World Health Organization. Obesity and overweight, fact sheet. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (2024).
Jaacks, L. M. et al. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 7, 231–240 (2019).
Google Scholar
Frank, P. et al. Overweight, obesity, and individual symptoms of depression: a multicohort study with replication in UK Biobank. Brain Behav. Immun. 105, 192–200 (2022).
Google Scholar
Avila, C. et al. An overview of links between obesity and mental health. Curr. Obes. Rep. 4, 303–310 (2015).
Google Scholar
Tam, B. T., Morais, J. A. & Santosa, S. Obesity and ageing: two sides of the same coin. Obes. Rev. 21, e12991 (2020).
Google Scholar
Bischof, G. N. & Park, D. C. Obesity and aging: consequences for cognition, brain structure, and brain function. Psychosom. Med. 77, 697–709 (2015).
Google Scholar
Pugazhenthi, S., Qin, L. & Reddy, P. H. Common neurodegenerative pathways in obesity, diabetes, and Alzheimer’s disease. Biochim. Biophys. Acta Mol. Basis Dis. 1863, 1037–1045 (2017).
Google Scholar
Jones, R. A., Lawlor, E. R., Griffin, S. J., van Sluijs, E. M. F. & Ahern, A. L. Impact of adult weight management interventions on mental health: a systematic review and meta-analysis protocol. BMJ Open 10, e031857 (2020).
Google Scholar
Payne, M. E. et al. Quality of life and mental health in older adults with obesity and frailty: associations with a weight loss intervention. J. Nutr. Health. Aging 22, 1259–1265 (2018).
Google Scholar
Zheng, S. et al. Effectiveness of holistic mobile health interventions on diet, and physical, and mental health outcomes: a systematic review and meta-analysis. EClinicalMedicine 66, 102309 (2023).
Google Scholar
Veronese, N. et al. Weight loss is associated with improvements in cognitive function among overweight and obese people: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 72, 87–94 (2017).
Google Scholar
Levakov, G. et al. The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity. eLife 12, e83604 (2023).
Google Scholar
Schmitt, L. O. & Gaspar, J. M. Obesity-induced brain neuroinflammatory and mitochondrial changes. Metabolites 13, 86 (2023).
Google Scholar
Guillemot-Legris, O. & Muccioli, G. G. Obesity-induced neuroinflammation: beyond the hypothalamus. Trends Neurosci. 40, 237–253 (2017).
Google Scholar
Roh, H. T., Cho, S. Y. & So, W. Y. Obesity promotes oxidative stress and exacerbates blood-brain barrier disruption after high-intensity exercise. J. Sport. Health. Sci. 6, 225–230 (2017).
Google Scholar
Santos, A. L. & Sinha, S. Obesity and aging: molecular mechanisms and therapeutic approaches. Ageing Res. Rev. 67, 101268 (2021).
Google Scholar
Neto, A., Fernandes, A. & Barateiro, A. The complex relationship between obesity and neurodegenerative diseases: an updated review. Front. Cell. Neurosci. 17, 1294420 (2023).
Google Scholar
Carnell, S. et al. Neural correlates of familial obesity risk and overweight in adolescence. NeuroImage 159, 236–247 (2017).
Google Scholar
Belfort-DeAguiar, R. et al. Humans with obesity have disordered brain responses to food images during physiological hyperglycemia. Am. J. Physiol. Endocrinol. Metab. 314, E522–E529 (2018).
Google Scholar
Blechert, J., Klackl, J., Miedl, S. F. & Wilhelm, F. H. To eat or not to eat: effects of food availability on reward system activity during food picture viewing. Appetite 99, 254–261 (2016).
Google Scholar
Demos, K. E. et al. The effects of experimental manipulation of sleep duration on neural response to food cues. Sleep 40, zsx125 (2017).
Google Scholar
Wiemerslage, L. et al. An obesity-associated risk allele within the FTO gene affects human brain activity for areas important for emotion, impulse control and reward in response to food images. Eur. J. Neurosci. 43, 1173–1180 (2016).
Google Scholar
Dodd, S. L., Long, J. D., Hou, J., Kahathuduwa, C. N. & O’Boyle, M. W. Brain activation and affective judgements in response to personal dietary images: an fMRI preliminary study. Appetite 148, 104561 (2020).
Google Scholar
Li, G. et al. Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions. Mol. Psychiatry 28, 1466–1479 (2023).
Google Scholar
Smith, E., Hay, P., Campbell, L. & Trollor, J. N. A review of the association between obesity and cognitive function across the lifespan: implications for novel approaches to prevention and treatment. Obes. Rev. 12, 740–755 (2011).
Google Scholar
Wang, C., Chan, J. S., Ren, L. & Yan, J. H. Obesity reduces cognitive and motor functions across the lifespan. Neural. Plast. 2016, 2473081 (2016).
Google Scholar
Anstey, K. J., Cherbuin, N., Budge, M. & Young, J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obes. Rev. 12, e426–e437 (2011).
Google Scholar
Schall, M., Iordanishvili, E., Mauler, J., Oros-Peusquens, A. M. & Shah, N. J. Increasing body mass index in an elderly cohort: effects on the quantitative MR parameters of the brain. J. Magn. Reson. Imaging 51, 514–523 (2020).
Google Scholar
Walther, K., Birdsill, A. C., Glisky, E. L. & Ryan, L. Structural brain differences and cognitive functioning related to body mass index in older females. Hum. Brain Mapp. 31, 1052–1064 (2010).
Google Scholar
Hamer, M. & Batty, G. D. Association of body mass index and waist-to-hip ratio with brain structure: UK Biobank study. Neurology 92, e594–e600 (2019).
Google Scholar
Zhang, Y. et al. Recovery of brain structural abnormalities in morbidly obese patients after bariatric surgery. Int. J. Obes. 40, 1558–1565 (2016).
Google Scholar
Almby, K. E. et al. Effects of gastric bypass surgery on the brain: simultaneous assessment of glucose uptake, blood flow, neural activity, and cognitive function during normo- and hypoglycemia. Diabetes 70, 1265–1277 (2021).
Google Scholar
Tuulari, J. J. et al. Bariatric surgery induces white and grey matter density recovery in the morbidly obese: a voxel-based morphometric study. Hum. Brain Mapp. 37, 3745–3756 (2016).
Google Scholar
Janssen, I. & Mark, A. E. Elevated body mass index and mortality risk in the elderly. Obes. Rev. 8, 41–59 (2007).
Google Scholar
Snijder, M. B., van Dam, R. M., Visser, M. & Seidell, J. C. What aspects of body fat are particularly hazardous and how do we measure them? Int. J. Epidemiol. 35, 83–92 (2006).
Google Scholar
Kumar, A. et al. Brain–muscle communication prevents muscle aging by maintaining daily physiology. Science 384, 563–572 (2024).
Google Scholar
Lee, H. et al. Obesity and muscle may have synergic effect more than independent effects on brain volume in community-based elderly. Eur. Radiol. 31, 2956–2966 (2021).
Google Scholar
Wang, R.-Z. et al. Body weight in neurological and psychiatric disorders: a large prospective cohort study. Nat. Ment. Health 2, 41–51 (2024).
Google Scholar
Grapsa, I. et al. Longitudinal examination of body mass index and cognitive function in older adults: the HELIAD study. Nutrients 15, 1795 (2023).
Google Scholar
Pflanz, C. P. et al. Central obesity is selectively associated with cerebral gray matter atrophy in 15,634 subjects in the UK Biobank. Int. J. Obes. 46, 1059–1067 (2022).
Google Scholar
Dekkers, I. A., Jansen, P. R. & Lamb, H. J. Obesity, brain volume, and white matter microstructure at MRI: a cross-sectional UK Biobank study. Radiology 291, 763–771 (2019).
Google Scholar
Kim, A. Y., Shim, J. H., Choi, H. J. & Baek, H. M. Comparison of volumetric and shape changes of subcortical structures based on 3-dimensional image between obesity and normal-weighted subjects using 3.0 T MRI. J. Clin. Neurosci. 73, 280–287 (2020).
Google Scholar
Franz, C. E. et al. Body mass trajectories and cortical thickness in middle-aged men: a 42-year longitudinal study starting in young adulthood. Neurobiol. Aging 79, 11–21 (2019).
Google Scholar
Ambikairajah, A., Tabatabaei-Jafari, H., Walsh, E., Hornberger, M. & Cherbuin, N. Longitudinal changes in fat mass and the hippocampus. Obesity 28, 1263–1269 (2020).
Google Scholar
Willette, A. A. & Kapogiannis, D. Does the brain shrink as the waist expands? Ageing Res. Rev. 20, 86–97 (2015).
Google Scholar
Driscoll, I. et al. Midlife obesity and trajectories of brain volume changes in older adults. Hum Brain. Mapp. 33, 2204–2210 (2012).
Google Scholar
Bobb, J. F., Schwartz, B. S., Davatzikos, C. & Caffo, B. Cross-sectional and longitudinal association of body mass index and brain volume. Hum. Brain Mapp. 35, 75–88 (2014).
Google Scholar
Park, B. Y. et al. Whole-brain functional connectivity correlates of obesity phenotypes. Hum. Brain Mapp. 41, 4912–4924 (2020).
Google Scholar
Berthoud, H. R., Munzberg, H. & Morrison, C. D. Blaming the brain for obesity: integration of hedonic and homeostatic mechanisms. Gastroenterology 152, 1728–1738 (2017).
Google Scholar
Kaplan, J. M., Seeley, R. J. & Grill, H. J. Daily caloric intake in intact and chronic decerebrate rats. Behav. Neurosci. 107, 876–881 (1993).
Google Scholar
Friedman, J. M. Leptin and the endocrine control of energy balance. Nat. Metab. 1, 754–764 (2019).
Google Scholar
Martín, M. & Ramos, S. Dietary flavonoids and insulin signaling in diabetes and obesity. Cells 10, 1474 (2021).
Google Scholar
Steinert, R. E. et al. Ghrelin, CCK, GLP-1, and PYY(3-36): secretory controls and physiological roles in eating and glycemia in health, obesity, and after RYGB. Physiol. Rev. 97, 411–463 (2017).
Google Scholar
Low, A. Y. T. et al. Reverse-translational identification of a cerebellar satiation network. Nature 600, 269–273 (2021).
Google Scholar
Tregellas, J. R. et al. Altered default network activity in obesity. Obesity 19, 2316–2321 (2011).
Google Scholar
McFadden, K. L., Cornier, M. A., Melanson, E. L., Bechtell, J. L. & Tregellas, J. R. Effects of exercise on resting-state default mode and salience network activity in overweight/obese adults. NeuroReport 24, 866–871 (2013).
Google Scholar
Li, G. et al. Bariatric surgery in obese patients reduced resting connectivity of brain regions involved with self-referential processing. Hum. Brain Mapp. 39, 4755–4765 (2018).
Google Scholar
Doucet, G. E., Rasgon, N., McEwen, B. S., Micali, N. & Frangou, S. Elevated body mass index is associated with increased integration and reduced cohesion of sensory-driven and internally guided resting-state functional brain networks. Cereb. Cortex 28, 988–997 (2018).
Google Scholar
Caunca, M. R. et al. Measures of obesity are associated with MRI markers of brain aging: The Northern Manhattan Study. Neurology 93, 791–803 (2019).
Google Scholar
Dake, M. D. et al. Obesity and brain vulnerability in normal and abnormal aging: a multimodal MRI study. J. Alzheimers Dis. Rep. 5, 65–77 (2021).
Google Scholar
van Galen, K. A. et al. Brain responses to nutrients are severely impaired and not reversed by weight loss in humans with obesity: a randomized crossover study. Nat. Metab. 5, 1059–1072 (2023).
Google Scholar
Dye, L., Boyle, N. B., Champ, C. & Lawton, C. The relationship between obesity and cognitive health and decline. Proc. Nutr. Soc. 76, 443–454 (2017).
Google Scholar
Cournot, M. et al. Relation between body mass index and cognitive function in healthy middle-aged men and women. Neurology 67, 1208–1214 (2006).
Google Scholar
Yim, C. Y. et al. The effect of overweight/obesity on cognitive function in euthymic individuals with bipolar disorder. Eur. Psychiatry 27, 223–228 (2012).
Google Scholar
Jaeger, J. Digit symbol substitution test: the case for sensitivity over specificity in neuropsychological testing. J. Clin. Psychopharmacol 38, 513–519 (2018).
Google Scholar
Fama, R. & Sullivan, E. V. Thalamic structures and associated cognitive functions: relations with age and aging. Neurosci. Biobehav. Rev. 54, 29–37 (2015).
Google Scholar
Burgaleta, M. et al. Subcortical regional morphology correlates with fluid and spatial intelligence. Hum. Brain Mapp. 35, 1957–1968 (2014).
Google Scholar
Thibaut, F. Basal ganglia play a crucial role in decision making. Dialogues Clin. Neurosci. 18, 3 (2016).
Google Scholar
Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).
Google Scholar
Calsolaro, V. & Edison, P. Novel GLP-1 (glucagon-like peptide-1) analogues and insulin in the treatment for Alzheimer’s disease and other neurodegenerative diseases. CNS Drugs 29, 1023–1039 (2015).
Google Scholar
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Google Scholar
Aminoff, E. M., Kveraga, K. & Bar, M. The role of the parahippocampal cortex in cognition. Trends. Cogn. Sci. 17, 379–390 (2013).
Google Scholar
Namboodiri, V. M. K. et al. Single-cell activity tracking reveals that orbitofrontal neurons acquire and maintain a long-term memory to guide behavioral adaptation. Nat. Neurosci. 22, 1110–1121 (2019).
Google Scholar
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Google Scholar
Bosello, O. & Vanzo, A. Obesity paradox and aging. Eat. Weight Disord. 26, 27–35 (2021).
Google Scholar
Heymsfield, S. B., Gallagher, D., Mayer, L., Beetsch, J. & Pietrobelli, A. Scaling of human body composition to stature: new insights into body mass index. Am. J. Clin. Nutr. 86, 82–91 (2007).
Google Scholar
Yang, J. et al. Trajectories of depressive symptoms during pregnancy and risk of premature birth: a multicenter and prospective cohort study. Psychiatry Res. 326, 115284 (2023).
Google Scholar
Zheng, M. et al. Nighttime sleep duration trajectories were associated with body mass index trajectories in early childhood. Pediatr. Obes. 16, e12766 (2020).
Google Scholar
Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61, 1402–1418 (2012).
Google Scholar
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219 (2004).
Google Scholar
Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48, 63–72 (2009).
Google Scholar
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).
Google Scholar
Achard, S. & Bullmore, E. Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3, e17 (2007).
Google Scholar
He, Y., Chen, Z. & Evans, A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J. Neurosci. 28, 4756–4766 (2008).
Google Scholar
Wang, J. et al. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum. Brain Mapp. 30, 1511–1523 (2009).
Google Scholar
Bielczyk, N. Z. et al. Thresholding functional connectomes by means of mixture modeling. NeuroImage 171, 402–414 (2018).
Google Scholar
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010).
Google Scholar
Fawns-Ritchie, C. & Deary, I. J. Reliability and validity of the UK Biobank cognitive tests. PLoS ONE 15, e0231627 (2020).
Google Scholar
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, 104927 (2022).
Google Scholar
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, 518–528 (2000).
Google Scholar
Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 1–38 (2014).
Google Scholar
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