Effects of a 2-Week Kinect-Based Mixed-Reality Exercise Program on Prediabetes: A Pilot Trial during COVID-19.

The regulation of metabolic flux is essential for life, yet our understanding of it remains incomplete. The ability to determine the role of specific enzymes in regulating metabolic flux rapidly becomes a nearly overwhelming challenge due to the various interacting pathways, multiple enzyme isoforms, and many different substrates that are involved. This complexity is further compounded by the fact that the enzymes themselves are regulated by multiple mechanisms. Moreover, the amount of an enzyme in a particular cell may be modulated by a variety of transcriptional and post-transcriptional mechanisms. Thus, it has long been apparent that an understanding of how metabolic flux through specific pathways is regulated requires detailed measurements within the context of model systems. In mammals, the opposing processes of glycolysis and gluconeogenesis are both essential for glucose homeostasis. As previously reviewed by Granner and Pilkis (1Granner D. Pilkis S. J. Biol. Chem. 1990; 265: 10173-10176Abstract Full Text PDF PubMed Google Scholar), the regulation of three substrate cycles (glucokinase (GK)/glucose-6-phosphatase, pyruvate kinase/pyruvate carboxylase/phosphoenolpyruvate carboxykinase (PEPCK), 1The abbreviations used are: PEPCK, phosphoenolpyruvate carboxykinase; GK, glucokinase; KO, knock-out; CREB, cAMP response element-binding protein. and phosphofructokinase/fructose-1,6-bisphosphatase) play an important role in hepatic glucose metabolism. Of these, two have garnered most of the attention: GK and the cytosolic isoform of PEPCK. Both are widely thought to play key roles in determining flux through the glycolytic and gluconeogenic pathways, respectively, in the liver. Since a variety of genetically altered mouse models have been generated and characterized to determine the role of these two enzymes in regulating metabolic flux they now serve as excellent examples for highlighting the indispensable role that genetically manipulated mice have played in assessing metabolic flux control within the intact animal. Moreover, the findings obtained illustrate that surprises are still possible despite decades of previous studies and that additional studies are still required. In this Minireview we compare and contrast the findings obtained for both GK and PEPCK, because, by doing so, some important points about the concept of metabolic control are reinforced. GK and PEPCK are both expressed and regulated in a manner that reflects their vastly different functions. The two enzymes can be generally thought of as being expressed in a largely non-overlapping set of cell types, as is illustrated in Fig. 1. While they are co-expressed in the liver, periportal and perivenous hepatocytes possess different amounts of these and other glycolytic and gluconeogenic enzymes, and consequently different metabolic capacities, due to gradients in oxygen, substrate, hormone, and mediator levels, as well as different cell-to-cell interactions (2Jungermann K. Diabetes Metab. 1992; 18: 81-86PubMed Google Scholar, 3Jungermann K. Kietzmann T. Annu. Rev. Nutr. 1996; 16: 179-203Crossref PubMed Scopus (422) Google Scholar). Two kinetically similar but structurally distinct isoforms of GK are made due to alternate promoters in the GK gene and the alternate RNA processing (4Jetton T.L. Liang Y. Pettepher C.C. Zimmerman E.C. Cox F.G. Horvath K. Matschinsky F.M. Magnuson M.A. J. Biol. Chem. 1994; 269: 3641-3654Abstract Full Text PDF PubMed Google Scholar, 5Schuit F.C. Huypens P. Heimberg H. Pipeleers D.G. Diabetes. 2001; 50: 1-11Crossref PubMed Scopus (319) Google Scholar, 6Corbett J.A. Trends Endocrinol. Metab. 2001; 12: 140-142Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar). One isoform is made in the liver, whereas the other is found in the pancreatic islet, enteroendocrine cells of the gut, and in certain parts of the brain. Studies of the role of GK in determining glycolytic flux have been limited largely to the liver and pancreatic β-cell, where the enzyme is regulated in a vastly different manner. Besides the use of alternate cell type-specific promoters, the liver expresses a regulatory protein that binds GK and sequesters it in the nucleus under certain metabolic conditions (7de la Iglesia N. Mukhtar M. Seoane J. Guinovart J.J. Agius L. J. Biol. Chem. 2000; 275: 10597-10603Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar, 8Grimsby J. Coffey J.W. Dvorozniak M.T. Magram J. Li G. Matschinsky F.M. Shiota C. Kaur S. Magnuson M.A. Grippo J.F. J. Biol. Chem. 2000; 275: 7826-7831Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar). Due to an S0.5 for glucose of about 8 mm and the lack of significant end product inhibition by glucose 6-phosphate, GK functions to regulate intracellular glycolytic flux in a manner that parallels changes in the blood glucose concentration (9Matschinsky F.M. Diabetes. 2002; 51: S394-S404Crossref PubMed Google Scholar). In the case of PEPCK, there are two different genes that encode either a mitochondrial or cytoplasmic isoform of the enzyme (10Hanson R.W. Reshef L. Annu. Rev. Biochem. 1997; 66: 581-611Crossref PubMed Scopus (634) Google Scholar). The cytoplasmic PEPCK isoform is found in the proximal tubule of the kidney, adipose, and intestine, in addition to hepatocytes (10Hanson R.W. Reshef L. Annu. Rev. Biochem. 1997; 66: 581-611Crossref PubMed Scopus (634) Google Scholar). In both the liver and kidney this enzyme has long been thought to play a role in determining gluconeogenic flux, although the gene is regulated differently in these two sites (10Hanson R.W. Reshef L. Annu. Rev. Biochem. 1997; 66: 581-611Crossref PubMed Scopus (634) Google Scholar, 11Drewnowsk K.D. Craig M.R. Digiovanni S.R. McCarty J.M. Moorman A.F. Lamers W.H. Schoolwerth A.C. J. Physiol. Pharmacol. 2002; 53: 3-20PubMed Google Scholar). Intestinal PEPCK may also contribute to the endogenous glucose production during diabetes (12Rajas F. Croset M. Zitoun C. Montano S. Mithieux G. Diabetes. 2000; 49: 1165-1168Crossref PubMed Scopus (89) Google Scholar). In contrast, in adipose tissues, PEPCK expression is thought to play an important role in glyceroneogenesis (10Hanson R.W. Reshef L. Annu. Rev. Biochem. 1997; 66: 581-611Crossref PubMed Scopus (634) Google Scholar, 13Beale E.G. Hammer R.E. Antoine B. Forest C. FASEB J. 2002; 16: 1695-1696Crossref PubMed Scopus (55) Google Scholar). Indeed, transgene-directed overexpression of PEPCK in adipose tissue causes increased glyceroneogenesis, free fatty acid re-esterification, and adipose mass, without any changes in insulin sensitivity, findings that provide direct support for this notion (14Franckhauser S. Munoz S. Pujol A. Casellas A. Riu E. Otaegui P. Su B. Bosch F. Diabetes. 2002; 51: 624-630Crossref PubMed Scopus (180) Google Scholar). The physiological regulation of glucose homeostasis involves multiple tissue-tissue interactions, mediated by a variety of hormones, the nervous system, as well as changes in the concentrations of specific nutrients. For instance, glycolytic flux in the liver is stimulated by insulin and inhibited by glucagon, two hormones secreted by the pancreatic islet. The secretion of both of these hormones is modulated by sympathetic, parasympathetic, and sensory innervation of the pancreas (15Ahren B. Diabetologia. 2000; 43: 393-410Crossref PubMed Scopus (701) Google Scholar). Centers regulating the neuronal input into hormone secretion are thought largely to reside in the hypothalamus and brain stem, two other sites where GK is found. Moreover, incretins, such as glucagon-like peptide-1 or glucose-dependent insulinotropic polypeptide that affect insulin secretion are secreted by enteroendocrine cells of the gut, cells that are also known to express GK (16Doyle M.E. Egan J.M. Recent Prog. Horm. Res. 2001; 56: 377-399Crossref PubMed Scopus (89) Google Scholar, 17Drucker D.J. Endocrinology. 2001; 142: 521-527Crossref PubMed Scopus (310) Google Scholar, 18Pamir N. Lynn F.C. Buchan A.M. Ehses J. Hinke S.A. Pospisilik J.A. Miyawaki K. Yamada Y. Seino Y. McIntosh C.H. Pederson R.A. Am. J. Physiol. 2003; 284: E931-E939Crossref PubMed Scopus (103) Google Scholar). Thus, there exists a highly dispersed yet integrated system of GK-containing cells that interact with each other in a complex manner to maintain glucose homeostasis. Similarly, the regulation of gluconeogenic flux, which varies under different metabolic conditions, also depends on a variety of different tissue-tissue interactions. Although the liver, kidney, and intestine all contain the enzymatic machinery necessary to make glucose, changes in the hormonal milieu have a significant impact on where glucose is made, as well as the substrates that are used as precursors. For instance, the liver primarily utilizes lactate and alanine, and to a smaller extent glycerol, for gluconeogenesis. However, alanine is less important as a substrate in the kidney, with both glycerol and glutamine being more important for glucose production from this site (19Gerich J.E. Meyer C. Woerle H.J. Stumvoll M. Diabetes Care. 2001; 24: 382-391Crossref PubMed Scopus (457) Google Scholar, 20Cersosimo E. Garlick P. Ferretti J. Diabetes. 2000; 49: 1186-1193Crossref PubMed Scopus (80) Google Scholar). Moreover, the small intestine is beginning to be viewed as playing a substantial role during certain metabolic states. During the postabsorptive state systemic glucose release occurs primarily from the liver and kidney, with no glucose release from the small intestine (12Rajas F. Croset M. Zitoun C. Montano S. Mithieux G. Diabetes. 2000; 49: 1165-1168Crossref PubMed Scopus (89) Google Scholar, 19Gerich J.E. Meyer C. Woerle H.J. Stumvoll M. Diabetes Care. 2001; 24: 382-391Crossref PubMed Scopus (457) Google Scholar). However, glucose production by the small intestine rises to 25% of that made in the whole body during insulinopenic states, such as during a 48-h fast or in streptozotocin-induced diabetes (21Croset M. Rajas F. Zitoun C. Hurot J.M. Montano S. Mithieux G. Diabetes. 2001; 50: 740-746Crossref PubMed Scopus (160) Google Scholar). The relative contributions of the kidney and liver may also change during an extended fast, with renal gluconeogenesis becoming somewhat more important (22Ekberg K. Landau B.R. Wajngot A. Chandramouli V. Efendic S. Brunengraber H. Wahren J. Diabetes. 1999; 48: 292-298Crossref PubMed Scopus (198) Google Scholar). For many years a predominant view pertaining to the regulation of metabolic flux was the presence of a rate-limiting step at or near the start of a pathway. However, this view began to give way in the late 1970s to the concept of variable metabolic control strength. Fundamental to this concept was the idea that control over a metabolic pathway might be distributed over a number of enzymes in a pathway, and the flux control strength of each enzyme could be quantified by metabolic control analysis in an isolated system (23Kacser H. Burns J.A. Biochem. Soc. Trans. 1979; 7: 1149-1160Crossref PubMed Scopus (326) Google Scholar, 24Groen A.K. van Roermund C.W. Vervoorn R.C. Tager J.M. Biochem. J. 1986; 237: 379-389Crossref PubMed Scopus (146) Google Scholar). This concept has had significant impact on how the regulation of metabolic flux is both viewed and studied. However, as this concept evolved, it also became clear that the control exerted by any particular enzyme might also vary under different states and thus in some cases might be virtually impossible to determine without a detailed empirical analysis (25Rognstad R. J. Biol. Chem. 1979; 254: 1875-1878Abstract Full Text PDF PubMed Google Scholar). It is now well recognized that, only by being able to precisely alter the expression of an enzyme in a graded manner, can the role of specific enzymes in regulating metabolic flux in a pathway be accurately assessed. Even so, it remains a frequent practice to attempt to infer the function of an enzyme using knowledge of its kinetics, location, or regulation. However, there is no guarantee that such inferences are correct. Fortunately it is no longer necessary to simply guess given the increasing ease and precision for performing genetic manipulations in the mouse, especially given the ability both to overexpress enzymes in specific tissues and to eliminate their expression in selected sites via the Cre/loxP system (26Le Y. Sauer B. Methods Mol. Biol. 2000; 136: 477-485PubMed Google Scholar). Both overexpression and conditional gene knock-out (KO) strategies proved to be valuable for determining the effects changing the expression of GK on an important metabolic marker, the blood glucose concentration (27Niswender K.D. Postic C. Jetton T.L. Bennett B.D. Piston D.W. Efrat S. Magnuson M.A. J. Biol. Chem. 1997; 272: 22564-22569Abstract Full Text Full Text PDF PubMed Scopus (45) Google Scholar, 28Niswender K.D. Shiota M. Postic C. Cherrington A.D. Magnuson M.A. J. Biol. Chem. 1997; 272: 22570-22575Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar, 29Postic C. Shiota M. Niswender K.D. Jetton T.L. Chen Y. Moates J.M. Shelton K.D. Lindner J. Cherrington A.D. Magnuson M.A. J. Biol. Chem. 1999; 274: 305-315Abstract Full Text Full Text PDF PubMed Scopus (1032) Google Scholar). As shown in Fig. 2A, a 50% reduction in GK gene expression leads to pronounced hyperglycemia. The total elimination of GK is incompatible with life because pups die within a week of birth from the effects of uncontrolled hyperglycemia. In contrast, a 50% increase in the amount of GK causes hypoglycemia, and doubling the amount of GK (via introduction of two extra copies of the entire GK gene locus) lowers the blood glucose concentration even more. When plotted, as shown in Fig. 2A, a curvilinear relationship between GK gene expression and the blood glucose concentration becomes readily apparent. These data clearly illustrate that small changes in the expression of GK are sufficient to have a major impact on the plasma glucose concentration, although they do not reveal which tissues are involved or precisely how they each contribute to this relationship. To determine the role of GK in particular tissues and to further explore the functional relationship between tissues that express this enzyme, both liver- and pancreatic β-cell-specific GK-KO mice have been generated and characterized (29Postic C. Shiota M. Niswender K.D. Jetton T.L. Chen Y. Moates J.M. Shelton K.D. Lindner J. Cherrington A.D. Magnuson M.A. J. Biol. Chem. 1999; 274: 305-315Abstract Full Text Full Text PDF PubMed Scopus (1032) Google Scholar). Liver-specific GK-KO mice exhibit nearly a 40% increase in blood glucose levels without a simultaneous increase in the plasma insulin levels. Hyperglycemic clamp experiments revealed that, although the basal glucose turnover rates are nearly normal, the rate of new hepatic glycogen synthesis and glucose turnover in response to hyperglycemia is only 10 and 40% that of animals that make normal amounts of hepatic GK, respectively. The effect of glucose on several glucose-responsive genes was also abnormal, further demonstrating a key role for GK in carbohydrate-induced responses by the liver (30Towle H.C. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 13476-13478Crossref PubMed Scopus (38) Google Scholar). Although these functional impairments might have been predicted using in vitro systems, one observation simply could not have been anticipated. Namely, the liver-specific GK-KO mice exhibited profoundly impaired insulin secretion. Mice lacking hepatic GK did not exhibit any increase in their plasma insulin levels under basal condition, despite the 40% increase in blood glucose levels, and the increase in insulin secretion in response to hyperglycemia was only 30% of normal. These a between rates of hepatic glycolysis and insulin secretion that is not The hyperglycemia that occurs in these mice be sufficient to insulin secretion because it has also been shown that small but changes in the blood glucose concentration have effects on insulin secretion H. S. A. Diabetes. 2001; 50: PubMed Google Scholar, A. J. S. J. Biol. Chem. 2002; Full Text Full Text PDF PubMed Scopus Google Scholar). performing a pancreatic β-cell-specific of GK different was it was that mice without any GK in die birth of Thus, this clearly that GK a role in regulating metabolic flux in Although this was long thought to be the this and a in isolated no for (29Postic C. Shiota M. Niswender K.D. Jetton T.L. Chen Y. Moates J.M. Shelton K.D. Lindner J. Cherrington A.D. Magnuson M.A. J. Biol. Chem. 1999; 274: 305-315Abstract Full Text Full Text PDF PubMed Scopus (1032) Google Scholar, D.W. Postic C. Shelton K.D. Magnuson M.A. J. Biol. Chem. 1999; 274: Full Text Full Text PDF PubMed Scopus Google Scholar). Mice that lack one functional of GK only in the exhibit a metabolic similar to that of the liver-specific GK-KO hyperglycemia and normal basal glucose turnover but more they exhibit impaired glucose turnover and insulin secretion during hyperglycemia due to the between GK in and insulin secretion (9Matschinsky F.M. Diabetes. 2002; 51: S394-S404Crossref PubMed Google Scholar, F.M. Diabetes. 1996; PubMed Scopus Google Scholar, F.M. B. Magnuson M.A. Diabetes. PubMed Scopus Google Scholar). Mice that make an increased amount of GK are to the of both hyperglycemia and with the of a M. Postic C. Y. Jetton T.L. K. D. J. Grippo J.F. Magnuson M.A. Cherrington A.D. Diabetes. 2001; 50: PubMed Scopus Google Scholar). This to of is thought to be due to increased hepatic GK which causes increased hepatic glucose via increased hepatic glucose and increased glycogen in response to hyperglycemia K.D. Shiota M. Postic C. Cherrington A.D. Magnuson M.A. J. Biol. Chem. 1997; 272: 22570-22575Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar). T. Riu E. Bosch F. A. FASEB J. 1996; PubMed Scopus Google have also that the streptozotocin-induced increase in the concentrations of glucose, and free fatty is smaller in GK between GK and glycolysis is clearly as a of the genetic studies that have to the of over of GK and serve as a for assessing the control strength of that the function of GK a in known as diabetes of the P. L. E. A. Niswender K. Magnuson M.A. Matschinsky F.M. Biochem. J. 1997; PubMed Scopus (45) Google Scholar, A. M. C. Moates M. Magnuson M.A. Matschinsky F.M. Diabetologia. 1999; PubMed Scopus Google Scholar). several have been that increase the of the enzyme and that are with the of of S. C. A. T. K. Magnuson M.A. Shiota C. Matschinsky F.M. F. Diabetes. 2002; 51: PubMed Scopus Google Scholar, B. P. M. E. A. A. M.A. Matschinsky F.M. N. J. PubMed Scopus Google Scholar). Thus, genetic studies in with analysis of the the experiments in mice with both of clearly that the changes in the of GK changes in the blood glucose concentration, an effect due to the metabolic control strength that GK on glycolysis in cells that express the The metabolic effects of the amount of PEPCK in mice have also been As for GK, the of several have been important in being able to the impact of changes in the expression of the gene and its role in determining metabolic flux and in the blood glucose concentration A. Pujol A. M. Bosch F. Proc. Natl. Acad. Sci. U. S. A. 1994; PubMed Scopus Google generated mice that overexpress a PEPCK via a of PEPCK that leads to increased expression in all normal sites of expression adipose In these PEPCK gene expression in the liver was and the blood glucose concentration during was 50% normal. Y. S. S. L. P. J.E. J. Biol. Chem. 2002; Full Text Full Text PDF PubMed Scopus Google made a mouse, also under control of a in which hepatic PEPCK gene expression in the liver was about that of The blood glucose concentration a fast in these animals was P. Shiota M. Shelton K.D. R. Postic C. Magnuson M.A. Mol. Biol. 2000; PubMed Scopus Google three distinct that made by gene using a Cre/loxP One was was normal but could be to via whereas the was mice these different animals with in PEPCK gene expression from to of normal to the analysis for GK, the blood glucose concentration in mice with variable amounts of PEPCK gene expression to was and is shown in Fig. This is on data from animals for the PEPCK which was at 30% because this reflects the blood glucose concentration in pups to P. Shiota M. Shelton K.D. R. Postic C. Magnuson M.A. Mol. Biol. 2000; PubMed Scopus Google found that mice with a and reduction in PEPCK gene expression and a normal blood glucose concentration a The points at and PEPCK are on the of Y. S. S. L. P. J.E. J. Biol. Chem. 2002; Full Text Full Text PDF PubMed Scopus Google and A. Pujol A. M. Bosch F. Proc. Natl. Acad. Sci. U. S. A. 1994; PubMed Scopus Google Scholar). When the of all three different are it is clear that the relationship between PEPCK gene expression and the blood glucose concentration is different from the curvilinear relationship for the expression of GK and the blood glucose there is a through the of GK the through the PEPCK is These clearly that PEPCK is essential for life but also that only a small amount of PEPCK is necessary for a normal blood glucose concentration in an animal. Indeed, the blood glucose concentration is by a of changes in PEPCK gene Even mice with a reduction in PEPCK are not only but they have nearly normal blood glucose concentrations a Although these findings to that PEPCK control over the blood glucose concentration, at the of expression or the normal they be with in PEPCK may not accurately changes in PEPCK and multiple may be there is an amount of PEPCK that serve to the from the of P. Shiota M. Shelton K.D. R. Postic C. Magnuson M.A. Mol. Biol. 2000; PubMed Scopus Google Scholar, P. Shiota M. P. A.D. Magnuson M.A. Diabetes. 2003; PubMed Scopus Google Scholar). Thus, the plasma glucose concentration may be a of metabolic that in the of in PEPCK gene For instance, changes in the expression of other genes in the liver and the rate of hepatic glucose production in mice with only a increase in hepatic PEPCK gene expression Y. S. S. L. P. J.E. J. Biol. Chem. 2002; Full Text Full Text PDF PubMed Scopus Google Scholar). These mice had a increase in both their plasma insulin concentration and impaired glucose with insulin Y. S. S. L. P. J.E. J. Biol. Chem. 2002; Full Text Full Text PDF PubMed Scopus Google Scholar). metabolic of these animals revealed increased rates of hepatic glucose production but normal glucose during clamp Thus, a increase in hepatic PEPCK may be to insulin with Y. S. S. L. P. J.E. J. Biol. Chem. 2002; Full Text Full Text PDF PubMed Scopus Google there was a in the amount of both insulin and in the liver. Mice that lack PEPCK only in the liver are also able to maintain although at the of hepatic a that also both the notion of the role of the liver in gluconeogenesis and the role of PEPCK in this In a of the liver-specific P. Shiota M. P. A.D. Magnuson M.A. Diabetes. 2003; PubMed Scopus Google have that from animals that lack hepatic PEPCK do not glucose from either lactate or This that the mitochondrial isoform of PEPCK, which is still in these not play any role in determining flux, which not be given that this isoform for only of in the liver of using both and metabolic P. Shiota M. P. A.D. Magnuson M.A. Diabetes. 2003; PubMed Scopus Google have also shown that mice lacking PEPCK in the liver have impaired gluconeogenesis from but not from glycerol, and that there is a increase in acid even pyruvate and is experiments using and that total body gluconeogenic flux from phosphoenolpyruvate is still of that in a normal mouse, despite the of hepatic PEPCK. These that tissues may be of the in gluconeogenesis by more the amount of As both the kidney and intestine, two other sites of PEPCK gene may contribute to systemic glucose production during certain metabolic states (12Rajas F. Croset M. Zitoun C. Montano S. Mithieux G. Diabetes. 2000; 49: 1165-1168Crossref PubMed Scopus (89) Google Scholar, 19Gerich J.E. Meyer C. Woerle H.J. Stumvoll M. Diabetes Care. 2001; 24: 382-391Crossref PubMed Scopus (457) Google Scholar). Indeed, renal gluconeogenesis has long been known to be sufficient to maintain nearly normal blood glucose levels in Proc. Natl. Acad. Sci. U. S. A. PubMed Scopus Google Scholar). it has been shown that, in endogenous glucose production only by about 50% of the liver T.L. N. A. B. J. 2002; Full Text Full Text PDF PubMed Scopus Google Scholar, N. D. A. M.A. S.A. Diabetes. 2000; 49: PubMed Scopus Google Scholar). 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GK near total control over glycolysis in tissues that express the enzyme, the is different for PEPCK, because changes in PEPCK gene expression within mice do not However, as as these data may it remains important to view it with some because a analysis of gluconeogenic flux in animals with different amounts of PEPCK has not yet been the obtained to are on their to that PEPCK determine gluconeogenic it is that control is distributed other enzymes in this pathway. it has been shown that a that gene expression by to is in the liver by and by insulin overexpression of in via use of a causes increased hepatic glucose production P. Chen G. J. J. G. J. Granner 2001; PubMed Scopus Google Scholar). PEPCK gene also leads to an increase in and two other key enzymes in the gluconeogenic pathway. Moreover, mice that a of the cAMP response element-binding protein gene or that express a exhibited both and a in pyruvate and PEPCK S. F. S. R. A. D. G. C. P. B. M. 2001; PubMed Scopus Google Scholar). These studies provide both additional support and a the control of gluconeogenesis may the regulation of a set of Thus, the control of gluconeogenic flux be a more with the findings of have this in the R.C. Annu. Rev. Nutr. 1999; PubMed Scopus Google Scholar, Annu. Rev. Nutr. PubMed Scopus Google Scholar, N. G. J. 1999; PubMed Scopus Google Scholar, M.R. PubMed Scopus Google Scholar). This is also with metabolic control that a the regulation of metabolic flux can be distributed over multiple enzymatic It is that the glucose homeostasis in the mouse are similar to in the and that genetic can now be readily that the mouse to serve as a model system for assessing the role of specific enzymes in determining metabolic use of conditional gene strategies that make use of as well as other In the overexpression of enzymes via the of mice by to because, as by the studies we have it is to be able to increase the expression of specific enzymes as to from

Lack of relationship between plasma insulin and glucagon levels and angiographically-documented coronary atherosclerosis.

Immune Health Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305 Australia.Cardiovascular disease (CVD) remains a major cause of global mortality. Poor diet quality, characterised by excessive consumption of energy-dense, nutrient-poor foods and insufficient intake of fruits, vegetables, and whole grains, is associated with an increased risk of CVD(1). This study compares the impact of two short-term dietary interventions, a Healthy Australian Diet adhering to national guidelines and a Typical Australian Diet representing current national consumption patterns, on several cardiometabolic outcomes. These outcomes include body weight, waist circumference (WC), body fat percentage (BFP), blood pressure (BP), fasting blood lipids and glucose concentrations. Data from an eight-week randomised, cross-over feeding study involving 34 adults (53% female, age 38.4 ± 18.1 years) were analysed, with participants randomly assigned to consume each diet for two weeks, separated by a two-week washout period. During each feeding phase, all food items were provided to ensure compliance. The Healthy Australian Diet adhered to the Australian Dietary Guidelines(2), including a balanced intake of the five food groups and meeting Acceptable Macronutrient Distribution Range targets(3), with saturated fat limited to ≤10% of energy. The Typical Australian Diet was formulated based on apparent consumption patterns in Australia(4), setting total fat intake at 40% of energy and total saturated fat at 15% of energy. Comprehensive data collection occurred at four key visits: week 0 (end of run-in; baseline 1), week 2 (post-feeding phase 1), week 4 (end of washout, baseline 2), and week 8 (post-feeding phase 2). Trained personnel measured WC using a tensible tape, while body weight and BFP measurements were obtained using bioimpedance analysis (Inbody 270; Biospace Co, Seoul, Korea). Blood pressure was recorded using the Uscom BP+ supra-systolic oscillometric central blood pressure device. Blood glucose and lipid (triglycerides, total-, low-density lipoprotein- [LDL] and high-density lipoprotein- [HDL] cholesterol) concentrations were measured after a 12-hour fast by an accredited pathology service. Differential changes in cardiometabolic variables between intervention groups were evaluated using linear mixed-effect models, adjusting for diet sequence, feeding phase, and subject ID as a random variable to account for potential autocorrelation. Post-hoc pairwise comparisons were conducted to assess the impact effects of each diet. There were no significant differences between the Healthy Australian Diet and the Typical Australian Diet with respect to weight, BFP, WC, blood triglycerides, systolic and diastolic BP. However, the Healthy Australian Diet led to a significantly greater decrease in total-, LDL-, HDL- and non-HDL cholesterol, and fasting blood glucose relative to the Typical Australian Diet (p<0.001). The results underscore the importance of promoting dietary intakes that align with Australian Dietary Guidelines for optimising the risk of CVD and impaired glucose tolerance. Conversely, the Typical Australian Dietary pattern demonstrated detrimental cardiometabolic effects over a short period of just two weeks.

Blood glucose concentrations are recognized to vary during hemodialysis (HD), with hypoglycemia reported with glucose-free dialysates. As glucose can be converted to lactate, and conversely lactate to glucose, we wished to study factors associated with peri-dialytic changes in blood glucose. We prospectively collected data including patient profile, dialysis prescription, hemodynamic parameters, medications, dialysis adequacy and monthly blood tests for three consecutive months. All patients used a 100mg/dl glucose dialysate. Linear mixed model, general estimated equation and binary logistic regression were used for analysis. We studied 157 sessions in 55 patients, median age 67.1 (58.5-72.6) years, 67% male, 71% diabetic, 40% prescribed insulin, dialysis vintage 20.4 (10.7-57.7) months. Mean single pool Kt/Vurea and normalized protein nitrogen appearance rate (nPNA) were 1.70±0.34 and 1.01±0.30g/kg/day respectively. Hypoglycemia (<70mg/dl) occurred during 10 sessions (6.4%). 25% of non-diabetes experienced hypoglycemia. The % change in peri-dialytic blood glucose was associated with the % change in lactate (estimate of fixed effect=0.23 p<0.001) and pre-HD glucose (estimate of fixed effect=0.09, p<0.001). The fall in glucose was not associated with urea clearance, consumption of food, administration of insulin or antidiabetic medications, nPNA, body mass index, or pyridoxine concentrations. Peri-dialytic hypoglycemia cannot simply be explained by dialyzer clearance, as the corresponding fall in lactate would potentially suggest increased gluconeogenesis. Despite using a glucose containing dialysate, asymptomatic hypoglycemia occurred in 6.4% of sessions, suggesting a role for peri-dialytic blood glucose monitoring and avoiding fasting during dialysis.

To evaluate day-to-day variability in blood glucose curves (BGCs) generated at home and at the clinic for cats with diabetes mellitus. Prospective study. 7 cats with diabetes mellitus. Procedures-BGCs generated at home on 2 consecutive days and within 1 week at the clinic were obtained twice. On each occasion, insulin dose, amount of food, and type of food were consistent for all 3 BGCs. Results of curves generated at home were compared with each other and with the corresponding clinic curve. Differences between blood glucose concentration determined after food was withheld (fasting), nadir concentration, time to nadir concentration, maximum concentration, and mean concentration during 12 hours had high coefficients of variation, as did the difference between fasting blood glucose and nadir concentrations and area under the curve of home curves. Differences between home curve variables were not smaller than those between home and clinic curves, indicating large day-to-day variability in both home and clinic curves. Evaluation of the paired home curves led to the same theoretical recommendation for adjustment of insulin dose on 6 of 14 occasions, and evaluation of home and clinic curves resulted in the same recommendation on 14 of 28 occasions. Four of the 6 paired home curves in cats with good glycemic control and 2 of the 8 paired home curves in cats with poor glycemic control led to the same recommendation. Considerable day-to-day variability was detected in BGCs generated at home. Cats with good glycemic control may have more reproducible curves generated during blood collection at home than cats with poorer control.

Variability in metabolic parameters, such as fasting blood glucose and cholesterol concentrations, blood pressure, and body weight can affect health outcomes. We investigated whether variability in these metabolic parameters has additive effects on the risk of mortality and cardiovascular outcomes in the general population. Using nationally representative data from the Korean National Health Insurance System, 6 748 773 people who were free of diabetes mellitus, hypertension, and dyslipidemia and who underwent ≥3 health examinations from 2005 to 2012 were followed to the end of 2015. Variability in fasting blood glucose and total cholesterol concentrations, systolic blood pressure, and body mass index was measured using the coefficient of variation, SD, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability. Participants were classified numerically according to the number of high-variability parameters (eg, a score of 4 indicated high variability in all 4 metabolic parameters). Cox proportional hazards models adjusting for age, sex, smoking, alcohol, regular exercise, income, and baseline levels of fasting blood glucose, systolic blood pressure, total cholesterol, and body mass index were used. There were 54 785 deaths (0.8%), 22 498 cases of stroke (0.3%), and 21 452 myocardial infarctions (0.3%) during a median follow-up of 5.5 years. High variability in each metabolic parameter was associated with a higher risk for all-cause mortality, myocardial infarction, and stroke. Furthermore, the risk of outcomes increased significantly with the number of high-variability metabolic parameters. In the multivariable-adjusted model comparing a score of 0 versus 4, the hazard ratios (95% CIs) were 2.27 (2.13-2.42) for all-cause mortality, 1.43 (1.25-1.64) for myocardial infarction, and 1.41 (1.25-1.60) for stroke. Similar results were obtained when modeling the variability using the SD, variability independent of the mean, and average real variability, and in various sensitivity analyses. High variability of fasting blood glucose and total cholesterol levels, systolic blood pressure, and body mass index was an independent predictor of mortality and cardiovascular events. There was a graded association between the number of high-variability parameters and cardiovascular outcomes.

Psyllium Lowers Blood Glucose and Insulin Concentrations in Horses

The Contribution of Physical Activity in Blood Glucose Concentration for People with Type 1 Diabetes

Abstract. Nilsson T, Svensson A, Lapidus L, Lindstedt G, Nyström E, Eggertsen R (Mölnlycke Primary Health Care and Research Centre; Göteborg University, Göteborg, Sweden). The relations of microalbuminuria to ambulatory blood pressure and myocardial wall thickness in a population. J Intern Med 1998; 244: 55–9.ObjectivesTo examine the relationship between microalbuminuria (20–200 μg min−1) and 24 h ambulatory blood pressure and heart wall thickness, in a representative population sample of men and women aged 56–65 years.DesignEvery second individual aged 56–65 years (n= 488) in the district, was invited for a health examination, which included determination of urinary albumin and creatinine (overnight sample). The highest and lowest decentile of urinary albumin/creatinine ratio were compared.SettingThe district of the Primary Health Care and Research Centre of Mölnlycke, Sweden.SubjectsAfter excluding 2 individuals with a urinary albumin excretion exceeding 200 μg min−1, 26 subjects (group 1) could be compared with 27 subjects in the lowest decentile (group 2).Main outcome measuresComparison between the determinations of the ambulatory blood pressure and echocardiographic variables in the two groups.ResultsGroup 1 had significantly higher 24 104 ambulatory blood pressure, and heart septum and posterior wall thickness as well as significantly higher fasting blood glucose and serum triglyceride concentrations. The differences in blood pressure (P &amp;lt; 0.05) but not heart wall thickness remained significant (P &amp;lt; 0.05) after excluding subjects with hypertension, angina pectoris, treated diabetes mellitus, and/or history of heart or cerebrovascular disease. When excluding individuals with both treated and untreated diabetes mellitus, fasting blood glucose concentration was higher in group 1. The waist‐hip ratio, weight and body mass index did not differ between the groups.ConclusionsThe findings indicate that microalbuminuria is related to signs of cardiovascular and metabolic influence and therefore could be a valuable tool for grading the risk of later cardiovascular morbidity.

Blood glucose concentrations are measured in diabetes to detect hyper- and hypo-glycaemia. Health care professionals need this information to diagnose diabetes, or states of impaired glucose tolerance, to adjust therapy and correct hyper- and hypo-glycaemia in established diabetes, to interpret signs and symptoms in patients (e.g. is confusion due to hypoglycaemia or another cause?), and to assess the risk of tissue complications developing in the future (the severity and duration of hyperglycaemia is clearly related to microvascular disease). The patient with diabetes measures blood glucose concentrations to take corrective action with food and insulin, to maintain good control, to check the safety of everyday activities (e.g. not driving when hypoglycaemic), to assess the impact of events and lifestyle and on control (exercise, diet, illness, psychological stress), and to ensure a good quality of life and the ‘peace of mind’ that knowledge of the blood glucose concentration gives. Glucose monitoring has traditionally been performed by intermittent sampling of blood glucose concentrations, either in hospital or by the patient testing their own blood glucose concentrations at home using finger-prick capillary blood samples applied to reagent strips and inserted into portable glucose meters – self-monitoring of blood glucose (SMBG). In addition, in the last decade or so, continuous glucose monitoring (CGM) has entered clinical practice as a supplement to SMBG, albeit with limited uptake at present. CGM is based on the implantation of needle-type glucose sensors, or microdialysis probes, into the subcutaneous tissue for measurement of interstitial glucose concentrations.

Barley mixed rice, "Mugi gohan," is traditionally eaten with yam paste in Japan. Both ingredients contain dietary fiber and reportedly reduce postprandial hyperglycemia. However, evidence supporting the benefits of combining barley mixed rice with yam paste is limited. In this study, we evaluated whether ingesting a combination of barley mixed rice and yam paste affected postprandial blood glucose concentration and insulin secretion. This study followed an open-label, randomized controlled crossover design, following the unified protocol of the Japanese Association for the Study of Glycemic Index. Fourteen healthy subjects each consumed four different test meals: white rice only, white rice with yam paste, barley mixed rice, and barley mixed rice with yam paste. We measured their postprandial blood glucose and insulin concentrations after every meal, and we calculated the area under curve for glucose and insulin. Participants had significantly reduced area under curve for glucose and insulin after eating barley mixed rice with yam paste compared to when they ate white rice only. Participants had similar area under curve for glucose and insulin after eating barley mixed rice only, or eating white rice with yam paste. Participants had lower blood glucose concentrations 15 min after eating barley mixed rice only, whilst eating white rice with yam paste did not maintain lower blood glucose after 15 min. Eating barley mixed rice with yam paste decreases postprandial blood glucose concentrations and reduces insulin secretion.

Management of Tight Intraoperative Glycemic Control During Off-Pump Coronary Artery Bypass Surgery in Diabetic and Nondiabetic Patients

The glucoregulatory role of insulin for adult subjects is undisputed. However, less is known about its secretion and actions in the neonatal period, either in healthy subjects, or in those at risk of disordered blood glucose homeostasis. We studied the relationships between blood glucose and plasma insulin concentrations in 52 healthy children, 67 appropriate birthweight for gestational age (AGA) term infants, 39 AGA preterm infants, and 59 infants who were hypoglycaemic or hyperglycaemic. In addition, 26 babies from the latter group underwent glucose turnover studies. In children and AGA infants, plasma insulin concentration was positively related to blood glucose concentration (Spearman rho, P: Children, 0.58, <0.001; AGA term, 0.31 <0.01; AGA preterm, 0.44, <0.01). However, at equivalent glucose concentrations, there was greater variation in insulin concentration and the median insulin concentration was higher in AGA infants than for children. In hypoglycaemic and hyperglycaemic infants, there was no relationship between blood glucose and plasma insulin levels, and, in turn, glucose production rates were not related to plasma insulin levels. These data suggest that, compared to older subjects, neonatal pancreatic insulin secretion is less closely linked to circulating blood glucose concentrations, and that insulin does not control glucose production rates in infants with disorders of blood glucose homeostasis.

Introduction: In developing countries more than 90% infants are born with low birth weight (LBW). In Bangladesh the incidence is about 30%. Newborn baby has got a definite difference in metabolic profile from the adult; this difference is much more pronounced in case of LBW newborn. Infants born prematurely or following intrauterine malnutrition may develop abnormally low plasma glucose concentration for a prolonged period as a result of the failure to adapt a metabolic and endocrine response.Glucose is an essential nutrient for the brain. Abnormally low levels (hypoglycaemia) can cause encephalopathy and have the potential to produce long-term neurological injury. Adequate knowledge about neonatal physiology is important in the management of LBW neonates in the neonatal nursery specially in the first 48 hours of life as most of the neonatal deaths occur in this critical period of time. Objectives: To evaluate the clinical features of hypoglycaemic low birth weight (LBW) neonates in the first 48 hours of life. Methods: This cross-sectional study was conducted on 56 LBW neonates in the inpatient departments of Paediatrics and Obstetrics of Combined Military Hospital (CMH), Dhaka between 15th September 2010 to 14th March 2011. LBW (Weight &lt;2500gm) neonates, both term and preterm, from birth to 48 hours of age and who were well at the time of blood collection were included in this study. After enrollment of the newborn, with all aseptic precaution blood samples were taken from cord (as basal) at birth, from venous blood at 2 hours and then from capillary blood at 4 hours, in between 12-24 hours and in between 24-48 hours by heel prick from each case . Cord and capillary blood glucose were measured by using “Accu-Check-Active”- glucometer. Blood glucose concentrations less than 2.6 mmol/L irrespective of weight and gestational age were considered as low for the purpose of analysis. Results: It was revealed from the study that the mean birth weight was 1.77 gm mean gestational age was 36.09 weeks. 30 ( 53.57% ) were intrauterine growth retarded (IUGR) and 26 (46.43% ) were appropriate for gestational age (AGA). Out of thirty IUGR, 24 (51.79%) infants had low blood glucose (&lt;2.6 mmol/L) levels. Twenty-two (75.86%) of 29 low birth weight infants showed low blood glucose at one of the five time points, 07 (17.07%) had low values on two hypoglycemia at 12-24 hours of age.The mean blood glucose concentration in LBW infants were 4.03 mmol/L at birth (cord), 2.33mmol/L at 2 hours, 2.96mmol/L, at 4 hours 3.14mmol/L, at 12—24hours and 3.43mmol/ L at 24—48hours of age. In the first 48 hours, the lowest blood glucose (mean) concentrations were found at 2 hours after delivery.Hypoglycaemia was most common among the preterm intrauterine growth retarded (IUGR) neonates at 2 hours after delivery. Most 23 (79.31%) of the hypoglycaemic infants were asymptomatic and 6 (20.69%) had symptoms.Early feeding prevents hypoglycaemia and appears to influence subsequent glucose values. Among the manifestations jitteriness, hyperalert, tachypnoea, high pitched cry, lethargy, apnoea and poor feeding were observed. Conclusion: The study findings may be helpful in the management of LBW neonates with hypoglycaemia and will encourage the mother in initiation of feeding immediately after delivery. DOI: http://dx.doi.org/10.3329/jafmc.v9i1.18726 Journal of Armed Forces Medical College Bangladesh Vol.9(1) 2013: 49-56

Blood glucose monitoring is an important part and means in the process of diabetes treatment and management. There is a high correlation between glucose concentration in interstitial fluid and glucose concentration in blood. Real-time monitoring of glucose concentration in interstitial fluid has important clinical significance. Continuous glucose monitoring (CGM) is an emerging glucose monitoring technology, which indirectly reflects the blood glucose level of the body through the glucose level in the interstitial fluid, providing continuous and comprehensive information and rules of blood glucose variation over a period of time. There are three main categories of CGM: minimally invasive implantation, non-invasive microtransparency and non-invasive technology. Noninvasive blood glucose monitoring technology is the future development direction, but accuracy and delay of blood glucose will be the biggest challenge to be overcome in clinical application. Key words: Blood glucose monitoring; Continuous glucose monitoring; Noninvasive glucose monitoring