@article {1128, title = {Age and cystatin C in healthy adults: a collaborative study.}, journal = {Nephrol Dial Transplant}, volume = {25}, year = {2010}, month = {2010 Feb}, pages = {463-9}, abstract = {

BACKGROUND: Kidney function declines with age, but a substantial portion of this decline has been attributed to the higher prevalence of risk factors for kidney disease at older ages. The effect of age on kidney function has not been well described in a healthy population across a wide age spectrum.

METHODS: The authors pooled individual-level cross-sectional data from 18 253 persons aged 28-100 years in four studies: the Cardiovascular Health Study; the Health, Aging and Body Composition Study; the Multi-Ethnic Study of Atherosclerosis and the Prevention of Renal and Vascular End-Stage Disease cohort. Kidney function was measured by cystatin C. Clinical risk factors for kidney disease included diabetes, hypertension, obesity, smoking, coronary heart disease, cerebrovascular disease, peripheral arterial disease and heart failure.

RESULTS: Across the age range, there was a strong, non-linear association of age with cystatin C concentration. This association was substantial, even among participants free of clinical risk factors for kidney disease; mean cystatin C levels were 46\% higher in participants 80 and older compared with those <40 years (1.06 versus 0.72 mg/L, P < 0.001). Participants with one or more risk factors had higher cystatin C concentrations for a given age, and the age association was slightly stronger (P < 0.001 for age and risk factor interaction).

CONCLUSIONS: There is a strong, non-linear association of age with kidney function, even in healthy individuals. An important area for research will be to investigate the mechanisms that lead to deterioration of kidney function in apparently healthy persons.

}, keywords = {Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Cystatin C, Humans, Kidney, Middle Aged, Reference Values}, issn = {1460-2385}, doi = {10.1093/ndt/gfp474}, author = {Odden, Michelle C and Tager, Ira B and Gansevoort, Ron T and Bakker, Stephan J L and Katz, Ronit and Fried, Linda F and Newman, Anne B and Canada, Robert B and Harris, Tamara and Sarnak, Mark J and Siscovick, David and Shlipak, Michael G} } @article {6301, title = {Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts.}, journal = {Kidney Int}, volume = {80}, year = {2011}, month = {2011 Jul}, pages = {93-104}, abstract = {

Both a low estimated glomerular filtration rate (eGFR) and albuminuria are known risk factors for end-stage renal disease (ESRD). To determine their joint contribution to ESRD and other kidney outcomes, we performed a meta-analysis of nine general population cohorts with 845,125 participants and an additional eight cohorts with 173,892 patients, the latter selected because of their high risk for chronic kidney disease (CKD). In the general population, the risk for ESRD was unrelated to eGFR at values between 75 and 105 ml/min per 1.73 m(2) but increased exponentially at lower levels. Hazard ratios for eGFRs averaging 60, 45, and 15 were 4, 29, and 454, respectively, compared with an eGFR of 95, after adjustment for albuminuria and cardiovascular risk factors. Log albuminuria was linearly associated with log ESRD risk without thresholds. Adjusted hazard ratios at albumin-to-creatinine ratios of 30, 300, and 1000 mg/g were 5, 13, and 28, respectively, compared with an albumin-to-creatinine ratio of 5. Albuminuria and eGFR were associated with ESRD, without evidence for multiplicative interaction. Similar associations were found for acute kidney injury and progressive CKD. In high-risk cohorts, the findings were generally comparable. Thus, lower eGFR and higher albuminuria are risk factors for ESRD, acute kidney injury and progressive CKD in both general and high-risk populations, independent of each other and of cardiovascular risk factors.

}, keywords = {Aged, Albuminuria, Cohort Studies, Creatinine, Female, Glomerular Filtration Rate, Humans, Kidney Failure, Chronic, Male, Middle Aged, Prognosis, Proportional Hazards Models, Risk Factors}, issn = {1523-1755}, doi = {10.1038/ki.2010.531}, author = {Gansevoort, Ron T and Matsushita, Kunihiro and van der Velde, Marije and Astor, Brad C and Woodward, Mark and Levey, Andrew S and de Jong, Paul E and Coresh, Josef} } @article {7380, title = {Age and association of kidney measures with mortality and end-stage renal disease.}, journal = {JAMA}, volume = {308}, year = {2012}, month = {2012 Dec 12}, pages = {2349-60}, abstract = {

CONTEXT: Chronic kidney disease (CKD) is prevalent in older individuals, but the risk implications of low estimated glomerular filtration rate (eGFR) and high albuminuria across the full age range are controversial.

OBJECTIVE: To evaluate possible effect modification (interaction) by age of the association of eGFR and albuminuria with clinical risk, examining both relative and absolute risks.

DESIGN, SETTING, AND PARTICIPANTS: Individual-level meta-analysis including 2,051,244 participants from 33 general population or high-risk (of vascular disease) cohorts and 13 CKD cohorts from Asia, Australasia, Europe, and North/South America, conducted in 1972-2011 with a mean follow-up time of 5.8 years (range, 0-31 years).

MAIN OUTCOME MEASURES: Hazard ratios (HRs) of mortality and end-stage renal disease (ESRD) according to eGFR and albuminuria were meta-analyzed across age categories after adjusting for sex, race, cardiovascular disease, diabetes, systolic blood pressure, cholesterol, body mass index, and smoking. Absolute risks were estimated using HRs and average incidence rates.

RESULTS: Mortality (112,325 deaths) and ESRD (8411 events) risks were higher at lower eGFR and higher albuminuria in every age category. In general and high-risk cohorts, relative mortality risk for reduced eGFR decreased with increasing age; eg, adjusted HRs at an eGFR of 45 mL/min/1.73 m2 vs 80 mL/min/1.73 m2 were 3.50 (95\% CI, 2.55-4.81), 2.21 (95\% CI, 2.02-2.41), 1.59 (95\% CI, 1.42-1.77), and 1.35 (95\% CI, 1.23-1.48) in age categories 18-54, 55-64, 65-74, and >=75 years, respectively (P <.05 for age interaction). Absolute risk differences for the same comparisons were higher at older age (9.0 [95\% CI, 6.0-12.8], 12.2 [95\% CI, 10.3-14.3], 13.3 [95\% CI, 9.0-18.6], and 27.2 [95\% CI, 13.5-45.5] excess deaths per 1000 person-years, respectively). For increased albuminuria, reduction of relative risk with increasing age was less evident, while differences in absolute risk were higher in older age categories (7.5 [95\% CI, 4.3-11.9], 12.2 [95\% CI, 7.9-17.6], 22.7 [95\% CI, 15.3-31.6], and 34.3 [95\% CI, 19.5-52.4] excess deaths per 1000 person-years, respectively by age category, at an albumin-creatinine ratio of 300 mg/g vs 10 mg/g). In CKD cohorts, adjusted relative hazards of mortality did not decrease with age. In all cohorts, ESRD relative risks and absolute risk differences at lower eGFR or higher albuminuria were comparable across age categories.

CONCLUSIONS: Both low eGFR and high albuminuria were independently associated with mortality and ESRD regardless of age across a wide range of populations. Mortality showed lower relative risk but higher absolute risk differences at older age.

}, keywords = {Adolescent, Adult, Age Factors, Aged, Albuminuria, Cohort Studies, Female, Glomerular Filtration Rate, Humans, Kidney, Kidney Failure, Chronic, Male, Middle Aged, Risk, Young Adult}, issn = {1538-3598}, doi = {10.1001/jama.2012.16817}, author = {Hallan, Stein I and Matsushita, Kunihiro and Sang, Yingying and Mahmoodi, Bakhtawar K and Black, Corri and Ishani, Areef and Kleefstra, Nanne and Naimark, David and Roderick, Paul and Tonelli, Marcello and Wetzels, Jack F M and Astor, Brad C and Gansevoort, Ron T and Levin, Adeera and Wen, Chi-Pang and Coresh, Josef} } @article {1406, title = {Association of mild to moderate chronic kidney disease with venous thromboembolism: pooled analysis of five prospective general population cohorts.}, journal = {Circulation}, volume = {126}, year = {2012}, month = {2012 Oct 16}, pages = {1964-71}, abstract = {

BACKGROUND: Recent findings suggest that chronic kidney disease (CKD) may be associated with an increased risk of venous thromboembolism (VTE). Given the high prevalence of mild-to-moderate CKD in the general population, in depth analysis of this association is warranted.

METHODS AND RESULTS: We pooled individual participant data from 5 community-based cohorts from Europe (second Nord-Tr{\o}ndelag Health Study [HUNT2], Prevention of Renal and Vascular End-stage Disease [PREVEND], and the Troms{\o} study) and the United States (Atherosclerosis Risks in Communities [ARIC] and Cardiovascular Health Study [CHS]) to assess the association of estimated glomerular filtration rate (eGFR), albuminuria, and CKD with objectively verified VTE. To estimate adjusted hazard ratios for VTE, categorical and continuous spline models were fit by using Cox regression with shared-frailty or random-effect meta-analysis. A total of 1178 VTE events occurred over 599 453 person-years follow-up. Relative to eGFR 100 mL/min per 1.73 m(2), hazard ratios for VTE were 1.29 (95\% confidence interval, 1.04-1.59) for eGFR 75, 1.31 (1.00-1.71) for eGFR 60, 1.82 (1.27-2.60) for eGFR 45, and 1.95 (1.26-3.01) for eGFR 30 mL/min per 1.73 m(2). In comparison with an albumin-to-creatinine ratio (ACR) of 5.0 mg/g, the hazard ratios for VTE were 1.34 (1.04-1.72) for ACR 30 mg/g, 1.60 (1.08-2.36) for ACR 300 mg/g, and 1.92 (1.19-3.09) for ACR 1000 mg/g. There was no interaction between clinical categories of eGFR and ACR (P=0.20). The adjusted hazard ratio for CKD, defined as eGFR <60 mL/min per 1.73 m(2) or albuminuria >=30 mg/g, (versus no CKD) was 1.54 (95\% confidence interval, 1.15-2.06). Associations were consistent in subgroups according to age, sex, and comorbidities, and for unprovoked versus provoked VTE, as well.

CONCLUSIONS: Both eGFR and ACR are independently associated with increased risk of VTE in the general population, even across the normal eGFR and ACR ranges.

}, keywords = {Aged, Cohort Studies, Europe, Female, Humans, Incidence, Male, Middle Aged, Prevalence, Renal Insufficiency, Chronic, Risk Factors, Severity of Illness Index, Venous Thromboembolism}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.112.113944}, author = {Mahmoodi, Bakhtawar K and Gansevoort, Ron T and N{\ae}ss, Inger Anne and Lutsey, Pamela L and Br{\ae}kkan, Sigrid K and Veeger, Nic J G M and Brodin, Ellen E and Meijer, Karina and Sang, Yingying and Matsushita, Kunihiro and Hallan, Stein I and Hammerstr{\o}m, Jens and Cannegieter, Suzanne C and Astor, Brad C and Coresh, Josef and Folsom, Aaron R and Hansen, John-Bjarne and Cushman, Mary} } @article {1385, title = {Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate.}, journal = {JAMA}, volume = {307}, year = {2012}, month = {2012 May 09}, pages = {1941-51}, abstract = {

CONTEXT: The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation more accurately estimates glomerular filtration rate (GFR) than the Modification of Diet in Renal Disease (MDRD) Study equation using the same variables, especially at higher GFR, but definitive evidence of its risk implications in diverse settings is lacking.

OBJECTIVE: To evaluate risk implications of estimated GFR using the CKD-EPI equation compared with the MDRD Study equation in populations with a broad range of demographic and clinical characteristics.

DESIGN, SETTING, AND PARTICIPANTS: A meta-analysis of data from 1.1 million adults (aged >= 18 years) from 25 general population cohorts, 7 high-risk cohorts (of vascular disease), and 13 CKD cohorts. Data transfer and analyses were conducted between March 2011 and March 2012.

MAIN OUTCOME MEASURES: All-cause mortality (84,482 deaths from 40 cohorts), cardiovascular mortality (22,176 events from 28 cohorts), and end-stage renal disease (ESRD) (7644 events from 21 cohorts) during 9.4 million person-years of follow-up; the median of mean follow-up time across cohorts was 7.4 years (interquartile range, 4.2-10.5 years).

RESULTS: Estimated GFR was classified into 6 categories (>=90, 60-89, 45-59, 30-44, 15-29, and <15 mL/min/1.73 m(2)) by both equations. Compared with the MDRD Study equation, 24.4\% and 0.6\% of participants from general population cohorts were reclassified to a higher and lower estimated GFR category, respectively, by the CKD-EPI equation, and the prevalence of CKD stages 3 to 5 (estimated GFR <60 mL/min/1.73 m(2)) was reduced from 8.7\% to 6.3\%. In estimated GFR of 45 to 59 mL/min/1.73 m(2) by the MDRD Study equation, 34.7\% of participants were reclassified to estimated GFR of 60 to 89 mL/min/1.73 m(2) by the CKD-EPI equation and had lower incidence rates (per 1000 person-years) for the outcomes of interest (9.9 vs 34.5 for all-cause mortality, 2.7 vs 13.0 for cardiovascular mortality, and 0.5 vs 0.8 for ESRD) compared with those not reclassified. The corresponding adjusted hazard ratios were 0.80 (95\% CI, 0.74-0.86) for all-cause mortality, 0.73 (95\% CI, 0.65-0.82) for cardiovascular mortality, and 0.49 (95\% CI, 0.27-0.88) for ESRD. Similar findings were observed in other estimated GFR categories by the MDRD Study equation. Net reclassification improvement based on estimated GFR categories was significantly positive for all outcomes (range, 0.06-0.13; all P < .001). Net reclassification improvement was similarly positive in most subgroups defined by age (<65 years and >=65 years), sex, race/ethnicity (white, Asian, and black), and presence or absence of diabetes and hypertension. The results in the high-risk and CKD cohorts were largely consistent with the general population cohorts.

CONCLUSION: The CKD-EPI equation classified fewer individuals as having CKD and more accurately categorized the risk for mortality and ESRD than did the MDRD Study equation across a broad range of populations.

}, keywords = {African Continental Ancestry Group, Aged, Algorithms, Asian Continental Ancestry Group, Cardiovascular Diseases, Cohort Studies, Decision Support Techniques, European Continental Ancestry Group, Female, Glomerular Filtration Rate, Humans, Kidney Failure, Chronic, Male, Middle Aged, Models, Theoretical, Risk Assessment, Sex Factors}, issn = {1538-3598}, doi = {10.1001/jama.2012.3954}, author = {Matsushita, Kunihiro and Mahmoodi, Bakhtawar K and Woodward, Mark and Emberson, Jonathan R and Jafar, Tazeen H and Jee, Sun Ha and Polkinghorne, Kevan R and Shankar, Anoop and Smith, David H and Tonelli, Marcello and Warnock, David G and Wen, Chi-Pang and Coresh, Josef and Gansevoort, Ron T and Hemmelgarn, Brenda R and Levey, Andrew S} } @article {7377, title = {Cystatin C versus creatinine in determining risk based on kidney function.}, journal = {N Engl J Med}, volume = {369}, year = {2013}, month = {2013 Sep 05}, pages = {932-43}, abstract = {

BACKGROUND: Adding the measurement of cystatin C to that of serum creatinine to determine the estimated glomerular filtration rate (eGFR) improves accuracy, but the effect on detection, staging, and risk classification of chronic kidney disease across diverse populations has not been determined.

METHODS: We performed a meta-analysis of 11 general-population studies (with 90,750 participants) and 5 studies of cohorts with chronic kidney disease (2960 participants) for whom standardized measurements of serum creatinine and cystatin C were available. We compared the association of the eGFR, as calculated by the measurement of creatinine or cystatin C alone or in combination with creatinine, with the rates of death (13,202 deaths in 15 cohorts), death from cardiovascular causes (3471 in 12 cohorts), and end-stage renal disease (1654 cases in 7 cohorts) and assessed improvement in reclassification with the use of cystatin C.

RESULTS: In the general-population cohorts, the prevalence of an eGFR of less than 60 ml per minute per 1.73 m(2) of body-surface area was higher with the cystatin C-based eGFR than with the creatinine-based eGFR (13.7\% vs. 9.7\%). Across all eGFR categories, the reclassification of the eGFR to a higher value with the measurement of cystatin C, as compared with creatinine, was associated with a reduced risk of all three study outcomes, and reclassification to a lower eGFR was associated with an increased risk. The net reclassification improvement with the measurement of cystatin C, as compared with creatinine, was 0.23 (95\% confidence interval [CI], 0.18 to 0.28) for death and 0.10 (95\% CI, 0.00 to 0.21) for end-stage renal disease. Results were generally similar for the five cohorts with chronic kidney disease and when both creatinine and cystatin C were used to calculate the eGFR.

CONCLUSIONS: The use of cystatin C alone or in combination with creatinine strengthens the association between the eGFR and the risks of death and end-stage renal disease across diverse populations. (Funded by the National Kidney Foundation and others.).

}, keywords = {Creatinine, Cystatin C, Glomerular Filtration Rate, Humans, Kidney Failure, Chronic, Kidney Function Tests, Reference Standards, Renal Insufficiency, Chronic, Risk, Risk Assessment}, issn = {1533-4406}, doi = {10.1056/NEJMoa1214234}, author = {Shlipak, Michael G and Matsushita, Kunihiro and Arnl{\"o}v, Johan and Inker, Lesley A and Katz, Ronit and Polkinghorne, Kevan R and Rothenbacher, Dietrich and Sarnak, Mark J and Astor, Brad C and Coresh, Josef and Levey, Andrew S and Gansevoort, Ron T} } @article {5854, title = {Hypertension and low HDL cholesterol were associated with reduced kidney function across the age spectrum: a collaborative study.}, journal = {Ann Epidemiol}, volume = {23}, year = {2013}, month = {2013 Mar}, pages = {106-11}, abstract = {

PURPOSE: To determine if the associations among established risk factors and reduced kidney function vary by age.

METHODS: We pooled cross-sectional data from 14,788 nondiabetics aged 40 to 100 years in 4 studies: Cardiovascular Health Study, Health, Aging, and Body Composition Study, Multi-Ethnic Study of Atherosclerosis, and Prevention of Renal and Vascular End-Stage Disease cohort.

RESULTS: Hypertension and low high-density lipoprotein (HDL) cholesterol were associated with reduced cystatin C-based estimated glomerular filtration rate (eGFR) across the age spectrum. In adjusted analyses, hypertension was associated with a 2.3 (95\% confidence interval [CI], 0.1, 4.4), 5.1 (95\% CI, 4.1, 6.1), and 6.9 (95\%~CI, 3.0, 10.4) mL/min/1.73 m(2) lower eGFR in participants 40 to 59, 60 to 79, and at least 80~years, respectively (P for interaction < .001). The association of low HDL cholesterol with reduced kidney function was also greater in the older age groups: 4.9 (95\% CI, 3.5, 6.3), 7.1 (95\% CI, 6.0, 8.3), 8.9 (95\% CI, 5.4, 11.9) mL/min/1.73 m(2) (P for interaction < .001). Smoking and obesity were associated with reduced kidney function in participants under 80 years. All estimates of the potential population impact of the risk factors were modest.

CONCLUSIONS: Hypertension, obesity, smoking, and low HDL cholesterol are modestly associated with reduced kidney function in nondiabetics. The associations of hypertension and HDL cholesterol with reduced kidney function seem to be stronger in older adults.

}, keywords = {Adult, Age Factors, Aged, Aged, 80 and over, Aging, Causality, Cholesterol, LDL, Cohort Studies, Comorbidity, Cross-Sectional Studies, Cystatin C, Female, Humans, Hypertension, Kidney Function Tests, Male, Netherlands, Obesity, Prevalence, Renal Insufficiency, Chronic, Risk Factors, Smoking, United States}, issn = {1873-2585}, doi = {10.1016/j.annepidem.2012.12.004}, author = {Odden, Michelle C and Tager, Ira B and Gansevoort, Ron T and Bakker, Stephan J L and Fried, Linda F and Newman, Anne B and Katz, Ronit and Satterfield, Suzanne and Harris, Tamara B and Sarnak, Mark J and Siscovick, David and Shlipak, Michael G} } @article {6424, title = {Association of kidney disease measures with ischemic versus hemorrhagic strokes: pooled analyses of 4 prospective community-based cohorts.}, journal = {Stroke}, volume = {45}, year = {2014}, month = {2014 Jul}, pages = {1925-31}, abstract = {

BACKGROUND AND PURPOSE: Although low glomerular filtration rate (GFR) and albuminuria are associated with increased risk of stroke, few studies compared their contribution to risk of ischemic versus hemorrhagic stroke separately. We contrasted the association of these kidney measures with ischemic versus hemorrhagic stroke.

METHODS: We pooled individual participant data from 4 community-based cohorts: 3 from the United States and 1 from The Netherlands. GFR was estimated using both creatinine and cystatin C, and albuminuria was quantified by urinary albumin-to-creatinine ratio (ACR). Associations of estimated GFR and ACR were compared for each stroke type (ischemic versus intraparenchymal hemorrhagic) using study-stratified Cox regression.

RESULTS: Among 29,595 participants (mean age, 61 [SD 12.5] years; 46\% men; 17\% black), 1261 developed stroke (12\% hemorrhagic) during 280,549 person-years. Low estimated GFR was significantly associated with increased risk of ischemic stroke, but not hemorrhagic stroke, whereas high ACR was associated with both stroke types. Adjusted hazard ratios for ischemic and hemorrhagic stroke at estimated GFR of 45 (versus 95) mL/min per 1.73 m2 were 1.30 (95\% confidence interval, 1.01-1.68) and 0.92 (0.47-1.81), respectively. In contrast, the corresponding hazard ratios for ACR of 300 (versus 5) mg/g were 1.62 (1.27-2.07) for ischemic and 2.57 (1.37-4.83) for hemorrhagic stroke, with significantly stronger association with hemorrhagic stroke (P=0.04). For hemorrhagic stroke, the association of elevated ACR was of similar magnitude as that of elevated systolic blood pressure.

CONCLUSIONS: Whereas albuminuria showed significant association with both stroke types, the association of decreased estimated GFR was only significant for ischemic stroke. The strong association of albuminuria with both stroke types warrants clinical attention and further investigations.

}, keywords = {Aged, Albuminuria, Brain Ischemia, Comorbidity, Female, Glomerular Filtration Rate, Humans, Intracranial Hemorrhages, Kidney Diseases, Male, Middle Aged, Netherlands, Proportional Hazards Models, Prospective Studies, Risk Factors, Stroke, United States}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.114.004900}, author = {Mahmoodi, Bakhtawar K and Yatsuya, Hiroshi and Matsushita, Kunihiro and Sang, Yinying and Gottesman, Rebecca F and Astor, Brad C and Woodward, Mark and Longstreth, W T and Psaty, Bruce M and Shlipak, Michael G and Folsom, Aaron R and Gansevoort, Ron T and Coresh, Josef} } @article {6554, title = {Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality.}, journal = {JAMA}, volume = {311}, year = {2014}, month = {2014 Jun 25}, pages = {2518-2531}, abstract = {

IMPORTANCE: The established chronic kidney disease (CKD) progression end point of end-stage renal disease (ESRD) or a doubling of serum creatinine concentration (corresponding to a change in estimated glomerular filtration rate [GFR] of -57\% or greater) is a late event.

OBJECTIVE: To characterize the association of decline in estimated GFR with subsequent progression to ESRD with implications for using lesser declines in estimated GFR as potential alternative end points for CKD progression. Because most people with CKD die before reaching ESRD, mortality risk also was investigated.

DATA SOURCES AND STUDY SELECTION: Individual meta-analysis of 1.7 million participants with 12,344 ESRD events and 223,944 deaths from 35 cohorts in the CKD Prognosis Consortium with a repeated measure of serum creatinine concentration over 1 to 3 years and outcome data.

DATA EXTRACTION AND SYNTHESIS: Transfer of individual participant data or standardized analysis of outputs for random-effects meta-analysis conducted between July 2012 and September 2013, with baseline estimated GFR values collected from 1975 through 2012.

MAIN OUTCOMES AND MEASURES: End-stage renal disease (initiation of dialysis or transplantation) or all-cause mortality risk related to percentage change in estimated GFR over 2 years, adjusted for potential confounders and first estimated GFR.

RESULTS: The adjusted hazard ratios (HRs) of ESRD and mortality were higher with larger estimated GFR decline. Among participants with baseline estimated GFR of less than 60 mL/min/1.73 m2, the adjusted HRs for ESRD were 32.1 (95\% CI, 22.3-46.3) for changes of -57\% in estimated GFR and 5.4 (95\% CI, 4.5-6.4) for changes of -30\%. However, changes of -30\% or greater (6.9\% [95\% CI, 6.4\%-7.4\%] of the entire consortium) were more common than changes of -57\% (0.79\% [95\% CI, 0.52\%-1.06\%]). This association was strong and consistent across the length of the baseline period (1 to 3 years), baseline estimated GFR, age, diabetes status, or albuminuria. Average adjusted 10-year risk of ESRD (in patients with a baseline estimated GFR of 35 mL/min/1.73 m2) was 99\% (95\% CI, 95\%-100\%) for estimated GFR change of -57\%, was 83\% (95\% CI, 71\%-93\%) for estimated GFR change of -40\%, and was 64\% (95\% CI, 52\%-77\%) for estimated GFR change of -30\% vs 18\% (95\% CI, 15\%-22\%) for estimated GFR change of 0\%. Corresponding mortality risks were 77\% (95\% CI, 71\%-82\%), 60\% (95\% CI, 56\%-63\%), and 50\% (95\% CI, 47\%-52\%) vs 32\% (95\% CI, 31\%-33\%), showing a similar but weaker pattern.

CONCLUSIONS AND RELEVANCE: Declines in estimated GFR smaller than a doubling of serum creatinine concentration occurred more commonly and were strongly and consistently associated with the risk of ESRD and mortality, supporting consideration of lesser declines in estimated GFR (such as a 30\% reduction over 2 years) as an alternative end point for CKD progression.

}, keywords = {Adult, Aged, Aged, 80 and over, Cohort Studies, Creatinine, Disease Progression, Endpoint Determination, Female, Glomerular Filtration Rate, Humans, Kidney Failure, Chronic, Male, Middle Aged, Reference Values, Risk}, issn = {1538-3598}, doi = {10.1001/jama.2014.6634}, author = {Coresh, Josef and Turin, Tanvir Chowdhury and Matsushita, Kunihiro and Sang, Yingying and Ballew, Shoshana H and Appel, Lawrence J and Arima, Hisatomi and Chadban, Steven J and Cirillo, Massimo and Djurdjev, Ognjenka and Green, Jamie A and Heine, Gunnar H and Inker, Lesley A and Irie, Fujiko and Ishani, Areef and Ix, Joachim H and Kovesdy, Csaba P and Marks, Angharad and Ohkubo, Takayoshi and Shalev, Varda and Shankar, Anoop and Wen, Chi Pang and de Jong, Paul E and Iseki, Kunitoshi and Stengel, B{\'e}n{\'e}dicte and Gansevoort, Ron T and Levey, Andrew S} } @article {6295, title = {Relative risks of chronic kidney disease for mortality and end-stage renal disease across races are similar.}, journal = {Kidney Int}, volume = {86}, year = {2014}, month = {2014 Oct}, pages = {819-27}, abstract = {

Some suggest race-specific cutpoints for kidney measures to define and stage chronic kidney disease (CKD), but evidence for race-specific clinical impact is limited. To address this issue, we compared hazard ratios of estimated glomerular filtration rates (eGFR) and albuminuria across races using meta-regression in 1.1 million adults (75\% Asians, 21\% Whites, and 4\% Blacks) from 45 cohorts. Results came mainly from 25 general population cohorts comprising 0.9 million individuals. The associations of lower eGFR and higher albuminuria with mortality and end-stage renal disease (ESRD) were largely similar across races. For example, in Asians, Whites, and Blacks, the adjusted hazard ratios (95\% confidence interval) for eGFR 45-59 versus 90-104 ml/min per 1.73 m(2) were 1.3 (1.2-1.3), 1.1 (1.0-1.2), and 1.3 (1.1-1.7) for all-cause mortality, 1.6 (1.5-1.7), 1.4 (1.2-1.7), and 1.4 (0.7-2.9) for cardiovascular mortality, and 27.6 (11.1-68.7), 11.2 (6.0-20.9), and 4.1 (2.2-7.5) for ESRD, respectively. The corresponding hazard ratios for urine albumin-to-creatinine ratio 30-299 mg/g or dipstick 1+ versus an albumin-to-creatinine ratio under 10 or dipstick negative were 1.6 (1.4-1.8), 1.7 (1.5-1.9), and 1.8 (1.7-2.1) for all-cause mortality, 1.7 (1.4-2.0), 1.8 (1.5-2.1), and 2.8 (2.2-3.6) for cardiovascular mortality, and 7.4 (2.0-27.6), 4.0 (2.8-5.9), and 5.6 (3.4-9.2) for ESRD, respectively. Thus, the relative mortality or ESRD risks of lower eGFR and higher albuminuria were largely similar among three major races, supporting similar clinical approach to CKD definition and staging, across races.

}, keywords = {Adult, African Continental Ancestry Group, Aged, Aged, 80 and over, Albuminuria, Asian Continental Ancestry Group, Cardiovascular Diseases, Cohort Studies, Creatinine, European Continental Ancestry Group, Female, Glomerular Filtration Rate, Humans, Kidney Failure, Chronic, Male, Middle Aged, Odds Ratio, Renal Insufficiency, Chronic, Risk Factors}, issn = {1523-1755}, doi = {10.1038/ki.2013.553}, author = {Wen, Chi Pang and Matsushita, Kunihiro and Coresh, Josef and Iseki, Kunitoshi and Islam, Muhammad and Katz, Ronit and McClellan, William and Peralta, Carmen A and Wang, Haiyan and de Zeeuw, Dick and Astor, Brad C and Gansevoort, Ron T and Levey, Andrew S and Levin, Adeera} } @article {6797, title = {A Meta-analysis of the Association of Estimated GFR, Albuminuria, Age, Race, and Sex With Acute Kidney Injury.}, journal = {Am J Kidney Dis}, volume = {66}, year = {2015}, month = {2015 Oct}, pages = {591-601}, abstract = {

BACKGROUND: Acute kidney injury (AKI) is a serious global public health problem. We aimed to quantify the risk of AKI associated with estimated glomerular filtration rate (eGFR), albuminuria (albumin-creatinine ratio [ACR]), age, sex, and race (African American and white).

STUDY DESIGN: Collaborative meta-analysis.

SETTING \& POPULATION: 8 general-population cohorts (1,285,049 participants) and 5 chronic kidney disease (CKD) cohorts (79,519 participants).

SELECTION CRITERIA FOR STUDIES: Available eGFR, ACR, and 50 or more AKI events.

PREDICTORS: Age, sex, race, eGFR, urine ACR, and interactions.

OUTCOME: Hospitalized with or for AKI, using Cox proportional hazards models to estimate HRs of AKI and random-effects meta-analysis to pool results.

RESULTS: 16,480 (1.3\%) general-population cohort participants had AKI over a mean follow-up of 4 years; 2,087 (2.6\%) CKD participants had AKI over a mean follow-up of 1 year. Lower eGFR and higher ACR were strongly associated with AKI. Compared with eGFR of 80mL/min/1.73m(2), the adjusted HR of AKI at eGFR of 45mL/min/1.73m(2) was 3.35 (95\% CI, 2.75-4.07). Compared with ACR of 5mg/g, the risk of AKI at ACR of 300mg/g was 2.73 (95\% CI, 2.18-3.43). Older age was associated with higher risk of AKI, but this effect was attenuated with lower eGFR or higher ACR. Male sex was associated with higher risk of AKI, with a slight attenuation in lower eGFR but not in higher ACR. African Americans had higher AKI risk at higher levels of eGFR and most levels of ACR.

LIMITATIONS: Only 2 general-population cohorts could contribute to analyses by race; AKI identified by diagnostic code.

CONCLUSIONS: Reduced eGFR and increased ACR are consistent strong risk factors for AKI, whereas associations of AKI with age, sex, and race may be weaker in more advanced stages of CKD.

}, keywords = {Acute Kidney Injury, Adolescent, Adult, African Americans, Age Distribution, Aged, Albuminuria, Continental Population Groups, European Continental Ancestry Group, Female, Glomerular Filtration Rate, Humans, Incidence, Male, Middle Aged, Predictive Value of Tests, Prognosis, Severity of Illness Index, Sex Distribution, Young Adult}, issn = {1523-6838}, doi = {10.1053/j.ajkd.2015.02.337}, author = {Grams, Morgan E and Sang, Yingying and Ballew, Shoshana H and Gansevoort, Ron T and Kimm, Heejin and Kovesdy, Csaba P and Naimark, David and Oien, Cecilia and Smith, David H and Coresh, Josef and Sarnak, Mark J and Stengel, B{\'e}n{\'e}dicte and Tonelli, Marcello} } @article {6796, title = {A Meta-analysis of the Association of Estimated GFR, Albuminuria, Diabetes Mellitus, and Hypertension With Acute Kidney Injury.}, journal = {Am J Kidney Dis}, volume = {66}, year = {2015}, month = {2015 Oct}, pages = {602-12}, abstract = {

BACKGROUND: Diabetes mellitus and hypertension are risk factors for acute kidney injury (AKI). Whether estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (ACR) remain risk factors for AKI in the presence and absence of these conditions is uncertain.

STUDY DESIGN: Meta-analysis of cohort studies.

SETTING \& POPULATION: 8 general-population (1,285,045 participants) and 5 chronic kidney disease (CKD; 79,519 participants) cohorts.

SELECTION CRITERIA FOR STUDIES: Cohorts participating in the CKD Prognosis Consortium.

PREDICTORS: Diabetes and hypertension status, eGFR by the 2009 CKD Epidemiology Collaboration creatinine equation, urine ACR, and interactions.

OUTCOME: Hospitalization with AKI, using Cox proportional hazards models to estimate HRs of AKI and random-effects meta-analysis to pool results.

RESULTS: During a mean follow-up of 4 years, there were 16,480 episodes of AKI in the general-population and 2,087 episodes in the CKD cohorts. Low eGFRs and high ACRs were associated with higher risks of AKI in individuals with or without diabetes and with or without hypertension. When compared to a common reference of eGFR of 80mL/min/1.73m(2) in nondiabetic patients, HRs for AKI were generally higher in diabetic patients at any level of eGFR. The same was true for diabetic patients at all levels of ACR compared with nondiabetic patients. The risk gradient for AKI with lower eGFRs was greater in those without diabetes than with diabetes, but similar with higher ACRs in those without versus with diabetes. Those with hypertension had a higher risk of AKI at eGFRs>60mL/min/1.73m(2) than those without hypertension. However, risk gradients for AKI with both lower eGFRs and higher ACRs were greater for those without than with hypertension.

LIMITATIONS: AKI identified by diagnostic code.

CONCLUSIONS: Lower eGFRs and higher ACRs are associated with higher risks of AKI among individuals with or without either diabetes or hypertension.

}, keywords = {Acute Kidney Injury, Adult, Aged, Comorbidity, Diabetes Mellitus, Disease Progression, Female, Glomerular Filtration Rate, Humans, Hypertension, Incidence, Kidney Failure, Chronic, Male, Middle Aged, Prognosis, Renal Insufficiency, Chronic}, issn = {1523-6838}, doi = {10.1053/j.ajkd.2015.02.338}, author = {James, Matthew T and Grams, Morgan E and Woodward, Mark and Elley, C Raina and Green, Jamie A and Wheeler, David C and de Jong, Paul and Gansevoort, Ron T and Levey, Andrew S and Warnock, David G and Sarnak, Mark J} } @article {7011, title = {Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels.}, journal = {J Med Genet}, volume = {53}, year = {2016}, month = {2016 Jul}, pages = {441-9}, abstract = {

BACKGROUND: So far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.

METHODS: We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from \~{}60 000 individuals in the discovery stage and \~{}90 000 samples in the replication stage.

RESULTS: Our study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.

CONCLUSIONS: This study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.

}, issn = {1468-6244}, doi = {10.1136/jmedgenet-2015-103439}, author = {van Leeuwen, Elisabeth M and Sabo, Aniko and Bis, Joshua C and Huffman, Jennifer E and Manichaikul, Ani and Smith, Albert V and Feitosa, Mary F and Demissie, Serkalem and Joshi, Peter K and Duan, Qing and Marten, Jonathan and van Klinken, Jan B and Surakka, Ida and Nolte, Ilja M and Zhang, Weihua and Mbarek, Hamdi and Li-Gao, Ruifang and Trompet, Stella and Verweij, Niek and Evangelou, Evangelos and Lyytik{\"a}inen, Leo-Pekka and Tayo, Bamidele O and Deelen, Joris and van der Most, Peter J and van der Laan, Sander W and Arking, Dan E and Morrison, Alanna and Dehghan, Abbas and Franco, Oscar H and Hofman, Albert and Rivadeneira, Fernando and Sijbrands, Eric J and Uitterlinden, Andr{\'e} G and Mychaleckyj, Josyf C and Campbell, Archie and Hocking, Lynne J and Padmanabhan, Sandosh and Brody, Jennifer A and Rice, Kenneth M and White, Charles C and Harris, Tamara and Isaacs, Aaron and Campbell, Harry and Lange, Leslie A and Rudan, Igor and Kolcic, Ivana and Navarro, Pau and Zemunik, Tatijana and Salomaa, Veikko and Kooner, Angad S and Kooner, Jaspal S and Lehne, Benjamin and Scott, William R and Tan, Sian-Tsung and de Geus, Eco J and Milaneschi, Yuri and Penninx, Brenda W J H and Willemsen, Gonneke and de Mutsert, Ren{\'e}e and Ford, Ian and Gansevoort, Ron T and Segura-Lepe, Marcelo P and Raitakari, Olli T and Viikari, Jorma S and Nikus, Kjell and Forrester, Terrence and McKenzie, Colin A and de Craen, Anton J M and de Ruijter, Hester M and Pasterkamp, Gerard and Snieder, Harold and Oldehinkel, Albertine J and Slagboom, P Eline and Cooper, Richard S and K{\"a}h{\"o}nen, Mika and Lehtim{\"a}ki, Terho and Elliott, Paul and van der Harst, Pim and Jukema, J Wouter and Mook-Kanamori, Dennis O and Boomsma, Dorret I and Chambers, John C and Swertz, Morris and Ripatti, Samuli and Willems van Dijk, Ko and Vitart, Veronique and Polasek, Ozren and Hayward, Caroline and Wilson, James G and Wilson, James F and Gudnason, Vilmundur and Rich, Stephen S and Psaty, Bruce M and Borecki, Ingrid B and Boerwinkle, Eric and Rotter, Jerome I and Cupples, L Adrienne and van Duijn, Cornelia M} } @article {7140, title = {Predicting Heart Failure With Preserved and Reduced Ejection Fraction: The International Collaboration on Heart Failure Subtypes.}, journal = {Circ Heart Fail}, volume = {9}, year = {2016}, month = {2016 Jun}, abstract = {

BACKGROUND: Heart failure (HF) is a prevalent and deadly disease, and preventive strategies focused on at-risk individuals are needed. Current HF prediction models have not examined HF subtypes. We sought to develop and validate risk prediction models for HF with preserved and reduced ejection fraction (HFpEF, HFrEF).

METHODS AND RESULTS: Of 28,820 participants from 4 community-based cohorts, 982 developed incident HFpEF and 909 HFrEF during a median follow-up of 12 years. Three cohorts were combined, and a 2:1 random split was used for derivation and internal validation, with the fourth cohort as external validation. Models accounted for multiple competing risks (death, other HF subtype, and unclassified HF). The HFpEF-specific model included age, sex, systolic blood pressure, body mass index, antihypertensive treatment, and previous myocardial infarction; it had good discrimination in derivation (c-statistic 0.80; 95\% confidence interval [CI], 0.78-0.82) and validation samples (internal: 0.79; 95\% CI, 0.77-0.82 and external: 0.76; 95\% CI: 0.71-0.80). The HFrEF-specific model additionally included smoking, left ventricular hypertrophy, left bundle branch block, and diabetes mellitus; it had good discrimination in derivation (c-statistic 0.82; 95\% CI, 0.80-0.84) and validation samples (internal: 0.80; 95\% CI, 0.78-0.83 and external: 0.76; 95\% CI, 0.71-0.80). Age was more strongly associated with HFpEF, and male sex, left ventricular hypertrophy, bundle branch block, previous myocardial infarction, and smoking with HFrEF (P value for each comparison <=0.02).

CONCLUSIONS: We describe and validate risk prediction models for HF subtypes and show good discrimination in a large sample. Some risk factors differed between HFpEF and HFrEF, supporting the notion of pathogenetic differences among HF subtypes.

}, issn = {1941-3297}, doi = {10.1161/CIRCHEARTFAILURE.115.003116}, author = {Ho, Jennifer E and Enserro, Danielle and Brouwers, Frank P and Kizer, Jorge R and Shah, Sanjiv J and Psaty, Bruce M and Bartz, Traci M and Santhanakrishnan, Rajalakshmi and Lee, Douglas S and Chan, Cheeling and Liu, Kiang and Blaha, Michael J and Hillege, Hans L and van der Harst, Pim and van Gilst, Wiek H and Kop, Willem J and Gansevoort, Ron T and Vasan, Ramachandran S and Gardin, Julius M and Levy, Daniel and Gottdiener, John S and de Boer, Rudolf A and Larson, Martin G} } @article {8570, title = {A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape.}, journal = {Nat Commun}, volume = {7}, year = {2016}, month = {2016 11 23}, pages = {13357}, abstract = {

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99\% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

}, keywords = {Anthropometry, Body Size, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Principal Component Analysis}, issn = {2041-1723}, doi = {10.1038/ncomms13357}, author = {Ried, Janina S and Jeff M, Janina and Chu, Audrey Y and Bragg-Gresham, Jennifer L and van Dongen, Jenny and Huffman, Jennifer E and Ahluwalia, Tarunveer S and Cadby, Gemma and Eklund, Niina and Eriksson, Joel and Esko, T{\~o}nu and Feitosa, Mary F and Goel, Anuj and Gorski, Mathias and Hayward, Caroline and Heard-Costa, Nancy L and Jackson, Anne U and Jokinen, Eero and Kanoni, Stavroula and Kristiansson, Kati and Kutalik, Zolt{\'a}n and Lahti, Jari and Luan, Jian{\textquoteright}an and M{\"a}gi, Reedik and Mahajan, Anubha and Mangino, Massimo and Medina-G{\'o}mez, Carolina and Monda, Keri L and Nolte, Ilja M and Perusse, Louis and Prokopenko, Inga and Qi, Lu and Rose, Lynda M and Salvi, Erika and Smith, Megan T and Snieder, Harold and Stan{\v c}{\'a}kov{\'a}, Alena and Ju Sung, Yun and Tachmazidou, Ioanna and Teumer, Alexander and Thorleifsson, Gudmar and van der Harst, Pim and Walker, Ryan W and Wang, Sophie R and Wild, Sarah H and Willems, Sara M and Wong, Andrew and Zhang, Weihua and Albrecht, Eva and Couto Alves, Alexessander and Bakker, Stephan J L and Barlassina, Cristina and Bartz, Traci M and Beilby, John and Bellis, Claire and Bergman, Richard N and Bergmann, Sven and Blangero, John and Bl{\"u}her, Matthias and Boerwinkle, Eric and Bonnycastle, Lori L and Bornstein, Stefan R and Bruinenberg, Marcel and Campbell, Harry and Chen, Yii-Der Ida and Chiang, Charleston W K and Chines, Peter S and Collins, Francis S and Cucca, Fracensco and Cupples, L Adrienne and D{\textquoteright}Avila, Francesca and de Geus, Eco J C and Dedoussis, George and Dimitriou, Maria and D{\"o}ring, Angela and Eriksson, Johan G and Farmaki, Aliki-Eleni and Farrall, Martin and Ferreira, Teresa and Fischer, Krista and Forouhi, Nita G and Friedrich, Nele and Gjesing, Anette Prior and Glorioso, Nicola and Graff, Mariaelisa and Grallert, Harald and Grarup, Niels and Gr{\"a}{\ss}ler, J{\"u}rgen and Grewal, Jagvir and Hamsten, Anders and Harder, Marie Neergaard and Hartman, Catharina A and Hassinen, Maija and Hastie, Nicholas and Hattersley, Andrew Tym and Havulinna, Aki S and Heli{\"o}vaara, Markku and Hillege, Hans and Hofman, Albert and Holmen, Oddgeir and Homuth, Georg and Hottenga, Jouke-Jan and Hui, Jennie and Husemoen, Lise Lotte and Hysi, Pirro G and Isaacs, Aaron and Ittermann, Till and Jalilzadeh, Shapour and James, Alan L and J{\o}rgensen, Torben and Jousilahti, Pekka and Jula, Antti and Marie Justesen, Johanne and Justice, Anne E and K{\"a}h{\"o}nen, Mika and Karaleftheri, Maria and Tee Khaw, Kay and Keinanen-Kiukaanniemi, Sirkka M and Kinnunen, Leena and Knekt, Paul B and Koistinen, Heikki A and Kolcic, Ivana and Kooner, Ishminder K and Koskinen, Seppo and Kovacs, Peter and Kyriakou, Theodosios and Laitinen, Tomi and Langenberg, Claudia and Lewin, Alexandra M and Lichtner, Peter and Lindgren, Cecilia M and Lindstr{\"o}m, Jaana and Linneberg, Allan and Lorbeer, Roberto and Lorentzon, Mattias and Luben, Robert and Lyssenko, Valeriya and M{\"a}nnist{\"o}, Satu and Manunta, Paolo and Leach, Irene Mateo and McArdle, Wendy L and McKnight, Barbara and Mohlke, Karen L and Mihailov, Evelin and Milani, Lili and Mills, Rebecca and Montasser, May E and Morris, Andrew P and M{\"u}ller, Gabriele and Musk, Arthur W and Narisu, Narisu and Ong, Ken K and Oostra, Ben A and Osmond, Clive and Palotie, Aarno and Pankow, James S and Paternoster, Lavinia and Penninx, Brenda W and Pichler, Irene and Pilia, Maria G and Polasek, Ozren and Pramstaller, Peter P and Raitakari, Olli T and Rankinen, Tuomo and Rao, D C and Rayner, Nigel W and Ribel-Madsen, Rasmus and Rice, Treva K and Richards, Marcus and Ridker, Paul M and Rivadeneira, Fernando and Ryan, Kathy A and Sanna, Serena and Sarzynski, Mark A and Scholtens, Salome and Scott, Robert A and Sebert, Sylvain and Southam, Lorraine and Spars{\o}, Thomas Hempel and Steinthorsdottir, Valgerdur and Stirrups, Kathleen and Stolk, Ronald P and Strauch, Konstantin and Stringham, Heather M and Swertz, Morris A and Swift, Amy J and T{\"o}njes, Anke and Tsafantakis, Emmanouil and van der Most, Peter J and van Vliet-Ostaptchouk, Jana V and Vandenput, Liesbeth and Vartiainen, Erkki and Venturini, Cristina and Verweij, Niek and Viikari, Jorma S and Vitart, Veronique and Vohl, Marie-Claude and Vonk, Judith M and Waeber, G{\'e}rard and Widen, Elisabeth and Willemsen, Gonneke and Wilsgaard, Tom and Winkler, Thomas W and Wright, Alan F and Yerges-Armstrong, Laura M and Hua Zhao, Jing and Zillikens, M Carola and Boomsma, Dorret I and Bouchard, Claude and Chambers, John C and Chasman, Daniel I and Cusi, Daniele and Gansevoort, Ron T and Gieger, Christian and Hansen, Torben and Hicks, Andrew A and Hu, Frank and Hveem, Kristian and Jarvelin, Marjo-Riitta and Kajantie, Eero and Kooner, Jaspal S and Kuh, Diana and Kuusisto, Johanna and Laakso, Markku and Lakka, Timo A and Lehtim{\"a}ki, Terho and Metspalu, Andres and Nj{\o}lstad, Inger and Ohlsson, Claes and Oldehinkel, Albertine J and Palmer, Lyle J and Pedersen, Oluf and Perola, Markus and Peters, Annette and Psaty, Bruce M and Puolijoki, Hannu and Rauramaa, Rainer and Rudan, Igor and Salomaa, Veikko and Schwarz, Peter E H and Shudiner, Alan R and Smit, Jan H and S{\o}rensen, Thorkild I A and Spector, Timothy D and Stefansson, Kari and Stumvoll, Michael and Tremblay, Angelo and Tuomilehto, Jaakko and Uitterlinden, Andr{\'e} G and Uusitupa, Matti and V{\"o}lker, Uwe and Vollenweider, Peter and Wareham, Nicholas J and Watkins, Hugh and Wilson, James F and Zeggini, Eleftheria and Abecasis, Goncalo R and Boehnke, Michael and Borecki, Ingrid B and Deloukas, Panos and van Duijn, Cornelia M and Fox, Caroline and Groop, Leif C and Heid, Iris M and Hunter, David J and Kaplan, Robert C and McCarthy, Mark I and North, Kari E and O{\textquoteright}Connell, Jeffrey R and Schlessinger, David and Thorsteinsdottir, Unnur and Strachan, David P and Frayling, Timothy and Hirschhorn, Joel N and M{\"u}ller-Nurasyid, Martina and Loos, Ruth J F} } @article {7575, title = {Measures of chronic kidney disease and risk of incident peripheral artery disease: a collaborative meta-analysis of individual participant data.}, journal = {Lancet Diabetes Endocrinol}, volume = {5}, year = {2017}, month = {2017 Sep}, pages = {718-728}, abstract = {

BACKGROUND: Some evidence suggests that chronic kidney disease is a risk factor for lower-extremity peripheral artery disease. We aimed to quantify the independent and joint associations of two measures of chronic kidney disease (estimated glomerular filtration rate [eGFR] and albuminuria) with the incidence of peripheral artery disease.

METHODS: In this collaborative meta-analysis of international cohorts included in the Chronic Kidney Disease Prognosis Consortium (baseline measurements obtained between 1972 and 2014) with baseline measurements of eGFR and albuminuria, at least 1000 participants (this criterion not applied to cohorts exclusively enrolling patients with chronic kidney disease), and at least 50 peripheral artery disease events, we analysed adult participants without peripheral artery disease at baseline at the individual patient level with Cox proportional hazards models to quantify associations of creatinine-based eGFR, urine albumin-to-creatinine ratio (ACR), and dipstick proteinuria with the incidence of peripheral artery disease (including hospitalisation with a diagnosis of peripheral artery disease, intermittent claudication, leg revascularisation, and leg amputation). We assessed discrimination improvement through c-statistics.

FINDINGS: We analysed 817 084 individuals without a history of peripheral artery disease at baseline from 21 cohorts. 18 261 cases of peripheral artery disease were recorded during follow-up across cohorts (median follow-up was 7{\textperiodcentered}4 years [IQR 5{\textperiodcentered}7-8{\textperiodcentered}9], range 2{\textperiodcentered}0-15{\textperiodcentered}8 years across cohorts). Both chronic kidney disease measures were independently associated with the incidence of peripheral artery disease. Compared with an eGFR of 95 mL/min per 1{\textperiodcentered}73 m2, adjusted hazard ratios (HRs) for incident study-specific peripheral artery disease was 1{\textperiodcentered}22 (95\% CI 1{\textperiodcentered}14-1{\textperiodcentered}30) at an eGFR of 45 mL/min per 1{\textperiodcentered}73 m2 and 2{\textperiodcentered}06 (1{\textperiodcentered}70-2{\textperiodcentered}48) at an eGFR of 15 mL/min per 1{\textperiodcentered}73 m2. Compared with an ACR of 5 mg/g, the adjusted HR for incident study-specific peripheral artery disease was 1{\textperiodcentered}50 (1{\textperiodcentered}41-1{\textperiodcentered}59) at an ACR of 30 mg/g and 2{\textperiodcentered}28 (2{\textperiodcentered}12-2{\textperiodcentered}44) at an ACR of 300 mg/g. The adjusted HR at an ACR of 300 mg/g versus 5 mg/g was 3{\textperiodcentered}68 (95\% CI 3{\textperiodcentered}00-4{\textperiodcentered}52) for leg amputation. eGFR and albuminuria contributed multiplicatively (eg, adjusted HR 5{\textperiodcentered}76 [4{\textperiodcentered}90-6{\textperiodcentered}77] for incident peripheral artery disease and 10{\textperiodcentered}61 [5{\textperiodcentered}70-19{\textperiodcentered}77] for amputation in eGFR <30 mL/min per 1{\textperiodcentered}73 m2 plus ACR >=300 mg/g or dipstick proteinuria 2+ or higher vs eGFR >=90 mL/min per 1{\textperiodcentered}73 m2 plus ACR <10 mg/g or dipstick proteinuria negative). Both eGFR and ACR significantly improved peripheral artery disease risk discrimination beyond traditional predictors, with a substantial improvement prediction of amputation with ACR (difference in c-statistic 0{\textperiodcentered}058, 95\% CI 0{\textperiodcentered}045-0{\textperiodcentered}070). Patterns were consistent across clinical subgroups.

INTERPRETATION: Even mild-to-moderate chronic kidney disease conferred increased risk of incident peripheral artery disease, with a strong association between albuminuria and amputation. Clinical attention should be paid to the development of peripheral artery disease symptoms and signs in people with any stage of chronic kidney disease.

FUNDING: American Heart Association, US National Kidney Foundation, and US National Institute of Diabetes and Digestive and Kidney Diseases.

}, issn = {2213-8595}, doi = {10.1016/S2213-8587(17)30183-3}, author = {Matsushita, Kunihiro and Ballew, Shoshana H and Coresh, Josef and Arima, Hisatomi and Arnl{\"o}v, Johan and Cirillo, Massimo and Ebert, Natalie and Hiramoto, Jade S and Kimm, Heejin and Shlipak, Michael G and Visseren, Frank L J and Gansevoort, Ron T and Kovesdy, Csaba P and Shalev, Varda and Woodward, Mark and Kronenberg, Florian} } @article {7549, title = {Predictors and outcomes of heart failure with mid-range ejection fraction.}, journal = {Eur J Heart Fail}, year = {2017}, month = {2017 Dec 11}, abstract = {

AIMS: While heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.

METHODS AND RESULTS: We pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49\%], HFpEF (EF >=50\%), and HFrEF (EF <=40\%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P~<= 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P~<= 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95\% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95\% CI 1.20-1.90, P~= 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95\% CI 1.41-1.73, P~= 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P~= 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P~= 0.78).

CONCLUSIONS: We found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

}, issn = {1879-0844}, doi = {10.1002/ejhf.1091}, author = {Bhambhani, Vijeta and Kizer, Jorge R and Lima, Jo{\~a}o A C and van der Harst, Pim and Bahrami, Hossein and Nayor, Matthew and de Filippi, Christopher R and Enserro, Danielle and Blaha, Michael J and Cushman, Mary and Wang, Thomas J and Gansevoort, Ron T and Fox, Caroline S and Gaggin, Hanna K and Kop, Willem J and Liu, Kiang and Vasan, Ramachandran S and Psaty, Bruce M and Lee, Douglas S and Brouwers, Frank P and Hillege, Hans L and Bartz, Traci M and Benjamin, Emelia J and Chan, Cheeling and Allison, Matthew and Gardin, Julius M and Januzzi, James L and Levy, Daniel and Herrington, David M and van Gilst, Wiek H and Bertoni, Alain G and Larson, Martin G and de Boer, Rudolf A and Gottdiener, John S and Shah, Sanjiv J and Ho, Jennifer E} } @article {7603, title = {Association of Cardiovascular Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction.}, journal = {JAMA Cardiol}, year = {2018}, month = {2018 Jan 10}, abstract = {

Importance: Nearly half of all patients with heart failure have preserved ejection fraction (HFpEF) as opposed to reduced ejection fraction (HFrEF), yet associations of biomarkers with future heart failure subtype are incompletely understood.

Objective: To evaluate the associations of 12 cardiovascular biomarkers with incident HFpEF vs HFrEF among adults from the general population.

Design, Setting, and Participants: This study included 4 longitudinal community-based cohorts: the Cardiovascular Health Study (1989-1990; 1992-1993 for supplemental African-American cohort), the Framingham Heart Study (1995-1998), the Multi-Ethnic Study of Atherosclerosis (2000-2002), and the Prevention of Renal and Vascular End-stage Disease study (1997-1998). Each cohort had prospective ascertainment of incident HFpEF and HFrEF. Data analysis was performed from June 25, 2015, to November 9, 2017.

Exposures: The following biomarkers were examined: N-terminal pro B-type natriuretic peptide or brain natriuretic peptide, high-sensitivity troponin T or I, C-reactive protein (CRP), urinary albumin to creatinine ratio (UACR), renin to aldosterone ratio, D-dimer, fibrinogen, soluble suppressor of tumorigenicity, galectin-3, cystatin C, plasminogen activator inhibitor 1, and interleukin 6.

Main Outcomes and Measures: Development of incident HFpEF and incident HFrEF.

Results: Among the 22 756 participants in these 4 cohorts (12 087 women and 10 669 men; mean [SD] age, 60 [13] years) in the study, during a median follow-up of 12 years, 633 participants developed incident HFpEF, and 841 developed HFrEF. In models adjusted for clinical risk factors of heart failure, 2 biomarkers were significantly associated with incident HFpEF: UACR (hazard ratio [HR], 1.33; 95\% CI, 1.20-1.48; P < .001) and natriuretic peptides (HR, 1.27; 95\% CI, 1.16-1.40; P < .001), with suggestive associations for high-sensitivity troponin (HR, 1.11; 95\% CI, 1.03-1.19; P = .008), plasminogen activator inhibitor 1 (HR, 1.22; 95\% CI, 1.03-1.45; P = .02), and fibrinogen (HR, 1.12; 95\% CI, 1.03-1.22; P = .01). By contrast, 6 biomarkers were associated with incident HFrEF: natriuretic peptides (HR, 1.54; 95\% CI, 1.41-1.68; P < .001), UACR (HR, 1.21; 95\% CI, 1.11-1.32; P < .001), high-sensitivity troponin (HR, 1.37; 95\% CI, 1.29-1.46; P < .001), cystatin C (HR, 1.19; 95\% CI, 1.11-1.27; P < .001), D-dimer (HR, 1.22; 95\% CI, 1.11-1.35; P < .001), and CRP (HR, 1.19; 95\% CI, 1.11-1.28; P < .001). When directly compared, natriuretic peptides, high-sensitivity troponin, and CRP were more strongly associated with HFrEF compared with HFpEF.

Conclusions and Relevance: Biomarkers of renal dysfunction, endothelial dysfunction, and inflammation were associated with incident HFrEF. By contrast, only natriuretic peptides and UACR were associated with HFpEF. These findings highlight the need for future studies focused on identifying novel biomarkers of the risk of HFpEF.

}, issn = {2380-6591}, doi = {10.1001/jamacardio.2017.4987}, author = {de Boer, Rudolf A and Nayor, Matthew and deFilippi, Christopher R and Enserro, Danielle and Bhambhani, Vijeta and Kizer, Jorge R and Blaha, Michael J and Brouwers, Frank P and Cushman, Mary and Lima, Jo{\~a}o A C and Bahrami, Hossein and van der Harst, Pim and Wang, Thomas J and Gansevoort, Ron T and Fox, Caroline S and Gaggin, Hanna K and Kop, Willem J and Liu, Kiang and Vasan, Ramachandran S and Psaty, Bruce M and Lee, Douglas S and Hillege, Hans L and Bartz, Traci M and Benjamin, Emelia J and Chan, Cheeling and Allison, Matthew and Gardin, Julius M and Januzzi, James L and Shah, Sanjiv J and Levy, Daniel and Herrington, David M and Larson, Martin G and van Gilst, Wiek H and Gottdiener, John S and Bertoni, Alain G and Ho, Jennifer E} } @article {7812, title = {The Association of Obesity and Cardiometabolic Traits With Incident~HFpEF and HFrEF.}, journal = {JACC Heart Fail}, volume = {6}, year = {2018}, month = {2018 Aug}, pages = {701-709}, abstract = {

OBJECTIVES: This study evaluated the associations of obesity and cardiometabolic traits with incident heart failure with preserved versus reduced ejection fraction (HFpEF vs. HFrEF). Given known sex differences in HF subtype, we examined men and women separately.

BACKGROUND: Recent studies suggest that obesity confers greater risk of HFpEF versus HFrEF. Contributions of associated metabolic traits to HFpEF are less clear.

METHODS: We studied 22,681 participants from 4 community-based cohorts followed for incident HFpEF versus HFrEF (ejection fraction >=50\% vs.~<50\%). We evaluated the association of body mass index (BMI) and cardiometabolic traits with incident HF subtype using Cox models.

RESULTS: The mean age was 60 {\textpm} 13 years, and 53\% were women. Over a median follow-up of 12 years, 628 developed incident HFpEF and 835 HFrEF. Greater BMI portended higher risk of HFpEF compared with HFrEF (hazard ratio [HR]: 1.34 per 1-SD increase in BMI; 95\% confidence interval [CI]: 1.24 to 1.45 vs. HR: 1.18; 95\% CI: 1.10 to 1.27). Similarly, insulin resistance (homeostatic model assessment of insulin resistance) was associated with HFpEF (HR: 1.20 per 1-SD; 95\% CI: 1.05 to 1.37), but not HFrEF (HR: 0.99; 95\% CI: 0.88 to 1.11; p~< 0.05 for difference HFpEF vs. HFrEF). We found that the differential association of BMI with HFpEF versus HFrEF was more pronounced among women (p for difference HFpEF vs. HFrEF~= 0.01) when compared with men (p~= 0.34).

CONCLUSIONS: Obesity and related cardiometabolic traits including insulin resistance are more strongly associated with risk of future HFpEF versus HFrEF. The differential risk of HFpEF with obesity seems particularly pronounced among~women and may underlie sex differences in HF subtypes.

}, issn = {2213-1787}, doi = {10.1016/j.jchf.2018.05.018}, author = {Savji, Nazir and Meijers, Wouter C and Bartz, Traci M and Bhambhani, Vijeta and Cushman, Mary and Nayor, Matthew and Kizer, Jorge R and Sarma, Amy and Blaha, Michael J and Gansevoort, Ron T and Gardin, Julius M and Hillege, Hans L and Ji, Fei and Kop, Willem J and Lau, Emily S and Lee, Douglas S and Sadreyev, Ruslan and van Gilst, Wiek H and Wang, Thomas J and Zanni, Markella V and Vasan, Ramachandran S and Allen, Norrina B and Psaty, Bruce M and van der Harst, Pim and Levy, Daniel and Larson, Martin and Shah, Sanjiv J and de Boer, Rudolf A and Gottdiener, John S and Ho, Jennifer E} } @article {7923, title = {Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.}, journal = {Eur Heart J}, year = {2018}, month = {2018 Nov 22}, abstract = {

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after {\textquoteright}recalibration{\textquoteright}, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at {\textquoteright}high{\textquoteright} 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10\%, 52\%, and 41\%, respectively, whereas RRS under-predicted by 10\%. Original versions of algorithms classified 29-39\% of individuals aged >=40 years as high risk. By contrast, recalibration reduced this proportion to 22-24\% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.

Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

}, issn = {1522-9645}, doi = {10.1093/eurheartj/ehy653}, author = {Pennells, Lisa and Kaptoge, Stephen and Wood, Angela and Sweeting, Mike and Zhao, Xiaohui and White, Ian and Burgess, Stephen and Willeit, Peter and Bolton, Thomas and Moons, Karel G M and van der Schouw, Yvonne T and Selmer, Randi and Khaw, Kay-Tee and Gudnason, Vilmundur and Assmann, Gerd and Amouyel, Philippe and Salomaa, Veikko and Kivimaki, Mika and Nordestgaard, B{\o}rge G and Blaha, Michael J and Kuller, Lewis H and Brenner, Hermann and Gillum, Richard F and Meisinger, Christa and Ford, Ian and Knuiman, Matthew W and Rosengren, Annika and Lawlor, Debbie A and V{\"o}lzke, Henry and Cooper, Cyrus and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Kauhanen, Jussi and Cooper, Jackie A and Rodriguez, Beatriz and Sundstr{\"o}m, Johan and Barrett-Connor, Elizabeth and Dankner, Rachel and Nietert, Paul J and Davidson, Karina W and Wallace, Robert B and Blazer, Dan G and Bj{\"o}rkelund, Cecilia and Donfrancesco, Chiara and Krumholz, Harlan M and Nissinen, Aulikki and Davis, Barry R and Coady, Sean and Whincup, Peter H and J{\o}rgensen, Torben and Ducimetiere, Pierre and Trevisan, Maurizio and Engstr{\"o}m, Gunnar and Crespo, Carlos J and Meade, Tom W and Visser, Marjolein and Kromhout, Daan and Kiechl, Stefan and Daimon, Makoto and Price, Jackie F and G{\'o}mez de la C{\'a}mara, Agustin and Wouter Jukema, J and Lamarche, Beno{\^\i}t and Onat, Altan and Simons, Leon A and Kavousi, Maryam and Ben-Shlomo, Yoav and Gallacher, John and Dekker, Jacqueline M and Arima, Hisatomi and Shara, Nawar and Tipping, Robert W and Roussel, Ronan and Brunner, Eric J and Koenig, Wolfgang and Sakurai, Masaru and Pavlovic, Jelena and Gansevoort, Ron T and Nagel, Dorothea and Goldbourt, Uri and Barr, Elizabeth L M and Palmieri, Luigi and Nj{\o}lstad, Inger and Sato, Shinichi and Monique Verschuren, W M and Varghese, Cherian V and Graham, Ian and Onuma, Oyere and Greenland, Philip and Woodward, Mark and Ezzati, Majid and Psaty, Bruce M and Sattar, Naveed and Jackson, Rod and Ridker, Paul M and Cook, Nancy R and D{\textquoteright}Agostino, Ralph B and Thompson, Simon G and Danesh, John and Di Angelantonio, Emanuele} } @article {7915, title = {Relationship of Estimated GFR and Albuminuria to Concurrent Laboratory Abnormalities: An Individual Participant Data Meta-analysis in a Global Consortium.}, journal = {Am J Kidney Dis}, year = {2018}, month = {2018 Oct 19}, abstract = {

RATIONALE \& OBJECTIVE: Chronic kidney disease (CKD) is complicated by abnormalities that reflect disruption in filtration, tubular, and endocrine functions of the kidney. Our aim was to explore the relationship of specific laboratory result abnormalities and hypertension with the estimated glomerular filtration rate (eGFR) and albuminuria CKD staging framework.

STUDY DESIGN: Cross-sectional individual participant-level analyses in a global consortium.

SETTING \& STUDY POPULATIONS: 17 CKD and 38 general population and high-risk cohorts.

SELECTION CRITERIA FOR STUDIES: Cohorts in the CKD Prognosis Consortium with data for eGFR and albuminuria, as well as a measurement of hemoglobin, bicarbonate, phosphorus, parathyroid hormone, potassium, or calcium, or hypertension.

DATA EXTRACTION: Data were obtained and analyzed between July 2015 and January~2018.

ANALYTICAL APPROACH: We modeled the association of eGFR and albuminuria with hemoglobin, bicarbonate, phosphorus, parathyroid hormone, potassium, and calcium values using linear regression and with hypertension and categorical definitions of each abnormality using logistic regression. Results were pooled using random-effects meta-analyses.

RESULTS: The CKD cohorts (n=254,666 participants) were 27\% women and 10\% black, with a mean age of 69 (SD, 12) years. The general population/high-risk cohorts (n=1,758,334) were 50\% women and 2\% black, with a mean age of 50 (16) years. There was a strong graded association between lower eGFR and all laboratory result abnormalities (ORs ranging from 3.27 [95\% CI, 2.68-3.97] to 8.91 [95\% CI, 7.22-10.99] comparing eGFRs of 15 to 29 with eGFRs of 45 to 59mL/min/1.73m), whereas albuminuria had equivocal or weak associations with abnormalities (ORs ranging from 0.77 [95\% CI, 0.60-0.99] to 1.92 [95\% CI, 1.65-2.24] comparing urinary albumin-creatinine ratio > 300 vs~< 30mg/g).

LIMITATIONS: Variations in study era, health care delivery system, typical diet, and laboratory assays.

CONCLUSIONS: Lower eGFR was strongly associated with higher odds of multiple laboratory result abnormalities. Knowledge of risk associations might help guide management in the heterogeneous group of patients with CKD.

}, issn = {1523-6838}, doi = {10.1053/j.ajkd.2018.08.013}, author = {Inker, Lesley A and Grams, Morgan E and Levey, Andrew S and Coresh, Josef and Cirillo, Massimo and Collins, John F and Gansevoort, Ron T and Gutierrez, Orlando M and Hamano, Takayuki and Heine, Gunnar H and Ishikawa, Shizukiyo and Jee, Sun Ha and Kronenberg, Florian and Landray, Martin J and Miura, Katsuyuki and Nadkarni, Girish N and Peralta, Carmen A and Rothenbacher, Dietrich and Schaeffner, Elke and Sedaghat, Sanaz and Shlipak, Michael G and Zhang, Luxia and van Zuilen, Arjan D and Hallan, Stein I and Kovesdy, Csaba P and Woodward, Mark and Levin, Adeera} } @article {8109, title = {A catalog of genetic loci associated with kidney function from analyses of a million individuals.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 06}, pages = {957-972}, abstract = {

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

}, keywords = {Chromosome Mapping, European Continental Ancestry Group, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Inheritance Patterns, Kidney Function Tests, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Renal Insufficiency, Chronic, Uromodulin}, issn = {1546-1718}, doi = {10.1038/s41588-019-0407-x}, author = {Wuttke, Matthias and Li, Yong and Li, Man and Sieber, Karsten B and Feitosa, Mary F and Gorski, Mathias and Tin, Adrienne and Wang, Lihua and Chu, Audrey Y and Hoppmann, Anselm and Kirsten, Holger and Giri, Ayush and Chai, Jin-Fang and Sveinbjornsson, Gardar and Tayo, Bamidele O and Nutile, Teresa and Fuchsberger, Christian and Marten, Jonathan and Cocca, Massimiliano and Ghasemi, Sahar and Xu, Yizhe and Horn, Katrin and Noce, Damia and van der Most, Peter J and Sedaghat, Sanaz and Yu, Zhi and Akiyama, Masato and Afaq, Saima and Ahluwalia, Tarunveer S and Almgren, Peter and Amin, Najaf and Arnl{\"o}v, Johan and Bakker, Stephan J L and Bansal, Nisha and Baptista, Daniela and Bergmann, Sven and Biggs, Mary L and Biino, Ginevra and Boehnke, Michael and Boerwinkle, Eric and Boissel, Mathilde and Bottinger, Erwin P and Boutin, Thibaud S and Brenner, Hermann and Brumat, Marco and Burkhardt, Ralph and Butterworth, Adam S and Campana, Eric and Campbell, Archie and Campbell, Harry and Canouil, Micka{\"e}l and Carroll, Robert J and Catamo, Eulalia and Chambers, John C and Chee, Miao-Ling and Chee, Miao-Li and Chen, Xu and Cheng, Ching-Yu and Cheng, Yurong and Christensen, Kaare and Cifkova, Renata and Ciullo, Marina and Concas, Maria Pina and Cook, James P and Coresh, Josef and Corre, Tanguy and Sala, Cinzia Felicita and Cusi, Daniele and Danesh, John and Daw, E Warwick and de Borst, Martin H and De Grandi, Alessandro and de Mutsert, Ren{\'e}e and de Vries, Aiko P J and Degenhardt, Frauke and Delgado, Graciela and Demirkan, Ayse and Di Angelantonio, Emanuele and Dittrich, Katalin and Divers, Jasmin and Dorajoo, Rajkumar and Eckardt, Kai-Uwe and Ehret, Georg and Elliott, Paul and Endlich, Karlhans and Evans, Michele K and Felix, Janine F and Foo, Valencia Hui Xian and Franco, Oscar H and Franke, Andre and Freedman, Barry I and Freitag-Wolf, Sandra and Friedlander, Yechiel and Froguel, Philippe and Gansevoort, Ron T and Gao, He and Gasparini, Paolo and Gaziano, J Michael and Giedraitis, Vilmantas and Gieger, Christian and Girotto, Giorgia and Giulianini, Franco and G{\"o}gele, Martin and Gordon, Scott D and Gudbjartsson, Daniel F and Gudnason, Vilmundur and Haller, Toomas and Hamet, Pavel and Harris, Tamara B and Hartman, Catharina A and Hayward, Caroline and Hellwege, Jacklyn N and Heng, Chew-Kiat and Hicks, Andrew A and Hofer, Edith and Huang, Wei and Hutri-K{\"a}h{\"o}nen, Nina and Hwang, Shih-Jen and Ikram, M Arfan and Indridason, Olafur S and Ingelsson, Erik and Ising, Marcus and Jaddoe, Vincent W V and Jakobsdottir, Johanna and Jonas, Jost B and Joshi, Peter K and Josyula, Navya Shilpa and Jung, Bettina and K{\"a}h{\"o}nen, Mika and Kamatani, Yoichiro and Kammerer, Candace M and Kanai, Masahiro and Kastarinen, Mika and Kerr, Shona M and Khor, Chiea-Chuen and Kiess, Wieland and Kleber, Marcus E and Koenig, Wolfgang and Kooner, Jaspal S and K{\"o}rner, Antje and Kovacs, Peter and Kraja, Aldi T and Krajcoviechova, Alena and Kramer, Holly and Kr{\"a}mer, Bernhard K and Kronenberg, Florian and Kubo, Michiaki and Kuhnel, Brigitte and Kuokkanen, Mikko and Kuusisto, Johanna and La Bianca, Martina and Laakso, Markku and Lange, Leslie A and Langefeld, Carl D and Lee, Jeannette Jen-Mai and Lehne, Benjamin and Lehtim{\"a}ki, Terho and Lieb, Wolfgang and Lim, Su-Chi and Lind, Lars and Lindgren, Cecilia M and Liu, Jun and Liu, Jianjun and Loeffler, Markus and Loos, Ruth J F and Lucae, Susanne and Lukas, Mary Ann and Lyytik{\"a}inen, Leo-Pekka and M{\"a}gi, Reedik and Magnusson, Patrik K E and Mahajan, Anubha and Martin, Nicholas G and Martins, Jade and M{\"a}rz, Winfried and Mascalzoni, Deborah and Matsuda, Koichi and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Metspalu, Andres and Mikaelsdottir, Evgenia K and Milaneschi, Yuri and Miliku, Kozeta and Mishra, Pashupati P and Mohlke, Karen L and Mononen, Nina and Montgomery, Grant W and Mook-Kanamori, Dennis O and Mychaleckyj, Josyf C and Nadkarni, Girish N and Nalls, Mike A and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M and Noordam, Raymond and O{\textquoteright}Connell, Jeffrey and O{\textquoteright}Donoghue, Michelle L and Olafsson, Isleifur and Oldehinkel, Albertine J and Orho-Melander, Marju and Ouwehand, Willem H and Padmanabhan, Sandosh and Palmer, Nicholette D and Palsson, Runolfur and Penninx, Brenda W J H and Perls, Thomas and Perola, Markus and Pirastu, Mario and Pirastu, Nicola and Pistis, Giorgio and Podgornaia, Anna I and Polasek, Ozren and Ponte, Belen and Porteous, David J and Poulain, Tanja and Pramstaller, Peter P and Preuss, Michael H and Prins, Bram P and Province, Michael A and Rabelink, Ton J and Raffield, Laura M and Raitakari, Olli T and Reilly, Dermot F and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M and Ridker, Paul M and Rivadeneira, Fernando and Rizzi, Federica and Roberts, David J and Robino, Antonietta and Rossing, Peter and Rudan, Igor and Rueedi, Rico and Ruggiero, Daniela and Ryan, Kathleen A and Saba, Yasaman and Sabanayagam, Charumathi and Salomaa, Veikko and Salvi, Erika and Saum, Kai-Uwe and Schmidt, Helena and Schmidt, Reinhold and Sch{\"o}ttker, Ben and Schulz, Christina-Alexandra and Schupf, Nicole and Shaffer, Christian M and Shi, Yuan and Smith, Albert V and Smith, Blair H and Soranzo, Nicole and Spracklen, Cassandra N and Strauch, Konstantin and Stringham, Heather M and Stumvoll, Michael and Svensson, Per O and Szymczak, Silke and Tai, E-Shyong and Tajuddin, Salman M and Tan, Nicholas Y Q and Taylor, Kent D and Teren, Andrej and Tham, Yih-Chung and Thiery, Joachim and Thio, Chris H L and Thomsen, Hauke and Thorleifsson, Gudmar and Toniolo, Daniela and T{\"o}njes, Anke and Tremblay, Johanne and Tzoulaki, Ioanna and Uitterlinden, Andr{\'e} G and Vaccargiu, Simona and van Dam, Rob M and van der Harst, Pim and van Duijn, Cornelia M and Velez Edward, Digna R and Verweij, Niek and Vogelezang, Suzanne and V{\"o}lker, Uwe and Vollenweider, Peter and Waeber, G{\'e}rard and Waldenberger, Melanie and Wallentin, Lars and Wang, Ya Xing and Wang, Chaolong and Waterworth, Dawn M and Bin Wei, Wen and White, Harvey and Whitfield, John B and Wild, Sarah H and Wilson, James F and Wojczynski, Mary K and Wong, Charlene and Wong, Tien-Yin and Xu, Liang and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M and Zhang, Weihua and Zonderman, Alan B and Rotter, Jerome I and Bochud, Murielle and Psaty, Bruce M and Vitart, Veronique and Wilson, James G and Dehghan, Abbas and Parsa, Afshin and Chasman, Daniel I and Ho, Kevin and Morris, Andrew P and Devuyst, Olivier and Akilesh, Shreeram and Pendergrass, Sarah A and Sim, Xueling and B{\"o}ger, Carsten A and Okada, Yukinori and Edwards, Todd L and Snieder, Harold and Stefansson, Kari and Hung, Adriana M and Heid, Iris M and Scholz, Markus and Teumer, Alexander and K{\"o}ttgen, Anna and Pattaro, Cristian} } @article {8207, title = {Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 Oct}, pages = {1459-1474}, abstract = {

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

}, issn = {1546-1718}, doi = {10.1038/s41588-019-0504-x}, author = {Tin, Adrienne and Marten, Jonathan and Halperin Kuhns, Victoria L and Li, Yong and Wuttke, Matthias and Kirsten, Holger and Sieber, Karsten B and Qiu, Chengxiang and Gorski, Mathias and Yu, Zhi and Giri, Ayush and Sveinbjornsson, Gardar and Li, Man and Chu, Audrey Y and Hoppmann, Anselm and O{\textquoteright}Connor, Luke J and Prins, Bram and Nutile, Teresa and Noce, Damia and Akiyama, Masato and Cocca, Massimiliano and Ghasemi, Sahar and van der Most, Peter J and Horn, Katrin and Xu, Yizhe and Fuchsberger, Christian and Sedaghat, Sanaz and Afaq, Saima and Amin, Najaf and Arnl{\"o}v, Johan and Bakker, Stephan J L and Bansal, Nisha and Baptista, Daniela and Bergmann, Sven and Biggs, Mary L and Biino, Ginevra and Boerwinkle, Eric and Bottinger, Erwin P and Boutin, Thibaud S and Brumat, Marco and Burkhardt, Ralph and Campana, Eric and Campbell, Archie and Campbell, Harry and Carroll, Robert J and Catamo, Eulalia and Chambers, John C and Ciullo, Marina and Concas, Maria Pina and Coresh, Josef and Corre, Tanguy and Cusi, Daniele and Felicita, Sala Cinzia and de Borst, Martin H and De Grandi, Alessandro and de Mutsert, Ren{\'e}e and de Vries, Aiko P J and Delgado, Graciela and Demirkan, Ayse and Devuyst, Olivier and Dittrich, Katalin and Eckardt, Kai-Uwe and Ehret, Georg and Endlich, Karlhans and Evans, Michele K and Gansevoort, Ron T and Gasparini, Paolo and Giedraitis, Vilmantas and Gieger, Christian and Girotto, Giorgia and G{\"o}gele, Martin and Gordon, Scott D and Gudbjartsson, Daniel F and Gudnason, Vilmundur and Haller, Toomas and Hamet, Pavel and Harris, Tamara B and Hayward, Caroline and Hicks, Andrew A and Hofer, Edith and Holm, Hilma and Huang, Wei and Hutri-K{\"a}h{\"o}nen, Nina and Hwang, Shih-Jen and Ikram, M Arfan and Lewis, Raychel M and Ingelsson, Erik and Jakobsdottir, Johanna and Jonsdottir, Ingileif and Jonsson, Helgi and Joshi, Peter K and Josyula, Navya Shilpa and Jung, Bettina and K{\"a}h{\"o}nen, Mika and Kamatani, Yoichiro and Kanai, Masahiro and Kerr, Shona M and Kiess, Wieland and Kleber, Marcus E and Koenig, Wolfgang and Kooner, Jaspal S and K{\"o}rner, Antje and Kovacs, Peter and Kr{\"a}mer, Bernhard K and Kronenberg, Florian and Kubo, Michiaki and Kuhnel, Brigitte and La Bianca, Martina and Lange, Leslie A and Lehne, Benjamin and Lehtim{\"a}ki, Terho and Liu, Jun and Loeffler, Markus and Loos, Ruth J F and Lyytik{\"a}inen, Leo-Pekka and M{\"a}gi, Reedik and Mahajan, Anubha and Martin, Nicholas G and M{\"a}rz, Winfried and Mascalzoni, Deborah and Matsuda, Koichi and Meisinger, Christa and Meitinger, Thomas and Metspalu, Andres and Milaneschi, Yuri and O{\textquoteright}Donnell, Christopher J and Wilson, Otis D and Gaziano, J Michael and Mishra, Pashupati P and Mohlke, Karen L and Mononen, Nina and Montgomery, Grant W and Mook-Kanamori, Dennis O and M{\"u}ller-Nurasyid, Martina and Nadkarni, Girish N and Nalls, Mike A and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M and Noordam, Raymond and O{\textquoteright}Connell, Jeffrey R and Olafsson, Isleifur and Padmanabhan, Sandosh and Penninx, Brenda W J H and Perls, Thomas and Peters, Annette and Pirastu, Mario and Pirastu, Nicola and Pistis, Giorgio and Polasek, Ozren and Ponte, Belen and Porteous, David J and Poulain, Tanja and Preuss, Michael H and Rabelink, Ton J and Raffield, Laura M and Raitakari, Olli T and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M and Rizzi, Federica and Robino, Antonietta and Rudan, Igor and Krajcoviechova, Alena and Cifkova, Renata and Rueedi, Rico and Ruggiero, Daniela and Ryan, Kathleen A and Saba, Yasaman and Salvi, Erika and Schmidt, Helena and Schmidt, Reinhold and Shaffer, Christian M and Smith, Albert V and Smith, Blair H and Spracklen, Cassandra N and Strauch, Konstantin and Stumvoll, Michael and Sulem, Patrick and Tajuddin, Salman M and Teren, Andrej and Thiery, Joachim and Thio, Chris H L and Thorsteinsdottir, Unnur and Toniolo, Daniela and T{\"o}njes, Anke and Tremblay, Johanne and Uitterlinden, Andr{\'e} G and Vaccargiu, Simona and van der Harst, Pim and van Duijn, Cornelia M and Verweij, Niek and V{\"o}lker, Uwe and Vollenweider, Peter and Waeber, G{\'e}rard and Waldenberger, Melanie and Whitfield, John B and Wild, Sarah H and Wilson, James F and Yang, Qiong and Zhang, Weihua and Zonderman, Alan B and Bochud, Murielle and Wilson, James G and Pendergrass, Sarah A and Ho, Kevin and Parsa, Afshin and Pramstaller, Peter P and Psaty, Bruce M and B{\"o}ger, Carsten A and Snieder, Harold and Butterworth, Adam S and Okada, Yukinori and Edwards, Todd L and Stefansson, Kari and Susztak, Katalin and Scholz, Markus and Heid, Iris M and Hung, Adriana M and Teumer, Alexander and Pattaro, Cristian and Woodward, Owen M and Vitart, Veronique and K{\"o}ttgen, Anna} } @article {8624, title = {Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline.}, journal = {Kidney Int}, year = {2020}, month = {2020 Oct 30}, abstract = {

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25\% or more and eGFRcrea under 60 mL/min/1.73m at follow-up among those with eGFRcrea 60 mL/min/1.73m or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95\% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

}, issn = {1523-1755}, doi = {10.1016/j.kint.2020.09.030}, author = {Gorski, Mathias and Jung, Bettina and Li, Yong and Matias-Garcia, Pamela R and Wuttke, Matthias and Coassin, Stefan and Thio, Chris H L and Kleber, Marcus E and Winkler, Thomas W and Wanner, Veronika and Chai, Jin-Fang and Chu, Audrey Y and Cocca, Massimiliano and Feitosa, Mary F and Ghasemi, Sahar and Hoppmann, Anselm and Horn, Katrin and Li, Man and Nutile, Teresa and Scholz, Markus and Sieber, Karsten B and Teumer, Alexander and Tin, Adrienne and Wang, Judy and Tayo, Bamidele O and Ahluwalia, Tarunveer S and Almgren, Peter and Bakker, Stephan J L and Banas, Bernhard and Bansal, Nisha and Biggs, Mary L and Boerwinkle, Eric and Bottinger, Erwin P and Brenner, Hermann and Carroll, Robert J and Chalmers, John and Chee, Miao-Li and Chee, Miao-Ling and Cheng, Ching-Yu and Coresh, Josef and de Borst, Martin H and Degenhardt, Frauke and Eckardt, Kai-Uwe and Endlich, Karlhans and Franke, Andre and Freitag-Wolf, Sandra and Gampawar, Piyush and Gansevoort, Ron T and Ghanbari, Mohsen and Gieger, Christian and Hamet, Pavel and Ho, Kevin and Hofer, Edith and Holleczek, Bernd and Xian Foo, Valencia Hui and Hutri-K{\"a}h{\"o}nen, Nina and Hwang, Shih-Jen and Ikram, M Arfan and Josyula, Navya Shilpa and K{\"a}h{\"o}nen, Mika and Khor, Chiea-Chuen and Koenig, Wolfgang and Kramer, Holly and Kr{\"a}mer, Bernhard K and Kuhnel, Brigitte and Lange, Leslie A and Lehtim{\"a}ki, Terho and Lieb, Wolfgang and Loos, Ruth J F and Lukas, Mary Ann and Lyytik{\"a}inen, Leo-Pekka and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Milaneschi, Yuri and Mishra, Pashupati P and Mononen, Nina and Mychaleckyj, Josyf C and Nadkarni, Girish N and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M and O{\textquoteright}Donoghue, Michelle L and Orho-Melander, Marju and Pendergrass, Sarah A and Penninx, Brenda W J H and Preuss, Michael H and Psaty, Bruce M and Raffield, Laura M and Raitakari, Olli T and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M and Rosenkranz, Alexander R and Rossing, Peter and Rotter, Jerome I and Sabanayagam, Charumathi and Schmidt, Helena and Schmidt, Reinhold and Sch{\"o}ttker, Ben and Schulz, Christina-Alexandra and Sedaghat, Sanaz and Shaffer, Christian M and Strauch, Konstantin and Szymczak, Silke and Taylor, Kent D and Tremblay, Johanne and Chaker, Layal and van der Harst, Pim and van der Most, Peter J and Verweij, Niek and V{\"o}lker, Uwe and Waldenberger, Melanie and Wallentin, Lars and Waterworth, Dawn M and White, Harvey D and Wilson, James G and Wong, Tien-Yin and Woodward, Mark and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M and Zhang, Yan and Snieder, Harold and Wanner, Christoph and B{\"o}ger, Carsten A and K{\"o}ttgen, Anna and Kronenberg, Florian and Pattaro, Cristian and Heid, Iris M} } @article {8488, title = {Sex-Specific Associations of Cardiovascular Risk Factors and Biomarkers With Incident Heart~Failure.}, journal = {J Am Coll Cardiol}, volume = {76}, year = {2020}, month = {2020 Sep 22}, pages = {1455-1465}, abstract = {

BACKGROUND: Whether cardiovascular (CV) disease risk factors and biomarkers associate differentially with heart failure (HF) risk in men and women is unclear.

OBJECTIVES: The purpose of this study was to evaluate sex-specific associations of CV risk factors and biomarkers with incident HF.

METHODS: The analysis was performed using data from 4 community-based cohorts with 12.5 years of follow-up. Participants (recruited between 1989 and 2002) were free of HF at baseline. Biomarker measurements included natriuretic peptides, cardiac troponins, plasminogen activator inhibitor-1, D-dimer, fibrinogen, C-reactive protein, sST2, galectin-3, cystatin-C, and urinary albumin-to-creatinine ratio.

RESULTS: Among 22,756 participants (mean age 60 {\textpm} 13 years, 53\% women), HF occurred in 2,095 participants (47\% women). Age, smoking, type 2 diabetes mellitus, hypertension, body mass index, atrial fibrillation, myocardial infarction, left ventricular hypertrophy, and left bundle branch block were strongly associated with HF in both sexes (p~<~0.001), and the combined clinical model had good discrimination in men (C-statistic~=~0.80) and in women (C-statistic~=~0.83). The majority of biomarkers were strongly and similarly associated with HF in both sexes. The clinical model improved modestly after adding natriuretic peptides in men (ΔC-statistic~=~0.006; likelihood ratio chi-square~=~146; p~<~0.001), and after adding cardiac troponins in women (ΔC-statistic~=~0.003; likelihood ratio chi-square~=~73; p~<~0.001).

CONCLUSIONS: CV risk factors are strongly and similarly associated with incident HF in both sexes, highlighting the similar importance of risk factor control in reducing HF risk in the community. There are subtle sex-related differences in the predictive value of individual biomarkers, but the overall improvement in HF risk estimation when included in~a~clinical HF risk prediction model is limited in both sexes.

}, issn = {1558-3597}, doi = {10.1016/j.jacc.2020.07.044}, author = {Suthahar, Navin and Lau, Emily S and Blaha, Michael J and Paniagua, Samantha M and Larson, Martin G and Psaty, Bruce M and Benjamin, Emelia J and Allison, Matthew A and Bartz, Traci M and Januzzi, James L and Levy, Daniel and Meems, Laura M G and Bakker, Stephan J L and Lima, Jo{\~a}o A C and Cushman, Mary and Lee, Douglas S and Wang, Thomas J and deFilippi, Christopher R and Herrington, David M and Nayor, Matthew and Vasan, Ramachandran S and Gardin, Julius M and Kizer, Jorge R and Bertoni, Alain G and Allen, Norrina B and Gansevoort, Ron T and Shah, Sanjiv J and Gottdiener, John S and Ho, Jennifer E and de Boer, Rudolf A} } @article {9112, title = {Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 Jun 13}, pages = {580}, abstract = {

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (n = 178,691, n = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

}, keywords = {Creatinine, Diabetes Mellitus, Diabetic Nephropathies, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Kidney}, issn = {2399-3642}, doi = {10.1038/s42003-022-03448-z}, author = {Winkler, Thomas W and Rasheed, Humaira and Teumer, Alexander and Gorski, Mathias and Rowan, Bryce X and Stanzick, Kira J and Thomas, Laurent F and Tin, Adrienne and Hoppmann, Anselm and Chu, Audrey Y and Tayo, Bamidele and Thio, Chris H L and Cusi, Daniele and Chai, Jin-Fang and Sieber, Karsten B and Horn, Katrin and Li, Man and Scholz, Markus and Cocca, Massimiliano and Wuttke, Matthias and van der Most, Peter J and Yang, Qiong and Ghasemi, Sahar and Nutile, Teresa and Li, Yong and Pontali, Giulia and G{\"u}nther, Felix and Dehghan, Abbas and Correa, Adolfo and Parsa, Afshin and Feresin, Agnese and de Vries, Aiko P J and Zonderman, Alan B and Smith, Albert V and Oldehinkel, Albertine J and De Grandi, Alessandro and Rosenkranz, Alexander R and Franke, Andre and Teren, Andrej and Metspalu, Andres and Hicks, Andrew A and Morris, Andrew P and T{\"o}njes, Anke and Morgan, Anna and Podgornaia, Anna I and Peters, Annette and K{\"o}rner, Antje and Mahajan, Anubha and Campbell, Archie and Freedman, Barry I and Spedicati, Beatrice and Ponte, Belen and Sch{\"o}ttker, Ben and Brumpton, Ben and Banas, Bernhard and Kr{\"a}mer, Bernhard K and Jung, Bettina and {\r A}svold, Bj{\o}rn Olav and Smith, Blair H and Ning, Boting and Penninx, Brenda W J H and Vanderwerff, Brett R and Psaty, Bruce M and Kammerer, Candace M and Langefeld, Carl D and Hayward, Caroline and Spracklen, Cassandra N and Robinson-Cohen, Cassianne and Hartman, Catharina A and Lindgren, Cecilia M and Wang, Chaolong and Sabanayagam, Charumathi and Heng, Chew-Kiat and Lanzani, Chiara and Khor, Chiea-Chuen and Cheng, Ching-Yu and Fuchsberger, Christian and Gieger, Christian and Shaffer, Christian M and Schulz, Christina-Alexandra and Willer, Cristen J and Chasman, Daniel I and Gudbjartsson, Daniel F and Ruggiero, Daniela and Toniolo, Daniela and Czamara, Darina and Porteous, David J and Waterworth, Dawn M and Mascalzoni, Deborah and Mook-Kanamori, Dennis O and Reilly, Dermot F and Daw, E Warwick and Hofer, Edith and Boerwinkle, Eric and Salvi, Erika and Bottinger, Erwin P and Tai, E-Shyong and Catamo, Eulalia and Rizzi, Federica and Guo, Feng and Rivadeneira, Fernando and Guilianini, Franco and Sveinbjornsson, Gardar and Ehret, Georg and Waeber, G{\'e}rard and Biino, Ginevra and Girotto, Giorgia and Pistis, Giorgio and Nadkarni, Girish N and Delgado, Graciela E and Montgomery, Grant W and Snieder, Harold and Campbell, Harry and White, Harvey D and Gao, He and Stringham, Heather M and Schmidt, Helena and Li, Hengtong and Brenner, Hermann and Holm, Hilma and Kirsten, Holgen and Kramer, Holly and Rudan, Igor and Nolte, Ilja M and Tzoulaki, Ioanna and Olafsson, Isleifur and Martins, Jade and Cook, James P and Wilson, James F and Halbritter, Jan and Felix, Janine F and Divers, Jasmin and Kooner, Jaspal S and Lee, Jeannette Jen-Mai and O{\textquoteright}Connell, Jeffrey and Rotter, Jerome I and Liu, Jianjun and Xu, Jie and Thiery, Joachim and Arnl{\"o}v, Johan and Kuusisto, Johanna and Jakobsdottir, Johanna and Tremblay, Johanne and Chambers, John C and Whitfield, John B and Gaziano, John M and Marten, Jonathan and Coresh, Josef and Jonas, Jost B and Mychaleckyj, Josyf C and Christensen, Kaare and Eckardt, Kai-Uwe and Mohlke, Karen L and Endlich, Karlhans and Dittrich, Katalin and Ryan, Kathleen A and Rice, Kenneth M and Taylor, Kent D and Ho, Kevin and Nikus, Kjell and Matsuda, Koichi and Strauch, Konstantin and Miliku, Kozeta and Hveem, Kristian and Lind, Lars and Wallentin, Lars and Yerges-Armstrong, Laura M and Raffield, Laura M and Phillips, Lawrence S and Launer, Lenore J and Lyytik{\"a}inen, Leo-Pekka and Lange, Leslie A and Citterio, Lorena and Klaric, Lucija and Ikram, M Arfan and Ising, Marcus and Kleber, Marcus E and Francescatto, Margherita and Concas, Maria Pina and Ciullo, Marina and Piratsu, Mario and Orho-Melander, Marju and Laakso, Markku and Loeffler, Markus and Perola, Markus and de Borst, Martin H and G{\"o}gele, Martin and Bianca, Martina La and Lukas, Mary Ann and Feitosa, Mary F and Biggs, Mary L and Wojczynski, Mary K and Kavousi, Maryam and Kanai, Masahiro and Akiyama, Masato and Yasuda, Masayuki and Nauck, Matthias and Waldenberger, Melanie and Chee, Miao-Li and Chee, Miao-Ling and Boehnke, Michael and Preuss, Michael H and Stumvoll, Michael and Province, Michael A and Evans, Michele K and O{\textquoteright}Donoghue, Michelle L and Kubo, Michiaki and K{\"a}h{\"o}nen, Mika and Kastarinen, Mika and Nalls, Mike A and Kuokkanen, Mikko and Ghanbari, Mohsen and Bochud, Murielle and Josyula, Navya Shilpa and Martin, Nicholas G and Tan, Nicholas Y Q and Palmer, Nicholette D and Pirastu, Nicola and Schupf, Nicole and Verweij, Niek and Hutri-K{\"a}h{\"o}nen, Nina and Mononen, Nina and Bansal, Nisha and Devuyst, Olivier and Melander, Olle and Raitakari, Olli T and Polasek, Ozren and Manunta, Paolo and Gasparini, Paolo and Mishra, Pashupati P and Sulem, Patrick and Magnusson, Patrik K E and Elliott, Paul and Ridker, Paul M and Hamet, Pavel and Svensson, Per O and Joshi, Peter K and Kovacs, Peter and Pramstaller, Peter P and Rossing, Peter and Vollenweider, Peter and van der Harst, Pim and Dorajoo, Rajkumar and Sim, Ralene Z H and Burkhardt, Ralph and Tao, Ran and Noordam, Raymond and M{\"a}gi, Reedik and Schmidt, Reinhold and de Mutsert, Ren{\'e}e and Rueedi, Rico and van Dam, Rob M and Carroll, Robert J and Gansevoort, Ron T and Loos, Ruth J F and Felicita, Sala Cinzia and Sedaghat, Sanaz and Padmanabhan, Sandosh and Freitag-Wolf, Sandra and Pendergrass, Sarah A and Graham, Sarah E and Gordon, Scott D and Hwang, Shih-Jen and Kerr, Shona M and Vaccargiu, Simona and Patil, Snehal B and Hallan, Stein and Bakker, Stephan J L and Lim, Su-Chi and Lucae, Susanne and Vogelezang, Suzanne and Bergmann, Sven and Corre, Tanguy and Ahluwalia, Tarunveer S and Lehtim{\"a}ki, Terho and Boutin, Thibaud S and Meitinger, Thomas and Wong, Tien-Yin and Bergler, Tobias and Rabelink, Ton J and Esko, T{\~o}nu and Haller, Toomas and Thorsteinsdottir, Unnur and V{\"o}lker, Uwe and Foo, Valencia Hui Xian and Salomaa, Veikko and Vitart, Veronique and Giedraitis, Vilmantas and Gudnason, Vilmundur and Jaddoe, Vincent W V and Huang, Wei and Zhang, Weihua and Wei, Wen Bin and Kiess, Wieland and M{\"a}rz, Winfried and Koenig, Wolfgang and Lieb, Wolfgang and G{\`a}o, Xin and Sim, Xueling and Wang, Ya Xing and Friedlander, Yechiel and Tham, Yih-Chung and Kamatani, Yoichiro and Okada, Yukinori and Milaneschi, Yuri and Yu, Zhi and Stark, Klaus J and Stefansson, Kari and B{\"o}ger, Carsten A and Hung, Adriana M and Kronenberg, Florian and K{\"o}ttgen, Anna and Pattaro, Cristian and Heid, Iris M} } @article {9093, title = {Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies.}, journal = {Kidney Int}, year = {2022}, month = {2022 Jun 16}, abstract = {

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95\% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95\% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.

}, issn = {1523-1755}, doi = {10.1016/j.kint.2022.05.021}, author = {Gorski, Mathias and Rasheed, Humaira and Teumer, Alexander and Thomas, Laurent F and Graham, Sarah E and Sveinbjornsson, Gardar and Winkler, Thomas W and G{\"u}nther, Felix and Stark, Klaus J and Chai, Jin-Fang and Tayo, Bamidele O and Wuttke, Matthias and Li, Yong and Tin, Adrienne and Ahluwalia, Tarunveer S and Arnl{\"o}v, Johan and {\r A}svold, Bj{\o}rn Olav and Bakker, Stephan J L and Banas, Bernhard and Bansal, Nisha and Biggs, Mary L and Biino, Ginevra and B{\"o}hnke, Michael and Boerwinkle, Eric and Bottinger, Erwin P and Brenner, Hermann and Brumpton, Ben and Carroll, Robert J and Chaker, Layal and Chalmers, John and Chee, Miao-Li and Chee, Miao-Ling and Cheng, Ching-Yu and Chu, Audrey Y and Ciullo, Marina and Cocca, Massimiliano and Cook, James P and Coresh, Josef and Cusi, Daniele and de Borst, Martin H and Degenhardt, Frauke and Eckardt, Kai-Uwe and Endlich, Karlhans and Evans, Michele K and Feitosa, Mary F and Franke, Andre and Freitag-Wolf, Sandra and Fuchsberger, Christian and Gampawar, Piyush and Gansevoort, Ron T and Ghanbari, Mohsen and Ghasemi, Sahar and Giedraitis, Vilmantas and Gieger, Christian and Gudbjartsson, Daniel F and Hallan, Stein and Hamet, Pavel and Hishida, Asahi and Ho, Kevin and Hofer, Edith and Holleczek, Bernd and Holm, Hilma and Hoppmann, Anselm and Horn, Katrin and Hutri-K{\"a}h{\"o}nen, Nina and Hveem, Kristian and Hwang, Shih-Jen and Ikram, M Arfan and Josyula, Navya Shilpa and Jung, Bettina and K{\"a}h{\"o}nen, Mika and Karabegovi{\'c}, Irma and Khor, Chiea-Chuen and Koenig, Wolfgang and Kramer, Holly and Kr{\"a}mer, Bernhard K and Kuhnel, Brigitte and Kuusisto, Johanna and Laakso, Markku and Lange, Leslie A and Lehtim{\"a}ki, Terho and Li, Man and Lieb, Wolfgang and Lind, Lars and Lindgren, Cecilia M and Loos, Ruth J F and Lukas, Mary Ann and Lyytik{\"a}inen, Leo-Pekka and Mahajan, Anubha and Matias-Garcia, Pamela R and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Milaneschi, Yuri and Mishra, Pashupati P and Mononen, Nina and Morris, Andrew P and Mychaleckyj, Josyf C and Nadkarni, Girish N and Naito, Mariko and Nakatochi, Masahiro and Nalls, Mike A and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M and Nutile, Teresa and O{\textquoteright}Donoghue, Michelle L and O{\textquoteright}Connell, Jeffrey and Olafsson, Isleifur and Orho-Melander, Marju and Parsa, Afshin and Pendergrass, Sarah A and Penninx, Brenda W J H and Pirastu, Mario and Preuss, Michael H and Psaty, Bruce M and Raffield, Laura M and Raitakari, Olli T and Rheinberger, Myriam and Rice, Kenneth M and Rizzi, Federica and Rosenkranz, Alexander R and Rossing, Peter and Rotter, Jerome I and Ruggiero, Daniela and Ryan, Kathleen A and Sabanayagam, Charumathi and Salvi, Erika and Schmidt, Helena and Schmidt, Reinhold and Scholz, Markus and Sch{\"o}ttker, Ben and Schulz, Christina-Alexandra and Sedaghat, Sanaz and Shaffer, Christian M and Sieber, Karsten B and Sim, Xueling and Sims, Mario and Snieder, Harold and Stanzick, Kira J and Thorsteinsdottir, Unnur and Stocker, Hannah and Strauch, Konstantin and Stringham, Heather M and Sulem, Patrick and Szymczak, Silke and Taylor, Kent D and Thio, Chris H L and Tremblay, Johanne and Vaccargiu, Simona and van der Harst, Pim and van der Most, Peter J and Verweij, Niek and V{\"o}lker, Uwe and Wakai, Kenji and Waldenberger, Melanie and Wallentin, Lars and Wallner, Stefan and Wang, Judy and Waterworth, Dawn M and White, Harvey D and Willer, Cristen J and Wong, Tien-Yin and Woodward, Mark and Yang, Qiong and Yerges-Armstrong, Laura M and Zimmermann, Martina and Zonderman, Alan B and Bergler, Tobias and Stefansson, Kari and B{\"o}ger, Carsten A and Pattaro, Cristian and K{\"o}ttgen, Anna and Kronenberg, Florian and Heid, Iris M} }