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Nagahisa T, Kosugi S, Yamaguchi S. Interactions between Intestinal Homeostasis and NAD + Biology in Regulating Incretin Production and Postprandial Glucose Metabolism. Nutrients 2023; 15:nu15061494. [PMID: 36986224 PMCID: PMC10052115 DOI: 10.3390/nu15061494] [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: 02/21/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
The intestine has garnered attention as a target organ for developing new therapies for impaired glucose tolerance. The intestine, which produces incretin hormones, is the central regulator of glucose metabolism. Glucagon-like peptide-1 (GLP-1) production, which determines postprandial glucose levels, is regulated by intestinal homeostasis. Nicotinamide phosphoribosyltransferase (NAMPT)-mediated nicotinamide adenine dinucleotide (NAD+) biosynthesis in major metabolic organs such as the liver, adipose tissue, and skeletal muscle plays a crucial role in obesity- and aging-associated organ derangements. Furthermore, NAMPT-mediated NAD+ biosynthesis in the intestines and its upstream and downstream mediators, adenosine monophosphate-activated protein kinase (AMPK) and NAD+-dependent deacetylase sirtuins (SIRTs), respectively, are critical for intestinal homeostasis, including gut microbiota composition and bile acid metabolism, and GLP-1 production. Thus, boosting the intestinal AMPK-NAMPT-NAD+-SIRT pathway to improve intestinal homeostasis, GLP-1 production, and postprandial glucose metabolism has gained significant attention as a novel strategy to improve impaired glucose tolerance. Herein, we aimed to review in detail the regulatory mechanisms and importance of intestinal NAMPT-mediated NAD+ biosynthesis in regulating intestinal homeostasis and GLP-1 secretion in obesity and aging. Furthermore, dietary and molecular factors regulating intestinal NAMPT-mediated NAD+ biosynthesis were critically explored to facilitate the development of new therapeutic strategies for postprandial glucose dysregulation.
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Affiliation(s)
- Taichi Nagahisa
- Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Shotaro Kosugi
- Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Shintaro Yamaguchi
- Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan
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Shah P. Genomic Editing and Diabetes. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1396:207-214. [DOI: 10.1007/978-981-19-5642-3_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Fletcher PA, Marinelli I, Bertram R, Satin LS, Sherman AS. Pulsatile Basal Insulin Secretion Is Driven by Glycolytic Oscillations. Physiology (Bethesda) 2022; 37:0. [PMID: 35378996 PMCID: PMC9191171 DOI: 10.1152/physiol.00044.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
In fasted and fed states, blood insulin levels are oscillatory. While this phenomenon is well studied at high glucose levels, comparatively little is known about its origin under basal conditions. We propose a possible mechanism for basal insulin oscillations based on oscillations in glycolysis, demonstrated using an established mathematical model. At high glucose, this is superseded by a calcium-dependent mechanism.
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Affiliation(s)
- P. A. Fletcher
- 1Laboratory of Biological Modeling, National Institutes of Health, Bethesda, Maryland
| | - I. Marinelli
- 2Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, United Kingdom
| | - R. Bertram
- 3Department of Mathematics and Programs in Neuroscience and Molecular Biophysics, Florida State University, Tallahassee, Florida
| | - L. S. Satin
- 4Department of Pharmacology and Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, Michigan
| | - A. S. Sherman
- 1Laboratory of Biological Modeling, National Institutes of Health, Bethesda, Maryland
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Laurenti MC, Arora P, Dalla Man C, Andrews JC, Rizza RA, Matveyenko A, Bailey KR, Cobelli C, Vella A. The relationship between insulin and glucagon concentrations in non-diabetic humans. Physiol Rep 2022; 10:e15380. [PMID: 35822422 PMCID: PMC9277417 DOI: 10.14814/phy2.15380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 06/02/2023] Open
Abstract
Abnormal postprandial suppression of glucagon in Type 2 diabetes (T2DM) has been attributed to impaired insulin secretion. Prior work suggests that insulin and glucagon show an inverse coordinated relationship. However, dysregulation of α-cell function in prediabetes occurs early and independently of changes in β-cells, which suggests insulin having a less significant role on glucagon control. We therefore, sought to examine whether hepatic vein hormone concentrations provide evidence to further support the modulation of glucagon secretion by insulin. As part of a series of experiments to measure the effect of diabetes-associated genetic variation in TCF7L2 on islet cell function, hepatic vein insulin and glucagon concentrations were measured at 2-minute intervals during fasting and a hyperglycemic clamp. The experiment was performed on 29 nondiabetic subjects (age = 46 ± 2 years, BMI 28 ± 1 Kg/m2 ) and enabled post-hoc analysis, using Cross-Correlation and Cross-Approximate Entropy (Cross-ApEn) to evaluate the interaction of insulin and glucose. Mean insulin concentrations rose from fasting (33 ± 4 vs. 146 ± 12 pmol/L, p < 0.01) while glucagon was suppressed (96 ± 8 vs. 62 ± 5 ng/L, p < 0.01) during the clamp. Cross-ApEn was used to measure pattern reproducibility in the two hormones using glucagon as control mechanism (0.78 ± 0.03 vs. 0.76 ± 0.03, fasting vs. hyperglycemia) and using insulin as a control mechanism (0.78 ± 0.02 vs. 0.76 ± 0.03, fasting vs. hyperglycemia). Values did not differ between the two scenarios. Cross-correlation analysis demonstrated a small in-phase coordination between insulin and glucagon concentrations during fasting, which inverted during hyperglycemia. This data suggests that the interaction between the two hormones is not driven by either. On a minute-to-minute basis, direct control and secretion of glucagon is not mediated (or restrained) by insulin.
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Affiliation(s)
- Marcello C. Laurenti
- Division of Endocrinology, Diabetes & MetabolismEndocrine Research Unit, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical SciencesRochesterMinnesotaUSA
| | - Praveer Arora
- Division of Endocrinology, Diabetes & MetabolismEndocrine Research Unit, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
| | - Chiara Dalla Man
- Department of Information EngineeringUniversity of PadovaPadovaItaly
| | - James C. Andrews
- Vascular and Interventional Radiology, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
| | - Robert A. Rizza
- Division of Endocrinology, Diabetes & MetabolismEndocrine Research Unit, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
| | - Aleksey Matveyenko
- Division of Endocrinology, Diabetes & MetabolismEndocrine Research Unit, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
| | - Kent R. Bailey
- Division of Biomedical Statistics and Informatics, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
| | - Claudio Cobelli
- Department of Woman and Child's HealthUniversity of PadovaPadovaItaly
| | - Adrian Vella
- Division of Endocrinology, Diabetes & MetabolismEndocrine Research Unit, Mayo Clinic, College of Medicine and ScienceRochesterMinnesotaUSA
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Marinelli I, Fletcher PA, Sherman AS, Satin LS, Bertram R. Symbiosis of Electrical and Metabolic Oscillations in Pancreatic β-Cells. Front Physiol 2021; 12:781581. [PMID: 34925070 PMCID: PMC8682964 DOI: 10.3389/fphys.2021.781581] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/08/2021] [Indexed: 11/13/2022] Open
Abstract
Insulin is secreted in a pulsatile pattern, with important physiological ramifications. In pancreatic β-cells, which are the cells that synthesize insulin, insulin exocytosis is elicited by pulses of elevated intracellular Ca2+ initiated by bursts of electrical activity. In parallel with these electrical and Ca2+ oscillations are oscillations in metabolism, and the periods of all of these oscillatory processes are similar. A key question that remains unresolved is whether the electrical oscillations are responsible for the metabolic oscillations via the effects of Ca2+, or whether the metabolic oscillations are responsible for the electrical oscillations due to the effects of ATP on ATP-sensitive ion channels? Mathematical modeling is a useful tool for addressing this and related questions as modeling can aid in the design of well-focused experiments that can test the predictions of particular models and subsequently be used to improve the models in an iterative fashion. In this article, we discuss a recent mathematical model, the Integrated Oscillator Model (IOM), that was the product of many years of development. We use the model to demonstrate that the relationship between calcium and metabolism in beta cells is symbiotic: in some contexts, the electrical oscillations drive the metabolic oscillations, while in other contexts it is the opposite. We provide new insights regarding these results and illustrate that what might at first appear to be contradictory data are actually compatible when viewed holistically with the IOM.
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Affiliation(s)
- Isabella Marinelli
- Centre for Systems Modelling and Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham, United Kingdom
| | - Patrick A Fletcher
- Laboratory of Biological Modeling, National Institutes of Health, Bethesda, MD, United States
| | - Arthur S Sherman
- Laboratory of Biological Modeling, National Institutes of Health, Bethesda, MD, United States
| | - Leslie S Satin
- Department of Pharmacology, Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Richard Bertram
- Programs in Neuroscience and Molecular Biophysics, Department of Mathematics, Florida State University, Tallahassee, FL, United States
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Redondo MJ, Warnock MV, Libman IM, Bocchino LE, Cuthbertson D, Geyer S, Pugliese A, Steck AK, Evans-Molina C, Becker D, Sosenko JM, Bacha F. TCF7L2 Genetic Variants Do Not Influence Insulin Sensitivity or Secretion Indices in Autoantibody-Positive Individuals at Risk for Type 1 Diabetes. Diabetes Care 2021; 44:2039-2044. [PMID: 34326068 PMCID: PMC8740915 DOI: 10.2337/dc21-0531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/10/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We aimed to test whether type 2 diabetes (T2D)-associated TCF7L2 genetic variants affect insulin sensitivity or secretion in autoantibody-positive relatives at risk for type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS We studied autoantibody-positive TrialNet Pathway to Prevention study participants (N = 1,061) (mean age 16.3 years) with TCF7L2 single nucleotide polymorphism (SNP) information and baseline oral glucose tolerance test (OGTT) to calculate indices of insulin sensitivity and secretion. With Bonferroni correction for multiple comparisons, P values < 0.0086 were considered statistically significant. RESULTS None, one, and two T2D-linked TCF7L2 alleles were present in 48.1%, 43.9%, and 8.0% of the participants, respectively. Insulin sensitivity (as reflected by 1/fasting insulin [1/IF]) decreased with increasing BMI z score and was lower in Hispanics. Insulin secretion (as measured by 30-min C-peptide index) positively correlated with age and BMI z score. Oral disposition index was negatively correlated with age, BMI z score, and Hispanic ethnicity. None of the indices were associated with TCF7L2 SNPs. In multivariable analysis models with age, BMI z score, ethnicity, sex, and TCF7L2 alleles as independent variables, C-peptide index increased with age, while BMI z score was associated with higher insulin secretion (C-peptide index), lower insulin sensitivity (1/IF), and lower disposition index; there was no significant effect of TCF7L2 SNPs on any of these indices. When restricting the analyses to participants with a normal OGTT (n = 743; 70%), the results were similar. CONCLUSIONS In nondiabetic autoantibody-positive individuals, TCF7L2 SNPs were not related to insulin sensitivity or secretion indices after accounting for BMI z score, age, sex, and ethnicity.
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Affiliation(s)
- Maria J Redondo
- Texas Children's Hospital, Baylor College of Medicine, Houston, TX
| | | | | | - Laura E Bocchino
- University of South Florida, Tampa, FL.,Jaeb Center for Health Research, Tampa, FL
| | | | - Susan Geyer
- University of South Florida, Tampa, FL.,Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | | | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
| | | | - Jay M Sosenko
- Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL
| | - Fida Bacha
- Texas Children's Hospital, Baylor College of Medicine, Houston, TX.,Children's Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture, Houston, TX
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Laurenti MC, Matveyenko A, Vella A. Measurement of Pulsatile Insulin Secretion: Rationale and Methodology. Metabolites 2021; 11:metabo11070409. [PMID: 34206296 PMCID: PMC8305896 DOI: 10.3390/metabo11070409] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/29/2022] Open
Abstract
Pancreatic β-cells are responsible for the synthesis and exocytosis of insulin in response to an increase in circulating glucose. Insulin secretion occurs in a pulsatile manner, with oscillatory pulses superimposed on a basal secretion rate. Insulin pulses are a marker of β-cell health, and secretory parameters, such as pulse amplitude, time interval and frequency distribution, are impaired in obesity, aging and type 2 diabetes. In this review, we detail the mechanisms of insulin production and β-cell synchronization that regulate pulsatile insulin secretion, and we discuss the challenges to consider when measuring fast oscillatory secretion in vivo. These include the anatomical difficulties of measuring portal vein insulin noninvasively in humans before the hormone is extracted by the liver and quickly removed from the circulation. Peripheral concentrations of insulin or C-peptide, a peptide cosecreted with insulin, can be used to estimate their secretion profile, but mathematical deconvolution is required. Parametric and nonparametric approaches to the deconvolution problem are evaluated, alongside the assumptions and trade-offs required for their application in the quantification of unknown insulin secretory rates from known peripheral concentrations. Finally, we discuss the therapeutical implication of targeting impaired pulsatile secretion and its diagnostic value as an early indicator of β-cell stress.
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Affiliation(s)
- Marcello C. Laurenti
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN 55905, USA; (M.C.L.); (A.M.)
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN 55905, USA
| | - Aleksey Matveyenko
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN 55905, USA; (M.C.L.); (A.M.)
| | - Adrian Vella
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN 55905, USA; (M.C.L.); (A.M.)
- Correspondence: ; Tel.: +1-507-255-6515; Fax: +1-507-255-4828
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Scialla S, Loppini A, Patriarca M, Heinsalu E. Hubs, diversity, and synchronization in FitzHugh-Nagumo oscillator networks: Resonance effects and biophysical implications. Phys Rev E 2021; 103:052211. [PMID: 34134340 DOI: 10.1103/physreve.103.052211] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/03/2021] [Indexed: 11/06/2022]
Abstract
Using the FitzHugh-Nagumo equations to represent the oscillatory electrical behavior of β-cells, we develop a coupled oscillator network model with cubic lattice topology, showing that the emergence of pacemakers or hubs in the system can be viewed as a natural consequence of oscillator population diversity. The optimal hub to nonhub ratio is determined by the position of the diversity-induced resonance maximum for a given set of FitzHugh-Nagumo equation parameters and is predicted by the model to be in a range that is fully consistent with experimental observations. The model also suggests that hubs in a β-cell network should have the ability to "switch on" and "off" their pacemaker function. As a consequence, their relative amount in the population can vary in order to ensure an optimal oscillatory performance of the network in response to environmental changes, such as variations of an external stimulus.
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Affiliation(s)
- Stefano Scialla
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Á. del Portillo 21, 00128 Rome, Italy
| | - Alessandro Loppini
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Á. del Portillo 21, 00128 Rome, Italy
| | - Marco Patriarca
- National Institute of Chemical Physics and Biophysics, Rävala 10, Tallinn 15042, Estonia
| | - Els Heinsalu
- National Institute of Chemical Physics and Biophysics, Rävala 10, Tallinn 15042, Estonia
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Laurenti MC, Dalla Man C, Varghese RT, Andrews JC, Jones JG, Barosa C, Rizza RA, Matveyenko A, De Nicolao G, Bailey KR, Cobelli C, Vella A. Insulin Pulse Characteristics and Insulin Action in Non-diabetic Humans. J Clin Endocrinol Metab 2021; 106:1702-1709. [PMID: 33606017 PMCID: PMC8344841 DOI: 10.1210/clinem/dgab100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Pulsatile insulin secretion is impaired in diseases such as type 2 diabetes that are characterized by insulin resistance. This has led to the suggestion that changes in insulin pulsatility directly impair insulin signaling. We sought to examine the effects of pulse characteristics on insulin action in humans, hypothesizing that a decrease in pulse amplitude or frequency is associated with impaired hepatic insulin action. METHODS We studied 29 nondiabetic subjects on two occasions. On 1 occasion, hepatic and peripheral insulin action was measured using a euglycemic clamp. The deuterated water method was used to estimate the contribution of gluconeogenesis to endogenous glucose production. On a separate study day, we utilized nonparametric stochastic deconvolution of frequently sampled peripheral C-peptide concentrations during fasting to reconstruct portal insulin secretion. In addition to measuring basal and pulsatile insulin secretion, we used approximate entropy to measure orderliness and Fourier transform to measure the average, and the dispersion of, insulin pulse frequencies. RESULTS In univariate analysis, basal insulin secretion (R2 = 0.16) and insulin pulse amplitude (R2 = 0.09) correlated weakly with insulin-induced suppression of gluconeogenesis. However, after adjustment for age, sex, and weight, these associations were no longer significant. The other pulse characteristics also did not correlate with the ability of insulin to suppress endogenous glucose production (and gluconeogenesis) or to stimulate glucose disappearance. CONCLUSIONS Overall, our data demonstrate that insulin pulse characteristics, considered independently of other factors, do not correlate with measures of hepatic and peripheral insulin sensitivity in nondiabetic humans.
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Affiliation(s)
- Marcello C Laurenti
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN, USA
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Ron T Varghese
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN, USA
| | - James C Andrews
- Vascular and Interventional Radiology, Mayo Clinic, Rochester, MN, USA
| | - John G Jones
- Center for Neurosciences, University of Coimbra, Coimbra, Portugal
| | - Cristina Barosa
- Center for Neurosciences, University of Coimbra, Coimbra, Portugal
| | - Robert A Rizza
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN, USA
| | - Aleksey Matveyenko
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN, USA
- Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Giuseppe De Nicolao
- Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy
| | - Kent R Bailey
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Adrian Vella
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN, USA
- Correspondence: Adrian Vella MD, Endocrine Research Unit, Mayo Clinic College of Medicine, 200 First ST SW, 5–194 Joseph, Rochester, MN 55905, USA.
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