Complex mechanisms linking neurocognitive dysfunction to insulin resistance and other metabolic dysfunction

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Abstract

Scientific evidence has established several links between metabolic and neurocognitive dysfunction, and epidemiologic evidence has revealed an increased risk of Alzheimer’s disease and vascular dementia in patients with diabetes. In July 2015, the National Institute of Diabetes, Digestive, and Kidney Diseases gathered experts from multiple clinical and scientific disciplines, in a workshop entitled “The Intersection of Metabolic and Neurocognitive Dysfunction”, to clarify the state-of-the-science on the mechanisms linking metabolic dysfunction, and insulin resistance and diabetes in particular, to neurocognitive impairment and dementia. This perspective is intended to serve as a summary of the opinions expressed at this meeting, which focused on identifying gaps and opportunities to advance research in this emerging area with important public health relevance.

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Most cited references 109

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Addiction-like reward dysfunction and compulsive eating in obese rats: Role for dopamine D2 receptors

Paul Johnson, Paul Kenny (2010)

We found that development of obesity was coupled with the emergence of a progressively worsening brain reward deficit. Similar changes in reward homeostasis induced by cocaine or heroin is considered a critical trigger in the transition from casual to compulsive drug-taking. Accordingly, we detected compulsive-like feeding behavior in obese but not lean rats, measured as palatable food consumption that was resistant to disruption by an aversive conditioned stimulus. Striatal dopamine D2 receptors (D2R) were downregulated in obese rats, similar to previous reports in human drug addicts. Moreover, lentivirus-mediated knockdown of striatal D2R rapidly accelerated the development of addiction-like reward deficits and the onset of compulsive-like food seeking in rats with extended access to palatable high-fat food. These data demonstrate that overconsumption of palatable food triggers addiction-like neuroadaptive responses in brain reward circuitries and drives the development of compulsive eating. Common hedonic mechanisms may therefore underlie obesity and drug addiction.

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Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies.

K. Anstey, N Cherbuin, M. Budge … (2011)

The relationship between body mass index (BMI) (in midlife and late-life) and dementia was investigated in meta-analyses of 16 articles reporting on 15 prospective studies. Follow-ups ranged from 3.2 to 36.0 years. Meta-analyses were conducted on samples including 25 624 participants evaluated for Alzheimer's disease (AD), 15 435 participants evaluated for vascular dementia (VaD) and 30 470 followed for any type of dementia (Any Dementia). Low BMI in midlife was associated with 1.96 [95% confidence interval (CI): 1.32, 2.92] times the risk of developing AD. The pooled relative risks for AD, VaD and Any Dementia for overweight BMI in midlife compared with normal BMI were 1.35 (95% CI:1.19, 1.54), 1.33 (95% CI: 1.02, 1.75) and 1.26 (95% CI: 1.10, 1.44), respectively. The pooled relative risks of AD and Any Dementia for obese BMI in midlife compared to normal BMI were 2.04 (95% CI: 1.59, 2.62) and 1.64 (95% CI: 1.34, 2.00), respectively. Continuous BMI in late-life was not associated with dementia. Small numbers of studies included in pooled analyses reduce generalizability of findings, and emphasize the need for publication of additional findings. We conclude that underweight, overweight and obesity in midlife increase dementia risk. Further research evaluating late-life BMI and dementia is required. © 2011 The Authors. obesity reviews © 2011 International Association for the Study of Obesity.

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Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments.

Laura Germine, Ken Nakayama, Bradley Duchaine … (2012)

With the increasing sophistication and ubiquity of the Internet, behavioral research is on the cusp of a revolution that will do for population sampling what the computer did for stimulus control and measurement. It remains a common assumption, however, that data from self-selected Web samples must involve a trade-off between participant numbers and data quality. Concerns about data quality are heightened for performance-based cognitive and perceptual measures, particularly those that are timed or that involve complex stimuli. In experiments run with uncompensated, anonymous participants whose motivation for participation is unknown, reduced conscientiousness or lack of focus could produce results that would be difficult to interpret due to decreased overall performance, increased variability of performance, or increased measurement noise. Here, we addressed the question of data quality across a range of cognitive and perceptual tests. For three key performance metrics-mean performance, performance variance, and internal reliability-the results from self-selected Web samples did not differ systematically from those obtained from traditionally recruited and/or lab-tested samples. These findings demonstrate that collecting data from uncompensated, anonymous, unsupervised, self-selected participants need not reduce data quality, even for demanding cognitive and perceptual experiments.

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Author and article information

Journal

Journal ID (nlm-ta): F1000Res

Journal ID (iso-abbrev): F1000Res

Journal ID (pmc): F1000Research

Title: F1000Research

Publisher: F1000Research (London, UK )

ISSN (Electronic): 2046-1402

Publication date (Electronic): 2 June 2016

Publication date Collection: 2016

Volume: 5

Electronic Location Identifier: 353

Affiliations

[1 ]National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

[2 ]Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

[3 ]Icahn School of Medicine and James J. Peters VAMC, New York, NY, USA

[4 ]Yale University School of Medicine, New Haven, CT, USA

[5 ]Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA

[6 ]Berenson-Allen Center for Noninvasive Brain Stimulation and Division for Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

[1 ]Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

[2 ]Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA

[3 ]Geriatric Research, Education & Clinical Center, Central Arkansas Veterans Healthcare System, Little Rock, AR, USA

[1 ]Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada

[2 ]Department of Neurology, Mashhad University of Medical Sciences, Mashhad, Iran

[3 ]Department of Clinical Neurological Sciences (CNS), University of Western Ontario, London, ON, Canada

National Library of Medicine & NIH

[1 ]Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

[2 ]Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA

[3 ]Geriatric Research, Education & Clinical Center, Central Arkansas Veterans Healthcare System, Little Rock, AR, USA

National Library of Medicine & NIH

Author notes

[a ] luke.stoeckel@ 123456nih.gov

L.E.S., Z.A., S.G., and D.S. wrote the first draft, revised, and edited the White Paper. Other named authors read and edited the paper. All authors approved the content.

Competing interests: No competing interests were disclosed.

Article

DOI: 10.12688/f1000research.8300.2

PMC ID: 4897751

PubMed ID: 27303627

SO-VID: 6e74eb42-bc11-44b6-9364-861f20c3feb6

License:

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions.

History

Date accepted : 31 May 2016

Funding

Funded by: National Institutes of Health

Award ID: R01 NS084965

Award ID: U01AG046170

Award ID: R01 DK 085579

Award ID: P50 AG005138

Award ID: R01 CA 180030

Award ID: R01 NS075685

Award ID: R01 DC 006706

Award ID: P30 AG10161

Award ID: R01 AG 040039

Award ID: R01HD069776

Award ID: R01NS073601

Award ID: R21 NS082870

Award ID: R21 MH099196

Award ID: R21 NS085491

Award ID: R21 HD07616

Award ID: UL1 RR025758

The following National Institutes of Health grant funding supported this work: R01 NS084965 (Z.A.); U01AG046170 (S.G.; Eric Schadt); R01 DK 085579 (D.S.); P50 AG005138 (S.G.; Mary Sano, PI); R01 CA 180030 (D.S.); R01 NS075685 (S.G.); R01 DC 006706 (D.S.); P30 AG10161 (Z.A. David A. Bennett, PI); R01 AG 040039 (Z.A.); R21 NS082870 (A.P.L.).

Complex mechanisms linking neurocognitive dysfunction to insulin resistance and other metabolic dysfunction

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Karger: Neurology and Neuroscience

Most cited references 109

Addiction-like reward dysfunction and compulsive eating in obese rats: Role for dopamine D2 receptors

Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies.

Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments.

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