Insulin Signaling in Type 2 Diabetes

Brännmark, Cecilia; Nyman, Elin; Fagerholm, Siri; Bergenholm, Linnéa; Ekstrand, Eva-Maria; Cedersund, Gunnar; Strålfors, Peter

doi:10.1074/jbc.m112.432062

Cited by 109 publications

(147 citation statements)

References 48 publications

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“…While the first two differences (i and ii) have been measured in adipocytes from T2D individuals, the reduced feedback signalling that was identified (iii) was a direct result of the modelling approach. With these differences (i-iii), data from both normal and insulin-resistant T2D individuals could be simulated using the same mathematical model [9,10]. The model can thus be used to simulate the acute intracellular effects of anti-diabetic drugs.…”

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

“…These signalling proteins were measured in primary adipocytes, both from non-diseased and T2D individuals. The data achieved from cells of T2D individuals displayed reduced insulin signalling throughout the network, when compared with data from non-diseased individuals [9,10]. In a mathematical modelling approach, all data could be analysed simultaneously.…”

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

“…The model can thus be used to simulate the acute intracellular effects of anti-diabetic drugs. Furthermore, this intracellular model has been connected to a whole-body model of glucoseinsulin dynamics and with even more detailed models for insulin binding to its receptor [9,22,24]. These insulin signalling models may, therefore, be further used in the study of metabolic evaluation of other drug targets.…”

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

“…In a series of papers [9,10,22,23], the intracellular insulin resistance mechanisms in human adipocytes have been unravelled, with a combination of systematic experimental and mathematical modelling approaches. The experimental data consist of both dynamic time-series (i.e.…”

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

“…Considering research in diabetes, multilevel models have helped us unravel how insulin resistance, i.e. a reduced cellular response to insulin, arises from specific parts of the intracellular protein interaction network, how this resistance spreads to the rest of the network and even how this resistance may affect other organs [9,10]. This multi-level model is currently being used by several drug development companies to help them identify promising drug targets.…”

Section: Introductionmentioning

confidence: 99%

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Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

et al. 2016

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One contribution of 12 to a theme issue 'The Human Physiome: a necessary key to the creative destruction of medicine'. We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

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Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

Section: Insulin Resistance Mechanismsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

et al. 2016

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Multivariate Analyses and the Bridging of Biology's ‘Math‐Gap’

Skjærvold

Brovold

Rahmati

et al. 2017

Encyclopedia of Analytical Chemistry

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This article shows how multivariate data modeling and multivariate metamodeling bridge the math‐gap in many sciences, e.g. in the biosciences. Biomedical science is constituted by two traditionally diverging lines of research, one being explorative, inductive, and holistic and the other being confirmative, deductive, and reductionistic. We argue in this article that both cultures are in dire need of more cross‐disciplinarity and openness to the knowledge and opportunities that lie on the other side of what we here call the ‘Math‐Gap in Bioscience’: The former tradition, which we choose to call ‘The House of Bio’, needs to expand to more powerful data‐driven statistical modeling and assessments in order to make sense out of their deluge of measurement data. The latter, which we here call ‘The House of Math’, needs to develop more and better mechanistic models to make possible the quantitative combination and effective utilization of today's enormous growth in biomedical knowledge and data. However, the use of mathematics and statistics in biomedical fields is hampered by lack of contact, curiosity, and respect between these two research cultures. To bridge this ‘Math‐Gap’ between them, we here propose a simple but powerful toolbox: soft multivariate modeling based on graphically interpreted, cross‐validated bilinear analysis. Nine different examples of data modeling and metamodeling show how this is successfully used in widely different fields of biomedical research – and from both research traditions.

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The role of autophagy in insulin resistance and glucolipid metabolism and potential use of autophagy modulating natural products in the treatment of type 2 diabetes mellitus

Yang,

Ding,

Chen

et al. 2024

Diabetes Metabolism Res

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Type 2 diabetes mellitus (T2DM) is a severe, long‐term condition characterised by disruptions in glucolipid and energy metabolism. Autophagy, a fundamental cellular process, serves as a guardian of cellular health by recycling and renewing cellular components. To gain a comprehensive understanding of the vital role that autophagy plays in T2DM, we conducted an extensive search for high‐quality publications across databases such as Web of Science, PubMed, Google Scholar, and SciFinder and used keywords like ‘autophagy’, ‘insulin resistance’, and ‘type 2 diabetes mellitus’, both individually and in combinations. A large body of evidence underscores the significance of activating autophagy in alleviating T2DM symptoms. An enhanced autophagic activity, either by activating the adenosine monophosphate‐activated protein kinase and sirtuin‐1 signalling pathways or inhibiting the mechanistic target of rapamycin complex 1 signalling pathway, can effectively improve insulin resistance and balance glucolipid metabolism in key tissues like the hypothalamus, skeletal muscle, liver, and adipose tissue. Furthermore, autophagy can increase β‐cell mass and functionality in the pancreas. This review provides a narrative summary of autophagy regulation with an emphasis on the intricate connection between autophagy and T2DM symptoms. It also discusses the therapeutic potentials of natural products with autophagy activation properties for the treatment of T2DM conditions. Our findings suggest that autophagy activation represents an innovative approach of treating T2DM.

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Insulin Signaling in Type 2 Diabetes

Cited by 109 publications

References 48 publications

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

Multivariate Analyses and the Bridging of Biology's ‘Math‐Gap’

The role of autophagy in insulin resistance and glucolipid metabolism and potential use of autophagy modulating natural products in the treatment of type 2 diabetes mellitus

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