scholarly journals Impact of Hyperglycemia on the Renin Angiotensin System in Early Human Type 1 Diabetes Mellitus

1999 ◽  
Vol 10 (8) ◽  
pp. 1778-1785
Author(s):  
JUDITH A. MILLER

Abstract. It has been demonstrated previously that moderate hyperglycemia without glucosuria can increase plasma renin activity and mean arterial pressure in young healthy males with early uncomplicated type 1 diabetes mellitus. This study was conducted to extend these observations by testing the hypothesis that mild to moderate hyperglycemia can affect renal function by increasing renin angiotensin system (RAS) activity in diabetic humans. The study included 10 men and women with early, uncomplicated type 1 diabetes (duration <5 yr), all ingesting a controlled sodium and protein diet. They were studied on four separate occasions, during a subdepressor dose of the angiotensin II (AngII) receptor blocker losartan, and during graded AngII infusion, 1.5 and 2.5 ng/kg per min, while euglycemic (blood glucose 4 to 6 mmol/L) and again while hyperglycemic without glucosuria (blood glucose 9 to 11 mmol/L), according to a randomized crossover design. Outcome measures included mean arterial pressure (MAP), GFR, effective renal plasma flow (ERPF), renal vascular resistance (RVR), filtration fraction (FF), and urine sodium excretion (UNaV) at baseline and in response to the above maneuvers. During hyperglycemic conditions, MAP was significantly higher compared with euglycemia, as were RVR and FF. After the administration of losartan, a significant renal and peripheral depressor effect was noted, with decreases in MAP, RVR, and FF, whereas during euglycemia the responses to losartan were minimal. AngII infusion resulted in elevations in MAP, RVR, and FF and a decline in UNaV during both glycemic phases, but the responses during hyperglycemia, most significantly at the 1.5 ng/kg per min infusion rate, were blunted. These data support the hypothesis that hyperglycemia affects renal function by activating the RAS. The mechanism remains obscure, but these contrasting responses may provide a link between the observations that maintenance of euglycemia and blockade of the RAS prevent or delay diabetic kidney disease, and furthermore, may clarify the mechanism whereby high glucose promotes renal disease progression in diabetes.

2011 ◽  
Vol 29 ◽  
pp. e377-e378
Author(s):  
L. Morais ◽  
I. Watanabe ◽  
M. Franco ◽  
D. Arita ◽  
M. Gabbay ◽  
...  

2003 ◽  
Vol 63 (1) ◽  
pp. 172-178 ◽  
Author(s):  
Norman K. Hollenberg ◽  
Deborah A. Price ◽  
Naomi D.L. Fisher ◽  
M. Cecilia Lansang ◽  
Bruce Perkins ◽  
...  

Diabetologia ◽  
2019 ◽  
Vol 62 (6) ◽  
pp. 1090-1093 ◽  
Author(s):  
Chantal Kopecky ◽  
Yuliya Lytvyn ◽  
Oliver Domenig ◽  
Marlies Antlanger ◽  
Johannes J. Kovarik ◽  
...  

2013 ◽  
Vol 84 (6) ◽  
pp. 1246-1253 ◽  
Author(s):  
David Z.I. Cherney ◽  
Bernard Zinman ◽  
Christopher R.J. Kennedy ◽  
Rahim Moineddin ◽  
Vesta Lai ◽  
...  

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).


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