Utility of combined inflammatory biomarkers for the identification of cognitive dysfunction in non-diabetic participants of the ELSA-Brasil

Adriana Cezaretto; Bianca de Almeida-Pititto; Gizelton Pereira Alencar; Claudia K Suemoto; Isabela Bensenor; Paulo A Lotufo; Sandra R G Ferreira; on the behalf of the ELSA-Brasil researchers

doi:10.1016/j.psyneuen.2019.01.003

Utility of combined inflammatory biomarkers for the identification of cognitive dysfunction in non-diabetic participants of the ELSA-Brasil

Psychoneuroendocrinology. 2019 May:103:61-66. doi: 10.1016/j.psyneuen.2019.01.003. Epub 2019 Jan 9.

Authors

Adriana Cezaretto¹, Bianca de Almeida-Pititto¹, Gizelton Pereira Alencar¹, Claudia K Suemoto², Isabela Bensenor³, Paulo A Lotufo³, Sandra R G Ferreira⁴; on the behalf of the ELSA-Brasil researchers

Affiliations

¹ Department of Epidemiology, School of Public Health, University of Sao Paulo, Brazil.
² Division of Geriatrics, School of Medicine, University of São Paulo, Brazil.
³ Department of Internal Medicine, University of Sao Paulo, Brazil.
⁴ Department of Epidemiology, School of Public Health, University of Sao Paulo, Brazil. Electronic address: sandrafv@usp.br.

PMID: 30641436
DOI: 10.1016/j.psyneuen.2019.01.003

Abstract

Introduction: Insulin resistance and low-grade inflammation are pathophysiological mechanisms shared by type 2 diabetes and dementia. A cluster of biomarkers that could help diagnosing cognitive dysfunction prior to the installation of insulin resistance is desirable. This ELSA sub-study examined whether a cluster of selected inflammatory biomarkers was associated with worse cognitive scores in non-diabetic participants.

Methods: A sample of 998 non-diabetic participants of ELSA-Brasil had their cognitive function assessed by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), a verbal fluency test and a trail making test. An inflammatory cluster was formed by using the k-means method. ANOVA was used to compare the tertiles of a composite global cognitive z-score with clinical and laboratory variables. Logistic regression modelling with forward stepwise model selection was performed considering cognitive performance as the outcome and the cluster as the independent variable of main interest. Models were stratified by sex and adjusted for age, insulin resistance and other confounders.

Results: The mean age was 45.7 ± 4.9 years and 54.8% were women, who had a higher frequency of university level, healthier behaviors and lower systolic and diastolic blood pressure (BP) levels, fasting plasma glucose, non-HDL cholesterol and E-selectin levels than men. Individuals in the highest tertile of the composite global cognitive z-score were more likely to be women, with university level, and lower mean values of body mass index, BP levels, and HOMA-IR than those in lower tertiles. Using logistic regression model, the cluster category of the highest grade of inflammation showed to be associated with worse cognitive performance in women only.

Conclusion: The association between a cluster of inflammation and worse cognitive performance seems to be useful to identify middle-aged women at risk for cognitive decline, independently of their state of insulin resistance.

Keywords: Cluster analysis; Cognitive function; Dementia; Prediabetes; Prevention; Subclinical inflammation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Biomarkers
Body Mass Index
Brazil
Cognition / physiology
Cognitive Dysfunction / diagnosis*
Cognitive Dysfunction / metabolism
Cognitive Dysfunction / physiopathology
Cross-Sectional Studies
Diabetes Mellitus, Type 2 / metabolism
Female
Humans
Inflammation / metabolism*
Insulin / blood
Insulin Resistance / physiology*
Logistic Models
Male
Middle Aged
Risk Factors
Sex Factors

Substances

Biomarkers
Insulin