chronic kidney disease epidemiology

It is well recognised that estimating GFR using the Modification of Diet in Renal Disease (MDRD) equation significantly underestimates reference GFR values in the normal-high range in people with and without diabetes [1]. In an attempt to overcome some of the limitations of the MDRD equation, the Chronic Kidney Disease- Epidemiology Collaboration (CKD-EPI) equation has been developed, using the same variables as the MDRD equation. In particular, it has been reported to reduce bias compared to the MDRD equation for GFR > 60 ml/ min/1.73 m 2 in various study populations [2, 3]. This re- duction in bias has been attributed to the characteristics of the populations from which the CKD-EPI and the MDRD equations were derived, with the mean measured GFR levels being 68 and 40 ml/min/1.73 m 2 in these re- spective populations [1, 4]. Unlike the MDRD equation, the CKD-EPI equation also contains a spline term for serum creatinine (at 62 μmol/L for females and 80 μmol/L for males) to account for the weaker relation- ship between serum creatinine and GFR at lower com- pared with higher creatinine levels [4, 5].

Participants aged 60 years or older in the Third Affiliated Hospital of Sun Yat-sen University, People’s Republic of China were enrolled between January 2010 and December 2012. Exclusion criteria included: 1) acute kidney function deterioration, edema, skeletal muscle atrophy, pleural effu- sion or ascites, malnutrition, amputation, heart failure, and ketoacidosis, or 2) on cimetidine or trimethoprim, or 3) being treated with dialysis at the time of the study. Study approval was obtained from the institutional review board at the Third Affiliated Hospital of Sun Yat-sen University. Informed consent of subjects was obtained prior to the beginning of the study.

CI: Confidence interval; CIRS-G: Cumulative Illness Rating Scale-Geriatrics; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration; COPD: Chronic Obstructive Pulmonary Disease; eGFR: estimated glomerular filtration rate; ESKD: End-stage kidney disease; GA: Geriatric assessment; GFR: Glomerular filtration rate; GOLD: Geriatric assessment in OLder patients starting Dialysis; HR: Hazard ratio; HRQOL: Health related quality of life; IQR: Interquartile range; MCID: Minimal clinically important difference; MCM: Maximal conservative management; OR: Odds ratio; QoL: Quality of Life; SD: Standard deviation; SF-36: Short-Form 36 Survey

ACR: Albumin-to-creatinine ratio; CKD: Chronic kidney disease; CKD- EPI: Chronic kidney disease epidemiology collaboration equation; CKD- MBD: CKD-mineral and bone disorders; C-STRIDE: Chinese cohort study of chronic kidney disease; CVD: Cardiovascular disease; DM: Diabetes mellitus; eGFR: Estimated glomerular filtration rate; ESRD: end-stage renal disease; HBP: High blood pressure; HDL: High-density lipoprotein; HRQoL: health- related quality of life; Hs-CRP: High-sensitivity C-reactive protein; iPTH: Intact parathyroid hormone; IQR: Interquartile range; KDOQI: Kidney disease outcomes quality initiative; KDQoL: Kidney disease quality of life; LDL: Low- density lipoprotein; LVESD: Left ventricular end-systolic diameter; MCS: Mental summary scores; PCS: Physical summary scores; SDs: Standard deviations; UA, uric acid

The estimated GFR (eGFR), calculated by different equa- tions, is commonly used for clinical care and research. The Modification of Diet in Renal Disease (MDRD) equation, initiated in 1999 and based on the serum creatinine level, is still applied clinically after several modifications [7, 8]. Recently, new equations such as the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations based on cystatin C and/or serum creatinine have been recommended for clinical applications [9, 10]. Some reports showed an improved accuracy of the eGFR using the cystatin C-based eq. [11, 12]. However, it is still controversial whether cystatin C-based GFR-estimating formulae are superior to serum creatinine-based ones [6].

BMI: Body mass index; CISTA: Research center on health, work and environment; CKD: Chronic kidney disease; CKD-EPI: Chronic kidney disease epidemiology collaboration; CKDu: Chronic kidney disease of undetermined cause; eGFR: Estimated glomerular filtration rate; ESRD: End-stage renal disease; GFR: Glomerular filtration rate; IQR: Interquartile range; ISICv4: International standard industrial classification of all economic activities, Rev4; MeN: Mesoamerican nephropathy; NCDs: Non-communicable diseases; NSAIDs: Non-steroidal anti-inflammatory drugs; SCr: Serum creatinine; SD: Standard deviation; UK: United Kingdom; UNAN- León: National Autonomous University of Nicaragua, León; USD: United States Dollars; UTI: Urinary tract infection

The current recommended gold standard for radiola- belled GFR measurement is by using exogenous markers such as iothalamate, Chromium 51 ethylenediamine- tetraacetic acid ( 51 Cr-EDTA EDTA) or iohexol. 51 Cr- EDTA is a well-recognised exogenous marker and it is widely used for the assessment of GFR [4, 5]. However, the use of these exogenous markers is expensive, labour intensive, time consuming and not widely available in our country. Therefore, estimated GFR (eGFR) calcula- tion is important to overcome this problem. The best eGFR equation, ideally, should have lower bias and limits of agreement with greater precision and accuracy. To date, Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equations are widely accepted to be used in clinical practices for GFR estimation [6, 7].

The glomerular filtration rate was estimated using the Chronic Kidney Disease EPIdemiology collaboration equation (CKD-EPI) [23]. This was analysed as a con- tinuous variable and in three classes eGFR < 90; 90–105; > 105 ml/min/1.73m 2 ; the threshold 90 ml/min/1.73m 2 was chosen as it is conventionally used to indicate kid- ney disease without chronic kidney failure [23]; the group with eGFR > 105 ml/min/1.73m 2 includes half of our healthy population. At baseline, the UACR was de- termined on two separate occasions several weeks apart, and the mean UACR calculated and analysed in three classes: undetected, detected below and detected above the sex-specific median.

BMI: body mass index; BSA: body surface area; CCr: creatinine clearance; CKD: chronic kidney disease; CKD EPI: chronic kidney disease epidemiology collaboration; CRMSE: combined root mean square error; eGFR: estimated glo- merular filtration rate; ESRD: end stage renal disease; GFR: glomerular filtration rate; Ht: height; K/DOQI: kidney disease outcomes quality initiative; MDRD: modifications of diet into renal diseases; mGFR: measured glomerular filtration

Clinical data were obtained by medical record review, including demographic factors, liver disease etiology, and co-morbid conditions (all defined by documentation in the medical record). Uric acid and serum creatinine levels assayed by the John Hopkins Hospital Laboratory using standard clinical laboratory techniques were re- trieved from the laboratory database. The Model for End-Stage Liver Disease (MELD) score at OLT was cal- culated in accordance with the United Network for Organ Sharing (UNOS) formula [16]. To calculate the MELD score, serum bilirubin, creatinine, and Inter- national Normalized Ratio (INR) values less than 1.0 were set to 1.0 to preclude negative values, and serum creatinine upper-limit values were set at 4.0 if the pa- tient required renal replacement therapy prior to trans- plantation. Renal function was assessed by estimating glomerular filtration rate (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula [17]. Categories of eGFR were created according to standard classifications [18]. Information regarding the primary calcineurin inhibitor (CNI) used after

ACR- Albumin to creatinine ratio; BUN – Blood urea nitrogen; CKD – Chronic Kidney disease; CKD-EPI – Chronic Kidney disease – Epidemiology Collaboration; CKDu – Chronic kidney disease of unknown aetiology; COPCORD – Community acquired program for the control of rheumatic disease; E-GFR – Estimated glomerular filtration rate; ESRD – End stage renal disease; IDMS – Isotope dilution mass spectroscope; JOABPEQ – Japanese orthopaedic association back pain evaluation questionnaire; MDRD – Modification of Diet in Renal disease; MOH – Ministry of Health; NCD – Non-communicable diseases; NCP – North Central Province; NSAID’s – Non-steroidal anti-inflammatory drugs; NWP – North Western Province; WHO – World Health Organization.

AKI: acute kidney injury; ART : antiretroviral treatment; CKD: chronic kidney disease; CKI-EPI: chronic kidney disease epidemiology collaboration; CrCl: creatinine clearance; D4T: stavudine; EFV: efavirenz; eGFR: estimated glomeru- lar filtration rate; FTC: emtricitabine; HIV: human immunodeficiency virus; IL: interleukin; LPV: lopinavir; MDRD: modification of diet in renal disease; NVP: nevirapine; PMTCT : prevention of mother to child transmission; RCT : ran- domised control trial; RD: renal dysfunction; sCr: serum creatinine; SNP: single nucleotide polymorphism; TD: tubular dysfunction; TDF: tenofovir disproxil fumarate; TAF: tenofovir alafenamide.

Kidney function is usually measured by estimating glomerular filtration rate (GFR), which is currently considered to be the best index. A direct measurement of GFR is possible, such as by assessing urinary iothalamate or inulin clearance, but this is cum- bersome and not suitable for route clinical or population screening. Several equations have been proposed to estimate GFR (eGFR) from serum creatinine and the currently recommended equation for adults is the Chronic Kidney Disease-Epidemiology Collab- oration (CKD-EPI) equation [6]. The CKD-EPI equation also takes age, sex and race into account, because of their association with muscle mass, which influences the gen- eration of creatinine. It is particularly challenging to accurately estimate eGFR in older adults, because the increase in serum creatinine reflecting reduced kidney function is paralleled by an age-related decrease in muscle mass [7]. Another issue is the need to calibrate serum creatinine assays across laboratories to use them to estimate GFR [8, 9]. Because creatinine depends on muscle mass and other factors, such as diet, that influence creatinine generation, there have been efforts to identify a marker of glomerular filtration that does not suffer from these limitations. Cystatin C, an endogenous protein produced by nearly all human cells that is freely filtered by the glomeruli, has recently been pro- posed as a new marker. Cystatin C-based equations to estimate GFR are now available [10–14]. Compared to creatinine, cystatin C-based equations better predicted all-cause mortality and cardiovascular events in people older than 65 years [15] as well as all-cause mortality and end-stage renal disease (ESRD) in general adult populations [11]. Cystatin C may be combined with creatinine to estimate GFR [11], as demonstrated by some recently published equations cited above [13, 14]. Markers of glomerular filtration (e.g. serum cre- atinine and cystatin C) and markers of kidney damage (e.g. albuminuria, renal biopsy find- ings) are also part of the tests used to define CKD-staging.

ACR: Albumin to creatinine ratio; ADPKD: Autosomal dominant polycystic kidney disease; CKD: Chronic kidney disease; CKD-EPI: Chronic kidney disease epidemiology collaboration; CT: Computed tomography; ESRD: End-stage renal disease; GFR: Glomerular filtration rate; GWAS: Genome-wide association studies; IDMS: Isotope dilution Mass Spectrometry; KDIGO: Kidney disease improving global outcomes; RAS: Renin-angiotensin aldosterone system; sCr: Serum creatinine; sUA: Serum uric acid; TGF- β : Transforming growth factor beta; TKV: Total kidney volume; TNF- α : Tumor necrosis factor alpha.

The severity of AKI was evaluated according to the sta- ging system devised by the Acute Kidney Injury Network [11]. The increase of creatinine was used as the main marker of the severity of AKI (increase of creatinine to equal or more than 3-fold of the baseline creatinine value). None of the patients had previously known chronic kidney disease. Therefore, basal creatinine values were estimated from normal glomerular filtration rates that are nor- mal for sex and age, standardized on body surface area (1.73 m 2 ) [12]. To calculate normal plasma creatinine levels, creatinine levels were backtraced from the CKD-EPI equation (Chronic Kidney Disease Epidemiology Collabor- ation) by solving the equation for this variable.

Methods: This was a retrospective, longitudinal, and descriptive study, including patients with T2DM, who were cared for from January 2014 until December 2014, at the Clínica de Diabetes, Hospital Regional “Gral. Ignacio Zaragoza", ISSSTE, Mexico City, Mexico. eGFR was calculated using three formulas: the chronic kidney disease – epidemiology collaboration (CKD-EPI), Cockcroft-Gault, and modification of diet in renal disease (MDRD), during two periods of observation, 3 and 6 months. The results were compared by Student t tests or Wilcoxon-Mann-Whitney test depending on the variable distribution. Pearson correlation was employed to determine the relation between the eGFR determined with each formula and the analyzed variables. Results: The mean age was 56.5±11.3 years in the group of 3 months’ follow-up (n=110) and 57.1±13.8 years in the group of 6 months’ follow-up (n=47). In both groups, the formula with the lowest percentages of cases of CKD was CKD-EPI and the difference of this formula had a basal and final significant positive correlation with the DBP.

5 mL fasting peripheral venous blood was collected from subjects for various biochemical investigations. Routine biochemical tests included blood urea, serum creatinine, sodium, potassium, calcium and uric acid were carried out in the hospital laboratory. Estimated glomerular filtration rate (eGFR) was calculated by using Modification of Diet in Renal Disease (MDRD) [8] and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [9]. Morning spot urine samples were collected for urine albumin and urine creatinine test. Serum creatinine and urine creatinine were measured by alkaline picrate jaffee´s kinetic method [10]. Urine micro-albumin was estimated by nephelometer (nephstar®, Goldsite Diagnostics). Albumin/creatinine ratio (ACR) was calculated by using urine micro-albumin and urine creatinine and were expressed in mg/g creatinine.

Demographics, and clinical and laboratory values at enroll- ment were extracted from an electronic data management system (http://www.phactaX.org). The estimated glomeru- lar filtration rate (eGFR) was estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation using creatinine [16]. Resting blood pressure was measured with mercury sphygmomanometers and cuffs of appropriate size three times for average blood pressure. Hypertension (HTN) was defined as a blood pressure re- cording ≥ 140/90 mmHg, a self-reported history of hyper- tension, or use of antihypertensive agents. Diabetes (DM) was defined by self-reporting or use of hypoglycemic medi- cations. Physical activity was quantified by the International Physical Activity Questionnaire. Subjects were categorized by total Metabolic Equivalent of Task (MET) - minutes/ week; “ high ” was defined as ≥ 3000 METs-minutes/week, “ moderate ” as 600 – 2999 METs-minutes/week and “ low ” as < 600 METs-minutes/week. Anemia was defined as hemoglobin < 13 g/dL for males, or < 12 g/dL for females. Two-dimensional echocardiography was conducted to measure cardiac parameters. LV mass was calculated by 0.8 x {1.04[(LVIDd + PWTd + SWTd) 3 - (LVIDd) 3 ]} + 0.6 g, where PWTd and SWTd are posterior wall thickness at end diastole and septal wall thickness at end diastole, re- spectively [17]. Left ventricular hypertrophy (LVH) was de- fined as LV mass/height 2.7 ≥ 47 g/m 2.7 in female and ≥ 50 g/ m 2.7 in male, [17, 18] because LV mass indexed to body sur- face area is problematic in that weight is affected by volume overload in CKD [19].

area; BW, body weight; CKD, chronic kidney disease; CKD-EPI, chronic kidney diseases epidemiology collaboration; eGFR, estimated glomerular filtration rate; ECFV, extracellular fluid volume; FFM, fat free mass; FM, fat mass; FLH, fore- limb height; GFR, glomerular filtration rate; HLH, hindlimb height; LFLC, left forelimb circumference; LHLC, left hindlimb circumference; LPHLC, left proxi- mal hindlimb circumference; MDRD, modification of diet in renal disease; PC, pelvic circumference; RFLC, right forelimb circumference; RHLC, right hindlimb circumference; RPHLC, right proximal hindlimb circumference; TBW, total body water; TC, thoracic circumference

We developed a structured survey instrument designed to test different factors related to TM practices among community members (Additional file 1: Appendix 1). The development of this survey has been described else- where, but in brief, the instrument was drafted by local and non-local experts from multiple disciplines includ- ing medicine, epidemiology, sociology, anthropology, and public health. It was independently translated into Swahili by two native speakers, and we conducted joint reviews of each version with a focus on the codability of words and concepts with difficult translations. To ensure the content validity of the survey instrument, we piloted it through multiple qualitative sessions. This was an it- erative process that involved several adjustments to the instrument as new themes and ideas emerged through- out the sessions. Many of the survey items and response categories were added directly based on the results of these qualitative piloting sessions [5]. In its final form, the survey instrument included nine items. It comprised open-ended questions related to types of TMs used by community members as well as close-ended questions related to frequency of use, reasons for use, modes of use, modes of access, and conditions treated by TMs.