Effects of a 13-Week Personalized Lifestyle Intervention Based on the Diabetes Subtype for People with Newly Diagnosed Type 2 Diabetes.
Iris M de HooghWilrike J PasmanAndré BoorsmaBen van OmmenSuzan WopereisPublished in: Biomedicines (2022)
A type 2 diabetes mellitus (T2DM) subtyping method that determines the T2DM phenotype based on an extended oral glucose tolerance test is proposed. It assigns participants to one of seven subtypes according to their β-cell function and the presence of hepatic and/or muscle insulin resistance. The effectiveness of this subtyping approach and subsequent personalized lifestyle treatment in ameliorating T2DM was assessed in a primary care setting. Sixty participants, newly diagnosed with (pre)diabetes type 2 and not taking diabetes medication, completed the intervention. Retrospectively collected data of 60 people with T2DM from usual care were used as controls. Bodyweight ( p < 0.01) and HbA1c ( p < 0.01) were significantly reduced after 13 weeks in the intervention group, but not in the usual care group. The intervention group achieved 75.0% diabetes remission after 13 weeks (fasting glucose ≤ 6.9 mmol/L and HbA1c < 6.5% (48 mmol/mol)); for the usual care group, this was 22.0%. Lasting (two years) remission was especially achieved in subgroups with isolated hepatic insulin resistance. Our study shows that a personalized diagnosis and lifestyle intervention for T2DM in a primary care setting may be more effective in improving T2DM-related parameters than usual care, with long-term effects seen especially in subgroups with hepatic insulin resistance.
Keyphrases
- glycemic control
- type diabetes
- insulin resistance
- blood glucose
- randomized controlled trial
- primary care
- metabolic syndrome
- weight loss
- healthcare
- newly diagnosed
- cardiovascular disease
- palliative care
- skeletal muscle
- quality improvement
- high fat diet
- adipose tissue
- polycystic ovary syndrome
- physical activity
- pain management
- systematic review
- study protocol
- emergency department
- clinical trial
- affordable care act
- machine learning
- cardiovascular risk factors
- general practice
- preterm birth