A Cross-Sectional Multicentre Study to Validate Insulin Sensitivity Index Cut-Offs for Detection of Metabolic Syndrome in Indian Adolescents with Type-1 Diabetes

Background

A previous study compared insulin sensitivity indices for the detection of double diabetes (DD) in Indian adolescents with type-1 diabetes (T1D) and derived a cut-off to predict future risk for the development of metabolic syndrome (MS) in adolescents with T1D. We conducted the current study with the aim to validate these cut-offs for detecting DD among Indian subjects with T1D from various geographical locations.

Methods

This multicentric cross-sectional study included 161 Indian adolescents with T1D. Demographic, anthropometric, clinical, and biochemical data were collected using standard protocols. Insulin sensitivity (IS) was calculated using various equations developed to determine insulin sensitivity in subjects with T1D. Metabolic syndrome was diagnosed using International Diabetes Federation (IDF) Consensus Definition 2017.

Results

We report 4.3% prevalence of MS in Indian adolescents with T1D with an additional 29.8% of study participants at risk of development of MS. Low High density lipoprotein (HDL) (23.6%) was the commonest abnormal component of the MS definition. Insulin sensitivity calculated by an equation derived by the SEARCH group was the most appropriate index to identify MS and metabolic risk in Indian adolescents with T1D. The proposed cut-off of 5.48 had high specificity, positive predictive value, and negative predictive value in identifying the risk of the development of DD.

Conclusions

Insulin sensitivity calculated by the equation proposed by the SEARCH group together with cut-offs derived in earlier study may be used effectively to identify risk of development of MS/DD in Indian adolescents with T1D from various geographical locations.

Comparison of insulin sensitivity indices for detection of double diabetes in Indian adolescents with type 1 diabetes

OBJECTIVES

The role of insulin sensitivity (IS) in the development and progression of metabolic syndrome (MS) in subjects with type-1 diabetes (T1D) is being increasingly recognized. As patients with T1D lack endogenous insulin secretion, measurement of insulin concentration by immunoassay or by indices such as homeostasis model of assessment for insulin resistance (HOMA-IR) is not helpful in assessing IS. Hence, some equations have been developed and validated against data from euglycemic-hyper-insulinemic clamp tests (the gold standard) to estimate IS. 1) To assess IS using available equations (EDC, SEARCH and CACTI) and relationship of IS with MS and microalbuminuria in adolescents with T1D, (2) To compare the predictive value of these equations for detection of MS and derive a cut-off to predict the future risk of development of MS and microalbuminuria and (3) To identify the most accurate non-invasive and easy-to-use equation for detecting patients with double diabetes (DD) in a clinical setting.

METHODS

This cross-sectional study included 181 adolescents aged 12-18 years with T1D. Demographic data and laboratory measurements were performed using standard protocols. IS was calculated using following equations:(1) EDC=24.31-12.22×(WHR)-3.29×(hypertension)-0.57×(HbA1c), (2) SEARCH=exp(4.64725-0.02032(waist)-0.09779(HbA1c)-0.00235(Triglycerides), (3)CACTI-exA=exp(4.1075-0.01299×(waist)-1.05819×(insulin dose)-0.00354×(Triglycerides)-0.00802×(DBP)).

RESULTS

IS determined by all three methods had significant negative correlation (p<0.05) with MS as well as with microalbuminuria. The cut-off value of 5.485 mg/kg/min by SEARCH method for determining IS had the highest sensitivity and specificity in identifying MS.

CONCLUSIONS

IS by SEARCH equation may be used in routine clinical practice to detect DD in Indian adolescents with T1D at risk of developing metabolic as well as microvascular complications.

Genomic risk prediction of coronary artery disease in women with breast cancer: a prospective cohort study

BACKGROUND

Advancements in cancer therapeutics have resulted in increases in cancer-related survival; however, there is a growing clinical dilemma. The current balancing of survival benefits and future cardiotoxic harms of oncotherapies has resulted in an increased burden of cardiovascular disease in breast cancer survivors. Risk stratification may help address this clinical dilemma. This study is the first to assess the association between a coronary artery disease-specific polygenic risk score and incident coronary artery events in female breast cancer survivors.

METHODS

We utilized the Studies in Epidemiology and Research in Cancer Heredity prospective cohort involving 12,413 women with breast cancer with genotype information and without a baseline history of cardiovascular disease. Cause-specific hazard ratios for association of the polygenic risk score and incident coronary artery disease (CAD) were obtained using left-truncated Cox regression adjusting for age, genotype array, conventional risk factors such as smoking and body mass index, as well as other sociodemographic, lifestyle, and medical variables.

RESULTS

Over a median follow-up of 10.3 years (IQR: 16.8) years, 750 incident fatal or non-fatal coronary artery events were recorded. A 1 standard deviation higher polygenic risk score was associated with an adjusted hazard ratio of 1.33 (95% CI 1.20, 1.47) for incident CAD.

CONCLUSIONS

This study provides evidence that a coronary artery disease-specific polygenic risk score can risk-stratify breast cancer survivors independently of other established cardiovascular risk factors.

A mass spectrometry database for identification of saponins in plants

Saponins constitute an important class of secondary metabolites of the plant kingdom. Here, we present a mass spectrometry-based database for rapid and easy identification of saponins henceforth referred to as saponin mass spectrometry database (SMSD). With a total of 4196 saponins, 214 of which were obtained from commercial sources. Through liquid chromatography-tandem high-resolution/mass spectrometry (HR/MS) analysis under negative ion mode, the fragmentation behavior for all parent fragment ions almost conformed to successive losses of sugar moieties, α-dissociation and McLafferty rearrangement of aglycones in high-energy collision induced dissociation. The saccharide moieties produced sugar fragment ions from m/z (monosaccharide) to m/z (polysaccharides). The parent and sugar fragment ions of other saponins were predicted using the above mentioned fragmentation pattern. The SMSD is freely accessible at http://47.92.73.208:8082/ or http://cpu-smsd.com (preferrably using google). It provides three search modes ("CLASSIFY", "SEARCH" and "METABOLITE"). Under the "CLASSIFY" function, saponins are classified with high predictive accuracies from all metabolites by establishment of logistic regression model through their mass data from HR/MS input as a csv file, where the first column is ID and the second column is mass. For the "SEARCH" function, saponins are searched against parent ions with certain mass tolerance in "MS Ion Search". Then, daughter ions with certain mass tolerance are input into "MS/MS Ion Search". The optimal candidates were screened out according to the match count and match rate values in comparison with fragment data in database. Additionally, another logistic regression model completely differentiated between parent and sugar fragment ions. This function designed in front web is conducive to search and recheck. With the "METABOLITE" function, saponins are searched using their common names, where both full and partial name searches are supported. With these modes, saponins of diverse chemical composition can be explored, grouped and identified with a high degree of predictive accuracy. This specialized database would aid in the identification of saponins in complex matrices particular in the study of traditional Chinese medicines or plant metabolomics.