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Cognitive Decline: Just Life, or a Preventable Disease? 

Feb 22, 2020
 
Editor: David L. Joffe, BSPharm, CDE, FACA

Author: Antonio Bess, Pharm D Candidate, Florida Agricultural & Mechanical University School of Pharmacy

Cognitive decline is associated with many diseases and medications, but the exact mechanisms are not clearly understood.

Diabetes, obesity, and declining cognitive function are all associated with increased prevalence with increasing age. Diabetes is a known risk factor for eye, kidney, neurological and cardiovascular diseases, but its effect on declining cognitive function has been in question. Previous studies have found associations between patients who have diabetes and poor glycemic control and significantly faster cognitive decline. Other studies have demonstrated a pattern in which diabetes, high blood pressure, and high body mass index in midlife predict dementia in late life. In this prospective study, individuals were followed for up to ten years to find associations between indices in diabetes, insulin resistance, obesity, inflammation, and blood pressure with cognitive decline. The indices of interest were measured separately among those with and without central obesity.  

The MonongahelaYoughiogheny Healthy Aging Team is a populationbased cohort of participants recruited randomly from 2006 to 2008, who were 65 and older, and were from a group of small towns in southwestern Pennsylvania. The study is focused on the epidemiology of cognitive decline and dementia in an area that still has not recovered economically from the collapse of the steel industry in the 1970s. Participants were analyzed at study entry, and annual follow up. To measure cognitive function, participants were given a panel of neuropsychological tests tapping the domains of attention/processing speed, executive function, memory, language, and visuospatial function. At study entry and annually, BP, BMI, waisthip ratio, and depressive symptoms using the modified Center for Epidemiologic Studies Depression scale (mCESD) were measured. 

Key variables at the time of blood draw, including age, sex, race (white vs. nonwhite), education (high school [HS] or less vs. more than HS), APOE*4 allele carrier status, mCESD score, BMI, WHR, systolic BP (SBP), and the following laboratory assay variables: CRP, glucose, HbA1c, insulin, HOMAIR, resistin, adiponectin, and GLP1 were all reviewed to identify predictors of cognitive decline.  Linear regression models were used to examine the relationships of each of the assay values to global cognitive decline, first individually and then jointly, unadjusted and adjusted for age, sex, education, APOE*4 carrier status, mCESD score, WHR, and SBP. The models were refit after stratifying by WHR, using sexspecific medians to equalize sample sizes and statistical power across groups. To account for potential biases posed by deaths or dropouts that did not allow for blood to be drawn, demographics and APOE*4 frequencies of the subgroup with fasting blood to all remaining participants were compared using Welchʼs ttest for continuous variables and Fisherʼs exact test for categorical variables. The main multivariable regression models were then refitted with inverse probability weighting (IPW) to account for potential attrition and nonresponse biases, using multiple imputations to address missing data where applicable. The machine learning approach of Classification and Regression Tree (CART) modeling, stratified by sexspecific median WHR, was used to identify variables and their respective optimal cut-offs for predicting slope of cognitive decline.

Among 1982 participants who were recruited and underwent full assessment at baseline from 2006 to 2008, only 478 individuals were able to provide fasting blood samples. Of this group of individuals, the median age was 82 years; 66.7% were women; 96.7% were white, and 49.0% had more than HS education. Compared to the 1504 original participants without fasting blood data, at baseline, these 478 were significantly younger (74.6 vs. 78.6 years; P < .001); more likely to be women (66.7% vs. 59.2%; P = .004); more likely to be of European descent (96.7% vs. 94.1%; P < .001); more likely to have at least HS education (49.0% vs. 38.6%; P < .001); but about equally likely to be APOE*4 carriers (19.3% vs. 21.5%; P = .350).  

In unadjusted analysis in the sample as a whole, faster cognitive decline was associated with greater age, less education, APOE*4 carriage, higher depression symptoms (mCESD score), and higher adiponectin level. HbA1c was significantly associated with cognitive decline. After stratifying by the median waist-hip ratio, HbA1c remained related to cognitive decline in those with higher waist-hip ratios. Faster cognitive decline was associated, in lower waist-hip ratio participants younger than 87 years, with adiponectin of 11 or greater; and in higher waist-hip ratio participants younger than 88 years, with HbA1c of 6.2% or greater. Higher adiponectin levels predicted a steeper cognitive decline in the lower waist-hip ratio group.  

Abdominal obesity plays a crucial role in cognitive decline in those with diabetes. The microvascular disease may play a more significant role than macrovascular disease. Midlife obesity contributes to cognitive decline but there was no midlife data in this study. Future studies should include a large minority, midlife population. Adiponectin levels need to be carefully assessed as well. 
 

Practice Pearls: 

  • In individuals younger than 88 years old, central obesity can lead to faster cognitive declines.
  • Obesity, diabetes, and aging contribute to cognitive decline, so it’s hard to distinguish the most significant risk.
  • Adiponectin may be a novel independent risk factor for cognitive decline and should be reviewed.

 

Ganguli, Mary, et al. “Aging, Diabetes, Obesity, and Cognitive Decline: A Population‐Based Study.” Journal of the American Geriatrics Society, John Wiley & Sons, Ltd, Feb. 2020, p. jgs.16321, doi:10.1111/jgs.16321. 

Ganguli, Mary, et al. Aging, Diabetes, Obesity, and Cognitive Decline: A Population-Based Study. 2020, pp. 1–8, doi:10.1111/jgs.16321. 

Tuligenga, Richard H., et al. “Midlife Type 2 Diabetes and Poor Glycaemic Control as Risk Factors for Cognitive Decline in Early Old Age: A Post-Hoc Analysis of the Whitehall II Cohort Study.” The Lancet Diabetes and Endocrinology, vol. 2, no. 3, Elsevier Limited, Mar. 2014, pp. 228–35, doi:10.1016/S2213-8587(13)70192-X. 

Cukierman, T., et al. “Cognitive Decline and Dementia in Diabetes – Systematic Overview of Prospective Observational Studies.” Diabetologia, vol. 48, no. 12, Springer, 8 Dec. 2005, pp. 2460–69, doi:10.1007/s00125-005-0023-4. 

 

Antonio Bess, Florida Agricultural and Mechanical University College of Pharmacy 

 

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