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Optimizing treatment goals for long-term health outcomes among patients with type 2 diabetes mellitus
Qian Shi1, Yilu Lin1, Vivian A Fonseca2
1Department of Health Policy and Management, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
Introduction:
Considerable confusions on treatment target have resulted from recent changes in guidelines. Evidence in medical guidelines came from clinical trials with highly selected patients, whereas treatment goals may differ in some subgroups. This study aimed to assess optimal treatment goals (A1C, blood pressure, low-density lipoprotein cholesterol (LDL-C)) for patients with type 2 diabetes mellitus (T2DM), which lead to optimal health outcomes by different treatment strategies.
Research Design And Methods:
A retrospective longitudinal study was conducted for veterans with T2DM by using US Veterans Affairs Administrative Database (2005-2015). Medical records were prepared for repeated evaluation performed at 6-month intervals and multivariate longitudinal regression was used to estimate the risk of microvascular and macrovascular complication events. Second-degree polynomial and splines were applied to identify the optimal goals in their associations with lowest risk of clinical outcomes, controlling for demographic characteristics, medical history, and medications.
Results:
A total of 124 651 patients with T2DM were selected, with mean of 6.72 follow-up years. In the general population, to achieve the lowest risk of microvascular and macrovascular complication, the optimal goals were A1C=6.81%, LDL-C=109.10 mg/dL; and A1C=6.76%, LDL-C=111.65 mg/dL, systolic blood pressure (SBP)=130.60 mmHg, respectively. The optimal goals differed between age and racial subgroups. Lower SBP for younger patients and lower LDL-C for black patients were associated with better health outcomes.
Conclusions:
Optimal treatment goals were identified and multi-faceted treatment strategies targeting hyperglycemia and hyperlipidemia and hypertension may improve health outcome in veterans with T2DM. In addition to guidelines' recommended goals, health systems may examine their own large diverse patients with T2DM for better quality of care.
Insights
Optimal treatment goals for type 2 diabetes mellitus (T2DM) patients were identified. Tailored strategies for A1C, blood pressure, and LDL-C in subgroups like younger or Black patients improve health outcomes.
Area of Science:
- Endocrinology
- Cardiology
- Nephrology
- Public Health
Background:
- Recent guideline changes for type 2 diabetes mellitus (T2DM) treatment targets have caused confusion.
- Clinical trial evidence often involves highly selected patients, potentially not reflecting real-world T2DM subgroups.
- Optimal health outcomes may necessitate individualized treatment goals beyond general recommendations.
Purpose of the Study:
- To determine optimal treatment goals for A1C, blood pressure, and LDL-C in patients with T2DM.
- To identify strategies that lead to the best health outcomes across diverse patient populations.
- To address discrepancies between guideline-based targets and subgroup-specific needs in T2DM management.
Main Methods:
- Retrospective longitudinal study using the US Veterans Affairs Administrative Database (2005-2015).
- Multivariate longitudinal regression analysis to assess risks of microvascular and macrovascular complications.
- Application of polynomial and spline models to identify optimal goal associations with lowest clinical outcome risks.
Main Results:
- Analysis of 124,651 T2DM patients with a mean follow-up of 6.72 years.
- Optimal goals for lowest complication risk: A1C ~6.8%, LDL-C ~110 mg/dL, and systolic blood pressure (SBP) ~130 mmHg.
- Subgroup analysis revealed that younger patients benefited from lower SBP, and Black patients from lower LDL-C.
Conclusions:
- Identified optimal, individualized treatment goals for T2DM management.
- Multi-faceted strategies targeting hyperglycemia, hyperlipidemia, and hypertension are crucial for veterans with T2DM.
- Health systems should analyze their own diverse T2DM patient data for enhanced quality of care.
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