Long-term obesity impacts brain morphology, functional connectivity and cognition in adults

被引：0
作者：
Zhang, Die [1 ]
Shen, Chenye [2 ]
Chen, Nanguang [2 ,3 ]
Liu, Chaoqiang [2 ]
Hu, Jun [4 ]
Lau, Kui Kai [5 ,6 ]
Wen, Zhibo [7 ]
Qiu, Anqi [1 ,2 ,8 ]
机构：
[1] Hong Kong Polytech Univ, Dept Hlth Technol & Informat, Hong Kong, Peoples R China
[2] Natl Univ Singapore, Dept Biomed Engn, Singapore, Singapore
[3] NUS Suzhou Res Inst, Suzhou, Peoples R China
[4] Peking Univ, Shenzhen Hosp, Shenzhen, Peoples R China
[5] Univ Hong Kong, LKS Fac Med, Sch Clin Med, Hong Kong, Peoples R China
[6] Univ Hong Kong, State Key Lab Brain & Cognit Sci, Hong Kong, Peoples R China
[7] Southern Med Univ, Zhujiang Hosp, Dept Radiol, Guangzhou, Peoples R China
[8] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21218 USA
来源：
NATURE MENTAL HEALTH | 2025年
基金：
新加坡国家研究基金会;
关键词：
BODY-MASS INDEX; MIDDLE-AGED MEN; NETWORK ACTIVITY; MORBIDLY OBESE; IMAGE; RISK; OVERWEIGHT; HEALTH; TRAJECTORIES; INTEGRATION;
D O I：
10.1038/s44220-025-00396-5
中图分类号：
R749 [精神病学];
学科分类号：
100205 ;
摘要：
Although obesity has been implicated in brain and cognitive health, the effect of longitudinal obesity trajectories on brain and cognitive aging remains insufficiently understood. Here, using multifaceted obesity measurements from the UK Biobank, we identified five distinct obesity trajectories: low-stable, moderate-stable, high-stable, increasing and decreasing. We observed that individuals in the decreasing trajectory showed minimal adverse effects on brain structure and cognitive performance, compared with the low-stable trajectory (low obesity levels over time). By contrast, the increasing and moderate- and high-stable trajectories were associated with progressively greater impairments in brain morphology, functional connectivity and cognitive abilities. Specifically, adverse effects extended from fronto-mesolimbic regions in the increasing trajectory to parietal and temporal regions in the moderate-stable trajectory, culminating in widespread brain abnormalities in the high-stable group. These findings highlight the dynamic relationship between obesity evolution and brain-cognitive health, underscoring the clinical importance of long-term monitoring and management of obesity through a multifaceted approach.
引用
收藏
页码：466 / 478
页数：21
相关论文
共 91 条
  • [81] Greve D.N., Fischl B., Accurate and robust brain image alignment using boundary-based registration, NeuroImage, 48, pp. 63-72, (2009)
  • [82] Finn E.S., Et al., Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity, Nat. Neurosci, 18, pp. 1664-1671, (2015)
  • [83] Achard S., Bullmore E., Efficiency and cost of economical brain functional networks, PLoS Comput. Biol, 3, (2007)
  • [84] He Y., Chen Z., Evans A., Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease, J. Neurosci, 28, pp. 4756-4766, (2008)
  • [85] Wang J., Et al., Parcellation-dependent small-world brain functional networks: a resting-state fMRI study, Hum. Brain Mapp, 30, pp. 1511-1523, (2009)
  • [86] Bielczyk N.Z., Et al., Thresholding functional connectomes by means of mixture modeling, NeuroImage, 171, pp. 402-414, (2018)
  • [87] Rubinov M., Sporns O., Complex network measures of brain connectivity: uses and interpretations, NeuroImage, 52, pp. 1059-1069, (2010)
  • [88] Fawns-Ritchie C., Deary I.J., Reliability and validity of the UK Biobank cognitive tests, PLoS ONE, 15, (2020)
  • [89] Fanelli G., Et al., The link between cognition and somatic conditions related to insulin resistance in the UK Biobank study cohort: a systematic review, Neurosci. Biobehav. Rev, 143, (2022)
  • [90] Arbuthnott K., Frank J., Trail making test, part B as a measure of executive control: validation using a set-switching paradigm, J. Clin. Exp. Neuropsychol, 22, pp. 518-528, (2000)
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