Depression is a major determinant of health-related quality of life in patients with diabetic nephropathy

Study design and study subjects

We recruited outpatients with DKD between April 2017 and March 2018 from the nephrology clinic of a single tertiary hospital. Inclusion criteria were pre-dialysis or dialysis patients with DKD aged ≥ 18 years and using hypoglycemic agents such as oral hypoglycemics or insulin. DKD was defined as biopsy-proven diabetic nephropathy or was clinically determined in diabetic patients with an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2with or without persistent proteinuria for 3 months or more and without any other specific reasonseven. Persistent proteinuria was defined as a urinary albumin/creatinine ratio (uACR) ≥ 30 mg/g or a urinary protein/creatinine ratio (uPCR) ≥ 0.2 g/g. eGFR was obtained using the creatinine equation from the Chronic Kidney Disease Epidemiology Collaboration8. All clinical studies were conducted according to the guidelines of the 2013 Declaration of Helsinki. All patients signed informed consent forms. The study protocol was approved by the Institutional Review Board of Samsung Medical Center.

Data collection and measurements

Demographic data on age, gender, body mass index (BMI), smoking status, comorbidities, medication history, duration of diabetes, and dialysis were collected through interview and a review of medical records at initial registration. Comorbidity burden was measured using the modified Charlson Comorbidity Index (CCI), which included the following comorbid conditions: myocardial infarction, congestive heart failure, peripheral vascular disease, stroke, dementia, chronic obstructive pulmonary disease, connective tissue disease, peptic ulcer, liver disease, diabetes mellitus, kidney failure, leukemia, lymphoma, solid tumor, liver disease and AIDS/HIV9. The age factor was excluded from the index to examine the influence of age on HRQoL independently of comorbidities. Anthropometric and blood pressure measurements were performed by trained personnel, and laboratory data, including white blood cell count, hemoglobin (Hb) levels, serum creatinine levels, HbA1c levels and uACR or uPCR values ​​were measured at baseline. For dialysis patients, the above measurements were taken mid-week before an HD (hemodialysis) session.

Health-related questionnaires were administered with the help of study personnel. For registered dialysis patients, the questionnaires were completed mid-week before a HD session. Sleep quality was measured using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K), which is comparable to the original version. It is a valid and reliable screening tool to identify “good” and “bad” sleepersten. The PSQI consists of 18 questions covering sleep quality, sleep onset latency, sleep efficiency, sleep duration, sleep disturbances, use of sleeping pills, and daytime dysfunction. Each item is scored from 0 to 3, giving an overall PSQI score ranging from 0 to 21, with higher scores indicating lower sleep quality. A total score > 5 indicates poor sleep, while a total score ≤ 5 indicates good sleep. To determine levels of anxiety and depression, the Hospital Anxiety and Depression Scale (HADS) was used.11.12. This scale is divided into an anxiety subscale (HADS-A) and a depression subscale (HADS-D), both of which contain seven interwoven items. Each item has 4 response categories ranging from 0 to 3, with total scores ranging from 0 to 21. For anxiety and depression, scores of 0 to 7 are considered normal, 8 to 10 are considered abnormal, and 11 to 21 are considered abnormal12. The Korean version of the International Physical Activity Questionnaire (IPAQ) – Short Form records physical activity over the past seven days13. The 7-point IPAQ was used to identify the total number of minutes spent in moderate- and vigorous-intensity physical activity, walking, and inactivity over the past seven days. Responses were converted to measures of metabolic equivalent task minutes per week (MET-min/week) according to the IPAQ scoring protocol14. Physical activity levels were categorized as low, moderate, and high. Low physical activity was defined as inactivity or activity that was not sufficient to fall into categories 2 or 3. Moderate physical activity was defined as one of three criteria: three or more days of vigorous activity with at least 20 minutes a day; five or more days of moderate-intensity activity or walking for at least 30 minutes a day; or five or more days of any combination of walking, moderate-intensity, or vigorous-intensity activity achieving a minimum of at least 600 MET-min/week. Vigorous physical activity was defined as one of the following criteria: vigorous-intensity activity for at least three days and an accumulation of at least 1500 MET-min/week or seven days or more of any combination of walking, moderate or vigorous intensity activities and accumulate at least 3000 MET-min/week.

Quality of life assessment

The SF-36 (version 2.0) was used to assess complete HRQoL15.16. It includes 36 questions, eight scales (physical functioning [PF]role limitation caused by physical problems [RF]body pain [BP]general health [GH]vitality [VT]social functioning [SF]role limitation caused by emotional issues [RE]and mental health [MH]), and two summary measures. Responses to each question were transformed into equivalent SF-36 scores, and scores ranged from 0 to 100, with higher numerical scores indicating better HRQOL. The PF, RF, BP, and GH subscales were summarized in a Physical Component Summary (PCS), while the VT, SF, RE, and MH subscales were summarized in a Mental Component Summary (MCS ). We defined low HRQoL as an SF-36 score > one standard deviation (SD) below the mean, referring to previous studies with patients with CKD17.18. Similarly, we defined poor physical health and poor mental health as PCS scores and MCS scores > one SD below the mean, respectively.

statistical analyzes

Data are expressed as percentiles for categorical variables, and as medians and interquartile ranges (IQR) for continuous variables. Chi-square analysis or Fisher’s exact test for categorical variables and Kruskal-Wallis test for continuous variables were used to determine differences in baseline characteristics across CRF stages. Differences in SF-36, PCS, and MCS scores between good sleepers and poor sleepers were assessed using the Mann-Whitney rank sum test. The Jonckheere-Terpstra test was used to determine the significance of the trend in continuous variables. Spearman’s correlation coefficients were used to examine relationships between clinical parameters and SF-36 scores. The association between low HRQOL assessed by SF-36 score and clinical factors was investigated using multivariate logistic regression analysis adjusted for age, sex, CCI score, smoking status, Hb, eGFR, and PSQI-K, HADS-A, HADS-D, and IPAQ scores, and results were reported as odds ratios (ORs) and confidence intervals at 95% (95% CI). For multiple logistic regression analysis, we selected confounders based on potential confounders that have been proven in previous studies, variables that showed significant association with low HRQOL in our univariate analysis, and our variables of interest. The goodness of fit of the logistic regression models was assessed using the area under the ROC curve (AUC)19. The multicollinearity of the variables was assessed using the variance inflation factor (VIF) with a reference value of 10 before interpreting the final output. Similarly, the association of clinical factors with low HRQOL, as assessed by PCS score and MCS score, has been investigated using the aforementioned methods. Additional logistic regression analyzes were performed excluding dialysis patients since dialysis treatment itself might be a mediator in the association between HRQoL and clinical factors. Due to the lack of a solid definition of low HRQoL, additional sensitivity analyzes with the aforementioned adjustments were performed using a linear regression model for the association of clinical factors with SF score. -36 as a continuous variable. cook distance20 was used in regression analysis to find influential outliers in a set of predictor variables. There were no missing data or significant outliers. All statistical analyzes were performed using SPSS version 25.0 for Windows (IBM, Armonk, NY, USA), SAS version 9.4 (SAS Institute, Cary, NC), and R 4.0.3 (Vienna, Austria; http: // A p a value

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