Disease System Analysis: Basic Disease Progression Models in Degenerative Disease

Post, Teun M.; Freijer, Jan; DeJongh, Joost; Danhof, Meindert

doi:10.1007/s11095-005-5641-5

Cited by 82 publications

(63 citation statements)

References 43 publications

(28 reference statements)

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“…The turnover components in the leptin equation do not reflect production and loss of leptin but physiological changes over a longer time period, similar to turnover models for describing disease progression (Post et al, 2005). Therefore, leptin k out does not reflect its plasma half-life.…”

Section: Discussionmentioning

confidence: 99%

Mechanism-Based Modeling of Nutritional and Leptin Influences on Growth in Normal and Type 2 Diabetic Rats

Landersdorfer¹,

DuBois²,

Almon³

et al. 2008

J Pharmacol Exp Ther

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Influences of genetic and nutritional factors on body weight, fat mass, and leptin production and effects of leptin were assessed in normal [Wistar-Kyoto (WKY)] and diabetic [GotoKakizaki (GK)] rats by mechanism-based modeling. The study included 60 WKY and 60 GK rats; half received high-fat diet (HF), and the others received normal rat chow (N). Body weights and food consumption were measured twice weekly. Six rats per group were sacrificed at 4, 8, 12, 16, and 20 weeks. Abdominal fat was weighed, and plasma leptin was measured by enzyme-linked immunosorbent assay. All data were comodeled using NONMEM version VI level 1.1 (first-order conditional estimation with interaction) (Beal SL, Boeckmann AJ, Sheiner LB, and NONMEM Project Group, NONMEM Users Guides, University of California, San Francisco, CA, 2007). Weight gain was modeled as differences between energy intake and metabolic rate based on allometrically scaled lean body mass (LBM).The GK had higher metabolic rates (1.15 kcal/day/g LBM 0.75 ) than WKY-N (0.92) and WKY-HF (1.02) rats and higher efficiency in transforming energy into body weight. Leptin effect was modeled as inhibition of food consumption. Total body fat was estimated from abdominal fat. Leptin production from fat was 4.7-fold higher for GK (3.03 ng/ml/day/g) than WKY (0.66 ng/ml/day/g). Leptin production rate from LBM was 0.53 ng/ ml/day/g for all groups. The IC 50 for inhibition of food intake by leptin was approximately 3-fold higher in GK versus WKY, indicating leptin resistance for the effect on food consumption in GK. The GK had similar intake of kilocalories but lower body weights and fat mass than WKY, possibly because of higher metabolic rates. Our mechanism-based model explains intrinsic reasons for differences in growth, food intake, and leptin concentrations among these two strains of rats.Type 2 diabetes (T2DM) is a major health risk in many countries and the number of diabetic patients and associated costs for health systems are increasing. More than 180 million people are diabetic worldwide, and this number was estimated to more than double by 2030 (Centers for Disease Control and Prevention, http://www.cdc.gov/diabetes/pubs/ factsheets.htm). In 2005, an estimated 1.1 million people died from diabetes, and such deaths were predicted to increase by more than 50% over the next 10 years (Centers for Disease Control and Prevention, http://www.cdc.gov/ diabetes/pubs/factsheets.htm).

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Section: Discussionmentioning

confidence: 99%

Mechanism-Based Modeling of Nutritional and Leptin Influences on Growth in Normal and Type 2 Diabetic Rats

Landersdorfer¹,

DuBois²,

Almon³

et al. 2008

J Pharmacol Exp Ther

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“…Chan and Holford (2001) reviewed drug treatment effects on disease progression in multiple disease scenarios, placing emphasis on understanding the natural disease progression in the absence of drug. Post and Danhof presented a variety of equations for describing disease progression using pharmacodynamic indirect response models, a mechanistic paradigm for describing turnover of response and drug effect (Dayneka et al, 1993;Post et al, 2005). Other diseases have been described, including bacterial cell growth and irreversible loss after drug therapy, fasting plasma glucose in diabetes, and pain and bone mineral density progression in osteoarthritis (Jusko, 1971;Ravn et al, 1996;Meunier et al, 1999;Frey et al, 2003;Pillai et al, 2004;de Winter et al, 2006).…”

mentioning

confidence: 99%

Modeling Corticosteroid Effects in a Rat Model of Rheumatoid Arthritis I: Mechanistic Disease Progression Model for the Time Course of Collagen-Induced Arthritis in Lewis Rats

Earp¹,

DuBois²,

Molano³

et al. 2008

J Pharmacol Exp Ther

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A mechanism-based model was developed to describe the time course of arthritis progression in the rat. Arthritis was induced in male Lewis rats with type II porcine collagen into the base of the tail. Disease progression was monitored by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST) concentrations, and tumor necrosis factor (TNF)-␣, interleukin (IL)-1␤, IL-6, and glucocorticoid receptor (GR) mRNA expression in paw tissue. Bone mineral density was determined by PIXImus II dual energy X-ray densitometry. Plasma CST was assayed by high-performance liquid chromatography. Cytokine and GR mRNA were determined by quantitative real-time polymerase chain reaction. Disease progression models were constructed from transduction and indirect response models and applied using S-ADAPT software. A delay in the onset of increased paw TNF-␣ and IL-6 mRNA concentrations was successfully characterized by simple transduction. This rise was closely followed by an up-regulation of GR mRNA and CST concentrations. Paw swelling and body weight responses peaked approximately 21 days after induction, whereas bone mineral density changes were greatest at 23 days after induction. After peak response, the time course in IL-1␤, IL-6 mRNA, and paw edema slowly declined toward a disease steady state. Model parameters indicate TNF-␣ and IL-1␤ mRNA most significantly induce paw edema, whereas IL-6 mRNA exerted the most influence on BMD. The model for bone mineral density captures rates of turnover of cancellous and cortical bone and the fraction of each in the different regions analyzed. This small systems model integrates and quantitates multiple factors contributing to arthritis in rats.

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“…Existing work on disease progression models have been proved effective for drug development and early intervention. For example, Post et al [12] proposed a family of models to describe the progression of degenerative diseases (such as type 2 diabetes and Parkinson's disease) as a function of disease process and treatment effects. De Winter et al [3] developed a mechanism based technique for modeling the progression of diabetes mellitus by tracking the interaction between several key indicators.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised learning of disease progression models

Wang

Sontag

Wang

2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

181

114

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Chronic diseases, such as Alzheimer's Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in early diagnosis and personalized care. Modeling disease progression based on real-world evidence is a very challenging task due to the incompleteness and irregularity of the observations, as well as the heterogeneity of the patient conditions. In this paper, we propose a probabilistic disease progression model that address these challenges. As compared to existing disease progression models, the advantage of our model is three-fold: 1) it learns a continuous-time progression model from discrete-time observations with non-equal intervals; 2) it learns the full progression trajectory from a set of incomplete records that only cover short segments of the progression; 3) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence, which are usually extremely sparse and noisy. We demonstrate the capabilities of our model by applying it to a real-world COPD patient cohort and deriving some interesting clinical insights.

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Disease System Analysis: Basic Disease Progression Models in Degenerative Disease

Abstract: Disease system analysis constitutes a scientific basis for the distinction between symptomatic versus protective drug effects in relation to specific disease processes as well as the identification of the exposure-response relationship during the time-course of disease.

Cited by 82 publications

References 43 publications

Mechanism-Based Modeling of Nutritional and Leptin Influences on Growth in Normal and Type 2 Diabetic Rats

Mechanism-Based Modeling of Nutritional and Leptin Influences on Growth in Normal and Type 2 Diabetic Rats

Modeling Corticosteroid Effects in a Rat Model of Rheumatoid Arthritis I: Mechanistic Disease Progression Model for the Time Course of Collagen-Induced Arthritis in Lewis Rats

Unsupervised learning of disease progression models

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