734 Letters to the Editor
adults who were younger than 59 years with those who were 59 years of age or older (and therefore were not focused on childhood obesity), is clearly referred to in the Results section of our paper. The paper by Choquet et al. (16), which was also focused on adults, was not cited within this context. Although each of these studies reported meritorious findings, extrapolating findings on the genetics of adult obesity to the pediatric setting would be unwise in our view because of the differences in physiology between these groups. Thus, the novel finding of our study to which Meyre refers is fully substantiated by the evidence, and our decision not to draw strong parallels to studies in adults is fully defensible in our view. ACKNOWLEDGMENTS Conflict of interest: none declared. REFERENCES 1. Meyre D. Re: “The association of common variants in PCSK1 with obesity: a HuGe review and meta-analysis” [letter]. Am J Epidemiol. 2015;181(9):732–733. 2. Stijnen P, Tuand K, Varga TV, et al. The association of common variants in PCSK1 with obesity: a HuGE review and meta-analysis. Am J Epidemiol. 2014;180(11): 1051–1065. 3. Meyre D, Delplanque J, Chèvre J-C, et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet. 2009;41(2):157–159. 4. Benzinou M, Creemers JWM, Choquet H, et al. Common nonsynonymous variants in PCSK1 confer risk of obesity. Nat Genet. 2008;40(8):943–945. 5. Goodman SN, Berlin JA. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Ann Intern Med. 1994;121(3):200–206. 6. Smith AH, Bates MN. Confidence limit analyses should replace power calculations in the interpretation of epidemiologic studies. Epidemiology. 1992;3(5):449–452. 7. Detsky AS, Sackett DL. When was a “negative” clinical trial big enough? How many patients you needed depends on what you found. Arch Intern Med. 1985;145(4):709–712. 8. Colegrave N, Ruxton GD. Confidence intervals are a more useful complement to nonsignificant tests than are power calculations. Behav Ecol. 2003;14(3):446–447. 9. Gardner MJ, Altman DG. Confidence intervals rather than P values: estimation rather than hypothesis testing. Br Med J (Clin Res Ed). 1986;292(6522):746–750.
10. Onwuegbuzie AJ, Leech NL. Post hoc power: a concept whose time has come. Underst Stat. 2004;3(4):201–230. 11. O’Keefe DJ. Brief report: post hoc power, observed power, a priori power, retrospective power, prospective power, achieved power: sorting out appropriate uses of statistical power analyses. Commun Methods Meas. 2007;1(4):291–299. 12. Sterne JA, Davey Smith G. Sifting the evidence-what’s wrong with significance tests? BMJ. 2001;322(7280): 226–231. 13. Wen W, Cho Y-S, Zheng W, et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet. 2012;44(3):307–311. 14. Liu C-T, Monda KL, Taylor KC, et al. Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLoS Genet. 2013;9(8):e1003681. 15. Kilpeläinen TO, Bingham SA, Khaw K-T, et al. Association of variants in the PCSK1 gene with obesity in the EPIC-Norfolk study. Hum Mol Genet. 2009;18(18):3496–3501. 16. Choquet H, Kasberger J, Hamidovic A, et al. Contribution of common PCSK1 genetic variants to obesity in 8,359 subjects from multi-ethnic American population. PLoS One. 2013;8(2):e57857.
Pieter Stijnen1, Krizia Tuand1, Tibor V. Varga2, Paul W. Franks2,3,4, Bert Aertgeerts5, and John W. M. Creemers1 (e-mail: [email protected]) 1 Laboratory of Biochemical Neuro-endocrinology, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium 2 Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University and Skåne University Hospital Malmö, Malmö, Sweden 3 Department of Public Health and Clinical Medicine, Faculty of Medicine, Umeå University, Umeå, Sweden 4 Department of Nutrition, Harvard School of Public Health, Boston, MA 5 Academic Center for General Practice, Department of Public health and Primary Care, Katholieke Universiteit Leuven, Leuven, Belgium
DOI: 10.1093/aje/kwv061; Advance Access publication: April 9, 2015
© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].
RE: “COFFEE CONSUMPTION AND MORTALITY FROM ALL CAUSES, CARDIOVASCULAR DISEASE, AND CANCER: A DOSE-RESPONSE META-ANALYSIS” In their recent meta-analysis, Crippa et al. (1) reported that coffee consumption is inversely associated with all-cause and cardiovascular disease mortality. The largest risk reduction (21%, 95% confidence interval: 16, 26) was observed for cardiovascular disease mortality at a level of 3 cups/day (1). This remarkable finding should encourage many people to continue, or even increase, their coffee consumption. For once, it will be fairly easy to comply with advice on adopting a healthier diet.
Coffee consumption has been reported to have a hypercholesterolemic effect, leading to adverse cardiovascular outcomes (2). Cafestol and kahweol occur naturally in coffee beans and have been identified as hypercholesterolemic compounds (3). The associations of coffee with serum lipoprotein concentrations are largely dependent on the method of its preparation. For example, cafestol and kahweol are not present in regular coffee made with drip coffee-makers, as they are Am J Epidemiol. 2015;181(9):732–735
Letters to the Editor 735
largely trapped by the use of a paper filter. Espresso, which is made using high-pressure hot water forced through a column of coffee in a percolator, has a different composition. In a recent meta-analysis, consumption of unfiltered coffee resulted in mean increases in low-density lipoprotein cholesterol concentrations of 11.9 mg/dL and mean increases in triglyceride concentrations of 18.8 mg/dL. Consumption of filtered coffee did not significantly change low-density lipoprotein cholesterol or triglyceride concentrations (4). The systematic review by Crippa et al. included 21 prospective studies published between 1987 and 2013 that had enrolled subjects from 1959 to 2008 (1). During this period, coffee was mostly filtered. Recently, capsule coffee machines have become popular (5). Therefore, an increasing proportion of the occidental population is switching its coffee-drinking behavior from filtered coffee to unfiltered coffee: Sales of the leading brand have leapt 30% per year over the last decade (6). Consequently, the dramatic cardiovascular protective effect of coffee reported by Crippa et al. (1) might not be applicable to drinkers of unfiltered coffee. Further studies should assess the difference in mortality between filtered and unfiltered coffee drinkers. ACKNOWLEDGMENTS
2. Rebello SA, van Dam RM. Coffee consumption and cardiovascular health: getting to the heart of the matter. Curr Cardiol Rep. 2013;15(10):403. 3. Cornelis MC, El-Sohemy A. Coffee, caffeine, and coronary heart disease. Curr Opin Clin Nutr Metab Care. 2007;10(6):745–751. 4. Cai L, Ma D, Zhang Y, et al. The effect of coffee consumption on serum lipids: a meta-analysis of randomized controlled trials. Eur J Clin Nutr. 2012;66(8):872–877. 5. Aubin HJ, Berlin I. Coffee drinking and mortality [letter]. N Engl J Med. 2012;367(6):576. 6. Alderman L. Nespresso and rivals vie for dominance in coffee war. The New York Times [online edition]. http://www.nytimes. com/2010/08/21/business/global/21coffee.html?pagewanted= all&_r=0. Published August 20, 2010. Accessed December 27, 2014.
Henri-Jean Aubin1,2,3 and Ivan Berlin3,4,5 (e-mail: [email protected]) 1 Hôpital Paul Brousse, Assistance Publique-Hôpitaux de Paris, Villejuif, France 2 Department of Psychiatry and Addiction Medicine, Hôpital Université Paris-Sud, Orsay, France 3 INSERM U1178, Paris, France 4 Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France 5 Université Pierre et Marie Curie, Paris, France
Conflict of interest: none declared. REFERENCES 1. Crippa A, Discacciati A, Larsson SC, et al. Coffee consumption and mortality from all causes, cardiovascular disease, and cancer: a dose-response meta-analysis. Am J Epidemiol. 2014;180(8): 763–775.
Am J Epidemiol. 2015;181(9):732–735
Editor’s note: In accordance with Journal policy, Crippa et al. were asked whether they wished to respond to this letter, but they chose not to do so. DOI: 10.1093/aje/kwv070; Advance Access publication: March 31, 2015
adults who were younger than 59 years with those who were 59 years of age or older (and therefore were not focused on childhood obesity), is clearly referred to in the Results section of our paper. The paper by Choquet et al. (16), which was also focused on adults, was not cited within this context. Although each of these studies reported meritorious findings, extrapolating findings on the genetics of adult obesity to the pediatric setting would be unwise in our view because of the differences in physiology between these groups. Thus, the novel finding of our study to which Meyre refers is fully substantiated by the evidence, and our decision not to draw strong parallels to studies in adults is fully defensible in our view. ACKNOWLEDGMENTS Conflict of interest: none declared. REFERENCES 1. Meyre D. Re: “The association of common variants in PCSK1 with obesity: a HuGe review and meta-analysis” [letter]. Am J Epidemiol. 2015;181(9):732–733. 2. Stijnen P, Tuand K, Varga TV, et al. The association of common variants in PCSK1 with obesity: a HuGE review and meta-analysis. Am J Epidemiol. 2014;180(11): 1051–1065. 3. Meyre D, Delplanque J, Chèvre J-C, et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet. 2009;41(2):157–159. 4. Benzinou M, Creemers JWM, Choquet H, et al. Common nonsynonymous variants in PCSK1 confer risk of obesity. Nat Genet. 2008;40(8):943–945. 5. Goodman SN, Berlin JA. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Ann Intern Med. 1994;121(3):200–206. 6. Smith AH, Bates MN. Confidence limit analyses should replace power calculations in the interpretation of epidemiologic studies. Epidemiology. 1992;3(5):449–452. 7. Detsky AS, Sackett DL. When was a “negative” clinical trial big enough? How many patients you needed depends on what you found. Arch Intern Med. 1985;145(4):709–712. 8. Colegrave N, Ruxton GD. Confidence intervals are a more useful complement to nonsignificant tests than are power calculations. Behav Ecol. 2003;14(3):446–447. 9. Gardner MJ, Altman DG. Confidence intervals rather than P values: estimation rather than hypothesis testing. Br Med J (Clin Res Ed). 1986;292(6522):746–750.
10. Onwuegbuzie AJ, Leech NL. Post hoc power: a concept whose time has come. Underst Stat. 2004;3(4):201–230. 11. O’Keefe DJ. Brief report: post hoc power, observed power, a priori power, retrospective power, prospective power, achieved power: sorting out appropriate uses of statistical power analyses. Commun Methods Meas. 2007;1(4):291–299. 12. Sterne JA, Davey Smith G. Sifting the evidence-what’s wrong with significance tests? BMJ. 2001;322(7280): 226–231. 13. Wen W, Cho Y-S, Zheng W, et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet. 2012;44(3):307–311. 14. Liu C-T, Monda KL, Taylor KC, et al. Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLoS Genet. 2013;9(8):e1003681. 15. Kilpeläinen TO, Bingham SA, Khaw K-T, et al. Association of variants in the PCSK1 gene with obesity in the EPIC-Norfolk study. Hum Mol Genet. 2009;18(18):3496–3501. 16. Choquet H, Kasberger J, Hamidovic A, et al. Contribution of common PCSK1 genetic variants to obesity in 8,359 subjects from multi-ethnic American population. PLoS One. 2013;8(2):e57857.
Pieter Stijnen1, Krizia Tuand1, Tibor V. Varga2, Paul W. Franks2,3,4, Bert Aertgeerts5, and John W. M. Creemers1 (e-mail: [email protected]) 1 Laboratory of Biochemical Neuro-endocrinology, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium 2 Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University and Skåne University Hospital Malmö, Malmö, Sweden 3 Department of Public Health and Clinical Medicine, Faculty of Medicine, Umeå University, Umeå, Sweden 4 Department of Nutrition, Harvard School of Public Health, Boston, MA 5 Academic Center for General Practice, Department of Public health and Primary Care, Katholieke Universiteit Leuven, Leuven, Belgium
DOI: 10.1093/aje/kwv061; Advance Access publication: April 9, 2015
© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].
RE: “COFFEE CONSUMPTION AND MORTALITY FROM ALL CAUSES, CARDIOVASCULAR DISEASE, AND CANCER: A DOSE-RESPONSE META-ANALYSIS” In their recent meta-analysis, Crippa et al. (1) reported that coffee consumption is inversely associated with all-cause and cardiovascular disease mortality. The largest risk reduction (21%, 95% confidence interval: 16, 26) was observed for cardiovascular disease mortality at a level of 3 cups/day (1). This remarkable finding should encourage many people to continue, or even increase, their coffee consumption. For once, it will be fairly easy to comply with advice on adopting a healthier diet.
Coffee consumption has been reported to have a hypercholesterolemic effect, leading to adverse cardiovascular outcomes (2). Cafestol and kahweol occur naturally in coffee beans and have been identified as hypercholesterolemic compounds (3). The associations of coffee with serum lipoprotein concentrations are largely dependent on the method of its preparation. For example, cafestol and kahweol are not present in regular coffee made with drip coffee-makers, as they are Am J Epidemiol. 2015;181(9):732–735
Letters to the Editor 735
largely trapped by the use of a paper filter. Espresso, which is made using high-pressure hot water forced through a column of coffee in a percolator, has a different composition. In a recent meta-analysis, consumption of unfiltered coffee resulted in mean increases in low-density lipoprotein cholesterol concentrations of 11.9 mg/dL and mean increases in triglyceride concentrations of 18.8 mg/dL. Consumption of filtered coffee did not significantly change low-density lipoprotein cholesterol or triglyceride concentrations (4). The systematic review by Crippa et al. included 21 prospective studies published between 1987 and 2013 that had enrolled subjects from 1959 to 2008 (1). During this period, coffee was mostly filtered. Recently, capsule coffee machines have become popular (5). Therefore, an increasing proportion of the occidental population is switching its coffee-drinking behavior from filtered coffee to unfiltered coffee: Sales of the leading brand have leapt 30% per year over the last decade (6). Consequently, the dramatic cardiovascular protective effect of coffee reported by Crippa et al. (1) might not be applicable to drinkers of unfiltered coffee. Further studies should assess the difference in mortality between filtered and unfiltered coffee drinkers. ACKNOWLEDGMENTS
2. Rebello SA, van Dam RM. Coffee consumption and cardiovascular health: getting to the heart of the matter. Curr Cardiol Rep. 2013;15(10):403. 3. Cornelis MC, El-Sohemy A. Coffee, caffeine, and coronary heart disease. Curr Opin Clin Nutr Metab Care. 2007;10(6):745–751. 4. Cai L, Ma D, Zhang Y, et al. The effect of coffee consumption on serum lipids: a meta-analysis of randomized controlled trials. Eur J Clin Nutr. 2012;66(8):872–877. 5. Aubin HJ, Berlin I. Coffee drinking and mortality [letter]. N Engl J Med. 2012;367(6):576. 6. Alderman L. Nespresso and rivals vie for dominance in coffee war. The New York Times [online edition]. http://www.nytimes. com/2010/08/21/business/global/21coffee.html?pagewanted= all&_r=0. Published August 20, 2010. Accessed December 27, 2014.
Henri-Jean Aubin1,2,3 and Ivan Berlin3,4,5 (e-mail: [email protected]) 1 Hôpital Paul Brousse, Assistance Publique-Hôpitaux de Paris, Villejuif, France 2 Department of Psychiatry and Addiction Medicine, Hôpital Université Paris-Sud, Orsay, France 3 INSERM U1178, Paris, France 4 Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France 5 Université Pierre et Marie Curie, Paris, France
Conflict of interest: none declared. REFERENCES 1. Crippa A, Discacciati A, Larsson SC, et al. Coffee consumption and mortality from all causes, cardiovascular disease, and cancer: a dose-response meta-analysis. Am J Epidemiol. 2014;180(8): 763–775.
Am J Epidemiol. 2015;181(9):732–735
Editor’s note: In accordance with Journal policy, Crippa et al. were asked whether they wished to respond to this letter, but they chose not to do so. DOI: 10.1093/aje/kwv070; Advance Access publication: March 31, 2015
