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 - Altunkaya, James
AU - Slieker, Roderick C.
AU - Beulens, Joline W. J.
AU - ‘t Hart, Leen M.
AU - Pearson, Ewan R.
AU - Elders, Petra J. M.
AU - Feenstra, Talitha L.
AU - Leal, Jose
N1 - Funding Information: reviewers for insightful feedback, which contributed a lot to enhancing the quality and clarity of the article. Funding. This project received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant 115881 (RHAPSODY). This joint undertaking receives support from the European Union’s Horizon 2020 (H2020 Health) research and innovation program and the European Federation of Pharmaceutical Industries and Associations. This work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 16.0097-2. Publisher Copyright: © 2023 by the American Diabetes Association.
PY - 2023/7/1
Y1 - 2023/7/1
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 Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study 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 with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist 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 with homogenous type 2 diabetes, treatment for individuals in the 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 10-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated 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 Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study 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 with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist 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 with homogenous type 2 diabetes, treatment for individuals in the 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 10-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated 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.
UR - http://www.scopus.com/inward/record.url?scp=85163699285&partnerID=8YFLogxK
U2 - https://doi.org/10.2337/dc22-2170
DO - https://doi.org/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 -