Skin fibroblasts as a tool for identifying the risk of nephropathy in the type 1 diabetic population

Millioni, Renato; Puricelli, Lucia; Iori, Elisabetta; Trevisan, Roberto; Tessari, Paolo

doi:10.1002/dmrr.1287

Cited by 15 publications

(10 citation statements)

References 79 publications

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“…Thus, fibroblasts from skin biopsies have been derived from both diabetic and normal patients to determine the mechanisms underlying DN (Millioni et al, 2012). However, it is still uncertain whether physiological changes observed in skin fibroblasts truly mimic those occurring in renal fibroblasts.…”

Section: Classical Strategies and Challenges For Studying Mechanisms mentioning

confidence: 99%

New Opportunities: Harnessing Induced Pluripotency for Discovery in Diabetes and Metabolism

2013

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The landmark discovery of induced pluripotent stem cells (iPSCs) by Shinya Yamanaka has transformed regenerative biology. Previously, insights into the pathogenesis of chronic human diseases have been hindered by the inaccessibility of patient samples. However, scientists are now able to convert patient fibroblasts into iPSCs and differentiate them into disease-relevant cell types. This ability opens new avenues for investigating disease pathogenesis and designing novel treatments. In this review, we highlight the uses of human iPSCs to uncover the underlying causes and pathological consequences of diabetes and metabolic syndromes, multi-factorial diseases whose etiologies have been difficult to unravel using traditional methodologies.

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Section: Classical Strategies and Challenges For Studying Mechanisms mentioning

confidence: 99%

New Opportunities: Harnessing Induced Pluripotency for Discovery in Diabetes and Metabolism

2013

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“…The Chinese population is about 19 hypertensive (see [22]). Generally speaking, hypertension in the Chinese does not owe to their being obese (see [23]).…”

Section: Methodsmentioning

confidence: 99%

Insulin Resistance: Regression and Clustering

Yoon

Assimes²,

Quertermous³

et al. 2014

PLoS ONE

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In this paper we try to define insulin resistance (IR) precisely for a group of Chinese women. Our definition deliberately does not depend upon body mass index (BMI) or age, although in other studies, with particular random effects models quite different from models used here, BMI accounts for a large part of the variability in IR. We accomplish our goal through application of Gauss mixture vector quantization (GMVQ), a technique for clustering that was developed for application to lossy data compression. Defining data come from measurements that play major roles in medical practice. A precise statement of what the data are is in Section 1. Their family structures are described in detail. They concern levels of lipids and the results of an oral glucose tolerance test (OGTT). We apply GMVQ to residuals obtained from regressions of outcomes of an OGTT and lipids on functions of age and BMI that are inferred from the data. A bootstrap procedure developed for our family data supplemented by insights from other approaches leads us to believe that two clusters are appropriate for defining IR precisely. One cluster consists of women who are IR, and the other of women who seem not to be. Genes and other features are used to predict cluster membership. We argue that prediction with “main effects” is not satisfactory, but prediction that includes interactions may be.

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“…Moreover, this model served for the diagnosis and investigation of diseases. For instance, HDF were used to study impairments in the wound healing process [ 13 ], skin fibrosis propensity in response to radiotherapy [ 14 ], oxidative phosphorylation impairment [ 15 ], and evaluation of the presence or the risk of metabolic diseases in patients [ 16 , 17 ]. HDF were also used to study neurodegenerative diseases such as Parkinsons [ 18 ] and Alzheimers [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Human Dermal Fibroblast: A Promising Cellular Model to Study Biological Mechanisms of Major Depression and Antidepressant Drug Response

Mesdom

Colle

Lebigot

et al. 2020

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Background: Human dermal fibroblasts (HDF) can be used as a cellular model relatively easily and without genetic engineering. Therefore, HDF represent an interesting tool to study several human diseases including psychiatric disorders. Despite major depressive disorder (MDD) being the second cause of disability in the world, the efficacy of antidepressant drug (AD) treatment is not sufficient and the underlying mechanisms of MDD and the mechanisms of action of AD are poorly understood. Objective: The aim of this review is to highlight the potential of HDF in the study of cellular mechanisms involved in MDD pathophysiology and in the action of AD response. Methods: The first part is a systematic review following PRISMA guidelines on the use of HDF in MDD research. The second part reports the mechanisms and molecules both present in HDF and relevant regarding MDD pathophysiology and AD mechanisms of action. Results: HDFs from MDD patients have been investigated in a relatively small number of works and most of them focused on the adrenergic pathway and metabolism-related gene expression as compared to HDF from healthy controls. The second part listed an important number of papers demonstrating the presence of many molecular processes in HDF, involved in MDD and AD mechanisms of action. Conclusion: The imbalance in the number of papers between the two parts highlights the great and still underused potential of HDF, which stands out as a very promising tool in our understanding of MDD and AD mechanisms of action.

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Skin fibroblasts as a tool for identifying the risk of nephropathy in the type 1 diabetic population

Cited by 15 publications

References 79 publications

New Opportunities: Harnessing Induced Pluripotency for Discovery in Diabetes and Metabolism

New Opportunities: Harnessing Induced Pluripotency for Discovery in Diabetes and Metabolism

Insulin Resistance: Regression and Clustering

Human Dermal Fibroblast: A Promising Cellular Model to Study Biological Mechanisms of Major Depression and Antidepressant Drug Response

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