The Effects of Lutein and Zeaxanthin Supplementation on Brain Morphology in Older Adults: A Randomized, Controlled Trial

Catherine M Mewborn, Cutter A Lindbergh, B Randy Hammond, Lisa M Renzi-Hammond, L Stephen Miller, Catherine M Mewborn, Cutter A Lindbergh, B Randy Hammond, Lisa M Renzi-Hammond, L Stephen Miller

Abstract

A growing literature emphasizes the importance of lifestyle factors such as nutrition in successful aging. The current study examined if one year of supplementation with lutein (L) and zeaxanthin (Z), two nutrients with known antioxidative properties and cognitive benefits, impacted structural brain outcomes in older adults using a double-blind, randomized, placebo-controlled trial design. Community-dwelling older adults (20 males and 27 females) aged 65-87 years (M = 71.8 years, SD = 6.04 years) were randomized into supplement (N = 33) and placebo groups (N = 14) using simple randomization. The supplement group received 10 mg L + 2 mg Z daily for 12 months while the placebo group received a visually identical, inert placebo. L and Z were measured via retinal concentrations (macular pigment optical density or MPOD). Structural brain outcomes, focusing on global and frontal-temporal lobe regions, were acquired using both T1-weighted and DTI MRI sequences. We hypothesized that the supplement group would increase, maintain, or show attenuated loss in hypothesized regions-of-interest (ROIs) while the placebo group would show age-related declines in brain structural integrity over the course of the trial. While results showed age-related declines for frontal and temporal gray and white matter volumes, as well as fornix white matter microstructure across both groups, only minimal differences were found between the supplement and placebo groups. However, exploratory analyses showed that individuals who responded better to supplementation (i.e., showed greater increases in MPOD) showed less decline in global and prefrontal gray matter volume than supplement "nonresponders." While results suggest that one year of L and Z supplementation may have limited effects on structural brain outcomes overall, there may be a subsample of individuals for whom supplementation of L and Z provides greater benefits. ClinicalTrials.gov number, NCT02023645.

Conflict of interest statement

During a portion of data collection time, LMRH was employed by Abbott Nutrition while holding a joint appointment at the University of Georgia. No other conflicts of interest exist for the study authors, including CMM, CAL, BRH, and LSM. All statistical analyses were completed independent of the supporting agencies.

Copyright © 2019 Catherine M. Mewborn et al.

Figures

Figure 1
Figure 1
CONSORT flow diagram.
Figure 2
Figure 2
Volumetric regions-of-interest (ROIs). The figure depicts the masks used for volumetric ROI analyses from left to right: prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex, medial temporal cortex, and hippocampus. Masks are superimposed on a representative T1-weighted image from a participant in our sample.
Figure 3
Figure 3
White matter microstructure regions-of-interest (ROIs). The figure depicts the masks used for white matter microstructure ROI analyses for the genu (red), fornix (blue), and anterior cingulum (yellow) in the sagittal view (a) and axial view (b). Masks are superimposed on a single-subject diffusion-weighted template in MNI space provided by Johns Hopkins University (JHU) in FMRIB's Software Library (FSL). The mean skeleton for the sample is overlaid on the diffusion-weighted image in bright green.
Figure 4
Figure 4
Changes in total gray matter volume. The figure shows changes in total gray matter volume (mm3 × 105) between supplement group “responders” and “nonresponders.”
Figure 5
Figure 5
Changes in prefrontal cortex volume. The figure shows changes in total gray matter volume (mm3 × 105) between supplement group “responders” and “nonresponders.”

References

    1. Couture M., Larivière N., Lefrançois R. Psychological distress in older adults with low functional independence: a multidimensional perspective. Archives of Gerontology and Geriatrics. 2005;41(1):101–111. doi: 10.1016/j.archger.2004.12.004.
    1. Puente A. N., Terry D. P., Faraco C. C., Brown C. L., Miller L. S. Functional impairment in mild cognitive impairment evidenced using performance-based measurement. Journal of Geriatric Psychiatry and Neurology. 2014;27(4):253–258. doi: 10.1177/0891988714532016.
    1. Harman D. Aging: a theory based on free radical and radiation chemistry. Journal of Gerontology. 1956;11:289–300. doi: 10.1093/geronj/11.3.298.
    1. Abdollahi M., Moridani M. Y., Aruoma O. I., Mostafalou S. Oxidative stress in aging. Oxidative Medicine and Cellular Longevity. 2014;2014:2. doi: 10.1155/2014/876834.876834
    1. Gu Y., Brickman A. M., Stern Y., et al. Mediterranean diet and brain structure in a multiethnic elderly cohort. Neurology. 2015;85(20):1744–1751. doi: 10.1212/WNL.0000000000002121.
    1. Raji C. A., Erickson K. I., Lopez O. L., et al. Regular fish consumption and age-related brain gray matter loss. American Journal of Preventive Medicine. 2014;47(4):444–451. doi: 10.1016/j.amepre.2014.05.037.
    1. Scarmeas N., Luchsinger J. A., Stern Y., et al. Mediterranean diet and magnetic resonance imaging-assessed cerebrovascular disease. Annals of Neurology. 2011;69(2):257–268. doi: 10.1002/ana.22317.
    1. Craft N. E., Haitema T. B., Garnett K. M., Fitch K. A., Dorey C. K. Carotenoid, tocopherol, and retinol concentrations in elderly human brain. The Journal of Nutrition, Health and Aging. 2004;8(3):156–162.
    1. Johnson E. J., Vishwanathan R., Johnson M. A., et al. Relationship between serum and brain carotenoids, α-tocopherol, and retinol concentrations and cognitive performance in the oldest old from the Georgia centenarian study. Journal of Aging Research. 2013;2013:13. doi: 10.1155/2013/951786.951786
    1. den Heijer T., Launer L. J., de Groot J. C., et al. Serum carotenoids and cerebral white matter lesions: the Rotterdam scan study. Journal of the American Geriatrics Society. 2001;49(5):642–646. doi: 10.1046/j.1532-5415.2001.49126.x.
    1. Lindbergh C. A., Renzi-Hammond L. M., Hammond B. R., et al. Lutein and zeaxanthin influence brain function in older adults: a randomized controlled trial. Journal of the International Neuropsychological Society. 2018;23(1):77–90. doi: 10.1017/S1355617717000534.
    1. Mewborn C. M., Terry D. P., Renzi-Hammond L. M., Hammond B. R., Miller L. S. Relation of retinal and serum lutein and zeaxanthin to white matter integrity in older adults: a diffusion tensor imaging study. Archives of Clinical Neuropsychology. 2017;33(7):861–874. doi: 10.1093/acn/acx109.
    1. Abe O., Yamasue H., Aoki S., et al. Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data. Neurobiology of Aging. 2008;29(1):102–116. doi: 10.1016/j.neurobiolaging.2006.09.003.
    1. Salat D. H., Kaye J. A., Janowsky J. S. Prefrontal gray and white matter volumes in healthy aging and Alzheimer disease. Archives of Neurology. 1999;56(3):338–344. doi: 10.1001/archneur.56.3.338.
    1. Bennett I. J., Madden D. J. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience. 2014;276(6):187–205. doi: 10.1016/j.neuroscience.2013.11.026.
    1. Head D., Buckner R. L., Shimony J. S., et al. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cerebral Cortex. 2004;14(4):410–423. doi: 10.1093/cercor/bhh003.
    1. Schulz K. F., Altman D. G., Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Annals of Internal Medicine. 2010;152(11):726–732. doi: 10.7326/0003-4819-152-11-201006010-00232.
    1. Hammond B. R., Miller L. S., Bello M. O., Lindbergh C. A., Mewborn C., Renzi-Hammond L. M. Effects of lutein/zeaxanthin supplementation on the cognitive function of community dwelling older adults: a randomized, double-masked, placebo-controlled trial. Frontiers in Aging Neuroscience. 2017;9 doi: 10.3389/fnagi.2017.00254.
    1. Morris J. C. The Clinical dementia rating (CDR): current version and scoring rules. Neurology. 1993;43(11):p. 2412. doi: 10.1212/wnl.43.11.2412-a.
    1. Wooten B. R., Hammond B. R. Spectral absorbance and spatial distribution of macular pigment using heterochromatic flicker photometry. Optometry and Vision Science. 2005;82(5):378–386. doi: 10.1097/01.OPX.0000162654.32112.A1.
    1. Wooten B. R., Hammond B. R., Land R. I., Snodderly D. M. A practical method for measuring macular pigment optical density. Investigative Ophthalmology and Visual Science. 1999;40(11):2481–2489.
    1. Fischl B., Salat D. H., Busa E., et al. Whole brain segmentation. Neuron. 2002;33(3):341–355. doi: 10.1016/S0896-6273(02)00569-X.
    1. Reuter M., Schmansky N. J., Rosas H. D., Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage. 2012;61(4):1402–1418. doi: 10.1016/j.neuroimage.2012.02.084.
    1. Desikan R. S., Ségonne F., Fischl B., et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31(3):968–980. doi: 10.1016/j.neuroimage.2006.01.021.
    1. Behrens T. E. J., Woolrich M. W., Jenkinson M., et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine. 2003;50(5):1077–1088. doi: 10.1002/mrm.10609.
    1. Smith S. M., Jenkinson M., Johansen-Berg H., et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage. 2006;31(4):1487–1505. doi: 10.1016/j.neuroimage.2006.02.024.
    1. Mori S., Oishi K., Jiang H., et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage. 2008;40(2):570–582. doi: 10.1016/j.neuroimage.2007.12.035.
    1. Jernigan T. L., Archibald S. L., Fennema-Notestine C., et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology of Aging. 2001;22(4):581–594. doi: 10.1016/S0197-4580(01)00217-2.
    1. Barrick T. R., Charlton R. A., Clark C. A., Markus H. S. White matter structural decline in normal ageing: a prospective longitudinal study using tract-based spatial statistics. NeuroImage. 2010;51(2):565–577. doi: 10.1016/j.neuroimage.2010.02.033.
    1. Teipel S. J., Meindl T., Wagner M., et al. Longitudinal changes in fiber tract integrity in healthy aging and mild cognitive impairment: a DTI follow-up study. Journal of Alzheimer’s Disease. 2010;22(2):507–522. doi: 10.3233/JAD-2010-100234.
    1. Sato Y., Suzuki R., Kobayashi M., et al. Involvement of cholesterol membrane transporter Niemann-Pick C1-Like 1 in the intestinal absorption of lutein. Journal of Pharmacy and Pharmaceutical Sciences. 2012;15(2):256–264. doi: 10.18433/j38k56.
    1. Tanumihardjo S. A., Li J., Dosti M. P. Lutein absorption is facilitated with cosupplementation of ascorbic acid in young adults. Journal of the American Dietetic Association. 2005;105(1):114–118. doi: 10.1016/j.jada.2004.10.011.
    1. Vogiatzoglou A., Refsum H., Johnston C., et al. Vitamin B12 status and rate of brain volume loss in community-dwelling elderly. Neurology. 2008;71(11):826–832. doi: 10.1212/01.wnl.0000325581.26991.f2.
    1. Akbaraly T. N., Singh-Manoux A., Marmot M. G., Brunner E. J. Education attenuates the association between dietary patterns and cognition. Dementia and Geriatric Cognitive Disorders. 2009;27(2):147–154. doi: 10.1159/000199235.
    1. Renzi L. M., Dengler M. J., Puente A., Miller L. S., Hammond B. R. Relationships between macular pigment optical density and cognitive function in unimpaired and mildly cognitively impaired older adults. Neurobiology of Aging. 2014;35(7):1695–1699. doi: 10.1016/j.neurobiolaging.2013.12.024.
    1. Renzi L. M., Hammond B. J., Dengler M., Roberts R. The relation between serum lipids and lutein and zeaxanthin in the serum and retina: results from cross-sectional, case-control and case study designs. Lipids in Health and Disease. 2012;11(33) doi: 10.1186/1476-511X-11-33.
    1. Ajana S., Weber D., Helmer C., et al. Plasma concentrations of lutein and zeaxanthin, macular pigment optical density, and their associations with cognitive performances among older adults. Investigative Opthalmology and Visual Science. 2018;59(5):1828–1835. doi: 10.1167/iovs.17-22656.
    1. Feeney J., Finucane C., Savva G. M., et al. Low macular pigment optical density is associated with lower cognitive performance in a large, population-based sample of older adults. Neurobiology of Aging. 2013;34(11):2449–2456. doi: 10.1016/j.neurobiolaging.2013.05.007.
    1. Vishwanathan R., Iannaccone A., Scott T. M., et al. Macular pigment optical density is related to cognitive function in older people. Age and Ageing. 2014;43(2):271–275. doi: 10.1093/ageing/aft210.
    1. Akbaraly N. T., Faure H., Gourlet V., Favier A., Berr C. Plasma carotenoid levels and cognitive performance in an elderly population: results of the EVA study. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2007;62(3):308–316. doi: 10.1093/gerona/62.3.308.
    1. Johnson E. J., Chung H.-Y., Caldarella S. M., Snodderly D. M. The influence of supplemental lutein and docosahexaenoic acid on serum, lipoproteins, and macular pigmentation. The American Journal of Clinical Nutrition. 2008;87(5):1521–1529. doi: 10.1093/ajcn/87.5.1521.
    1. The Age-Related Eye Disease Study 2 (AREDS2) Research Group. Lutein + zeaxanthin and omega-3 fatty acids for age-related macular degeneration: the Age-Related Eye Disease Study 2 (AREDS2) randomized clinical trial. JAMA. 2013;309(19):2005–2015. doi: 10.1001/jama.2013.4997.

Source: PubMed

Подписаться