Hyang Kim

High sensitivity c-reactive protein as an independent risk factor for essential hypertension

BACKGROUND C-reactive protein (CRP), one of the hepatic acute phase reactants, has been associated with decreased endothelium-dependent relaxation, a potential risk factor for hypertension. However, the relationship between CRP and hypertension has not been well elucidated. The aim of this study is to assess whether circulating levels of CRP are independently related to essential hypertension. METHODS We evaluated the relationship between high sensitivity CRP with blood pressure (BP) and several cardiovascular risk factors in a cross-sectional survey of 8347 apparently healthy Korean persons. The CRP was measured by nephelometry. RESULTS The subjects consisted of 4813 men and 3534 women, aged >/=20 years. Mean (SD) age and CRP level of the population were 47.1 (11.5) years and 1.12 (1.72) mg/L. Overall hypertension prevalence was 34%. There was a significant positive association between BP and the CRP level (P <.0001). After adjustment for age, sex, fasting blood sugar, triglyceride, low-density lipoprotein, body mass index, waist-hip ratio, high-density lipoprotein, the prevalence of hypertension by CRP was 1.267 (95% confidence interval [CI] 1.079-1.487, P =.004), 1.253 (95% CI 1.062-1.477, P =.007), and 1.451 (95% CI 1.231-1.711, P <.001) times higher in subjects in the second, third, and fourth quartiles of CRP, as compared to subjects in the first quartile. CONCLUSIONS Our results suggest that the CRP level may be an independent risk factor for the development of hypertension in Korean persons. However, because of the cross-sectional nature of our study, this finding should be confirmed in prospective cohort studies, aimed at elucidating the role of CRP in the prediction, diagnosis, and management of hypertension.

Insulin resistance and C‐reactive protein as independent risk factors for non‐alcoholic fatty liver disease in non‐obese Asian men

Background and Aim:  Although insulin resistance is often considered the link between obesity and non‐alcoholic fatty liver disease (NAFLD), the role of insulin resistance, independent of obesity, as a NAFLD risk factor in non‐obese men has been less well established. Systemic inflammation may be accompanied by insulin resistance in healthy subjects. The goal of the present study was to examine if insulin resistance and systemic inflammatory markers are independent predictors of NAFLD in non‐obese men.

Metabolically Healthy Obesity and Development of Chronic Kidney Disease: A Cohort Study.

Context The risk for chronic kidney disease (CKD) among obese patients without metabolic abnormalities is unknown. Contribution In this cohort study of South Korean men and women, metabolically healthy overweight and obese participants had increased incidence of CKD compared with normal-weight participants. Caution Body mass index was a marker of obesity and was assessed only once at baseline. Implication Physicians should monitor metabolically healthy obese and overweight patients for CKD and counsel them about maintaining a healthy weight and lifestyle. Chronic kidney disease (CKD) is a major clinical and public health problem (1). It is a precursor for end-stage renal disease and a strong risk factor for cardiovascular morbidity and mortality (2). Its prevalence is increasing worldwide along with the growing prevalence of obesity and metabolic disease (3). Indeed, obesitymediated by hypertension, insulin resistance, hyperglycemia, dyslipidemia, and other metabolic abnormalitiesis a major risk factor for CKD (4). Although the role of obesity-induced metabolic abnormalities in CKD development is well-established, metabolically healthy obese (MHO) persons, seem to have a favorable profile with no metabolic abnormalities (5, 6). The association between MHO and CKD, however, is largely unknown. The only study available found no association (7), but the comparison between MHO and normal-weight participants could be biased because the reference group included overweight participants, and metabolically healthy participants were defined as those with fewer than 2 metabolic components. Therefore, we examined the association between categories of body mass index (BMI) and CKD in a large sample of metabolically healthy men and women who had health screening examinations. Methods Study Population The Kangbuk Samsung Health Study is a cohort study of South Korean men and women aged 18 years or older who had a comprehensive annual or biennial health examination at the clinics of the Kangbuk Samsung Hospital Health Screening Centers in Seoul and Suwon, South Korea (8). More than 80% of participants were employees of various companies and local governmental organizations and their spouses. In South Korea, the Industrial Safety and Health Act requires all employees to receive annual or biennial health screening examinations, offered free of charge. The remaining participants registered for the screening examinations on their own. Our analysis included all persons who had comprehensive health examinations from 1 January 2002 to 31 December 2009 and had at least 1 other screening examination before 31 December 2013 (that is, they all had a baseline visit and 1 follow-up visit [n=175859]) (Figure 1). We excluded persons who had metabolic abnormalities (5, 9, 10) or evidence of kidney disease at baseline (n=108263). We excluded those with fasting glucose levels of 100 mg/dL or greater or who used glucose-lowering agents; blood pressure (BP) of 130/85 mm Hg or greater or who used BP-lowering agents; triglyceride levels of 150 mg/dL or greater or who used lipid-lowering agents; high-density lipoprotein (HDL) cholesterol levels less than 40 mg/dL in men or less than 50 mg/dL in women; insulin resistance, defined as homeostasis model assessment of insulin resistance (HOMA-IR) scores of 2.5 or greater (11); estimated glomerular filtration rate (GFR) less than 60 mL/min/1.73 m2; proteinuria; history of CKD; or history of cancer. Among eligible participants (n=67596), we further excluded those with missing values in any of the study variables (n=5347 [7.9%]). The final sample size was 62249 participants (Figure 1), all of whom were metabolically healthy and did not have markers of kidney disease at baseline. This study was approved by the Institutional Review Board of the Kangbuk Samsung Hospital, which exempted the requirement for informed consent because we only accessed deidentified data routinely collected as part of health screening examinations. Figure 1. Study flow diagram. CKD = chronic kidney disease; HDL = high-density lipoprotein. * Participants in the screening program could have >1 criterion that made them ineligible for the study. Eligible participants could have missing data in >1 study variable. Measurements Data on medical history, medication use, family history, physical activity, alcohol intake, smoking habits, and education level were collected by a standardized, self-administered questionnaire. Anthropometry data, BP, and blood samples were obtained by trained staff during the examinations (8, 12). Smoking status was categorized as never, former, or current. Alcohol consumption was categorized as none, moderate (20 g per day), or high (>20 g per day). The weekly frequency of moderate- or vigorous-intensity physical activity was also assessed. Sitting BP, height, and weight were measured by trained nurses. Height was measured to the nearest 1 cm with a stadiometer while the participant stood barefoot. Weight was measured to the nearest 0.1 kg on a bioimpedance analyzer (InBody 3.0 and Inbody 720, Biospace), which was validated for reproducibility and accuracy of body composition measurements (13) and calibrated every morning before testing started. Body mass index was calculated as weight in kilograms divided by height in meters squared and was classified according to Asian-specific criteria (14) (underweight, BMI <18.5 kg/m2; normal weight, BMI of 18.5 to 22.9 kg/m2; overweight, BMI of 23 to 24.9 kg/m2; and obese, BMI 25 kg/m2). Blood specimens were sampled from the antecubital vein after at least a 10-hour fast. The methods for measuring serum levels of glucose, uric acid, total cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides, HDL cholesterol, aspartate aminotransferase, alanine aminotransferase, -glutamyltransferase, insulin, and high-sensitivity C-reactive protein (hsCRP) have been reported elsewhere (8, 12). The Department of Laboratory Medicine of the Kangbuk Samsung Hospital has been accredited by the Korean Society for Laboratory Medicine and the Korean Association of Quality Assurance for Clinical Laboratories and participates in the College of American Pathologists Proficiency Testing survey. Insulin resistance was assessed with the HOMA-IR equation (fasting insulin [uU/mL]fasting glucose [mmol/L] 22.5). An ultrasonographic diagnosis of fatty liver was defined as a diffuse increase of fine echoes in the liver parenchyma compared with the kidney or spleen parenchyma (15, 16). During the study period, serum creatinine levels were measured with the kinetic alkaline picrate method (Jaffe method) in an automated chemistry analyzer (from 2002 to 2009, we used the Advia 1650a Autoanalyzer [Bayer Diagnostics]; from 2010 to 2013, we used the Modular D2400 [Roche]). The within-batch and total coefficients of variation were 1.8% to 3.9% for low-level and 1.4% to 1.8% for high-level quality control specimens throughout the study. Because the laboratory method that was used to measure serum creatinine levels from 2002 to 2009 was not traceable to isotope-dilution mass spectrometry, we estimated GFR by using the 4-variable Modification of Diet in Renal Disease Study equation (17). The conclusions did not change if we used the Chronic Kidney Disease Epidemiology Collaboration equation (18) for GFR estimation (data not shown). Urine protein was measured semiquantitatively by urine dipstick (URiSCAN Urine test strips, YD Diagnostics) tested on fresh, midstream urine samples and was reported in the following 6 grades: absent, trace, 1+, 2+, 3+, and 4+ (corresponding to protein levels of undetectable, 10 mg/dL, 30 mg/dL, 100 mg/dL, 300 mg/dL, and 1000 mg/dL, respectively). Proteinuria was defined as a grade of 1+ or greater. Statistical Analysis Person-years of follow-up were calculated from the date of the baseline health examination until the date of CKD diagnosis or the last screening examination, whichever came first. The cumulative incidence of CKD for baseline BMI categories (<18.5, 18.5 to 22.9, 23.0 to 24.9, or 25.0 kg/m2) were standardized to the empirical distribution of baseline confounders in the overall study sample with inverse probability weighting (19, 20). We first fitted a multinomial logistic regression to estimate each participants probability of being in his or her own BMI category given the observed confounders. Stabilized weights were then calculated as the inverse of the estimated conditional probabilities of exposure, further rescaled by the overall proportion of participants in each BMI category to reduce variability of weights across groups and to avoid influential observations involving extremely obese persons (19). For risk analyses, we fitted a spline-based, parametric survival model (21) according to the stabilized weights and stratified by BMI category to obtain smooth estimates of the CKD cumulative incidence curves that would have been seen in the entire population if every participant had been in each category (20). This survival model parameterized stratum-specific log cumulative hazards as distinct natural cubic splines of log time with 3 internal knots at the 25th, 50th, and 75th percentiles; allowed for interval-censored events (incident CKD occurred at an unknown time point between the visit at which CKD was diagnosed and the previous visit); and used robust SEs for spline parameters that accounted for the correlation induced by weighting (21). For comparison, we also applied weighted KaplanMeier methods to estimate nonparametric cumulative incidence curves for each BMI category. We used the previously mentioned weighted, spline-based survival model to calculate adjusted differences in cumulative incidences of CKD at 2, 5, and 10 years of follow-up of normal-weight participants compared with those in the other BMI categories. We calculated 95% CIs by applying delta methods to the robust variance estimates of spline parameters. In addition to risk differences, we

Body fat distribution and insulin resistance: beyond obesity in nonalcoholic fatty liver disease among overweight men.

Objective: The aim of this study was to characterize the relationship between nonalcoholic fatty liver disease (NAFLD) and body fat distribution and insulin resistance in a sample of non-diabetic overweight men. Subjects and Methods: We conducted a cross-sectional survey of 117 overweight men with NAFLD, as well as 117 controls, who were matched with regard to age and body mass index. None of the study subjects exhibited signs of alcohol abuse, hepatitis B or C, diabetes or fasting hyperglycemia, or hypertension. The diagnosis of NAFLD was based on dual findings of elevated alanine aminotransferase levels and sonographically-determined fatty liver. Body fat distribution was assessed via bioelectrical impedance. Insulin resistance was evaluated via homeostasis model assessment (HOMA-IR). Results: The risk of developing NAFLD was found to be profoundly associated with elevated measurements of waist circumference, fat mass, percentage of body fat and abdominal fat, iron, triglycerides, apolipoprotein B, and results of HOMA-IR. Multivariate analysis revealed that NAFLD was significantly associated with elevated measurements of waist circumference, iron, apolipoprotein B, and HOMA-IR. Conclusions: Our study provides evidence for a profound and dose-dependent association of NAFLD with central adiposity, insulin resistance in overweight men lacking complications of metabolic syndrome. Overweight subjects with insulin resistance or central adiposity were at more risk of NAFLD than were those subjects with less insulin resistance or central adiposity, even those with a similar degree of obesity.

Serum Uric Acid as a Predictor for the Development of Nonalcoholic Fatty Liver Disease in Apparently Healthy Subjects: A 5-Year Retrospective Cohort Study.

BACKGROUND/AIMS This study evaluated the relationship between hyperuricemia and nonalcoholic fatty liver disease (NAFLD) by comparing the incidence rates of NAFLD in relation to serum uric acid levels in apparently healthy subjects during a 5-year period. METHODS Among 15,638 healthy Korean subjects who participated in a health-screening program in 2003 and 2008, respectively, 4954 subjects without other risk factors were enrolled in this study. We compared the incidence rates of NAFLD in 2008 with respect to baseline uric acid levels. RESULTS In 2003, serum uric acid levels were categorized into the following quartiles: 0.6-3.9, 3.9-4.8, 4.8-5.9, and 5.9-12.6 mg/dL. The incidence of NAFLD in 2008 increased with the level of baseline uric acid (5.6%, 9.8%, 16.2%, and 20.9%, respectively; p<0.05). Multiple logistic regression analysis demonstrated that hyperuricemia was associated with the development of NAFLD. When compared to the subjects in quartile 1, the odds ratio (OR) for the incidence of NAFLD for quartiles 2, 3, and 4 were 1.53 (95% confidence interval [CI], 1.09-2.16; p=0.014], 1.69 (95% CI, 1.17-2.44; p=0.005), and 1.84 (95% CI, 1.25-2.71; p=0.002), respectively. CONCLUSIONS High serum uric acid levels appear to be associated with an increased risk of the development of NAFLD.

Prediction of mortality in patients undergoing maintenance hemodialysis by Charlson Comorbidity Index using ICD-10 database.

Background/Aims: Many patients with end-stage renal disease have additional comorbidities that are important to clinical study and impact the patient’s outcome. The Charlson Comorbidity Index (CCI) is a popular tool and a strong predictor of outcome in end-stage renal disease patients. We obtained comorbidity data from the hospital discharge database using the International Classification of Disease, 10th revision (ICD-10) and analyzed the mortality rate in incident patients undergoing maintenance hemodialysis (HD). Methods: We evaluated the medical records of a total of 456 patients on HD (58 ± 14 years of age, 56% males). We calculated CCI scores at the start of HD with information from the hospital discharge summary according to the ICD-10 code. We then analyzed patient mortality according to these CCI scores. Results: The percentages of patients that had diabetes with end-organ damage (51.1%), congestive heart failure (9.9%), coronary artery disease (8.1%) and stroke (6.8%) were identified. CCI scores were 5.09 ± 2.01 (range 2–11). Four comorbidity groups were established by quartile ranking of the CCI scores: low, moderate, high and very high. The mortality rates were: 0.83, 7.70, 14.09 and 18.69 deaths/100 patient-years, respectively (p = 0.001). Compared with the low comorbidity group, the hazard ratios for mortality were 9.22 (95% CI 3.29–25.84) for the moderate group, 16.77 (95% CI 5.97–47.11) for the high group, and 22.37 (95% CI 8.08–61.93) for the very high group. Conclusions: The CCI scores using the ICD-10 database information were significant predictors of mortality in incident patients undergoing maintenance HD.

Insomnia in diabetic hemodialysis patients: Prevalence and risk factors by a multicenter study

Background: Insomnia is one of the most common problems in dialysis patients, and likely to contribute impairment in quality of life, which has a positive correlation with patients’ survival. In diabetic patients, morbidity and mortality are substantially higher than in the nondiabetic counterparts, and also the incidence of sleep disturbances. However, there is no means to predict sleep disturbance in the dialysis patients especially in diabetics. To define the prevalence and risk factors for insomnia in diabetic patients on hemodialysis, we undertook a cross-sectional multicenter study. Methods: Eighty-two diabetic patients (50 men/32 women, aged 58.7 ± 9.23 years) on maintenance hemodialysis for more than 6 months from 12 different hospitals were enrolled. The demographic data, subjective symptoms, depression scale, and insomnia were assessed by questionnaires, and lean body mass, BMI, Kt/V, subjective global assessment, nursing assessment score (NAS), and biochemical parameters were examined. Results: The number of patients with and without insomnia were 56 and 26, respectively, which amounted to 68.2% for insomnia. NAS (28.1 ± 3.81 vs. 30.8 ± 2.88, p = 0.002), serum albumin concentration (3.82 ± 0.44 vs. 4.09 ± 0.36 g/dl, p = 0.008), and depression scale (25.2 ± 12.1 vs. 18.9 ± 10.3, p = 0.025) were significantly different between them. Patients with insomnia were older (60.5 ± 9.0 vs. 56. 1 ± 9.60 years, p = 0.053) and felt pain (38.5 vs. 15.3%, p = 0.06) more frequently than those without insomnia. The scale of depression was correlated with NAS (r = –0.455, p < 0.001) and the serum albumin concentration was correlated with NAS (r = 0.337, p = 0.002). NAS, age, and serum albumin concentration were the major risk factors for insomnia in logistic regression analysis. Conclusion: The prevalence of insomnia in diabetic hemodialysis patients was 68.2%. Age, nutritional status, and depression were the major risk factors for sleep disturbance in diabetic patients.

Urinary stones following renal transplantation.

Background: The formation of urinary tract stones following renal transplantation is a rare complication. The clinical features of stones after transplantation differ from those of non-transplant patients. Renal colic or pain is usually absent and rarely resembles acute rejection. Methods: We retrospectively studied 849 consecutive kidney transplant patients in The Rogosin Institute/The Weill-Cornell Medical Center, New York who were transplanted between 1980 and 1997 and had functioning grafts for more than 3 months, to determine the incidence of stone formation, composition, risk factors and patient outcome. Results: At our center, urinary stones were diagnosed in 15 patients (1.8%) of 849 functioning renal grafts for 3 or more months. Of the 15 patients, 10 were males and 5 were females in their third and fourth decade. Eight patients received their transplant from living donors and 7 from cadaveric donors. The stones were first diagnosed between 3 and 109 months after transplantation (mean 17.8 months) and 5 patients had recurrent episodes. The stones were located in the bladder in 11 cases (73.3%), transplanted kidney in 3 cases and in multiple sites in one case. The size of stones varied from 3.4 mm to 40 mm (mean 12 mm). The composition of stones was a mixed form of calcium oxalate and calcium phosphate in 5 cases and 4 patients had infected stones consisting of struvite or mixed form of struvite and calcium phosphate. Factors predisposing to stone formation included tertiary hyperparathyroidism (n=8), hypercalciuria (n=5), recurrent urinary tract infection (n=5), hypocitraturia (n=4), and obstructive uropathy (n=2). Many cases had more than one risk factor. Clinically, painless hematuria was observed in 6 patients and dysuria without bacteriuria in 5 patients. None had renal colic or severe pain at any time. There were no changes in graft function at diagnosis and after removal of stones. Five patients passed stones spontaneously and 8 patients underwent cystoscopy for stone removal. Conclusion: Urinary stone formation following kidney transplantation is a rare complication (1.8%). Hyperparathyroidism, hypercalciuria, recurrent urinary tract infection and hypocitraturia are the most common risk factors, but often there are multiple factors which predispose to stone formation. To detect stones and determine their location and size, ultrasonography appears to be the most useful diagnostic tool. Prompt diagnosis, the removal of stones and stone-preventive measures can prevent adverse effects on renal graft outcome.

Effect of N-Acetylcysteine on cystatin C-Based renaL function after Elective coronary angiography (ENABLE Study): A prospective, randomized trial

BACKGROUND Several studies have reported the role of N-acetylcysteine on the prevention of contrast induced nephropathy (CIN) with conflicting results. To date, the effect of acetylcysteine on cystatin C-based CIN has not been described. This study was designed to examine the incidence of cystatin C-based CIN and investigate the effect of N-acetylcysteine on the prevention of CIN after coronary angiography (CAG). METHODS We conducted a prospective, randomized trial on 166 patients (80 patients in N-acetylcysteine group and 86 patients in control group) that underwent elective CAG with apparently normal renal function. Serum cystatin C and creatinine concentrations were measured before, and at 24 and 48 h after CAG. RESULTS The overall incidence of cystatin C-based CIN among all study subjects was 10.2% (5.0% in N-acetylcysteine group and 15.1% in control group, p<0.05) and that of serum creatinine-based CIN was 6% (3.8% in N-acetylcysteine group and 8.1% in control group, p=NS). Kappa analysis between cystatin C-based CIN and serum creatinine-based CIN showed a substantial agreement (k=0.64). Multivariate logistic regression analysis showed that N-acetylcysteine administration was independently protective against the development of cystatin C-based CIN (Odd ratio[95% confidence interval] 0.255[0.066 to 0.994]) but there was a trend toward protection against that of serum creatinine-based CIN. CONCLUSIONS This study suggests that in patients with apparently normal renal function, prophylactic oral N-acetylcysteine administration is effective at preventing cystatin C-based CIN development after elective coronary angiography and/or intervention, and that serum cystatin C might be a more sensitive marker of the early CIN than serum creatinine.

The Clinical Significance of Serum and Urinary Neopterin Levels in Several Renal Diseases

Neopterin is a pyrazino-pyrimidine compound, and is known to be a marker associated with cell-mediated immunity in various diseases. We hypothesized that the levels of serum and urine neopterin would be elevated in renal disease, and would correlate with certain clinical parameters. We evaluated serum and urinary neopterin levels in patients with several renal diseases, including nephrotic syndrome (NS, n=19), chronic renal failure (CRF, n=8), end stage renal disease (ESRD, n=64) and controls (n=22). Serum neopterin was elevated in patients with CRF and ESRD, as compared to controls. Urinary neopterin levels were also found to be elevated in patients with CRF and ESRD, as compared to controls. Serum neopterin levels showed significant positive correlation with age, serum BUN and creatinine levels, and inverse correlation with WBC, hemoglobin, hematocrit, serum albumin and total iron binding capacity. Urine neopterin levels exhibited positive correlation with age and serum creatinine levels, and inverse correlation with WBC, hemoglobin, hematocrit, BUN and serum albumin. In conclusion, increased serum and urinary neopterin levels were found in some patients with renal disease and were correlated with certain clinical parameters.