Clustering patterns of metabolic syndrome: A cross-sectional study in children and adolescents in Kyiv

Abstract

ObjectiveThe aim: to identify subgroups by cluster analysis according parameters: original homeostatic model of insulin resistance (HOMA-1 IR), updated computer model of insulin resistance (HOMA-2 IR), β-cell function (%B) and insulin sensitivity (%S) for the prognosis of different variants of metabolic syndrome in children for more individualized treatment selection.Patients and methodsThe observational cross-sectional study on 75 children aged from 10 to 17 with metabolic syndrome according to the International Diabetes Federation criteria was conducted at the Cardiology Department of Children's Clinical Hospital No.6 in Kyiv. HOMA-1 IR was calculated as follows: fasting insulin (µIU/ml) × fasting glucose (mmol/L)/22.5. HOMA-2 IR with %B and %S were calculated according to the computer model in [http://www.dtu.ox.ac.uk]. All biochemical analysis were carried out using Cobas 6000 analyzer and Roche Diagnostics (Switzerland). The statistical analysis was performed using STATISTICA 7.0 and Easy R. The hierarchical method Ward was used for cluster analysis according the parameters: HOMA-1 IR, HOMA-2 IR, %B and %S.ResultsFour clusters were identified from the dendrogram, which could predict four variants in the course of metabolic syndrome such that children in cluster 1 would have the worst values of the studied parameters and those in cluster 4 – the best. It was found that HOMA-1 IR was much higher in cluster 1 (6.32 ± 0.66) than in cluster 4 (2.19 ± 0.13). HOMA-2 IR was also much higher in cluster 1 (3.80 ± 0.34) than in cluster 4 (1.31 ± 0.06). By the analysis of variance using Scheffe's multiple comparison method, a statistically significant difference was obtained between the laboratory parameters among the subgroups: HOMA-1 IR (p < 0,001), glucose (p < 0.001), insulin (p < 0,001), HOMA-2 IR (p < 0.001), %B (p < 0.001), %S (p < 0.001), TG (p = 0.005) and VLDL-C (p = 0.002).ConclusionsA cluster analysis revealed that the first two subgroups of children had the worst insulin resistance and lipid profile parameters. It was found positive correlation between HOMA-1 IR, HOMA-2 IR, %B and %S with lipid metabolism parameters TG and VLDL-C and negative correlation between %B and HDL-C in children with metabolic syndrome (MetS).The risk of getting a high TG result in the blood analysis in children with MetS was significantly dependent with the HOMA-2 IR >2.26.

Cardiorespiratory Monitoring of Children and Adolescents With Metabolic Syndrome Aged 10-17 Years: A Cross-Sectional Descriptive Study

“…The patient was found to have reduced saturation, a high sleep fragmentation index, low pulse rate variability and low pulse wave variability, which may indicate autonomic neuropathy and a nocturnal increase in blood pressure. The study group was predominantly male, suggesting a possible gender predisposition to MetS, consistent with previous studies [17].…”

Mechanisms and risk factors of metabolic syndrome in children and adolescents

“…In physiological conditions, leptin stimulates the oxidation of FFAs and the uptake of glucose, thus preventing the accumulation of lipids in non-adipose tissues. When the abundance of FFAs is no longer compensated by leptin activity, they are shifted to the nonoxidative metabolic pathway and detrimental metabolites able to induce β cells death are produced [ 26 , 33 , 34 ]. Another adipokine strongly related to the pathogenesis of MetS is adiponectin, which is known to exert a variety of protective functions on metabolism and to induce an anti-inflammatory effect via inhibition of TLRs and secretion of anti-inflammatory cytokines [ 15 – 17 , 28 , 29 , 32 ].…”

Сlinical Characteristics of Children and Adolescents With Metabolic Syndrome in Kyiv

“…Ñë³ä çàçíà÷èòè, ùî IDF -öå ºäèíå âèçíà÷åííÿ, â ÿêîìó àáäîì³íàëüíå îaeèð³ííÿ º îáîâ'ÿçêîâèì êðèòåð³ºì. Íàäàííÿ ïåðâèííîñò³ â ä³àãíîñòèö³ âèì³ðþâàííþ îêðóaeíîñò³ òà볿 º íàóêîâî îá-´ðóíòîâàíèì ÷èñëåííèìè äîñë³äaeåííÿìè [9,10,11]. Òàê, çã³äíî ç ìåòà-àíàë³çîì 2018 ðîêó, ä³òè ç³ ñï³ââ³äíîøåííÿì îêðóaeíîñò³ òà볿 äî çðîñòó (WtHR) ≥0,5 ìàþòü â÷åòâåðî âèùèé ðèçèê êëàñòåðíèõ êðèòåð³¿â ÌÑ ³ ùî öåé ðèçèê âèïðàâäîâóº ïî÷àòîê ïðîô³ëàêòè÷íèõ çàõîä³â [12].…”

“…It should be noted that the IDF is the only definition in which abdominal obesity is a mandatory criterion. The primary diagnostic role of waist circumference measurement is scientifically supported by numerous studies [9,10,11]. For example, according to a 2018 meta-analysis, children with a waist-to-height ratio (WHtR) ≥ 0.5 have a fourfold higher risk of meeting cluster criteria for MetS, and this risk justifies the initiation of preventive measures [12].…”