Precision medicine in type 2 diabetes
© 2018 The Association for the Publication of the Journal of Internal Medicine..
The Precision Medicine Initiative defines precision medicine as 'an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person'. This approach will facilitate more accurate treatment and prevention strategies in contrast to a one-size-fits-all approach, in which disease treatment and prevention strategies are developed for generalized usage. Diabetes is clearly more heterogeneous than the conventional subclassification into type 1 and type 2 diabetes. Monogenic forms of diabetes like MODY and neonatal diabetes have paved the way for precision medicine in diabetes, as carriers of unique mutations require unique treatment. Diagnosis of diabetes in the past has been dependent upon measuring one metabolite, glucose. By instead including six variables in a clustering analysis, we could break down diabetes into five distinct subgroups, with better prediction of disease progression and outcome. The severe insulin-resistant diabetes (SIRD) cluster showed the highest risk of kidney disease and highest prevalence of nonalcoholic fatty liver disease, whereas patients in the insulin-deficient cluster 2 (SIDD) had the highest risk of retinopathy. In the future, this will certainly be improved and expanded by including genetic, epigenetic and other biomarker to allow better prediction of outcome and choice of more precise treatment.
Errataetall: |
CommentIn: J Intern Med. 2019 Jul;286(1):112-114. - PMID 30957916 |
---|---|
Medienart: |
E-Artikel |
Erscheinungsjahr: |
2019 |
---|---|
Erschienen: |
2019 |
Enthalten in: |
Zur Gesamtaufnahme - volume:285 |
---|---|
Enthalten in: |
Journal of internal medicine - 285(2019), 1 vom: 07. Jan., Seite 40-48 |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Prasad, R B [VerfasserIn] |
---|
Links: |
---|
Themen: |
Biomarkers |
---|
Anmerkungen: |
Date Completed 21.11.2019 Date Revised 21.11.2019 published: Print-Electronic CommentIn: J Intern Med. 2019 Jul;286(1):112-114. - PMID 30957916 Citation Status MEDLINE |
---|
doi: |
10.1111/joim.12859 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM290361788 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM290361788 | ||
003 | DE-627 | ||
005 | 20231225064550.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231225s2019 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1111/joim.12859 |2 doi | |
028 | 5 | 2 | |a pubmed24n0967.xml |
035 | |a (DE-627)NLM290361788 | ||
035 | |a (NLM)30403316 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Prasad, R B |e verfasserin |4 aut | |
245 | 1 | 0 | |a Precision medicine in type 2 diabetes |
264 | 1 | |c 2019 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 21.11.2019 | ||
500 | |a Date Revised 21.11.2019 | ||
500 | |a published: Print-Electronic | ||
500 | |a CommentIn: J Intern Med. 2019 Jul;286(1):112-114. - PMID 30957916 | ||
500 | |a Citation Status MEDLINE | ||
520 | |a © 2018 The Association for the Publication of the Journal of Internal Medicine. | ||
520 | |a The Precision Medicine Initiative defines precision medicine as 'an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person'. This approach will facilitate more accurate treatment and prevention strategies in contrast to a one-size-fits-all approach, in which disease treatment and prevention strategies are developed for generalized usage. Diabetes is clearly more heterogeneous than the conventional subclassification into type 1 and type 2 diabetes. Monogenic forms of diabetes like MODY and neonatal diabetes have paved the way for precision medicine in diabetes, as carriers of unique mutations require unique treatment. Diagnosis of diabetes in the past has been dependent upon measuring one metabolite, glucose. By instead including six variables in a clustering analysis, we could break down diabetes into five distinct subgroups, with better prediction of disease progression and outcome. The severe insulin-resistant diabetes (SIRD) cluster showed the highest risk of kidney disease and highest prevalence of nonalcoholic fatty liver disease, whereas patients in the insulin-deficient cluster 2 (SIDD) had the highest risk of retinopathy. In the future, this will certainly be improved and expanded by including genetic, epigenetic and other biomarker to allow better prediction of outcome and choice of more precise treatment | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Research Support, Non-U.S. Gov't | |
650 | 4 | |a Review | |
650 | 4 | |a diabetes | |
650 | 7 | |a Biomarkers |2 NLM | |
700 | 1 | |a Groop, L |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Journal of internal medicine |d 1992 |g 285(2019), 1 vom: 07. Jan., Seite 40-48 |w (DE-627)NLM012608750 |x 1365-2796 |7 nnns |
773 | 1 | 8 | |g volume:285 |g year:2019 |g number:1 |g day:07 |g month:01 |g pages:40-48 |
856 | 4 | 0 | |u http://dx.doi.org/10.1111/joim.12859 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 285 |j 2019 |e 1 |b 07 |c 01 |h 40-48 |