4.4 Article

Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment

Journal

GEROSCIENCE
Volume 44, Issue 4, Pages 2319-2336

Publisher

SPRINGER
DOI: 10.1007/s11357-022-00588-2

Keywords

Pattern; Brain ageing; Positron emission tomography; Glucose metabolism

Funding

  1. National Natural Science Foundation of China [61633018, 82020108013, 61603236, 81830059, 81801052]
  2. National Key Research and Development Program of China [2016YFC1306300, 2018YFC1312000, 2018YFC1707704]
  3. 111 Project [D20031]
  4. Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]
  5. Beijing Municipal Commission of Health and Family Planning [PXM2020_026283_000002]

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Exploring individual hallmarks of brain ageing is important. In this study, the age-related glucose metabolism pattern (ARGMP) is proposed as a potential index to characterize brain ageing in cognitively normal elderly individuals and predict high conversion risk into cognitive impairment.
Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected F-18-fluorodeoxyglucose (F-18-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 similar to 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.

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