36
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Habitual coffee consumption and cognitive function: a Mendelian randomization meta-analysis in up to 415,530 participants

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Coffee’s long-term effect on cognitive function remains unclear with studies suggesting both benefits and adverse effects. We used Mendelian randomization to investigate the causal relationship between habitual coffee consumption and cognitive function in mid- to later life. This included up to 415,530 participants and 300,760 coffee drinkers from 10 meta-analysed European ancestry cohorts. In each cohort, composite cognitive scores that capture global cognition and memory were computed using available tests. A genetic score derived using CYP1A1/2 (rs2472297) and AHR (rs6968865) was chosen as a proxy for habitual coffee consumption. Null associations were observed when examining the associations of the genetic score with global and memory cognition (β = −0.0007, 95% C.I. −0.009 to 0.008, P = 0.87; β = −0.001, 95% C.I. −0.005 to 0.002, P = 0.51, respectively), with high consistency between studies (P heterogeneity > 0.4 for both). Domain specific analyses using available cognitive measures in the UK Biobank also did not support effects by habitual coffee intake for reaction time, pairs matching, reasoning or prospective memory (P ≥ 0.05 for all). Despite the power to detect very small effects, our meta-analysis provided no evidence for causal long-term effects of habitual coffee consumption on global cognition or memory.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

          Illustrating bias due to conditioning on a collider.

          That conditioning on a common effect of exposure and outcome may cause selection, or collider-stratification, bias is not intuitive. We provide two hypothetical examples to convey concepts underlying bias due to conditioning on a collider. In the first example, fever is a common effect of influenza and consumption of a tainted egg-salad sandwich. In the second example, case-status is a common effect of a genotype and an environmental factor. In both examples, conditioning on the common effect imparts an association between two otherwise independent variables; we call this selection bias.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Caffeinated and Decaffeinated Coffee Consumption and Risk of Type 2 Diabetes: A Systematic Review and a Dose-Response Meta-analysis

            OBJECTIVE Previous meta-analyses identified an inverse association of coffee consumption with the risk of type 2 diabetes. However, an updated meta-analysis is needed because new studies comparing the trends of association for caffeinated and decaffeinated coffee have since been published. RESEARCH DESIGN AND METHODS PubMed and Embase were searched for cohort or nested case-control studies that assessed the relationship of coffee consumption and risk of type 2 diabetes from 1966 to February 2013. A restricted cubic spline random-effects model was used. RESULTS Twenty-eight prospective studies were included in the analysis, with 1,109,272 study participants and 45,335 cases of type 2 diabetes. The follow-up duration ranged from 10 months to 20 years. Compared with no or rare coffee consumption, the relative risk (RR; 95% CI) for diabetes was 0.92 (0.90–0.94), 0.85 (0.82–0.88), 0.79 (0.75–0.83), 0.75 (0.71–0.80), 0.71 (0.65–0.76), and 0.67 (0.61–0.74) for 1–6 cups/day, respectively. The RR of diabetes for a 1 cup/day increase was 0.91 (0.89–0.94) for caffeinated coffee consumption and 0.94 (0.91–0.98) for decaffeinated coffee consumption (P for difference = 0.17). CONCLUSIONS Coffee consumption was inversely associated with the risk of type 2 diabetes in a dose-response manner. Both caffeinated and decaffeinated coffee was associated with reduced diabetes risk.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Coffee, CYP1A2 genotype, and risk of myocardial infarction.

              The association between coffee intake and risk of myocardial infarction (MI) remains controversial. Coffee is a major source of caffeine, which is metabolized by the polymorphic cytochrome P450 1A2 (CYP1A2) enzyme. Individuals who are homozygous for the CYP1A2*1A allele are "rapid" caffeine metabolizers, whereas carriers of the variant CYP1A2*1F are "slow" caffeine metabolizers. To determine whether CYP1A2 genotype modifies the association between coffee consumption and risk of acute nonfatal MI. Cases (n = 2014) with a first acute nonfatal MI and population-based controls (n = 2014) living in Costa Rica between 1994 and 2004, matched for age, sex, and area of residence, were genotyped by restriction fragment-length polymorphism polymerase chain reaction. A food frequency questionnaire was used to assess the intake of caffeinated coffee. Relative risk of nonfatal MI associated with coffee intake, calculated using unconditional logistic regression. Fifty-five percent of cases (n = 1114) and 54% of controls (n = 1082) were carriers of the slow *1F allele. For carriers of the slow *1F allele, the multivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of nonfatal MI associated with consuming less than 1, 1, 2 to 3, and 4 or more cups of coffee per day were 1.00 (reference), 0.99 (0.69-1.44), 1.36 (1.01-1.83), and 1.64 (1.14-2.34), respectively. Corresponding ORs (95% CIs) for individuals with the rapid *1A/*1A genotype were 1.00, 0.75 (0.51-1.12), 0.78 (0.56-1.09), and 0.99 (0.66-1.48) (P = .04 for gene x coffee interaction). For individuals younger than the median age of 59 years, the ORs (95% CIs) associated with consuming less than 1, 1, 2 to 3, or 4 or more cups of coffee per day were 1.00, 1.24 (0.71-2.18), 1.67 (1.08-2.60), and 2.33 (1.39-3.89), respectively, among carriers of the *1F allele. The corresponding ORs (95% CIs) for those with the *1A/*1A genotype were 1.00, 0.48 (0.26-0.87), 0.57 (0.35-0.95), and 0.83 (0.46-1.51). Intake of coffee was associated with an increased risk of nonfatal MI only among individuals with slow caffeine metabolism, suggesting that caffeine plays a role in this association.
                Bookmark

                Author and article information

                Contributors
                Elina.Hypponen@unisa.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 May 2018
                14 May 2018
                2018
                : 8
                : 7526
                Affiliations
                [1 ]ISNI 0000 0000 8994 5086, GRID grid.1026.5, Australian Centre for Precision Health, , University of South Australia, ; Adelaide, Australia
                [2 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, ; Bristol, UK
                [3 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, UK Centre for Tobacco and Alcohol Studies (UKCTAS) and School of Experimental Psychology, , University of Bristol, ; Bristol, UK
                [4 ]ISNI 0000 0001 0941 4873, GRID grid.10858.34, Center for Life Course Health Research, , University of Oulu, ; Oulu, Finland
                [5 ]ISNI 0000 0004 4685 4917, GRID grid.412326.0, Oulu University Hospital, ; Oulu, Finland
                [6 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; Stockholm, Sweden
                [7 ]ISNI 0000 0001 2097 1371, GRID grid.1374.1, Research Centre of Applied and Preventive Cardiovascular Medicine, , University of Turku, ; Turku, Finland
                [8 ]Helsinki Collegium for Advanced Studies, Helsinki, Finland
                [9 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, Department of Psychology and Logopedics, Faculty of medicine, , University of Helsinki, ; Helsinki, Finland
                [10 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Public Health and Caring Sciences, , Clinical Nutrition and Metabolism. Uppsala University, ; Uppsala, Sweden
                [11 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Surgical Sciences, , Orthopaedics, Uppsala University, ; Uppsala, Sweden
                [12 ]ISNI 0000 0001 2193 314X, GRID grid.8756.c, Institute of Health & Wellbeing, , University of Glasgow, ; Glasgow, UK
                [13 ]ISNI 0000 0004 4685 4917, GRID grid.412326.0, Unit of Primary Health Care, , Oulu University Hospital, ; Oulu, Finland
                [14 ]ISNI 0000 0001 2314 6254, GRID grid.5509.9, Department of Clinical Chemistry, , Fimlab Laboratories and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Life Sciences, University of Tampere, ; Tampere, Finland
                [15 ]ISNI 0000 0001 2314 6254, GRID grid.5509.9, Department of Clinical Physiology, , Tampere University Hospital and Faculty of Medicine and Life Sciences, University of Tampere, ; Tampere, Finland
                [16 ]ISNI 0000 0001 2314 6254, GRID grid.5509.9, Department of Pediatrics, , Tampere University Hospital and Faculty of Medicine and Life Sciences, University of Tampere, ; Tampere, Finland
                [17 ]ISNI 0000 0001 1013 0499, GRID grid.14758.3f, Department of Public Health Solutions, , National Institute for Health and Welfare, ; Helsinki, Finland
                [18 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, ; Oxford, OX3 7BN, UK
                [19 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Medical Sciences, , Cardiovascular Epidemiology, Uppsala University, ; Uppsala, Sweden
                [20 ]ISNI 0000000121901201, GRID grid.83440.3b, Population, Policy and Practice, , UCL Great Ormond Street Institute of Child Health, ; London, WC1N 1EH UK
                [21 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, Department of General Practice and Primary Health Care, , University of Helsinki and Helsinki University Hospital, ; Helsinki, Finland
                [22 ]ISNI 0000 0004 0409 6302, GRID grid.428673.c, Folkhälsan Research Center, ; Helsinki, Finland
                [23 ]ISNI 0000 0004 0628 215X, GRID grid.410552.7, Department of Clinical Physiology and Nuclear Medicine, , Turku University Hospital, ; Turku, Finland
                [24 ]ISNI 0000 0001 0941 4873, GRID grid.10858.34, Department of Psychiatry, , Research Unit of Clinical Neuroscience, University of Oulu, ; Oulu, Finland
                [25 ]ISNI 0000 0004 4685 4917, GRID grid.412326.0, Department of Psychiatry, , University Hospital of Oulu, ; Oulu, Finland
                [26 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Epidemiology and Biostatistics, , MRC–PHE Centre for Environment & Health, School of Public Health, Imperial College London, ; London, UK
                [27 ]ISNI 0000 0001 0941 4873, GRID grid.10858.34, Biocenter Oulu, University of Oulu, ; Oulu, Finland
                [28 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Medicine, , Division of Cardiovascular Medicine, Stanford University School of Medicine, ; Stanford, CA 94305 USA
                [29 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Medical Sciences, , Molecular Epidemiology and Science for Life Laboratory, Uppsala University, ; Uppsala, Sweden
                [30 ]ISNI 0000000419368956, GRID grid.168010.e, Stanford Cardiovascular Institute, Stanford University, ; CA, 94305 USA
                [31 ]ISNI 0000 0004 1936 8024, GRID grid.8391.3, University of Exeter Medical School, ; Exeter, United Kingdom
                [32 ]GRID grid.430453.5, South Australian Health and Medical Research Institute, ; Adelaide, Australia
                Author information
                http://orcid.org/0000-0002-4310-5297
                http://orcid.org/0000-0003-3850-1487
                http://orcid.org/0000-0001-5585-3420
                http://orcid.org/0000-0002-2452-1500
                http://orcid.org/0000-0002-4049-993X
                Article
                25919
                10.1038/s41598-018-25919-2
                5951917
                29760501
                ed59954b-d91c-4c21-a8e4-7918f6e4bf00
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 September 2017
                : 24 April 2018
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                Uncategorized
                Uncategorized

                Comments

                Comment on this article