kidney damage during the inflammation process. For example, 14 and 15-EET mitigated ischemia/reperfusion-caused renal tubular epithelial cell damage. Many drug candidates that target the AA metabolism pathways are being developed to treat kidney inflammation. These observations support an extraordinary interest in a wide range of studies on drug interventions aiming to control AA metabolism and kidney inflammation.

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Section: Mechanism Of Aa-induced Renal Inflammationmentioning

confidence: 99%

Arachidonic Acid Metabolism and Kidney Inflammation

Wang

Chen

et al. 2019

IJMS

245

171

View full text Add to dashboard Cite

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“…Animal models have demonstrated that AGP-1 can reverse renal fibrosis and inflammation and preserve tubular epithelial structure, and as such it has been suggested to have a potential therapeutic role in LN [ 76 , 77 ]. As a urinary biomarker, AGP-1 levels were higher in patients with JSLE compared to JIA controls, and in particular in JSLE patients with LN flares [ 11 , 46 , 48 , 49 , 60 ], correlated to LN activity and could predict with fair-good accuracy a LN flare [ 47 , 55 , 59 ]. AGP-1 levels were higher when crescents, tubular cell necrosis and mesangial proliferation were present on renal biopsy [ 47 , 48 ].…”

Section: Renal Biomarkersmentioning

confidence: 99%

“…AGP-1 levels were higher when crescents, tubular cell necrosis and mesangial proliferation were present on renal biopsy [ 47 , 48 ]. Furthermore urinary AGP-1 levels increased at least 3 months before a clinically detectable flare of LN and could predict a flare up to 12 months in advance [ 49 , 59 ]. AGP-1 is also an excellent predictor of non-responders to therapy [ 54 ].…”

Section: Renal Biomarkersmentioning

confidence: 99%

Biomarkers Associated with Organ-Specific Involvement in Juvenile Systemic Lupus Erythematosus

Greenan-Barrett

Doolan

Shah

et al. 2021

IJMS

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Juvenile systemic lupus erythematosus (JSLE) is characterised by onset before 18 years of age and more severe disease phenotype, increased morbidity and mortality compared to adult-onset SLE. Management strategies in JSLE rely heavily on evidence derived from adult-onset SLE studies; therefore, identifying biomarkers associated with the disease pathogenesis and reflecting particularities of JSLE clinical phenotype holds promise for better patient management and improved outcomes. This narrative review summarises the evidence related to various traditional and novel biomarkers that have shown a promising role in identifying and predicting specific organ involvement in JSLE and appraises the evidence regarding their clinical utility, focusing in particular on renal biomarkers, while also emphasising the research into cardiovascular, haematological, neurological, skin and joint disease-related JSLE biomarkers, as well as genetic biomarkers with potential clinical applications.

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“…MSM have been used to explore the natural history of diseases and conditions as diverse as lung transplantation, 3 cardiovascular diseases, 4 chronic myeloid leukemia, 5 colon cancer, 6 and psoriatic arthritis 7 . Moreover, they have been applied to large disease cohorts and registry data, primarily for prediction of patient prognosis 8,9 . Both general statistical methods and software to fit models to observed data are available 10‐12 …”

Section: Introductionmentioning

confidence: 99%

“…7 Moreover, they have been applied to large disease cohorts and registry data, primarily for prediction of patient prognosis. 8,9 Both general statistical methods and software to fit models to observed data are available. [10][11][12] MSM have been used in secondary analysis of RCT data to better understand the mechanisms underlying primary analysis results, for example, the illness-death model to explore disease recurrence/progression patterns in chronic myeloid leukemia.…”

mentioning

confidence: 99%

Power and sample size for multistate model analysis of longitudinal discrete outcomes in disease prevention trials

Smith

Nixon

Sharples

2021

Statistics in Medicine

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For clinical trials where participants pass through a number of discrete health states resulting in longitudinal measures over time, there are several potential primary estimands for the treatment effect. Incidence or time to a particular health state are commonly used outcomes but the choice of health state may not be obvious and these estimands do not make full use of the longitudinal assessments. Multistate models have been developed for some diseases and conditions with the purpose of understanding their natural history and have been used for secondary analysis to understand mechanisms of action of treatments. There is little published on the use of multistate models as the primary analysis method and potential implications on design features, such as assessment schedules. We illustrate methods via analysis of data from a motivating example; a Phase III clinical trial of pressure ulcer prevention strategies. We clarify some of the possible estimands that might be considered and we show, via a simulation study, that under some circumstances the sample size could be reduced by half using a multistate model based analysis, without adversely affecting the power of the trial.

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A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity

Cited by 13 publications

References 49 publications

Arachidonic Acid Metabolism and Kidney Inflammation

Arachidonic Acid Metabolism and Kidney Inflammation

Biomarkers Associated with Organ-Specific Involvement in Juvenile Systemic Lupus Erythematosus

Power and sample size for multistate model analysis of longitudinal discrete outcomes in disease prevention trials

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