In a recent study published in Diabetes Care, researchers examined the association between prediabetes and brain aging. They also investigated whether modifiable lifestyle factors could attenuate the association.
Study: Diabetes, Prediabetes, and Brain Aging: The Role of Healthy Lifestyle. Image Credit: Josh Namdar/Shutterstock.com
Background
Diabetes is a significant risk factor for dementia, although its impact on early brain aging is unknown. Diabetes has been related to global brain shrinkage, an increased burden of small-vessel disease, and microstructural lesions before cognitive symptoms, according to magnetic resonance imaging (MRI).
Prediabetes has been associated with mild cerebrovascular and neurodegenerative abnormalities, although its relationship with cognitive decline and dementia is debatable.
The brain age gap (BAG) signifies a divergence from normal aging, which increases the risk of cognitive decline and dementia.
Early diagnosis of accelerated brain aging might aid in the prompt identification and management of persons at risk for dementia. However, longitudinal evidence on the link between prediabetes and brain age is limited, and lifestyle choices may impact the relationship.
About the study
In the present study, researchers explored the impact of a healthy lifestyle on dementia risk among individuals with prediabetes or diabetes.
The study included 31,229 dementia-free adult United Kingdom Biobank participants aged 40 to 70 years. Exclusion criteria included chronic neurological disorders, insulin-dependent diabetes, and individuals with missing glycated hemoglobin (HbA1c).
Researchers ascertained prediabetes and diabetes using the American Diabetes Association criteria, medical history, medications, and HbA1c values at baseline. HbA1c ≥6.5% indicates diabetes, 5.7% to 6.4% indicates prediabetes, and <5.7% indicates normoglycemia. Diabetes was well-controlled (HbA1c <7%), moderately controlled (≥7.0 to <8%), or poorly controlled (≥8.0%).
Participant follow-ups over 11 years included one or two brain MRI scans. Brain age was estimated from 1,079 MRI image-derived phenotypes (IDPs) using the least-absolute shrinkage and selection operator regression (LASSO) model with Bayesian optimization and without feature selection.
The researchers obtained the IDPs using T1-weighted MRI, T2-weighted MRI, T2-weighted with fluid-attenuated inversion recovery (FLAIR), diffusion MRI, and functional MRI (fMRI) at rest and task stages. The team derived BAG by subtracting chronological age from brain age.
Linear regressions estimated beta coefficients for the association between glycemic status and BAG, and restricted cubic splines modeled nonlinear associations between HbA1c and BAG. Researchers also performed a joint exposure analysis by including a variable that combined glycemic status and lifestyle into linear regressions.
Study covariates included sociodemographic factors, cardiometabolic risk factors, and lifestyle behaviors (physical activity, smoking, and alcohol intake). Sociodemographic factors were education and socioeconomic status. Cardiometabolic risk factors included body mass index (BMI), blood pressure, antihypertensive medications, high-density lipoprotein (HDL) cholesterol, and triglycerides.
In sensitivity analysis, researchers used BAG estimates by other machine learning models and non-imputed data, considering the MRI assessment center as a covariate and excluding individuals with prodromal or undiagnosed dementia.
Results
Among the study participants, the mean age was 55 years; 53% were female, 13,518 (43%) had prediabetes, and 1,149 (3.7%) had diabetes. Compared to normoglycemic individuals, those with prediabetes tended to be older, male, less educated, physically inactive, with cardiometabolic risk factors, and had a lower socioeconomic status.
Prediabetes (β=0.2) and diabetes (β=2.0) were related to significantly high BAG values. The team found brain age to exceed chronological age by 0.5 years in prediabetes individuals and by 2.3 years in those with diabetes. Diabetes considerably increased BAG over time, an annual increase of 0.3 years, and BAG increased to 4.2 years among individuals with poor glycemic control.
The link between prediabetes and high BAG was especially robust among males with at least two risk factors for cardiometabolic disease. Specifically, the brain age among males with prediabetes exceeded their chronological age by 0.8 years, compared to 0.3 years for females.
Furthermore, BAG increased to 2.6 years for males with diabetes against 1.8 years for females. Similarly, among those with at least two factors that increase cardiometabolic risk, prediabetes and diabetes were linked with BAG values of 1.3 and 3.1 years, respectively, compared to 0.2 and 2.0 years among those with fewer cardiometabolic risk factors.
However, healthy lifestyle habits (high physical activity, no excessive drinking, and no smoking) attenuated the relationship of diabetes with BAG.
The brain age of diabetes patients practicing healthy lifestyles exceeded their chronological age by 0.8 years, and that of individuals with non-optimal lifestyles exceeded their chronological age by 2.5 years. Practicing healthy lifestyle habits was associated with a 1.7-year decrease in BAG. Sensitivity analyses produced similar findings.
Conclusion
The study found that diabetes and prediabetes are associated with accelerated brain aging, making these illnesses suitable targets for lifestyle interventions to maintain brain health.
Males with poor cardiometabolic health showed the most robust associations, suggesting that preventative actions may benefit them the most. Adopting a healthy lifestyle, including physical activity, no smoking, and avoiding heavy drinking, may help mitigate these consequences.
Natural nutrients nicotinamide and pyridoxine reverse muscle aging