Unraveling the Intricate Connections between Brain Aging, Body Composition, and Metabolic Indices
Poster No:
2629
Submission Type:
Abstract Submission
Authors:
Huei-Yu Tsai1, Han-Jui Lee2,3, Chen-Yuan Kuo1,4, Yu-Chung Tsao5, Pei-Lin Lee6, Kun-Hsien Chou1,7, Chung-Jung Lin2, Ching-Po Lin1,6,8
Institutions:
1Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan, 3School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 4Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, 5Division of Occupational Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 6Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, 7Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, 8Department of Education and Research, Taipei City Hospital,, Taipei, Taiwan
First Author:
Co-Author(s):
Han-Jui Lee
Department of Radiology, Taipei Veterans General Hospital|School of Medicine, National Yang Ming Chiao Tung University
Taipei, Taiwan|Taipei, Taiwan
Department of Radiology, Taipei Veterans General Hospital|School of Medicine, National Yang Ming Chiao Tung University
Taipei, Taiwan|Taipei, Taiwan
Chen-Yuan Kuo
Institute of Neuroscience, National Yang Ming Chiao Tung University|Department of Neurology, Neurological Institute, Taipei Veterans General Hospital
Taipei, Taiwan|Taipei, Taiwan
Institute of Neuroscience, National Yang Ming Chiao Tung University|Department of Neurology, Neurological Institute, Taipei Veterans General Hospital
Taipei, Taiwan|Taipei, Taiwan
Pei-Lin Lee
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University
Taipei, Taiwan
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University
Taipei, Taiwan
Kun-Hsien Chou
Institute of Neuroscience, National Yang Ming Chiao Tung University|Brain Research Center, National Yang Ming Chiao Tung University
Taipei, Taiwan|Taipei, Taiwan
Institute of Neuroscience, National Yang Ming Chiao Tung University|Brain Research Center, National Yang Ming Chiao Tung University
Taipei, Taiwan|Taipei, Taiwan
Ching-Po Lin
Institute of Neuroscience, National Yang Ming Chiao Tung University|Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University|Department of Education and Research, Taipei City Hospital,
Taipei, Taiwan|Taipei, Taiwan|Taipei, Taiwan
Institute of Neuroscience, National Yang Ming Chiao Tung University|Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University|Department of Education and Research, Taipei City Hospital,
Taipei, Taiwan|Taipei, Taiwan|Taipei, Taiwan
Introduction:
The demographic shift toward an aging population poses a contemporary challenge, necessitating a comprehensive understanding of aging's multifaceted impact. Previous research has demonstrated the diverse effects of aging on brain structure, cognitive function, body composition, and metabolic processes [1,2]. These age-related changes significantly influence the risk of morbidity and disability in the elderly, exhibiting substantial individual variations [3]. While neuroimaging-derived indicators of brain biological age have emerged as a recognized measure of brain health [2], a gap persists in understanding the association among brain biological age, body composition, and metabolic profiles. This study addressed this gap by investigating the intricate relationship between brain biological age, metabolic indices, and specific body compositions defined by MR imaging. Through identifying these key factors, our research aimed to provide clinicians and the general population with valuable insights for navigating a healthier aging trajectory in the future.
Methods:
A total of 458 subjects (age range: 20.5-82.5 years, 280 males and 178 females) were retrospectively recruited from Taipei Veterans General Hospital. Participants underwent abdominal and brain MRI scans using two 3T MRI scanners, with a comprehensive collection of biochemical markers, including total cholesterol, low-density lipoprotein, high-density lipoprotein, triglycerides, fasting glucose (Glu), high-sensitivity C-reactive protein (hs-CRP), aspartate aminotransferase (AST), and other relevant metabolic indices. Body composition metrics, such as total abdominal muscle area (TAMA), skeletal muscle index (SMI), and body surface area (BSA), were quantified using abdominal MRI. Additionally, brain T1-weighted MRI data were gathered from 2675 healthy participants (age range:18-92 years, 1247 males and 1428 females) as a training dataset to construct the structural covariance network (SCN)-based brain age prediction model.
The brain age model was developed followed previously described procedures [4]. We extracted 800 SCN based features for both gray matter volume and density to construct a brain age estimator. The radial basis function (RBF) kernel-based support vector regression (SVR) brain age estimator was then applied to the study cohort to estimate individual brain predicted age and brain age gap (BAG). Subsequently, the partial correlation analyses were used to examine associations between BAG, body compositions, and metabolic indices adjusting for confounding variables including age, age2, gender, total intracranial volume, smoking, and drinking. The significance threshold was set at a false discovery rate-corrected p-value < 0.05. Additionally, general linear models were employed to explore the potential interaction effect between gender, significant metabolic and body composition indices on BAG.
The brain age model was developed followed previously described procedures [4]. We extracted 800 SCN based features for both gray matter volume and density to construct a brain age estimator. The radial basis function (RBF) kernel-based support vector regression (SVR) brain age estimator was then applied to the study cohort to estimate individual brain predicted age and brain age gap (BAG). Subsequently, the partial correlation analyses were used to examine associations between BAG, body compositions, and metabolic indices adjusting for confounding variables including age, age2, gender, total intracranial volume, smoking, and drinking. The significance threshold was set at a false discovery rate-corrected p-value < 0.05. Additionally, general linear models were employed to explore the potential interaction effect between gender, significant metabolic and body composition indices on BAG.
Results:
Our brain age prediction model accurately predicted chronological age in the training dataset (MAE = 4.26 years, r = 0.945) and satisfactory generalizability in the study dataset (MAE = 6.11 years, r = 0.900) (Fig 1). Our results showed significant negative correlations between BAG and TAMA, BMI-adjusted SMI, and BSA. Conversely, BAG exhibited positive correlations with hs-CRP, AST, and Glu (Table 1). Additionally, the change of BAG in male group was also associated with TAMA, BMI-adjusted SMI, BSA, hs-CRP and Glu, but not in female group. However, the interaction between gender and these biomarkers on BAG was not significant.
Conclusions:
In summary, our study provided insights into the associations between body composition, biochemical markers and BAG. The observed correlations suggested that lower skeletal muscle mass, increasing hs-CRP, and increasing blood glucose were associated with a larger BAG. These findings contributed to understanding the factors influencing brain aging and highlighted the importance of healthy aging interventions in future research.
Lifespan Development:
Aging
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Physiology, Metabolism and Neurotransmission :
Physiology, Metabolism and Neurotransmission Other 1
Keywords:
Aging
Blood
Machine Learning
MRI
STRUCTURAL MRI
Other - brain age, body composition, visceral fat, metabolism
1|2Indicates the priority used for review
Provide references using author date format
2. Ponti, F., et al., Aging and Imaging Assessment of Body Composition: From Fat to Facts. Front Endocrinol (Lausanne), 2019. 10: p. 861.
3. Wrigglesworth, J., et al., Factors associated with brain ageing - a systematic review. BMC Neurol, 2021. 21(1): p. 312.
4. Kuo, C.-Y., et al., Large-scale structural covariance networks predict age in middle-to-late adulthood: A novel brain aging biomarker. Cerebral Cortex, 2020. 30(11): p. 5844-5862.