Hippocampal volume reduction is associated with direct measure of insulin resistance in adults. (January 2022)
- Record Type:
- Journal Article
- Title:
- Hippocampal volume reduction is associated with direct measure of insulin resistance in adults. (January 2022)
- Main Title:
- Hippocampal volume reduction is associated with direct measure of insulin resistance in adults
- Authors:
- Frangou, Sophia
Abbasi, Fahim
Watson, Katie
Haas, Shalaila S.
Antoniades, Mathilde
Modabbernia, Amirhossein
Myoraku, Alison
Robakis, Thalia
Rasgon, Natalie - Abstract:
- Highlights: Neuroimaging and metabolic data analyzed by unsupervised machine learning. Insulin resistance generally negatively correlated with hippocampal subfield volumes. Lower hippocampal volume subgroup had higher BMI, insulin resistance, and leptin. Abstract: Hippocampal integrity is highly susceptible to metabolic dysfunction, yet its mechanisms are not well defined. We studied 126 healthy individuals aged 23–61 years. Insulin resistance (IR) was quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Body mass index (BMI), adiposity, fasting insulin, glucose, leptin as well as structural neuroimaing with automatic hippocampal subfield segmentation were performed. Data analysis using unsupervised machine learning (k-means clustering) identified two subgroups reflecting a pattern of more pronounced hippocampal volume reduction being concurrently associated with greater adiposity and insulin resistance; the hippocampal volume reductions were uniform across subfields. Individuals in the most deviant subgroup were predominantly women (79 versus 42 %) with higher BMI [27.9 (2.5) versus 30.5 (4.6) kg/m 2 ], IR (SSPG concentration, [156 (61) versus 123 (70) mg/dL] and leptinemia [21.7 (17.0) versus 44.5 (30.4) μg/L]. The use of person-based modeling in healthy individuals suggests that adiposity, insulin resistance and compromised structural hippocampal integrity behave as a composite phenotype; female sex emerged as riskHighlights: Neuroimaging and metabolic data analyzed by unsupervised machine learning. Insulin resistance generally negatively correlated with hippocampal subfield volumes. Lower hippocampal volume subgroup had higher BMI, insulin resistance, and leptin. Abstract: Hippocampal integrity is highly susceptible to metabolic dysfunction, yet its mechanisms are not well defined. We studied 126 healthy individuals aged 23–61 years. Insulin resistance (IR) was quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Body mass index (BMI), adiposity, fasting insulin, glucose, leptin as well as structural neuroimaing with automatic hippocampal subfield segmentation were performed. Data analysis using unsupervised machine learning (k-means clustering) identified two subgroups reflecting a pattern of more pronounced hippocampal volume reduction being concurrently associated with greater adiposity and insulin resistance; the hippocampal volume reductions were uniform across subfields. Individuals in the most deviant subgroup were predominantly women (79 versus 42 %) with higher BMI [27.9 (2.5) versus 30.5 (4.6) kg/m 2 ], IR (SSPG concentration, [156 (61) versus 123 (70) mg/dL] and leptinemia [21.7 (17.0) versus 44.5 (30.4) μg/L]. The use of person-based modeling in healthy individuals suggests that adiposity, insulin resistance and compromised structural hippocampal integrity behave as a composite phenotype; female sex emerged as risk factor for this phenotype. … (more)
- Is Part Of:
- Neuroscience research. Volume 174(2022)
- Journal:
- Neuroscience research
- Issue:
- Volume 174(2022)
- Issue Display:
- Volume 174, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 174
- Issue:
- 2022
- Issue Sort Value:
- 2022-0174-2022-0000
- Page Start:
- 19
- Page End:
- 24
- Publication Date:
- 2022-01
- Subjects:
- Insulin resistance -- Hippocampal volume -- Adiposity
Neurosciences -- Research -- Periodicals
Neurosciences -- Research -- Japan -- Periodicals
Neurology -- Periodicals
Neurosciences -- Periodicals
Neurosciences -- Recherche -- Périodiques
Neurosciences -- Recherche -- Japon -- Périodiques
Neurosciences -- Research
Japan
Periodicals
612.8 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01680102 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.neures.2021.07.006 ↗
- Languages:
- English
- ISSNs:
- 0168-0102
- Deposit Type:
- Legaldeposit
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