Long noncoding RNAs (CTC-471J1.2, NeST) as epigenetic risk factors of active juvenile lupus nephritis: a case-control study.

BACKGROUND: Measurement of the circulating levels of long-non-coding RNAs (lncRNAs) in lupus nephritis (LN) patients could dramatically explore more insights about the disease pathogenesis. Hence, we aimed to quantify the level of expression of CTC-471J1.2 and NeST in LN patients and to correlate it with the disease activity. METHOD: This case-control study was conducted on a group of children with juvenile LN attending to Mansoura University Children's Hospital (MUCH). Demographics, clinical, and laboratory findings were collected besides the measurement of lncRNAs by quantitative real-time PCR. RESULTS: The expression level of lncRNAs-CTC-471J1.2 was significantly down-regulated in children with active LN versus inactive cases or controls. In contrast, the NeST was significantly up-regulated in active LN cases. A significant correlation was found between CTC-471J1.2 expression and LN activity parameters. Additionally, both lncRNAs showed a reasonable sensitivity and specificity in differentiation of active LN. A regression analysis model revealed that CTC-471J1.2 and NeST were independent predictors of active nephritis. CONCLUSION: The expression level of circulatory lncRNAs-CTC-471J1.2 and NeST can be used as sensitive and specific biomarkers for active LN. Furthermore, both could serve as predictors for nephritis activity.

Serum apelin-12 and obesity-related markers in Egyptian children with Down syndrome.

Children with Down syndrome (DS) exhibit higher overweight/obesity rates than their typically developing peers. Apelin-12 is a bioactive adipokine that exerts vital roles in obesity-related cardiometabolic comorbidities. To date, apelin-12 has not been investigated in obese-DS. This study aimed to explore the possible association between serum apelin-12 and obesity-related markers and to evaluate the efficiency of apelin-12 in the prediction of metabolic syndrome (MetS) in obese-DS compared to BMI Z-score matched obese-control. The cross-sectional study included 150 prepubertal children classified into three groups; obese-DS (n = 50), obese-control (n = 50), and normal-weight-control (n = 50). Anthropometric parameters, body adiposity, fasting serum levels of blood glucose (FBG), insulin, lipid profile, and apelin-12 were evaluated. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from FBG and insulin. MetS was defined using Adult Treatment Panel III criteria modified for the pediatric age group. ROC curves were analyzed to evaluate the efficiency of apelin-12 in predicting MetS in obesity groups. Obese-DS exhibited higher body adiposity with marked central fat distribution, atherogenic lipid profile, and higher HOMA-IR compared to obese-control. Apelin-12 was significantly higher in obese-DS and obese-DS with MetS compared to obese-control and obese-control with MetS respectively (p < 0.001). The increase in apelin-12 with higher obesity grades was pronounced in obese-DS. Apelin-12 strongly correlated with body adiposity, several MetS risk factors, and HOMA-IR in obese-DS. Significantly higher AUC for apelin-12 in the diagnosis of MetS among obese-DS than obese-control (AUC = 0.948 vs. AUC = 0.807; p = 0.04). CONCLUSIONS:  The current study supports the crucial role of apelin-12 in obesity-related clinical and biochemical markers and in MetS in obese-DS and obese-control. Serum apelin-12 is a potential diagnostic biomarker for MetS with greater performance in obese-DS than obese-control raising its potential for clinical and therapeutic applications. WHAT IS KNOWN: • Obese-DS children displayed excess body adiposity, Pronounced central fat distribution, atherogenic lipid profile, higher HOMA-IR, and higher prevalence of MetS than obese-control. WHAT IS NEW: • Higher serum apelin-12 was observed in obese-DS and obese-DS with MetS than obese-control and obese-control with MetS respectively. The increase in apelin-12 level with increasing obesity grades was more pronounced in obese-DS. • Apelin-12 strongly correlated with obesity-related markers and MetS components in obese-DS. Apelin-12 performed better as a diagnostic biomarker for MetS in obese-DS than obese-control.

How multisystem inflammatory syndrome in children discriminated from Kawasaki disease: a differentiating score based on an inception cohort study.

BACKGROUND: About 25-50% of multisystem inflammatory syndrome in children (MIS-C) patients meet the criteria for diagnosis of Kawasaki disease (KD). The differentiation of both conditions is so challenging on clinical practice as the management of both is time dependant and precise diagnosis is fundamental. METHOD: Data were collected from children < 18 years old hospitalized with MIS-C or KD. Patient demographics, clinical, and laboratory data were compared, and a discrimination score was created to assist in clinical differentiation. RESULTS: 72 patients with MIS-C and 18 with KD were included in the study. Patients with MIS-C had a higher prevalence of abdominal pain (p = 0.02), vomiting (p = 0.03), and cervical lymphadenopathy (p = 0.02) compared with KD cases. MIS-C patients had higher liver enzymes (aspartate aminotransferase (AST) (p = 0.04), alanine aminotransferase (ALT) (p = 0.03), serum creatinine (p = 0.03), and lower platelet count nadir (p = 0.02) than KD. Four variables were detected in the regression analysis model, and the independent predictors were utilized to generate a scoring model that distinguished MIS-C from KD with an area under the curve of 0.70. CONCLUSION: This study constructed a prediction model for differentiation of MIS-C from KD based on clinical and laboratory profiles. This model will be valuable to guide clinicians in the treatment decisions. Key Points • Children with MIS-C are more likely to have gastrointestinal symptoms, cervical lymphadenopathy, and respiratory involvement than KD patients. • Elevated liver enzymes and lower platelet count are more pronounced laboratory findings in MIS-C than KD. • This study constructed a prediction model for differentiation of MIS-C from KD based on clinical and laboratory profiles. This model will be valuable to guide clinicians in the treatment decisions.