Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

Xinyu  Li; Anoukh  van Giessen; Roderick C. Slieker; Joline W. Beulens; James  Altunkaya; Ewan R. Pearson; Petra J. M. Elders; Talitha L. Feenstra; Jose  Leal

doi:10.2337/dc22-2170

Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

Xinyu Li (Lead / Corresponding author), Anoukh van Giessen, Roderick C. Slieker, Joline W. Beulens, James Altunkaya, Ewan R. Pearson, Petra J. M. Elders, Talitha L. Feenstra, Jose Leal

Population Health and Genomics

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Abstract

Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c.

Research Design and Methods: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist’s subgroups.

Results: Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained.

Conclusions: Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Original language	English
Pages (from-to)	1395-1403
Number of pages	9
Journal	Diabetes Care
Volume	46
Issue number	7
Early online date	5 May 2023
DOIs	https://doi.org/10.2337/dc22-2170
Publication status	Published - Jul 2023

Keywords

Type 2 Diabetes
Economic Analysis
Cost Effectiveness
Cost Analysis
Disease Modeling
Diagnosis/Stratification
Reclassification
Clusters
Guidelines
Healthcare
Models
Healthcare Costs

ASJC Scopus subject areas

Advanced and Specialised Nursing
Internal Medicine
Endocrinology, Diabetes and Metabolism

UN SDGs

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

Access to Document

10.2337/dc22-2170

Author Accepted ManuscriptAccepted author manuscript, 634 KB

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Li, X., van Giessen, A., Slieker, R. C., Beulens, J. W., Altunkaya, J., Pearson, E. R., Elders, P. J. M., Feenstra, T. L., & Leal, J. (2023). Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups. Diabetes Care, 46(7), 1395-1403. https://doi.org/10.2337/dc22-2170

@article{12d7d892c187466d9a6c4a69ae5bf7fc,

title = "Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups",

abstract = "Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c.Research Design and Methods: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist{\textquoteright}s subgroups.Results: Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained.Conclusions: Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.",

keywords = "Type 2 Diabetes, Economic Analysis, Cost Effectiveness, Cost Analysis, Disease Modeling, Diagnosis/Stratification, Reclassification, Clusters, Guidelines, Healthcare, Models, Healthcare Costs",

author = "Xinyu Li and {van Giessen}, Anoukh and Slieker, {Roderick C.} and Beulens, {Joline W.} and James Altunkaya and Pearson, {Ewan R.} and Elders, {Petra J. M.} and Feenstra, {Talitha L.} and Jose Leal",

note = "Funding Information: This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115881 (RHAPSODY). This Joint Undertaking receives support from the European Union{\textquoteright}s Horizon 2020 research and innovation programme and EFPIA. This work is supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0097. The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. Copyright: {\textcopyright}2023 by the American Diabetes Association.",

year = "2023",

month = jul,

doi = "10.2337/dc22-2170",

language = "English",

volume = "46",

pages = "1395--1403",

journal = "Diabetes Care",

issn = "0149-5992",

publisher = "American Diabetes Association Inc.",

number = "7",

}

Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups. / Li, Xinyu (Lead / Corresponding author); van Giessen, Anoukh ; Slieker, Roderick C. et al.
In: Diabetes Care, Vol. 46, No. 7, 07.2023, p. 1395-1403.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment

T2 - A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

AU - Li, Xinyu

AU - van Giessen, Anoukh

AU - Slieker, Roderick C.

AU - Beulens, Joline W.

AU - Altunkaya, James

AU - Pearson, Ewan R.

AU - Elders, Petra J. M.

AU - Feenstra, Talitha L.

AU - Leal, Jose

N1 - Funding Information: This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115881 (RHAPSODY). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This work is supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0097. The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. Copyright: ©2023 by the American Diabetes Association.

PY - 2023/7

Y1 - 2023/7

N2 - Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c.Research Design and Methods: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist’s subgroups.Results: Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained.Conclusions: Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

AB - Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c.Research Design and Methods: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist’s subgroups.Results: Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained.Conclusions: Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

KW - Type 2 Diabetes

KW - Economic Analysis

KW - Cost Effectiveness

KW - Cost Analysis

KW - Disease Modeling

KW - Diagnosis/Stratification

KW - Reclassification

KW - Clusters

KW - Guidelines

KW - Healthcare

KW - Models

KW - Healthcare Costs

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U2 - 10.2337/dc22-2170

DO - 10.2337/dc22-2170

M3 - Article

C2 - 37146005

SN - 0149-5992

VL - 46

SP - 1395

EP - 1403

JO - Diabetes Care

JF - Diabetes Care

IS - 7

ER -

Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

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RHAPSODY (Joint with 25 other parties)

Cite this

Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

RHAPSODY (Joint with 25 other parties)

Cite this