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3 results on '"Aditya, Surapeneni"'

1. Albuminuria Testing in Hypertension and Diabetes

Author

Jung-Im Shin, Alex R. Chang, Morgan E. Grams, Josef Coresh, Shoshana H. Ballew, Aditya Surapaneni, Kunihiro Matsushita, Henk J.G. Bilo, Juan J. Carrero, Gabriel Chodick, Kenn B. Daratha, Simerjot K. Jassal, Girish N. Nadkarni, Robert G. Nelson, Christoph Nowak, Nikita Stempniewicz, Keiichi Sumida, Jamie P. Traynor, Mark Woodward, Yingying Sang, Ron T. Gansevoort, John Chalmers, Katherine Tuttle, Radica Alicic, Sterling McPherson, Cami Jones, Gurmukteshwar Singh, Jamie Green, H. Lester Kirchner, Varda Shalev, Erwin P. Bottinger, Ruth J.F. Loos, Stephen B. Ellis, John Cuddeback, Elizabeth Ciemins, Emily Carbonara, Stephan Dunning, William C. Knowler, Helen C. Looker, Lyane M. Kieneker, Stephan J.L. Bakker, Hans L. Hillege, Pim van der Harst, Jacklyn Bergstrom, Joachim Ix, Csaba P. Kovesdy, Praveen Potukuchi, Marco Trevisan, Carl Gustaf Elinder, Björn Wettermark, Johan Ärnlöv, Patrick B. Mark, Peter C. Thomson, Colin C. Geddes, Gijs W.D. Landman, Kornelis J.J. van Hateren, Nanne Kleefstra, Orlando Gutierrez, Tsuneo Konta, Andrew S. Levey, Kevan Polkinghorne, Elke Schäffner, Jingsha Chen, Aditya Surapeneni, Groningen Kidney Center (GKC), and Cardiovascular Centre (CVC)

Subjects
Adult, medicine.medical_specialty, Article, Diabetes mellitus, Internal medicine, Internal Medicine, Diabetes Mellitus, Medicine, Albuminuria, Humans, Renal Insufficiency, Chronic, Aged, business.industry, Incidence (epidemiology), Odds ratio, Middle Aged, medicine.disease, Blood pressure, Meta-analysis, Creatinine, Cohort, Hypertension, medicine.symptom, business, Kidney disease, Glomerular Filtration Rate
Abstract

Albuminuria is an under-recognized component of chronic kidney disease definition, staging, and prognosis. Guidelines, particularly for hypertension, conflict on recommendations for urine albumin-to-creatinine ratio (ACR) measurement. Separately among 1 344 594 adults with diabetes and 2 334 461 nondiabetic adults with hypertension from the chronic kidney disease Prognosis Consortium, we assessed ACR testing, estimated the prevalence and incidence of ACR ≥30 mg/g and developed risk models for ACR ≥30 mg/g. The ACR screening rate (cohort range) was 35.1% (12.3%–74.5%) in diabetes and 4.1% (1.3%–20.7%) in hypertension. Screening was largely unrelated to the predicted risk of prevalent albuminuria. The median prevalence of ACR ≥30 mg/g across cohorts was 32.1% in diabetes and 21.8% in hypertension. Higher systolic blood pressure was associated with a higher prevalence of albuminuria (odds ratio [95% CI] per 20 mm Hg in diabetes, 1.50 [1.42–1.60]; in hypertension, 1.36 [1.28–1.45]). The ratio of undetected (due to lack of screening) to detected ACR ≥30 mg/g was estimated at 1.8 in diabetes and 19.5 in hypertension. Among those with ACR

Published
2021

2. Development of Risk Prediction Equations for Incident Chronic Kidney Disease

Author

Robert G, Nelson Morgan, E Grams, Shoshana H, Ballew, Yingying, Sang, Fereidoun, Azizi, Steven J, Chadban, Layal, Chaker, Stephan C, Dunning, Caroline, Fox, Yoshihisa, Hirakawa, Kunitoshi, Iseki, Joachim, Ix, Tazeen H, Jafar, Anna, Köttgen, David M, J Naimark, Takayoshi, Ohkubo, Gordon J, Prescott Casey, M Rebholz, Charumathi, Sabanayagam, Toshimi, Sairenchi, Ben, Schöttker, Yugo, Shibagaki, Marcello, Tonelli, Luxia, Zhang, Ron T, Gansevoort, Kunihiro, Matsushita, Mark, Woodward, Josef, Coresh, Varda, Shalev, CKD Prognosis Consortium, John, Chalmers, Hisatomi, Arima, Vlado, Perkovic, Morgan, Grams, Kevan, Polkinghorne, Robert, Atkins, Steven, Chadban, Ming-Hui, Zhao, Fang, Wang, Jinwei, Wang, Frank, Sacks, Gary, Curhan, Michael, Shlipak, Mark, Sarnak, Ronit, Katz, Jade, Hiramoto, Hiroyasu, Iso, Isao, Muraki, Kazumasa, Yamagishi, Mitsumasa, Umesawa, Hermann, Brenner, Kai-Uwe, Saum, Dietrich, Rothenbacher, Shih-Jen, Hwang, Alex, Chang, Jamie, Green, Gurmukteshwar, Singh, H Lester, Kirchner, Corri, Black, Angharad, Marks, Gordon, Prescott, Laura, Clark, Nick, Fluck, Cirillo, Massimo, Stein, Hallan, Marius, Øvrehus, Knut, Langlo, Solfrid, Romundstad, Fujiko, Irie, Adolfo, Correa, Casey, Rebholz, Bessie, Young, L Ebony, Boulware, Stanford, Mwasongwe, Tsuyoshi, Watanabe, Kunihiro, Yamagata, Koichi, Asahi, Gabriel, Chodick, Carmen, Peralta, Erwin, Bottinger, Girish, Nadkarni, Stephen, Ellis, Rajiv, Nadukuru, Timothy, Kenealy, C Raina, Elley, John, Collins, Paul, Drury, Kei, Asayama, Masahiro, Kikuya, Hirohito, Metoki, Masaaki, Nakayama, Chiho, Iseki, Robert, Nelson, Helen, Looker, William, Knowler, Ron, Gansevoort, Stephan, Bakker, Hiddo, Heerspink, Simerjot, Jassal, Jaclyn, Bergstrom, Elizabeth, Barrett-Connor, Csaba, Kovesdy, Keiichi, Sumida, Kamyar, Kalantar-Zadeh, Sanaz, Sedaghat, Arfan, Ikram, Ewout, Hoorn, Abbas, Dehghan, Juan, Carrero, Marie, Evans, Carl-Gustaf, Elinder, Tien, Wong, Ching-Yu, Cheng, Chi-Pang, Wen, Chwen-Keng, Tsao, Min-Kuang, Tsai, Chien-Hua, Chen, Farhad, Hosseinpanah, Farzad, Hadaegh, Mohammadhassan, Mirbolouk, Marit, Solbu, Bjørn, Eriksen, Trond, Jenssen, Anne, Eggen, Lars, Lannfelt, Anders, Larsson, Johan, Ärnlöv, Henk, Bilo, Gijs, Landman, Kornelis, Hateren, Nanne, Kleefstra, Stephan, Dunning, Nikita, Stempniewicz, John, Cuddeback, Elizabeth, Ciemins, Shoshana, Ballew, Andrew, Levey, Jingsha, Chen, Lucia, Kwak, Aditya, Surapeneni, Mark, Woodward., Robert, G, Nelson, Morgan, E, Gram, Shoshana, H, Ballew, Yingying, Sang, Fereidoun, Azizi, Steven, J, Chadban, Layal, Chaker, Stephan, C, Dunning, Caroline, Fox, Yoshihisa, Hirakawa, Kunitoshi, Iseki, Joachim, Ix, Tazeen, H, Jafar, Anna, Köttgen, David, M, J, Naimark, Takayoshi, Ohkubo, Gordon, J, Prescott, Casey, M, Rebholz, Charumathi, Sabanayagam, Toshimi, Sairenchi, Ben, Schöttker, Yugo, Shibagaki, Marcello, Tonelli, Luxia, Zhang, Ron, T, Gansevoort, Kunihiro, Matsushita, Mark, Woodward, Josef, Coresh, Varda, Shalev, CKD Prognosis, Consortium, John, Chalmer, Hisatomi, Arima, Vlado, Perkovic, Morgan, Gram, Kevan, Polkinghorne, Robert, Atkin, Steven, Chadban, Ming-Hui, Zhao, Fang, Wang, Jinwei, Wang, Frank, Sack, Gary, Curhan, Michael, Shlipak, Mark, Sarnak, Ronit, Katz, Jade, Hiramoto, Hiroyasu, Iso, Isao, Muraki, Kazumasa, Yamagishi, Mitsumasa, Umesawa, Hermann, Brenner, Kai-Uwe, Saum, Dietrich, Rothenbacher, Shih-Jen, Hwang, Alex, Chang, Jamie, Green, Gurmukteshwar, Singh, H, Lester, Kirchner, Corri, Black, Angharad, Mark, Gordon, Prescott, Laura, Clark, Nick, Fluck, Cirillo, Massimo, Stein, Hallan, Marius, Øvrehu, Knut, Langlo, Solfrid, Romundstad, Fujiko, Irie, Adolfo, Correa, Casey, Rebholz, Bessie, Young, L, Ebony, Boulware, Stanford, Mwasongwe, Tsuyoshi, Watanabe, Kunihiro, Yamagata, Koichi, Asahi, Gabriel, Chodick, Carmen, Peralta, Erwin, Bottinger, Girish, Nadkarni, Stephen, Elli, Rajiv, Nadukuru, Timothy, Kenealy, C, Raina, Elley, John, Collin, Paul, Drury, Kei, Asayama, Masahiro, Kikuya, Hirohito, Metoki, Masaaki, Nakayama, Chiho, Iseki, Robert, Nelson, Helen, Looker, William, Knowler, Ron, Gansevoort, Stephan, Bakker, Hiddo, Heerspink, Simerjot, Jassal, Jaclyn, Bergstrom, Elizabeth, Barrett-Connor, Csaba, Kovesdy, Keiichi, Sumida, Kamyar, Kalantar-Zadeh, Sanaz, Sedaghat, Arfan, Ikram, Ewout, Hoorn, Abbas, Dehghan, Juan, Carrero, Marie, Evan, Carl-Gustaf, Elinder, Tien, Wong, Ching-Yu, Cheng, Chi-Pang, Wen, Chwen-Keng, Tsao, Min-Kuang, Tsai, Chien-Hua, Chen, Farhad, Hosseinpanah, Farzad, Hadaegh, Mohammadhassan, Mirbolouk, Marit, Solbu, Bjørn, Eriksen, Trond, Jenssen, Anne, Eggen, Lars, Lannfelt, Anders, Larsson, Johan, Ärnlöv, Henk, Bilo, Gijs, Landman, Kornelis, Hateren, Nanne, Kleefstra, Stephan, Dunning, Nikita, Stempniewicz, John, Cuddeback, Elizabeth, Ciemin, Shoshana, Ballew, Andrew, Levey, Jingsha, Chen, Lucia, Kwak, Aditya, Surapeneni, Mark, Woodward., Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Epidemiology, and Internal Medicine

Subjects
Male, medicine.medical_specialty, Renal function, Disease, Risk Assessment, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Interquartile range, Internal medicine, Diabetes mellitus, medicine, CKD, Humans, 030212 general & internal medicine, 0101 mathematics, Renal Insufficiency, Chronic, Statistic, Aged, Original Investigation, business.industry, 010102 general mathematics, General Medicine, Middle Aged, Models, Theoretical, medicine.disease, MODEL, RENAL-DISEASE, Albuminuria, Female, medicine.symptom, business, Body mass index, Glomerular Filtration Rate, Kidney disease
Abstract

IMPORTANCE Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.OBJECTIVE To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).DESIGN, SETTING, AND PARTICIPANTS Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).EXPOSURES Demographic and clinical factors.MAIN OUTCOMES AND MEASURES Incident eGFR of less than 60 mL/min/1.73 m(2).RESULTS Among 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A(1c), and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.CONCLUSIONS AND RELEVANCE Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.

Published
2019

3. 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

Keiichi, Sumida, Girish N, Nadkarni, Morgan E, Grams, Yingying, Sang, Shoshana H, Ballew, Josef, Coresh, Kunihiro, Matsushita, Aditya, Surapaneni, Nigel, Brunskill, Steve J, Chadban, Alex R, Chang, Massimo, Cirillo, Kenn B, Daratha, Ron T, Gansevoort, Amit X, Garg, Licia, Iacoviello, Takamasa, Kayama, Tsuneo, Konta, Csaba P, Kovesdy, James, Lash, Brian J, Lee, Rupert W, Major, Marie, Metzger, Katsuyuki, Miura, David M J, Naimark, Robert G, Nelson, Simon, Sawhney, Nikita, Stempniewicz, Mila, Tang, Raymond R, Townsend, Jamie P, Traynor, José M, Valdivielso, Jack, Wetzels, Kevan R, Polkinghorne, Hiddo J L, Heerspink, Aditya, Surapeneni, Groningen Kidney Center (GKC), Cardiovascular Centre (CVC), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Centre de recherche en épidémiologie et santé des populations (CESP), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay

Subjects
Male, musculoskeletal diseases, medicine.medical_specialty, [SDV]Life Sciences [q-bio], Urology, Renal function, Urine, Urinalysis, 01 natural sciences, Sensitivity and Specificity, 03 medical and health sciences, 0302 clinical medicine, immune system diseases, Diabetes mellitus, Internal Medicine, Medicine, Albuminuria, Humans, Mass Screening, 030212 general & internal medicine, 0101 mathematics, Stage (cooking), Renal Insufficiency, Chronic, skin and connective tissue diseases, Reagent Strips, Kidney, business.industry, 010102 general mathematics, General Medicine, Dipstick, Middle Aged, medicine.disease, Prognosis, 3. Good health, Proteinuria, medicine.anatomical_structure, Creatinine, Female, medicine.symptom, Renal disorders Radboud Institute for Health Sciences [Radboudumc 11], business, Kidney disease
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: 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: National Institute of Diabetes and Digestive and Kidney Diseases and National Kidney Foundation.

Published
2020

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