To Evaluate the Effectiveness of a Whole-System Medicine Approach-Based AIWeLL DM™ Protocol on Diabetes: A Single-Arm Pre-Post Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 129
- 试验地点
- 1
- 主要终点
- Complete Blood Count (CBC), Urine Routine Examination, Diabetic Profile, Lipid Profile, Liver Profile, Kidney Profile
研究概览
简要总结
Ayurveda, a traditional system of medicine, offers a holistic framework for disease diagnosis, treatment, and prevention. It emphasizes individual constitution (Prakriti) and pathological state (Vikriti), providing a personalized approach to healthcare. Prakriti-based assessments have been correlated with biochemical, genetic, and microbiome markers, demonstrating their potential for phenotyping individuals and predicting predisposition to metabolic disorders, including diabetes. Integrating Ayurvedic principles with modern biomedical approaches may enhance the early identification of at-risk individuals, improve personalized treatment strategies, and support a more comprehensive model for diabetes management and complication prevention.
This study aims to evaluate the effectiveness of a Whole-System Medicine approach incorporating Ayurvedic principles and modern diagnostics through the AIWeLL DM™ Protocol. By leveraging a personalized, multi-dimensional framework, this study seeks to improve diabetes management outcomes and explore potential avenues for disease reversal in select cases.
研究设计
- 研究类型
- Interventional
- 分配方式
- Not Applicable
- 盲法
- Not Applicable
入排标准
- 年龄范围
- 25.00 Year(s) 至 60.00 Year(s)(—)
- 性别
- All
入选标准
- •Type 2 diabetes patients, prediabetes
- •Duration of Diabetes should be less than 10 years.
排除标准
- •Type 1 diabetes patients
- •Gestational diabetes
- •Smokers and alcoholics
- •T2DM patients under insulin
- •Pregnancy and Lactation
- •Diabetes patients with co-morbidities.
结局指标
主要结局
Complete Blood Count (CBC), Urine Routine Examination, Diabetic Profile, Lipid Profile, Liver Profile, Kidney Profile
时间窗: Before treatment, after treatment
次要结局
- CGM indices, Gut microbiome composition (16sRNA metagenomic sequencing)(Before treatment, after treatment)
