Does insulin resistance influence neurodegeneration in non-diabetic Alzheimer’s subjects?
Background Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucos...
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| Vydáno v: | Alzheimer's research & therapy Ročník 13; číslo 1; s. 47 - 11 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London
BioMed Central
17.02.2021
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1758-9193, 1758-9193 |
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| Abstract | Background
Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects.
Methods
In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function.
Results
In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs.
Conclusions
In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. |
|---|---|
| AbstractList | Background
Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects.
Methods
In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function.
Results
In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs.
Conclusions
In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. Type 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects.BACKGROUNDType 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects.In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function.METHODSIn total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function.In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs.RESULTSIn this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs.In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration.CONCLUSIONSIn non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. Type 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects. In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function. In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs. In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. Background Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects. Methods In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function. Results In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs. Conclusions In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. Type 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects. In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function. In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs. In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. Background Type 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects. Methods In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function. Results In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs. Conclusions In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. Keywords: Alzheimer's disease, Insulin resistance, Magnetic resonance imaging, Positron emission tomography imaging Abstract Background Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD aetiopathogenesis in non-diabetic patients is still debated. Here we evaluated the influence of insulin resistance on brain glucose metabolism, grey matter volume and white matter lesions (WMLs) in non-diabetic AD subjects. Methods In total, 130 non-diabetic AD subjects underwent MRI and [18F]FDG PET scans with arterial cannula insertion for radioactivity measurement. T1 Volumetric and FLAIR sequences were acquired on a 3-T MRI scanner. These subjects also had measurement of glucose and insulin levels after a 4-h fast on the same day of the scan. Insulin resistance was calculated by the updated homeostatic model assessment (HOMA2). For [18F]FDG analysis, cerebral glucose metabolic rate (rCMRGlc) parametric images were generated using spectral analysis with arterial plasma input function. Results In this non-diabetic AD population, HOMA2 was negatively associated with hippocampal rCMRGlc, along with total grey matter volumes. No significant correlation was observed between HOMA2, hippocampal volume and WMLs. Conclusions In non-diabetic AD, peripheral insulin resistance is independently associated with reduced hippocampal glucose metabolism and with lower grey matter volume, suggesting that peripheral insulin resistance might influence AD pathology by its action on cerebral glucose metabolism and on neurodegeneration. |
| ArticleNumber | 47 |
| Audience | Academic |
| Author | Knight, Lucy Karim, Salman Junaid, Kehinde Ridha, Basil H. Walker, Zuzana Edison, Paul Donaldson, Andrew Love, Sharon Brooks, David J. Frangou, Eleni Busza, Gail Tan, Tricia Tadros, George Lawrence, Robert M. Underwood, Ben Holscher, Christian Malik, Naghma Raza, Sanara Kshemendran, Sajeev Harrison, John Prasanna, Aparna Ritchie, Craig W. Macharouthu, Ajayverma Livingston, Nicholas R. Calsolaro, Valeria Koranteng, Paul Thacker, Simon Carver, Stefan Bannister, Carol Mate, Vandana Passmore, Anthony Peter van der Doef, Thalia Nilforooshan, Ramin Femminella, Grazia Daniela Coulthard, Elizabeth Ballard, Clive McFarlane, Brady Russell, Gregor Holmes, Clive Archer, Hilary McGuinness, Bernadette |
| Author_xml | – sequence: 1 givenname: Grazia Daniela surname: Femminella fullname: Femminella, Grazia Daniela organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 2 givenname: Nicholas R. surname: Livingston fullname: Livingston, Nicholas R. organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 3 givenname: Sanara surname: Raza fullname: Raza, Sanara organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 4 givenname: Thalia surname: van der Doef fullname: van der Doef, Thalia organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 5 givenname: Eleni surname: Frangou fullname: Frangou, Eleni organization: University of Oxford – sequence: 6 givenname: Sharon surname: Love fullname: Love, Sharon organization: University of Oxford – sequence: 7 givenname: Gail surname: Busza fullname: Busza, Gail organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 8 givenname: Valeria surname: Calsolaro fullname: Calsolaro, Valeria organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 9 givenname: Stefan surname: Carver fullname: Carver, Stefan organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 10 givenname: Clive surname: Holmes fullname: Holmes, Clive organization: University of Southampton – sequence: 11 givenname: Craig W. surname: Ritchie fullname: Ritchie, Craig W. organization: University of Edinburgh – sequence: 12 givenname: Robert M. surname: Lawrence fullname: Lawrence, Robert M. organization: South West London and St George’s Mental Health NHS Trust – sequence: 13 givenname: Brady surname: McFarlane fullname: McFarlane, Brady organization: Southern Health NHS Foundation Trust – sequence: 14 givenname: George surname: Tadros fullname: Tadros, George organization: Heart of England NHS Foundation Trust – sequence: 15 givenname: Basil H. surname: Ridha fullname: Ridha, Basil H. organization: Brighton and Sussex University Hospital Trust – sequence: 16 givenname: Carol surname: Bannister fullname: Bannister, Carol organization: King’s College London – sequence: 17 givenname: Zuzana surname: Walker fullname: Walker, Zuzana organization: Mental Health Unit, St. Margaret’s Hospital – sequence: 18 givenname: Hilary surname: Archer fullname: Archer, Hilary organization: North Bristol NHS Trust – sequence: 19 givenname: Elizabeth surname: Coulthard fullname: Coulthard, Elizabeth organization: North Bristol NHS Trust – sequence: 20 givenname: Ben surname: Underwood fullname: Underwood, Ben organization: Cambridgeshire and Peterborough NHS Foundation Trust – sequence: 21 givenname: Aparna surname: Prasanna fullname: Prasanna, Aparna organization: Black Country Partnership NHS Foundation Trust – sequence: 22 givenname: Paul surname: Koranteng fullname: Koranteng, Paul organization: Northamptonshire Healthcare NHS Foundation Trust – sequence: 23 givenname: Salman surname: Karim fullname: Karim, Salman organization: Lancashire Care NHS Foundation Trust – sequence: 24 givenname: Kehinde surname: Junaid fullname: Junaid, Kehinde organization: Nottinghamshire Healthcare NHS Foundation Trust – sequence: 25 givenname: Bernadette surname: McGuinness fullname: McGuinness, Bernadette organization: Queen’s University Belfast – sequence: 26 givenname: Anthony Peter surname: Passmore fullname: Passmore, Anthony Peter organization: Queen’s University Belfast – sequence: 27 givenname: Ramin surname: Nilforooshan fullname: Nilforooshan, Ramin organization: Surrey and Borders Partnership NHS Foundation Trust – sequence: 28 givenname: Ajayverma surname: Macharouthu fullname: Macharouthu, Ajayverma organization: NHS Ayrshire and Arran – sequence: 29 givenname: Andrew surname: Donaldson fullname: Donaldson, Andrew organization: NHS Lanarkshire – sequence: 30 givenname: Simon surname: Thacker fullname: Thacker, Simon organization: Derbyshire Healthcare NHS Foundation Trust – sequence: 31 givenname: Gregor surname: Russell fullname: Russell, Gregor organization: Bradford District Care NHS Foundation Trust – sequence: 32 givenname: Naghma surname: Malik fullname: Malik, Naghma organization: North West Boroughs Partnership NHS Foundation Trust – sequence: 33 givenname: Vandana surname: Mate fullname: Mate, Vandana organization: Cornwall Partnership NHS Foundation Trust – sequence: 34 givenname: Lucy surname: Knight fullname: Knight, Lucy organization: Somerset Partnership NHS Foundation Trust – sequence: 35 givenname: Sajeev surname: Kshemendran fullname: Kshemendran, Sajeev organization: South Staffordshire and Shropshire Healthcare NHS Foundation Trust – sequence: 36 givenname: Tricia surname: Tan fullname: Tan, Tricia organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London – sequence: 37 givenname: Christian surname: Holscher fullname: Holscher, Christian organization: Research and Experimental Center, Henan University of Chinese Medicine – sequence: 38 givenname: John surname: Harrison fullname: Harrison, John organization: Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam – sequence: 39 givenname: David J. surname: Brooks fullname: Brooks, David J. organization: Newcastle University – sequence: 40 givenname: Clive surname: Ballard fullname: Ballard, Clive organization: University of Exeter Medical School – sequence: 41 givenname: Paul surname: Edison fullname: Edison, Paul email: paul.edison@imperial.ac.uk organization: Division of Neurology, Neurology Imaging Unit, Department of Brain Sciences, Imperial College London |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33597002$$D View this record in MEDLINE/PubMed |
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| Keywords | Insulin resistance Alzheimer’s disease Magnetic resonance imaging Positron emission tomography imaging |
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Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin... Type 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin resistance on AD... Background Type 2 diabetes is a risk factor for Alzheimer's disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin... Background Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral insulin... Abstract Background Type 2 diabetes is a risk factor for Alzheimer’s disease (AD), and AD brain shows impaired insulin signalling. The role of peripheral... |
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