Low cerebral blood flow is associated with lower memory function in metabolic syndrome

Alex C Birdsill, Cynthia M Carlsson, Auriel A Willette, Ozioma C Okonkwo, Sterling C Johnson, Guofan Xu, Jennifer M Oh, Catherine L Gallagher, Rebecca L Koscik, Erin M Jonaitis, Bruce P Hermann, Asenath LaRue, Howard A Rowley, Sanjay Asthana, Mark A Sager, Barbara B Bendlin, Alex C Birdsill, Cynthia M Carlsson, Auriel A Willette, Ozioma C Okonkwo, Sterling C Johnson, Guofan Xu, Jennifer M Oh, Catherine L Gallagher, Rebecca L Koscik, Erin M Jonaitis, Bruce P Hermann, Asenath LaRue, Howard A Rowley, Sanjay Asthana, Mark A Sager, Barbara B Bendlin

Abstract

Background: Metabolic syndrome (MetS)--a cluster of cardiovascular risk factors--is linked with cognitive decline and dementia. However, the brain changes underlying this link are presently unknown. In this study, we tested the relationship between MetS, cerebral blood flow (CBF), white matter hyperintensity burden, and gray matter (GM) volume in cognitively healthy late middle-aged adults. Additionally, the extent to which MetS was associated with cognitive performance was assessed.

Design and methods: Late middle-aged adults from the Wisconsin Registry for Alzheimer's Prevention (N = 69, mean age = 60.4 years) underwent a fasting blood draw, arterial spin labeling perfusion MRI, T1-weighted MRI, T2FLAIR MRI, and neuropsychological testing. MetS was defined as abnormalities on three or more factors, including abdominal obesity, triglycerides, HDL-cholesterol, blood pressure, and fasting glucose.

Results: Mean GM CBF was 15% lower in MetS compared to controls. Voxel-wise image analysis indicated that the MetS group had lower CBF across a large portion of the cortical surface, with the exception of medial and inferior parts of the occipital and temporal lobes. The MetS group also had lower immediate memory function; a mediation analysis indicated this relationship was partially mediated by CBF. Among the MetS factors, abdominal obesity and elevated triglycerides were most strongly associated with lower CBF.

Conclusions: The results underscore the importance of reducing the number of cardiovascular risk factors for maintaining CBF and cognition in an aging population.

Copyright © 2012 The Obesity Society.

Figures

Figure 1
Figure 1
Mean CBF is displayed by groups defined by the number of MetS factors present in an individual. CBF is adjusted by reference cluster and inversion time.
Figure 2
Figure 2
Participants with metabolic syndrome showed significantly lower CBF in large portions of the cortical surface of the frontal and parietal lobes, and the lateral and superior portions of the temporal and occipital lobes. Voxel-wise results are shown here at p

Figure 3

The first model displays the…

Figure 3

The first model displays the total effect, c, between MetS and immediate memory.…

Figure 3
The first model displays the total effect, c, between MetS and immediate memory. The second uses CBF as a mediator that is partially accounting for the effect between MetS and immediate memory. The indirect effect, ab = −0.41, is the portion of the effect accounted for by CBF. Significance of the mediation was determined using bootstrapping (k = 5000) with 95% confidence intervals of the indirect effect [−0.82, − 0.08]. Age, reference cluster, and inversion time were controlled.
Figure 3
Figure 3
The first model displays the total effect, c, between MetS and immediate memory. The second uses CBF as a mediator that is partially accounting for the effect between MetS and immediate memory. The indirect effect, ab = −0.41, is the portion of the effect accounted for by CBF. Significance of the mediation was determined using bootstrapping (k = 5000) with 95% confidence intervals of the indirect effect [−0.82, − 0.08]. Age, reference cluster, and inversion time were controlled.

References

    1. Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120:1640–5.
    1. Ervin RB. National health statistics reports; no 13. National Center for Health Statistics; Hyattsville, MD: 2009. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006. 2009.
    1. Centers for Disease Control and Prevention and The Merck Company Foundation. The State of Aging and Health in America, 2007. The Merck Company Foundation; Whitehouse Station, N.J: 2007.
    1. Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol. 2006;5:735–41.
    1. Vanhanen M, Koivisto K, Moilanen L, et al. Association of metabolic syndrome with Alzheimer disease. Neurology. 2006;67:843–7.
    1. Yaffe K, Kanaya A, Lindquist K, et al. The metabolic syndrome, inflammation, and risk of cognitive decline. Jama. 2004;292:2237–42.
    1. Fitzpatrick AL, Kuller LH, Lopez OL, et al. Midlife and late-life obesity and the risk of dementia: cardiovascular health study. Arch Neurol. 2009;66:336–42.
    1. Xu W, Qiu C, Gatz M, Pedersen NL, Johansson B, Fratiglioni L. Mid- and late-life diabetes in relation to the risk of dementia: a population-based twin study. Diabetes. 2009;58:71–7.
    1. Frisardi V, Solfrizzi V, Seripa D, et al. Metabolic-cognitive syndrome: a cross-talk between metabolic syndrome and Alzheimer’s disease. Ageing Res Rev. 2010;9:399–417.
    1. Rogers RL, Meyer JS, McClintic K, Mortel KF. Reducing Hypertriglyceridemia in Elderly Patients with Cerebrovascular Disease Stabilizes or Improves Cognition and Cerebral Perfusion. Angiology. 1989;40:260–9.
    1. Muller M, van der Graaf Y, Visseren FL, Mali WP, Geerlings MI. Hypertension and longitudinal changes in cerebral blood flow: The SMART-MR study. Ann Neurol. 2012
    1. Carlsson CM, Xu G, Wen Z, et al. Effects of Atorvastatin on Cerebral Blood Flow in Middle-Aged Adults at Risk for Alzheimer’s Disease: A Pilot Study. Curr Alzheimer Res. 2011 Epub ahead of print.
    1. Willeumier KC, Taylor DV, Amen DG. Elevated BMI is associated with decreased blood flow in the prefrontal cortex using SPECT imaging in healthy adults. Obesity (Silver Spring) 2011;19:1095–7.
    1. Johnson NA, Jahng GH, Weiner MW, et al. Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. Radiology. 2005;234:851–9.
    1. Sager MA, Hermann B, La Rue A. Middle-aged children of persons with Alzheimer’s disease: APOE genotypes and cognitive function in the Wisconsin Registry for Alzheimer’s Prevention. J Geriatr Psychiatry Neurol. 2005;18:245–9.
    1. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34:939–44.
    1. Dowling NM, Hermann B, La Rue A, Sager MA. Latent structure and factorial invariance of a neuropsychological test battery for the study of preclinical Alzheimer’s disease. Neuropsychology. 2010;24:742–56.
    1. Spreen O, Strauss E. A compendium of neuropsychological tests: administration, norms, and commentary. 2. Oxford University Press; New York: 1998.
    1. Wechsler D. WAIS-III: Wechsler Adult Intelligence Scale. Psychological Corporation; 1997.
    1. Trenerry MR. Stroop Neuropsychological Screening Test Manual. Psychological Assessment Resources; 1989.
    1. Reitan RM, Wolfson D. The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. Neuropsychology Press; 1993.
    1. Backman L, Jones S, Berger AK, Laukka EJ, Small BJ. Cognitive impairment in preclinical Alzheimer’s disease: a meta-analysis. Neuropsychology. 2005;19:520–31.
    1. Ye FQ, Frank JA, Weinberger DR, McLaughlin AC. Noise reduction in 3D perfusion imaging by attenuating the static signal in arterial spin tagging (ASSIST) Magn Reson Med. 2000;44:92–100.
    1. Dai W, Garcia D, de Bazelaire C, Alsop DC. Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magn Reson Med. 2008;60:1488–97.
    1. Garcia DM, Duhamel G, Alsop DC. Efficiency of inversion pulses for background suppressed arterial spin labeling. Magn Reson Med. 2005;54:366–72.
    1. Xu G, Rowley HA, Wu G, et al. Reliability and precision of pseudo-continuous arterial spin labeling perfusion MRI on 3. 0 T and comparison with 15O-water PET in elderly subjects at risk for Alzheimer’s disease. NMR Biomed. 2010;23:286–93.
    1. Tosun D, Mojabi P, Weiner MW, Schuff N. Joint analysis of structural and perfusion MRI for cognitive assessment and classification of Alzheimer’s disease and normal aging. Neuroimage. 2010;52:186–97.
    1. Yakushev I, Hammers A, Fellgiebel A, et al. SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage. 2009;44:43–50.
    1. Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839–51.
    1. Keihaninejad S, Heckemann RA, Fagiolo G, Symms MR, Hajnal JV, Hammers A. A robust method to estimate the intracranial volume across MRI field strengths (1. 5T and 3T) Neuroimage. 2010;50:1427–37.
    1. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59:3774–83.
    1. O’brien R. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality & Quantity. 2007;41:673–90.
    1. MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593–614.
    1. Carr DB, Utzschneider KM, Hull RL, et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes. 2004;53:2087–94.
    1. Buckner RL, Snyder AZ, Shannon BJ, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25:7709–17.
    1. Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET Evaluation of Cerebral Metabolic Decline in Dementia: A Potential Outcome Measure in Alzheimer’s Disease Treatment Studies. Am J Psychiatry. 2002;159:738–45.
    1. Craft S, Zallen G, Baker LD. Glucose and memory in mild senile dementia of the Alzheimer type. J Clin Exp Neuropsychol. 1992;14:253–67.
    1. Yaffe K, Weston AL, Blackwell T, Krueger KA. The metabolic syndrome and development of cognitive impairment among older women. Arch Neurol. 2009;66:324–8.
    1. Wolk DA, Dickerson BC. Fractionating verbal episodic memory in Alzheimer’s disease. Neuroimage. 2011;54:1530–9.

Source: PubMed

3
Prenumerera