About
Determine how lean vs obese participants enriched for metabolic dysfunction, compare on:
1) memory and cognitive tests; 2) behavior, general health, well-being, and personality; 3) brain structure; and 4) brain function
Specific Aims:
1) Determine if biological markers predict cognitive, behavioral, or brain outcomes
2) Elucidate how body composition measures predict cognitive, behavioral, or brain outcomes.
3) Discern if biological factors including genes, free fatty acids, immune system signaling proteins, or metabolic/stress hormones predict brain outcomes.
Health conditions: obesity and type 2 diabetes
The outcomes from SIM-Bio-SIS will signal to the lay community that obesity is important for one's current cognitive function and brain health, not just for chronic diseases that take years or decades to manifest. These results will indirectly improve the prevention of metabolic related disorders by demonstrating the impact of dysmetabolism on brain structure and function to encourage healthy lifestyles throughout society. Additionally, these results will provide preliminary evidence that will lay the foundation for subsequent intervention protocols focused on lowering body weight and/or improving metabolic function to see if behavioral and brain outcomes improve. It is unknown which aspects of brain structure/function are affected by obesity or Type 2 diabetes. To assess these relationships, we will be the first researchers to systematically assess:
1) if individuals with obesity or metabolic dysfunction show worse cognition and memory, as well as increased feelings of anxiety, sadness, and difficulty in regulating `negative` emotion in thousands of subjects;
2) If obesity or metabolic dysfunction affect brain structure and brain function during different tests; and
3) if fat, muscle, bone composition, or markers in the blood explain why obesity results in altered behavior, brain structure and brain function.
The subset of subjects with imaging variables will be used for all analyses where N = 11,000 subjects
14 Publications
| Pub ID | Title | Author(s) | Year | Journal |
| 12185 | A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks | Mohammad Fili (+13) | 2024 | Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring |
| 14692 | A multi-stage feature selection method to improve classification of potential super-agers and cognitive decliners using structural brain MRI data - a UK biobank study | Parvin Mohammadiarvejeh (+14) | 2024 | GeroScience |
| 4078 | APOE, TOMM40, and sex interactions on neural network connectivity | Tianqi Li (+12) | 2021 | Neurobiology of Aging |
| 4017 | Aging-related changes in fluid intelligence, muscle and adipose mass, and sex-specific immunologic mediation: A longitudinal UK Biobank study | Brandon S Klinedinst (+8) | 2019 | Brain Behavior and Immunity |
| 7707 | Alzheimer's Disease Genetic Influences Impact the Associations between Diet and Resting-State Functional Connectivity: A Study from the UK Biobank | Tianqi Li (+6) | 2023 | Nutrients |
| 8005 | Associations Between Insulin-Like Growth Factor-1 and Resting-State Functional Connectivity in Cognitively Unimpaired Midlife Adults | Tianqi Li (+8) | 2023 | Journal of Alzheimer's Disease |
| 13989 | Associations of Coffee and Tea Consumption on Neural Network Connectivity: Unveiling the Role of Genetic Factors in Alzheimer's Disease Risk | Tianqi Li (+5) | 2024 | Nutrients |
| 12530 | Beer, wine, and spirits differentially influence body composition in older white adults-a United Kingdom Biobank study | Brittany A. Larsen (+7) | 2022 | Obesity Science & Practice |
| 12231 | Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories - a UK Biobank Random Forest classification study | Parvin Mohammadiarvejeh (+11) | 2022 | GeroScience |
| 15917 | Decoding cognitive aging: how white matter tracts and demographics distinguish potential Super-Agers | Mohammad Fili (+3) | 2025 | GeroScience |
| 7894 | Exploring the secrets of super-aging: a UK Biobank study on brain health and cognitive function | Brandon S. Klinedinst (+10) | 2023 | GeroScience |
| 10237 | Using machine learning to predict COVID-19 infection and severity risk among 4510 aged adults: a UK Biobank cohort study | Auriel A. Willette (+12) | 2022 | Scientific Reports |
| 11777 | Vitamin B6, B12, and Folate's Influence on Neural Networks in the UK Biobank Cohort | Tianqi Li (+2) | 2024 | Nutrients |
| 5997 | Walking in the Light: How History of Physical Activity, Sunlight, and Vitamin D Account for Body Fat - A UK Biobank Study | Brandon S. Klinedinst (+15) | 2020 | Obesity |