Katharina Paulmichl1, Mensud Hatunic2, Kurt Højlund3, Aleksandra Jotic4, Michael Krebs5, Asimina Mitrakou6, Francesca Porcellati7, Andrea Tura8, Peter Bergsten9, Anders Forslund10, Hannes Manell11, Kurt Widhalm12, Daniel Weghuber13, Christian-Heinz Anderwald. 1. Department of Pediatrics, Division of Pediatric Gastroenterology, Hepatology and Nutrition, Paracelsus Medical University, Salzburg, Austria; Obesity Research Unit, Paracelsus Medical University, Salzburg, Austria. 2. Mater Misericordiae University Hospital, Dublin, Ireland; 3. Department of Endocrinology, Odense University Hospital, and Department of Clinical Research and Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark; 4. Clinic for Endocrinology, Diabetes and Metabolic Disorders, Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 5. Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria; michael.krebs@meduniwien.ac.at d.weghuber@salk.at. 6. Department of Clinical Therapeutics, Alexandra Hospital, Medical School of National and Kapodistrian University, Athens, Greece; 7. Department of Internal Medicine, University of Perugia, Perugia, Italy; 8. Metabolic Unit, CNR Institute of Neuroscience (IN-CNR), Padua, Italy; 9. Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden; 10. Uppsala University Children's Hospital, Uppsala, Sweden; 11. Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden; Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden; 12. Department of Pediatrics, Division of Pediatric Gastroenterology, Hepatology and Nutrition, Paracelsus Medical University, Salzburg, Austria; 13. Department of Pediatrics, Division of Pediatric Gastroenterology, Hepatology and Nutrition, Paracelsus Medical University, Salzburg, Austria; Obesity Research Unit, Paracelsus Medical University, Salzburg, Austria. michael.krebs@meduniwien.ac.at d.weghuber@salk.at.
Abstract
BACKGROUND: The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio was introduced as a tool to estimate insulin resistance, because circulating lipid measurements are available in routine settings. Insulin, C-peptide, and free fatty acids are components of other insulin-sensitivity indices but their measurement is expensive. Easier and more affordable tools are of interest for both pediatric and adult patients. METHODS: Study participants from the Relationship Between Insulin Sensitivity and Cardiovascular Disease [43.9 (8.3) years, n = 1260] as well as the Beta-Cell Function in Juvenile Diabetes and Obesity study cohorts [15 (1.9) years, n = 29] underwent oral-glucose-tolerance tests and euglycemic clamp tests for estimation of whole-body insulin sensitivity and calculation of insulin sensitivity indices. To refine the TG/HDL ratio, mathematical modeling was applied including body mass index (BMI), fasting TG, and HDL cholesterol and compared to the clamp-derived M-value as an estimate of insulin sensitivity. Each modeling result was scored by identifying insulin resistance and correlation coefficient. The Single Point Insulin Sensitivity Estimator (SPISE) was compared to traditional insulin sensitivity indices using area under the ROC curve (aROC) analysis and χ(2) test. RESULTS: The novel formula for SPISE was computed as follows: SPISE = 600 × HDL-C(0.185)/(TG(0.2) × BMI(1.338)), with fasting HDL-C (mg/dL), fasting TG concentrations (mg/dL), and BMI (kg/m(2)). A cutoff value of 6.61 corresponds to an M-value smaller than 4.7 mg · kg(-1) · min(-1) (aROC, M:0.797). SPISE showed a significantly better aROC than the TG/HDL-C ratio. SPISE aROC was comparable to the Matsuda ISI (insulin sensitivity index) and equal to the QUICKI (quantitative insulin sensitivity check index) and HOMA-IR (homeostasis model assessment-insulin resistance) when calculated with M-values. CONCLUSIONS: The SPISE seems well suited to surrogate whole-body insulin sensitivity from inexpensive fasting single-point blood draw and BMI in white adolescents and adults.
BACKGROUND: The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio was introduced as a tool to estimate insulin resistance, because circulating lipid measurements are available in routine settings. Insulin, C-peptide, and free fatty acids are components of other insulin-sensitivity indices but their measurement is expensive. Easier and more affordable tools are of interest for both pediatric and adult patients. METHODS: Study participants from the Relationship Between Insulin Sensitivity and Cardiovascular Disease [43.9 (8.3) years, n = 1260] as well as the Beta-Cell Function in Juvenile Diabetes and Obesity study cohorts [15 (1.9) years, n = 29] underwent oral-glucose-tolerance tests and euglycemic clamp tests for estimation of whole-body insulin sensitivity and calculation of insulin sensitivity indices. To refine the TG/HDL ratio, mathematical modeling was applied including body mass index (BMI), fasting TG, and HDL cholesterol and compared to the clamp-derived M-value as an estimate of insulin sensitivity. Each modeling result was scored by identifying insulin resistance and correlation coefficient. The Single Point Insulin Sensitivity Estimator (SPISE) was compared to traditional insulin sensitivity indices using area under the ROC curve (aROC) analysis and χ(2) test. RESULTS: The novel formula for SPISE was computed as follows: SPISE = 600 × HDL-C(0.185)/(TG(0.2) × BMI(1.338)), with fasting HDL-C (mg/dL), fasting TG concentrations (mg/dL), and BMI (kg/m(2)). A cutoff value of 6.61 corresponds to an M-value smaller than 4.7 mg · kg(-1) · min(-1) (aROC, M:0.797). SPISE showed a significantly better aROC than the TG/HDL-C ratio. SPISE aROC was comparable to the Matsuda ISI (insulin sensitivity index) and equal to the QUICKI (quantitative insulin sensitivity check index) and HOMA-IR (homeostasis model assessment-insulin resistance) when calculated with M-values. CONCLUSIONS: The SPISE seems well suited to surrogate whole-body insulin sensitivity from inexpensive fasting single-point blood draw and BMI in white adolescents and adults.
Authors: Benedetta Salvatori; Tina Linder; Daniel Eppel; Micaela Morettini; Laura Burattini; Christian Göbl; Andrea Tura Journal: Cardiovasc Diabetol Date: 2022-10-18 Impact factor: 8.949
Authors: Linda J Andes; Yiling J Cheng; Deborah B Rolka; Edward W Gregg; Giuseppina Imperatore Journal: JAMA Pediatr Date: 2020-02-03 Impact factor: 16.193