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Acta Diabetologica

, Volume 55, Issue 6, pp 557–568 | Cite as

Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting

  • N. Simioni
  • C. Berra
  • M. Boemi
  • A. C. Bossi
  • R. Candido
  • G. Di Cianni
  • S. Frontoni
  • S. Genovese
  • P. Ponzani
  • V. Provenzano
  • G. T. Russo
  • L. Sciangula
  • A. Lapolla
  • C. Bette
  • M. C. Rossi
  • ReaL (NN2211-4118) Study Group*
Open Access
Original Article

Abstract

Aims

There is an unmet need among healthcare providers to identify subgroups of patients with type 2 diabetes who are most likely to respond to treatment.

Methods

Data 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.

Results

Data 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.

Conclusions

Glycaemic response to liraglutide is largely driven by baseline HbA1c levels and, to a lesser extent, by diabetes duration.

Keywords

Liraglutide Type 2 diabetes Response to therapy RECPAM analysis GLP-1RA 

Introduction

Liraglutide is a once-daily human glucagon-like peptide-1 (GLP-1) analogue available for the treatment of type 2 diabetes (T2D), and its efficacy and safety have been demonstrated in the Liraglutide Effect and Action in Diabetes (LEAD) study programme [1, 2, 3, 4, 5, 6, 7]. Liraglutide has also cardioprotective benefits in patients with T2D at increased risk of cardiovascular disease [8]. Liraglutide was approved in the EU in 2009, and data from real-world observational studies have further demonstrated that the benefits of liraglutide on glycated haemoglobin (HbA1c) and body weight loss were consistent with those obtained in the randomised LEAD trials [9]. Long-term studies indicated that the benefits were sustained for up to 3 years [10, 11].

Liraglutide has been demonstrated to have benefits across a diverse spectrum of patients with T2D, but the extent of HbA1c improvement differs within patient groups having different demographics and clinical characteristics [12]. Thus, there is an unmet need to identify subgroups of patients with T2D receiving liraglutide who are most likely to have the greatest response to treatment. This information would help healthcare providers individualise treatment options and assess cost benefits. Patients and healthcare professionals could benefit from a more detailed understanding of factors associated with improved response to liraglutide.

The ReaL study (ClinicalTrials.gov identifier: NCT02255266) was the largest observational study of liraglutide in Italian clinical practice, showing that 43.5% of patients achieved HbA1c reduction ≥ 1% (10.9 mmol/mol) after 12 months of treatment (primary endpoint). This manuscript reports findings from a secondary analysis performed to identify subgroups or classes of patients with T2D who were more likely to have an improved response to liraglutide owing to specific combinations of clinical and socio-demographic characteristics.

Materials and methods

ReaL was an observational, retrospective, longitudinal, multicentre study involving 45 Italian diabetes clinics throughout the country. The design and methods of this real-world study have been previously reported [13]. Briefly, all consecutive patients aged ≥ 18 years diagnosed with T2D and receiving their first prescription of liraglutide in 2011 were eligible for the study. This study was conducted in accordance with the Declaration of Helsinki (last amended by 59th WMA General Assembly, Seoul, October 2013) and the Guidelines for Good Pharmacoepidemiology Practices (ICH-GPP Revision 2, April, 2007). A written informed consent, approved by an independent ethics committee, was signed by all patients before data collection. Data on a range of key clinical variables were obtained from electronic medical records. Information on fasting plasma glucose (FPG), body weight, body mass index (BMI), diabetes duration, presence of diabetes complications, liraglutide treatment, and treatment with other oral antidiabetic drugs (OADs) was extracted at the date of the first liraglutide prescription at baseline in 2011 and after 12 months. The frequency of patients achieving HbA1c reduction ≥ 1% (10.9 mmol/mol) after 12 months’ treatment (primary endpoint) was calculated. This primary endpoint was selected because it represents a mean effect seen in randomised clinical trials of liraglutide and is a strong indicator of effectiveness that is meaningful to both patients and clinicians. It is also in line with the trend in clinical care to individualise specific HbA1c targets. Information on side effects and adverse events was not explored, since it was not available in the electronic medical records in a standardised format.

Statistical analysis

Results are expressed as mean and standard deviation (SD) for continuous variables, and proportion and percentages for categorical measures, respectively. Between-group patient characteristics were compared with a Mann–Whitney U test or Student’s t test (as appropriate) for continuous variables, or a Chi-square test for categorical variables. Univariate logistic regression was used to identify baseline characteristics of patients who achieved the primary endpoint (HbA1c reduction ≥ 1.0% [10.9 mmol/mol] at 12 months), compared with those who did not.

Multivariate logistic regression analysis was performed to identify independent factors associated with the endpoint after adjustment for other variables. Covariates included in the multivariate analysis were age, sex, diabetes duration, baseline HbA1c, FPG, BMI, presence of diabetes complications, treatment at the first prescription of liraglutide (baseline), treatment modality, liraglutide dose, hypertension, dyslipidaemia, and estimated glomerular filtration rate (eGFR) levels. Standardised criteria which were used for diagnosis of hypertension were not established a priori for this study. Data were collected from electronic medical records, but in the Italian national guidelines, hypertension and dyslipidaemia cut-offs are blood pressure (BP) values ≥ 140/90 mmHg and low-density lipoprotein (LDL)-cholesterol ≥ 100 mg/dl, respectively. Covariates used in the multivariate analysis were chosen based on clinical judgment and did not depend on reaching statistical significance in the univariate analysis. Results are shown as odds ratios (ORs) and 95% confidence intervals (CI).

RECursive Partitioning and AMalgamation (RECPAM) analysis, a tree-based statistical method that integrates standard regression and tree-growing techniques, was used to detect potential interactions among the different variables in predicting reduction of at least 1% in HbA1c and identify homogeneous and distinct subgroups of patients with increased likelihood of reaching the endpoint [14]. In diabetes, RECPAM analysis has been previously used to identify: patients with T2D at risk of microalbuminuria [15], factors associated with impaired quality of life in patients using continuous subcutaneous insulin infusion [16], and patients at higher risk of cardiovascular disease [17]. The RECPAM analysis was performed using SAS® (Release 9.4 Cary, NC, USA) and a macro-routine written by F. Pellegrini and updated by M. Scardapane and G. Lucisano. At each partitioning step, the RECPAM method automatically chose the covariate and best binary split to maximise the difference in risk of experiencing the outcome. The algorithm stopped when user-defined stopping rules were met. In this case, each final class was required to have at least 100 patients in total and 30 patients with the target endpoint.

The set of variables tested in the RECPAM analysis was the same tested in the multivariate logistic regression analysis. Continuous variables were not categorised so as to allow the algorithm to choose the natural cut-off points when identifying distinct subgroups of patients. For each subgroup or class, the proportions (%) of patients reaching the endpoint and the likelihood (ORs and 95% CI) to reach the endpoint versus the reference subgroup were obtained. Finally, to detect additional global correlates (i.e. variables playing a role for all patients, irrespective of the interactions detected by RECPAM), a logistic regression model with RECPAM-identified subgroups and all the covariates ruled out by the algorithm was performed. No imputation was used for missing data, and sensitivity analyses were not performed.

Results

A total of 1723 patients were included in the analysis. Baseline characteristics, including diabetes complications and prior treatment regimens, are shown in Table 1. At baseline, most patients were being treated with metformin, either as monotherapy (n = 803, 46.6%) or with sulphonylureas (n = 457, 26.5%). Few patients (n = 100, 5.8%) received insulin. Most patients received liraglutide as an add-on to previous therapies (63.2%), with 33.4% replacing another prior drug with liraglutide, and 3.4% reducing the number of prior therapies. Mean BMI at baseline was 35.6 ± 5.9 kg/m2, with 83.3% of patients considered to have obesity (BMI > 30 kg/m2).
Table 1

Baseline characteristics of 1723 patients with type 2 diabetes prior to starting liraglutide treatment

Variable

Category

Value

Age (years)

 

58.9 ± 9.5

Sex (%)

Female

45.1

Diabetes duration (years)

 

9.6 ± 7.1

HbA1c (% points)

 

8.3 ± 1.4

(67 ± 15.3 mmol/mol)

Fasting plasma glucose (mg/dL)

 

171.8 ± 52.2

BMI (kg/m2)

 

35.6 ± 5.9

Presence of diabetes complications (%)

  

 Coronary heart disease

No

86.9

Yes

13.1

 Stroke

No

98.1

Yes

1.9

 Peripheral vascular disease

No

93.3

Yes

6.7

 Diabetic retinopathy

No

81.5

Yes

18.5

 Sensory-motor neuropathy

No

86.5

Yes

13.5

Baseline treatment (%)

Metformin

46.6

 

Other monotherapy

7.6

 

Metformin + SU

26.5

 

Other dual

8.6

 

≥3 OADs

3.7

 

Insulin ± OADs

7

Liraglutide treatment modality (%)

Switch

33.4

 

Add-on

63.2

 

Reduce

3.4

Systolic blood pressure (mmHg)

 

139.3 ± 18.1

Diastolic blood pressure (mmHg)

 

81.3 ± 10.0

Hypertension (≥ 140/90 mmHg) (%)

No

39.8

Yes

60.2

Total cholesterol (mg/dL)

 

180.8 ± 39.8

HDL-cholesterol (mg/dL)

 

45.0 ± 10.9

LDL-cholesterol (mg/dL)

 

102.9 ± 35.3

Dyslipidaemia (%)

No

34.4

Yes

65.6

eGFR (%)

≤ 30 mL/min/1.73 m2

0.1

> 30– < 60 mL/min/1.73 m2

11.4

≥ 60– < 90 mL/min/1.73 m2

43.1

≥ 90 mL/min/1.73 m2

45.4

Values are mean ± SD or %

Add-on, liraglutide added to prior therapy; BMI, body mass Index; eGFR, estimated glomerular filtration rate (using the Chronic Kidney Disease-Epidemiology Collaboration formula); HbA1c, glycated haemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; reduce, number of prior OADs was reduced with addition of liraglutide; OAD, oral antidiabetic drug; SU, sulphonylurea; switch, switch to liraglutide from prior therapy

By 12 months (primary endpoint analysis), a total of 194/1723 (11.2%) patients had discontinued liraglutide treatment. For those with a known reason (n = 166), most (n = 75/166) were owing to lack of effectiveness. An additional 35 discontinued due to liraglutide intolerance, 28 owing to gastrointestinal side effects, and 20 discontinued for other reasons. A total of 19 patients were non-adherent to therapy. At 12 months, there were 1325 (76.9%) patients with HbA1c values available at both baseline and 12 months, and 577/1325 (43.5%) reached the primary endpoint (HbA1c reduction ≥ 1.0% [10.9 mmol/mol]).

Patients who reached the endpoint had a shorter mean diabetes duration (9.1 ± 6.9 vs. 10.0 ± 7.0 years, p = 0.04), higher mean HbA1c at baseline (9.0 ± 1.4 [75 ± 15.3 mmol/mol] vs. 7.7 ± 1.0% [61 ± 10.9 mmol/mol], p < 0.0001), higher mean diastolic BP (82.6 ± 10.0 vs. 80.3 ± 9.8 mmHg, p = 0.0002) and higher mean total cholesterol levels (183.1 ± 41.8 vs. 177.2 ± 37.4 mg/dL, p = 0.02) compared to those who failed to reach the primary endpoint. Mean BMI was nearly identical in the two groups (35.6 ± 5.8 vs. 35.5 ± 5.8 kg/m2, p = 0.72), and there were no significant differences in mean high-density lipoprotein (HDL)-cholesterol (p = 0.11) or mean LDL-cholesterol (p = 0.16). There were no significant differences between the two groups in the proportion of patients using antihypertensive or lipid-lowering medications or other diabetes treatments at baseline.

Logistic regression analysis

The odds of achieving the primary endpoint, by patient characteristic, are shown in Table 2. In the univariate analysis, higher HbA1c at baseline was associated with significantly higher odds (OR 2.78; 95% CI [2.43; 3.18]; p < 0.0001). Shorter diabetes duration was associated with a significantly lower odds of reaching the endpoint (OR 0.98; 95% CI [0.97; 1.00]; p = 0.04). Higher diastolic BP (OR 1.02; 95% CI [1.01; 1.04]; p = 0.0002) and higher total cholesterol (OR 1.00; 95% CI [1.00; 1.01]; p = 0.0203) were also associated with significantly increased odds of reaching the endpoint. Other patient characteristics, such as age, sex, BMI, presence of various diabetes complications, dyslipidaemia or eGFR levels, were not significantly associated with odds of reaching the endpoint.
Table 2

Univariate and multivariate analysis of factors predicting reduction of HbA1c ≥ 1.0% (10.9 mmol/mol) among 1325 patientsa after 12 months of treatment with liraglutide

Variable

Category

Univariate logistic regression

Multivariate logistic regression

Odds ratio (95% CI)

p-value

Odds ratio (95% CI)b

p-value

Age

N/A

1.00 (0.99; 1.01)

0.9689

1.02 (1.00; 1.04)

0.02

Diabetes duration (years, continuous)

N/A

0.98 (0.97; 1.00)

0.04

0.97 (0.94; 0.99)

0.007

HbA1c (continuous)

N/A

2.78 (2.43; 3.18)

< 0.0001

3.52 (2.90; 4.27)

< 0.0001

BMI kg/m2 (continuous)

N/A

1.00 (0.98; 1.02)

0.7207

1.01 (0.98; 1.03)

0.61

Baseline treatment

Metformin

1.00c

 

1.00c

N/A

Other monotherapy

1.17 (0.76; 1.80)

0.4651

0.91 (0.52; 1.59)

0.75

Metformin + SU

1.01 (0.77; 1.32)

0.9528

0.50 (0.34; 0.72)

0.0002

Other dual

1.01 (0.67; 1.52)

0.9615

0.59 (0.34; 1.02)

0.06

≥ 3 OADs

1.12 (0.62; 2.02)

0.7025

0.41 (0.19; 0.88)

0.02

Insulin ± OADs

1.00 (0.63; 1.58)

0.9963

0.44 (0.23; 0.85)

0.02

Liraglutide dose

1.8

1.00c

 

1.00c

N/A

1.2

1.43 (1.12; 1.82)

0.0037

1.91 (1.40; 2.61)

< 0.0001

Liraglutide treatment modality

Switch

1.00c

 

1.00c

N/A

Add-on

1.74 (1.38; 2.20)

< 0.0001

1.86 (1.38; 2.51)

< 0.0001

Reduce

0.56 (0.26; 1.21)

0.1418

0.62 (0.24; 1.59)

0.32

Sex

Female

1.00c

   

Male

1.09 (0.88; 1.35)

0.4459

  

Fasting plasma glucose (mg/dL, continuous)

N/A

1.01 (1.01; 1.02)

< 0.0001

  

Diabetic retinopathy

No

1.00c

   

Yes

1.17 (0.87; 1.57)

0.2896

  

Sensory-motor neuropathy

No

1.00c

   

Yes

1.10 (0.79; 1.52)

0.5731

  

Coronary heart disease

No

1.00c

   

Yes

0.85 (0.62; 1.18)

0.3408

  

Stroke

No

1.00c

   

Yes

0.82 (0.38; 1.76)

0.6052

  

Peripheral vascular disease

No

1.00c

   

Yes

0.85 (0.55; 1.32)

0.4702

  

Blood pressure (mm Hg)

≤ 130/80

1.00c

   

131–139/81–89

1.25 (0.77; 2.03)

0.3652

  

≥ 140/90

1.11 (0.86; 1.44)

0.4247

  

Systolic BP (mm Hg, continuous)

N/A

1.00 (1.00; 1.01)

0.5617

  

Diastolic BP (mm Hg, continuous)

N/A

1.02 (1.01; 1.04)

0.0002

  

Hypertension

No

1.00c

   

Yes

0.91 (0.69; 1.19)

0.4815

  

Total cholesterol (mg/dL, continuous)

N/A

1.00 (1.00; 1.01)

0.0203

  

HDL-cholesterol (mg/dL, continuous)

N/A

1.0 (1.0; 1.0)

0.1091

  

LDL-cholesterol (mg/dL, continuous)

N/A

1.0 (1.0; 1.0)

0.1566

  

Dyslipidaemia

No

1.00c

   

Yes

0.98 (0.77; 1.24)

0.8573

  

eGFR

> 90

1.00c

   

61–90

0.97 (0.73; 1.29)

0.8471

  

31–60

0.63 (0.39; 1.02)

0.0603

  

0–30

nc

nc

  

Add-on, liraglutide added to prior therapy; BMI, body mass index; BP, blood pressure; CI, confidence interval; eGFR, estimated glomerular filtration rate; HbA1c, glycated haemoglobin; LDL, low-density lipoprotein; N/A, not applicable; nc, not calculated; OAD, oral antidiabetic drug; reduce, number of prior OADs was reduced with addition of liraglutide; SU, sulphonylurea; switch, switch to liraglutide from prior therapy

aPatients who had HbA1c data recorded at 12 months

bAdjusted for age, sex, duration of diabetes, baseline HbA1c, FPG, BMI, presence of diabetes complications, hypertension, dyslipidaemia, eGFR levels, treatment scheme at the first prescription of liraglutide, treatment modality, and liraglutide dosage

cReference category

Prior treatment (including insulin) was not significantly associated with reaching the primary endpoint (p > 0.05). However, after adjusting for potential confounding in the multivariate analysis, all prior treatment regimens (except for other dual therapy, p = 0.06) were associated with a significantly lower odds of achieving the endpoint compared with metformin monotherapy (Table 2). Regarding treatment modality, patients who had liraglutide added to their prior therapy had a significantly higher odds of achieving the primary endpoint (OR 1.74 95% CI [1.38; 2.20]; p < 0.0001) compared with patients who switched to liraglutide from their previous therapy. Those results were confirmed in the multivariate analysis.

The proportion of patients using liraglutide at higher doses increased with successive follow-up, with over a third (36.1%) using 1.8 mg at 12 months compared to 5.3% at baseline. Patients using liraglutide 1.2 mg had an increased odds (OR 1.43; 95% CI [1.12; 1.82]; p = 0.0037) of reaching the endpoint compared to those using the highest dose (1.8 mg).

RECPAM analysis

The RECPAM analysis identified five distinct patient subgroups or classes with increasing odds of achieving an HbA1c reduction ≥ 1.0% (10.9 mmol/mol) after 12 months (Fig. 1, Table 3). The proportion of patients reaching the endpoint ranged from 16.3% (reference group) to 83.1%. The splitting variables indicated that baseline HbA1c and, to some extent, diabetes duration were the primary drivers of degree of response to liraglutide, whereas other patient-related factors were not identified as important in discriminating responder subgroups. With patients having baseline HbA1c ≤ 7.5% (58 mmol/mol) considered the reference class (OR = 1.00), the odds of patients in the other classes achieving the endpoint were: Class 4: OR 2.6; 95% CI [1.7; 4.1], patients with HbA1c between 7.5% (58 mmol/mol) and 8.2% (66 mmol/mol), diabetes duration > 5 years; Class 3: OR 6.3; 95% CI [3.8; 10.2], HbA1c between 7.5% (58 mmol/mol) and 8.2% (66 mmol/mol), diabetes duration < 5 years; Class 2: OR 8.5; 95% CI [5.5; 13.1], HbA1c between 8.2% (66 mmol/mol) and 9.1% (76 mmol/mol); and Class 1: OR 28.7; 95% CI [17.8; 46.2], HbA1c > 9.1%.
Fig. 1

Subgroups of patients with type 2 diabetes with different odds of achieving a HbA1c reduction ≥ 1.0% (10.9 mmo/mol) after 12 months of treatment with liraglutide, identified using RECPAM analysis. The tree-growing algorithm modelled the odds for achieving HbA1c reduction ≥ 1.0%-point using multivariate logistic regression. Splitting variables were automatically selected by the RECPAM routine among the covariates used in the multivariate analysis and are shown between branches. Cut-offs sending patients to the left or right sibling were also automatically chosen by the RECPAM routine and are reported on the relative branches.  %, proportion of patients in subgroup achieving a reduction in HbA1c ≥ 1.0% (10.9 mmol/mol); circles indicate subgroups of patients and squares indicate final RECPAM classes. Numbers inside circles and squares indicate number of patients achieving HbA1c reduction ≥ 1.0% (10.9 mmol/mol). HbA1c, glycated haemoglobin; OR, unadjusted odds ratio (95% confidence interval); RECPAM, RECursive Partitioning and AMalgamation

Table 3

Clinical characteristics, at baseline and after 12 months of treatment with liraglutide, by RECPAM class

 

RECPAM classification

Class 1

n = 219

Class 2

n = 194

Class 3

n = 106

Class 4

n = 197

Class 5

n = 306

p-value

Splitting variables

HbA1c > 9.1%

[76 mmol/mol]

8.2% < HbA1c ≥ 9.1%

[66 < HbA1c ≥ 76 mmol/mol]

7.5% < HbA1c ≥ 8.2%

[58 < HbA1c ≥ 66 mmol/mol]

Diabetes duration

≤ 5 years

7.5% < HbA1c ≥ 8.2%

[58 < HbA1c ≥ 66 mmol/mol]

Diabetes duration

> 5 years

HbA1c ≤ 7.5%

[58 mmol/mol]

 

Unadjusted odds of HbA1c being reduced by ≥ 1.0%

28.7

(17.8; 46.2)

8.5

(5.5; 13.1)

6.3

(3.8; 10.2)

2.6

(1.7; 4.1)

1.00a

 

Patient characteristic

      

Baseline HbA1c (%)

10.2 ± 1.0

[88 ± 10.9 mmol/mol]

8.7 ± 0.3

[72 ± 3.3 mmol/mol]

7.9 ± 0.2

[63 ± 2.2 mmol/mol]

7.9 ± 0.2

[63 ± 2.2 mmol/mol]

7.0 ± 0.5

[53 ± 5.5 mmol/mol]

< 0.0001

Change in HbA1c (%)

− 2.2 ± 1.5

[88 ± 16.4 mmol/mol]

− 1.0 ± 1.1

[88 ± 12.0 mmol/mol]

− 0.9 ± 1.0

[88 ± 10.9 mmol/mol]

− 0.5 ± 0.9

[88 ± 9.8 mmol/mol]

− 0.1 ± 0.8

[88 ± 8.7 mmol/mol]

< 0.0001

Baseline FPG (mg/dl)

223.0 ± 56.7

181.5 ± 41.1

157.3 ± 28.9

159.7 ± 33.2

137.5 ± 28.5

< 0.0001

Change in FPG (mg/dl)

− 59.1 ± 63.7

− 28.9 ± 49.9

− 20.6 ± 40.3

− 14.4 ± 35.5

− 7.1 ± 33.0

0.0002

Baseline BMI (Kg/m2)

35.6 ± 5.6

35.3 ± 5.6

37.2 ± 6.3

34.1 ± 5.6

35.7 ± 6.2

< 0.0001

Change in BMI (Kg/m2)

− 0.9 ± 2.2

− 1.6 ± 2.0

− 1.3 ± 1.9

− 1.1 ± 1.7

− 1.3 ± 2.1

0.02

Baseline weight (Kg)

101.5 ± 18.5

98.3 ± 17.7

103.9 ± 19.1

93.9 ± 17.4

100.2 ± 19.2

< 0.0001

Change in weight (Kg)

− 2.5 ± 6.1

− 4.3 ± 5.3

− 3.7 ± 5.2

− 3.1 ± 4.7

− 3.7 ± 5.8

0.03

Age (years)

57.7 ± 9.4

60.7 ± 8.0

56.0 ± 9.1

61.2 ± 9.3

59.2 ± 8.9

< 0.0001

Sex (% male)

57.5

55.2

48.1

52.3

56.9

0.46

Duration diabetes (years)

10.2 ± 6.9

11.2 ± 7.3

2.9 ± 1.5

12.1 ± 6.3

9.1 ± 6.8

< 0.0001

Baseline treatment (%)

     

< 0.0001

Metformin only

34.7

34

71.7

38.6

60.8

 

Other monotherapy

7.3

7.7

8.5

8.6

7.2

 

Metformin + SU

35.6

35.6

13.2

31.5

14.4

 

Other dual therapies

7.3

11.3

3.8

7.6

10.1

 

≥ 3 OADs

5.5

4.1

1.9

5.6

2.6

 

Insulin ± OADs

9.6

7.2

0.9

8.1

4.9

 

Treatment modality (%)

     

0.34

Switch

31.5

37.6

34.0

36.5

38.2

 

Add-on

67.1

59.8

65.1

61.4

57.8

 

Reduction

1.4

2.6

0.9

2.0

3.9

 

Liraglutide dosage (%)

     

0.0007

0.6

4.1

4.6

5.7

5.1

7.8

 

1.2

55.3

50.5

65.1

58.9

66.7

 

1.8

40.6

44.8

29.2

36.0

25.5

 

Baseline SBP (mmHg)

142.0 ± 18.4

140.3 ± 16.6

138.0 ± 17.9

140.3 ± 18.7

137.4 ± 16.8

0.09

Change in SBP (mmHg)

− 4.2 ± 18.5

− 2.6 ± 16.7

− 4.4 ± 16.0

− 6.3 ± 19.2

− 5.4 ± 17.6

0.57

Baseline DBP (mmHg)

83.5 ± 10.6

81.2 ± 9.4

81.7 ± 10.1

81.0 ± 9.6

80.0 ± 10.0

0.02

Change in DBP (mmHg)

− 1.8 ± 11.2

− 0.6 ± 9.6

− 1.0 ± 10.6

− 2.4 ± 11.0

− 1.7 ± 11.1

0.60

Baseline total cholesterol (mg/dl)

187.9 ± 43.6

181.5 ± 36.3

185.2 ± 38.1

175.2 ± 34.9

174.8 ± 38.0

0.007

Change in total cholesterol (mg/dl)

− 16.2 ± 40.1

− 9.8 ± 32.3

− 19.9 ± 39.6

− 7.2 ± 34.7

− 7.1 ± 31.0

0.06

Baseline HDL-cholesterol (mg/dl)

42.9 ± 9.5

45.2 ± 11.5

43.5 ± 10.9

46.5 ± 12.0

44.7 ± 10.4

0.07

Change in HDL-cholesterol (mg/dl)

0.6 ± 7.1

1.6 ± 8.3

1.6 ± 7.5

1.8 ± 8.2

0.9 ± 7.9

0.42

Baseline LDL-cholesterol (mg/dl)

104.2 ± 38.8

104.4 ± 30.7

108.5 ± 36.2

96.9 ± 32.1

101.1 ± 32.8

0.13

Change in LDL-cholesterol (mg/dl)

− 9.4 ± 35.7

− 10.8 ± 30.9

− 20.4 ± 36.2

− 7.3 ± 31.8

− 8.7 ± 30.6

0.15

Baseline triglycerides (mg/dl)

211.6 ± 120.0

169.6 ± 80.1

182.9 ± 81.8

163.7 ± 77.9

150.8 ± 75.6

<0.0001

Change in triglycerides

− 35.4 ± 110.2

− 7.3 ± 85.8

− 16.6 ± 82.2

− 11.4 ± 64.6

− 0.4 ± 60.8

0.002

Baseline albuminuria (mg/l)

73.7 ± 150.3

39.0 ± 92.0

37.2 ± 55.7

40.6 ± 64.9

38.0 ± 84.1

0.07

Change in albuminuria (mg/l)

− 20.0 ± 119.2

0.6 ± 58.6

− 1.2 ± 43.3

− 15.2 ± 75.6

− 13.2 ± 89.7

0.91

Baseline eGFR (%)

     

0.16

0–60

5.5

8.2

2.8

7.6

8.2

 

61–90

62.1

57.2

68.9

64.5

55.2

 

> 90

32.4

34.5

28.3

27.9

36.6

 

Values are mean ± SD unless otherwise stated

BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycated haemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; n, number of subjects in class; OAD, oral antidiabetic drug; RECPAM, RECursive Partitioning and AMalgamation; SBP, systolic blood pressure; SU, sulphonylurea

aReference category for odds ratio

Although all RECPAM classes showed HbA1c reduction, the patient subgroup with the greatest odds of achieving an HbA1c reduction ≥ 1.0% (10.9 mmol/mol) can be described as having the following: mean HbA1c of 10.2% (88 mmol/mol), mean FPG of 223.0 mg/dL, mean diabetes duration of 10.2 years at baseline, metformin treatment ± sulphonylureas at initiation of liraglutide treatment, and liraglutide as an adjunct to prior therapy (versus discontinuation of prior treatment) (Table 3). Each RECPAM class showed a reduction in mean weight, ranging from 2.5 to 4.3 kg, after 12 months’ treatment with liraglutide. There was no obvious relationship between mean HbA1c reduction and mean weight loss. A final logistic model adjusted with other covariates deemed clinically important and with RECPAM classes forced into the model is shown in Table 4. The final logistic model with both the RECPAM classes and the covariates not entering the tree forced in the model (Table 4) showed that additional global variables associated with the likelihood of reaching the endpoint were baseline treatment scheme, liraglutide dosage and treatment modality.
Table 4

Final logistic modela showing key factors predicting reduction of HbA1c ≥ 1.0% [10.9 mmol/mol] among 1325 patients after 12 months of treatment with liraglutide, with RECPAM classes forced in the model

Factor

OR (95% CI)

p-value

RECPAM classes

  

 Class 1

33.69

(18.10–62.74)

< 0.0001

 Class 2

10.33

(6.23–17.12)

< 0.0001

 Class 3

5.72

(3.35–9.76)

< 0.0001

 Class 2

2.89

(1.80–4.65)

< 0.0001

 Class 5

1.00b

 

Baseline treatment

  

 Other monotherapies

0.93

(0.51–1.69)

0.81

 Metformin + sulphonylurea

0.47

(0.31–0.70)

0.0002

 Other dual therapies (metformin + TZD, metformin + glinides, SU + TZD)

0.73

(0.40–1.31)

0.29

 ≥ 3 OADs

0.39

(0.17–0.88)

0.02

 Insulin ± OADs

0.47

(0.24–0.94)

0.03

 Metformin only

1.00b

 

Liraglutide dosage (mg)

  

 0.6

1.02

(0.49–2.12)

0.95

 1.2

2.05

(1.45–2.90)

< 0.0001

 1.8

1.00b

 

Liraglutide treatment modality

  

 Add-on to existing treatment

1.79

(1.29–2.50)

0.0005

 Reduction of no. of drug classes

0.52

(0.17–1.63)

0.26

 Switch from another drug class

1.00b

 

BMI, body mass index; CI, confidence interval; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, glycated haemoglobin; OAD, oral antidiabetic drug; OR, odds ratio; SU, sulphonylurea; TZD, thiazolidinedione

aModel was adjusted for age, sex, FPG, BMI, presence of diabetes complications, hypertension, dyslipidaemia, and eGFR levels

bReference category

Discussion

This is the first RECPAM analysis to identify distinct groups of patients with T2D who were prescribed liraglutide in routine clinical practice according to their predicted degree of response to liraglutide treatment. These data can improve clinical practice by providing a deeper knowledge of factors influencing liraglutide’s impact on metabolic control. The key message of this analysis is that only baseline HbA1c and to a lesser extent diabetes duration were predictive of liraglutide effectiveness. Furthermore, these results for the first time clarify that HbA1c reduction can exceed 2.0% when baseline levels are > 9.0%. This finding has important clinical and health policy implications for the Italian Drugs Agency (AIFA) regulations, considering that patients with HbA1c ≥ 8.5% are currently excluded from the GLP-1 receptor agonists’ reimbursement policy, which requires HbA1c between 7.5 (58 mmol/mol) and 8.5% (69 mmol/mol) (AIFA regulations).

Different patterns have been reported in clinical trials with regard to dose response with liraglutide. In this study, patients using the 1.2-mg liraglutide dose as maintenance dose were more likely to reach the primary endpoint than those using the higher maintenance dose (1.8 mg). This is likely due to an indication bias because patients struggling to achieve good glycaemic control were up-titrated to the higher dose, but owing to their disease severity, they still did not respond as well as healthier patients who did not require an increased dose. Escalation from the starting liraglutide dose of 0.6–1.2 mg likely occurred earlier after initiation, whereas when escalation to 1.8 mg occurred, it tended to be later in the study.

In line with existing findings [18, 19, 20], we found that the higher the baseline HbA1c level, the higher the reduction achieved. Multivariate analysis showed that the likelihood of reaching the endpoint increased by 3.5 times for every 1% HbA1c increase at baseline. In addition, by applying the RECPAM analysis, the study showed that the likelihood of reaching the endpoint was 28 times higher with baseline HbA1c > 9.1% as compared to baseline levels < 7.5%. In the EVIDENCE study [21], conducted in France by general practitioners and specialists, on 2029 patients, there was a mean (± SD) HbA1c reduction from baseline of 1.01 ± 1.54% (from 8.46 ± 1.46 to 7.44% ± 1.20; p < 0.0001); after 2 years, 29.9% (95% CI 27.7; 31.2) of patients still had HbA1c ≤ 7.0%; in the cohort treated within specialist care settings (N = 1398), HbA1c reduction was − 0.8%.

In the current study, although there were differences in the degree of liraglutide response, each RECPAM class showed decreases in HbA1c from baseline after 12 months of treatment. As might be expected, a greater proportion of patients with the poorest glycaemic control at baseline achieved the primary endpoint of HbA1c reduction ≥ 1.0% (10.9 mmol/mol) after 12 months, since it would be incrementally more difficult to achieve that degree of absolute HbA1c reduction in patients already at or near glycaemic targets. Nevertheless, these results suggest that there is a distinct subgroup of patients for whom liraglutide treatment can help achieve HbA1c reductions in excess of 2.0% (21.9 mmol/mol), a finding that may have important clinical implications.

The RECPAM algorithm selected only baseline HbA1c and diabetes duration as important splitting variables when creating the responder subgroups or classes. This indicated that other patient variables were less important in determining the degree of response to liraglutide. Although BMI was not selected by the algorithm, this too may be because of the high prevalence of obesity in the sample.

Multivariate logistic regression with RECPAM categories forced into the model further confirmed that liraglutide is best used as an add-on to, rather than replacement for, prior treatment regimens (generally OADs) in T2D (OR 1.79; 95% CI [1.29; 2.50]). This finding is in line with current treatment guidelines [22]. Interestingly, the largest patient subgroup (n = 306, RECPAM Class 5) (Table 3) had comparatively good HbA1c control (≤ 7.5% [58 mmol/mol]), suggesting that there is also a patient subgroup who may initiate liraglutide to pair the glycaemic control to weight loss.

Regarding the role of diabetes duration, a previous study on liraglutide reported a higher efficacy in patients with short diabetes duration [12], while the ReaL study [13] found improvements in metabolic control also in patients with long diabetes duration. RECPAM analysis clarifies that diabetes duration can play a role mainly for patients with HbA1c levels between 7.5 and 8.2%; in particular, one in two patients with diabetes duration ≤ 5 years reached the endpoint, compared to one in three for a diabetes duration > 5 years. The role of BMI and previous therapy as independent predictors emerging in other studies [19, 23] was not confirmed in our study.

A strength of this study was the large sample size. Use of real-world data also makes the findings more generalisable to patient populations seen in regular clinical practice. The observational nature of the study may introduce bias in the selection of patients who were prescribed liraglutide; however, consecutive enrolment of all patients was adopted to minimise this. Since these results reflect the clinical usage of liraglutide in Italy, they may not be generalisable to countries with different usage patterns. As a retrospective study based on electronic medical records, the completeness of information depended on the ability of participating centres to record clinical data. It should be noted that data completeness was judged satisfactory (i.e. 97.2–56.3% complete for the adjustment variables used). Insulin secretion capacity was not evaluated as a potential predictor of HbA1c reduction with liraglutide, although several studies have suggested the usefulness of this parameter in predicting the effectiveness of liraglutide [24, 25]. This would be useful to explore in future studies. We cannot exclude the involvement of other factors, besides HbA1c and partly diabetes duration, in determining HbA1c reduction through liraglutide, but we analysed all factors easily available to diabetologists to guide routine clinical practice.

In conclusion, in this study, glycaemic response to liraglutide was largely driven by baseline HbA1c levels and to a lesser extent by diabetes duration. The clinical benefit seems to be maximised when used as an add-on to prior therapies. All RECPAM classes showed weight loss, which appeared independent of mean HbA1c reduction. RECPAM analyses suggest an urgent need to revise the AIFA criteria for reimbursement due to the finding that HbA1c reduction can exceed 2.0% in people with HbA1c > 9.0%.

Notes

Acknowledgements

It is with regret that we announce the death of co-author Dr M. Boemi, to whom this article is dedicated. He participated actively in the drafting and approved the final version of this manuscript. The authors thank Antonio Nicolucci, Michele Sacco and Marco Scardapane (CORESEARCH – Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy) for data management, data analysis, and assistance with manuscript preparation. We thank Cristiano Bette, Margit Kaltoft and Elena Startseva (Novo Nordisk), for their review and input to the manuscript. Medical writing assistance and editorial/submission support were provided by Gary Patronek and Izabel James, of Watermeadow Medical, an Ashfield Company, part of UDG Healthcare plc, funded by Novo Nordisk A/S. This study was funded by Novo Nordisk A/S. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

*ReaL Study Group

Expert board and writing committee: Natalino Simioni1, Cesare Berra2, Massimo Boemi3, Antonio Carlo Bossi4, Riccardo Candido5, Graziano Di Cianni6, Simona Frontoni7, Stefano Genovese8, Paola Ponzani9, Vincenzo Provenzano10, Giuseppina Russo11, Luigi Sciangula12, Annunziata Lapolla13, Cristiano Bette14, Maria Chiara Rossi15 on behalf of the ReaL (NN2211-4118) Study Group*

1Presidio Ospedaliero di Cittadella, Cittadella, Padua, Italy, 2Humanitas Research Hospital, Rozzano (MI), Italy, 3IRCCS INRCA, Ancona, Italy, 4Ospedale Treviglio Caravaggio, Treviglio, Italy, 5Ass 1 Triestina, Trieste, Italy, 6Ospedale di Livorno, Livorno, Italy, 7University of Rome Tor Vergata, Rome, Italy, 8Centro Cardiologico Monzino, Milan, Milan, Italy, 9Ospedale La Colletta, ASL3 Genovese, Arenzano, Italy, 10Centro Regionale di Riferimento Diabetologia ed Impianto Microinfusori Sicilia, Partinico, Palermo, Italy, 11University of Messina, Messina, Italy, 12IRCCS Multimedica - Ospedale di Castellanza, Varese, Italy, 13University of Padua, Padua, Italy 14Novo Nordisk Spa, Rome, Italy, 15CORESEARCH – Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy.

Investigators: Riccardo Fornengo, Ospedale Civile Chivasso (Torino); Antonio Carlo Bossi, Ospedale Treviglio (Caravaggio), Fabrizio Querci, Ospedale Pesenti Fenaroli (Alzano Lombardo); Paolo Desenzani, Spedali Civili di Brescia (Montichiari); Luigi Sciangula, Diabetologia Presidio Polispecialistico di Mariano Comense ASST Lariana, Como; Stefano Genovese, IRCCS Multimedica (Milano), Piermarco Piatti, IRCCS (Milano); Enrica Chebat, Ospedale Luigi Sacco (Milano); Cesare Celeste Berra, Humanitas Clinical and Research Center, Rozzano (MI); Paolo Erpoli, Ospedale Sant’Antonio Abate (Milano); Bruno Fattor, Ospedale Generale Regionale (Bolzano), Sandro Inchiostro, Ospedale San Lorenzo (Trento); Alberto Marangoni, Presidio Ospedaliero di Bassano Del Grappa (Vicenza); Loris Confortin, Presidio Ospedaliero Di Castelfranco Veneto (Treviso); Anna Coracina, Presidio Ospedaliero Di Montebelluna (Treviso); Carmela Vinci, Presidio Ospedaliero di San Dona’ Di Piave (Venezia); Natalino Simioni, Presidio Ospedaliero di Cittadella (Padova), Annunziata Lapolla, Complesso Ospedaliero Dei Colli (Padova); Riccardo Candido, ASS 1 Triestina (Trieste); Giuseppe Felace, Ospedale S Giovanni Dei Battuti (Udine); Paola Ponzani, Ospedale La Colletta dii Arenzano (Genova); Marcello Monesi, Ospedale Sant’Anna Di Cona (Ferrara); Francesca Pellicano, Ospedale S Maria Delle Croci (Ravenna); Fabio Baccetti, Ospedale Ss Giacomo e Cristoforo (Massa Carrara); Michele Di Carlo, Ospedale Civile Campo Di Marte (Lucca); Graziano Di Cianni, Ospedale Di Livorno (Livorno); Massimo Boemi, IRCCS INRCA (Ancona); Simona Frontoni, University of Rome Tor Vergata (Roma); Sergio Leotta, Ospedale Sandro Pertini (Roma); Davide Lauro, Policlinico Tor Vergata (Roma); Claudio Ventura, Ospedale Israelitico (Roma); Dario Pitocco, Policlinico A. Gemelli (Roma); Franco Tuccinardi, Ospedale Monsignor Diliegro (Latina); Gaetano Leto, Ospedale Regionale S. Maria Goretti (Latina); Pasquale Alfidi, Ospedale SS Filippo Nicola (Avezzano); Ercole D’Ugo, ASL 2 Lanciano Vasto Chieti (Vasto); Stefania Donatelli, ASL 2 Lanciano Vasto Chieti (Chieti); Ercole Memoli, ASL Avellino (Avellino); Damiano Gullo, Ospedale Nuovo Garibaldi (Catania); Giuseppina Russo, Policlinico G Martino (Messina); Manfredi Rizzo, Policlinico Universita P. Giaccone (Palermo); Vincenzo Provenzano, Ospedale Civile Di Partinico (Palermo); Giuseppe Mattina, ASP Palermo (Palermo); Mario Rizzo, Ospedale Buccheri La Ferla (Palermo).

Other responsible parties: Contract research organisation (CRO) responsible for the submissions to Independent Ethics Committees (IECs), study closure procedures, creation and delivery of study files: TFS People S.r.l - Via Lucrezio Caro 63, 00193, Rome, Italy (Simona Foglietta). CRO responsible for data management: Istituto di Ricerche Farmacologiche Mario Negri - Via Giuseppe La Masa 19, 20156, Milan, Italy (Roberto Latini). CRO responsible for statistical analysis and medical writing: CORESEARCH – Center for Outcomes Research and clinical Epidemiology - S.r.l.Via Tiziano Vecellio 2, 65124, Pescara, Italy (Antonio Nicolucci, Maria Chiara Rossi, Michele Sacco, Marco Scardapane).

Compliance with ethical standards

Conflict of interest

Simioni N: Consulting fees from Novo Nordisk, Lilly, Boehringer Ingelheim and Abbott; member of advisory boards for Novo Nordisk, Lilly and Boehringer Ingelheim; investigator in clinical trials sponsored by Novo Nordisk. Berra C: consulting fees from Novo Nordisk, Lilly, Boehringer Ingelheim, Sanofi, Johnson & Johnson and Bayer; research support from AstraZeneca and Takeda; member of advisory boards for Novo Nordisk, Lilly, Boehringer Ingelheim, AstraZeneca and Sanofi; investigator in clinical trials sponsored by Lilly and Sanofi. Boemi M: member of advisory boards for Lilly, Boehringer Ingelheim and Sanofi; investigator in clinical trials sponsored by Novo Nordisk, Boehringer Ingelheim and Merck SD. Bossi AC: investigator in clinical trials sponsored by Novo Nordisk, Artsana, Lilly, Bayer and Sanofi; consulting fees from AstraZeneca, Roche, Johnson & Johnson and Takeda; research support from Merck SD and Sigma-Tau; member of advisory board for Boehringer Ingelheim. Candido R: investigator in clinical trials sponsored by Novo Nordisk, Lilly and Merck SD; consulting fees from AstraZeneca, Roche and Johnson & Johnson; member of advisory board for Boehringer Ingelheim. Di Cianni G: investigator in clinical trials sponsored by Novo Nordisk, AstraZeneca and Sanofi; member of advisory boards for Lilly and Sanofi. Frontoni S: member of advisory boards for Novo Nordisk, Lilly, AstraZeneca, Johnson & Johnson, Takeda and Sigma-Tau; investigator in clinical trials sponsored by Novo Nordisk and Boehringer Ingelheim. Genovese S: consulting fees from Novo Nordisk, Lilly, Boehringer Ingelheim, AstraZeneca, Merck SD, Sanofi, Johnson & Johnson, Takeda, Abbott Diabetes Care, Bristol Myers & Squibb, Janssen, Lifescan, Menarini and Novartis; member of advisory boards for Novo Nordisk, Boehringer Ingelheim, AstraZeneca, Merck SD, Sanofi, Johnson & Johnson, Takeda, Abbott Diabetes Care, Bruno Farmaceutici, Janssen, Lifescan and Novartis; research support from Novartis; investigator in clinical trials sponsored by Novo Nordisk, Lilly, Boehringer Ingelheim, AstraZeneca, Merck SD, Takeda, Janssen, Novartis and Sanofi. Ponzani P: investigator in clinical trials sponsored by Boehringer Ingelheim, Sanofi, Johnson & Johnson, Bayer and Novartis; member of advisory boards for Novo Nordisk and AstraZeneca. Provenzano V: consulting fees from Novo Nordisk, Lilly, Boehringer Ingelheim, AstraZeneca, Merck SD, Sanofi and Takeda; member of advisory boards for Novo Nordisk, Lilly, Boehringer Ingelheim, AstraZeneca and Sanofi; investigator in clinical trials sponsored by Novo Nordisk, Lilly, Boehringer Ingelheim, AstraZeneca, Merck SD, Sanofi and Roche. Russo GT: investigator in clinical trials sponsored by Lilly, Boehringer Ingelheim, Merck SD, Sanofi and Johnson & Johnson; member of advisory boards for Novo Nordisk, Lilly and Boehringer Ingelheim, member of advisory boards for, and consulting fees from, Novo Nordisk, Lilly and Boehringer Ingelheim. Sciangula L: member of advisory boards for Novo Nordisk, Lilly, AstraZeneca and Johnson & Johnson; consulting fees from Roche; investigator in clinical trials sponsored by Novo Nordisk. Lapolla A: investigator in clinical trials sponsored by Novo Nordisk, Lilly, Boehringer Ingelheim and Sanofi. Bette C: employee of Novo Nordisk SpA (Rome, Italy). Rossi MC: research grant from Novo Nordisk, Sanofi, Dexcom, AstraZeneca, Sigma-Tau, Eli Lilly, Artsana and Medtronic.

Ethical approval

This study was conducted in accordance with the Declaration of Helsinki and the Guidelines for Good Pharmacoepidemiology Practices. According to Italian law (Italian Republic. Determination of the Italian Medicines Agency of March 20, 2008. Official Gazette of the Italian Republic. General Series No. 76; March 31, 2008), prior to study initiation, the protocol, patient informed consent form and patient enrolment procedures were reviewed and approved by an Independent Ethics Committee (IEC). The study protocol was submitted to the Coordinating Centre IEC in advance, then after its official approval, the study documentation was submitted to the local IECs of all participating centres.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • N. Simioni
    • 1
  • C. Berra
    • 2
  • M. Boemi
    • 3
  • A. C. Bossi
    • 4
  • R. Candido
    • 5
  • G. Di Cianni
    • 6
  • S. Frontoni
    • 7
  • S. Genovese
    • 8
  • P. Ponzani
    • 9
  • V. Provenzano
    • 10
  • G. T. Russo
    • 11
  • L. Sciangula
    • 12
  • A. Lapolla
    • 13
  • C. Bette
    • 14
  • M. C. Rossi
    • 15
  • ReaL (NN2211-4118) Study Group*
  1. 1.Presidio Ospedaliero di CittadellaCittadella, PaduaItaly
  2. 2.Humanitas Research HospitalRozzanoItaly
  3. 3.IRCCS INRCAAnconaItaly
  4. 4.ASST Bergamo OvestTreviglioItaly
  5. 5.Azienda Sanitaria Universitaria Integrata di TriesteTriesteItaly
  6. 6.Ospedale di LivornoLivornoItaly
  7. 7.University of Rome Tor VergataRomeItaly
  8. 8.IRCCS Centro Cardiologico MonzinoMilanItaly
  9. 9.Ospedale La Colletta, ASL3 GenoveseArenzanoItaly
  10. 10.Centro Regionale di Riferimento Diabetologia ed Impianto Microinfusori SiciliaPartinico, PalermoItaly
  11. 11.University of MessinaMessinaItaly
  12. 12.IRCCS Multimedica - Ospedale di CastellanzaVareseItaly
  13. 13.University of PaduaPaduaItaly
  14. 14.Novo Nordisk SpaRomeItaly
  15. 15.CORESEARCH – Center for Outcomes Research and Clinical EpidemiologyPescaraItaly

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