Alcohol use and cognitive performance: a comparison between Greece and the United States

Abstract

Objectives

To examine associations between alcohol use and cognitive performance among older adults in Greece and the United States, and assess potential differences due to differing drinking practices in the two countries.

Methods

Data came from Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) and National Alzheimer’s Coordinating Center Uniform Dataset (NACC). We examined those aged 65–90 years at baseline who had no cognitive impairment and complete data for cognitive and alcohol use variables (N = 1110 from HELIAD; N = 2455 from NACC). We examined associations between current alcohol use and frequency of such use with cognitive performance on various cognitive tasks stratified by gender.

Results

In NACC, use of alcohol was associated with better cognitive performance. Men drinkers performed better than non-drinkers on Trail A (standardized mean 0.07 vs. −0.24, p<.001), Trail B (0.06 vs. −0.19, p=.001), and women drinkers performed better on Trail A (0.04 vs. −0.09, p=.016), Trail B (0.04 vs. −0.10, p=.005), verbal fluency (Animals: 0.05 vs. −0.13, p<.001; Vegetables: 0.04 vs. −0.09, p=.027), and MoCA (0.03 vs. −0.08, p=.039). In HELIAD, fewer differences were seen with only women drinkers exhibiting better performance than non-drinkers on the Boston Naming Task (0.11 vs. −0.05, p=.016). In general, more frequent drinkers performed better on cognitive tasks than less frequent drinkers, although this was only statistically significant in the NACC dataset.

Conclusion

While drinking alcohol may be associated with better cognitive performance across both the US and Greece, more research is needed to assess the cultural factors that may modify this association.

Keywords:

• Cognitive performance: alcohol use: frequency: international comparison

Disclosure statement

All authors report no conflicts of interest.

Additional information

Funding

This study was supported by funding from the Alzheimer’s Association (GEENA-Q-19-594339) and the National Institutes of Health (K01AG061239, R01AG062483, P30AG059299, P30AG062429, R01AG049810). The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).