Hypertensive Nephropathy

Each participating center will enroll approximately 250 consecutive individuals over a 5-year period from 2011 until 2015, totaling 2,450 adult patients with CKD who provide written informed consent. The participating individuals will be monitored for approximately 10 years until death or until ESRD occurs.
The KNOW-CKD will enroll ethnic Korean patients with CKD who range in age between 20 years and 75 years. The CKD stages from 1 to 5 (predialysis), based on the eGFR, is calculated using the four-variable Modification of Diet in Renal Disease (MDRD) equation as follows:
eGFR (ml/min per 1.73 m²) = 175 × [serum Cr (mg/dl)] ^-1.154 × [age]^-0.203 × [0.742 if female] × [1.212 if black], using serum creatinine concentrations measured at a central laboratory and an assay traceable to the international reference material [12 (link)].
Excluded subjects are those who 1) are unable or unwilling to give written consent, 2) have previously received chronic dialysis or organ transplantation, 3) have heart failure (NYHA class 3 or 4) or liver cirrhosis (Child-Pugh class 2 or 3), 4) have a past or current history of malignancy, 5) are currently pregnant, or 6) have a single kidney due to trauma or kidney donation.
We defined and allocated the specific causes of the CKD into four subgroups: glomerulonephritis (GN), diabetic nephropathy (DN), hypertensive nephropathy (HTN), and polycystic kidney disease (PKD). The definition of the subgroup is defined by the pathologic diagnosis, in the event that the biopsy result is available. Otherwise, the subgroup classification depends on the clinical diagnosis. GN is defined by the presence of glomerular hematuria or albuminuria with or without an underlying systemic disease causing glomerulonephritis. The diagnosis of DN is based on albuminuria in a subject with type 2 diabetes mellitus and the presence of diabetic retinopathy. HTN is defined by the patient’s hypertension history and the absence of a systemic illness associated with renal damage. Unified ultrasound criteria [13 (link)] will be used to diagnose PKD. The other causative diseases will be categorized as ‘unclassified’.

Since GBD 2010, we have used the World Cancer Research Fund criteria for convincing or probable evidence of risk–outcome pairs.¹⁶ For GBD 2019, we completely updated our systematic reviews for 81 risk–outcome pairs. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowcharts on these reviews are available in appendix 1 (section 4). Convincing evidence requires more than one study type, at least two cohorts, no substantial unexplained heterogeneity across studies, good-quality studies to exclude the risk of confounding and selection bias, and biologically plausible dose–response gradients. For GBD, for a newly proposed or evaluated risk–outcome pair, we additionally required that there was a significant association (p<0·05) after taking into account sources of potential bias. To avoid risk–outcome pairs repetitively entering and leaving the analysis with each cycle of GBD, the criteria for exclusion requires that with the available studies the association has a p value greater than 0·1. On the basis of these reviews and meta-regressions, 12 risk–outcome pairs included in GBD 2017 were excluded from GBD 2019: vitamin A deficiency and lower respiratory infections; zinc deficiency and lower respiratory infections; diet low in fruits and four outcomes: lip and oral cavity cancer, nasopharynx cancer, other pharynx cancer, and larynx cancer; diet low in whole grains and two outcomes: intracerebral haemorrhage and subarachnoid haemorrhage; intimate partner violence and maternal abortion and miscarriage; and high FPG and three outcomes: chronic kidney disease due to hypertension, chronic kidney disease due to glomerulonephritis, and chronic kidney disease due to other and unspecified causes. In addition, on the basis of multiple requests to begin capturing important dimensions of climate change into GBD, we evaluated the direct relationship between high and low non-optimal temperatures on all GBD disease and injury outcomes. Rather than rely on a heterogeneous literature with a small number of studies examining relationships with specific diseases and injuries, we analysed individual-level cause of death data for all locations with available information on daily temperature, location, and International Classification of Diseases-coded cause of death. These data totalled 58·9 million deaths covering eight countries. On the basis of this analysis, 27 GBD cause Level 3 outcomes met the inclusion criteria for each non-optimal risk factor (appendix 1 section 2.2.1) and were included in this analysis. Other climate-related relationships, such as between precipitation or humidity and health outcomes, have not yet been evaluated.

Trained experts in the medical record section evaluated patients’ medical records to extract information such as name, sex, father’s name, national identification number, home and work address, date of birth, admission time, discharge date, insurance coverage, final diagnosis (based on the International Classification of Diseases 10 (ICD10) codes), and prescribed medications. Then, they entered the data into the hospital information system (HIS). The treatment deputy of the relevant university regularly supervises data entry. In addition, the Office of Statistics and Information Technology of the Ministry of Health constantly evaluates and monitors the HIS of all hospitals in the country (Rabiei et al., 2017 (link)). In the current study, hospitals with coronary care units (CCU), emergency wards for cardiac or respiratory patients, or cardiology, internal medicine, and respiratory diseases wards (where the target patients were likely to be admitted) in Isfahan were identified in coordination with the Deputy of Treatment as a supervisory body for all hospitals. From 24 hospitals in Isfahan, 15 eligible hospitals (Sina, Shariati, Sepahan, Askarieh, Amin, Chamran, Sadoughi, Gharazi, Khanevadeh, Noor, Alzahra, Kashani, Amiralmomenin, Isabne Maryam, and Feiz) met the criteria and were chosen to be considered in this study (Rabiei et al., 2017 (link)). We collected private, governmental, social security insurance, university-affiliated, and military hospital records. Therefore, the CAPACITY study includes nearly all of the population admitted for HCD in Isfahan within the study period (Rabiei et al., 2017 (link); Karbakhsh et al., 2022 (link)).
Patients referred to the hospital were examined by the on-call doctors and were presented with various differential diagnoses. Then, the doctor in charge would conduct the necessary investigations to determine the definite diagnosis, and then at the time of the patient’s discharge, would record the final diagnosis in the patient’s file in HIS. Therefore, during the hospitalization, other differential diagnoses are checked and ruled out, and the final and relatively definite diagnosis of the disease based on the examination by the specialist doctor was registered in HIS with the ICD10 code. More details on quality control procedures can be retrieved elsewhere (Rabiei et al., 2017 (link)). In the CAPACITY study, the elective admissions of database were deleted and only the emergency hospitalized patients were considered. The diagnoses were recorded based on ICD10 codes. In the ICD10 approach, subgroups of HCD included essential (primary) hypertension (I10), hypertensive heart disease (I11), hypertensive renal disease (I12), hypertensive heart and renal disease (I13), and secondary hypertension (I15), which were considered the clinical criteria for inclusion in the study. Total HCD admissions were considered the sum of I10-I15. All non-elective and emergency patients with first HCD events were included in the current study. According to patients’ addresses in hospital records, participants who lived in Isfahan, and were hospitalized either in one of 15 hospitals (university-affiliated, private, social security insurance, military, and governmental) in Isfahan or died due to any hypertensive cardiovascular events from 20 March 2011, to 20 March 2012, were included in the study. In the current study, patients under the age of 18 were excluded because of the few hospital admissions of HCD. More details about data collection and quality control procedures were previously reported (Rabiei et al., 2017 (link)).

This was a longitudinal study of a prospective cohort of CKD patients in Korea, called KNOW-CKD (KoreaN cohort study for Outcome in patients With Chronic Kidney Disease). KNOW-CKD is a multicenter prospective cohort study that enrolled adult predialysis patients with CKD stages G1 to G5^{11 (link)}. Patients were classified into four groups according to the specific cause of CKD at enrollment: glomerulonephritis (GN), diabetic nephropathy (DN), hypertensive nephropathy (HTN), and Polycystic kidney disease (PKD). Each group classification was determined based on pathologic diagnosis if a biopsy result was available (27.6% of total patients: 66.5% of GN group, 6.4% of DN group, 7.3% of HTN group and 1.4% of PKD group). Otherwise, group classifications were based on clinical diagnoses. The biopsy-proven GN consisted as following – 40% IgA nephropathy, 7% focal segment glomerular sclerosis, 6% membranous nephropathy, 5% crescentic GN, 2.4% minimal change disease, and 1.5% lupus nephritis. Non-biopsy-proven GN was defined as the clinical history manifesting chronic GN and the presence of albuminuria or glomerular hematuria with or without an underlying systemic disease causing GN. The active GN population taking immunosuppressant at enrollment was excluded to minimize the heterogeneity by treatment. Diagnosis of DN was strictly based on albuminuria in a patient with type 2 diabetes and the presence of diabetic retinopathy. To exclude DN patients who may have combined GN, diabetic patients with glomerular hematuria were not included in the DN group. HTN was diagnosed by a history of hypertension and the absence of a systemic illness associated with kidney damage. Only the patients with proteinuria < 1.5 g/day and a proportion of urine albumin < 50% of urine protein were included in HTN to exclude the GN population. To diagnose PKD, unified ultrasound criteria were used^{12 (link)}. Other causative diseases was categorized as ‘unclassified’ and excluded from our analysis.
A total of 2238 patients enrolled in the study from April 2011 to February 2016. After excluding patients with unclassified etiology or without follow-up data, 2070 patients were finally analyzed in this study for survival analysis with follow up until March 31, 2020. To determine the annual eGFR change and trajectory, we included only those patients (n = 1952) with more than two creatinine measurements (Fig. 1). Written informed consent from each patient was collected voluntarily at the time of enrollment. The study was approved by the institutional review board of each participating hospital: Chonnam National University Hospital (CNUH-2011-092), Eulji General Hospital (201105-01), Gil Hospital (GIRBA2553), Kangbuk Samsung Medical Center (2011-01-076), Pusan Paik Hospital (11-091), Seoul National University Bundang Hospital (B-1106/129-008), Seoul National University Hospital (H-1704-025-842), Seoul St. Mary’s Hospital (KC11OIMI0441), and Yonsei University Severance Hospital (4-2011-0163). This study follows the guidelines of the 2008 Declaration of Helsinki.Figure 1

Flowchart of enrolled study patients. eGFR, estimated glomerular filtration rate; IDMS, isotope dilution mass spectrometry.

Demographic details and medication history were collected at enrollment. Serum creatinine was measured at each study visit by a central laboratory (Lab Genomics, Seoul, Republic of Korea) using an isotope dilution mass spectrometry-traceable method. For eGFR, the CKD -EPI equation based on serum creatinine was used^{13 (link)}. After the baseline visit, patients were followed-up at 6 and 12 months and then every 1 year until death or drop-out and follow-up events were recorded. In case of loss to follow-up, patients were censored for kidney and CVD events at the last follow-up visit. Death and the cause of death were collected using either hospital medical records or data from the National Database of Statistics Korea using the Korean resident registration number. Data were collected until whichever came first: drop-out, death, or March 31, 2020.
Both kidney failure and the composite of kidney failure and/or creatinine doubling were used as kidney outcomes. Kidney failure was defined as starting maintenance dialysis (required for longer than 3 months) or receiving kidney transplantation. Another outcome was the composite outcome of CVD and all-cause death. CVD was defined as any first event of the following that needed hospitalization, intervention, or therapy during the follow-up period : acute myocardial infarction, unstable angina which needed admission due to aggravated coronary ischemic symptoms, percutaneous coronary artery intervention or coronary bypass graft surgery, ischemic or hemorrhagic cerebral stroke, cerebral artery aneurysm, congestive heart failure, symptomatic arrhythmia, aggravated valvular heart meant by requiring hospital admission, any pericardial disease that required hospital admissions such as pericarditis, pericardial effusion, or cardiac tamponade, abdominal aortic aneurysm, or severe peripheral arterial disease (Table S1).
The chi-square test or Anova was used to compare the baseline characteristics. Non-normally distributed variables such as parathyroid hormone, urine protein/creatinine, and high sensitivity C-reactive protein were compared by Kruskal–Wallis test. The four groups had significant differences in baseline characteristics including age and baseline eGFR; we therefore used the overlap propensity score (PS) weighting method to minimize the effects of confounding factors on outcomes^{14 (link)}. Overlap weighting is a PS method that tries to mimic important attributes of randomized clinical trials. This method can overcome the potential limitation of adjusting the difference in measured characteristics using classic PS methods of inverse probability of treatment weighting (IPTW). Overlap weighting overcomes these limitations by assigning weights to each patient that are proportional to the probability of that patient belonging to the opposite group^{15 (link)}. PSs were calculated using a logistic model with the following variables since they showed significant differences among the four groups: age, sex, body mass index, CKD stage, mean blood pressure, CVD, hemoglobin, serum uric acid, calcium, phosphorous, albumin, total cholesterol, high-density lipoprotein, low-density lipoprotein, fasting blood sugar, intact parathyroid hormone, urine protein-to-creatinine ratio, high-sensitivity C-reactive protein, diuretics use, statin use, and angiotensin converting enzyme inhibitor or angiotensin receptor blocker use in this study. The log10 transformed values were used for PS calculation with the non-normally distributed variables such as parathyroid hormone, urine protein-to-creatinine ratio, and high sensitivity C-reactive protein. The patients in the compared group were weighted by the probability of the reference group (1-PS), and the patients in the reference group were weighted by the probability of the compared group (PS). For two groups of CKD causes, we applied the overlap weighting method to each set, resulting in a total of 6 sets. To visually compare distributions of balance, the density plots were created (Figure S1). Additionally, the standardized mean difference (SMD) was calculated to check good balance after the overlap weighting method was applied. This is calculated by the absolute value of the difference in mean among groups divided by the standard deviation. The SMD less than or equal to 0.10 means good balance after weighting^{15 (link)}. In outcome comparison analysis, a Cox proportional hazard model was used for kidney outcomes, and a cause-specific hazard model was used for the composite of CVD and death. In the competing risk model for the composite of CVD and death, kidney failure was considered a competing risk since many patients who started kidney replacement therapy were no longer followed for further event thereafter. Results are presented as hazard ratios (HRs) and 95% confidence intervals (95% CI). To estimate annual eGFR change, generalized linear mixed models were constructed with random intercepts and slopes with an unstructured model for the correlation structure. The results were expressed as estimates (standard errors). In the adjusted models, the variables used in PS score calculation were further adjusted. Spaghetti plots showing the individual trajectories of eGFR during follow-up were drawn to determine patterns of eGFR decline according to cause of CKD. P for the quadratic term was tested using polynomial mixed models with random intercepts and slopes. A P value less than 0.05 was considered statistically significant. SAS 9.4 (SAS Institute, Cary, NC, USA) and R version 3.5.3 (Foundation for Statistical Computing, Vienna, Austria) were used.