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1. The contribution of sodium reduction and potassium increase to the blood pressure lowering observed in the Salt Substitute and Stroke Study

Author: Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, Neal, Bruce, Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, and Neal, Bruce
Abstract: The Salt Substitute and Stroke Study (SSaSS) demonstrated significant reductions in systolic blood pressure (SBP), and the risk of stroke, major cardiovascular events and total mortality with the use of potassium-enriched salt. The contribution of sodium reduction versus potassium increase to these effects is unknown. We identified four different data sources describing the association between sodium reduction, potassium supplementation and change in SBP. We then fitted a series of models to estimate the SBP reductions expected for the differences in sodium and potassium intake in SSaSS, derived from 24-h urine collections. The proportions of the SBP reduction separately attributable to sodium reduction and potassium supplementation were calculated. The observed SBP reduction in SSaSS was -3.3 mmHg with a corresponding mean 15.2 mmol reduction in 24-h sodium excretion and a mean 20.6 mmol increase in 24-h potassium excretion. Assuming 90% of dietary sodium intake and 70% of dietary potassium intake were excreted through urine, the models projected falls in SBP of between -1.67 (95% confidence interval: -4.06 to +0.73) mmHg and -5.33 (95% confidence interval: -8.58 to -2.08) mmHg. The estimated proportional contribution of sodium reduction to the SBP fall ranged between 12 and 39% for the different models fitted. Sensitivity analyses assuming different proportional urinary excretion of dietary sodium and potassium intake showed similar results. In every model, the majority of the SBP lowering effect in SSaSS was estimated to be attributable to the increase in dietary potassium rather than the fall in dietary sodium.
Published: 2024
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2. The contribution of sodium reduction and potassium increase to the blood pressure lowering observed in the Salt Substitute and Stroke Study

Author: Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, Neal, Bruce, Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, and Neal, Bruce
Abstract: The Salt Substitute and Stroke Study (SSaSS) demonstrated significant reductions in systolic blood pressure (SBP), and the risk of stroke, major cardiovascular events and total mortality with the use of potassium-enriched salt. The contribution of sodium reduction versus potassium increase to these effects is unknown. We identified four different data sources describing the association between sodium reduction, potassium supplementation and change in SBP. We then fitted a series of models to estimate the SBP reductions expected for the differences in sodium and potassium intake in SSaSS, derived from 24-h urine collections. The proportions of the SBP reduction separately attributable to sodium reduction and potassium supplementation were calculated. The observed SBP reduction in SSaSS was -3.3 mmHg with a corresponding mean 15.2 mmol reduction in 24-h sodium excretion and a mean 20.6 mmol increase in 24-h potassium excretion. Assuming 90% of dietary sodium intake and 70% of dietary potassium intake were excreted through urine, the models projected falls in SBP of between -1.67 (95% confidence interval: -4.06 to +0.73) mmHg and -5.33 (95% confidence interval: -8.58 to -2.08) mmHg. The estimated proportional contribution of sodium reduction to the SBP fall ranged between 12 and 39% for the different models fitted. Sensitivity analyses assuming different proportional urinary excretion of dietary sodium and potassium intake showed similar results. In every model, the majority of the SBP lowering effect in SSaSS was estimated to be attributable to the increase in dietary potassium rather than the fall in dietary sodium.
Published: 2024
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3. The contribution of sodium reduction and potassium increase to the blood pressure lowering observed in the Salt Substitute and Stroke Study

Author: Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, Neal, Bruce, Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, and Neal, Bruce
Abstract: The Salt Substitute and Stroke Study (SSaSS) demonstrated significant reductions in systolic blood pressure (SBP), and the risk of stroke, major cardiovascular events and total mortality with the use of potassium-enriched salt. The contribution of sodium reduction versus potassium increase to these effects is unknown. We identified four different data sources describing the association between sodium reduction, potassium supplementation and change in SBP. We then fitted a series of models to estimate the SBP reductions expected for the differences in sodium and potassium intake in SSaSS, derived from 24-h urine collections. The proportions of the SBP reduction separately attributable to sodium reduction and potassium supplementation were calculated. The observed SBP reduction in SSaSS was -3.3 mmHg with a corresponding mean 15.2 mmol reduction in 24-h sodium excretion and a mean 20.6 mmol increase in 24-h potassium excretion. Assuming 90% of dietary sodium intake and 70% of dietary potassium intake were excreted through urine, the models projected falls in SBP of between -1.67 (95% confidence interval: -4.06 to +0.73) mmHg and -5.33 (95% confidence interval: -8.58 to -2.08) mmHg. The estimated proportional contribution of sodium reduction to the SBP fall ranged between 12 and 39% for the different models fitted. Sensitivity analyses assuming different proportional urinary excretion of dietary sodium and potassium intake showed similar results. In every model, the majority of the SBP lowering effect in SSaSS was estimated to be attributable to the increase in dietary potassium rather than the fall in dietary sodium.
Published: 2024
Full Text: View/download PDF

4. The contribution of sodium reduction and potassium increase to the blood pressure lowering observed in the Salt Substitute and Stroke Study

Author: Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, Neal, Bruce, Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, and Neal, Bruce
Abstract: The Salt Substitute and Stroke Study (SSaSS) demonstrated significant reductions in systolic blood pressure (SBP), and the risk of stroke, major cardiovascular events and total mortality with the use of potassium-enriched salt. The contribution of sodium reduction versus potassium increase to these effects is unknown. We identified four different data sources describing the association between sodium reduction, potassium supplementation and change in SBP. We then fitted a series of models to estimate the SBP reductions expected for the differences in sodium and potassium intake in SSaSS, derived from 24-h urine collections. The proportions of the SBP reduction separately attributable to sodium reduction and potassium supplementation were calculated. The observed SBP reduction in SSaSS was -3.3 mmHg with a corresponding mean 15.2 mmol reduction in 24-h sodium excretion and a mean 20.6 mmol increase in 24-h potassium excretion. Assuming 90% of dietary sodium intake and 70% of dietary potassium intake were excreted through urine, the models projected falls in SBP of between -1.67 (95% confidence interval: -4.06 to +0.73) mmHg and -5.33 (95% confidence interval: -8.58 to -2.08) mmHg. The estimated proportional contribution of sodium reduction to the SBP fall ranged between 12 and 39% for the different models fitted. Sensitivity analyses assuming different proportional urinary excretion of dietary sodium and potassium intake showed similar results. In every model, the majority of the SBP lowering effect in SSaSS was estimated to be attributable to the increase in dietary potassium rather than the fall in dietary sodium.
Published: 2024
Full Text: View/download PDF

5. The contribution of sodium reduction and potassium increase to the blood pressure lowering observed in the Salt Substitute and Stroke Study

Author: Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, Neal, Bruce, Huang, Liping, Li, Qiang, Wu, Jason H. Y., Tian, Maoyi, Yin, Xuejun, Yu, Jie, Liu, Yishu, Zhang, Xinyi, Wu, Yangfeng, Paige, Ellie, Trieu, Kathy, Marklund, Matti, Rodgers, Anthony, and Neal, Bruce
Abstract: The Salt Substitute and Stroke Study (SSaSS) demonstrated significant reductions in systolic blood pressure (SBP), and the risk of stroke, major cardiovascular events and total mortality with the use of potassium-enriched salt. The contribution of sodium reduction versus potassium increase to these effects is unknown. We identified four different data sources describing the association between sodium reduction, potassium supplementation and change in SBP. We then fitted a series of models to estimate the SBP reductions expected for the differences in sodium and potassium intake in SSaSS, derived from 24-h urine collections. The proportions of the SBP reduction separately attributable to sodium reduction and potassium supplementation were calculated. The observed SBP reduction in SSaSS was -3.3 mmHg with a corresponding mean 15.2 mmol reduction in 24-h sodium excretion and a mean 20.6 mmol increase in 24-h potassium excretion. Assuming 90% of dietary sodium intake and 70% of dietary potassium intake were excreted through urine, the models projected falls in SBP of between -1.67 (95% confidence interval: -4.06 to +0.73) mmHg and -5.33 (95% confidence interval: -8.58 to -2.08) mmHg. The estimated proportional contribution of sodium reduction to the SBP fall ranged between 12 and 39% for the different models fitted. Sensitivity analyses assuming different proportional urinary excretion of dietary sodium and potassium intake showed similar results. In every model, the majority of the SBP lowering effect in SSaSS was estimated to be attributable to the increase in dietary potassium rather than the fall in dietary sodium.
Published: 2024
Full Text: View/download PDF

6. The ATHENA COVID-19 Study: Cohort profile and first findings for people diagnosed with COVID-19 in Queensland, 1 January to 31 December 2020

Author: Welsh, Jennifer, Korda, Rosemary, Paige, Ellie, Morgan, Mark A, Law, Hsei Di, Stanton, Tony, Bourne, Zoltan, Tolosa, M Ximena, Greaves, Kim, Welsh, Jennifer, Korda, Rosemary, Paige, Ellie, Morgan, Mark A, Law, Hsei Di, Stanton, Tony, Bourne, Zoltan, Tolosa, M Ximena, and Greaves, Kim
Abstract: Background : To date, there are limited Australian data on characteristics of people diagnosed with COVID-19 and on how these characteristics relate to outcomes. The ATHENA COVID-19 Study was established to describe health outcomes and investigate predictors of outcomes for all people diagnosed with COVID-19 in Queensland by linking COVID-19 notification, hospital, general practice and death registry data. This paper reports on the establishment and first findings for the ATHENA COVID-19 Study. Methods : Part 1 of the ATHENA COVID-19 Study used Notifiable Conditions System data from 1 January 2020 to 31 December 2020, linked to: Emergency Department Collection data for the same period; Queensland Health Admitted Patient Data Collections (from 1 January 2010 to 30 January 2021); and Deaths Registrations data (from 1 January 2020 to 17 January 2021). Results : To 31 December 2020, a total of 1,254 people had been diagnosed with SARS-CoV-2 infection in Queensland: half were female (49.8%); two-thirds (67.7%) were aged 20–59 years; and there was an over-representation of people living in less-disadvantaged areas. More than half of people diagnosed (57.6%) presented to an emergency department (ED); 21.2% were admitted to hospital as an inpatient (median length of stay 11 days); 1.4% were admitted to an intensive care unit (82.4% of these required ventilation); and there were six deaths. Analysis of factors associated with these outcomes was limited due to small case numbers: people living in less-disadvantaged areas had a lower risk of being admitted to hospital (test for trend, p < 0.001), while those living in more remote areas were less likely than people living in major cities to present to an ED (test for trend: p=0.007), which may reflect differential health care access rather than health outcomes per se. Increasing age (test for trend, p < 0.001) and being a current/recent smoker (age-sex-adjusted relative risk: 1.61; 95% confidence interval: 1.00, 2.61) were asso
Published: 2021

7. Living Guidelines for absolute cardiovascular risk: assessment and management

Author: Paige, Ellie, Agostino, Jason, Phillips, Christine, Elliott, Julian, Cadilhac, Dominique, Connell, Cia, Kowalski, Melanie, O'Donoughue Jenkins, Lily, Daluwatta, Amanda, Banks, Emily, Paige, Ellie, Agostino, Jason, Phillips, Christine, Elliott, Julian, Cadilhac, Dominique, Connell, Cia, Kowalski, Melanie, O'Donoughue Jenkins, Lily, Daluwatta, Amanda, and Banks, Emily
Published: 2019

8. Psychological distress and medication use for secondary prevention of cardiovascular events: Evidence from a large-scale population-based cohort study

Author: Welsh, Jennifer, Paige, Ellie, Banks, Emily, Joshy, Grace, Brieger, David, Korda, Rosemary, Welsh, Jennifer, Paige, Ellie, Banks, Emily, Joshy, Grace, Brieger, David, and Korda, Rosemary
Abstract: Objective Cardiac patients with psychological distress have a poorer prognosis than patients without distress, potentially reflecting differences in preventive care. We aimed to examine distress-related variation in guideline-recommended medication use for secondary prevention of cardiovascular disease (CVD). Methods Baseline questionnaire data from the 45 and Up Study (collected 2006–2009) were linked to hospitalisation, pharmaceutical dispensing and death records (to exclude those who died). Among participants hospitalised with myocardial infarction, angina, stroke/transient ischaemic attack in the six years before the questionnaire, Modified Poisson regression was used to estimate relative risks (RR) for distress (Kessler 10 scores: low[10- < 12], mild[12- < 16], moderate[16- < 22] and high[22–50]) and use of both blood pressure- and lipid-lowering medications, and use of neither medication in the three months following the questionnaire, adjusting for sociodemographic and health characteristics. Results Among 7598 participants, 34.0% had low, 35.4% mild, 18.3% moderate and 12.3% high psychological distress. Around two-thirds (63.4%) were using both medications and the proportion declined with increasing levels of distress: RRs were 1.01(95%CI:0.97–1.05), 0.95(0.90–1.00) and 0.91(0.86–0.97) for mild, moderate and high compared to low distress, respectively (p(trend) = 0.001). The proportion using neither medication was 9.1% and increased with increasing distress: RRs for mild, moderate and high compared to low distress were 0.99(0.82–1.19), 1.30(1.06–1.59) and 1.60(1.28–1.98), respectively (p(trend) < 0.001). Conclusion Patients with psychological distress may need more support to optimise their use of secondary CVD prevention medications. Increasing the use of these medications for distressed patients may improve prognosis for patients with distress and improve population-level secondary prevention of CVD more broadly.
Published: 2019

9. Aboriginal and Torres Strait Islander absolute cardiovascular risk assessment and management: systematic review of evidence to inform national guidelines

Author: Paige, Ellie, O'Donoughue Jenkins, Lily, Agostino, Jason, Pennings, Susan, Wade, Vicki, Lovett, Raymond, Daluwatta, Amanda, McLoughlin, Kirstie, Banks, Emily, Paige, Ellie, O'Donoughue Jenkins, Lily, Agostino, Jason, Pennings, Susan, Wade, Vicki, Lovett, Raymond, Daluwatta, Amanda, McLoughlin, Kirstie, and Banks, Emily
Abstract: Australia’s absolute cardiovascular disease (CVD) risk assessment algorithm1 first examines whether individuals meet criteria for clinically determined high CVD risk and, in those not meeting these criteria, applies the Framingham Risk Equation to estimate an individual’s risk of having a CVD event in the next 5 years. The same risk equation is used for Aboriginal and Torres Strait Islander people and non-Indigenous Australians, although there is variation in underlying risk across the two populations, with the former experiencing a greater burden of cardiovascular risk factors.
Published: 2019

10. Interleukin-6 Receptor and Abdominal Aortic Aneurysm Growth Rates

Author: Paige, Ellie, Clément, Marc, Lareyre, Fabien, Sweeting, Michael, Raffort, Juliette, Grenier, Céline, Finigan, Alison, Harrison, James, Peters, James E., Sun, Benjamin B., Butterworth, Adam, Paige, Ellie, Clément, Marc, Lareyre, Fabien, Sweeting, Michael, Raffort, Juliette, Grenier, Céline, Finigan, Alison, Harrison, James, Peters, James E., Sun, Benjamin B., and Butterworth, Adam
Abstract: The Asp358Ala variant (rs2228145; A>C) in the IL (interleukin)-6 receptor (IL6R) gene has been implicated in the development of abdominal aortic aneurysms (AAAs), but its effect on AAA growth over time is not known. We aimed to investigate the clinical association between the IL6R-Asp358Ala variant and AAA growth and to assess the effect of blocking the IL-6 signaling pathway in mouse models of aortic aneurysm rupture or dissection.
Published: 2019

11. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
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12. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

13. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

14. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

15. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

16. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
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17. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., and Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group
Published: 2018

18. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., and Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group
Published: 2018

19. Risk thresholds for alcohol consumption - Authors' reply

Author: Wood, Angela M., Kaptoge, Stephen, Paige, Ellie, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Paige, Ellie, Di Angelantonio, Emanuele, and Danesh, John
Abstract: To define alcohol consumption thresholds associated with lowest risk for all-cause mortality and cardiovascular disease subtypes, we analysed individual-participant data from 599 912 current drinkers without previous cardiovascular disease.1 Among current drinkers, the threshold for lowest risk of all-cause mortality was about 100 g per week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk-based thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data challenge the concept that moderate alcohol consumption is universally associated with lower cardiovascular disease risk, and suggest implications for low-risk limits in guidelines.
Published: 2018

20. Genomic atlas of the human plasma proteome

Author: Sun, Benjamin B., Maranville, Joseph C., Peters, James E., Stacey, David, Staley, James R., Blackshaw, James, Burgess, Stephen, Jiang, Tao, Paige, Ellie, Surendran, Praveen, Oliver-Williams, Clare, Sun, Benjamin B., Maranville, Joseph C., Peters, James E., Stacey, David, Staley, James R., Blackshaw, James, Burgess, Stephen, Jiang, Tao, Paige, Ellie, Surendran, Praveen, and Oliver-Williams, Clare
Abstract: Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.
Published: 2018

21. Landmark models for optimizing the use of repeated measurements of risk factors in electronic health records to predict future disease risk

Author: Paige, Ellie, Barrett, Jessica, Sweeting, Michael, Nazareth, Irwin D., Paige, Ellie, Barrett, Jessica, Sweeting, Michael, and Nazareth, Irwin D.
Abstract: The benefits of using electronic health records for disease risk screening and personalized heathcare decisions are becoming increasingly recognized. We present a computationally feasible statistical approach to address the methodological challenges in utilizing historical repeat measures of multiple risk factors recorded in electronic health records to systematically identify patients at high risk of future disease. The approach is principally based on a two-stage dynamic landmark model. The first stage estimates current risk factor values from all available historical repeat risk factor measurements by landmark-age-specific multivariate linear mixed-effects models with correlated random-intercepts, which account for sporadically recorded repeat measures, unobserved data and measurements errors. The second stage predicts future disease risk from a sex-stratified Cox proportional hazards model, with estimated current risk factor values from the first stage. Methods are exemplified by developing and validating a dynamic 10-year cardiovascular disease risk prediction model using electronic primary care records for age, diabetes status, hypertension treatment, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol from 41,373 individuals in 10 primary care practices in England and Wales contributing to The Health Improvement Network (1997-2016). Using cross-validation, the model was well-calibrated (Brier score = 0.041 [95%CI: 0.039, 0.042]) and had good discrimination (C-index = 0.768 [95%CI: 0.759, 0.777]).
Published: 2018

22. Mid- and Long-Term Health Risks in Living Kidney Donors: A Systematic Review and Meta-analysis

Author: O'Keeffe, Linda M, Ramond, Anna, Oliver-Williams, Clare, Willeit, Peter, Paige, Ellie, Trotter, Patrick, Evans, Jonathan, Wadström, Jonas, Nicholson, Michael, Collett, Dave, Di Angelantonio, Emanuele, O'Keeffe, Linda M, Ramond, Anna, Oliver-Williams, Clare, Willeit, Peter, Paige, Ellie, Trotter, Patrick, Evans, Jonathan, Wadström, Jonas, Nicholson, Michael, Collett, Dave, and Di Angelantonio, Emanuele
Abstract: Background: Long-term health risks for adults who donate kidneys are unclear. Purpose: To summarize evidence about mid- and long-term health risks associated with living kidney donation in adults. Data Sources: PubMed, Embase, Scopus, and PsycINFO without language restriction from April 1964 to July 2017. Study Selection: Observational studies with at least 1 year of follow-up that compared health outcomes in adult living kidney donors versus nondonor populations. Data Extraction: Two investigators independently extracted study data and assessed study quality. Data Synthesis: 52 studies, comprising 118 426 living kidney donors and 117 656 nondonors, were included. Average follow-up was 1 to 24 years. No evidence suggested higher risk for all-cause mortality, cardiovascular disease, hypertension, type 2 diabetes, or adverse psychosocial health outcomes in living kidney donors than in nondonor populations. Donors had higher diastolic blood pressure, lower estimated glomerular filtration rates, and higher risk for end-stage renal disease (ESRD) (relative risk [RR], 8.83 [95% CI, 1.02 to 20.93]) and preeclampsia in female donors (RR, 2.12 [CI, 1.06 to 4.27]). Despite the increased RR, donors had low absolute risk for ESRD (incidence rate, 0.5 event [CI, 0.1 to 4.9 events] per 1000 person-years) and preeclampsia (incidence rate, 5.9 events [CI, 2.9 to 8.9 events] per 100 pregnancies). Limitation: Generalizability was limited by selected control populations, few studies reported pregnancy-related outcomes, and few studies were from low- and middle-income countries. Conclusion: Although living kidney donation is associated with higher RRs for ESRD and preeclampsia, the absolute risk for these outcomes remains low. Compared with nondonor populations, living kidney donors have no increased risk for other major chronic diseases, such as type 2 diabetes, or for adverse psychosocial outcomes. Primary Funding Source: National Health Service Blood and Transplant and Nati
Published: 2018

23. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M, Kaptoge, Stephen, Butterworth, Adam S, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S, Sweeting, Michael, Burgess, Stephen, Bell, Steven, Tjønneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J, Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F, Ford, Ian, Sattar, Naveed, Astle, William, Lazo, Mariana, Thompson, Simon G, Ferrari, Pietro, Leon, David A, Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Stevens, David, Butterworth, Adam, Koulman, Albert, Selmer, Randi M, Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Veikko, Salomaa, Nordestgaard, Børge G, Yeap, Bu B, Flecther, Astrid, Melander, Olle, Kuller, Lewis H, Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Verschuren, W M Monique, Arndt, Volker, Franco, Oscar H, Wennberg, Patrik, Gallacher, John, Gómez de la Cámara, Agustín, Völzke, Henry, Dahm, Christina C, Dale, Caroline E, Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T, Kaaks, Rudolf, Simons, Leon A, Lagiou, Pagona, Schoufour, Josje D, Boer, Jolanda M.A, Key, Timothy J, Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W, Taylor, James O, Sacerdote, Carlotta, Wallace, Robert B, Quiros, J. Ramon, Rimm, Eric B, Tumino, Rosario, Blazer III, Dan G, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Salomaa, Veikko, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J, Psaty, Bruce M, Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M, Grioni, Sara, Palli, Domenico, Huerta, José M, Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C, Panagiotakos, Demosthenes B, Karakatsani, Anna, Trichopoulou, Antonia, Kühn, Tilman, Grobbee, Diederick E, Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Després, Jean-Pierre, Cushman, Mary, Cooper, Jackie A, Rodriguez, Carlos J, Sakurai, Masaru, Shaw, Jonathan E, Knuiman, Matthew, Fletcher, Astrid, Voortman, Trudy, Meisinger, Christa, Dallongeville, Jean-Pierre, Gillumn, Richard F, Ford, Ian Ford, Thompson, Simon, Davey Smith, George, de la Cámara, Agustín Gómez, Bergmann, Manuela M, Crespo, Carlos J, Boer, Jolanda M A, Quiros, J Ramon, Blazer, Dan G, Wood, Angela M, Kaptoge, Stephen, Butterworth, Adam S, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S, Sweeting, Michael, Burgess, Stephen, Bell, Steven, Tjønneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J, Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F, Ford, Ian, Sattar, Naveed, Astle, William, Lazo, Mariana, Thompson, Simon G, Ferrari, Pietro, Leon, David A, Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Stevens, David, Butterworth, Adam, Koulman, Albert, Selmer, Randi M, Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Veikko, Salomaa, Nordestgaard, Børge G, Yeap, Bu B, Flecther, Astrid, Melander, Olle, Kuller, Lewis H, Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Verschuren, W M Monique, Arndt, Volker, Franco, Oscar H, Wennberg, Patrik, Gallacher, John, Gómez de la Cámara, Agustín, Völzke, Henry, Dahm, Christina C, Dale, Caroline E, Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T, Kaaks, Rudolf, Simons, Leon A, Lagiou, Pagona, Schoufour, Josje D, Boer, Jolanda M.A, Key, Timothy J, Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W, Taylor, James O, Sacerdote, Carlotta, Wallace, Robert B, Quiros, J. Ramon, Rimm, Eric B, Tumino, Rosario, Blazer III, Dan G, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Salomaa, Veikko, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J, Psaty, Bruce M, Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M, Grioni, Sara, Palli, Domenico, Huerta, José M, Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C, Panagiotakos, Demosthenes B, Karakatsani, Anna, Trichopoulou, Antonia, Kühn, Tilman, Grobbee, Diederick E, Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Després, Jean-Pierre, Cushman, Mary, Cooper, Jackie A, Rodriguez, Carlos J, Sakurai, Masaru, Shaw, Jonathan E, Knuiman, Matthew, Fletcher, Astrid, Voortman, Trudy, Meisinger, Christa, Dallongeville, Jean-Pierre, Gillumn, Richard F, Ford, Ian Ford, Thompson, Simon, Davey Smith, George, de la Cámara, Agustín Gómez, Bergmann, Manuela M, Crespo, Carlos J, Boer, Jolanda M A, Quiros, J Ramon, and Blazer, Dan G
Abstract: BACKGROUND: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. METHODS:We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose–response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12·5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5·6 years [5th–95th percentile 1·04–13·5]) from 71 011 participants from 37 studies. FINDINGS: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5·4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around o
Published: 2018

24. Socioeconomic variation in absolute cardiovascular disease risk and treatment in the Australian population

Author: Paige, Ellie, Welsh, Jennifer, Agostino, Jason, Calabria, Bianca, Banks, Emily, Korda, Rosemary, Paige, Ellie, Welsh, Jennifer, Agostino, Jason, Calabria, Bianca, Banks, Emily, and Korda, Rosemary
Abstract: Cardiovascular disease (CVD), preventable through appropriate management of absolute CVD risk, disproportionately affects socioeconomically disadvantaged individuals. The aim of this study was to estimate absolute and relative socioeconomic inequalities in absolute CVD risk and treatment in the Australian population using cross-sectional representative data on 4,751 people aged 45-74 from the 2011-12 Australian Health Survey. Poisson regression was used to calculate prevalence differences (PD) and ratios (PR) for prior CVD, high 5-year absolute risk of a primary CVD event and guideline-recommended medication use, in relation to socioeconomic position (SEP, measured by education). After adjusting for age and sex, the prevalence of high absolute risk of a primary CVD event among those of low, intermediate and high SEP was 12.6%, 10.9% and 7.7% (PD, low vs. high=5.0 [95% CI: 2.3, 7.7], PR=1.6 [1.2, 2.2]) and for prior CVD was 10.7%, 9.1% and 6.7% (PD=4.0 [1.4, 6.6], PR=1.6 [1.1, 2.2]). The proportions using preventive medication use among those with high primary risk were 21.3%, 19.5% and 29.4% for low, intermediate and high SEP and for prior CVD, were 37.8%, 35.7% and 17.7% (PD=20.1 [9.7, 30.5], PR=2.1 [1.3, 3.5]). Proportions at high primary risk and not using medications among those of low, intermediate and high SEP were 10.6%, 8.8% and 4.7% and with prior CVD not using medications were 8.5%, 6.3% and 4.1%. Findings indicate substantial potential to prevent CVD and reduce inequalities through appropriate management of high absolute risk in the population.
Published: 2018

25. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., and Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group
Published: 2018

26. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

27. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Public Health Epidemiologie, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiovasculaire Epidemiologie, Cardiovasculaire Epi Team 9, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group, Public Health Epidemiologie, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiovasculaire Epidemiologie, Cardiovasculaire Epi Team 9, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam, Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela, Crespo, Carlos, van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M.A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Grobbee, Diederick E., and Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group
Published: 2018

28. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

29. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
Full Text: View/download PDF

30. Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author: Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Wood, Angela M., Kaptoge, Stephen, Butterworth, Adam S., Willeit, Peter, Warnakula, Samantha, Bolton, Thomas, Paige, Ellie, Paul, Dirk S., Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M., Verschuren, W. M. Monique, Sato, Shinichi, Njolstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Borge G., Yeap, Bu B., Fletcher, Astrid, Melander, Olle, Kuller, Lewis H., Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyrus, Arndt, Volker, Franco, Oscar H., Wennberg, Patrik, Gallacher, John, de la Camara, Agustin Gomez, Volzke, Henry, Dahm, Christina C., Dale, Caroline E., Bergmann, Manuela M., Crespo, Carlos J., van der Schouw, Yvonne T., Kaaks, Rudolf, Simons, Leon A., Lagiou, Pagona, Schoufour, Josje D., Boer, Jolanda M. A., Key, Timothy J., Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W., Taylor, James O., Sacerdote, Carlotta, Wallace, Robert B., Quiros, J. Ramon, Tumino, Rosario, Blazer, Dan G., II, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J., Psaty, Bruce M., Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M., Grioni, Sara, Palli, Domenico, Huerta, Jose M., Price, Jackie, Sundstrom, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C., Panagiotakos, Demosthenes B., Karakatsani, Anna, Trichopoulou, Antonia, Kuhn, Tilman, Grobbee, Diederick E., Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Despres, Jean-Pierre, Cushman, Mary, Cooper, Jackie A., Rodriguez, Carlos J., Sakurai, Masaru, Shaw, Jonathan E., Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjonneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J., Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F., Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G., Ferrari, Pietro, Leon, David A., Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, and Danesh, John
Abstract: Background: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around
Published: 2018
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31. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
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32. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
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33. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
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34. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
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35. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
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36. Review of evidence for the alignment of guidelines on Aboriginal and Torres Strait Islander absolute cardiovascular disease risk: A report prepared for the Australian Government Department of Health

Author: Paige, Ellie, Agostino, Jason, Phillips, Christine, Wade, Vicki, O'Donoughue Jenkins, Lily, McLoughlin, Kirstie, Ford, Laura, Pennings, Susan, Daluwatta, Amanda, Korda, Rosemary, Greaves, Kim, Lovett, Raymond, Banks, Emily, Paige, Ellie, Agostino, Jason, Phillips, Christine, Wade, Vicki, O'Donoughue Jenkins, Lily, McLoughlin, Kirstie, Ford, Laura, Pennings, Susan, Daluwatta, Amanda, Korda, Rosemary, Greaves, Kim, Lovett, Raymond, and Banks, Emily
Abstract: Policy context: Cardiovascular disease (CVD) is highly preventable. CVD continues to be the largest contributor to mortality within the Aboriginal and Torres Strait Islander population and rates of CVD are disproportionately higher within the Australian Aboriginal and Torres Strait Islander population compared to the non-Indigenous population. Improving uptake of current evidence based solutions such as the absolute risk approach to CVD within the Aboriginal and Torres Strait Islander population is important to address this disparity. Although there are several tools available supporting an absolute CVD risk approach, clinical uptake is limited due to a number of factors including an outdated continued reliance on the ‘single risk factor’ approach to prevention, diagnosis and treatment of CVD. A major barrier to uptake is inconsistent messages in the current clinical practice guidelines. Key messages: There are three main guidelines on the absolute CVD risk approach for Aboriginal and Torres Strait Islander peoples in Australia: The NVDPA Guidelines for the Management of Absolute Cardiovascular Disease Risk; The Central Australian Rural Practitioners Association Standard Treatment Manual; and the RACGP National Guide to a Preventive Health Assessment for Aboriginal and Torres Strait Islander People. There is considerable alignment between the existing guidelines, including the need for an absolute risk approach, conditions conferring automatic high risk, use of the Framingham risk equation as the basis of calculating absolute risk, and the need to treat people at a greater than 15% risk of a primary CVD event over the next five years. The guidelines diverge materially in relation to four recommendations: 1) the age at which to commence absolute CVD risk assessment; 2) whether or not calculated risk scores should be adjusted upward by 5%; 3) how often CVD risk should be assessed; and 4) treatment targets for blood pressure. Available evidence indicates that CVD event
Published: 2017

37. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

38. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

39. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

40. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

41. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017

42. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W.M.M., Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan L.J., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan Håkan, Willeit, Johann, Onat, Altan, De La Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew W., Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela M., Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W.M.M., Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan L.J., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan Håkan, Willeit, Johann, Onat, Altan, De La Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew W., Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela M.
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (Cindex) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017

43. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

44. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017

45. Tobacco smoking and all-cause mortality in a large Australian cohort study: findings from a mature epidemic with current low smoking prevalence

Author: Banks, Emily, Joshy, Grace, Weber, Marianne F, Liu, Bette, Grenfell, Robert, Egger, Sam, Paige, Ellie, Lopez, Alan D, Sitas, Freddy, Beral, Valerie, Banks, Emily, Joshy, Grace, Weber, Marianne F, Liu, Bette, Grenfell, Robert, Egger, Sam, Paige, Ellie, Lopez, Alan D, Sitas, Freddy, and Beral, Valerie
Abstract: BACKGROUND The smoking epidemic in Australia is characterised by historic levels of prolonged smoking, heavy smoking, very high levels of long-term cessation, and low current smoking prevalence, with 13% of adults reporting that they smoked daily in 2013. Large-scale quantitative evidence on the relationship of tobacco smoking to mortality in Australia is not available despite the potential to provide independent international evidence about the contemporary risks of smoking. METHODS This is a prospective study of 204,953 individuals aged ≥45 years sampled from the general population of New South Wales, Australia, who joined the 45 and Up Study from 2006-2009, with linked questionnaire, hospitalisation, and mortality data to mid-2012 and with no history of cancer (other than melanoma and non-melanoma skin cancer), heart disease, stroke, or thrombosis. Hazard ratios (described here as relative risks, RRs) for all-cause mortality among current and past smokers compared to never-smokers were estimated, adjusting for age, education, income, region of residence, alcohol, and body mass index. RESULTS Overall, 5,593 deaths accrued during follow-up (874,120 person-years; mean: 4.26 years); 7.7% of participants were current smokers and 34.1% past smokers at baseline. Compared to never-smokers, the adjusted RR (95% CI) of mortality was 2.96 (2.69-3.25) in current smokers and was similar in men (2.82 (2.49-3.19)) and women (3.08 (2.63-3.60)) and according to birth cohort. Mortality RRs increased with increasing smoking intensity, with around two- and four-fold increases in mortality in current smokers of ≤14 (mean 10/day) and ≥25 cigarettes/day, respectively, compared to never-smokers. Among past smokers, mortality diminished gradually with increasing time since cessation and did not differ significantly from never-smokers in those quitting prior to age 45. Current smokers are estimated to die an average of 10 years earlier than non-smokers. CONCLUSIONS In Australia, up to two
Published: 2015

46. Using Australian Pharmaceutical Benefits Scheme data for pharmacoepidemiological research: challenges and approaches

Author: Paige, Ellie, Kemp-Casey, Anna, Korda, Rosemary, Banks, Emily, Paige, Ellie, Kemp-Casey, Anna, Korda, Rosemary, and Banks, Emily
Abstract: The Pharmaceutical Benefits Scheme (PBS) dataset provides detailed information about subsidised medicines dispensed in Australia and is increasingly used for pharmacoepidemiological research. Use of the PBS dataset provides unique opportunities for such research, but comes with its own set of challenges that must be considered and addressed. This paper outlines some issues that commonly arise when using PBS data − relating to accurate identification of medicine dispensings and how to define medicine exposure − and suggests some possible approaches for dealing with them. The paper is intended as an introductory resource for researchers.
Published: 2015

47. The relationship between body mass index and hospitalisation rates, days in hospital and costs: Findings from a large prospective linked data study

Author: Korda, Rosemary, Joshy, Grace, Paige, Ellie, Butler, James, Jorm, Louisa R, Liu, Bette, Bauman, Adrian, Banks, Emily, Korda, Rosemary, Joshy, Grace, Paige, Ellie, Butler, James, Jorm, Louisa R, Liu, Bette, Bauman, Adrian, and Banks, Emily
Abstract: Background: Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation-admissions, days and costs-associated with above-normal BMI. Methods: Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated. Results: There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%). Conclusions: The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would re
Published: 2015

48. A record linkage study of antidepressant medication use and weight change in Australian adults

Author: Paige, Ellie, Korda, Rosemary, Kemp-Casey, Anna, Dobbins, Timothy, Banks, Emily, Rodgers, Bryan, Paige, Ellie, Korda, Rosemary, Kemp-Casey, Anna, Dobbins, Timothy, Banks, Emily, and Rodgers, Bryan
Abstract: Objective: Antidepressant use is widespread. While weight gain is a commonly reported side-effect of antidepressant use and has the potential to affect population health, there is little large-scale population-based evidence on the issue, particularly for long-term use (≥12months). The aim of this study is to investigate the association between antidepressant use and weight change, including whether this relationship varies according to antidepressant class, recency of use, duration of use and dose. Methods: Annual percentage weight change was calculated from self-reported weight at two time-points from 20,751 participants aged ≥45years from the 45 and Up Study - a population-based cohort study from New South Wales, Australia. Antidepressant use, ascertained from linked pharmaceutical data, from 19months before baseline until end of follow-up (mean=3.3years of follow-up), was categorised as current, past-only, non-persistent or non-use. The association between antidepressant use and weight change was modelled using linear and multinomial logistic regressions and according to antidepressant class, recency, duration and dose. Results: Antidepressants were dispensed to 23% of participants (n=4748) during the study period. Current antidepressant users were significantly more likely to gain >3% of their body weight annually than non-users (adjusted relative risk ratio=1.19; 95% confidence interval: [1.03, 1.38]); the risk increased with increasing dose among current users (p[trend]=0.003). Risk of weight gain did not vary significantly according to antidepressant class, recency or duration of use; however, statistical power was limited. No significant associations were found between antidepressant use and weight loss. Conclusion: Current antidepressant use was associated with modest but statistically significant annual gains in weight, with similar effects observed across the different classes of antidepressants used.
Published: 2015

49. Income-related inequalities in chronic conditions, physical functioning and psychological distress among older people in Australia: cross-sectional findings from the 45 and up study

Author: Korda, Rosemary, Paige, Ellie, Yiengprugsawan, Vasoontara, Latz, Isabel, Friel, Sharon, Korda, Rosemary, Paige, Ellie, Yiengprugsawan, Vasoontara, Latz, Isabel, and Friel, Sharon
Abstract: BACKGROUND: The burden of chronic disease continues to rise as populations age. There is relatively little published on the socioeconomic distribution of this burden in older people. This study quantifies absolute and relative income-related inequalities in prevalence of chronic diseases, severe physical functioning limitation and high psychological distress in mid-age and older people in Australia. METHODS: Cross-sectional study of 208,450 participants in the 45 and Up Study, a population-based cohort of men and women aged 45–106 years from New South Wales, Australia. Chronic conditions included self-reported heart disease, diabetes, Parkinson’s disease, cancer and osteoarthritis; physical functioning limitation (severe/not) was measured using Medical Outcomes Study measures and psychological distress (high/not) using the Kessler Psychological Distress Scale. For each outcome, prevalence was estimated in relation to annual household income (6 categories). Prevalence differences (PDs) and ratios (PRs) were generated, comparing the lowest income category (<$20,000) to the highest (≥$70,000), using Poisson regression with robust standard errors, weighted for age, sex and region of residence. Analyses were stratified by age group (45–64, 65–79 and ≥80 years) and sex and adjusted for age and country of birth. RESULTS: With few exceptions, there were income gradients in the prevalence of chronic conditions among all age-sex groups, with prevalence decreasing with increasing income. Of the chronic diseases, PDs were highest for diabetes (ranging between 5.69% and 10.36% across age-sex groups) and in women, also for osteoarthritis (5.72% to 8.14%); PRs were highest for osteoarthritis in men aged 45–64 years (4.01), otherwise they were highest for diabetes (1.78 to 3.43). Inequalities were very high for both physical functioning limitation and psychological distress, particularly among those aged 45–64 (PDs between 18.67% and 29.23% and PRs between 4.63 and 16.51). Absolute
Published: 2014

50. How weight change is modelled in population studies can affect research findings: empirical results from a large-scale cohort study

Author: Paige, Ellie, Korda, Rosemary, Banks, Emily, Rodgers, Bryan, Paige, Ellie, Korda, Rosemary, Banks, Emily, and Rodgers, Bryan
Abstract: Objectives: To investigate how results of the association between education and weight change vary when weight change is defined and modelled in different ways. Design: Longitudinal cohort study. Participants: 60 404 men and women participating in the Social, Environmental and Economic Factors (SEEF) subcomponent of the 45 and Up Study—a population-based cohort study of people aged 45 years or older, residing in New South Wales, Australia. Outcome measures: The main exposure was selfreported education, categorised into four groups. The outcome was annual weight change, based on change in self-reported weight between the 45 and Up Study baseline questionnaire and SEEF questionnaire (completed an average of 3.3 years later). Weight change was modelled in four different ways: absolute change (kg) modelled as (1) a continuous variable and (2) a categorical variable (loss, maintenance and gain), and relative (%) change modelled as (3) a continuous variable and (4) a categorical variable. Different cutpoints for defining weight-change categories were also tested. Results: When weight change was measured categorically, people with higher levels of education (compared with no school certificate) were less likely to lose or to gain weight. When weight change was measured as the average of a continuous measure, a null relationship between education and annual weight change was observed. No material differences in the education and weight-change relationship were found when comparing weight change defined as an absolute (kg) versus a relative (%) measure. Results of the logistic regression were sensitive to different cut-points for defining weight-change categories. Conclusions: Using average weight change can obscure important directional relationship information and, where possible, categorical outcome measurements should be included in analyses.
Published: 2014

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