http://informahealthcare.com/arp ISSN: 1381-3455 (print), 1744-4160 (electronic) Arch Physiol Biochem, 2014; 120(3): 91–98 ! 2014 Informa UK Ltd. DOI: 10.3109/13813455.2014.911330 ORIGINAL ARTICLE Novel individual metabolic profile characterizes the protein kinase B-alpha (pkba / ) in vivo model Claudia Eberle 1,2 , Markus Niessen 1 , Brian A. Hemmings 3 , Oliver Tschopp 1 , and Christoph Ament 4 1 Universita ¨tsSpital Zu ¨rich, Abteilung fu ¨r Endokrinologie, Diabetologie & Klin. Erna ¨hrung, 8091 Zu ¨rich, Switzerland, 2 Hochschule Fulda – University of Applied Science, 36039 Fulda, Germany, 3 Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland, and 4 Institute for Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany Abstract Context: Type 2 diabetes and associated co-morbidities run epidemic waves worldwide. Since pathophysiological constellations are individual and display a wide spread of dysmetabolic profiles personalized health care assessments start to emerge. Therefore, we established a specific in silico assessment tool targeting metabolic characterizations individually. Materials and methods: Values obtained from oral glucose and intraperitoneal insulin tolerance tests performed on pkb / mice (KO) as well as age- and gender-matched controls (WT) were analysed using our established in silico model. Results: Generally, male pkb / mice (KO) presented significantly increased insulin sensitivity at an age of 6 months compared with age-matched WTs (p ¼ 0.036). Female KO and WT groups displayed improved glucose sensitivities compared with age-matched males (for WT p  0.011). Discussion and conclusion: Specific metabolic characterization should be assessed individually. Therefore, our in silico model enables novel insights inaugurating specific primary preventive strategies targeting individual metabolic profiling precisely. Keywords Diabetes, individualized medicine, in silico modelling, metabolic profiling, protein kinase B-alpha (pkb) History Received 23 December 2013 Revised 6 March 2014 Accepted 28 March 2014 Published online 28 April 2014 Introduction Three hundred and forty seven million people have been diagnosed with diabetes worldwide (Danaei et al., 2011) – the estimated number of unreported cases is approximately still higher globally. However, pathophysiological features of diabetes and related metabolic as well as cardiovascular co-morbidities vary between individuals and hence disease progression is unique in each case. Furthermore, genetic as well as environmental factors, such as life style and nutritional behaviour and pharmacological treatment, do clearly influ- ence further progresses of metabolic as well as associated diseases and their underlying pathogenic mechanisms altering metabolic outcomes individually (Smith et al., 2010). From a clinical point of view a broad variety of pathophysiological metabolic outcomes exist. As a conse- quence, we urgently need to develop personalized health care assessments addressing individual diagnostic as well as therapeutic strategies (Eberle et al., 2012b; Eberle & Ament, 2012c). In order to provide a basis for individualized metabolic profiling, we have previously presented an in silico model as a unifying framework, which allows to derive dynamic metabolic profiles on an individual basis thereby enabling preventive diagnostic as well as therapeutic assessments (Eberle et al., 2013). Multiple individual data can be integrated as, for example, measurements from oral glucose tolerance tests (OGTT) and intraperitoneal insulin tolerance tests (IPITT), respectively. Model outcome is a set of diagnostic parameters that specifically reflect the personal metabolic profile. This allows defining personalized strategies to target T2D as well as associated metabolic co-morbidities early on with individual risk assessments and tailored diagnostic as well as therapeutic strategies. The in silico model distinguishes between altered insulin concentrations leading to modified glucose concentrations; for example, due to receptor-dependent defects (insulin sensitivity k X ) or dysfunction of -cells upon glucose- stimulation (glucose sensitivity). It is also able to distinguish combined alterations consisting of common variations of both. Therefore, several model parameters were introduced: k X for insulin sensitivity, and k G1 , k G2 and k G3 to describe glucose sensitivities. For the latter, we differentiate into three distinct contributions: The short-term plasma-dependent glu- cose sensitivity k G1 , the long-term plasma-dependent glucose sensitivity k G2 , and the incretin effect k G3 triggered by orally administered glucose. Here we apply this concept to the previously described protein kinase B-alpha (pkb / ) in vivo model (Yang et al., 2003). The phosphoinositide-dependent serine-threonine Correspondence: Claudia Eberle, Hochschule Fulda – University of Applied Science, Marquardstrabe 35, 36039 Fulda, Germany. Tel: +49- 661-9640-6328. Fax: +49-661-9640-649. E-mail: claudia.eberle@ hs-fulda.de