BA19 - Omega-3 fatty acid biomarkers and cognitive decline in WHIMS

Investigator Names and Contact Information

William S. Harris (HARRISW@sanfordhealth.org)

Introduction/Intent

Dementia represents a diverse category of syndromes characterized by deficits in memory, cognitive function, and behavior. The greater life expectancy for women translates into a high lifetime risk of dementia and a greater need for long-term disability care. Based on 30-year follow-up data from Framingham, a middle-aged woman in North America of European descent has a 1 in 5 risk of developing dementia of any type³. The prevalence of dementia has been estimated to be approximately 6-10% of individuals age 65 and older, rising from 1-2% of those aged 65 to 74, and rising to 30% or more of those aged 85 and older.⁴ Dementia increases the mean annual healthcare expenditure per patient by over $5000 in 2006 dollars, with 75% of the costs related to higher rates of hospitalization and expenditures on skilled nursing facilities.⁵ In 2005, the U.S. spent an estimated $100 billion providing care for 3 million patients with dementia, and the U.S. population aged 65 and older is expected to increase from 12% in 2000 to 20% by 2030. The number of persons aged 80 and older also expected to double to 19.5 million by 2030.⁶ By 2011, current costs are expected to double, and by 2050 Medicare costs associated with dementia may be as much as $1 trillion.⁷ There is thus a pressing need to discover treatments or preventive strategies to reduce the burden of dementia.

Current therapies for treating dementia modify symptoms but not the course of the disease.⁸ To decrease the impact of dementia, effective strategies for identifying those at risk as well as treatments for preserving or slowing declining in cognitive function and promote independence among older adults are likely to have an important public health impact. It has been estimated that delaying the age of onset of dementia by just 5 years would reduce the lifetime risk of dementia by approximately half.³ In a recent editorial, Rosenberg noted that developing strategies to forestall the development of dementia is of highest priority, since the treatment of established disease with either drugs or diet has not been effective: “Studies of nutritional associations with brain function during the elongated prodromal period of age-related neurodegeneration and decline offer an opportunity for early intervention to maintain brain function and slow progression to dementia, which is costly economically and in terms of quality of life.”⁹ The context of Rosenberg’s editorial was a series of new studies linking omega-3 fatty acid (FA) intakes inversely to risk for mental decline (see below). RBC omega-3 levels have been shown to be a valid biomarker of omega-3 FA intake.¹⁰ The demonstration of an inverse correlation between this biomarker and cognitive decline would suggest that a diet higher in omega-3 FAs may slow the rate of decline in mental status. Although it is questionable that increased omega-3 intakes can reverse pre-existing disease, they very well may be able to forestall the development of cognitive dysfunction. Raising intakes of omega-3 fatty acids is both safe and achievable and could ultimately reduce the burden of age-related cognitive decline in the US.

Our primary hypothesis is that RBC omega‐3 FA content is inversely correlated with risk for cognitive decline – whether measured by cognitive testing, case adjudication, or brain MRI ‐ inpostmenopausal women. The cognitive test data allow us to assess preclinical relationships that may signal early risk. WHIMS has demonstrated that even small mean changes in cognitive test scores may be associated with marked differences in the risk of clinical events.^{1, 2} Associations with ischemic lesionvolumes and atrophy, as assessed by MRI, allow us to assess both preclinical and clinical neuropathology and may help signal mechanisms. WHIMS PD cases have been sub‐typed (Alzheimer’s, vascular, mixed, etc.), which allows us to examine the consistency of FA relations among these classifications. Similarly, with WHISCA, we will examine the consistency of FA relations across various cognitive domains. The relations between RBC omega‐3 FAs and time to incident CI and to the conversion from mild CI to PD will be explored. Together, these analyses permit a comprehensive examination of relations between a biomarker of tissue FA status and a spectrum of cognitive functional states and disease.

Aims

1. In WHIMS, to determine the relations that RBC omega-3 FA content has with a) baseline global cognitive function, b) changes in global cognitive function over 10+ years, c) time to incident probable dementia (PD) and combined cognitive impairment (CI; PD and/or mild CI), and d) incidence of PD and CI over follow-up period.
2. In WHISCA, to determine the relations that RBC omega-3 FA content has with a) baseline domain-specific cognitive function and b) changes in domain-specific cognitive function over 10+ years of follow-up.
3. In WHIMS-MRI, to determine the relations that RBC omega-3 FA content has with a) baseline total and region-specific ischemic lesion volume, and b) baseline total and region-specific brain volumes.
4. In WHIMS, to determine the effects of random assignment to hormone therapy (HT) on RBC omega-3 FA content.

Many additional studies or exploratory analyses will be possible using the FA data generated here, including:

• Use of the entire RBC FA profile (“metabolomic” approach; not just the omega-3 FAs) as marker of risk for PD/CI.
• Correlations between FA intakes from WHI diet records and RBC FA content (i.e., validation of essential FA intakes from a Food Frequency Questionnaire). Is the biomarker more closely associated with cognitive status than estimated omega-3 FA intakes?
• Interactions between omega-3 biomarker levels and other dietary factors (such as alcohol, caloric intake, fruits/vegetables, whole grains, vitamin E, folic acid, etc.) on the risk for cognitive decline
• Correlations between omega-3 biomarkers and cardiovascular risk factors (e.g., hypertension, diabetes, smoking, and hyperlipidemia)
• Interactions between omega-3 biomarkers and cardiovascular risk factors (e.g., hypertension, diabetes, smoking, and hyperlipidemia) on the risk for cognitive decline
• Relations between omega-3 biomarkers and risk for cardiovascular disease, for fractures, and for cancer; all adjudicated endpoints also captured within the WHIMS cohort

Since RBC FAs will be part of permanent dataset for WHI, a wide variety of other future studies will be possible as endpoints continue to accumulate, including interactions with genetic markers (especially apoE genotypes) and with environmental pollutants (e.g., mercury or other heavy metals).

Results/Findings

See publications: 1259, 1558. WHI publications by study lists published WHI papers that have been generated by ancillary studies. A complete list of WHI papers is available in the Bibliography section of this website

Ms1259 - Ammann EM, Pottala JV, Harris WS, Espeland MA, Wallace R, Denburg NL, Carnahan RM, Robinson JG. ω-3 fatty acids and domain-specific cognitive aging: secondary analyses of data from WHISCA. Neurology. 2013 Oct 22;81(17):1484-91. doi: 10.1212/WNL.0b013e3182a9584c. Epub 2013 Sep 25.​

Ms1558* - ​Pottala JV, Espeland MA, Polreis J, Robinson J, Harris WS. Correcting the Effects of -20​C Storage and Aliquot Size on Erythrocyte Fatty Acid Content in the Women's Health Initiative. Lipids. 2012 Sep;47(9):835-46.doi:10.1007/s11745-012-3693-y. Epub 2012 Jul 11.

*Please note the following correction to Ms1558: In the article two references are incorrect. In the next to last paragraph of the Discussion, the last sentence includes '...National Health Interveiw Survey [19], and are recommended for clinical research [20].' These reference numbers should actually be [21] and [22] as follows:

[21] Schenker N, Raghunathan TE, Chiu PL, Makuc DM, Zhang G, Cohen AJ. Multiple Imputation of Missing Income Data in the National Health Interview Survey. Journal of the American Statistical Association. 2006;101(475):925-933.

[22] Newgard CD, Haukoos JS. Advanced statistics: missing data in clinical research--part 2: multiple imputation. Acad Emerg Med. 2007;14(7):669-678.​

References

1. Rapp SR, Espeland MA, Shumaker SA, et al. Effect of Estrogen Plus Progestin on Global Cognitive Function in Postmenopausal Women: The Women's Health Initiative Memory Study: A Randomized Controlled Trial. JAMA. 2003;289(20):2663‐2672.
2. Espeland MA, Rapp SR, Shumaker SA, et al. Conjugated Equine Estrogens and Global Cognitive Function in Postmenopausal Women: Women's Health Initiative Memory Study. JAMA. June 23, 2004 2004;291(24):2959‐2968.
3. Seshadri S, Wolf P. Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study. Lancet Neurol. 2007;6(12):1106‐1114.
4. Hendrie HC. Epidemiology of Dementia and Alzheimer's Disease. Am. J. Geriatr. Psychiatry.1998;6:S3‐18.
5. Hill JW, Futterman R, Duttagupta S, Mastey V, Lloyd JR, Fillit H. Alzheimer's disease and related dementias increase costs of comorbidities in managed Medicare. Neurology. 2002;58:62‐70.
6. Centers for Disease Control and Prevention. Trends in aging ‐ United States and worldwide. MMWR Morb Mortal Wkly Rep. 2003;52:101‐106.
7. Alzheimer Association. Alzheimer 's disease facts and figures; Date accessed April 24, 2007.
8. Brayne C, Fox C, Boustani M. Dementia Screening in Primary Care: Is It Time? JAMA. 2007;298:2409‐2411.
9. Rosenberg IH. Rethinking brain food. Am J Clin Nutr. November 1, 2007 2007;86(5):1259‐1260.
10. Sands SA, Reid K, Windsor S, Harris W. The impact of age, body mass index, and fish intake on the EPA and DHA content of human erythrocytes. Lipids. 2005;40:in press.
11. Fitzpatrick AL, Kuller LH, Ives DG, et al. Incidence and Prevalence of Dementia in the CardiovTascular Health Study. Journal of the American Geriatrics Society. 2004;52(2):195