Predictors of response in initial users of metformin and sulphonylurea derivatives: a systematic review

2015 ◽  
Vol 32 (7) ◽  
pp. 853-864 ◽  
Author(s):  
D. P. Martono ◽  
R. Lub ◽  
H. J. Lambers Heerspink ◽  
E. Hak ◽  
B. Wilffert ◽  
...  
2016 ◽  
Vol 225 ◽  
pp. 345-352 ◽  
Author(s):  
John Rickard ◽  
Henry Michtalik ◽  
Ritu Sharma ◽  
Zackary Berger ◽  
Emmanuel Iyoha ◽  
...  

Rheumatology ◽  
2012 ◽  
Vol 52 (6) ◽  
pp. 1022-1032 ◽  
Author(s):  
N. Maricar ◽  
M. J. Callaghan ◽  
D. T. Felson ◽  
T. W. O'Neill

2020 ◽  
Vol 23 (5) ◽  
pp. 613-623
Author(s):  
Verinder Sharma ◽  
Mustaq Khan ◽  
Christine Baczynski ◽  
Isabel Boate

2020 ◽  
Vol 55 (6) ◽  
pp. 1320-1331
Author(s):  
Carlos E. Rodriguez‐Martinez ◽  
Monica P. Sossa‐Briceño ◽  
Jose A. Castro‐Rodriguez

2018 ◽  
Vol 31 (6) ◽  
pp. 283-302 ◽  
Author(s):  
Caroline Masse-Sibille ◽  
Bennabi Djamila ◽  
Giustiniani Julie ◽  
Haffen Emmanuel ◽  
Vandel Pierre ◽  
...  

Background: Geriatric depression is a heterogeneous disorder that increases morbidity and mortality in a population that is already vulnerable. Predicting response and remission to antidepressants could help clinicians to optimize the management of antidepressants and reduce the consequences of depression. Method: The aim of this article is to present results of a systematic review of the literature on predictive factors related to antidepressant response and remission in older adults with depression. Main Findings: We identified sociodemographic, clinical, neuropsychological, neuroimaging, and genetic factors that could be potential predictors of outcomes. Inconsistent findings and methodological differences among studies, however, limit the generalizability and application of these predictors in clinical practice. The results of our review confirm that geriatric depression includes many subgroups of patients with particular endophenotypes that may influence the course of depression. Conclusion: Further studies are needed to characterize depression subgroups in order to better understand the pathophysiology of late life depression and to find specific predictors for each group of patients.


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