Diabetes care · 2026
To Cluster or Not to Cluster? A Comparison of Approaches to Targeting Type 2 Diabetes Glucose-Lowering Therapy in the TriMaster Crossover Trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Güdemann Laura M, Angwin Catherine, Holman Rury R, Sattar Naveed, Pearson Ewan R, Murray Leech Jacques, Patel Kashyap A, Hattersley Andrew T, Dennis John M, Shields Beverley M, Jones Angus G
Аннотация
OBJECTIVE: Different precision treatment approaches have been proposed for type 2 diabetes, but robust comparisons are lacking. We compared the utility of proposed approaches targeting glucose-lowering therapy in the TriMaster three-way crossover trial.
RESEARCH DESIGN AND METHODS: We evaluated four previously reported precision treatment strategies for their ability to predict overall 4-month HbA1c response and within-person differential response to sitagliptin, canagliflozin, and pioglitazone in 309 adults with type 2 diabetes who received all three medications. Approaches included allocation to clusters based on routine features and HOMA, direct outcome prediction using a routine-features treatment-selection model, type 2 diabetes cluster-specific partitioned polygenic scores (PPSs), and a model based on discriminative dimensionality reduction tree (DDRTree) analysis.
RESULTS: The routine-features model and cluster approach were both strongly associated with overall and differential HbA1c responses (P < 0.0001). The routine-features model identified the most effective therapy with significant benefit across all drug comparisons, whereas clinical clusters showed benefit for only one cluster-drug comparison (severe insulin-resistant diabetes: SGLT2i vs. thiazolidinedione [TZD], P = 0.005). DDRTree was not associated with treatment response. Of eight PPSs, only two showed modest association with differential response (lipodystrophy PPS had a greater response to SGLT2 vs. TZD; and β-cell dysfunction, negative proinsulin PPS had a greater response to TZD vs. dipeptidyl peptidase 4 inhibitor). Treatment allocation based on the routine-features model resulted in greater HbA1c reduction than cluster-based allocation (0.27% [95% CI 0.18, 0.35], 2.9 mmol/mol [95% CI 2.0, 3.8] vs. 0.16% [95% CI 0.07, 0.25], 1.7 mmol/mol [95% CI 0.8, 2.7]).
CONCLUSIONS: Clinical features are more strongly associated with differential glycemic response than PPS, and direct outcome prediction outperforms data-driven subgrouping for optimizing short-term HbA1c reduction.
RESEARCH DESIGN AND METHODS: We evaluated four previously reported precision treatment strategies for their ability to predict overall 4-month HbA1c response and within-person differential response to sitagliptin, canagliflozin, and pioglitazone in 309 adults with type 2 diabetes who received all three medications. Approaches included allocation to clusters based on routine features and HOMA, direct outcome prediction using a routine-features treatment-selection model, type 2 diabetes cluster-specific partitioned polygenic scores (PPSs), and a model based on discriminative dimensionality reduction tree (DDRTree) analysis.
RESULTS: The routine-features model and cluster approach were both strongly associated with overall and differential HbA1c responses (P < 0.0001). The routine-features model identified the most effective therapy with significant benefit across all drug comparisons, whereas clinical clusters showed benefit for only one cluster-drug comparison (severe insulin-resistant diabetes: SGLT2i vs. thiazolidinedione [TZD], P = 0.005). DDRTree was not associated with treatment response. Of eight PPSs, only two showed modest association with differential response (lipodystrophy PPS had a greater response to SGLT2 vs. TZD; and β-cell dysfunction, negative proinsulin PPS had a greater response to TZD vs. dipeptidyl peptidase 4 inhibitor). Treatment allocation based on the routine-features model resulted in greater HbA1c reduction than cluster-based allocation (0.27% [95% CI 0.18, 0.35], 2.9 mmol/mol [95% CI 2.0, 3.8] vs. 0.16% [95% CI 0.07, 0.25], 1.7 mmol/mol [95% CI 0.8, 2.7]).
CONCLUSIONS: Clinical features are more strongly associated with differential glycemic response than PPS, and direct outcome prediction outperforms data-driven subgrouping for optimizing short-term HbA1c reduction.