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Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting
by
Boemi, Massimo
, Ponzani, Paola
, Russo, Giuseppina
, G Di Cianni
, Genovese, Stefano
, Sciangula, Luigi
, Bette, C
, Rossi, Maria Chiara
, Boemi, M
, Sciangula, L
, Lapolla, Annunziata
, Graziano Di Cianni
, Provenzano, V
, Simioni, N
, Candido, Riccardo
, Frontoni, S
, Lapolla, A
, Cristiano Bette
, Rossi, M C
, Bossi, A C
, Bossi, Antonio Carlo
, Candido, R
, Berra, C
, Frontoni, Simona
, Genovese, S
, Simioni, Natalino
, Ponzani, P
, Provenzano, Vincenzo
, Berra, Cesare
, Russo, G T
in
Algorithms
/ Antidiabetics
/ Blood pressure
/ Body mass index
/ Cardiovascular disease
/ Cholesterol
/ Clinical outcomes
/ Demographics
/ Diabetes
/ Diabetes mellitus
/ Diabetes mellitus (non-insulin dependent)
/ Drug therapy
/ Electronic health records
/ Electronic medical records
/ Hemoglobin
/ High density lipoprotein
/ Hypertension
/ Insulin
/ Lipoproteins
/ Medical prognosis
/ Medical records
/ Metabolic disorders
/ Multivariate analysis
/ Patients
/ Regression analysis
/ Statistical analysis
/ Variables
2018
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Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting
by
Boemi, Massimo
, Ponzani, Paola
, Russo, Giuseppina
, G Di Cianni
, Genovese, Stefano
, Sciangula, Luigi
, Bette, C
, Rossi, Maria Chiara
, Boemi, M
, Sciangula, L
, Lapolla, Annunziata
, Graziano Di Cianni
, Provenzano, V
, Simioni, N
, Candido, Riccardo
, Frontoni, S
, Lapolla, A
, Cristiano Bette
, Rossi, M C
, Bossi, A C
, Bossi, Antonio Carlo
, Candido, R
, Berra, C
, Frontoni, Simona
, Genovese, S
, Simioni, Natalino
, Ponzani, P
, Provenzano, Vincenzo
, Berra, Cesare
, Russo, G T
in
Algorithms
/ Antidiabetics
/ Blood pressure
/ Body mass index
/ Cardiovascular disease
/ Cholesterol
/ Clinical outcomes
/ Demographics
/ Diabetes
/ Diabetes mellitus
/ Diabetes mellitus (non-insulin dependent)
/ Drug therapy
/ Electronic health records
/ Electronic medical records
/ Hemoglobin
/ High density lipoprotein
/ Hypertension
/ Insulin
/ Lipoproteins
/ Medical prognosis
/ Medical records
/ Metabolic disorders
/ Multivariate analysis
/ Patients
/ Regression analysis
/ Statistical analysis
/ Variables
2018
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Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting
by
Boemi, Massimo
, Ponzani, Paola
, Russo, Giuseppina
, G Di Cianni
, Genovese, Stefano
, Sciangula, Luigi
, Bette, C
, Rossi, Maria Chiara
, Boemi, M
, Sciangula, L
, Lapolla, Annunziata
, Graziano Di Cianni
, Provenzano, V
, Simioni, N
, Candido, Riccardo
, Frontoni, S
, Lapolla, A
, Cristiano Bette
, Rossi, M C
, Bossi, A C
, Bossi, Antonio Carlo
, Candido, R
, Berra, C
, Frontoni, Simona
, Genovese, S
, Simioni, Natalino
, Ponzani, P
, Provenzano, Vincenzo
, Berra, Cesare
, Russo, G T
in
Algorithms
/ Antidiabetics
/ Blood pressure
/ Body mass index
/ Cardiovascular disease
/ Cholesterol
/ Clinical outcomes
/ Demographics
/ Diabetes
/ Diabetes mellitus
/ Diabetes mellitus (non-insulin dependent)
/ Drug therapy
/ Electronic health records
/ Electronic medical records
/ Hemoglobin
/ High density lipoprotein
/ Hypertension
/ Insulin
/ Lipoproteins
/ Medical prognosis
/ Medical records
/ Metabolic disorders
/ Multivariate analysis
/ Patients
/ Regression analysis
/ Statistical analysis
/ Variables
2018
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Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting
Journal Article
Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting
2018
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Overview
AimsThere is an unmet need among healthcare providers to identify subgroups of patients with type 2 diabetes who are most likely to respond to treatment.MethodsData were taken from electronic medical records of participants of an observational, retrospective study in Italy. We used logistic regression models to assess the odds of achieving glycated haemoglobin (HbA1c) reduction ≥ 1.0% point after 12-month treatment with liraglutide (primary endpoint), according to various patient-related factors. RECursive Partitioning and AMalgamation (RECPAM) analysis was used to identify distinct homogeneous patient subgroups with different odds of achieving the primary endpoint.ResultsData from 1325 patients were included, of which 577 (43.5%) achieved HbA1c reduction ≥ 1.0% point (10.9 mmol/mol) after 12 months. Logistic regression showed that for each additional 1% HbA1c at baseline, the odds of reaching this endpoint were increased 3.5 times (95% CI: 2.90–4.32). By use of RECPAM analysis, five distinct responder subgroups were identified, with baseline HbA1c and diabetes duration as the two splitting variables. Patients in the most poorly controlled subgroup (RECPAM Class 1, mean baseline HbA1c > 9.1% [76 mmol/mol]) had a 28-fold higher odds of reaching the endpoint versus patients in the best-controlled group (mean baseline HbA1c ≤ 7.5% [58 mmol/mol]). Mean HbA1c reduction from baseline was as large as − 2.2% (24 mol/mol) in the former versus − 0.1% (1.1 mmol/mol) in the latter. Mean weight reduction ranged from 2.5 to 4.3 kg across RECPAM subgroups.ConclusionsGlycaemic response to liraglutide is largely driven by baseline HbA1c levels and, to a lesser extent, by diabetes duration.
Publisher
Springer Nature B.V
Subject
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