Literature DB >> 31903769

The Need for Precision Medicine to be Applied to Diabetes.

David C Klonoff1, Jose C Florez2,3,4, Michael German5,6,7, Alexander Fleming8.   

Abstract

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

Entities:  

Keywords:  diabetes; genes; omics; pharmacogenetics; phenotype; precision medicine

Year:  2020        PMID: 31903769      PMCID: PMC7645141          DOI: 10.1177/1932296819894295

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  35 in total

1.  Pharmacogenetics in type 2 diabetes: still a conundrum in clinical practice.

Authors:  Antonio Brunetti; Eusebio Chiefari; Daniela Patrizia Foti
Journal:  Expert Rev Endocrinol Metab       Date:  2017-04-17

Review 2.  Translational genomics and precision medicine: Moving from the lab to the clinic.

Authors:  Eleftheria Zeggini; Anna L Gloyn; Anne C Barton; Louise V Wain
Journal:  Science       Date:  2019-09-27       Impact factor: 47.728

3.  Personalized medicine: from genotypes, molecular phenotypes and the quantified self, towards improved medicine.

Authors:  Joel T Dudley; Jennifer Listgarten; Oliver Stegle; Steven E Brenner; Leopold Parts
Journal:  Pac Symp Biocomput       Date:  2015

Review 4.  Diabetes at the crossroads: relevance of disease classification to pathophysiology and treatment.

Authors:  R David Leslie; Jerry Palmer; Nanette C Schloot; Ake Lernmark
Journal:  Diabetologia       Date:  2016-01       Impact factor: 10.122

5.  A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease.

Authors:  Robert A Scott; Daniel F Freitag; Li Li; Audrey Y Chu; Praveen Surendran; Robin Young; Niels Grarup; Alena Stancáková; Yuning Chen; Tibor V Varga; Hanieh Yaghootkar; Jian'an Luan; Jing Hua Zhao; Sara M Willems; Jennifer Wessel; Shuai Wang; Nisa Maruthur; Kyriaki Michailidou; Ailith Pirie; Sven J van der Lee; Christopher Gillson; Ali Amin Al Olama; Philippe Amouyel; Larraitz Arriola; Dominique Arveiler; Iciar Aviles-Olmos; Beverley Balkau; Aurelio Barricarte; Inês Barroso; Sara Benlloch Garcia; Joshua C Bis; Stefan Blankenberg; Michael Boehnke; Heiner Boeing; Eric Boerwinkle; Ingrid B Borecki; Jette Bork-Jensen; Sarah Bowden; Carlos Caldas; Muriel Caslake; L Adrienne Cupples; Carlos Cruchaga; Jacek Czajkowski; Marcel den Hoed; Janet A Dunn; Helena M Earl; Georg B Ehret; Ele Ferrannini; Jean Ferrieres; Thomas Foltynie; Ian Ford; Nita G Forouhi; Francesco Gianfagna; Carlos Gonzalez; Sara Grioni; Louise Hiller; Jan-Håkan Jansson; Marit E Jørgensen; J Wouter Jukema; Rudolf Kaaks; Frank Kee; Nicola D Kerrison; Timothy J Key; Jukka Kontto; Zsofia Kote-Jarai; Aldi T Kraja; Kari Kuulasmaa; Johanna Kuusisto; Allan Linneberg; Chunyu Liu; Gaëlle Marenne; Karen L Mohlke; Andrew P Morris; Kenneth Muir; Martina Müller-Nurasyid; Patricia B Munroe; Carmen Navarro; Sune F Nielsen; Peter M Nilsson; Børge G Nordestgaard; Chris J Packard; Domenico Palli; Salvatore Panico; Gina M Peloso; Markus Perola; Annette Peters; Christopher J Poole; J Ramón Quirós; Olov Rolandsson; Carlotta Sacerdote; Veikko Salomaa; María-José Sánchez; Naveed Sattar; Stephen J Sharp; Rebecca Sims; Nadia Slimani; Jennifer A Smith; Deborah J Thompson; Stella Trompet; Rosario Tumino; Daphne L van der A; Yvonne T van der Schouw; Jarmo Virtamo; Mark Walker; Klaudia Walter; Jean E Abraham; Laufey T Amundadottir; Jennifer L Aponte; Adam S Butterworth; Josée Dupuis; Douglas F Easton; Rosalind A Eeles; Jeanette Erdmann; Paul W Franks; Timothy M Frayling; Torben Hansen; Joanna M M Howson; Torben Jørgensen; Jaspal Kooner; Markku Laakso; Claudia Langenberg; Mark I McCarthy; James S Pankow; Oluf Pedersen; Elio Riboli; Jerome I Rotter; Danish Saleheen; Nilesh J Samani; Heribert Schunkert; Peter Vollenweider; Stephen O'Rahilly; Panos Deloukas; John Danesh; Mark O Goodarzi; Sekar Kathiresan; James B Meigs; Margaret G Ehm; Nicholas J Wareham; Dawn M Waterworth
Journal:  Sci Transl Med       Date:  2016-06-01       Impact factor: 17.956

6.  A combined analysis of 48 type 2 diabetes genetic risk variants shows no discriminative value to predict time to first prescription of a glucose lowering drug in Danish patients with screen detected type 2 diabetes.

Authors:  Malene Hornbak; Kristine Højgaard Allin; Majken Linnemann Jensen; Cathrine Juel Lau; Daniel Witte; Marit Eika Jørgensen; Annelli Sandbæk; Torsten Lauritzen; Åsa Andersson; Oluf Pedersen; Torben Hansen
Journal:  PLoS One       Date:  2014-08-26       Impact factor: 3.240

7.  A Global Overview of Precision Medicine in Type 2 Diabetes.

Authors:  Hugo Fitipaldi; Mark I McCarthy; Jose C Florez; Paul W Franks
Journal:  Diabetes       Date:  2018-10       Impact factor: 9.461

8.  Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data.

Authors:  John M Dennis; Beverley M Shields; William E Henley; Angus G Jones; Andrew T Hattersley
Journal:  Lancet Diabetes Endocrinol       Date:  2019-04-29       Impact factor: 44.867

9.  Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Authors:  Quincy A Hathaway; Skyler M Roth; Mark V Pinti; Daniel C Sprando; Amina Kunovac; Andrya J Durr; Chris C Cook; Garrett K Fink; Tristen B Cheuvront; Jasmine H Grossman; Ghadah A Aljahli; Andrew D Taylor; Andrew P Giromini; Jessica L Allen; John M Hollander
Journal:  Cardiovasc Diabetol       Date:  2019-06-11       Impact factor: 9.951

Review 10.  Gene-environment and gene-treatment interactions in type 2 diabetes: progress, pitfalls, and prospects.

Authors:  Paul W Franks; Ewan Pearson; Jose C Florez
Journal:  Diabetes Care       Date:  2013-05       Impact factor: 19.112

View more
  3 in total

1.  Digital Connectivity: The Sixth Vital Sign.

Authors:  David C Klonoff; Trisha Shang; Jennifer Y Zhang; Eda Cengiz; Chhavi Mehta; David Kerr
Journal:  J Diabetes Sci Technol       Date:  2021-05-12

2.  Rapid Point-of-Care Test for Determination of C-Peptide Levels.

Authors:  Paturi V Rao; Eric Bean; Dhanalakshmi Nair-Schaef; Siting Chen; Steven C Kazmierczak; Charles T Roberts; Srinivasa R Nagalla
Journal:  J Diabetes Sci Technol       Date:  2021-03-17

3.  Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment.

Authors:  John M Dennis
Journal:  Diabetes       Date:  2020-08-25       Impact factor: 9.461

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.