Beyond the Surface: Delving Deeper Into Leukoaraiosis With Multivoxel Magnetic Resonance Spectroscopy

Objective: To investigate metabolite changes in patients with leukoaraiosis employing multivoxel magnetic resonance spectroscopy (MRS) with the focus on periventricular white matter and explicate the biochemical alterations associated with leukoaraiosis and their impact on lesion load.

Methods: This prospective study was conducted on 64 patients with a known history of leukoaraiosis (mean age, 66.40±8.96 years; 54 men and 10 women) referred for magnetic resonance imaging, wherein MRS was performed. For comparison, 128 age- and gender-matched healthy individuals (mean age, 61.98±8.18 years; 40 men and 88 women) who comprised the control group also underwent MRS. We correlated metabolite ratios (NAA/Cr, NAA/Cho, and Cho/Cr) analyzed on MRS with lesion load measured by semiautomated software.

Results: The NAA/Cr ratio was significantly lower, whereas the NAA/Cho ratio was significantly higher in the control group compared with the patients with leukoaraiosis (P <.0001) . The Cho/Cr ratio was also significantly higher in the controls compared with the patients with leukoaraiosis (P <.0034) . This suggests that patients with leukoaraiosis exhibit significant metabolic differences compared with healthy controls. We observed no correlation between the metabolite ratios and lesion load, which indicates that the degree of white matter hyperintensities is not related to the metabolic changes in leukoaraiosis.

Conclusions: This study explicates the understanding of leukoaraiosis and underscores the potential of MRS as a biomarker for early diagnosis of leukoaraiosis.

1. Wardlaw JM, Smith EE, Biessels GJ, et al; STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–838. PMID: 23867200. PMCID: PMC3714437. https://doi.org/10.1016/S1474-4422(13)70124-8

2. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666. PMID: 20660506. PMCID: PMC2910261. https://doi.org/10.1136/bmj.c3666

3. Wardlaw JM, Valdés Hernández MC, Muñoz-Maniega S. What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc. 2015;4(6):001140. Published correction appears in J Am Heart Assoc. 2016;5(1):e002006. PMID: 26104658. PMCID: PMC4599520. https://doi.org/10.1161/JAHA.114.001140

4. Ben Salem D, Walker PM, Bejot Y, et al. N-acetylaspartate/ creatine and choline/creatine ratios in the thalami, insular cortex and white matter as markers of hypertension and cognitive impairment in the elderly. Hypertens Res. 2008;31(10):1851–1857. PMID: 19015591. https://doi.org/10.1291/hypres.31.1851

5. Cudalbu C, Mlynárik V, Gruetter R. Handling macromolecule signals in the quantification of the neurochemical profile. J Alzheimers Dis. 2012;31 Suppl 3:S101–S115. PMID: 22543852. https://doi.org/10.3233/JAD-2012-120100

6. Waragai M, Moriya M, Nojo T. Decreased N-acetyl aspartate/myo-inositol ratio in the posterior cingulate cortex shown by magnetic resonance spectroscopy may be one of the risk markers of preclinical Alzheimer’s disease: a 7-year follow-up study. J Alzheimers Dis. 2017;60(4):1411–1427. PMID: 28968236. PMCID: PMC5676849. https://doi.org/10.3233/JAD-170450

7. Modrego PJ, Fayed N. Longitudinal magnetic resonance spectroscopy as marker of cognitive deterioration in mild cognitive impairment. Am J Alzheimers Dis Other Demen. 2011;26(8):631– 636. PMID: 22323830. PMCID: PMC10845573. https://doi.org/10.1177/1533317511433809

8. Viana-Baptista M, Bugalho P, Jordão C, et al. Cognitive function correlates with frontal white matter apparent diffusion coefficients in patients with leukoaraiosis. J Neurol. 2008;255(3):360– 366. PMID: 18338199. https://doi.org/10.1007/s00415-008-0661-9

9. Kim SJ, Kwon OD, Han EB, et al. Impact of number of medications and age on adherence to antihypertensive medications: a nationwide population-based study. Medicine (Baltimore). 2019;98(49):e17825. PMID: 31804305. PMCID: PMC6919523. https://doi.org/10.1097/MD.0000000000017825

10. Maresova P, Javanmardi E, Barakovic S, et al. Consequences of chronic diseases and other limitations associated with old age - a scoping review. BMC Public Health. 2019;19(1):1431. PMID: 31675997. PMCID: PMC6823935. https://doi.org/10.1186/s12889-019-7762-5

11. Regitz-Zagrosek V. Sex and gender differences in health. Science & Society Series on Sex and Science. EMBO Rep. 2012;13(7):596–603. PMID: 22699937. PMCID: PMC3388783. https://doi.org/10.1038/embor.2012.87

12. Mauvais-Jarvis F. Sex differences in metabolic homeostasis, diabetes, and obesity. Biol Sex Differ. 2015;6:14. PMID: 26339468. PMCID: PMC4559072. https://doi.org/10.1186/s13293-015-0033-y

13. Lin Q, Huang WQ, Ma QL, et al. Incidence and risk factors of leukoaraiosis from 4683 hospitalized patients: a cross-sectional study. Medicine (Baltimore). 2017;96(39):e7682. PMID: 28953609. PMCID: PMC5626252. https://doi.org/10.1097/MD.0000000000007682

14. Kara F, Joers JM, Deelchand DK, et al. 1H MR spectroscopy biomarkers of neuronal and synaptic function are associated with tau deposition in cognitively unimpaired older adults. Neurobiol Aging. 2022;112:16–26. PMID: 35038671. PMCID: PMC8976711. https://doi.org/10.1016/j.neurobiolaging.2021.12.010

15. Barker PB, Lin DDM. In vivo proton MR spectroscopy of the human brain. Progress in Nuclear Magnetic Resonance Spectroscopy. 2006;49(2):99–128. https://doi.org/10.1016/j.pnmrs.2006.06.002

16. Soares DP, Law M. Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol. 2009;64(1):12–21. PMID: 19070693. https://doi.org/10.1016/j.crad.2008.07.002

17. Govindaraju V, Young K, Maudsley AA. Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed. 2000;13(3):129–153. Published correction appears in NMR Biomed. 2015;28(7):923–924. PMID: 10861994. https://doi.org/10.1002/1099-1492(200005)13:33.0.co;2-v

18. Gouw AA, Seewann A, van der Flier WM, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. J Neurol Neurosurg Psychiatry. 2011;82(2):126–135. PMID: 20935330. https://doi.org/10.1136/jnnp.2009.204685

19. Valdés Hernández Mdel C, Morris Z, Dickie DA, et al. Close correlation between quantitative and qualitative assessments of white matter lesions. Neuroepidemiology. 2013;40(1):13–22. PMID: 23075702. https://doi.org/10.1159/000341859

20. Hilal S, Mok V, Youn YC, Wong A, Ikram MK, Chen CL. Prevalence, risk factors and consequences of cerebral small vessel diseases: data from three Asian countries. J Neurol Neurosurg Psychiatry. 2017;88(8):669–674. PMID: 28600443. https://doi.org/10.1136/jnnp-2016-315324

21. Guo J, Yao C, Chen H, et al. The relationship between Cho/ NAA and glioma metabolism: implementation for margin delineation of cerebral gliomas. Acta Neurochir (Wien). 2012;154(8):1361– 1370. PMID: 22729482. PMCID: PMC3407558. https://doi.org/10.1007/s00701-012-1418-x

22. Hund-Georgiadis M, Norris DG, Guthke T, von Cramon DY. Characterization of cerebral small vessel disease by proton spectroscopy and morphological magnetic resonance. Cerebrovasc Dis. 2001;12(2):82–90. PMID: 11490101. https://doi.org/10.1159/000047686

23. Kantarci K, Jack CR Jr, Xu YC, et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: a 1H MRS study. Neurology. 2000;55(2):210–217. PMID: 10908893. PMCID: PMC2771162. https://doi.org/10.1212/wnl.55.2.210.