Associations between urinary caffeine and caffeine metabolites and cognitive function in older adults

ABSTRACT

Background

The effects of caffeine on cognitive impairment have not been conclusively determined. This study aimed to objectively assess the correlation between the urinary caffeine and caffeine metabolites and cognitive decline in older adults.

Methods

Data on urinary caffeine and caffeine metabolites and the cognitive performance of participants aged 60 years and older were extracted from the National Health and Nutrition Examination Surveys 2011–2014. Binary logistic regression and restricted cubic splines (RCS) analyses were used to evaluate the association between urinary caffeine and caffeine metabolites and cognitive performance.

Results

Eight hundred twenty-seven individuals were enrolled in this cross-sectional study. We observed that 1-methylxanthine, 3-methylxanthine, 7-methylxanthine, 1,3-dimethylxanthine, 1,7-dimethylxanthine, and 3,7-dimethylxanthine levels were significantly and inversely associated with cognitive decline. The RCS results suggested an approximately linear dose–response relationship between the aforementioned metabolites and cognitive performance. Moreover, the effects of urinary caffeine and caffeine metabolites on cognitive function assessed using the AFT were more evident in men.

Conclusions

Our study suggested that urinary caffeine and caffeine metabolite levels were associated with a reduced risk of cognitive impairment in a linear manner, especially in men.

KEYWORDS:

• Caffeine
• caffeine metabolites
• urine‌
• cognitive function
• ‌CERAD W-L
• AFT
• DSST‌
• older adults
• NHANES

Data availability statement

These data were derived from the following resources available in the public domain: https://wwwn.cdc.gov/nchs/nhanes/default.aspx.

Ethics approval

Human subject research is not taken into account in this work due to the data were obtained from free, publicly available database (National health and nutritional examination survey).

Acknowledgements

We acknowledged all participants involved in NHANES.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Xihang Fu

Xihang Fu, PhD is a PhD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Huiru Li

Huiru Li, MD is a MD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Xinzhen Chen

Xinzhen Chen, PhD is a PhD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Jinliang Cai

Jinliang Cai, MD is a MD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Ting Yao

Ting Yao, MD is a MD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Lingling Song

Lingling Song, MD is a MD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Manqiu Cen

Manqiu Cen, MD is a MD student at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Jing Wu, PhD is a professor at the Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.