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Huang LY, Liu CH, Chen FY, Kuo CH, Pitrone P, Liu JS. Aging Affects Insulin Resistance, Insulin Secretion, and Glucose Effectiveness in Subjects with Normal Blood Glucose and Body Weight. Diagnostics (Basel) 2023; 13:2158. [PMID: 37443552 DOI: 10.3390/diagnostics13132158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/18/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
AIM Several studies have demonstrated that factors including diabetes, including insulin resistance (IR), glucose effectiveness (GE), and the first and second phase of insulin secretion (FPIS, SPIS) could easily be calculated using basic characteristics and biochemistry profiles. Aging is accompanied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging in the different phases of insulin secretion (ISEC), i.e., the first and second phase of insulin secretion (FPIS, SPIS), and glucose effectiveness (GE). METHODS In total, 169 individuals (43 men and 126 women) recruited from the data bank of the Meei-Jaw (MJ) Health Screening Center and Cardinal Tien Hospital Data Access Center between 1999 and 2008, with a similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. The IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS, and GE). The equations that are used to calculate the DF in the present study were built and published by our group. RESULTS The age of the participants ranged from 18 to 78 years. Men had higher FPIS but lower HDL-C levels than women (2.067 ± 0.159, 1.950 ± 0.186 μU/min and 1.130 ± 0.306, 1.348 ± 0.357 mmol/dl, accordingly). The results of the Pearson correlation revealed that age was negatively related to the IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). At the same time, the FPIS was also only found to be negatively correlated with age in females (r = -0.238, p = 0.003), but there was no difference in the SPIS and age among both genders. CONCLUSIONS We have found that in Chinese subjects with a normal FPG level (90 mg/dL) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to the IR and GE among both genders. Only the FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest, are GE, FPIS, and IR. These results should be interpreted with caution because of the small sample size.
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Affiliation(s)
- Li-Ying Huang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan
| | - Chi-Hao Liu
- Division of Nephrology, Department of Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 80284, Taiwan
| | - Fang-Yu Chen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan
| | - Chun-Heng Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei 24352, Taiwan
| | - Pietro Pitrone
- Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98158 Messina, Italy
| | - Jhih-Syuan Liu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
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Piersanti A, Abdul Rahman NHB, Gobl C, Burattini L, Kautzky-Willer A, Pacini G, Tura A, Morettini M. Model-Based Assessment of Hepatic and Extrahepatic Insulin Clearance from Short Insulin-Modified IVGTT in Women with a History of Gestational Diabetes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4311-4314. [PMID: 34892175 DOI: 10.1109/embc46164.2021.9630405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Insulin clearance is an integral component of insulin metabolism. Yet, little is known about separate contribution of hepatic and extrahepatic insulin clearance in type 2 diabetes and in high-risk populations, such as women who experienced gestational diabetes mellitus (pGDM). A model-based method was recently proposed to assess both contributions from 3-hour insulin-modified intravenous glucose tolerance test (IM-IVGTT); the aim of this study was to assess the reliability of short (1 hour) IM-IVGTT in the application of such model-based method and to evaluate the role of the two contributions in determining insulin clearance in pGDM. A total of 115 pGDM women and 41 who remained healthy during pregnancy (CNT) were analyzed early postpartum and underwent a 3-hour IMIVGTT. Peripheral insulin clearance (CLP), hepatic fractional extraction (FEL) and extrahepatic distribution volume (VP) were estimated by performing a best-fit procedure on insulin IMIVGTT data considering firstly the overall 3-hour duration and then limiting data to 1 hour. Results showed no significant difference in parameter values between the 3-hour and the 1-hour IM-IVGTT. Comparison between pGDM and CNT (1-hour) showed no significant difference in CLp (0.23 [0.29] vs. 0.27 [0.43] L·min-1; p=0.64), FEL (50.2 [15.1] vs. 50.9 [11.7] %; p=0.63) and VP (2.01 [2.99] vs. 2.70 [4.00] L; p=0.92). In conclusion, short IM-IVGTT provides a reliable assessment of hepatic and extrahepatic insulin clearance through such model-based method. Its application to the study of pGDM women showed no alteration in hepatic and extrahepatic contributions with respect to women who had a healthy pregnancy.Clinical Relevance- This study proves the reliability of short (1 hour) IM-IVGTT to assess hepatic and extrahepatic insulin clearance in women who experienced gestational diabetes.
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Tura A, Göbl C, Morettini M, Burattini L, Kautzky-Willer A, Pacini G. Insulin clearance is altered in women with a history of gestational diabetes progressing to type 2 diabetes. Nutr Metab Cardiovasc Dis 2020; 30:1272-1280. [PMID: 32513580 DOI: 10.1016/j.numecd.2020.04.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/27/2020] [Accepted: 04/06/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND AND AIMS Insulin clearance is a relevant process in glucose homeostasis. In this observational study, we aimed to assess insulin clearance (ClINS) in women with former gestational diabetes (fGDM) both early after delivery and after a follow-up. METHODS AND RESULTS We analysed 59 fGDM women, and 16 women not developing GDM (CNT). All women underwent an oral glucose tolerance test (OGTT) yearly, and an insulin-modified intravenous glucose tolerance test (IVGTT) at baseline and at follow-up end (until 7 years). Both IVGTT and OGTT ClINS was assessed as insulin secretion to plasma insulin ratio. We also defined IVGTT first (0-10 min) and second phase (10-180 min) ClINS. We found that 14 fGDM women progressed to type 2 diabetes (PROG), whereas 45 women remained diabetes-free (NONPROG). At baseline, IVGTT ClINS showed alterations in PROG, especially in second phase (0.88 ± 0.10 l·min-1 in PROG, 0.60 ± 0.06 in NONPROG, 0.54 ± 0.07 in CNT, p ≤ 0.03). Differences in ClINS were not found from OGTT. Cox regression analysis showed second phase ClINS as significant type 2 diabetes predictor (hazard ratio = 1.90, 95% confidence interval 1.09-3.30, p = 0.02). CONCLUSION This study showed that insulin clearance derived from an insulin-modified IVGTT is notably altered in women with history of GDM progressing towards type 2 diabetes.
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Affiliation(s)
- Andrea Tura
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy.
| | - Christian Göbl
- Department of Obstetrics and Gynecology, Division of Obstetrics and Feto-Maternal Medicine, Medical University of Vienna, Vienna, Austria
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Alexandra Kautzky-Willer
- Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Giovanni Pacini
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy
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Stefanovski D, Moate PJ, Frank N, Ward GM, Localio AR, Punjabi NM, Boston RC. Metabolic modeling using statistical and spreadsheet software: Application to the glucose minimal model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 191:105353. [PMID: 32113102 DOI: 10.1016/j.cmpb.2020.105353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/14/2020] [Accepted: 01/21/2020] [Indexed: 06/10/2023]
Abstract
Kinetic non-linear metabolic models are used extensively in medical research and increasingly for clinical diagnostic purposes. An example of such a model is the Glucose Minimal Model by Bergman and colleagues [1]. This model is similar to pharmacokinetic/pharmacodynamic models in that like pharmacokinetic/pharmacodynamic models, it is based on a small number of fairly simple ordinary differential equations and it aims to determine how the changing concentration of one blood constituent influences the concentration of another constituent. Although such models may appear prima facie, to be relatively simple, they have gained a reputation of being difficult to fit to data, especially in a consistent and repeatable fashion. Consequently, researchers and clinicians have generally relied on dedicated software packages to do this type of modeling. This article describes the use of statistical and spreadsheet software for fitting the Glucose Minimal Model to data from an insulin modified intravenous glucose tolerance test (IM-IVGTT). A novel aspect of the modeling is that the differential equations that are normally used to describe insulin action and the disposition of plasma glucose are first solved and expressed in their explicit forms so as to facilitate the estimation of Glucose Minimal Model parameters using the nonlinear (nl) optimization procedure within statistical and spreadsheet software. The most important clinical parameter obtained from the Glucose Minimal Model is insulin sensitivity (SI). Using IM-IVGTT data from 42 horses in one experiment and 48 horses in a second experiment, we demonstrate that estimates of SI derived from the Glucose Minimal Model fitted to data using STATA and Excel, are highly concordant with SI estimates obtained using the industry standard software, MinMod Millennium. This work demonstrates that there is potential for statistical and spreadsheet software to be applied to a wide range of kinetic non-linear modeling problems.
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Affiliation(s)
- D Stefanovski
- Department of Clinical Studies - New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA, United States.
| | - P J Moate
- Agriculture Research Division, Department of Economic Development Jobs Transport and Resources, Ellinbank Centre, Ellinbank, VIC 3821, Australia
| | - N Frank
- Department of Clinical Sciences, Tufts Cummings School of Veterinary Medicine, North Grafton, MA, United States
| | - G M Ward
- Department of Endocrinology and Diabetes, St. Vincent's Hospital Melbourne, Melbourne, Australia
| | - A R Localio
- Division of Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA, United States
| | - N M Punjabi
- Division of Pulmonary and Critical Care Medicine (N.M.P.), Johns Hopkins University, Baltimore, MD, United States
| | - R C Boston
- Department of Clinical Studies - New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA, United States
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Morettini M, Castriota C, Göbl C, Kautzky-Willer A, Pacini G, Burattini L, Tura A. Glucose Effectiveness from Short Insulin-Modified IVGTT and Its Application to the Study of Women with Previous Gestational Diabetes Mellitus. Diabetes Metab J 2020; 44:286-294. [PMID: 31950770 PMCID: PMC7188979 DOI: 10.4093/dmj.2019.0016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/24/2019] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND This study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (SG), namely calculated SG (CSG), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to apply it to study women with previous gestational diabetes mellitus (pGDM). METHODS CSG was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from normal tolerance to type 2 diabetes mellitus (T2DM). CSG was then tested on a population of women with pGDM (n=57). Each subject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference SG. RESULTS CSG was described as CSG=1.06×10⁻²+5.71×10⁻²×KG/Gpeak, KG being the mean slope (absolute value) of loge glucose in 10-25- and 25-50-minute intervals, and Gpeak being the maximum of the glucose curve. Good agreement between CSG and SG in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (<5% observations outside limits of agreement), and by the test for equivalence (equivalence margin not higher than one standard deviation). At baseline, the PROG subgroup showed significantly lower SG and CSG values compared to the NONPROG subgroup (P<0.03). CONCLUSION CSG is a valid SG predictor. In the pGDM group, glucose effectiveness appeared to be impaired in women progressing to T2DM.
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Affiliation(s)
- Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Carlo Castriota
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Christian Göbl
- Division of Obstetrics and Feto-Maternal Medicine, Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
| | - Alexandra Kautzky-Willer
- Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Giovanni Pacini
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Andrea Tura
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy.
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Morettini M, Di Nardo F, Ingrillini L, Fioretti S, Göbl C, Kautzky-Willer A, Tura A, Pacini G, Burattini L. Glucose effectiveness and its components in relation to body mass index. Eur J Clin Invest 2019; 49:e13099. [PMID: 30838644 DOI: 10.1111/eci.13099] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 02/14/2019] [Accepted: 03/03/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND Obesity is known to induce a deterioration of insulin sensitivity (SI ), one of the insulin-dependent components of glucose tolerance. However, few studies investigated whether obesity affects also the insulin-independent component, that is glucose effectiveness (SG ). This cross-sectional study aimed to analyse SG and its components in different body mass index (BMI) categories. MATERIALS AND METHODS Three groups of subjects spanning different BMI (kg m-2 ) categories underwent a 3-h frequently sampled intravenous glucose tolerance test: Lean (LE; 18.5 ≤ BMI < 25, n = 73), Overweight (OW; 25 ≤ BMI < 30, n = 90), and Obese (OB; BMI ≥ 30, n = 41). OB has been further divided into two subgroups, namely Obese I (OB-I; 30 ≤ BMI < 35, n = 27) and Morbidly Obese (OB-M; BMI ≥ 35, n = 14). Minimal model analysis provided SG and its components at zero (GEZI) and at basal (BIE) insulin. RESULTS Values for SG were 1.98 ± 1.30 × 10-2 ·min-1 in all subjects grouped and 2.38 ± 1.23, 1.84 ± 0.82, 1.59 ± 0.61 10-2 ·min-1 in LE, OW and OB, respectively. In all subjects grouped, a significant inverse linear correlation was found between the log-transformed values of SG and BMI (r = -0.3, P < 0.0001). SG was significantly reduced in OW and OB with respect to LE (P < 0.001) but no significant difference was detected between OB and OW (P = 0.35) and between OB-I and OB-M (P = 0.25). Similar results were found for GEZI. BIE was not significantly different among NW, OW and OB (P = 0.11) and between OB-I and OB-M (P ≥ 0.07). CONCLUSIONS SG and its major component GEZI deteriorate in overweight individuals compared to those in the normal BMI range, without further deterioration when BMI increases above 30 kg m-2 .
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Affiliation(s)
- Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Francesco Di Nardo
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Laura Ingrillini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Sandro Fioretti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynecology, Division of Obstetrics and Feto-maternal Medicine, Medical University of Vienna, Vienna, Austria
| | - Alexandra Kautzky-Willer
- Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Andrea Tura
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy
| | - Giovanni Pacini
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
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