The Gut–Brain Axis in the Neuropsychological Disease Model of Obesity: A Classical Movie Revised by the Emerging Director “Microbiome”
Abstract
:1. Introduction
2. Why a Neuropsychological View of Obesity?
2.1. Neurobiological Aspects of Obesity’s Pathogenesis
2.2. Obesity and Neurological Comorbidities
3. The Microbiota–Gut–Brain Axis
3.1. Microbiome and Energy Harvest
- GM influences energy homeostasis by regulating gene expression via complex mechanisms started by SCFAs and monosaccharides [156]. In particular, the commensal microorganisms stimulate monosaccharide cellular uptake [157] and induce lipogenesis by activating the transcription factors carbohydrate response element binding protein (ChREBP) and sterol response element binding protein (SREBP) [155]. Triacylglycerols, produced trought hepatic lipogenesis, are thus sent from the liver to the blood in the form of very low-density lipoprotein and chylomicrons.
- HF diet triggers an increased absorption of bacterial lipopolysaccharide (LPS) (an endotoxin in the cell wall of Gram-negative bacteria) from the gut lumen to the bloodstream inducing low-grade inflammation, by activating B cells or dendritic cells activating and cytokine production [158]. The inflammation could also be stimulated by endotoxemia condition [157]; moreover, also the damaged gut barrier might contribute to this metabolic endotoxaemia [159].
- GM induces a suppression of angiopoietin-like protein 4 (ANGPTL4) in the intestinal epithelium. ANGPTL4 is a circulating enzyme produced by liver, gut and adipose tissue that inhibits LPL; its suppression provokes an increase of triacylglycerol storage in the adipose tissue [127].
3.2. Microbiome and the Brain
3.3. The Role of Microbiome-Driven Inflammation
4. Conclusions and Future Directions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Study | Sample Size | Patients’ Features | Findings | Ref. |
---|---|---|---|---|
Central nervous system disorders | ||||
Holtkamp et al., 2004 | 97 men | Children with attention-deficit/hyperactivity disorder (ADHD) | Obesity development independent of ADHD diagnosis | [69] |
Elias et al., 2005 | 551 men, 872 women | Individuals with healthy body weight, overweight, obese | In men, obesity association with adverse cognitive effects | [59] |
Cournot et al., 2006 | 1660 men, 1576 women | Healthy workers (32–62 years old) | Higher BMI association with lower cognitive scoresand higher cognitive decline | [60] |
Boeka et al., 2008 | 20 men, 48 women | Caucasian and African American extremely obese patients | Evidence of specific cognitive dysfunction in extremely obese individuals | [73] |
Sabia et al., 2009 | 3788 men, 1343 women | White individuals | Multiple effects of obesity on cognition | [61] |
Hassing et al., 2010 | 140 men, 277 women | Swedish twin registry | Midlife overweight association to lower overall cognitive function in old age | [62] |
Anstey et al., 2011 | 71529 individuals | Participants evaluated for any type of dementia | Overweight and obesity in midlife increase dementia risk | [64] |
Dahl et al., 2013 | 280 men, 377 women | Swedish adoption and twin study of ageing | Midlife overweight or obesity responsible of lower cognitive function and cognitive decline in late life | [63] |
Yau et al., 2014 | 30 obese, 30 lean adolescents | Obese without insulin resistance or metabolic syndrome | Uncomplicated obesity may result in subtle brain alterations | [72] |
Cheke et al., 2016 | 14 men, 36 women | 8 Obese individuals, 16 overweight, 26 lean | Higher BMI association with lower performance on the what-where-when memory task | [66] |
Navas et al., 2016 | 35 men, 44 women | 38 Normal weight, 21 overweight, 20 obese | Obesity is linked to a propensity to make risky decisions | [67] |
Kummer et al., 2016 | 92 patients and 19 controls | Children and adolescents: autism spectrum disorder (ASD) andADHD | Higher risk of overweight and obesity in ASD and ADHD | [70] |
Peripheral nervous system diseases | ||||
Ylitalo et al., 2011 | 2514 adults aged ≥ 40 years | Individuals with peripheral neuropathy,peripheral vascular disease (PVD), alower-extremity diseases (LEDs). | Obesity and cardiometabolic clustering markedly increased the likelihood of LEDs | [92] |
Tesfaye et al., 2005 | 1172 patients | Patients with type 1 diabetes mellitus. | Higher BMI independently associated with the incidence of neuropathy. | [93] |
Ziegler et al., 2008 | 195 patients and 198 controls | Population-based MONICA/KORA Augsburg Surveys aged 25–74 years. | Waist circumference association with peripheral arterial disease (PAD) | [94] |
Singleton et al., 2014 | 21 obese, 51 lean controls | Non-diabetic obese patients referred for Roux en Y bariatric surgery compared with lean controls. | Asymptomatic neuropathy is common in very obese patients independent of glucose control | [95] |
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Niccolai, E.; Boem, F.; Russo, E.; Amedei, A. The Gut–Brain Axis in the Neuropsychological Disease Model of Obesity: A Classical Movie Revised by the Emerging Director “Microbiome”. Nutrients 2019, 11, 156. https://doi.org/10.3390/nu11010156
Niccolai E, Boem F, Russo E, Amedei A. The Gut–Brain Axis in the Neuropsychological Disease Model of Obesity: A Classical Movie Revised by the Emerging Director “Microbiome”. Nutrients. 2019; 11(1):156. https://doi.org/10.3390/nu11010156
Chicago/Turabian StyleNiccolai, Elena, Federico Boem, Edda Russo, and Amedeo Amedei. 2019. "The Gut–Brain Axis in the Neuropsychological Disease Model of Obesity: A Classical Movie Revised by the Emerging Director “Microbiome”" Nutrients 11, no. 1: 156. https://doi.org/10.3390/nu11010156