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1. The Kidney Failure Risk Equation: Evaluation of Novel Input Variables including eGFR Estimated Using the CKD-EPI 2021 Equation in 59 Cohorts

Author: Grams, M.E., Brunskill, N.J., Ballew, Shoshana H., Sang, Y., Coresh, J., Matsushita, K., Surapaneni, A., Bell, S., Carrero, J.J., Chodick, G., Evans, M., Heerspink, H.J., Inker, L.A., Iseki, K., Kalra, P.A., Kirchner, H.L., Lee, B.J., Levin, A., Major, R.W., Medcalf, J., Nadkarni, G.N., Naimark, D.M.J., Ricardo, A.C., Sawhney, S., Sood, M.M., Staplin, N., Stempniewicz, N., Stengel, B., Sumida, K., Traynor, J.P., Brand, J. van den, Wen, C.P., Woodward, M., Yang, J.W., Wang, A.Y., Tangri, N., Grams, M.E., Brunskill, N.J., Ballew, Shoshana H., Sang, Y., Coresh, J., Matsushita, K., Surapaneni, A., Bell, S., Carrero, J.J., Chodick, G., Evans, M., Heerspink, H.J., Inker, L.A., Iseki, K., Kalra, P.A., Kirchner, H.L., Lee, B.J., Levin, A., Major, R.W., Medcalf, J., Nadkarni, G.N., Naimark, D.M.J., Ricardo, A.C., Sawhney, S., Sood, M.M., Staplin, N., Stempniewicz, N., Stengel, B., Sumida, K., Traynor, J.P., Brand, J. van den, Wen, C.P., Woodward, M., Yang, J.W., Wang, A.Y., and Tangri, N.
Abstract: Item does not contain fulltext, SIGNIFICANCE STATEMENT: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict 2- and 5-year risk of kidney failure in populations with eGFR <60 ml/min per 1.73 m 2 . However, the CKD-EPI 2021 creatinine equation for eGFR is now recommended for use but has not been fully tested in the context of KFRE. In 59 cohorts comprising 312,424 patients with CKD, the authors assessed the predictive performance and calibration associated with the use of the CKD-EPI 2021 equation and whether additional variables and accounting for the competing risk of death improves the KFRE's performance. The KFRE generally performed well using the CKD-EPI 2021 eGFR in populations with eGFR <45 ml/min per 1.73 m 2 and was not improved by adding the 2-year prior eGFR slope and cardiovascular comorbidities. BACKGROUND: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict kidney failure risk in people with GFR <60 ml/min per 1.73 m 2 . METHODS: Using 59 cohorts with 312,424 patients with CKD, we tested several modifications to the KFRE for their potential to improve the KFRE: using the CKD-EPI 2021 creatinine equation for eGFR, substituting 1-year average ACR for single-measure ACR and 1-year average eGFR in participants with high eGFR variability, and adding 2-year prior eGFR slope and cardiovascular comorbidities. We also assessed calibration of the KFRE in subgroups of eGFR and age before and after accounting for the competing risk of death. RESULTS: The KFRE remained accurate and well calibrated overall using the CKD-EPI 2021 eGFR equation. The other modifications did not improve KFRE performance. In subgroups of eGFR 45-59 ml/min per 1.73 m 2 and in older adults using the 5-year time horizon, the KFRE demonstrated systematic underprediction and overprediction, respectively. We developed and tested a new model with a spline term in eGFR and incorporating the competing risk
Published: 2023

2. Major cardiovascular events and subsequent risk of kidney failure with replacement therapy: a CKD Prognosis Consortium study

Author: Mark, P.B., Carrero, J.J., Matsushita, K., Sang, Y., Ballew, S.H., Grams, M.E., Coresh, J., Surapaneni, A., Brunskill, N.J., Chalmers, J., Chan, L., Chang, A.R., Chinnadurai, R., Chodick, G., Cirillo, M., Zeeuw, D. de, Evans, M., Garg, A.X., Gutierrez, O.M., Heerspink, H.J.L., Heine, G.H., Herrington, W.G., Ishigami, J., Kronenberg, F., Lee, J.Y., Levin, A., Major, R.W., Marks, A., Nadkarni, G.N., Naimark, D.M.J., Nowak, C., Rahman, M., Sabanayagam, C., Sarnak, M., Sawhney, S., Schneider, M.P., Shalev, V., Shin, J.I., Siddiqui, M.K., Stempniewicz, N., Sumida, K., Valdivielso, J.M., Brand, J. van den, Yee-Moon Wang, A, Wheeler, D.C., Zhang, L., Visseren, F.L.J., Stengel, B., Mark, P.B., Carrero, J.J., Matsushita, K., Sang, Y., Ballew, S.H., Grams, M.E., Coresh, J., Surapaneni, A., Brunskill, N.J., Chalmers, J., Chan, L., Chang, A.R., Chinnadurai, R., Chodick, G., Cirillo, M., Zeeuw, D. de, Evans, M., Garg, A.X., Gutierrez, O.M., Heerspink, H.J.L., Heine, G.H., Herrington, W.G., Ishigami, J., Kronenberg, F., Lee, J.Y., Levin, A., Major, R.W., Marks, A., Nadkarni, G.N., Naimark, D.M.J., Nowak, C., Rahman, M., Sabanayagam, C., Sarnak, M., Sawhney, S., Schneider, M.P., Shalev, V., Shin, J.I., Siddiqui, M.K., Stempniewicz, N., Sumida, K., Valdivielso, J.M., Brand, J. van den, Yee-Moon Wang, A, Wheeler, D.C., Zhang, L., Visseren, F.L.J., and Stengel, B.
Abstract: Item does not contain fulltext, AIMS: Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT). METHODS AND RESULTS: The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9-3.3], 2.0 (1.9-2.1), 4.5 (4.2-4.9), 2.8 (2.7-3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43-50) within 3 months] after adjustment for other CVD subtype incidence. CONCLUSION: Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed.
Published: 2023

3. Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis

Author: Grams, M.E., Coresh, J., Matsushita, K., Ballew, S.H., Sang, Y., Surapaneni, A., Pinho, N. Alencar de, Anderson, A., Appel, L.J., Ärnlöv, J., Azizi, F., Bansal, N., Bell, S., Bilo, H.J.G., Brunskill, N.J., Carrero, J.J., Chadban, S., Chalmers, J., Chen, J., Ciemins, E., Cirillo, M., Ebert, N., Evans, M., Ferreiro, Alejandro, Fu, E.L., Fukagawa, M., Green, J.A., Gutierrez, O.M., Herrington, W.G., Hwang, S.J., Inker, L.A., Iseki, K., Jafar, T., Jassal, S.K., Jha, V., Kadota, A., Katz, R., Köttgen, A., Konta, T., Kronenberg, F., Lee, B.J., Lees, J., Levin, A., Looker, H.C., Major, R., Cohen, C. Melzer, Mieno, M., Miyazaki, M., Moranne, O., Muraki, I., Naimark, D., Nitsch, D., Oh, W., Pena, M., Purnell, T.S., Sabanayagam, C., Satoh, M., Sawhney, S., Schaeffner, E., Schöttker, B., Shen, J.I., Shlipak, M.G., Sinha, S., Stengel, B., Sumida, K., Tonelli, M., Valdivielso, J.M., Zuilen, A.D. van, Visseren, F.L.J., Wang, A.Y., Wen, C.P., Wheeler, D.C., Yatsuya, H., Yamagata, K., Yang, J.W., Young, A., Zhang, Haitao, Zhang, L., Wetzels, J.F.M., Levey, A.S., Gansevoort, R.T., Grams, M.E., Coresh, J., Matsushita, K., Ballew, S.H., Sang, Y., Surapaneni, A., Pinho, N. Alencar de, Anderson, A., Appel, L.J., Ärnlöv, J., Azizi, F., Bansal, N., Bell, S., Bilo, H.J.G., Brunskill, N.J., Carrero, J.J., Chadban, S., Chalmers, J., Chen, J., Ciemins, E., Cirillo, M., Ebert, N., Evans, M., Ferreiro, Alejandro, Fu, E.L., Fukagawa, M., Green, J.A., Gutierrez, O.M., Herrington, W.G., Hwang, S.J., Inker, L.A., Iseki, K., Jafar, T., Jassal, S.K., Jha, V., Kadota, A., Katz, R., Köttgen, A., Konta, T., Kronenberg, F., Lee, B.J., Lees, J., Levin, A., Looker, H.C., Major, R., Cohen, C. Melzer, Mieno, M., Miyazaki, M., Moranne, O., Muraki, I., Naimark, D., Nitsch, D., Oh, W., Pena, M., Purnell, T.S., Sabanayagam, C., Satoh, M., Sawhney, S., Schaeffner, E., Schöttker, B., Shen, J.I., Shlipak, M.G., Sinha, S., Stengel, B., Sumida, K., Tonelli, M., Valdivielso, J.M., Zuilen, A.D. van, Visseren, F.L.J., Wang, A.Y., Wen, C.P., Wheeler, D.C., Yatsuya, H., Yamagata, K., Yang, J.W., Young, A., Zhang, Haitao, Zhang, L., Wetzels, J.F.M., Levey, A.S., and Gansevoort, R.T.
Abstract: Item does not contain fulltext, IMPORTANCE: Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or albuminuria) affects approximately 14% of adults in the US. OBJECTIVE: To evaluate associations of lower eGFR based on creatinine alone, lower eGFR based on creatinine combined with cystatin C, and more severe albuminuria with adverse kidney outcomes, cardiovascular outcomes, and other health outcomes. DESIGN, SETTING, AND PARTICIPANTS: Individual-participant data meta-analysis of 27 503 140 individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from 114 cohorts (albuminuria) from 1980 to 2021. EXPOSURES: The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria estimated as urine albumin to creatinine ratio (UACR). MAIN OUTCOMES AND MEASURES: The risk of kidney failure requiring replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization, coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. The analyses were performed within each cohort and summarized with random-effects meta-analyses. RESULTS: Within the population using eGFR based on creatinine alone (mean age, 54 years [SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the mean eGFR was 90 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 11 mg/g (IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C (mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years [SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone or based on creatinine and cystatin C) and higher UACR were each significantly associ
Published: 2023

4. Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis.

Author: Grams, M.E. and Grams, M.E.
Subjects: Radboudumc 11: Renal disorders Nephrology.
Published: 2023

5. Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes

Author: Grams, M.E., Brunskill, N.J., Ballew, S.H., Sang, Y., Coresh, J., Matsushita, K., Surapaneni, A., Brand, J. van den, Yee-Moon Wang, A, Tangri, N., Grams, M.E., Brunskill, N.J., Ballew, S.H., Sang, Y., Coresh, J., Matsushita, K., Surapaneni, A., Brand, J. van den, Yee-Moon Wang, A, and Tangri, N.
Abstract: Item does not contain fulltext
Published: 2022

6. Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis: An individual participant-based meta-analysis.

Author: Sumida K., Nadkarni G.N., Grams M.E., Sang Y., Ballew S.H., Coresh J., Matsushita K., Surapaneni A., Brunskill N., Chadban S.J., Chang A.R., Cirillo M., Daratha K.B., Gansevoort R.T., Garg A.X., Iacoviello L., Kayama T., Konta T., Kovesdy C.P., Lash J., Lee B.J., Major R.W., Metzger M., Miura K., Naimark D.M.J., Nelson R.G., Sawhney S., Stempniewicz N., Tang M., Townsend R.R., Traynor J.P., Valdivielso J.M., Wetzels J., Polkinghorne K.R., Heerspink H.J.L., Sumida K., Nadkarni G.N., Grams M.E., Sang Y., Ballew S.H., Coresh J., Matsushita K., Surapaneni A., Brunskill N., Chadban S.J., Chang A.R., Cirillo M., Daratha K.B., Gansevoort R.T., Garg A.X., Iacoviello L., Kayama T., Konta T., Kovesdy C.P., Lash J., Lee B.J., Major R.W., Metzger M., Miura K., Naimark D.M.J., Nelson R.G., Sawhney S., Stempniewicz N., Tang M., Townsend R.R., Traynor J.P., Valdivielso J.M., Wetzels J., Polkinghorne K.R., and Heerspink H.J.L.
Abstract: Background: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. Objective(s): To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. Design(s): Individual participant-based meta-analysis. Setting(s): 12 research and 21 clinical cohorts. Participant(s): 919 383 adults with same-day measures of ACR and PCR or dipstick protein. Measurements: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR >=30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR >=300 mg/g). Result(s): Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. Limitation(s): Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. Conclusion(s): Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. Primary
Published: 2021

7. Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis: An individual participant-based meta-analysis.

Author: Sumida K., Nadkarni G.N., Grams M.E., Sang Y., Ballew S.H., Coresh J., Matsushita K., Surapaneni A., Brunskill N., Chadban S.J., Chang A.R., Cirillo M., Daratha K.B., Gansevoort R.T., Garg A.X., Iacoviello L., Kayama T., Konta T., Kovesdy C.P., Lash J., Lee B.J., Major R.W., Metzger M., Miura K., Naimark D.M.J., Nelson R.G., Sawhney S., Stempniewicz N., Tang M., Townsend R.R., Traynor J.P., Valdivielso J.M., Wetzels J., Polkinghorne K.R., Heerspink H.J.L., Sumida K., Nadkarni G.N., Grams M.E., Sang Y., Ballew S.H., Coresh J., Matsushita K., Surapaneni A., Brunskill N., Chadban S.J., Chang A.R., Cirillo M., Daratha K.B., Gansevoort R.T., Garg A.X., Iacoviello L., Kayama T., Konta T., Kovesdy C.P., Lash J., Lee B.J., Major R.W., Metzger M., Miura K., Naimark D.M.J., Nelson R.G., Sawhney S., Stempniewicz N., Tang M., Townsend R.R., Traynor J.P., Valdivielso J.M., Wetzels J., Polkinghorne K.R., and Heerspink H.J.L.
Abstract: Background: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. Objective(s): To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. Design(s): Individual participant-based meta-analysis. Setting(s): 12 research and 21 clinical cohorts. Participant(s): 919 383 adults with same-day measures of ACR and PCR or dipstick protein. Measurements: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR >=30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR >=300 mg/g). Result(s): Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. Limitation(s): Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. Conclusion(s): Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. Primary
Published: 2021

8. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis : An Individual Participant-Based Meta-analysis

Author: Sumida, K., Nadkarni, G.N., Grams, M.E., Sang, Y., Ballew, Shoshana H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S.J., Chang, A.R., Cirillo, M., Daratha, K.B., Gansevoort, R.T., Garg, A.X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C.P., Lash, J., Lee, B.J., Major, R.W., Metzger, M., Miura, K., Naimark, D.M.J., Nelson, R.G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R.R., Traynor, J.P., Valdivielso, J.M., Wetzels, J., Polkinghorne, K.R., Heerspink, H.J., Sumida, K., Nadkarni, G.N., Grams, M.E., Sang, Y., Ballew, Shoshana H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S.J., Chang, A.R., Cirillo, M., Daratha, K.B., Gansevoort, R.T., Garg, A.X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C.P., Lash, J., Lee, B.J., Major, R.W., Metzger, M., Miura, K., Naimark, D.M.J., Nelson, R.G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R.R., Traynor, J.P., Valdivielso, J.M., Wetzels, J., Polkinghorne, K.R., and Heerspink, H.J.
Abstract: Item does not contain fulltext, BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. DESIGN: Individual participant-based meta-analysis. SETTING: 12 research and 21 clinical cohorts. PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein. MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g). RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. PRIMARY FUNDING SOURCE: Nati
Published: 2020

9. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis : An Individual Participant-Based Meta-analysis

Author: Sumida, K., Nadkarni, G.N., Grams, M.E., Sang, Y., Ballew, Shoshana H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S.J., Chang, A.R., Cirillo, M., Daratha, K.B., Gansevoort, R.T., Garg, A.X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C.P., Lash, J., Lee, B.J., Major, R.W., Metzger, M., Miura, K., Naimark, D.M.J., Nelson, R.G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R.R., Traynor, J.P., Valdivielso, J.M., Wetzels, J., Polkinghorne, K.R., Heerspink, H.J., Sumida, K., Nadkarni, G.N., Grams, M.E., Sang, Y., Ballew, Shoshana H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S.J., Chang, A.R., Cirillo, M., Daratha, K.B., Gansevoort, R.T., Garg, A.X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C.P., Lash, J., Lee, B.J., Major, R.W., Metzger, M., Miura, K., Naimark, D.M.J., Nelson, R.G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R.R., Traynor, J.P., Valdivielso, J.M., Wetzels, J., Polkinghorne, K.R., and Heerspink, H.J.
Abstract: Item does not contain fulltext, BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. DESIGN: Individual participant-based meta-analysis. SETTING: 12 research and 21 clinical cohorts. PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein. MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g). RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. PRIMARY FUNDING SOURCE: Nati
Published: 2020

10. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis : An Individual Participant-Based Meta-analysis

Author: Sumida, K., Nadkarni, G.N., Grams, M.E., Sang, Y., Ballew, Shoshana H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S.J., Chang, A.R., Cirillo, M., Daratha, K.B., Gansevoort, R.T., Garg, A.X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C.P., Lash, J., Lee, B.J., Major, R.W., Metzger, M., Miura, K., Naimark, D.M.J., Nelson, R.G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R.R., Traynor, J.P., Valdivielso, J.M., Wetzels, J., Polkinghorne, K.R., Heerspink, H.J., Sumida, K., Nadkarni, G.N., Grams, M.E., Sang, Y., Ballew, Shoshana H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S.J., Chang, A.R., Cirillo, M., Daratha, K.B., Gansevoort, R.T., Garg, A.X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C.P., Lash, J., Lee, B.J., Major, R.W., Metzger, M., Miura, K., Naimark, D.M.J., Nelson, R.G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R.R., Traynor, J.P., Valdivielso, J.M., Wetzels, J., Polkinghorne, K.R., and Heerspink, H.J.
Abstract: Item does not contain fulltext, BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. DESIGN: Individual participant-based meta-analysis. SETTING: 12 research and 21 clinical cohorts. PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein. MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g). RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. PRIMARY FUNDING SOURCE: Nati
Published: 2020

11. Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium

Author: Chang, A.R., Grams, M.E., Ballew, Shoshana H., Bilo, H., Correa, A., Evans, M., Gutierrez, O.M., Hosseinpanah, F., Iseki, K., Kenealy, T., Klein, B., Kronenberg, F., Lee, B.J., Li, Y., Miura, K., Navaneethan, S.D., Roderick, P.J., Valdivielso, J.M., Visseren, F.L., Zhang, L., Gansevoort, R.T., Hallan, S.I., Levey, A.S., Matsushita, K., Wetzels, J.F.M., Shalev, V., Woodward, M., Chang, A.R., Grams, M.E., Ballew, Shoshana H., Bilo, H., Correa, A., Evans, M., Gutierrez, O.M., Hosseinpanah, F., Iseki, K., Kenealy, T., Klein, B., Kronenberg, F., Lee, B.J., Li, Y., Miura, K., Navaneethan, S.D., Roderick, P.J., Valdivielso, J.M., Visseren, F.L., Zhang, L., Gansevoort, R.T., Hallan, S.I., Levey, A.S., Matsushita, K., Wetzels, J.F.M., Shalev, V., and Woodward, M.
Abstract: Contains fulltext : 203008.pdf (publisher's version ) (Open Access), OBJECTIVE: To evaluate the associations between adiposity measures (body mass index, waist circumference, and waist-to-height ratio) with decline in glomerular filtration rate (GFR) and with all cause mortality. DESIGN: Individual participant data meta-analysis. SETTING: Cohorts from 40 countries with data collected between 1970 and 2017. PARTICIPANTS: Adults in 39 general population cohorts (n=5 459 014), of which 21 (n=594 496) had data on waist circumference; six cohorts with high cardiovascular risk (n=84 417); and 18 cohorts with chronic kidney disease (n=91 607). MAIN OUTCOME MEASURES: GFR decline (estimated GFR decline >/=40%, initiation of kidney replacement therapy or estimated GFR <10 mL/min/1.73 m(2)) and all cause mortality. RESULTS: Over a mean follow-up of eight years, 246 607 (5.6%) individuals in the general population cohorts had GFR decline (18 118 (0.4%) end stage kidney disease events) and 782 329 (14.7%) died. Adjusting for age, sex, race, and current smoking, the hazard ratios for GFR decline comparing body mass indices 30, 35, and 40 with body mass index 25 were 1.18 (95% confidence interval 1.09 to 1.27), 1.69 (1.51 to 1.89), and 2.02 (1.80 to 2.27), respectively. Results were similar in all subgroups of estimated GFR. Associations weakened after adjustment for additional comorbidities, with respective hazard ratios of 1.03 (0.95 to 1.11), 1.28 (1.14 to 1.44), and 1.46 (1.28 to 1.67). The association between body mass index and death was J shaped, with the lowest risk at body mass index of 25. In the cohorts with high cardiovascular risk and chronic kidney disease (mean follow-up of six and four years, respectively), risk associations between higher body mass index and GFR decline were weaker than in the general population, and the association between body mass index and death was also J shaped, with the lowest risk between body mass index 25 and 30. In all cohort types, associations between higher waist circumference and higher waist-to-heig
Published: 2019

12. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies

Author: Coresh, J., Heerspink, H.J., Sang, Y., Matsushita, K., Arnlov, J., Astor, B.C., Black, C., Brunskill, N.J., Carrero, J.J., Feldman, H.I., Fox, C.S., Inker, L.A., Ishani, A., Ito, S., Jassal, S., Konta, T., Polkinghorne, K., Romundstad, S., Solbu, M.D., Stempniewicz, N., Stengel, B., Tonelli, M., Umesawa, M., Waikar, S.S., Wen, C.P., Wetzels, J.F.M., Woodward, M., Grams, M.E., Kovesdy, C.P., Levey, A.S., Gansevoort, R.T., Coresh, J., Heerspink, H.J., Sang, Y., Matsushita, K., Arnlov, J., Astor, B.C., Black, C., Brunskill, N.J., Carrero, J.J., Feldman, H.I., Fox, C.S., Inker, L.A., Ishani, A., Ito, S., Jassal, S., Konta, T., Polkinghorne, K., Romundstad, S., Solbu, M.D., Stempniewicz, N., Stengel, B., Tonelli, M., Umesawa, M., Waikar, S.S., Wen, C.P., Wetzels, J.F.M., Woodward, M., Grams, M.E., Kovesdy, C.P., Levey, A.S., and Gansevoort, R.T.
Abstract: Item does not contain fulltext, BACKGROUND: Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. METHODS: In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. FINDINGS: Between July, 2015, and June, 2018, we transferred and analysed data from 28 cohorts in the CKD-P
Published: 2019

13. Evaluating Glomerular Filtration Rate Slope as a Surrogate End Point for ESKD in Clinical Trials: An Individual Participant Meta-Analysis of Observational Data

Author: Grams, M.E., Sang, Y., Ballew, Shoshana H., Matsushita, K., Astor, B.C., Carrero, J.J., Chang, A.R., Inker, L.A., Kenealy, T., Kovesdy, C.P., Lee, B.J., Levin, A., Naimark, D., Pena, M.J., Schold, J.D., Shalev, V., Wetzels, J.F.M., Woodward, M., Gansevoort, R.T., Levey, A.S., Coresh, J., Grams, M.E., Sang, Y., Ballew, Shoshana H., Matsushita, K., Astor, B.C., Carrero, J.J., Chang, A.R., Inker, L.A., Kenealy, T., Kovesdy, C.P., Lee, B.J., Levin, A., Naimark, D., Pena, M.J., Schold, J.D., Shalev, V., Wetzels, J.F.M., Woodward, M., Gansevoort, R.T., Levey, A.S., and Coresh, J.
Abstract: Item does not contain fulltext, BACKGROUND: Decline in eGFR is a biologically plausible surrogate end point for the progression of CKD in clinical trials. However, it must first be tested to ensure strong associations with clinical outcomes in diverse populations, including patients with higher eGFR. METHODS: To investigate the association between 1-, 2-, and 3-year changes in eGFR (slope) with clinical outcomes over the long term, we conducted a random effects meta-analysis of 3,758,551 participants with baseline eGFR>/=60 ml/min per 1.73 m(2) and 122,664 participants with eGFR<60 ml/min per 1.73 m(2) from 14 cohorts followed for an average of 4.2 years. RESULTS: Slower eGFR decline by 0.75 ml/min per 1.73 m(2) per year over 2 years was associated with lower risk of ESKD in participants with baseline eGFR>/=60 ml/min per 1.73 m(2) (adjusted hazard ratio, 0.70; 95% CI, 0.68 to 0.72) and eGFR<60 ml/min per 1.73 m(2) (0.71; 95% CI, 0.68 to 0.74). The relationship was stronger with 3-year slope. For a rapidly progressing population with predicted 5-year risk of ESKD of 8.3%, an intervention that reduced eGFR decline by 0.75 ml/min per 1.73 m(2) per year over 2 years would reduce the ESKD risk by 1.6%. For a hypothetical low-risk population with a predicted 5-year ESKD risk of 0.58%, the same intervention would reduce the risk by only 0.13%. CONCLUSIONS: Slower decline in eGFR was associated with lower risk of subsequent ESKD, even in participants with eGFR>/=60 ml/min per 1.73 m(2), but those with the highest risk would be expected to benefit the most.
Published: 2019

14. Inconsistencies in Reporting Studies of Lactic Acidosis - Reply.

Author: Lazarus B., Grams M.E., Lazarus B., and Grams M.E.
Published: 2019

15. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies.

Author: Ichikawa K., Collins J.F., Drury P.L., Ellis S.G., Nadukuru R., Metzger M., Flamant M., Houillier P., Haymann J.-P., Froissart M., Kenealy T., Elley R.C., Cuddeback J.K., Ciemins E.L., Stempniewicz R., Nelson R.G., Knowler W.C., Bakker S.J., Major R.W., Medcalf J.F., Shepherd D., Barrett-Connor E., Bergstrom J., Ix J.H., Molnar M.Z., Maciulaitis R., Manley T., Smith K., Stockbridge N., Thompson A., Vetter T., Willis K., Zhang L., Coresh J., Heerspink H.J.L., Sang Y., Matsushita K., Arnlov J., Astor B.C., Black C., Brunskill N.J., Carrero J.-J., Feldman H.I., Fox C.S., Inker L.A., Ishani A., Ito S., Jassal S., Konta T., Polkinghorne K., Romundstad S., Solbu M.D., Stempniewicz N., Stengel B., Tonelli M., Umesawa M., Waikar S.S., Wen C.-P., Wetzels J.F.M., Woodward M., Grams M.E., Kovesdy C.P., Levey A.S., Gansevoort R.T., Appel L.J., Greene T., Chen T.K., Chalmers J., Arima H., Perkovic V., Levin A., Djurdjev O., Tang M., Nally J., Navaneethan S.D., Schold J.D., Weldegiorgis M., Herrington W.G., Smith M., Hsu C.Y., Hwang S.-J., Chang A.R., Kirchner H.L., Green J.A., Ho K., Marks A., Prescott G., Clark L.E., Fluck N., Shalev V., Chodick G., Blankestijn P.J., Van Zuilen A., Van den Brand J.A., Sarnak M.J., Bottinger E., Nadkarni G.N., Sumida K., de Zeeuw D., Brenner B., Qureshi A.R., Elinder C.-G., Runesson B., Evans M., Segelmark M., Stendahl M., Schon S., Naimark D.M., Tangri N., Sud M., Hirayama A., Bilo H.J., Landman G.W., Van Hateren K.J., Kleefstra N., Hallan S.I., Ballew S.H., Chen J., Kwak L., Surapaneni A., Parving H.-H., Rodby R.A., Rohde R.D., Lewis J.B., Lewis E., Perrone R.D., Abebe K.Z., Hou F.F., Xie D., Hunsicker L.G., Imai E., Kobayashi F., Makino H., Remuzzi G., Ruggenenti P., Eckardt K.-U., Gudmundsdottir H., Ichikawa K., Collins J.F., Drury P.L., Ellis S.G., Nadukuru R., Metzger M., Flamant M., Houillier P., Haymann J.-P., Froissart M., Kenealy T., Elley R.C., Cuddeback J.K., Ciemins E.L., Stempniewicz R., Nelson R.G., Knowler W.C., Bakker S.J., Major R.W., Medcalf J.F., Shepherd D., Barrett-Connor E., Bergstrom J., Ix J.H., Molnar M.Z., Maciulaitis R., Manley T., Smith K., Stockbridge N., Thompson A., Vetter T., Willis K., Zhang L., Coresh J., Heerspink H.J.L., Sang Y., Matsushita K., Arnlov J., Astor B.C., Black C., Brunskill N.J., Carrero J.-J., Feldman H.I., Fox C.S., Inker L.A., Ishani A., Ito S., Jassal S., Konta T., Polkinghorne K., Romundstad S., Solbu M.D., Stempniewicz N., Stengel B., Tonelli M., Umesawa M., Waikar S.S., Wen C.-P., Wetzels J.F.M., Woodward M., Grams M.E., Kovesdy C.P., Levey A.S., Gansevoort R.T., Appel L.J., Greene T., Chen T.K., Chalmers J., Arima H., Perkovic V., Levin A., Djurdjev O., Tang M., Nally J., Navaneethan S.D., Schold J.D., Weldegiorgis M., Herrington W.G., Smith M., Hsu C.Y., Hwang S.-J., Chang A.R., Kirchner H.L., Green J.A., Ho K., Marks A., Prescott G., Clark L.E., Fluck N., Shalev V., Chodick G., Blankestijn P.J., Van Zuilen A., Van den Brand J.A., Sarnak M.J., Bottinger E., Nadkarni G.N., Sumida K., de Zeeuw D., Brenner B., Qureshi A.R., Elinder C.-G., Runesson B., Evans M., Segelmark M., Stendahl M., Schon S., Naimark D.M., Tangri N., Sud M., Hirayama A., Bilo H.J., Landman G.W., Van Hateren K.J., Kleefstra N., Hallan S.I., Ballew S.H., Chen J., Kwak L., Surapaneni A., Parving H.-H., Rodby R.A., Rohde R.D., Lewis J.B., Lewis E., Perrone R.D., Abebe K.Z., Hou F.F., Xie D., Hunsicker L.G., Imai E., Kobayashi F., Makino H., Remuzzi G., Ruggenenti P., Eckardt K.-U., and Gudmundsdottir H.
Abstract: Background: Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. Method(s): In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. Finding(s): Between July, 2015, and June, 2018, we transferred and analysed data from 28 cohorts in the C
Published: 2019

16. Proton pump inhibitors in kidney disease.

Author: Lazarus B., Grams M.E., Lazarus B., and Grams M.E.
Published: 2019

17. Inconsistencies in Reporting Studies of Lactic Acidosis - Reply.

Author: Lazarus B., Grams M.E., Lazarus B., and Grams M.E.
Published: 2019

18. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies.

Author: Ichikawa K., Collins J.F., Drury P.L., Ellis S.G., Nadukuru R., Metzger M., Flamant M., Houillier P., Haymann J.-P., Froissart M., Kenealy T., Elley R.C., Cuddeback J.K., Ciemins E.L., Stempniewicz R., Nelson R.G., Knowler W.C., Bakker S.J., Major R.W., Medcalf J.F., Shepherd D., Barrett-Connor E., Bergstrom J., Ix J.H., Molnar M.Z., Maciulaitis R., Manley T., Smith K., Stockbridge N., Thompson A., Vetter T., Willis K., Zhang L., Coresh J., Heerspink H.J.L., Sang Y., Matsushita K., Arnlov J., Astor B.C., Black C., Brunskill N.J., Carrero J.-J., Feldman H.I., Fox C.S., Inker L.A., Ishani A., Ito S., Jassal S., Konta T., Polkinghorne K., Romundstad S., Solbu M.D., Stempniewicz N., Stengel B., Tonelli M., Umesawa M., Waikar S.S., Wen C.-P., Wetzels J.F.M., Woodward M., Grams M.E., Kovesdy C.P., Levey A.S., Gansevoort R.T., Appel L.J., Greene T., Chen T.K., Chalmers J., Arima H., Perkovic V., Levin A., Djurdjev O., Tang M., Nally J., Navaneethan S.D., Schold J.D., Weldegiorgis M., Herrington W.G., Smith M., Hsu C.Y., Hwang S.-J., Chang A.R., Kirchner H.L., Green J.A., Ho K., Marks A., Prescott G., Clark L.E., Fluck N., Shalev V., Chodick G., Blankestijn P.J., Van Zuilen A., Van den Brand J.A., Sarnak M.J., Bottinger E., Nadkarni G.N., Sumida K., de Zeeuw D., Brenner B., Qureshi A.R., Elinder C.-G., Runesson B., Evans M., Segelmark M., Stendahl M., Schon S., Naimark D.M., Tangri N., Sud M., Hirayama A., Bilo H.J., Landman G.W., Van Hateren K.J., Kleefstra N., Hallan S.I., Ballew S.H., Chen J., Kwak L., Surapaneni A., Parving H.-H., Rodby R.A., Rohde R.D., Lewis J.B., Lewis E., Perrone R.D., Abebe K.Z., Hou F.F., Xie D., Hunsicker L.G., Imai E., Kobayashi F., Makino H., Remuzzi G., Ruggenenti P., Eckardt K.-U., Gudmundsdottir H., Ichikawa K., Collins J.F., Drury P.L., Ellis S.G., Nadukuru R., Metzger M., Flamant M., Houillier P., Haymann J.-P., Froissart M., Kenealy T., Elley R.C., Cuddeback J.K., Ciemins E.L., Stempniewicz R., Nelson R.G., Knowler W.C., Bakker S.J., Major R.W., Medcalf J.F., Shepherd D., Barrett-Connor E., Bergstrom J., Ix J.H., Molnar M.Z., Maciulaitis R., Manley T., Smith K., Stockbridge N., Thompson A., Vetter T., Willis K., Zhang L., Coresh J., Heerspink H.J.L., Sang Y., Matsushita K., Arnlov J., Astor B.C., Black C., Brunskill N.J., Carrero J.-J., Feldman H.I., Fox C.S., Inker L.A., Ishani A., Ito S., Jassal S., Konta T., Polkinghorne K., Romundstad S., Solbu M.D., Stempniewicz N., Stengel B., Tonelli M., Umesawa M., Waikar S.S., Wen C.-P., Wetzels J.F.M., Woodward M., Grams M.E., Kovesdy C.P., Levey A.S., Gansevoort R.T., Appel L.J., Greene T., Chen T.K., Chalmers J., Arima H., Perkovic V., Levin A., Djurdjev O., Tang M., Nally J., Navaneethan S.D., Schold J.D., Weldegiorgis M., Herrington W.G., Smith M., Hsu C.Y., Hwang S.-J., Chang A.R., Kirchner H.L., Green J.A., Ho K., Marks A., Prescott G., Clark L.E., Fluck N., Shalev V., Chodick G., Blankestijn P.J., Van Zuilen A., Van den Brand J.A., Sarnak M.J., Bottinger E., Nadkarni G.N., Sumida K., de Zeeuw D., Brenner B., Qureshi A.R., Elinder C.-G., Runesson B., Evans M., Segelmark M., Stendahl M., Schon S., Naimark D.M., Tangri N., Sud M., Hirayama A., Bilo H.J., Landman G.W., Van Hateren K.J., Kleefstra N., Hallan S.I., Ballew S.H., Chen J., Kwak L., Surapaneni A., Parving H.-H., Rodby R.A., Rohde R.D., Lewis J.B., Lewis E., Perrone R.D., Abebe K.Z., Hou F.F., Xie D., Hunsicker L.G., Imai E., Kobayashi F., Makino H., Remuzzi G., Ruggenenti P., Eckardt K.-U., and Gudmundsdottir H.
Abstract: Background: Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. Method(s): In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. Finding(s): Between July, 2015, and June, 2018, we transferred and analysed data from 28 cohorts in the C
Published: 2019

19. Proton pump inhibitors in kidney disease.

Author: Lazarus B., Grams M.E., Lazarus B., and Grams M.E.
Published: 2019

20. Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium

Author: Chang, A.R., Grams, M.E., Ballew, Shoshana H., Bilo, H., Correa, A., Evans, M., Gutierrez, O.M., Hosseinpanah, F., Iseki, K., Kenealy, T., Klein, B., Kronenberg, F., Lee, B.J., Li, Y., Miura, K., Navaneethan, S.D., Roderick, P.J., Valdivielso, J.M., Visseren, F.L., Zhang, L., Gansevoort, R.T., Hallan, S.I., Levey, A.S., Matsushita, K., Wetzels, J.F.M., Shalev, V., Woodward, M., Chang, A.R., Grams, M.E., Ballew, Shoshana H., Bilo, H., Correa, A., Evans, M., Gutierrez, O.M., Hosseinpanah, F., Iseki, K., Kenealy, T., Klein, B., Kronenberg, F., Lee, B.J., Li, Y., Miura, K., Navaneethan, S.D., Roderick, P.J., Valdivielso, J.M., Visseren, F.L., Zhang, L., Gansevoort, R.T., Hallan, S.I., Levey, A.S., Matsushita, K., Wetzels, J.F.M., Shalev, V., and Woodward, M.
Abstract: Contains fulltext : 203008.pdf (publisher's version ) (Open Access), OBJECTIVE: To evaluate the associations between adiposity measures (body mass index, waist circumference, and waist-to-height ratio) with decline in glomerular filtration rate (GFR) and with all cause mortality. DESIGN: Individual participant data meta-analysis. SETTING: Cohorts from 40 countries with data collected between 1970 and 2017. PARTICIPANTS: Adults in 39 general population cohorts (n=5 459 014), of which 21 (n=594 496) had data on waist circumference; six cohorts with high cardiovascular risk (n=84 417); and 18 cohorts with chronic kidney disease (n=91 607). MAIN OUTCOME MEASURES: GFR decline (estimated GFR decline >/=40%, initiation of kidney replacement therapy or estimated GFR <10 mL/min/1.73 m(2)) and all cause mortality. RESULTS: Over a mean follow-up of eight years, 246 607 (5.6%) individuals in the general population cohorts had GFR decline (18 118 (0.4%) end stage kidney disease events) and 782 329 (14.7%) died. Adjusting for age, sex, race, and current smoking, the hazard ratios for GFR decline comparing body mass indices 30, 35, and 40 with body mass index 25 were 1.18 (95% confidence interval 1.09 to 1.27), 1.69 (1.51 to 1.89), and 2.02 (1.80 to 2.27), respectively. Results were similar in all subgroups of estimated GFR. Associations weakened after adjustment for additional comorbidities, with respective hazard ratios of 1.03 (0.95 to 1.11), 1.28 (1.14 to 1.44), and 1.46 (1.28 to 1.67). The association between body mass index and death was J shaped, with the lowest risk at body mass index of 25. In the cohorts with high cardiovascular risk and chronic kidney disease (mean follow-up of six and four years, respectively), risk associations between higher body mass index and GFR decline were weaker than in the general population, and the association between body mass index and death was also J shaped, with the lowest risk between body mass index 25 and 30. In all cohort types, associations between higher waist circumference and higher waist-to-heig
Published: 2019

21. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies

Author: Coresh, J., Heerspink, H.J., Sang, Y., Matsushita, K., Arnlov, J., Astor, B.C., Black, C., Brunskill, N.J., Carrero, J.J., Feldman, H.I., Fox, C.S., Inker, L.A., Ishani, A., Ito, S., Jassal, S., Konta, T., Polkinghorne, K., Romundstad, S., Solbu, M.D., Stempniewicz, N., Stengel, B., Tonelli, M., Umesawa, M., Waikar, S.S., Wen, C.P., Wetzels, J.F.M., Woodward, M., Grams, M.E., Kovesdy, C.P., Levey, A.S., Gansevoort, R.T., Coresh, J., Heerspink, H.J., Sang, Y., Matsushita, K., Arnlov, J., Astor, B.C., Black, C., Brunskill, N.J., Carrero, J.J., Feldman, H.I., Fox, C.S., Inker, L.A., Ishani, A., Ito, S., Jassal, S., Konta, T., Polkinghorne, K., Romundstad, S., Solbu, M.D., Stempniewicz, N., Stengel, B., Tonelli, M., Umesawa, M., Waikar, S.S., Wen, C.P., Wetzels, J.F.M., Woodward, M., Grams, M.E., Kovesdy, C.P., Levey, A.S., and Gansevoort, R.T.
Abstract: Item does not contain fulltext, BACKGROUND: Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. METHODS: In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. FINDINGS: Between July, 2015, and June, 2018, we transferred and analysed data from 28 cohorts in the CKD-P
Published: 2019

22. Evaluating Glomerular Filtration Rate Slope as a Surrogate End Point for ESKD in Clinical Trials: An Individual Participant Meta-Analysis of Observational Data

Author: Grams, M.E., Sang, Y., Ballew, Shoshana H., Matsushita, K., Astor, B.C., Carrero, J.J., Chang, A.R., Inker, L.A., Kenealy, T., Kovesdy, C.P., Lee, B.J., Levin, A., Naimark, D., Pena, M.J., Schold, J.D., Shalev, V., Wetzels, J.F.M., Woodward, M., Gansevoort, R.T., Levey, A.S., Coresh, J., Grams, M.E., Sang, Y., Ballew, Shoshana H., Matsushita, K., Astor, B.C., Carrero, J.J., Chang, A.R., Inker, L.A., Kenealy, T., Kovesdy, C.P., Lee, B.J., Levin, A., Naimark, D., Pena, M.J., Schold, J.D., Shalev, V., Wetzels, J.F.M., Woodward, M., Gansevoort, R.T., Levey, A.S., and Coresh, J.
Abstract: Item does not contain fulltext, BACKGROUND: Decline in eGFR is a biologically plausible surrogate end point for the progression of CKD in clinical trials. However, it must first be tested to ensure strong associations with clinical outcomes in diverse populations, including patients with higher eGFR. METHODS: To investigate the association between 1-, 2-, and 3-year changes in eGFR (slope) with clinical outcomes over the long term, we conducted a random effects meta-analysis of 3,758,551 participants with baseline eGFR>/=60 ml/min per 1.73 m(2) and 122,664 participants with eGFR<60 ml/min per 1.73 m(2) from 14 cohorts followed for an average of 4.2 years. RESULTS: Slower eGFR decline by 0.75 ml/min per 1.73 m(2) per year over 2 years was associated with lower risk of ESKD in participants with baseline eGFR>/=60 ml/min per 1.73 m(2) (adjusted hazard ratio, 0.70; 95% CI, 0.68 to 0.72) and eGFR<60 ml/min per 1.73 m(2) (0.71; 95% CI, 0.68 to 0.74). The relationship was stronger with 3-year slope. For a rapidly progressing population with predicted 5-year risk of ESKD of 8.3%, an intervention that reduced eGFR decline by 0.75 ml/min per 1.73 m(2) per year over 2 years would reduce the ESKD risk by 1.6%. For a hypothetical low-risk population with a predicted 5-year ESKD risk of 0.58%, the same intervention would reduce the risk by only 0.13%. CONCLUSIONS: Slower decline in eGFR was associated with lower risk of subsequent ESKD, even in participants with eGFR>/=60 ml/min per 1.73 m(2), but those with the highest risk would be expected to benefit the most.
Published: 2019

23. Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium

Author: Chang, A.R., Grams, M.E., Ballew, Shoshana H., Bilo, H., Correa, A., Evans, M., Gutierrez, O.M., Hosseinpanah, F., Iseki, K., Kenealy, T., Klein, B., Kronenberg, F., Lee, B.J., Li, Y., Miura, K., Navaneethan, S.D., Roderick, P.J., Valdivielso, J.M., Visseren, F.L., Zhang, L., Gansevoort, R.T., Hallan, S.I., Levey, A.S., Matsushita, K., Wetzels, J.F.M., Shalev, V., Woodward, M., Chang, A.R., Grams, M.E., Ballew, Shoshana H., Bilo, H., Correa, A., Evans, M., Gutierrez, O.M., Hosseinpanah, F., Iseki, K., Kenealy, T., Klein, B., Kronenberg, F., Lee, B.J., Li, Y., Miura, K., Navaneethan, S.D., Roderick, P.J., Valdivielso, J.M., Visseren, F.L., Zhang, L., Gansevoort, R.T., Hallan, S.I., Levey, A.S., Matsushita, K., Wetzels, J.F.M., Shalev, V., and Woodward, M.
Abstract: Contains fulltext : 203008.pdf (publisher's version ) (Open Access), OBJECTIVE: To evaluate the associations between adiposity measures (body mass index, waist circumference, and waist-to-height ratio) with decline in glomerular filtration rate (GFR) and with all cause mortality. DESIGN: Individual participant data meta-analysis. SETTING: Cohorts from 40 countries with data collected between 1970 and 2017. PARTICIPANTS: Adults in 39 general population cohorts (n=5 459 014), of which 21 (n=594 496) had data on waist circumference; six cohorts with high cardiovascular risk (n=84 417); and 18 cohorts with chronic kidney disease (n=91 607). MAIN OUTCOME MEASURES: GFR decline (estimated GFR decline >/=40%, initiation of kidney replacement therapy or estimated GFR <10 mL/min/1.73 m(2)) and all cause mortality. RESULTS: Over a mean follow-up of eight years, 246 607 (5.6%) individuals in the general population cohorts had GFR decline (18 118 (0.4%) end stage kidney disease events) and 782 329 (14.7%) died. Adjusting for age, sex, race, and current smoking, the hazard ratios for GFR decline comparing body mass indices 30, 35, and 40 with body mass index 25 were 1.18 (95% confidence interval 1.09 to 1.27), 1.69 (1.51 to 1.89), and 2.02 (1.80 to 2.27), respectively. Results were similar in all subgroups of estimated GFR. Associations weakened after adjustment for additional comorbidities, with respective hazard ratios of 1.03 (0.95 to 1.11), 1.28 (1.14 to 1.44), and 1.46 (1.28 to 1.67). The association between body mass index and death was J shaped, with the lowest risk at body mass index of 25. In the cohorts with high cardiovascular risk and chronic kidney disease (mean follow-up of six and four years, respectively), risk associations between higher body mass index and GFR decline were weaker than in the general population, and the association between body mass index and death was also J shaped, with the lowest risk between body mass index 25 and 30. In all cohort types, associations between higher waist circumference and higher waist-to-heig
Published: 2019

24. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies

Author: Coresh, J., Heerspink, H.J., Sang, Y., Matsushita, K., Arnlov, J., Astor, B.C., Black, C., Brunskill, N.J., Carrero, J.J., Feldman, H.I., Fox, C.S., Inker, L.A., Ishani, A., Ito, S., Jassal, S., Konta, T., Polkinghorne, K., Romundstad, S., Solbu, M.D., Stempniewicz, N., Stengel, B., Tonelli, M., Umesawa, M., Waikar, S.S., Wen, C.P., Wetzels, J.F.M., Woodward, M., Grams, M.E., Kovesdy, C.P., Levey, A.S., Gansevoort, R.T., Coresh, J., Heerspink, H.J., Sang, Y., Matsushita, K., Arnlov, J., Astor, B.C., Black, C., Brunskill, N.J., Carrero, J.J., Feldman, H.I., Fox, C.S., Inker, L.A., Ishani, A., Ito, S., Jassal, S., Konta, T., Polkinghorne, K., Romundstad, S., Solbu, M.D., Stempniewicz, N., Stengel, B., Tonelli, M., Umesawa, M., Waikar, S.S., Wen, C.P., Wetzels, J.F.M., Woodward, M., Grams, M.E., Kovesdy, C.P., Levey, A.S., and Gansevoort, R.T.
Abstract: Item does not contain fulltext, BACKGROUND: Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. METHODS: In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. FINDINGS: Between July, 2015, and June, 2018, we transferred and analysed data from 28 cohorts in the CKD-P
Published: 2019

25. Evaluating Glomerular Filtration Rate Slope as a Surrogate End Point for ESKD in Clinical Trials: An Individual Participant Meta-Analysis of Observational Data.

Author: Grams, M.E. and Grams, M.E.
Subjects: Radboudumc 11: Renal disorders RIHS: Radboud Institute for Health Sciences.
Published: 2019

26. Serum potassium and adverse outcomes across the range of kidney function: a CKD Prognosis Consortium meta-analysis

Author: Kovesdy, C.P., Matsushita, K., Sang, Y., Brunskill, N.J., Carrero, J.J., Chodick, G., Hasegawa, T., Heerspink, H.L., Hirayama, A., Landman, G.W., Levin, A., Nitsch, D., Wheeler, D.C., Coresh, J., Hallan, S.I., Wetzels, J.F.M., Shalev, V., Grams, M.E., Kovesdy, C.P., Matsushita, K., Sang, Y., Brunskill, N.J., Carrero, J.J., Chodick, G., Hasegawa, T., Heerspink, H.L., Hirayama, A., Landman, G.W., Levin, A., Nitsch, D., Wheeler, D.C., Coresh, J., Hallan, S.I., Wetzels, J.F.M., Shalev, V., and Grams, M.E.
Abstract: Item does not contain fulltext, Aims: Both hypo- and hyperkalaemia can have immediate deleterious physiological effects, and less is known about long-term risks. The objective was to determine the risks of all-cause mortality, cardiovascular mortality, and end-stage renal disease associated with potassium levels across the range of kidney function and evaluate for consistency across cohorts in a global consortium. Methods and results: We performed an individual-level data meta-analysis of 27 international cohorts [10 general population, 7 high cardiovascular risk, and 10 chronic kidney disease (CKD)] in the CKD Prognosis Consortium. We used Cox regression followed by random-effects meta-analysis to assess the relationship between baseline potassium and adverse outcomes, adjusted for demographic and clinical characteristics, overall and across strata of estimated glomerular filtration rate (eGFR) and albuminuria. We included 1 217 986 participants followed up for a mean of 6.9 years. The average age was 55 +/- 16 years, average eGFR was 83 +/- 23 mL/min/1.73 m2, and 17% had moderate- to-severe increased albuminuria levels. The mean baseline potassium was 4.2 +/- 0.4 mmol/L. The risk of serum potassium of >5.5 mmol/L was related to lower eGFR and higher albuminuria. The risk relationship between potassium levels and adverse outcomes was U-shaped, with the lowest risk at serum potassium of 4-4.5 mmol/L. Compared with a reference of 4.2 mmol/L, the adjusted hazard ratio for all-cause mortality was 1.22 [95% confidence interval (CI) 1.15-1.29] at 5.5 mmol/L and 1.49 (95% CI 1.26-1.76) at 3.0 mmol/L. Risks were similar by eGFR, albuminuria, renin-angiotensin-aldosterone system inhibitor use, and across cohorts. Conclusions: Outpatient potassium levels both above and below the normal range are consistently associated with adverse outcomes, with similar risk relationships across eGFR and albuminuria.
Published: 2018

27. Lactic Acidosis, Metformin Use, and Dose-Response Association - Reply.

Author: Grams M.E., Shin J.-I., Lazarus B., Grams M.E., Shin J.-I., and Lazarus B.
Published: 2018

28. Association of metformin use with risk of lactic acidosis across the range of kidney function: A community-based cohort study.

Author: Lazarus B., Sang Y., Alexander G.C., Secora A., Inker L.A., Coresh J., Chang A.R., Grams M.E., Shin J.-I., Wu A., Lazarus B., Sang Y., Alexander G.C., Secora A., Inker L.A., Coresh J., Chang A.R., Grams M.E., Shin J.-I., and Wu A.
Abstract: Importance: Approximately 1 million patients in the United States with type 2 diabetes mellitus and mild-to-moderate kidney disease do not receive guideline-directed therapy with metformin. This may reflect uncertainty regarding the risk of acidosis in patients with chronic kidney disease. Objective(s): To quantify the association between metformin use and hospitalization with acidosis across the range of estimated glomerular filtration rate (eGFR), accounting for change in eGFR stage over time. Design, Setting, and Participant(s): Community-based cohort of 75 413 patients with diabetes in Geisinger Health System, with time-dependent assessment of eGFR stage from January 2004 until January 2017. Results were replicated in 67 578 new metformin users and 14 439 new sulfonylurea users from 2010 to 2015, sourced from 350 private US health systems. Exposures: Metformin use. Main Outcomes and Measures: Hospitalization with acidosis (International Classification of Diseases, Ninth Revision, Clinical Modification code of 276.2). Result(s): In the primary cohort (n = 75 413), mean (SD) patient age was 60.4 (15.5) years, and 51%(n = 38 480) of the participants were female. There were 2335 hospitalizations with acidosis over a median follow-up of 5.7 years (interquartile range, 2.5-9.9 years). Compared with alternative diabetes management, time-dependent metformin use was not associated with incident acidosis overall (adjusted hazard ratio [HR], 0.98; 95%CI, 0.89-1.08) or in patients with eGFR 45 to 59 mL/min/1.73m2 (adjusted HR, 1.16; 95%CI, 0.95-1.41) and eGFR 30 to 44 mL/min/1.73m2 (adjusted HR, 1.09; 95%CI, 0.83-1.44). On the other hand, metformin use was associated with an increased risk of acidosis at eGFR less than 30 mL/min/1.73m2 (adjusted HR, 2.07; 95%CI, 1.33-3.22). Results were consistent when new metformin users were compared with new sulfonylurea users (adjusted HR for eGFR 30-44 mL/min/1.73m2, 0.77; 95%CI, 0.29-2.05), in a propensity-matched cohort (adjusted HR
Published: 2018

29. Global, regional, and national incidence, prevalence, and years lived with disability for 354 Diseases and Injuries for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017.

Author: She J., Thankappan K.R., Thomas N., Tilahun B., To Q.G., Tonelli M., Topor-Madry R., Topouzis F., Torre A.E., Tortajada-Girbes M., Touvier M., Tovani-Palone M.R., Towbin J.A., Tran B.X., Tran K.B., Troeger C.E., Truelsen T.C., Tsilimbaris M.K., Tsoi D., Tudor Car L., Tuzcu E.M., Ukwaja K.N., Ullah I., Undurraga E.A., Unutzer J., Updike R.L., Usman M.S., Uthman O.A., Vaduganathan M., Vaezi A., Valdez P.R., Varughese S., Vasankari T.J., Venketasubramanian N., Villafaina S., Violante F.S., Vladimirov S.K., Vlassov V., Vollset S.E., Vosoughi K., Vujcic I.S., Wagnew F.S., Waheed Y., Waller S.G., Wang Y., Wang Y.-P., Weiderpass E., Weintraub R.G., Weiss D.J., Weldegebreal F., Weldegwergs K.G., Werdecker A., West T.E., Whiteford H.A., Widecka J., Wijeratne T., Wilner L.B., Wilson S., Winkler A.S., Wiyeh A.B., Wiysonge C.S., Wolfe C.D.A., Woolf A.D., Wu S., Wu Y.-C., Wyper G.M.A., Xavier D., Xu G., Yadgir S., Yadollahpour A., Yahyazadeh Jabbari S.H., Yamada T., Yan L.L., Yano Y., Yaseri M., Yasin Y.J., Yeshaneh A., Yimer E.M., Yip P., Yisma E., Yonemoto N., Yoon S.-J., Yotebieng M., Younis M.Z., Yousefifard M., Yu C., Zadnik V., Zaidi Z., Zaman S.B., Zamani M., Zare Z., Zeleke A.J., Zenebe Z.M., Zhang K., Zhao Z., Zhou M., Zodpey S., Zucker I., Vos T., Murray C.J.L., De Courten B., Fox J., James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., Abdollahpour I., Abdulkader R.S., Abebe Z., Abera S.F., Abil O.Z., Abraha H.N., Abu-Raddad L.J., Abu-Rmeileh N.M.E., Accrombessi M.M.K., Acharya D., Acharya P., Ackerman I.N., Adamu A.A., Adebayo O.M., Adekanmbi V., Adetokunboh O.O., Adib M.G., Adsuar J.C., Afanvi K.A., Afarideh M., Afshin A., Agarwal G., Agesa K.M., Aggarwal R., Aghayan S.A., Agrawal S., Ahmadi A., Ahmadi M., Ahmadieh H., Ahmed M.B., Aichour A.N., Aichour I., Aichour M.T.E., Akinyemiju T., Akseer N., Al-Aly Z., Al-Eyadhy A., Al-Mekhlafi H.M., Al-Raddadi R.M., Alahdab F., Alam K., Alam T., Alashi A., Alavian S.M., Alene K.A., Alijanzadeh M., Alizadeh-Navaei R., Aljunid S.M., Alkerwi A., Alla F., Allebeck P., Alouani M.M.L., Altirkawi K., Alvis-Guzman N., Amare A.T., Aminde L.N., Ammar W., Amoako Y.A., Anber N.H., Andrei C.L., Androudi S., Animut M.D., Anjomshoa M., Ansha M.G., Antonio C.A.T., Anwari P., Arabloo J., Arauz A., Aremu O., Ariani F., Armoon B., Arnlov J., Arora A., Artaman A., Aryal K.K., Asayesh H., Asghar R.J., Ataro Z., Atre S.R., Ausloos M., Avila-Burgos L., Avokpaho E.F.G.A., Awasthi A., Ayala Quintanilla B.P., Ayer R., Azzopardi P.S., Babazadeh A., Badali H., Badawi A., Bali A.G., Ballesteros K.E., Ballew S.H., Banach M., Banoub J.A.M., Banstola A., Barac A., Barboza M.A., Barker-Collo S.L., Barnighausen T.W., Barrero L.H., Baune B.T., Bazargan-Hejazi S., Bedi N., Beghi E., Behzadifar M., Bejot Y., Belachew A.B., Belay Y.A., Bell M.L., Bello A.K., Bensenor I.M., Bernabe E., Bernstein R.S., Beuran M., Beyranvand T., Bhala N., Bhattarai S., Bhaumik S., Bhutta Z.A., Biadgo B., Bijani A., Bikbov B., Bilano V., Bililign N., Bin Sayeed M.S., Bisanzio D., Blacker B.F., Blyth F.M., Bou-Orm I.R., Boufous S., Bourne R., Brady O.J., Brainin M., Brant L.C., Brazinova A., Breitborde N.J.K., Brenner H., Briant P.S., Briggs A.M., Briko A.N., Britton G., Brugha T., Buchbinder R., Busse R., Butt Z.A., Cahuana-Hurtado L., Cano J., Cardenas R., Carrero J.J., Carter A., Carvalho F., Castaneda-Orjuela C.A., Castillo Rivas J., Castro F., Catala-Lopez F., Cercy K.M., Cerin E., Chaiah Y., Chang A.R., Chang H.-Y., Chang J.-C., Charlson F.J., Chattopadhyay A., Chattu V.K., Chaturvedi P., Chiang P.P.-C., Chin K.L., Chitheer A., Choi J.-Y.J., Chowdhury R., Christensen H., Christopher D.J., Cicuttini F.M., Ciobanu L.G., Cirillo M., Claro R.M., Collado-Mateo D., Cooper C., Coresh J., Cortesi P.A., Cortinovis M., Costa M., Cousin E., Criqui M.H., Cromwell E.A., Cross M., Crump J.A., Dadi A.F., Dandona L., Dandona R., Dargan P.I., Daryani A., Das Gupta R., Das Neves J., Dasa T.T., Davey G., Davis A.C., Davitoiu D.V., De La Hoz F.P., De Leo D., De Neve J.-W., Degefa M.G., Degenhardt L., Deiparine S., Dellavalle R.P., Demoz G.T., Deribe K., Dervenis N., Des Jarlais D.C., Dessie G.A., Dey S., Dharmaratne S.D., Dinberu M.T., Dirac M.A., Djalalinia S., Doan L., Dokova K., Doku D.T., Dorsey E.R., Doyle K.E., Driscoll T.R., Dubey M., Dubljanin E., Duken E.E., Duncan B.B., Duraes A.R., Ebrahimi H., Ebrahimpour S., Echko M.M., Edvardsson D., Effiong A., Ehrlich J.R., El Bcheraoui C., El Sayed Zaki M., El-Khatib Z., Elkout H., Elyazar I.R.F., Enayati A., Endries A.Y., Er B., Erskine H.E., Eshrati B., Eskandarieh S., Esteghamati A., Esteghamati S., Fakhim H., Fallah Omrani V., Faramarzi M., Fareed M., Farhadi F., Farid T.A., Farinha C.S.E., Farioli A., Faro A., Farvid M.S., Farzadfar F., Feigin V.L., Fentahun N., Fereshtehnejad S.-M., Fernandes E., Fernandes J.C., Ferrari A.J., Feyissa G.T., Filip I., Fischer F., Fitzmaurice C., Foigt N.A., Foreman 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H.W., Hoffman H.J., Hole M.K., Homaie Rad E., Hoogar P., Hosgood H.D., Hosseini S.M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Hotez P.J., Hoy D.G., Hsairi M., Htet A.S., Hu G., Huang J.J., Huynh C.K., Iburg K.M., Ikeda C.T., Ileanu B., Ilesanmi O.S., Iqbal U., Irvani S.S.N., Irvine C.M.S., Islam S., Islami F., Jacobsen K.H., Jahangiry L., Jahanmehr N., Jain S.K., Jakovljevic M., Javanbakht M., Jayatilleke A.U., Jeemon P., Jha R.P., Jha V., Ji J.S., Johnson C.O., Jonas J.B., Jozwiak J.J., Jungari S.B., Jurisson M., Kabir Z., Kadel R., Kahsay A., Kalani R., Kanchan T., Karami M., Karami Matin B., Karch A., Karema C., Karimi N., Karimi S.M., Kasaeian A., Kassa D.H., Kassa G.M., Kassa T.D., Kassebaum N.J., Katikireddi S.V., Kawakami N., Kazemi Karyani A., Keighobadi M.M., Keiyoro P.N., Kemmer L., Kemp G.R., Kengne A.P., Keren A., Khader Y.S., Khafaei B., Khafaie M.A., Khajavi A., Khalil I.A., Khan E.A., Khan M.S., Khan M.A., Khang Y.-H., Khazaei M., Khoja A.T., Khosravi A., Khosravi M.H., Kiadaliri A.A., Kiirithio D.N., Kim C.-I., Kim D., Kim P., Kim Y.-E., Kim Y.J., Kimokoti R.W., Kinfu Y., Kisa A., Kissimova-Skarbek K., Kivimaki M., Knudsen A.K.S., Kocarnik J.M., Kochhar S., Kokubo Y., Kolola T., Kopec J.A., Kosen S., Kotsakis G.A., Koul P.A., Koyanagi A., Kravchenko M.A., Krishan K., Krohn K.J., Kuate Defo B., Kucuk Bicer B., Kumar G.A., Kumar M., Kyu H.H., Lad D.P., Lad S.D., Lafranconi A., Lalloo R., Lallukka T., Lami F.H., Lansingh V.C., Latifi A., Lau K.M.-M., Lazarus J.V., Leasher J.L., Ledesma J.R., Lee P.H., Leigh J., Leung J., Levi M., Lewycka S., Li S., Li Y., Liao Y., Liben M.L., Lim L.-L., Lim S.S., Liu S., Lodha R., Looker K.J., Lopez A.D., Lorkowski S., Lotufo P.A., Low N., Lozano R., Lucas T.C.D., Lucchesi L.R., Lunevicius R., Lyons R.A., Ma S., Macarayan E.R.K., Mackay M.T., Madotto F., Magdy Abd El Razek H., Magdy Abd El Razek M., Maghavani D.P., Mahotra N.B., Mai H.T., Majdan M., Majdzadeh R., Majeed A., Malekzadeh R., Malta D.C., Mamun A.A., Manda A.-L., Manguerra H., Manhertz T., Mansournia M.A., Mantovani L.G., Mapoma C.C., Maravilla J.C., Marcenes W., Marks A., Martins-Melo F.R., Martopullo I., Marz W., Marzan M.B., Mashamba-Thompson T.P., Massenburg B.B., Mathur M.R., Matsushita K., Maulik P.K., Mazidi M., McAlinden C., McGrath J.J., McKee M., Mehndiratta M.M., Mehrotra R., Mehta K.M., Mehta V., Mejia-Rodriguez F., Mekonen T., Melese A., Melku M., Meltzer M., Memiah P.T.N., Memish Z.A., Mendoza W., Mengistu D.T., Mengistu G., Mensah G.A., Mereta S.T., Meretoja A., Meretoja T.J., Mestrovic T., Mezerji N.M.G., Miazgowski B., Miazgowski T., Millear A.I., Miller T.R., Miltz B., Mini G.K., Mirarefin M., Mirrakhimov E.M., Misganaw A.T., Mitchell P.B., Mitiku H., Moazen B., Mohajer B., Mohammad K.A., Mohammadifard N., Mohammadnia-Afrouzi M., Mohammed M.A., Mohammed S., Mohebi F., Moitra M., Mokdad A.H., Molokhia M., Monasta L., Moodley Y., Moosazadeh M., Moradi G., Moradi-Lakeh M., Moradinazar M., Moraga P., Morawska L., Moreno Velasquez I., Morgado-Da-Costa J., Morrison S.D., Moschos M.M., Mousavi S.M., Mruts K.B., Muche A.A., Muchie K.F., Mueller U.O., Muhammed O.S., Mukhopadhyay S., Muller K., Mumford J.E., Murhekar M., Musa J., Musa K.I., Mustafa G., Nabhan A.F., Nagata C., Naghavi M., Naheed A., Nahvijou A., Naik G., Naik N., Najafi F., Naldi L., Nam H.S., Nangia V., Nansseu J.R., Nascimento B.R., Natarajan G., Neamati N., Negoi I., Negoi R.I., Neupane S., Newton C.R.J., Ngunjiri J.W., Nguyen A.Q., Nguyen H.L.T., Nguyen H.T., Nguyen L.H., Nguyen M., Nguyen N.B., Nguyen S.H., Nichols E., Ningrum D.N.A., Nixon M.R., Nolutshungu N., Nomura S., Norheim O.F., Noroozi M., Norrving B., Noubiap J.J., Nouri H.R., Nourollahpour Shiadeh M., Nowroozi M.R., Nsoesie E.O., Nyasulu P.S., Odell C.M., Ofori-Asenso R., Ogbo F.A., Oh I.-H., Oladimeji O., Olagunju A.T., Olagunju T.O., Olivares P.R., Olsen H.E., Olusanya B.O., Ong K.L., Ong S.K., Oren E., Ortiz A., Ota E., Otstavnov S.S., overland S., Owolabi M.O., Mahesh P.A., Pacella R., Pakpour A.H., Pana A., Panda-Jonas S., Parisi A., Park E.-K., Parry C.D.H., Patel S., Pati S., Patil S.T., Patle A., Patton G.C., Paturi V.R., Paulson K.R., Pearce N., Pereira D.M., Perico N., Pesudovs K., Pham H.Q., Phillips M.R., Pigott D.M., Pillay J.D., Piradov M.A., Pirsaheb M., Pishgar F., Plana-Ripoll O., Plass D., Polinder S., Popova S., Postma M.J., Pourshams A., Poustchi H., Prabhakaran D., Prakash S., Prakash V., Purcell C.A., Purwar M.B., Qorbani M., Quistberg D.A., Radfar A., Rafay A., Rafiei A., Rahim F., Rahimi K., Rahimi-Movaghar A., Rahimi-Movaghar V., Rahman M., Ur Rahman M.H., Rahman M.A., Rahman S.U., Rai R.K., Rajati F., Ram U., Ranjan P., Ranta A., Rao P.C., Rawaf D.L., Rawaf S., Reddy K.S., Reiner R.C., Reinig N., Reitsma M.B., Remuzzi G., Renzaho A.M.N., Resnikoff S., Rezaei S., Rezai M.S., Ribeiro A.L.P., Robinson S.R., Roever L., Ronfani L., Roshandel G., Rostami A., Roth G.A., Roy A., Rubagotti E., Sachdev P.S., Sadat N., Saddik B., Sadeghi E., Saeedi Moghaddam S., Safari H., Safari Y., Safari-Faramani R., Safdarian M., Safi S., Safiri S., Sagar R., Sahebkar A., Sahraian M.A., Sajadi H.S., Salam N., Salama J.S., Salamati P., Saleem K., Saleem Z., Salimi Y., Salomon J.A., Salvi S.S., Salz I., Samy A.M., Sanabria J., Sang Y., Santomauro D.F., Santos I.S., Santos J.V., Santric Milicevic M.M., Sao Jose B.P., Sardana M., Sarker A.R., Sarrafzadegan N., Sartorius B., Sarvi S., Sathian B., Satpathy M., Sawant A.R., Sawhney M., Saxena S., Saylan M., Schaeffner E., Schmidt M.I., Schneider I.J.C., Schottker B., Schwebel D.C., Schwendicke F., Scott J.G., Sekerija M., Sepanlou S.G., Servan-Mori E., Seyedmousavi S., Shabaninejad H., Shafieesabet A., Shahbazi M., Shaheen A.A., Shaikh M.A., Shams-Beyranvand M., Shamsi M., Shamsizadeh M., Sharafi H., Sharafi K., Sharif M., Sharif-Alhoseini M., Sharma M., Sharma R., Sheikh A., Shi P., Shibuya K., Shigematsu M., Shiri R., Shirkoohi R., Shishani K., Shiue I., Shokraneh F., Shoman H., Shrime M.G., Si S., Siabani S., Siddiqi T.J., Sigfusdottir I.D., Sigurvinsdottir R., Silva J.P., Silveira D.G.A., Singam N.S.V., Singh J.A., Singh N.P., Singh V., Sinha D.N., Skiadaresi E., Slepak E.L.N., Sliwa K., Smith D.L., Smith M., Soares Filho A.M., Sobaih B.H., Sobhani S., Sobngwi E., Soneji S.S., Soofi M., Soosaraei M., Sorensen R.J.D., Soriano J.B., Soyiri I.N., Sposato L.A., Sreeramareddy C.T., Srinivasan V., Stanaway J.D., Stein D.J., Steiner C., Steiner T.J., Stokes M.A., Stovner L.J., Subart M.L., Sudaryanto A., Sufiyan M.B., Sunguya B.F., Sur P.J., Sutradhar I., Sykes B.L., Sylte D.O., Tabares-Seisdedos R., Tadakamadla S.K., Tadesse B.T., Tandon N., Tassew S.G., Tavakkoli M., Taveira N., Taylor H.R., Tehrani-Banihashemi A., Tekalign T.G., Tekelemedhin S.W., Tekle M.G., Temesgen H., Temsah M.-H., Temsah O., Terkawi A.S., and Teweldemedhin M.
Abstract: Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Method(s): We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we
Published: 2018

30. Lactic Acidosis, Metformin Use, and Dose-Response Association - Reply.

Author: Grams M.E., Shin J.-I., Lazarus B., Grams M.E., Shin J.-I., and Lazarus B.
Published: 2018

31. Association of metformin use with risk of lactic acidosis across the range of kidney function: A community-based cohort study.

Author: Lazarus B., Sang Y., Alexander G.C., Secora A., Inker L.A., Coresh J., Chang A.R., Grams M.E., Shin J.-I., Wu A., Lazarus B., Sang Y., Alexander G.C., Secora A., Inker L.A., Coresh J., Chang A.R., Grams M.E., Shin J.-I., and Wu A.
Abstract: Importance: Approximately 1 million patients in the United States with type 2 diabetes mellitus and mild-to-moderate kidney disease do not receive guideline-directed therapy with metformin. This may reflect uncertainty regarding the risk of acidosis in patients with chronic kidney disease. Objective(s): To quantify the association between metformin use and hospitalization with acidosis across the range of estimated glomerular filtration rate (eGFR), accounting for change in eGFR stage over time. Design, Setting, and Participant(s): Community-based cohort of 75 413 patients with diabetes in Geisinger Health System, with time-dependent assessment of eGFR stage from January 2004 until January 2017. Results were replicated in 67 578 new metformin users and 14 439 new sulfonylurea users from 2010 to 2015, sourced from 350 private US health systems. Exposures: Metformin use. Main Outcomes and Measures: Hospitalization with acidosis (International Classification of Diseases, Ninth Revision, Clinical Modification code of 276.2). Result(s): In the primary cohort (n = 75 413), mean (SD) patient age was 60.4 (15.5) years, and 51%(n = 38 480) of the participants were female. There were 2335 hospitalizations with acidosis over a median follow-up of 5.7 years (interquartile range, 2.5-9.9 years). Compared with alternative diabetes management, time-dependent metformin use was not associated with incident acidosis overall (adjusted hazard ratio [HR], 0.98; 95%CI, 0.89-1.08) or in patients with eGFR 45 to 59 mL/min/1.73m2 (adjusted HR, 1.16; 95%CI, 0.95-1.41) and eGFR 30 to 44 mL/min/1.73m2 (adjusted HR, 1.09; 95%CI, 0.83-1.44). On the other hand, metformin use was associated with an increased risk of acidosis at eGFR less than 30 mL/min/1.73m2 (adjusted HR, 2.07; 95%CI, 1.33-3.22). Results were consistent when new metformin users were compared with new sulfonylurea users (adjusted HR for eGFR 30-44 mL/min/1.73m2, 0.77; 95%CI, 0.29-2.05), in a propensity-matched cohort (adjusted HR
Published: 2018

32. Global, regional, and national incidence, prevalence, and years lived with disability for 354 Diseases and Injuries for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017.

Author: She J., Thankappan K.R., Thomas N., Tilahun B., To Q.G., Tonelli M., Topor-Madry R., Topouzis F., Torre A.E., Tortajada-Girbes M., Touvier M., Tovani-Palone M.R., Towbin J.A., Tran B.X., Tran K.B., Troeger C.E., Truelsen T.C., Tsilimbaris M.K., Tsoi D., Tudor Car L., Tuzcu E.M., Ukwaja K.N., Ullah I., Undurraga E.A., Unutzer J., Updike R.L., Usman M.S., Uthman O.A., Vaduganathan M., Vaezi A., Valdez P.R., Varughese S., Vasankari T.J., Venketasubramanian N., Villafaina S., Violante F.S., Vladimirov S.K., Vlassov V., Vollset S.E., Vosoughi K., Vujcic I.S., Wagnew F.S., Waheed Y., Waller S.G., Wang Y., Wang Y.-P., Weiderpass E., Weintraub R.G., Weiss D.J., Weldegebreal F., Weldegwergs K.G., Werdecker A., West T.E., Whiteford H.A., Widecka J., Wijeratne T., Wilner L.B., Wilson S., Winkler A.S., Wiyeh A.B., Wiysonge C.S., Wolfe C.D.A., Woolf A.D., Wu S., Wu Y.-C., Wyper G.M.A., Xavier D., Xu G., Yadgir S., Yadollahpour A., Yahyazadeh Jabbari S.H., Yamada T., Yan L.L., Yano Y., Yaseri M., Yasin Y.J., Yeshaneh A., Yimer E.M., Yip P., Yisma E., Yonemoto N., Yoon S.-J., Yotebieng M., Younis M.Z., Yousefifard M., Yu C., Zadnik V., Zaidi Z., Zaman S.B., Zamani M., Zare Z., Zeleke A.J., Zenebe Z.M., Zhang K., Zhao Z., Zhou M., Zodpey S., Zucker I., Vos T., Murray C.J.L., De Courten B., Fox J., James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., Abdollahpour I., Abdulkader R.S., Abebe Z., Abera S.F., Abil O.Z., Abraha H.N., Abu-Raddad L.J., Abu-Rmeileh N.M.E., Accrombessi M.M.K., Acharya D., Acharya P., Ackerman I.N., Adamu A.A., Adebayo O.M., Adekanmbi V., Adetokunboh O.O., Adib M.G., Adsuar J.C., Afanvi K.A., Afarideh M., Afshin A., Agarwal G., Agesa K.M., Aggarwal R., Aghayan S.A., Agrawal S., Ahmadi A., Ahmadi M., Ahmadieh H., Ahmed M.B., Aichour A.N., Aichour I., Aichour M.T.E., Akinyemiju T., Akseer N., Al-Aly Z., Al-Eyadhy A., Al-Mekhlafi H.M., Al-Raddadi R.M., Alahdab F., Alam K., Alam T., Alashi A., Alavian S.M., Alene K.A., Alijanzadeh M., Alizadeh-Navaei R., Aljunid S.M., Alkerwi A., Alla F., Allebeck P., Alouani M.M.L., Altirkawi K., Alvis-Guzman N., Amare A.T., Aminde L.N., Ammar W., Amoako Y.A., Anber N.H., Andrei C.L., Androudi S., Animut M.D., Anjomshoa M., Ansha M.G., Antonio C.A.T., Anwari P., Arabloo J., Arauz A., Aremu O., Ariani F., Armoon B., Arnlov J., Arora A., Artaman A., Aryal K.K., Asayesh H., Asghar R.J., Ataro Z., Atre S.R., Ausloos M., Avila-Burgos L., Avokpaho E.F.G.A., Awasthi A., Ayala Quintanilla B.P., Ayer R., Azzopardi P.S., Babazadeh A., Badali H., Badawi A., Bali A.G., Ballesteros K.E., Ballew S.H., Banach M., Banoub J.A.M., Banstola A., Barac A., Barboza M.A., Barker-Collo S.L., Barnighausen T.W., Barrero L.H., Baune B.T., Bazargan-Hejazi S., Bedi N., Beghi E., Behzadifar M., Bejot Y., Belachew A.B., Belay Y.A., Bell M.L., Bello A.K., Bensenor I.M., Bernabe E., Bernstein R.S., Beuran M., Beyranvand T., Bhala N., Bhattarai S., Bhaumik S., Bhutta Z.A., Biadgo B., Bijani A., Bikbov B., Bilano V., Bililign N., Bin Sayeed M.S., Bisanzio D., Blacker B.F., Blyth F.M., Bou-Orm I.R., Boufous S., Bourne R., Brady O.J., Brainin M., Brant L.C., Brazinova A., Breitborde N.J.K., Brenner H., Briant P.S., Briggs A.M., Briko A.N., Britton G., Brugha T., Buchbinder R., Busse R., Butt Z.A., Cahuana-Hurtado L., Cano J., Cardenas R., Carrero J.J., Carter A., Carvalho F., Castaneda-Orjuela C.A., Castillo Rivas J., Castro F., Catala-Lopez F., Cercy K.M., Cerin E., Chaiah Y., Chang A.R., Chang H.-Y., Chang J.-C., Charlson F.J., Chattopadhyay A., Chattu V.K., Chaturvedi P., Chiang P.P.-C., Chin K.L., Chitheer A., Choi J.-Y.J., Chowdhury R., Christensen H., Christopher D.J., Cicuttini F.M., Ciobanu L.G., Cirillo M., Claro R.M., Collado-Mateo D., Cooper C., Coresh J., Cortesi P.A., Cortinovis M., Costa M., Cousin E., Criqui M.H., Cromwell E.A., Cross M., Crump J.A., Dadi A.F., Dandona L., Dandona R., Dargan P.I., Daryani A., Das Gupta R., Das Neves J., Dasa T.T., Davey G., Davis A.C., Davitoiu D.V., De La Hoz F.P., De Leo D., De Neve J.-W., Degefa M.G., Degenhardt L., Deiparine S., Dellavalle R.P., Demoz G.T., Deribe K., Dervenis N., Des Jarlais D.C., Dessie G.A., Dey S., Dharmaratne S.D., Dinberu M.T., Dirac M.A., Djalalinia S., Doan L., Dokova K., Doku D.T., Dorsey E.R., Doyle K.E., Driscoll T.R., Dubey M., Dubljanin E., Duken E.E., Duncan B.B., Duraes A.R., Ebrahimi H., Ebrahimpour S., Echko M.M., Edvardsson D., Effiong A., Ehrlich J.R., El Bcheraoui C., El Sayed Zaki M., El-Khatib Z., Elkout H., Elyazar I.R.F., Enayati A., Endries A.Y., Er B., Erskine H.E., Eshrati B., Eskandarieh S., Esteghamati A., Esteghamati S., Fakhim H., Fallah Omrani V., Faramarzi M., Fareed M., Farhadi F., Farid T.A., Farinha C.S.E., Farioli A., Faro A., Farvid M.S., Farzadfar F., Feigin V.L., Fentahun N., Fereshtehnejad S.-M., Fernandes E., Fernandes J.C., Ferrari A.J., Feyissa G.T., Filip I., Fischer F., Fitzmaurice C., Foigt N.A., Foreman K.J., Frank T.D., Fukumoto T., Fullman N., Furst T., Furtado J.M., Futran N.D., Gall S., Ganji M., Gankpe F.G., Garcia-Basteiro A.L., Gardner W.M., Gebre A.K., Gebremedhin A.T., Gebremichael T.G., Gelano T.F., Geleijnse J.M., Genova-Maleras R., Geramo Y.C.D., Gething P.W., Gezae K.E., Ghadiri K., Ghasemi Falavarjani K., Ghasemi-Kasman M., Ghimire M., Ghosh R., Ghoshal A.G., Giampaoli S., Gill P.S., Gill T.K., Ginawi I.A., Giussani G., Gnedovskaya E.V., Goldberg E.M., Goli S., Gomez-Dantes H., Gona P.N., Gopalani S.V., Gorman T.M., Goulart A.C., Goulart B.N.G., Grada A., Grams M.E., Grosso G., Gugnani H.C., Guo Y., Gupta P.C., Gupta R., Gupta T., Gyawali B., Haagsma J.A., Hachinski V., Hafezi-Nejad N., Haghparast Bidgoli H., Hagos T.B., Hailu G.B., Haj-Mirzaian A., Hamadeh R.R., Hamidi S., Handal A.J., Hankey G.J., Hao Y., Harb H.L., Harikrishnan S., Haro J.M., Hasan M., Hassankhani H., Hassen H.Y., Havmoeller R., Hawley C.N., Hay R.J., Hay S.I., Hedayatizadeh-Omran A., Heibati B., Hendrie D., Henok A., Herteliu C., Heydarpour S., Hibstu D.T., Hoang H.T., Hoek H.W., Hoffman H.J., Hole M.K., Homaie Rad E., Hoogar P., Hosgood H.D., Hosseini S.M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Hotez P.J., Hoy D.G., Hsairi M., Htet A.S., Hu G., Huang J.J., Huynh C.K., Iburg K.M., Ikeda C.T., Ileanu B., Ilesanmi O.S., Iqbal U., Irvani S.S.N., Irvine C.M.S., Islam S., Islami F., Jacobsen K.H., Jahangiry L., Jahanmehr N., Jain S.K., Jakovljevic M., Javanbakht M., Jayatilleke A.U., Jeemon P., Jha R.P., Jha V., Ji J.S., Johnson C.O., Jonas J.B., Jozwiak J.J., Jungari S.B., Jurisson M., Kabir Z., Kadel R., Kahsay A., Kalani R., Kanchan T., Karami M., Karami Matin B., Karch A., Karema C., Karimi N., Karimi S.M., Kasaeian A., Kassa D.H., Kassa G.M., Kassa T.D., Kassebaum N.J., Katikireddi S.V., Kawakami N., Kazemi Karyani A., Keighobadi M.M., Keiyoro P.N., Kemmer L., Kemp G.R., Kengne A.P., Keren A., Khader Y.S., Khafaei B., Khafaie M.A., Khajavi A., Khalil I.A., Khan E.A., Khan M.S., Khan M.A., Khang Y.-H., Khazaei M., Khoja A.T., Khosravi A., Khosravi M.H., Kiadaliri A.A., Kiirithio D.N., Kim C.-I., Kim D., Kim P., Kim Y.-E., Kim Y.J., Kimokoti R.W., Kinfu Y., Kisa A., Kissimova-Skarbek K., Kivimaki M., Knudsen A.K.S., Kocarnik J.M., Kochhar S., Kokubo Y., Kolola T., Kopec J.A., Kosen S., Kotsakis G.A., Koul P.A., Koyanagi A., Kravchenko M.A., Krishan K., Krohn K.J., Kuate Defo B., Kucuk Bicer B., Kumar G.A., Kumar M., Kyu H.H., Lad D.P., Lad S.D., Lafranconi A., Lalloo R., Lallukka T., Lami F.H., Lansingh V.C., Latifi A., Lau K.M.-M., Lazarus J.V., Leasher J.L., Ledesma J.R., Lee P.H., Leigh J., Leung J., Levi M., Lewycka S., Li S., Li Y., Liao Y., Liben M.L., Lim L.-L., Lim S.S., Liu S., Lodha R., Looker K.J., Lopez A.D., Lorkowski S., Lotufo P.A., Low N., Lozano R., Lucas T.C.D., Lucchesi L.R., Lunevicius R., Lyons R.A., Ma S., Macarayan E.R.K., Mackay M.T., Madotto F., Magdy Abd El Razek H., Magdy Abd El Razek M., Maghavani D.P., Mahotra N.B., Mai H.T., Majdan M., Majdzadeh R., Majeed A., Malekzadeh R., Malta D.C., Mamun A.A., Manda A.-L., Manguerra H., Manhertz T., Mansournia M.A., Mantovani L.G., Mapoma C.C., Maravilla J.C., Marcenes W., Marks A., Martins-Melo F.R., Martopullo I., Marz W., Marzan M.B., Mashamba-Thompson T.P., Massenburg B.B., Mathur M.R., Matsushita K., Maulik P.K., Mazidi M., McAlinden C., McGrath J.J., McKee M., Mehndiratta M.M., Mehrotra R., Mehta K.M., Mehta V., Mejia-Rodriguez F., Mekonen T., Melese A., Melku M., Meltzer M., Memiah P.T.N., Memish Z.A., Mendoza W., Mengistu D.T., Mengistu G., Mensah G.A., Mereta S.T., Meretoja A., Meretoja T.J., Mestrovic T., Mezerji N.M.G., Miazgowski B., Miazgowski T., Millear A.I., Miller T.R., Miltz B., Mini G.K., Mirarefin M., Mirrakhimov E.M., Misganaw A.T., Mitchell P.B., Mitiku H., Moazen B., Mohajer B., Mohammad K.A., Mohammadifard N., Mohammadnia-Afrouzi M., Mohammed M.A., Mohammed S., Mohebi F., Moitra M., Mokdad A.H., Molokhia M., Monasta L., Moodley Y., Moosazadeh M., Moradi G., Moradi-Lakeh M., Moradinazar M., Moraga P., Morawska L., Moreno Velasquez I., Morgado-Da-Costa J., Morrison S.D., Moschos M.M., Mousavi S.M., Mruts K.B., Muche A.A., Muchie K.F., Mueller U.O., Muhammed O.S., Mukhopadhyay S., Muller K., Mumford J.E., Murhekar M., Musa J., Musa K.I., Mustafa G., Nabhan A.F., Nagata C., Naghavi M., Naheed A., Nahvijou A., Naik G., Naik N., Najafi F., Naldi L., Nam H.S., Nangia V., Nansseu J.R., Nascimento B.R., Natarajan G., Neamati N., Negoi I., Negoi R.I., Neupane S., Newton C.R.J., Ngunjiri J.W., Nguyen A.Q., Nguyen H.L.T., Nguyen H.T., Nguyen L.H., Nguyen M., Nguyen N.B., Nguyen S.H., Nichols E., Ningrum D.N.A., Nixon M.R., Nolutshungu N., Nomura S., Norheim O.F., Noroozi M., Norrving B., Noubiap J.J., Nouri H.R., Nourollahpour Shiadeh M., Nowroozi M.R., Nsoesie E.O., Nyasulu P.S., Odell C.M., Ofori-Asenso R., Ogbo F.A., Oh I.-H., Oladimeji O., Olagunju A.T., Olagunju T.O., Olivares P.R., Olsen H.E., Olusanya B.O., Ong K.L., Ong S.K., Oren E., Ortiz A., Ota E., Otstavnov S.S., overland S., Owolabi M.O., Mahesh P.A., Pacella R., Pakpour A.H., Pana A., Panda-Jonas S., Parisi A., Park E.-K., Parry C.D.H., Patel S., Pati S., Patil S.T., Patle A., Patton G.C., Paturi V.R., Paulson K.R., Pearce N., Pereira D.M., Perico N., Pesudovs K., Pham H.Q., Phillips M.R., Pigott D.M., Pillay J.D., Piradov M.A., Pirsaheb M., Pishgar F., Plana-Ripoll O., Plass D., Polinder S., Popova S., Postma M.J., Pourshams A., Poustchi H., Prabhakaran D., Prakash S., Prakash V., Purcell C.A., Purwar M.B., Qorbani M., Quistberg D.A., Radfar A., Rafay A., Rafiei A., Rahim F., Rahimi K., Rahimi-Movaghar A., Rahimi-Movaghar V., Rahman M., Ur Rahman M.H., Rahman M.A., Rahman S.U., Rai R.K., Rajati F., Ram U., Ranjan P., Ranta A., Rao P.C., Rawaf D.L., Rawaf S., Reddy K.S., Reiner R.C., Reinig N., Reitsma M.B., Remuzzi G., Renzaho A.M.N., Resnikoff S., Rezaei S., Rezai M.S., Ribeiro A.L.P., Robinson S.R., Roever L., Ronfani L., Roshandel G., Rostami A., Roth G.A., Roy A., Rubagotti E., Sachdev P.S., Sadat N., Saddik B., Sadeghi E., Saeedi Moghaddam S., Safari H., Safari Y., Safari-Faramani R., Safdarian M., Safi S., Safiri S., Sagar R., Sahebkar A., Sahraian M.A., Sajadi H.S., Salam N., Salama J.S., Salamati P., Saleem K., Saleem Z., Salimi Y., Salomon J.A., Salvi S.S., Salz I., Samy A.M., Sanabria J., Sang Y., Santomauro D.F., Santos I.S., Santos J.V., Santric Milicevic M.M., Sao Jose B.P., Sardana M., Sarker A.R., Sarrafzadegan N., Sartorius B., Sarvi S., Sathian B., Satpathy M., Sawant A.R., Sawhney M., Saxena S., Saylan M., Schaeffner E., Schmidt M.I., Schneider I.J.C., Schottker B., Schwebel D.C., Schwendicke F., Scott J.G., Sekerija M., Sepanlou S.G., Servan-Mori E., Seyedmousavi S., Shabaninejad H., Shafieesabet A., Shahbazi M., Shaheen A.A., Shaikh M.A., Shams-Beyranvand M., Shamsi M., Shamsizadeh M., Sharafi H., Sharafi K., Sharif M., Sharif-Alhoseini M., Sharma M., Sharma R., Sheikh A., Shi P., Shibuya K., Shigematsu M., 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Yoon S.-J., Yotebieng M., Younis M.Z., Yousefifard M., Yu C., Zadnik V., Zaidi Z., Zaman S.B., Zamani M., Zare Z., Zeleke A.J., Zenebe Z.M., Zhang K., Zhao Z., Zhou M., Zodpey S., Zucker I., Vos T., Murray C.J.L., De Courten B., Fox J., James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., Abdollahpour I., Abdulkader R.S., Abebe Z., Abera S.F., Abil O.Z., Abraha H.N., Abu-Raddad L.J., Abu-Rmeileh N.M.E., Accrombessi M.M.K., Acharya D., Acharya P., Ackerman I.N., Adamu A.A., Adebayo O.M., Adekanmbi V., Adetokunboh O.O., Adib M.G., Adsuar J.C., Afanvi K.A., Afarideh M., Afshin A., Agarwal G., Agesa K.M., Aggarwal R., Aghayan S.A., Agrawal S., Ahmadi A., Ahmadi M., Ahmadieh H., Ahmed M.B., Aichour A.N., Aichour I., Aichour M.T.E., Akinyemiju T., Akseer N., Al-Aly Z., Al-Eyadhy A., Al-Mekhlafi H.M., Al-Raddadi R.M., Alahdab F., Alam K., Alam T., Alashi A., Alavian S.M., Alene K.A., Alijanzadeh M., Alizadeh-Navaei R., 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Sayeed M.S., Bisanzio D., Blacker B.F., Blyth F.M., Bou-Orm I.R., Boufous S., Bourne R., Brady O.J., Brainin M., Brant L.C., Brazinova A., Breitborde N.J.K., Brenner H., Briant P.S., Briggs A.M., Briko A.N., Britton G., Brugha T., Buchbinder R., Busse R., Butt Z.A., Cahuana-Hurtado L., Cano J., Cardenas R., Carrero J.J., Carter A., Carvalho F., Castaneda-Orjuela C.A., Castillo Rivas J., Castro F., Catala-Lopez F., Cercy K.M., Cerin E., Chaiah Y., Chang A.R., Chang H.-Y., Chang J.-C., Charlson F.J., Chattopadhyay A., Chattu V.K., Chaturvedi P., Chiang P.P.-C., Chin K.L., Chitheer A., Choi J.-Y.J., Chowdhury R., Christensen H., Christopher D.J., Cicuttini F.M., Ciobanu L.G., Cirillo M., Claro R.M., Collado-Mateo D., Cooper C., Coresh J., Cortesi P.A., Cortinovis M., Costa M., Cousin E., Criqui M.H., Cromwell E.A., Cross M., Crump J.A., Dadi A.F., Dandona L., Dandona R., Dargan P.I., Daryani A., Das Gupta R., Das Neves J., Dasa T.T., Davey G., Davis A.C., Davitoiu D.V., De La Hoz F.P., De Leo D., De Neve J.-W., Degefa M.G., Degenhardt L., Deiparine S., Dellavalle R.P., Demoz G.T., Deribe K., Dervenis N., Des Jarlais D.C., Dessie G.A., Dey S., Dharmaratne S.D., Dinberu M.T., Dirac M.A., Djalalinia S., Doan L., Dokova K., Doku D.T., Dorsey E.R., Doyle K.E., Driscoll T.R., Dubey M., Dubljanin E., Duken E.E., Duncan B.B., Duraes A.R., Ebrahimi H., Ebrahimpour S., Echko M.M., Edvardsson D., Effiong A., Ehrlich J.R., El Bcheraoui C., El Sayed Zaki M., El-Khatib Z., Elkout H., Elyazar I.R.F., Enayati A., Endries A.Y., Er B., Erskine H.E., Eshrati B., Eskandarieh S., Esteghamati A., Esteghamati S., Fakhim H., Fallah Omrani V., Faramarzi M., Fareed M., Farhadi F., Farid T.A., Farinha C.S.E., Farioli A., Faro A., Farvid M.S., Farzadfar F., Feigin V.L., Fentahun N., Fereshtehnejad S.-M., Fernandes E., Fernandes J.C., Ferrari A.J., Feyissa G.T., Filip I., Fischer F., Fitzmaurice C., Foigt N.A., Foreman K.J., Frank T.D., Fukumoto T., Fullman N., Furst T., Furtado J.M., Futran N.D., Gall S., Ganji M., Gankpe F.G., Garcia-Basteiro A.L., Gardner W.M., Gebre A.K., Gebremedhin A.T., Gebremichael T.G., Gelano T.F., Geleijnse J.M., Genova-Maleras R., Geramo Y.C.D., Gething P.W., Gezae K.E., Ghadiri K., Ghasemi Falavarjani K., Ghasemi-Kasman M., Ghimire M., Ghosh R., Ghoshal A.G., Giampaoli S., Gill P.S., Gill T.K., Ginawi I.A., Giussani G., Gnedovskaya E.V., Goldberg E.M., Goli S., Gomez-Dantes H., Gona P.N., Gopalani S.V., Gorman T.M., Goulart A.C., Goulart B.N.G., Grada A., Grams M.E., Grosso G., Gugnani H.C., Guo Y., Gupta P.C., Gupta R., Gupta T., Gyawali B., Haagsma J.A., Hachinski V., Hafezi-Nejad N., Haghparast Bidgoli H., Hagos T.B., Hailu G.B., Haj-Mirzaian A., Hamadeh R.R., Hamidi S., Handal A.J., Hankey G.J., Hao Y., Harb H.L., Harikrishnan S., Haro J.M., Hasan M., Hassankhani H., Hassen H.Y., Havmoeller R., Hawley C.N., Hay R.J., Hay S.I., Hedayatizadeh-Omran A., Heibati B., Hendrie D., Henok A., Herteliu C., Heydarpour S., Hibstu D.T., Hoang H.T., Hoek H.W., Hoffman H.J., Hole M.K., Homaie Rad E., Hoogar P., Hosgood H.D., Hosseini S.M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Hotez P.J., Hoy D.G., Hsairi M., Htet A.S., Hu G., Huang J.J., Huynh C.K., Iburg K.M., Ikeda C.T., Ileanu B., Ilesanmi O.S., Iqbal U., Irvani S.S.N., Irvine C.M.S., Islam S., Islami F., Jacobsen K.H., Jahangiry L., Jahanmehr N., Jain S.K., Jakovljevic M., Javanbakht M., Jayatilleke A.U., Jeemon P., Jha R.P., Jha V., Ji J.S., Johnson C.O., Jonas J.B., Jozwiak J.J., Jungari S.B., Jurisson M., Kabir Z., Kadel R., Kahsay A., Kalani R., Kanchan T., Karami M., Karami Matin B., Karch A., Karema C., Karimi N., Karimi S.M., Kasaeian A., Kassa D.H., Kassa G.M., Kassa T.D., Kassebaum N.J., Katikireddi S.V., Kawakami N., Kazemi Karyani A., Keighobadi M.M., Keiyoro P.N., Kemmer L., Kemp G.R., Kengne A.P., Keren A., Khader Y.S., Khafaei B., Khafaie M.A., Khajavi A., Khalil I.A., Khan E.A., Khan M.S., Khan M.A., Khang Y.-H., Khazaei M., Khoja A.T., Khosravi A., Khosravi M.H., 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Velasquez I., Morgado-Da-Costa J., Morrison S.D., Moschos M.M., Mousavi S.M., Mruts K.B., Muche A.A., Muchie K.F., Mueller U.O., Muhammed O.S., Mukhopadhyay S., Muller K., Mumford J.E., Murhekar M., Musa J., Musa K.I., Mustafa G., Nabhan A.F., Nagata C., Naghavi M., Naheed A., Nahvijou A., Naik G., Naik N., Najafi F., Naldi L., Nam H.S., Nangia V., Nansseu J.R., Nascimento B.R., Natarajan G., Neamati N., Negoi I., Negoi R.I., Neupane S., Newton C.R.J., Ngunjiri J.W., Nguyen A.Q., Nguyen H.L.T., Nguyen H.T., Nguyen L.H., Nguyen M., Nguyen N.B., Nguyen S.H., Nichols E., Ningrum D.N.A., Nixon M.R., Nolutshungu N., Nomura S., Norheim O.F., Noroozi M., Norrving B., Noubiap J.J., Nouri H.R., Nourollahpour Shiadeh M., Nowroozi M.R., Nsoesie E.O., Nyasulu P.S., Odell C.M., Ofori-Asenso R., Ogbo F.A., Oh I.-H., Oladimeji O., Olagunju A.T., Olagunju T.O., Olivares P.R., Olsen H.E., Olusanya B.O., Ong K.L., Ong S.K., Oren E., Ortiz A., Ota E., Otstavnov S.S., overland S., Owolabi M.O., Mahesh P.A., Pacella R., Pakpour A.H., Pana A., Panda-Jonas S., Parisi A., Park E.-K., Parry C.D.H., Patel S., Pati S., Patil S.T., Patle A., Patton G.C., Paturi V.R., Paulson K.R., Pearce N., Pereira D.M., Perico N., Pesudovs K., Pham H.Q., Phillips M.R., Pigott D.M., Pillay J.D., Piradov M.A., Pirsaheb M., Pishgar F., Plana-Ripoll O., Plass D., Polinder S., Popova S., Postma M.J., Pourshams A., Poustchi H., Prabhakaran D., Prakash S., Prakash V., Purcell C.A., Purwar M.B., Qorbani M., Quistberg D.A., Radfar A., Rafay A., Rafiei A., Rahim F., Rahimi K., Rahimi-Movaghar A., Rahimi-Movaghar V., Rahman M., Ur Rahman M.H., Rahman M.A., Rahman S.U., Rai R.K., Rajati F., Ram U., Ranjan P., Ranta A., Rao P.C., Rawaf D.L., Rawaf S., Reddy K.S., Reiner R.C., Reinig N., Reitsma M.B., Remuzzi G., Renzaho A.M.N., Resnikoff S., Rezaei S., Rezai M.S., Ribeiro A.L.P., Robinson S.R., Roever L., Ronfani L., Roshandel G., Rostami A., Roth G.A., Roy A., Rubagotti E., Sachdev P.S., Sadat N., Saddik B., Sadeghi E., Saeedi Moghaddam S., Safari H., Safari Y., Safari-Faramani R., Safdarian M., Safi S., Safiri S., Sagar R., Sahebkar A., Sahraian M.A., Sajadi H.S., Salam N., Salama J.S., Salamati P., Saleem K., Saleem Z., Salimi Y., Salomon J.A., Salvi S.S., Salz I., Samy A.M., Sanabria J., Sang Y., Santomauro D.F., Santos I.S., Santos J.V., Santric Milicevic M.M., Sao Jose B.P., Sardana M., Sarker A.R., Sarrafzadegan N., Sartorius B., Sarvi S., Sathian B., Satpathy M., Sawant A.R., Sawhney M., Saxena S., Saylan M., Schaeffner E., Schmidt M.I., Schneider I.J.C., Schottker B., Schwebel D.C., Schwendicke F., Scott J.G., Sekerija M., Sepanlou S.G., Servan-Mori E., Seyedmousavi S., Shabaninejad H., Shafieesabet A., Shahbazi M., Shaheen A.A., Shaikh M.A., Shams-Beyranvand M., Shamsi M., Shamsizadeh M., Sharafi H., Sharafi K., Sharif M., Sharif-Alhoseini M., Sharma M., Sharma R., Sheikh A., Shi P., Shibuya K., Shigematsu M., Shiri R., Shirkoohi R., Shishani K., Shiue I., Shokraneh F., Shoman H., Shrime M.G., Si S., Siabani S., Siddiqi T.J., Sigfusdottir I.D., Sigurvinsdottir R., Silva J.P., Silveira D.G.A., Singam N.S.V., Singh J.A., Singh N.P., Singh V., Sinha D.N., Skiadaresi E., Slepak E.L.N., Sliwa K., Smith D.L., Smith M., Soares Filho A.M., Sobaih B.H., Sobhani S., Sobngwi E., Soneji S.S., Soofi M., Soosaraei M., Sorensen R.J.D., Soriano J.B., Soyiri I.N., Sposato L.A., Sreeramareddy C.T., Srinivasan V., Stanaway J.D., Stein D.J., Steiner C., Steiner T.J., Stokes M.A., Stovner L.J., Subart M.L., Sudaryanto A., Sufiyan M.B., Sunguya B.F., Sur P.J., Sutradhar I., Sykes B.L., Sylte D.O., Tabares-Seisdedos R., Tadakamadla S.K., Tadesse B.T., Tandon N., Tassew S.G., Tavakkoli M., Taveira N., Taylor H.R., Tehrani-Banihashemi A., Tekalign T.G., Tekelemedhin S.W., Tekle M.G., Temesgen H., Temsah M.-H., Temsah O., Terkawi A.S., and Teweldemedhin M.
Abstract: Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Method(s): We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we
Published: 2018

33. Serum potassium and adverse outcomes across the range of kidney function: a CKD Prognosis Consortium meta-analysis

Author: Kovesdy, C.P., Matsushita, K., Sang, Y., Brunskill, N.J., Carrero, J.J., Chodick, G., Hasegawa, T., Heerspink, H.L., Hirayama, A., Landman, G.W., Levin, A., Nitsch, D., Wheeler, D.C., Coresh, J., Hallan, S.I., Wetzels, J.F.M., Shalev, V., Grams, M.E., Kovesdy, C.P., Matsushita, K., Sang, Y., Brunskill, N.J., Carrero, J.J., Chodick, G., Hasegawa, T., Heerspink, H.L., Hirayama, A., Landman, G.W., Levin, A., Nitsch, D., Wheeler, D.C., Coresh, J., Hallan, S.I., Wetzels, J.F.M., Shalev, V., and Grams, M.E.
Abstract: Item does not contain fulltext, Aims: Both hypo- and hyperkalaemia can have immediate deleterious physiological effects, and less is known about long-term risks. The objective was to determine the risks of all-cause mortality, cardiovascular mortality, and end-stage renal disease associated with potassium levels across the range of kidney function and evaluate for consistency across cohorts in a global consortium. Methods and results: We performed an individual-level data meta-analysis of 27 international cohorts [10 general population, 7 high cardiovascular risk, and 10 chronic kidney disease (CKD)] in the CKD Prognosis Consortium. We used Cox regression followed by random-effects meta-analysis to assess the relationship between baseline potassium and adverse outcomes, adjusted for demographic and clinical characteristics, overall and across strata of estimated glomerular filtration rate (eGFR) and albuminuria. We included 1 217 986 participants followed up for a mean of 6.9 years. The average age was 55 +/- 16 years, average eGFR was 83 +/- 23 mL/min/1.73 m2, and 17% had moderate- to-severe increased albuminuria levels. The mean baseline potassium was 4.2 +/- 0.4 mmol/L. The risk of serum potassium of >5.5 mmol/L was related to lower eGFR and higher albuminuria. The risk relationship between potassium levels and adverse outcomes was U-shaped, with the lowest risk at serum potassium of 4-4.5 mmol/L. Compared with a reference of 4.2 mmol/L, the adjusted hazard ratio for all-cause mortality was 1.22 [95% confidence interval (CI) 1.15-1.29] at 5.5 mmol/L and 1.49 (95% CI 1.26-1.76) at 3.0 mmol/L. Risks were similar by eGFR, albuminuria, renin-angiotensin-aldosterone system inhibitor use, and across cohorts. Conclusions: Outpatient potassium levels both above and below the normal range are consistently associated with adverse outcomes, with similar risk relationships across eGFR and albuminuria.
Published: 2018

34. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

Author: Tangri, N., Grams, M.E., Levey, A.S., Coresh, J., Appel, L.J., Astor, B.C., Chodick, G., Collins, A.J., Djurdjev, O., Elley, C.R., Evans, M., Garg, A.X., Hallan, S.I., Inker, L.A., Ito, S., Jee, S.H., Kovesdy, C.P., Kronenberg, F., Heerspink, H.J., Marks, A., Nadkarni, G.N., Navaneethan, S.D., Nelson, R.G., Titze, S., Sarnak, M.J., Stengel, B., Woodward, M., Iseki, K., Wetzels, J.F.M., et al., Tangri, N., Grams, M.E., Levey, A.S., Coresh, J., Appel, L.J., Astor, B.C., Chodick, G., Collins, A.J., Djurdjev, O., Elley, C.R., Evans, M., Garg, A.X., Hallan, S.I., Inker, L.A., Ito, S., Jee, S.H., Kovesdy, C.P., Kronenberg, F., Heerspink, H.J., Marks, A., Nadkarni, G.N., Navaneethan, S.D., Nelson, R.G., Titze, S., Sarnak, M.J., Stengel, B., Woodward, M., Iseki, K., Wetzels, J.F.M., and et al.
Abstract: Item does not contain fulltext, IMPORTANCE: Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed. OBJECTIVE: To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis. DATA SOURCES: Thirty-one cohorts, including 721,357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014. STUDY SELECTION: Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease. DATA EXTRACTION AND SYNTHESIS: Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed. MAIN OUTCOMES AND MEASURES: Kidney failure (treatment by dialysis or kidney transplant). RESULTS: During a median follow-up of 4 years of 721,357 participants with CKD, 23,829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibrat
Published: 2016

35. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

Author: Tangri, N., Grams, M.E., Levey, A.S., Coresh, J., Appel, L.J., Astor, B.C., Chodick, G., Collins, A.J., Djurdjev, O., Elley, C.R., Evans, M., Garg, A.X., Hallan, S.I., Inker, L.A., Ito, S., Jee, S.H., Kovesdy, C.P., Kronenberg, F., Heerspink, H.J., Marks, A., Nadkarni, G.N., Navaneethan, S.D., Nelson, R.G., Titze, S., Sarnak, M.J., Stengel, B., Woodward, M., Iseki, K., Wetzels, J.F.M., et al., Tangri, N., Grams, M.E., Levey, A.S., Coresh, J., Appel, L.J., Astor, B.C., Chodick, G., Collins, A.J., Djurdjev, O., Elley, C.R., Evans, M., Garg, A.X., Hallan, S.I., Inker, L.A., Ito, S., Jee, S.H., Kovesdy, C.P., Kronenberg, F., Heerspink, H.J., Marks, A., Nadkarni, G.N., Navaneethan, S.D., Nelson, R.G., Titze, S., Sarnak, M.J., Stengel, B., Woodward, M., Iseki, K., Wetzels, J.F.M., and et al.
Abstract: Item does not contain fulltext, IMPORTANCE: Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed. OBJECTIVE: To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis. DATA SOURCES: Thirty-one cohorts, including 721,357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014. STUDY SELECTION: Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease. DATA EXTRACTION AND SYNTHESIS: Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed. MAIN OUTCOMES AND MEASURES: Kidney failure (treatment by dialysis or kidney transplant). RESULTS: During a median follow-up of 4 years of 721,357 participants with CKD, 23,829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibrat
Published: 2016

36. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

Author: Tangri, N., Grams, M.E., Levey, A.S., Coresh, J., Appel, L.J., Astor, B.C., Chodick, G., Collins, A.J., Djurdjev, O., Elley, C.R., Evans, M., Garg, A.X., Hallan, S.I., Inker, L.A., Ito, S., Jee, S.H., Kovesdy, C.P., Kronenberg, F., Heerspink, H.J., Marks, A., Nadkarni, G.N., Navaneethan, S.D., Nelson, R.G., Titze, S., Sarnak, M.J., Stengel, B., Woodward, M., Iseki, K., Wetzels, J.F.M., et al., Tangri, N., Grams, M.E., Levey, A.S., Coresh, J., Appel, L.J., Astor, B.C., Chodick, G., Collins, A.J., Djurdjev, O., Elley, C.R., Evans, M., Garg, A.X., Hallan, S.I., Inker, L.A., Ito, S., Jee, S.H., Kovesdy, C.P., Kronenberg, F., Heerspink, H.J., Marks, A., Nadkarni, G.N., Navaneethan, S.D., Nelson, R.G., Titze, S., Sarnak, M.J., Stengel, B., Woodward, M., Iseki, K., Wetzels, J.F.M., and et al.
Abstract: Item does not contain fulltext, IMPORTANCE: Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed. OBJECTIVE: To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis. DATA SOURCES: Thirty-one cohorts, including 721,357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014. STUDY SELECTION: Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease. DATA EXTRACTION AND SYNTHESIS: Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed. MAIN OUTCOMES AND MEASURES: Kidney failure (treatment by dialysis or kidney transplant). RESULTS: During a median follow-up of 4 years of 721,357 participants with CKD, 23,829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibrat
Published: 2016

37. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.

Author: Shlipak M.G., Tummalapalli S.L., Boulware L.E., Grams M.E., Ix J.H., Jha V., Kengne A.-P., Madero M., Mihaylova B., Tangri N., Cheung M., Jadoul M., Winkelmayer W.C., Zoungas S., Abraham G., Ademi Z., Alicic R.Z., de Boer I., Deo R., Ding X., Ebert N., Fowler K.J., Fried L.F., Gansevoort R.T., Garcia-Garcia G., Hemmelgarn B.R., Lee Harding J., Hudson J.Q., Iseki K., Jotwani V., Karliner L.S., Levey A.S., Liew A., Lin P.J., Luk A.O.Y., Martinez V., Moran A.E., Nguyen M., Obrador G.T., O'Donoghue D., Pavkov M.E., Pavlinac J., Powe N.R., Seegmiller J.C., Shen J.I., Shroff R., Sola L., Taal M.W., Tattersall J., Vassalotti J.A., Weir M.R., Zomer E., Shlipak M.G., Tummalapalli S.L., Boulware L.E., Grams M.E., Ix J.H., Jha V., Kengne A.-P., Madero M., Mihaylova B., Tangri N., Cheung M., Jadoul M., Winkelmayer W.C., Zoungas S., Abraham G., Ademi Z., Alicic R.Z., de Boer I., Deo R., Ding X., Ebert N., Fowler K.J., Fried L.F., Gansevoort R.T., Garcia-Garcia G., Hemmelgarn B.R., Lee Harding J., Hudson J.Q., Iseki K., Jotwani V., Karliner L.S., Levey A.S., Liew A., Lin P.J., Luk A.O.Y., Martinez V., Moran A.E., Nguyen M., Obrador G.T., O'Donoghue D., Pavkov M.E., Pavlinac J., Powe N.R., Seegmiller J.C., Shen J.I., Shroff R., Sola L., Taal M.W., Tattersall J., Vassalotti J.A., Weir M.R., and Zomer E.
Abstract: Chronic kidney disease (CKD) causes substantial global morbidity and increases cardiovascular and all-cause mortality. Unlike other chronic diseases with established strategies for screening, there has been no consensus on whether health systems and governments should prioritize early identification and intervention for CKD. Guidelines on evaluating and managing early CKD are available but have not been universally adopted in the absence of incentives or quality measures for prioritizing CKD care. The burden of CKD falls disproportionately upon persons with lower socioeconomic status, who have a higher prevalence of CKD, limited access to treatment, and poorer outcomes. Therefore, identifying and treating CKD at the earliest stages is an equity imperative. In 2019, Kidney Disease: Improving Global Outcomes (KDIGO) held a controversies conference entitled "Early Identification and Intervention in CKD." Participants identified strategies for screening, risk stratification, and treatment for early CKD and the key health system and economic factors for implementing these processes. A consensus emerged that CKD screening coupled with risk stratification and treatment should be implemented immediately for high-risk persons and that this should ideally occur in primary or community care settings with tailoring to the local context.Copyright © 2020 Kidney Disease: Improving Global Outcomes (KDIGO)

38. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.

Author: Shlipak M.G., Tummalapalli S.L., Boulware L.E., Grams M.E., Ix J.H., Jha V., Kengne A.-P., Madero M., Mihaylova B., Tangri N., Cheung M., Jadoul M., Winkelmayer W.C., Zoungas S., Abraham G., Ademi Z., Alicic R.Z., de Boer I., Deo R., Ding X., Ebert N., Fowler K.J., Fried L.F., Gansevoort R.T., Garcia-Garcia G., Hemmelgarn B.R., Lee Harding J., Hudson J.Q., Iseki K., Jotwani V., Karliner L.S., Levey A.S., Liew A., Lin P.J., Luk A.O.Y., Martinez V., Moran A.E., Nguyen M., Obrador G.T., O'Donoghue D., Pavkov M.E., Pavlinac J., Powe N.R., Seegmiller J.C., Shen J.I., Shroff R., Sola L., Taal M.W., Tattersall J., Vassalotti J.A., Weir M.R., Zomer E., Shlipak M.G., Tummalapalli S.L., Boulware L.E., Grams M.E., Ix J.H., Jha V., Kengne A.-P., Madero M., Mihaylova B., Tangri N., Cheung M., Jadoul M., Winkelmayer W.C., Zoungas S., Abraham G., Ademi Z., Alicic R.Z., de Boer I., Deo R., Ding X., Ebert N., Fowler K.J., Fried L.F., Gansevoort R.T., Garcia-Garcia G., Hemmelgarn B.R., Lee Harding J., Hudson J.Q., Iseki K., Jotwani V., Karliner L.S., Levey A.S., Liew A., Lin P.J., Luk A.O.Y., Martinez V., Moran A.E., Nguyen M., Obrador G.T., O'Donoghue D., Pavkov M.E., Pavlinac J., Powe N.R., Seegmiller J.C., Shen J.I., Shroff R., Sola L., Taal M.W., Tattersall J., Vassalotti J.A., Weir M.R., and Zomer E.
Abstract: Chronic kidney disease (CKD) causes substantial global morbidity and increases cardiovascular and all-cause mortality. Unlike other chronic diseases with established strategies for screening, there has been no consensus on whether health systems and governments should prioritize early identification and intervention for CKD. Guidelines on evaluating and managing early CKD are available but have not been universally adopted in the absence of incentives or quality measures for prioritizing CKD care. The burden of CKD falls disproportionately upon persons with lower socioeconomic status, who have a higher prevalence of CKD, limited access to treatment, and poorer outcomes. Therefore, identifying and treating CKD at the earliest stages is an equity imperative. In 2019, Kidney Disease: Improving Global Outcomes (KDIGO) held a controversies conference entitled "Early Identification and Intervention in CKD." Participants identified strategies for screening, risk stratification, and treatment for early CKD and the key health system and economic factors for implementing these processes. A consensus emerged that CKD screening coupled with risk stratification and treatment should be implemented immediately for high-risk persons and that this should ideally occur in primary or community care settings with tailoring to the local context.Copyright © 2020 Kidney Disease: Improving Global Outcomes (KDIGO)

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