Prediction of excessive weight gain in insulin treated patients with type 2 diabetes

Simon Lebech Cichosz; Louise Lundby-Christensen; Mette D Johansen; Lise Tarnow; Thomas Peter Almdal; Ole K Hejlesen; The C I M T Trial Group

doi:10.1111/1753-0407.12418

Prediction of excessive weight gain in insulin treated patients with type 2 diabetes

Simon Lebech Cichosz, Louise Lundby-Christensen, Mette D Johansen, Lise Tarnow, Thomas Peter Almdal, Ole K Hejlesen, The C I M T Trial Group

4 Citations (Scopus)

Abstract

BACKGROUND: Weight gain is an ongoing challenge when initiating insulin therapy in patients with type 2 diabetes mellitus (T2DM). However, if prediction of insulin-associated weight gain was possible on an individual level, targeted initiatives could be implemented to reduce weight gain. The aim of the present study was to identify predictors of weight gain in insulin-treated patients with T2DM.

METHODS: In all, 412 individuals with T2DM were, in addition to metformin or placebo, randomized into 18-month treatment groups with three different insulin analog treatment regimens (biphasic, aspart, detemir). Participants with excessive weight gain were defined as the group with weight gain in the 4th quartile (>6.2 kg).We developed a pattern classification method to predict individuals prone to excessive weight gain.

RESULTS: Over the 18-month treatment period, median weight gain among all 412 patients was 2.4 kg (95% prediction interval [PI] -5.6, 12.4 kg), whereas median weight gain for those in the upper 4th quartile (n = 103) was 8.9 kg (95% PI 6.3, 15.2 kg). No clinical baseline data were strong predictors of excessive weight gain. However, the weight gain during the first 3 months of the trial and the subsequent dose of insulin yielded a useful predictor for weight gain at the 18-month follow-up. Combining these two predictors into a prediction model with other clinical available information produced a receiver operating characteristic area under the curve of 0.80.

CONCLUSIONS: We have developed a prediction model that could help identify a substantial proportion of individuals with T2DM prone to large weight gain during insulin therapy.

Original language	English
Journal	Journal of Diabetes
Volume	9
Issue number	4
Pages (from-to)	325-331
Number of pages	7
ISSN	1753-0393
DOIs	https://doi.org/10.1111/1753-0407.12418
Publication status	Published - 1 Apr 2017
Externally published	Yes

Keywords

Aged
Biphasic Insulins
Blood Glucose
Chi-Square Distribution
Diabetes Mellitus, Type 2
Drug Therapy, Combination
Female
Follow-Up Studies
Hemoglobin A, Glycosylated
Humans
Hypoglycemic Agents
Insulin Aspart
Insulin Detemir
Logistic Models
Male
Metformin
Middle Aged
Prognosis
Time Factors
Treatment Outcome
Weight Gain
Journal Article
Randomized Controlled Trial

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1111/1753-0407.12418

1753-0407.12418Final published version, 419 KB

Cite this

@article{7e98d8422ea54ca0b5ab2b46589ce15d,

title = "Prediction of excessive weight gain in insulin treated patients with type 2 diabetes",

abstract = "BACKGROUND: Weight gain is an ongoing challenge when initiating insulin therapy in patients with type 2 diabetes mellitus (T2DM). However, if prediction of insulin-associated weight gain was possible on an individual level, targeted initiatives could be implemented to reduce weight gain. The aim of the present study was to identify predictors of weight gain in insulin-treated patients with T2DM.METHODS: In all, 412 individuals with T2DM were, in addition to metformin or placebo, randomized into 18-month treatment groups with three different insulin analog treatment regimens (biphasic, aspart, detemir). Participants with excessive weight gain were defined as the group with weight gain in the 4th quartile (>6.2 kg).We developed a pattern classification method to predict individuals prone to excessive weight gain.RESULTS: Over the 18-month treatment period, median weight gain among all 412 patients was 2.4 kg (95% prediction interval [PI] -5.6, 12.4 kg), whereas median weight gain for those in the upper 4th quartile (n = 103) was 8.9 kg (95% PI 6.3, 15.2 kg). No clinical baseline data were strong predictors of excessive weight gain. However, the weight gain during the first 3 months of the trial and the subsequent dose of insulin yielded a useful predictor for weight gain at the 18-month follow-up. Combining these two predictors into a prediction model with other clinical available information produced a receiver operating characteristic area under the curve of 0.80.CONCLUSIONS: We have developed a prediction model that could help identify a substantial proportion of individuals with T2DM prone to large weight gain during insulin therapy.",

keywords = "Aged, Biphasic Insulins, Blood Glucose, Chi-Square Distribution, Diabetes Mellitus, Type 2, Drug Therapy, Combination, Female, Follow-Up Studies, Hemoglobin A, Glycosylated, Humans, Hypoglycemic Agents, Insulin Aspart, Insulin Detemir, Logistic Models, Male, Metformin, Middle Aged, Prognosis, Time Factors, Treatment Outcome, Weight Gain, Journal Article, Randomized Controlled Trial",

author = "Cichosz, {Simon Lebech} and Louise Lundby-Christensen and Johansen, {Mette D} and Lise Tarnow and Almdal, {Thomas Peter} and Hejlesen, {Ole K} and Group, {The C I M T Trial}",

note = "{\textcopyright} 2016 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.",

year = "2017",

month = apr,

day = "1",

doi = "10.1111/1753-0407.12418",

language = "English",

volume = "9",

pages = "325--331",

journal = "Journal of Diabetes",

issn = "1753-0393",

publisher = "Wiley-Blackwell",

number = "4",

}

TY - JOUR

T1 - Prediction of excessive weight gain in insulin treated patients with type 2 diabetes

AU - Cichosz, Simon Lebech

AU - Lundby-Christensen, Louise

AU - Johansen, Mette D

AU - Tarnow, Lise

AU - Almdal, Thomas Peter

AU - Hejlesen, Ole K

AU - Group, The C I M T Trial

PY - 2017/4/1

Y1 - 2017/4/1

N2 - BACKGROUND: Weight gain is an ongoing challenge when initiating insulin therapy in patients with type 2 diabetes mellitus (T2DM). However, if prediction of insulin-associated weight gain was possible on an individual level, targeted initiatives could be implemented to reduce weight gain. The aim of the present study was to identify predictors of weight gain in insulin-treated patients with T2DM.METHODS: In all, 412 individuals with T2DM were, in addition to metformin or placebo, randomized into 18-month treatment groups with three different insulin analog treatment regimens (biphasic, aspart, detemir). Participants with excessive weight gain were defined as the group with weight gain in the 4th quartile (>6.2 kg).We developed a pattern classification method to predict individuals prone to excessive weight gain.RESULTS: Over the 18-month treatment period, median weight gain among all 412 patients was 2.4 kg (95% prediction interval [PI] -5.6, 12.4 kg), whereas median weight gain for those in the upper 4th quartile (n = 103) was 8.9 kg (95% PI 6.3, 15.2 kg). No clinical baseline data were strong predictors of excessive weight gain. However, the weight gain during the first 3 months of the trial and the subsequent dose of insulin yielded a useful predictor for weight gain at the 18-month follow-up. Combining these two predictors into a prediction model with other clinical available information produced a receiver operating characteristic area under the curve of 0.80.CONCLUSIONS: We have developed a prediction model that could help identify a substantial proportion of individuals with T2DM prone to large weight gain during insulin therapy.

AB - BACKGROUND: Weight gain is an ongoing challenge when initiating insulin therapy in patients with type 2 diabetes mellitus (T2DM). However, if prediction of insulin-associated weight gain was possible on an individual level, targeted initiatives could be implemented to reduce weight gain. The aim of the present study was to identify predictors of weight gain in insulin-treated patients with T2DM.METHODS: In all, 412 individuals with T2DM were, in addition to metformin or placebo, randomized into 18-month treatment groups with three different insulin analog treatment regimens (biphasic, aspart, detemir). Participants with excessive weight gain were defined as the group with weight gain in the 4th quartile (>6.2 kg).We developed a pattern classification method to predict individuals prone to excessive weight gain.RESULTS: Over the 18-month treatment period, median weight gain among all 412 patients was 2.4 kg (95% prediction interval [PI] -5.6, 12.4 kg), whereas median weight gain for those in the upper 4th quartile (n = 103) was 8.9 kg (95% PI 6.3, 15.2 kg). No clinical baseline data were strong predictors of excessive weight gain. However, the weight gain during the first 3 months of the trial and the subsequent dose of insulin yielded a useful predictor for weight gain at the 18-month follow-up. Combining these two predictors into a prediction model with other clinical available information produced a receiver operating characteristic area under the curve of 0.80.CONCLUSIONS: We have developed a prediction model that could help identify a substantial proportion of individuals with T2DM prone to large weight gain during insulin therapy.

KW - Aged

KW - Biphasic Insulins

KW - Blood Glucose

KW - Chi-Square Distribution

KW - Diabetes Mellitus, Type 2

KW - Drug Therapy, Combination

KW - Female

KW - Follow-Up Studies

KW - Hemoglobin A, Glycosylated

KW - Humans

KW - Hypoglycemic Agents

KW - Insulin Aspart

KW - Insulin Detemir

KW - Logistic Models

KW - Male

KW - Metformin

KW - Middle Aged

KW - Prognosis

KW - Time Factors

KW - Treatment Outcome

KW - Weight Gain

KW - Journal Article

KW - Randomized Controlled Trial

U2 - 10.1111/1753-0407.12418

DO - 10.1111/1753-0407.12418

M3 - Journal article

C2 - 27130075

SN - 1753-0393

VL - 9

SP - 325

EP - 331

JO - Journal of Diabetes

JF - Journal of Diabetes

IS - 4

ER -

Prediction of excessive weight gain in insulin treated patients with type 2 diabetes

Abstract

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this