NEPHROLOGY / RESEARCH PAPER
The Association between Coffee and Caffeine Consumption and Renal Function: Insight from Individual-Level Data, Mendelian Randomization, and Meta-Analysis
| 1 | Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Strand, London, UK, United Kingdom |
| 2 | MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom, United Kingdom |
| 3 | Department of Clinical Biochemistry, Royal Free Campus, University College London Medical School, University College London (UCL), London, UK, United Kingdom |
| 4 | Department of Family Medicine and Public Health, Institute of Medicine, University of Opole, Opole, Poland, United Kingdom |
| 5 | Department of Nephrology, "Dr. C. I. Parhon" Clinical Hospital Iasi, Iasi, Romania, Romania |
| 6 | 6Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK, United Kingdom |
| 7 | Department of Preventive Cardiology and Lipidology, Medical University of Lodz, Poland, Poland |
CORRESPONDING AUTHOR
Maciej Banach
Department of Preventive Cardiology and Lipidology, Medical University of Lodz, Poland, Sterlinga 1/3, 91-425, Lodz, Poland
Department of Preventive Cardiology and Lipidology, Medical University of Lodz, Poland, Sterlinga 1/3, 91-425, Lodz, Poland
Submission date: 2021-12-12
Acceptance date: 2021-12-14
Online publication date: 2021-12-14
Arch Med Sci 2022;18(4)
KEYWORDS
TOPICS
ABSTRACT
Introduction:
By applying on two-sample Mendelian randomization and systematic review and meta-analysis we investigated the association between caffeine and coffee intake with prevalent CKD and markers of renal function.
Material and methods:
For the individual data analysis we analysed the NHANES data on renal function markers and caffeine intake. MR was implemented by using summary-level data from the largest ever GWAS conducted on coffee intake (N=91,462) and kidney function.
Results:
Finally, we included the data of 18,436 participants, 6.9% had prevalent CKD (based on eGFR). Caffeine intake for general population was 131.1±1.1 mg. The percentage of participants with CKD, by caffeine quartile was 16.6% in the first (lowest) quartile, 13.9% in the second, 12.2% in the third and 11.0% in the top quartile (p<0.001). After adjustment, for increasing quartiles for caffeine consumption, mean urine albumin, albumin-creatinine ratio and estimated glomerular filtration rate (GFR) did not change significantly (p>0.234). In fully adjusted logistic regression models, there was no significant difference in chances of CKD prevalence (p-trend=0.745). In the same line, results of MR showed no impact of coffee intake on CKD (IVW=β: -0.0191, SE: 0.069, p=0.781), on eGFR (overall= IVW= β: -0.0005, SE: 0.005, p=0.926) both in diabetic (IVW= β: -0.006, SE: 0.009, p=0.478), and non-diabetic patients (IVW= β: -6.772, SE: 0.006, p=0.991). Results from the meta-analysis indicted that coffee consumption was not significantly associated with CKD (OR: 0.85, 95%CI 0.71-1.02, p=0.090, n=6 studies, I2=0.32).
Conclusions:
By implementing on different strategies, we have highlighted no significant association between coffee consumption with renal function and chance of CKD.
By applying on two-sample Mendelian randomization and systematic review and meta-analysis we investigated the association between caffeine and coffee intake with prevalent CKD and markers of renal function.
Material and methods:
For the individual data analysis we analysed the NHANES data on renal function markers and caffeine intake. MR was implemented by using summary-level data from the largest ever GWAS conducted on coffee intake (N=91,462) and kidney function.
Results:
Finally, we included the data of 18,436 participants, 6.9% had prevalent CKD (based on eGFR). Caffeine intake for general population was 131.1±1.1 mg. The percentage of participants with CKD, by caffeine quartile was 16.6% in the first (lowest) quartile, 13.9% in the second, 12.2% in the third and 11.0% in the top quartile (p<0.001). After adjustment, for increasing quartiles for caffeine consumption, mean urine albumin, albumin-creatinine ratio and estimated glomerular filtration rate (GFR) did not change significantly (p>0.234). In fully adjusted logistic regression models, there was no significant difference in chances of CKD prevalence (p-trend=0.745). In the same line, results of MR showed no impact of coffee intake on CKD (IVW=β: -0.0191, SE: 0.069, p=0.781), on eGFR (overall= IVW= β: -0.0005, SE: 0.005, p=0.926) both in diabetic (IVW= β: -0.006, SE: 0.009, p=0.478), and non-diabetic patients (IVW= β: -6.772, SE: 0.006, p=0.991). Results from the meta-analysis indicted that coffee consumption was not significantly associated with CKD (OR: 0.85, 95%CI 0.71-1.02, p=0.090, n=6 studies, I2=0.32).
Conclusions:
By implementing on different strategies, we have highlighted no significant association between coffee consumption with renal function and chance of CKD.
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