VA Pittsburgh Healthcare System

INTRODUCTION: There is no consensus definition of acute renal failure (ARF) in critically ill patients. More than 30 different definitions have been used in the literature, creating much confusion and making comparisons difficult. Similarly, strong debate exists on the validity and clinical relevance of animal models of ARF; on choices of fluid management and of end-points for trials of new interventions in this field; and on how information technology can be used to assist this process. Accordingly, we sought to review the available evidence, make recommendations and delineate key questions for future studies. METHODS: We undertook a systematic review of the literature using Medline and PubMed searches. We determined a list of key questions and convened a 2-day consensus conference to develop summary statements via a series of alternating breakout and plenary sessions. In these sessions, we identified supporting evidence and generated recommendations and/or directions for future research. RESULTS: We found sufficient consensus on 47 questions to allow the development of recommendations. Importantly, we were able to develop a consensus definition for ARF. In some cases it was also possible to issue useful consensus recommendations for future investigations. We present a summary of the findings. (Full versions of the six workgroups' findings are available on the internet at http://www.ADQI.net) CONCLUSION: Despite limited data, broad areas of consensus exist for the physiological and clinical principles needed to guide the development of consensus recommendations for defining ARF, selection of animal models, methods of monitoring fluid therapy, choice of physiological and clinical end-points for trials, and the possible role of information technology.

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

BACKGROUND: Patients with nonischemic dilated cardiomyopathy are at substantial risk for sudden death from cardiac causes. However, the value of prophylactic implantation of an implantable cardioverter-defibrillator (ICD) to prevent sudden death in such patients is unknown. METHODS: We enrolled 458 patients with nonischemic dilated cardiomyopathy, a left ventricular ejection fraction of less than 36 percent, and premature ventricular complexes or nonsustained ventricular tachycardia. A total of 229 patients were randomly assigned to receive standard medical therapy, and 229 to receive standard medical therapy plus a single-chamber ICD. RESULTS: Patients were followed for a mean (+/-SD) of 29.0+/-14.4 months. The mean left ventricular ejection fraction was 21 percent. The vast majority of patients were treated with angiotensin-converting-enzyme (ACE) inhibitors (86 percent) and beta-blockers (85 percent). There were 68 deaths: 28 in the ICD group, as compared with 40 in the standard-therapy group (hazard ratio, 0.65; 95 percent confidence interval, 0.40 to 1.06; P=0.08). The mortality rate at two years was 14.1 percent in the standard-therapy group (annual mortality rate, 7 percent) and 7.9 percent in the ICD group. There were 17 sudden deaths from arrhythmia: 3 in the ICD group, as compared with 14 in the standard-therapy group (hazard ratio, 0.20; 95 percent confidence interval, 0.06 to 0.71; P=0.006). CONCLUSIONS: In patients with severe, nonischemic dilated cardiomyopathy who were treated with ACE inhibitors and beta-blockers, the implantation of a cardioverter-defibrillator significantly reduced the risk of sudden death from arrhythmia and was associated with a nonsignificant reduction in the risk of death from any cause.

Although descriptive and etiological approaches to psychopathology have made notable advances, they seem to have reached a plateau. After reviewing the six approaches to etiology that now preempt the field—ecological, developmental, learning, genetic, internal environment, and neurophysiological models—a second-order model, vulnerability, is proposed as the common denominator, and methods for finding markers of vulnerability are suggested in the hope of revitalizing the field. It is assumed that exogenous and/or endogenous challengers elicit a crisis in all humans, but depending on the intensity of the elicited stress and the threshold for tolerating it, that is, one's vulnerability, the crisis will either be contained homeostatically or lead to an episode of disorder. Vulnerability and episode stand in a trait-state relation, and markers for each must be provided to distinguish between them.

BACKGROUND: The effectiveness of surgery versus observation for men with localized prostate cancer detected by means of prostate-specific antigen (PSA) testing is not known. METHODS: From November 1994 through January 2002, we randomly assigned 731 men with localized prostate cancer (mean age, 67 years; median PSA value, 7.8 ng per milliliter) to radical prostatectomy or observation and followed them through January 2010. The primary outcome was all-cause mortality; the secondary outcome was prostate-cancer mortality. RESULTS: During the median follow-up of 10.0 years, 171 of 364 men (47.0%) assigned to radical prostatectomy died, as compared with 183 of 367 (49.9%) assigned to observation (hazard ratio, 0.88; 95% confidence interval [CI], 0.71 to 1.08; P=0.22; absolute risk reduction, 2.9 percentage points). Among men assigned to radical prostatectomy, 21 (5.8%) died from prostate cancer or treatment, as compared with 31 men (8.4%) assigned to observation (hazard ratio, 0.63; 95% CI, 0.36 to 1.09; P=0.09; absolute risk reduction, 2.6 percentage points). The effect of treatment on all-cause and prostate-cancer mortality did not differ according to age, race, coexisting conditions, self-reported performance status, or histologic features of the tumor. Radical prostatectomy was associated with reduced all-cause mortality among men with a PSA value greater than 10 ng per milliliter (P=0.04 for interaction) and possibly among those with intermediate-risk or high-risk tumors (P=0.07 for interaction). Adverse events within 30 days after surgery occurred in 21.4% of men, including one death. CONCLUSIONS: Among men with localized prostate cancer detected during the early era of PSA testing, radical prostatectomy did not significantly reduce all-cause or prostate-cancer mortality, as compared with observation, through at least 12 years of follow-up. Absolute differences were less than 3 percentage points. (Funded by the Department of Veterans Affairs Cooperative Studies Program and others; PIVOT ClinicalTrials.gov number, NCT00007644.).

Clostridium difficile infection (CDI) is a leading cause of hospital-associated gastrointestinal illness and places a high burden on our health-care system. Patients with CDI typically have extended lengths-of-stay in hospitals, and CDI is a frequent cause of large hospital outbreaks of disease. This guideline provides recommendations for the diagnosis and management of patients with CDI as well as for the prevention and control of outbreaks while supplementing previously published guidelines. New molecular diagnostic stool tests will likely replace current enzyme immunoassay tests. We suggest treatment of patients be stratified depending on whether they have mild-to-moderate, severe, or complicated disease. Therapy with metronidazole remains the choice for mild-to-moderate disease but may not be adequate for patients with severe or complicated disease. We propose a classification of disease severity to guide therapy that is useful for clinicians. We review current treatment options for patients with recurrent CDI and recommendations for the control and prevention of outbreaks of CDI.

BACKGROUND AND PURPOSE: Mononuclear phagocytes are highly plastic cells that assume diverse phenotypes in response to microenvironmental signals. The phenotype-specific roles of microglia/macrophages in ischemic brain injury are poorly understood. A comprehensive characterization of microglia/macrophage polarization after ischemia may advance our knowledge of poststroke damage/recovery. METHODS: Focal transient cerebral ischemia was induced in mice for 60 minutes; animals were euthanized at 1 to 14 days of reperfusion. Reverse-transcriptase polymerase chain reaction and immunohistochemical staining for M1 and M2 markers were performed to characterize phenotypic changes in brain cells, including microglia and infiltrating macrophages. In vitro experiments using a transwell system, a conditioned medium transfer system, or a coculture system allowing cell-to-cell contacts were used to further elucidate the effect of neuronal ischemia on microglia/macrophage polarization and, conversely, the effect of microglia/macrophage phenotype on the fate of ischemic neurons. RESULTS: Local microglia and newly recruited macrophages assume the M2 phenotype at early stages of ischemic stroke but gradually transformed into the M1 phenotype in peri-infarct regions. In vitro experiments revealed that ischemic neurons prime microglial polarization toward M1 phenotype. M1-polarized microglia or M1-conditioned media exacerbated oxygen glucose deprivation-induced neuronal death. In contrast, maintaining the M2 phenotype of microglia protected neurons against oxygen glucose deprivation. CONCLUSIONS: Our results suggest that microglia/macrophages respond dynamically to ischemic injury, experiencing an early "healthy" M2 phenotype, followed by a transition to a "sick" M1 phenotype. These dual and opposing roles of microglia/macrophages suggest that stroke therapies should be shifted from simply suppressing microglia/macrophage toward adjusting the balance between beneficial and detrimental microglia/macrophage responses.

Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of >90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD.

Importance: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective: To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures: All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures: Derived phenotype (α, β, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results: The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). Conclusions and Relevance: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

4. Does inpatient management of hyperglycemia represent a safety concern? 5. What systems need to be in place to achieve these recommendations? 6. Is treatment of inpatient hyperglycemia cost-effective? 7. What are the optimal strategies for transition to outpatient care? 8. What are areas for future research?

RATIONALE: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment. OBJECTIVES: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes. METHODS: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France. MEASUREMENTS: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. MAIN RESULTS: The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were <or= 1.1% among patients in class I and <or= 1.9% among patients in class II. CONCLUSIONS: Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

BACKGROUND: Cystatin C is a serum measure of renal function that appears to be independent of age, sex, and lean muscle mass. We compared creatinine and cystatin C levels as predictors of mortality from cardiovascular causes and from all causes in the Cardiovascular Health Study, a cohort study of elderly persons living in the community. METHODS: Creatinine and cystatin C were measured in serum samples collected from 4637 participants at the study visit in 1992 or 1993; follow-up continued until June 30, 2001. For each measure, the study population was divided into quintiles, with the fifth quintile subdivided into thirds (designated 5a, 5b, and 5c). RESULTS: Higher cystatin C levels were directly associated, in a dose-response manner, with a higher risk of death from all causes. As compared with the first quintile, the hazard ratios (and 95 percent confidence intervals) for death were as follows: second quintile, 1.08 (0.86 to 1.35); third quintile, 1.23 (1.00 to 1.53); fourth quintile, 1.34 (1.09 to 1.66); quintile 5a, 1.77 (1.34 to 2.26); 5b, 2.18 (1.72 to 2.78); and 5c, 2.58 (2.03 to 3.27). In contrast, the association of creatinine categories with mortality from all causes appeared to be J-shaped. As compared with the two lowest quintiles combined (cystatin C level, < or =0.99 mg per liter), the highest quintile of cystatin C (> or =1.29 mg per liter) was associated with a significantly elevated risk of death from cardiovascular causes (hazard ratio, 2.27 [1.73 to 2.97]), myocardial infarction (hazard ratio, 1.48 [1.08 to 2.02]), and stroke (hazard ratio, 1.47 [ 1.09 to 1.96]) after multivariate adjustment. The fifth quintile of creatinine, as compared with the first quintile, was not independently associated with any of these three outcomes. CONCLUSIONS: Cystatin C, a serum measure of renal function, is a stronger predictor of the risk of death and cardiovascular events in elderly persons than is creatinine.

Physicians are often asked to make prognostic assessments but often worry that their assessments will prove inaccurate. Prognostic systems were developed to enhance the accuracy of such assessments. This paper describes an approach for evaluating prognostic systems based on the accuracy (calibration and discrimination) and generalizability (reproducibility and transportability) of the system's predictions. Reproducibility is the ability to produce accurate predictions among patients not included in the development of the system but from the same population. Transportability is the ability to produce accurate predictions among patients drawn from a different but plausibly related population. On the basis of the observation that the generalizability of a prognostic system is commonly limited to a single historical period, geographic location, methodologic approach, disease spectrum, or follow-up interval, we describe a working hierarchy of the cumulative generalizability of prognostic systems. This approach is illustrated in a structured review of the Dukes and Jass staging systems for colon and rectal cancer and applied to a young man with colon cancer. Because it treats the development of the system as a "black box" and evaluates only the performance of the predictions, the approach can be applied to any system that generates predicted probabilities. Although the Dukes and Jass staging systems are discrete, the approach can also be applied to systems that generate continuous predictions and, with some modification, to systems that predict over multiple time periods. Like any scientific hypothesis, the generalizability of a prognostic system is established by being tested and being found accurate across increasingly diverse settings. The more numerous and diverse the settings in which the system is tested and found accurate, the more likely it will generalize to an untested setting.

BACKGROUND: Combination therapy with angiotensin-converting-enzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs) decreases proteinuria; however, its safety and effect on the progression of kidney disease are uncertain. Methods We provided losartan (at a dose of 100 mg per day) to patients with type 2 diabetes, a urinary albumin-to-creatinine ratio (with albumin measured in milligrams and creatinine measured in grams) of at least 300, and an estimated glomerular filtration rate (GFR) of 30.0 to 89.9 ml per minute per 1.73 m(2) of body-surface area and then randomly assigned them to receive lisinopril (at a dose of 10 to 40 mg per day) or placebo. The primary end point was the first occurrence of a change in the estimated GFR (a decline of ≥ 30 ml per minute per 1.73 m(2) if the initial estimated GFR was ≥ 60 ml per minute per 1.73 m(2) or a decline of ≥ 50% if the initial estimated GFR was <60 ml per minute per 1.73 m(2)), end-stage renal disease (ESRD), or death. The secondary renal end point was the first occurrence of a decline in the estimated GFR or ESRD. Safety outcomes included mortality, hyperkalemia, and acute kidney injury. Results The study was stopped early owing to safety concerns. Among 1448 randomly assigned patients with a median follow-up of 2.2 years, there were 152 primary end-point events in the monotherapy group and 132 in the combination-therapy group (hazard ratio with combination therapy, 0.88; 95% confidence interval [CI], 0.70 to 1.12; P=0.30). A trend toward a benefit from combination therapy with respect to the secondary end point (hazard ratio, 0.78; 95% CI, 0.58 to 1.05; P=0.10) decreased with time (P=0.02 for nonproportionality). There was no benefit with respect to mortality (hazard ratio for death, 1.04; 95% CI, 0.73 to 1.49; P=0.75) or cardiovascular events. Combination therapy increased the risk of hyperkalemia (6.3 events per 100 person-years, vs. 2.6 events per 100 person-years with monotherapy; P<0.001) and acute kidney injury (12.2 vs. 6.7 events per 100 person-years, P<0.001). Conclusions Combination therapy with an ACE inhibitor and an ARB was associated with an increased risk of adverse events among patients with diabetic nephropathy. (Funded by the Cooperative Studies Program of the Department of Veterans Affairs Office of Research and Development; VA NEPHRON-D ClinicalTrials.gov number, NCT00555217.).

Mitochondrial dysregulation is strongly implicated in Parkinson disease. Mutations in PTEN-induced kinase 1 (PINK1) are associated with familial parkinsonism and neuropsychiatric disorders. Although overexpressed PINK1 is neuroprotective, less is known about neuronal responses to loss of PINK1 function. We found that stable knockdown of PINK1 induced mitochondrial fragmentation and autophagy in SH-SY5Y cells, which was reversed by the reintroduction of an RNA interference (RNAi)-resistant plasmid for PINK1. Moreover, stable or transient overexpression of wild-type PINK1 increased mitochondrial interconnectivity and suppressed toxin-induced autophagy/mitophagy. Mitochondrial oxidant production played an essential role in triggering mitochondrial fragmentation and autophagy in PINK1 shRNA lines. Autophagy/mitophagy served a protective role in limiting cell death, and overexpressing Parkin further enhanced this protective mitophagic response. The dominant negative Drp1 mutant inhibited both fission and mitophagy in PINK1-deficient cells. Interestingly, RNAi knockdown of autophagy proteins Atg7 and LC3/Atg8 also decreased mitochondrial fragmentation without affecting oxidative stress, suggesting active involvement of autophagy in morphologic remodeling of mitochondria for clearance. To summarize, loss of PINK1 function elicits oxidative stress and mitochondrial turnover coordinated by the autophagic and fission/fusion machineries. Furthermore, PINK1 and Parkin may cooperate through different mechanisms to maintain mitochondrial homeostasis. Mitochondrial dysregulation is strongly implicated in Parkinson disease. Mutations in PTEN-induced kinase 1 (PINK1) are associated with familial parkinsonism and neuropsychiatric disorders. Although overexpressed PINK1 is neuroprotective, less is known about neuronal responses to loss of PINK1 function. We found that stable knockdown of PINK1 induced mitochondrial fragmentation and autophagy in SH-SY5Y cells, which was reversed by the reintroduction of an RNA interference (RNAi)-resistant plasmid for PINK1. Moreover, stable or transient overexpression of wild-type PINK1 increased mitochondrial interconnectivity and suppressed toxin-induced autophagy/mitophagy. Mitochondrial oxidant production played an essential role in triggering mitochondrial fragmentation and autophagy in PINK1 shRNA lines. Autophagy/mitophagy served a protective role in limiting cell death, and overexpressing Parkin further enhanced this protective mitophagic response. The dominant negative Drp1 mutant inhibited both fission and mitophagy in PINK1-deficient cells. Interestingly, RNAi knockdown of autophagy proteins Atg7 and LC3/Atg8 also decreased mitochondrial fragmentation without affecting oxidative stress, suggesting active involvement of autophagy in morphologic remodeling of mitochondria for clearance. To summarize, loss of PINK1 function elicits oxidative stress and mitochondrial turnover coordinated by the autophagic and fission/fusion machineries. Furthermore, PINK1 and Parkin may cooperate through different mechanisms to maintain mitochondrial homeostasis. Parkinson disease is an age-related neurodegenerative disease that affects ∼1% of the population worldwide. The causes of sporadic cases are unknown, although mitochondrial or oxidative toxins such as 1-methyl-4-phenylpyridinium, 6-hydroxydopamine (6-OHDA), 3The abbreviations used are: 6-OHDA, 6-hydroxydopamine; AV, autophagic vacuole; Drp1, dynamin-related protein-1; Drp1-DN, dominant negative Drp1; LC3, microtubule-associated protein light chain 3; PD, Parkinson disease/parkinsonian disorder; PINK1, PTEN-induced kinase 1; ROS, reactive oxygen species; siRNA, small interfering RNA; RNAi, RNA interference; shRNA, short hairpin RNA; HA, hemagglutinin; GFP, green fluorescent protein; RFP, red fluorescent protein; DAPI, 4′,6-diamidino-2-phenylindole; ERK, extracellular signal-regulated kinase; MnTBAP, manganese(III) tetrakis(4-benzoic acid)porphyrin. and rotenone reproduce features of the disease in animal and cell culture models (1Bove J. Prou D. Perier C. Przedborski S. NeuroRx. 2005; 2: 484-494Crossref PubMed Scopus (575) Google Scholar). Abnormalities in mitochondrial respiration and increased oxidative stress are observed in cells and tissues from parkinsonian patients (2Hoepken H.H. Gispert S. Morales B. Wingerter O. Del Turco D. Mulsch A. Nussbaum R.L. Muller K. Drose S. Brandt U. Deller T. Wirth B. Kudin A.P. Kunz W.S. Auburger G. Neurobiol. Dis. 2007; 25: 401-411Crossref PubMed Scopus (170) Google Scholar, 3Exner N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar), which also exhibit increased mitochondrial autophagy (4Zhu J.-H. Guo F. Shelburne J. Watkins S. Chu C.T. Brain Pathol. 2003; 13: 473-481Crossref PubMed Scopus (211) Google Scholar). Furthermore, mutations in parkinsonian genes affect oxidative stress response pathways and mitochondrial homeostasis (5Abeliovich A. Flint Beal M. J. Neurochem. 2006; 99: 1062-1072Crossref PubMed Scopus (94) Google Scholar). Thus, disruption of mitochondrial homeostasis represents a major factor implicated in the pathogenesis of sporadic and inherited parkinsonian disorders (PD). The PARK6 locus involved in autosomal recessive and early-onset PD encodes for PTEN-induced kinase 1 (PINK1) (6Valente E.M. Abou-Sleiman P.M. Caputo V. Muqit M.M. Harvey K. Gispert S. Ali Z. Del Turco D. Bentivoglio A.R. Healy D.G. Albanese A. Nussbaum R. Gonzalez-Maldonado R. Deller T. Salvi S. Cortelli P. Gilks W.P. Latchman D.S. Harvey R.J. Dallapiccola B. Auburger G. Wood N.W. Science. 2004; 304: 1158-1160Crossref PubMed Scopus (2701) Google Scholar, 7Valente E.M. Salvi S. Ialongo T. Marongiu R. Elia A.E. Caputo V. Romito L. Albanese A. Dallapiccola B. Bentivoglio A.R. Ann. Neurol. 2004; 56: 336-341Crossref PubMed Scopus (418) Google Scholar). PINK1 is a cytosolic and mitochondrially localized 581-amino acid serine/threonine kinase that possesses an N-terminal mitochondrial targeting sequence (6Valente E.M. Abou-Sleiman P.M. Caputo V. Muqit M.M. Harvey K. Gispert S. Ali Z. Del Turco D. Bentivoglio A.R. Healy D.G. Albanese A. Nussbaum R. Gonzalez-Maldonado R. Deller T. Salvi S. Cortelli P. Gilks W.P. Latchman D.S. Harvey R.J. Dallapiccola B. Auburger G. Wood N.W. Science. 2004; 304: 1158-1160Crossref PubMed Scopus (2701) Google Scholar, 8Zhou C. Huang Y. Shao Y. May J. Prou D. Perier C. Dauer W. Schon E.A. Przedborski S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 12022-12027Crossref PubMed Scopus (264) Google Scholar). The primary sequence also includes a putative transmembrane domain important for orientation of the PINK1 domain (8Zhou C. Huang Y. Shao Y. May J. Prou D. Perier C. Dauer W. Schon E.A. Przedborski S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 12022-12027Crossref PubMed Scopus (264) Google Scholar), a conserved kinase domain homologous to calcium calmodulin kinases, and a C-terminal domain that regulates autophosphorylation activity (9Silvestri L. Caputo V. Bellacchio E. Atorino L. Dallapiccola B. Valente E.M. Casari G. Hum. Mol. Genet. 2005; 14: 3477-3492Crossref PubMed Scopus (393) Google Scholar, 10Sim C.H. Lio D.S. Mok S.S. Masters C.L. Hill A.F. Culvenor J.G. Cheng H.C. Hum. Mol. Genet. 2006; 15: 3251-3262Crossref PubMed Scopus (124) Google Scholar). Overexpression of wild-type PINK1, but not its PD-associated mutants, protects against several toxic insults in neuronal cells (6Valente E.M. Abou-Sleiman P.M. Caputo V. Muqit M.M. Harvey K. Gispert S. Ali Z. Del Turco D. Bentivoglio A.R. Healy D.G. Albanese A. Nussbaum R. Gonzalez-Maldonado R. Deller T. Salvi S. Cortelli P. Gilks W.P. Latchman D.S. Harvey R.J. Dallapiccola B. Auburger G. Wood N.W. Science. 2004; 304: 1158-1160Crossref PubMed Scopus (2701) Google Scholar, 11Deng H. Jankovic J. Guo Y. Xie W. Le W. Biochem. 2005; PubMed Scopus Google Scholar, A. T. E. N. M. M. F. Y. H. S. L. E. P. B. P. A. J. 2005; PubMed Scopus Google Scholar). Mitochondrial targeting is for Neurobiol. Dis. 2007; PubMed Scopus Google but not of the of PINK1 C. S. T. P. A. D.S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar), involvement of that mitochondrial (8Zhou C. Huang Y. Shao Y. May J. Prou D. Perier C. Dauer W. Schon E.A. Przedborski S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 12022-12027Crossref PubMed Scopus (264) Google Scholar). PINK1 activity is for its a in the of PINK1 its to C. S. T. P. A. D.S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar). Although PINK1 mutations not to mitochondrial PD-associated mutations the in loss of Neurobiol. Dis. 2007; PubMed Scopus Google Scholar, A. M. R. S. Proc. Natl. Acad. Sci. U. S. A. 2005; PubMed Scopus Google Scholar). that PINK1 and Parkin N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar, C. Guo M. 2006; PubMed Scopus Google Scholar, J. S. Y. S. S. E. J. M. J. 2006; PubMed Scopus Google Scholar, Y. S. Y. Huang Z. Y. L. Beal Vogel H. B. Proc. Natl. Acad. Sci. U. S. A. 2006; PubMed Scopus Google to oxidative stress A. S. Z. A.S. E.A. G. L. I. K. E. J. L. P. J. S. Latchman D. Wood N.W. 2008; PubMed Scopus Google Scholar, D. L. H. J. W.S. S. K. J. R. Z. Proc. Natl. Acad. Sci. U. S. A. 2006; PubMed Scopus Google and mitochondrial Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar). cells from PINK1 mutant patients exhibit mitochondrial fragmentation with by RNA interference in cells N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar). are by a of autophagic for to C.T. PubMed Scopus Google Scholar, N. Y. T. 27: PubMed Scopus Google Scholar). Interestingly, mitochondrial fission autophagic by the PD M. J. M. 2008; PubMed Scopus Google Scholar, J. Chu C.T. 2008; PubMed Scopus Google Scholar). Moreover, mitochondrial fragmentation and increased autophagy are observed in neurodegenerative and Parkinson (4Zhu J.-H. Guo F. Shelburne J. Watkins S. Chu C.T. Brain Pathol. 2003; 13: 473-481Crossref PubMed Scopus (211) Google Scholar, K. G. A. H. R.L. Y. M. S. G. J. Neurosci. PubMed Google Scholar, J. A. C. A. J. Neurol. 2005; PubMed Scopus Google Scholar, Chu C.T. Neurol. 2008; Google Scholar). Although of mitochondria in was to a as observed mitochondrial or limiting of in the of mitochondrial autophagy I. S. Biochem. 2007; PubMed Scopus Google Scholar, L. V. PubMed Scopus Google Scholar). mitochondrially localized may an important role in models oxidative mitochondrial J. Chu C.T. 2008; PubMed Scopus Google Scholar, C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google Scholar, C.T. J. R. 2007; PubMed Scopus Google Scholar). is involved in the of protein T. K. M. A. Y. R. M. K. I. H. N. 2006; PubMed Scopus Google Scholar, I. L. J. Biochem. 2004; PubMed Scopus Google Scholar, B. C. Z. S. F. R. Genet. 2004; PubMed Scopus Google and of M. J. E. K. Z. Proc. Natl. Acad. Sci. U. S. A. 2007; PubMed Scopus Google Scholar). disruption of function in of mitochondria with decreased calcium Y. Chu C.T. K. J. 2006; PubMed Scopus Google Scholar), an important role for autophagy in mitochondrial homeostasis Y. Chu C.T. K. J. 2006; PubMed Scopus Google Scholar, Y. H. R. W. S. 2007; PubMed Scopus Google Scholar). which the of PINK1 mitochondrial N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar), was found to autophagy of mitochondria D. A. R.J. J. 2008; PubMed Scopus Google Scholar). to cell by the PD toxins and J. Chu C.T. 2008; PubMed Scopus Google Scholar, Chu C.T. Neurol. 2008; Google Scholar, C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google Scholar, C.T. J. R. 2007; PubMed Scopus Google Scholar), in cells a in kinase Chu C.T. J. Neurochem. 2008; 105: PubMed Scopus Google Scholar). autophagy a and or autophagy a of that to Chu C.T. Neurol. 2008; Google Scholar). mitochondrial fragmentation N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google and increased mitochondrial autophagy (4Zhu J.-H. Guo F. Shelburne J. Watkins S. Chu C.T. Brain Pathol. 2003; 13: 473-481Crossref PubMed Scopus (211) Google in cells or tissues of PD or not the loss of PINK1 function in neuronal cells knockdown of PINK1 to mitochondrial fragmentation and increased autophagy and stable or transient overexpression of PINK1 the Autophagy/mitophagy was increased mitochondrial oxidant production and of The that PINK1 is important for the of mitochondrial suggesting that coordinated of mitochondrial and autophagy cell associated with loss of PINK1 function. with A. T. E. N. M. M. F. Y. H. S. L. E. P. B. P. A. J. 2005; PubMed Scopus Google and in C. S. T. P. A. D.S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google for Parkin in the was from and or wild-type and dominant negative Drp1 in the by of The shRNA for Drp1 S. 2007; PubMed Scopus Google Scholar). and by of S. T. T. 2007; PubMed Scopus Google Scholar). of PINK1 a in kinase of PINK1 was the of and for in and fluorescent of and SH-SY5Y cell a cell was as C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google Scholar). stable cell SH-SY5Y cells with in or and with 1 in for in of was by with and PINK1 was To stable PINK1-deficient SH-SY5Y with PINK1 or shRNA and as that was To for or of PINK1 shRNA targeting the N-terminal and the a sequence in the kinase domain by for PINK1 and and the protein was by for PINK1 and cells and with and cells in the or and with cells an a of or and a cells with and with an the function of of a for with or manganese(III) tetrakis(4-benzoic was used to A. Y. T. Y. R. Y. PubMed Scopus Google Scholar), and was used for to the interfering RNA for proteins and as C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google Scholar), and SH-SY5Y cells with siRNA, Atg7 siRNA, or the to C.T. PubMed Scopus Google Scholar). from SH-SY5Y cells as C. M. Chu C.T. 2007; PubMed Scopus Google Scholar). as C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google and a and with and as C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google or and by against the of was in a was 1 for as J. Chu C.T. 2008; PubMed Scopus Google and by the of cells or Mitochondrial with as C. M. Chu C.T. 2007; PubMed Scopus Google Scholar). To the of used a by Watkins of the for which the The of was to cell by the of the function in the of and Mitochondrial in cells with was as J. Chu C.T. 2008; PubMed Scopus Google Scholar). of the as a of autophagy was a for as C.T. PubMed Scopus Google Scholar, J. Chu C.T. 2008; PubMed Scopus Google Scholar). To of mitochondrial was for for from the The green of cells with or was to to as and to The mitochondrial The was as an of mitochondrial with used as a of mitochondrial as of mitochondrial fission and are as from of with of and the mechanisms associated with in PINK1 a of SH-SY5Y cell that or exhibit decreased of PINK1. a the a and a of PINK1. the a and a are by with the or by shRNA to PINK1 of PINK1 with mitochondria with in the and stable of PINK1 a C-terminal and overexpressed PINK1 and PINK1 is also observed in both and and 1 is in the mitochondrial and cytosolic was not observed but increased in of the with or with not The of PINK1 knockdown in stable PINK1 shRNA was and protein by and not PINK1 shRNA in both the and of PINK1 with cell of PINK1 in PINK1 shRNA with cell by and of PINK1 Mitochondrial and that stable mitochondrial and a of knockdown of PINK1 short hairpin and mitochondrial with The of of mitochondrial was in PINK1 knockdown cells to in disease (4Zhu J.-H. Guo F. Shelburne J. Watkins S. Chu C.T. Brain Pathol. 2003; 13: 473-481Crossref PubMed Scopus (211) Google and PD cells P. Neurol. PubMed Scopus Google Scholar). The of and cell was with cells suggesting that loss of PINK1 mitochondrial with a of and as as mitochondria with to the PINK1 knockdown cell the of mitochondria not to an autophagic as the of cell in overexpressing cells and PINK1 Mitochondrial mitochondrial are by cell by to the morphologic of of PINK1 with of known of mitochondrial The for mitochondria in an and of mitochondrial interconnectivity and mitochondrial was by with the fission protein dynamin-related protein and dominant negative Drp1 that in increased mitochondrial fission and E. L. Mol. PubMed Scopus Google Scholar, M. B. S. A. A. M. C.L. R.J. J. PubMed Scopus Google transient or stable overexpression of wild-type PINK1 increased mitochondrial interconnectivity and with stable cells and stable knockdown of PINK1 induced a fragmentation of the mitochondrial with cells mitochondrial an of of of mitochondria that PINK1-deficient exhibit mitochondria with cells, which a of and overexpression of PINK1 increased mitochondrial PINK1 of and fluorescent and of autophagy to the of PINK1 knockdown C.T. PubMed Scopus Google Scholar). of the protein protein light chain is essential for N. Y. T. 27: PubMed Scopus Google Scholar). The is in the is localized to and by Y. N. T. A. T. T. E. Y. T. J. PubMed Scopus Google Scholar). knockdown of PINK1 increased the to in PINK1 shRNA that loss of PINK1 autophagy The of was in cells with a of or which in S. T. T. 2007; PubMed Scopus Google Scholar). of PINK1 increased the of and and that and are in cell lines. of which A. Y. T. Y. R. Y. PubMed Scopus Google Scholar), autophagic in the PINK1-deficient PINK1 shRNA stable cell with and for of with as an of mitophagy J. Chu C.T. 2008; PubMed Scopus Google Scholar, S. I. 2006; 2: PubMed Scopus Google Scholar). We found that stable knockdown of PINK1 induced autophagic mitochondrial and and to and decreased of mitochondria as by for mitochondrial and proteins and about a in mitochondrial in shRNA overexpression of PINK1 an cells with or RNAi of Atg7 or proteins reversed the loss of mitochondrial autophagic of mitochondria in PINK1-deficient cells PINK1 Overexpression PINK1 J. Chu C.T. 2008; PubMed Scopus Google Scholar). stable knockdown of PINK1 stable PINK1 overexpression suppressed induced autophagy and mitophagy with stable and shRNA that transient of which the sequence by the shRNA, reversed the increased autophagy observed in PINK1 and is also active in against C. S. T. P. A. D.S. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar), which is associated with autophagic cell C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google Scholar). of PINK1 Mitochondrial and the mechanisms the of PINK1 knockdown mitochondrial used a mitochondrially fluorescent knockdown of PINK1 increased mitochondrial as by that loss of PINK1 mitochondrial and and Moreover, which to affect both and extracellular to of through the and MnTBAP, a that to mitochondria and 2003; 2: PubMed Scopus Google Scholar), reversed mitochondrial induced by loss of PINK1 to to in cells with and of Drp1-DN, which reversed the of PINK1 knockdown mitochondrial not in cell suggesting that of mitochondrial fragmentation in PINK1-deficient cells. shRNA PINK1 knockdown with and with or to the of autophagy induced by PINK1 inhibited autophagy induced by PINK1 also a autophagy and that production is for both mitochondrial fragmentation and autophagy but that its may important for autophagy Mitochondrial for Mitochondrial and in PINK1-deficient mitochondrial fragmentation observed in PINK1 shRNA cell was by We stable and PINK1 shRNA with the activity for in increased mitochondrial interconnectivity in SH-SY5Y cells of mitochondrial interconnectivity and that fission is for the mitochondrial remodeling observed in PINK1-deficient and Interestingly, with as a of autophagy in decreased and mitophagy in to to observed in wild-type and that the mitochondrial fission/fusion regulates mitochondrial induced by PINK1 for Mitochondrial in PINK1 shRNA but for Mitochondrial the of autophagy mitochondrial production and mitochondrial targeting the essential autophagy proteins Atg7 and J. Chu C.T. 2008; PubMed Scopus Google Scholar, C. Guo F. Watkins S. Y. Chu C.T. J. Pathol. 2007; PubMed Scopus Google Scholar, Chu C.T. J. Neurochem. 2008; 105: PubMed Scopus Google Scholar). knockdown of proteins inhibited autophagy in PINK1-deficient but the increased mitochondrial that autophagy of mitochondrial oxidative stress Interestingly, for or Atg7 also reversed the mitochondrial fragmentation observed in cell to to found in cells with of PINK1 and with the in that autophagy is involved in remodeling mitochondria for a role of Thus, fission/fusion and autophagic cooperate in the of mitochondrial remodeling and induced by PINK1 of and Mitochondrial in PINK1 with the that loss of PINK1 is in neuronal cells A. S. Z. A.S. E.A. G. L. I. K. E. J. L. P. J. S. Latchman D. Wood N.W. 2008; PubMed Scopus Google Scholar), found that knockdown of PINK1 a in cell in SH-SY5Y cells associated with an in cell in culture The role of autophagy in cell in PINK1-deficient cells was to autophagy and a of to autophagic further in cell was observed in PINK1 shRNA cells with or Atg7 siRNA, the of which the of stable cell mitochondrial induced by increased cell in cell suggesting that mitochondrial fission is also a protective in the of PINK1 mitophagic responses a role for autophagy in the of PINK1 as a to Parkin and in PINK1-deficient the that Parkin is to mitochondria and autophagy D. A. R.J. J. 2008; PubMed Scopus Google Scholar), the of Parkin in PINK1 knockdown cells. and that transient of a in SH-SY5Y cells increased the of and the of that with mitochondria cell in PINK1 knockdown cell the transient of Parkin mitochondrial morphologic to that transient of Parkin reversed cell induced by loss of PINK1 are to the of PD and Although the of cases are the of genes to parkinsonian pathways oxidative stress, mitochondrial and protein R. Brain Pathol. PubMed Scopus Google Scholar, Science. 2003; PubMed Scopus Google Scholar). the role of proteins implicated in autosomal recessive of PD of for the mechanisms that to or disease The that PINK1 a role in mitochondrial mitochondrial oxidative stress, and and that coordinated of and Parkin pathways are important protective responses for the of the with the that PINK1 is for mitochondrial and homeostasis (2Hoepken H.H. Gispert S. Morales B. Wingerter O. Del Turco D. Mulsch A. Nussbaum R.L. Muller K. Drose S. Brandt U. Deller T. Wirth B. Kudin A.P. Kunz W.S. Auburger G. Neurobiol. Dis. 2007; 25: 401-411Crossref PubMed Scopus (170) Google Scholar, 3Exner N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar, E.M. Abou-Sleiman P.M. Caputo V. Muqit M.M. Harvey K. Gispert S. Ali Z. Del Turco D. Bentivoglio A.R. Healy D.G. Albanese A. Nussbaum R. Gonzalez-Maldonado R. Deller T. Salvi S. Cortelli P. Gilks W.P. Latchman D.S. Harvey R.J. Dallapiccola B. Auburger G. Wood N.W. Science. 2004; 304: 1158-1160Crossref PubMed Scopus (2701) Google Scholar), found that stable knockdown of PINK1 mitochondrial and mitochondrial and overexpression of PINK1 mitochondrial a role of PINK1 in to a for PINK1 mitochondrial and that both the fission and autophagy are essential for the mitochondrial remodeling that of PINK1-deficient mitochondria and that this mitophagic response a protective mitochondrial as a coordinated of mitochondrial fission and autophagy to of mitochondria Parkin to and the autophagy of mitochondria D. A. R.J. J. 2008; PubMed Scopus Google Scholar). this to mitochondria by PINK1 Parkin the mitophagic response in PINK1 shRNA cell mitochondrial and cell of mitochondrial fission cell by Parkin is not to the mitochondrial a of mechanisms of in which PINK1 mitochondrial and Parkin mitochondria for clearance. Although and cell mitochondrial fragmentation in PINK1-deficient cells N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar), in the of for mitochondria in mutant loss of function PINK1 Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar, Y. Y. L. Beal A. Vogel H. B. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar). of PINK1 also a in mitochondrial T. J. Proc. Natl. Acad. Sci. U. S. A. 2008; 105: PubMed Scopus Google Scholar). Although mitochondria are small and in PINK1-deficient SH-SY5Y cells, also mitochondrial and mitochondria by this a or the of Parkin to mitochondrial morphologic in PINK1-deficient cells to or through of mitochondrial by that may and N. Treske B. Paquet D. Holmstrom K. Schiesling C. Gispert S. Carballo-Carbajal I. Berg D. Hoepken H.H. Gasser T. Kruger R. Winklhofer K.F. Vogel F. Reichert A.S. Auburger G. Kahle P.J. Schmid B. Haass C. J. Neurosci. 2007; 27: 12413-12418Crossref PubMed Scopus (435) Google Scholar, A. S. Z. A.S. E.A. G. L. I. K. E. J. L. P. J. S. Latchman D. Wood N.W. 2008; PubMed Scopus Google Scholar). is also that fission observed in RNAi in response to mitochondrial to PINK1 which is not observed in to its is that both fission and are induced in response to stress and but the the of and mechanisms in a a role in mitochondrial homeostasis in D. Google Scholar). is known about the of Mitochondrial fission mitophagy in G. A. H. G. L. S. G. J. M. J. J. 2008; 27: PubMed Scopus Google Scholar), and found that loss of PINK1 both mitochondrial fission and the autophagic is not for of mitochondria to but also to mitochondrial fragmentation and of autophagy to increased of but mitochondria autophagy as a of of the autophagy reversed the fragmentation induced by PINK1 and Interestingly, cell in that of different and of mitochondria I. S. Biochem. 2007; PubMed Scopus Google Scholar). Thus, coordinated of fission and autophagic to mitochondrial remodeling for autophagic turnover in PINK1-deficient cells. disease tissues is mitochondrial autophagy associated with increased mitochondrial kinase (4Zhu J.-H. Guo F. Shelburne J. Watkins S. Chu C.T. Brain Pathol. 2003; 13: 473-481Crossref PubMed Scopus (211) Google Scholar). The that overexpression of PINK1 mitochondrial the that mitochondrially localized may a role in mitochondrial The that protein kinase of Drp1 fission S. 2007; PubMed Scopus Google Scholar, C. J. 2007; PubMed Scopus Google that may also mitochondrial fission/fusion although of the PINK1 is known to affect mitochondrial H. K. N. S. S. D. Harvey K. E. Harvey R.J. N. Wood N.W. J. 2007; PubMed Scopus Google Scholar, L. 2007; PubMed Scopus Google Scholar). mitochondrial is strongly implicated in the pathogenesis of PD, mitophagy may a to by may are mitochondrial respiration Chu C.T. Neurol. 2008; Google Scholar). in this of PINK1 a role and although the further Chu C.T. J. Neurochem. 2008; 105: PubMed Scopus Google Scholar, Y. K. T. J. Neurosci. 2007; PubMed Scopus Google Scholar). RNAi and overexpression an role for PINK1 in mitochondrial homeostasis by mitochondrial oxidative stress, mitochondrial and in PINK1-deficient cells is increased and fission and autophagy an active role in mitochondrial remodeling by PINK1 and coordinated of fission and mitophagy may to associated with mitochondria in recessive PD We Watkins and the for the of for with and We and for and of the for with

Neuropsychiatric symptoms (NPS) are common in dementia and in predementia syndromes such as mild cognitive impairment (MCI). NPS in MCI confer a greater risk for conversion to dementia in comparison to MCI patients without NPS. NPS in older adults with normal cognition also confers a greater risk of cognitive decline in comparison to older adults without NPS. Mild behavioral impairment (MBI) has been proposed as a diagnostic construct aimed to identify patients with an increased risk of developing dementia, but who may or may not have cognitive symptoms. We propose criteria that include MCI in the MBI framework, in contrast to prior definitions of MBI. Although MBI and MCI can co-occur, we suggest that they are different and that both portend a higher risk of dementia. These MBI criteria extend the previous literature in this area and will serve as a template for validation of the MBI construct from epidemiologic, neurobiological, treatment, and prevention perspectives.

BACKGROUND: Renal insufficiency has been associated with cardiovascular disease events and mortality in several prospective studies, but the mechanisms for the elevated risk are not clear. Little is known about the association of renal insufficiency with inflammatory and procoagulant markers, which are potential mediators for the cardiovascular risk of kidney disease. METHODS AND RESULTS: The cross-sectional association of renal insufficiency with 8 inflammatory and procoagulant factors was evaluated using baseline data from the Cardiovascular Health Study, a population-based cohort study of 5888 subjects aged > or =65 years. C-reactive protein, fibrinogen, factor VIIc, and factor VIIIc levels were measured in nearly all participants; interleukin-6, intercellular adhesion molecule-1, plasmin-antiplasmin complex, and D-dimer levels were measured in nearly half of participants. Renal insufficiency was defined as a serum creatinine level > or =1.3 mg/dL in women and > or =1.5 mg/dL in men. Multivariate linear regression was used to compare adjusted mean levels of each biomarker in persons with and without renal insufficiency after adjustment for other baseline characteristics. Renal insufficiency was present in 647 (11%) of Cardiovascular Health Study participants. After adjustment for baseline differences, levels of C-reactive protein, fibrinogen, interleukin-6, factor VIIc, factor VIIIc, plasmin-antiplasmin complex, and D-dimer were significantly greater among persons with renal insufficiency (P<0.001). In participants with clinical, subclinical, and no cardiovascular disease at baseline, the positive associations of renal insufficiency with these inflammatory and procoagulant markers were similar. CONCLUSION: Renal insufficiency was independently associated with elevations in inflammatory and procoagulant biomarkers. These pathways may be important mediators leading to the increased cardiovascular risk of persons with kidney disease.

Atrial fibrillation (AF) affects more than 33 million individuals worldwide1 and has a complex heritability2. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF. This large, multi-ethnic genome-wide association study identifies 97 loci significantly associated with atrial fibrillation. These loci are enriched for genes involved in cardiac development, electrophysiology, structure and contractile function.

We provide a framework for health services-related researchers, practitioners, and policy makers to guide future health disparities research in areas ranging from detecting differences in health and health care to understanding the determinants that underlie disparities to ultimately designing interventions that reduce and eliminate these disparities. To do this, we identified potential selection biases and definitions of vulnerable groups when detecting disparities. The key factors to understanding disparities were multilevel determinants of health disparities, including individual beliefs and preferences, effective patient-provider communication; and the organizational culture of the health care system. We encourage interventions that yield generalizable data on their effectiveness and that promote further engagement of communities, providers, and policymakers to ultimately enhance the application and the impact of health disparities research.