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Are there any seasonal changes of cognitive impairment, depression, sleep disorders and quality of life in hemodialysis patients?
General Hospital Psychiatry 35 (2013) 28–32
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General Hospital Psychiatry j o u r n a l h o m e p a g e : h t t p : / / w w w. g h p j o u r n a l . c o m
Are there any seasonal changes of cognitive impairment, depression, sleep disorders and quality of life in hemodialysis patients? Baris Afsar, M.D. a,⁎, Alper Kirkpantur, M.D. b a b
Division of Nephrology, Department of Internal Medicine, Konya Numune State Hospital, 42690 Konya, Turkey RFM Renal Treatment Services,Yenimahalle, Ankara, Turkey
a r t i c l e
i n f o
Article history: Received 2 May 2012 Revised 16 August 2012 Accepted 21 August 2012 Keywords: Cognitive function Depression Hemodialysis Quality of life Sleep
a b s t r a c t Objective: Cognitive impairment, depression, sleep disorders and impaired quality of life are very common in hemodialysis (HD) patients. However, whether there are any seasonal changes of cognitive impairment, depression, sleep disorders and quality of life in HD patients is not known. Methods: The laboratory parameters, depressive symptoms, health-related quality of life, sleep quality (SQ) and cognitive function, were measured twice. Results: A total of 66 HD patients were enrolled. Pre-dialysis systolic blood pressure (BP) and pre-dialysis diastolic BP were higher, whereas predialysis creatinine and sodium were lower in January compared to July. Among domains of Short Form 36 (SF-36), physical functioning, role-physical limitation, general health perception, vitality, role emotional, Physical Component Summary Score (PCS) were higher, whereas Beck Depression Inventory (BDI) score was lower in July compared to January. Stepwise linear regression analysis revealed that only change in albumin and smoking status were related with seasonal change of BDI scores. Additionally only change in Mental Component Summary score of SF-36 were related with change in PCS score of SF-36 scores. Conclusions: Depressive symptoms and quality of life but not SQ and cognitive function showed seasonal variability in HD patients. © 2013 Elsevier Inc. All rights reserved.
1. Introduction Evidence is emerging that cognitive impairment, depression, sleep disorders, and impaired quality of life are very common in patients with chronic renal failure patients [1–3]. Additionally, these disorders are associated with increased risk of morbidity and mortality in these patients [4]. For example, Elder et al clearly demonstrated that in a pooled 11,351 patients in 308 dialysis units, the relative risk of mortality was 16% higher for hemodialysis (HD) patients with poor sleep quality (SQ) [5]. On the other hand Kalantar Zadeh et al. found that The Cox proportional regression relative risk of death for each 10 unit decrease in Short Form 36 (SF-36) was 2.07 (95% CI, 1.08–3.98; P=.02) [6]. Thus a good understanding of these conditions in renal failure patients is very important for their prevention, early intervention and management. Indeed many studies have been performed and showed that various economical, social, psychological factors, uremic encephalopathy, clinical and subclinical cerebrovascular disease and various comorbidities (ane-
⁎ Corresponding author. Department of Nephrology, Konya Numune State Hospital, 42690 Konya, Turkey. Tel.: +90 332 235 45 00; fax: +90 332 235 67 86. E-mail address: [email protected] (B. Afsar). 0163-8343/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.genhosppsych.2012.08.007
mia, hypertension, diabetes, malnutrition, etc.) were related with these disorders [7]. An elegant study by Kao et al. demonstrated that quality of life scores were worse in HD patients and closely related with higher depression scores [8]. It was also demonstrated that sleep disorders were common in HD patients and were related with lower albumin, higher inflammation, higher depression and worse quality of life [9,10]. Previously it was demonstrated that various clinical and laboratory parameters showed seasonal variations in (HD) patients. Cheung et al. demonstrated that various variables such as systolic and diastolic blood pressures (BPs), protein intake, blood urea nitrogen sodium, bicarbonate, albumin, and hematocrit showed seasonal variations in HD patients [11]. Kovacic et al. also demonstrated that blood urea nitrogen and ultrafiltration rates differed significantly [12]. However to the best of our knowledge no previous report analyzed seasonal variations of cognitive function, depressive behavior, sleep disorders and quality of life in HD patients. This is surprising since all these conditions are as important as laboratory values since they are related with morbidity and mortality. Thus, the current study was performed to analyze whether any seasonal variations exists regarding cognitive function, depressive behavior, sleep disorders, and quality of life in HD patients.
B. Afsar, A. Kirkpantur / General Hospital Psychiatry 35 (2013) 28–32
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2. Materials and methods The observational study was performed on regular HD patients with end-stage renal disease receiving HD therapy thrice weekly. The study was in accordance with the declaration of Helsinki and local ethical approval and informed consent was obtained before enrolment. The inclusion criteria for the patient enrollment was described as: patients N18 years of age with a minimum 1-year HD duration, patients without acute coronary syndrome in last 3 months, patients not taking antidepressants, patients without dementia and Alzheimer disease patients who wanted to participate the study. We recorded the sociodemographic and clinical characteristics of the patients including age, gender, living status (living alone or with partner), education status (illiterate, elementary school, secondary school, high school, and university graduate), marital status, economical status (monthly money income satisfactory or unsatisfactory), smoking status, previous renal transplantation (present or absent), etiologies of renal disease, presence of coronary artery disease, diabetes mellitus, and cerebrovascular disease. None of the patients was taking antidepressants and nutritional support and active liver disease during the study period. There were no patients in our study showing antibodies against human immunodeficiency virus, and there were no intravenous drug users. Body mass index was calculated as the ratio of dry weight in kilograms (end-dialysis weight) to height squared (in square meters). Interdialytic weight gain (IDWG) was calculated as: IDWG % = ½ðPredialysis Body Weight−Postdialysis Dry Body WeightÞ = Postdialysis Dry Body Weight × 100 Interdialytic weight gain was determined at the beginning of each HD session. Patients with an ischemic leg ulcer, patients who had peripheral revascularization procedures within last 6 months or amputation for critical limb ischemia, patients with Alzheimer disease and patients who were taking antidepressants were excluded. Patients who had suffered from acute coronary syndrome, myocardial infarction, angina pectoris, or coronary revascularization procedure (coronary stent replacement and coronary artery by-pass graft surgery) within last 3 months were not included. Coronary artery disease was defined as the presence of previous myocardial infarction, angina pectoris or coronary revascularization procedure. All included HD patients were undergone complete physical examination. The dialysis prescription in our study included 4–5 h of HD, thrice weekly for all patients with flow rates of 300–400 ml/min, using standard bicarbonate dialysis solution. All patients were clinically euvolemic. Urea kinetic modeling was performed in order to assess the delivered equilibrated dose of dialysis. Hemodialysis dose was evaluated using the following formula: spKt = V = −Ln ðR−0:008 × tÞ + ð4−½3:5 × RÞ × UF = W where spKt/V is a single-pool Kt/V, R is the ratio of post dialysis to pre-dialysis serum urea nitrogen, t is time on dialysis in hours, UF is the amount of ultrafiltration in liters and W is post-dialysis body weight in kilograms. The laboratory parameters including pre-dialysis blood urea nitrogen and creatinine, hemoglobin, albumin, high sensitive C-reactive protein (hs-CRP), calcium and phosphorus, serum iron, ferritin, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglyceride, and intact parathyroid hormone were measured twice (first in January and second in July) before the beginning of HD session. Postdialysis serum urea nitrogen levels, used to calculate urea reduction ratio, were also measured. After being given a brief explanation, measurements of depressive behavior using Beck Depression Inventory (BDI), health-related quality of life (HRQOL) using Short Form 36 (SF-36), SQ using Pittsburg Sleep Quality Index (PSQI) and cognitive function using standardized mini mental state examination (SMMSE) were performed twice (first in January and second in July) for each patient during regularly scheduled dialysis treatments. Assistance was available for patients who were illiterate. 2.1. Beck depression inventory The BDI, which was originally introduced by Beck et al., is a 21-item self-reported inventory that measures characteristic attitudes and symptoms of depression [13]. The 21 items are answered on a four-point Likert scale, in which 0 represents the absence of a problem and 3 represents the extreme severity of a problem. The total score ranges from 0 to 63. The BDI is documented as a valid index of depression and BDI scores correlate well with the diagnostic criteria for depression. It has been found to be a useful screening tool in HD patients [14]. 2.2. Quality of life assessment In order to evaluate the HRQOL of the patients, a short form of medical outcomes study (SF-36) was used [15]. The test consists of 36 items, which are assigned to eight subscales. Each subscale is scored with a range from 0 to 100. The higher the scale the better is the HRQOL. These 8 subscales can be summarized in a Physical Component Summary Score (PCS) and Mental Component Summary Score (MCS). SF-36 has been commonly used and validated in patients with end stage renal disease [16]. 2.3. Measurement of cognitive function The SMMSE was used for the analysis of cognitive function. The SMMSE scores range from 30 (unimpaired) to 0 (impaired) [17]. It provides a global score of cognitive ability that correlates with function in activities of daily living. The SMMSE measures various domains of cognitive function including orientation to time and place, registration, concentration, short-term recall, naming familiar items, repeating a common expression, and the ability to read and follow written instructions, write a sentence, construct a diagram, and follow a three-step verbal command. The SMMSE takes approximately 10 minutes to administer, provides a baseline score of cognitive function and pinpoints specific deficits that can aid in forming a diagnosis. The SMMSE is a reliable instrument that allows practitioners to accurately measure cognitive deficits and deterioration over time [18]. The Turkish version of the SMMSE has been validated and shown to be reliable in the Turkish population [19].
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B. Afsar, A. Kirkpantur / General Hospital Psychiatry 35 (2013) 28–32
2.4. Measurement of SQ The PSQI consists of 19 self-rated questions and five questions rated by the bed partner or roommate. The latter five questions are used for clinical information only and are not included in the scoring of the PSQI. The 19 self-rated questions assess a wide variety of factors and are grouped into seven component scores, each weighted equally on a 0–3 scale. According to the scoring guidelines provided by Buysse et al., the 19 items are analyzed to yield 7 sleep components: subjective SQ, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. The seven component scores are then summed to yield a global PSQI score, which ranges from 0 to 21. Higher scores indicate poorer SQ [20]. The validation of this index in Turkish patients has been performed by Agargun et al. [21]. 2.5. Statistics Statistical analysis was performed using SPSS 15.0 (SPSS Inc, Evanston, IL, USA). Results were considered statistically significant if two-tailed P value was less than .05. Data was checked for normality. For comparison of continuous variables between January and July, the paired t test or Wilcoxon signed rank test was used depending on whether the data were distributed normally or not. Linear regression analysis was performed to analyze the independent factors related with change in BDI scores and PCS scores of sf-36 between January and July. 3. Results Initially 82 patients were recruited. One patient with acute coronary syndrome, 1 patient with limp amputation, 1 patients with Alzheimer disease, 3 patients taking anti-depressants, 5 patients who did not want to participate, 2 patients with renal transplantation and 3 patients going to other centers were not included. The final population consisted of 66 patients. The etiologies of end stage renal disease were as follows: Diabetes mellitus (n:22), hypertension (n:13), glomerulonephritis (n:9), amyloidosis (n:5), vesicourethral reflux and pyelonephritis (n:4), nephrolithiaisis (n:2), polycystic kidney disease (n:3), ischemic nephropathy (n:1), and unknown (n:7). The baseline demographic characteristics of the patients were shown in Table 1. The comparative data analyzed in January and in July regarding clinical and laboratory variables are shown in Table 2. The comparative data analyzed in January and in July regarding quality of life, depression scores, SQ and cognitive function were shown in Table 3. Linear regression analysis revealed that among independent factors only Δalbumin and smoking status were related with seasonal change of BDI scores (ΔBDI score) as a dependent parameter. (ΔBDI score: difference of BDI scores between January and July) (Table 4). Additionally among independent factors (as mentioned above plus ΔBDI score) only ΔMCS score were related with ΔPCS scores (as an independent parameter) (B:1.525, Confidence Interval:0.360-2.691, P:0.011). 4. Discussion In the current study, to the best of our knowledge, we firstly demonstrated that depressive behavior and quality of life but not SQ Table 1 Baseline sociodemographic characteristics of 66 HD patients Parameter Male/female (n) Age (years) (mean±S.D.) Hemodialysis duration (months) Presence of coronary artery disease (n) Presence of hypertension (n) Smoker/nonsmoker (n) Presence of cerebrovascular accident (n) HD access (fistula-Greft-catheter) (n) Transplantation history (n) Educational status (Illiterate-primary school-secondary school-tertiary school-university) (n) Marital status (married/unmarried) (n) Living alone (yes/no) (n) Money income (satisfactory/unsatisfactory) (n)
42/24 49.7±12.9 66.6±58.3 26/40 45/21 27/39 9/57 54-4-8 3/66 13-25-9-13-6 61/5 10/56 40/26
and mental function showed seasonal variations between winter and summer months in HD patients. We observed that the more the decrease of BDI scores in July as compared to January, the more the serum albumin levels became higher in July compared to January. Also we found that the decrease of BDI scores is more prominent in July as compared to January in non-smoker patients. Additionally increase of PCS scores in July as compared to January was only related with increase of MCS score in July as compared to January. The depressive symptoms of HD patients were higher in January compared to July in the present study, although we don't know the exact mechanisms speculation can be made. It was demonstrated that exposure of the eyes to light of appropriate intensity and duration, at an appropriate time of day, can have marked effects on the affective and physical symptoms of depressive illness [22]. Thus lack of sunlight and short days on winter may negatively impact patients' mood. Indeed many studies have demonstrated seasonal increase of depressive symptoms in winter months called “winter depression” [23,24]. Accordingly light deficiency, especially shortened photoperiods, is the hypothesized etiological mechanism behind winter type depression and bright light treatment is the treatment of choice [25]. One would not be surprised by these findings, as certain neurotransmitters of thermoregulation, such as norepinephrine, dopamine, and
Table 2 The comparative clinical and laboratory data of 66 HD patients in January and July
Body mass indexa Interdialytic weight gain (%)a spKt/Va Pre-dialysis systolic BP (mmHg)a Pre dialysis diastolic BP (mmHg) a Predialysis creatinine (μmol/L) a Predialysis albumin (g/L) a Predialysis hemoglobin (g/L) a Predialysis serum iron (μg/dl) a Predialysis ferritin (ng/ml) a hs-CRP (mg/dl) a Intact parathyroid hormone (pg/ml) a Predialysis sodium (mmol/L) a Predialysis potassium (mmol/L) a Predialysis glucose (mmol/L) a Predialysis total cholesterol (mmol/L) a Triglyceride (mmol/L) a HDL cholesterol (mmol/L) a LDL cholesterol (mmol/L) a Predialysis calcium (mmol/L) a Pre dialysis phosphorus (mmol/L) a sp, Single pool. a Mean±S.D. ⁎ P: based on Wilcoxon. ⁎⁎ P: based on paired t test.
January
July
P
24.85±4.16 3.92±1.36 1.42±0.15 140.4±17.9 81.5±8.9 716.1±212.2 36.8±4.7 109.4±17.6 75.5±35.0 416.2±271.2 1.55±2.52 330.3±317.3 134.7±3.0 5.13±0.68 6.74±2.87 4.33±1.19 2.03±0.69 0.91±0.24 2.46±0.75 2.24±0.18 1.71±0.48
24.67±4.17 3.88±1.57 1.39±0.23 137.6±23.7 80.0±11.1 735.5±231.6 37.4±5.8 112.9±11.7 71.0±25.4 451.3±295.1 1.37±1.93 327.4±156,3 135.3±3.1 5.25±0.69 6.79±2.66 4.36±1.06 2.01±0.92 0.90±0.29 2.52±0.81 2.24±0.24 1.66±0.53
.302⁎⁎ .335⁎ .087⁎⁎ .004⁎⁎ .032⁎ .025⁎⁎ .366⁎⁎ .059⁎⁎ .392⁎ .293⁎ .091⁎ .054⁎ .041⁎⁎ .063⁎⁎ .507⁎ .762⁎ .064⁎ .571⁎ .429⁎ .914⁎⁎ .184⁎
B. Afsar, A. Kirkpantur / General Hospital Psychiatry 35 (2013) 28–32 Table 3 The comparative data of quality of life, depression scores, SQ and cognitive function in January and in July in 66 HD patients Parameter
January
July
P
Physical functioninga Role-physical limitationa Bodily paina General health perceptiona Vitalitya Social functioninga Role emotionala Mental healtha PCSa MCSa PSQI Score BDI Score SMMSE Score
59,7±14.3 61.1±15.5 47.0±11.8 60.1±13.8 60.2±12.9 63.1±17.4 62.9±16.9 64.9±15.0 42.3±4.0 46.2±8.1 4.62±3.1 15.9±10,9 24.4±3.43
61.0±15.4 61.8±16.3 47.4±12.7 61.4±14.5 61.1±12.8 63.0±17.7 64.0±17.2 65.0±15.6 44.2±7.3 46.4±8.3 4.59±3.1 14.4±10.0 24.2±3.36
b.0001⁎⁎ .032⁎⁎ .581⁎⁎ .017⁎⁎ .018⁎⁎ .602 .002⁎⁎ .551⁎⁎ .001⁎ .147⁎ .848⁎ b.0001⁎ .123⁎⁎
a Mean±S.D. ⁎ P: based on Wilcoxon. ⁎⁎ P: based on paired t test.
serotonin, are also key neurotransmitters of mood regulation [26]. Another factor may be the effect of ambient temperature. Shapira et al. examined 11 years of admissions data in a large, non-seasonal affective disorder population of both bipolar depressive and unipolar patients and discovered a positive correlation between admission rates and monthly temperatures in bipolar depressives, but not major depressive disorder patients [27]. We found that change in albumin levels were independently related with change in BDI score. As the difference of depressive symptoms between winter and summer increase, the change of albumin levels between winter and summer also increase. Thus, the lower levels of albumin during winter compared to summer were associated with higher levels of depression in winter compared to summer. To say it in other way, patients who did not experience significant increase of albumin levels in summer when compared to winter do not show significant optimization of depressive symptoms in summer compared to winter. We don't know the exact mechanisms regarding the relationships of ΔBDI score, Δalbumin. However the relationship between depression (as evaluated by BDI and albumin levels were shown before [28]. Additionally smoking status was related with less pronounced decrease of BDI scores in July when compared to January. To put it in another way, patients who smoke; feel depressive both in winter and in summer and do not show wide seasonal variation with respect to depression. The cause of this relationship in not known, and further research is necessary. Apart from PCS, Most of SF-36 domains related with physical function were higher in July compared to January in the present study. Again this finding may be related to the shortening hours of sunlight [29,30]. In one study it was shown that the best physical health was during the summer and the worst was during the winter [31]. Such findings were consistent with previous studies of the general population [32]. Thus, we extend previous findings and demonstrated that HD patients also exhibit seasonal variation with respect to physical performance which is better in summer then in winter. Previously seasonal changes in sleep have been described in both clinical and normal populations [30,33–35]. Numerous studies have found a higher incidence of depression with hypersomnia during the winter months than in other seasons [24,35]. Also a pattern of recurrent winter hypersomnia has also been described [36]. However, we found no difference of sleep disorders and cognitive function between January and July. Thus, unlike depression and quality of life, we suggest that sleep disorders and cognitive function did not show seasonal variation at least in HD patients. We believe that our findings have some potential implications for health care professionals. Firstly, since both depression and quality of
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life are closely related with morbidity and mortality in HD patients, this current study opens areas of new research whether deaths are higher in winter months in HD patients especially due to depression and bad life quality. If this is so, it should also be investigated whether timely intervention to these conditions will decreased morbidity and mortality given the fact that despite all major improvements and wide use of new therapeutic agents in HD patients morbidity and mortality was still high. Thus, the importance of timely intervention to improve depressive behavior and quality of life could not be overemphasized. Secondly, the albumin levels are lower in January as compared to July. This may be due to higher interdialytic weight gain in January so the dilutional hypoalbuminemia may occur. However the IDWG % was found to be higher in July compared to January (1.57±0.19 vs. 1.36± 0.17, Pb.0001). Thus, the lower levels of albumin in January may not be due to hypervolemia. Another hypothesis may be decreased appetite in January. Although we did not evaluate appetite in the current study, depression may inhibit appetite in winter months and lead to hypoalbuminemia. In support of our idea, we found that the changes in albumin levels were inversely associated with change in depressive behavior. Since hypoalbuminemia is strongly correlated with mortality in HD patients, whether improvement in depression will increase, the appetite and albumin levels and decrease in mortality should also be investigated. Regarding the finding that only ΔMCS score was related with ΔPCS score, we can only speculate that patients who feel well with mental dimension will have more urge to increase their daily activity. This study has limitations that deserve mention. Firstly, since our study is cross sectional, cause-and-effect relationship cannot be suggested. Secondly, our results are valid for only local patients and not a representative of the whole HD population. Thirdly, we rely on BDI to measure depressive symptoms, and patients with clinical depression may be missed without complete psychiatric evaluation. Lastly, our findings are only valid for winter and summer and not for spring and autumn. In conclusion we demonstrated for the first time that depressive symptoms and quality of life but not SQ and cognitive function showed seasonal variability in HD patients. Studies are needed to highlight the factors related with this seasonal variation in HD patients.
Table 4 The linear regression analyses of factors independently associated with seasonal change in BDI score
Age Gender (Δ% IDWG) ΔSBP ΔDBP HD vintage, Marital status, Presence coronary artery disease, Presence of diabetes mellitus, Presence of cerebrovascular disease, Smoking status, Living status, economical status ΔAlbumin ΔHemoglobin ΔPSQI score ΔSMMSE score ΔPCS ΔMCS
B
95% CI
beta
P
−0.022 0.201 −0.350 0.040 0.015 0.008 −0.408 −0.138 1.083 −0.022
−0.099 to 0.054 −1.359 to 1.762 −2.112 to 1.413 0.022–0.203 0.006–0.265 −0.005 to 0.020 −1.123 to (−3.096) −2.074 to 1.799 −0.653 to 2.819 −2.185 to 2.141
−0.101 0.034 −0.053 0.108 0.023 0.157 −0.038 −0.024 0.179 −0.003
.563 .796 .692 .621 .907 .235 .816 .887 .215 .984
−1.470 0.320 −0.177 2.207 −0.010 0.091 −0.076 −0.142 −0.209
−2.410 to (−0.470) −2.674 to 3.315 −1.797 to 1.443 0.696 to 3.718 −0.496 to 0.476 0.025 to 0.745 −0.599 to 0.447 −0.036 to (−0.319) −1.888 to 0.076
−0.311 0.041 −0.031 0.397 −0.005 0.037 −0.036 −0.220 −0.125
.042 .830 .827 .005 .967 .778 .771 .114 .098
R2: 0.478; DURBİN WATSON: 1.952. Δ denotes the differences between January and July; %IDWG, percent difference of interdialytic weight gain; SBP: systolic BP; DBP, diastolic BP.
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General Hospital Psychiatry j o u r n a l h o m e p a g e : h t t p : / / w w w. g h p j o u r n a l . c o m
Are there any seasonal changes of cognitive impairment, depression, sleep disorders and quality of life in hemodialysis patients? Baris Afsar, M.D. a,⁎, Alper Kirkpantur, M.D. b a b
Division of Nephrology, Department of Internal Medicine, Konya Numune State Hospital, 42690 Konya, Turkey RFM Renal Treatment Services,Yenimahalle, Ankara, Turkey
a r t i c l e
i n f o
Article history: Received 2 May 2012 Revised 16 August 2012 Accepted 21 August 2012 Keywords: Cognitive function Depression Hemodialysis Quality of life Sleep
a b s t r a c t Objective: Cognitive impairment, depression, sleep disorders and impaired quality of life are very common in hemodialysis (HD) patients. However, whether there are any seasonal changes of cognitive impairment, depression, sleep disorders and quality of life in HD patients is not known. Methods: The laboratory parameters, depressive symptoms, health-related quality of life, sleep quality (SQ) and cognitive function, were measured twice. Results: A total of 66 HD patients were enrolled. Pre-dialysis systolic blood pressure (BP) and pre-dialysis diastolic BP were higher, whereas predialysis creatinine and sodium were lower in January compared to July. Among domains of Short Form 36 (SF-36), physical functioning, role-physical limitation, general health perception, vitality, role emotional, Physical Component Summary Score (PCS) were higher, whereas Beck Depression Inventory (BDI) score was lower in July compared to January. Stepwise linear regression analysis revealed that only change in albumin and smoking status were related with seasonal change of BDI scores. Additionally only change in Mental Component Summary score of SF-36 were related with change in PCS score of SF-36 scores. Conclusions: Depressive symptoms and quality of life but not SQ and cognitive function showed seasonal variability in HD patients. © 2013 Elsevier Inc. All rights reserved.
1. Introduction Evidence is emerging that cognitive impairment, depression, sleep disorders, and impaired quality of life are very common in patients with chronic renal failure patients [1–3]. Additionally, these disorders are associated with increased risk of morbidity and mortality in these patients [4]. For example, Elder et al clearly demonstrated that in a pooled 11,351 patients in 308 dialysis units, the relative risk of mortality was 16% higher for hemodialysis (HD) patients with poor sleep quality (SQ) [5]. On the other hand Kalantar Zadeh et al. found that The Cox proportional regression relative risk of death for each 10 unit decrease in Short Form 36 (SF-36) was 2.07 (95% CI, 1.08–3.98; P=.02) [6]. Thus a good understanding of these conditions in renal failure patients is very important for their prevention, early intervention and management. Indeed many studies have been performed and showed that various economical, social, psychological factors, uremic encephalopathy, clinical and subclinical cerebrovascular disease and various comorbidities (ane-
⁎ Corresponding author. Department of Nephrology, Konya Numune State Hospital, 42690 Konya, Turkey. Tel.: +90 332 235 45 00; fax: +90 332 235 67 86. E-mail address: [email protected] (B. Afsar). 0163-8343/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.genhosppsych.2012.08.007
mia, hypertension, diabetes, malnutrition, etc.) were related with these disorders [7]. An elegant study by Kao et al. demonstrated that quality of life scores were worse in HD patients and closely related with higher depression scores [8]. It was also demonstrated that sleep disorders were common in HD patients and were related with lower albumin, higher inflammation, higher depression and worse quality of life [9,10]. Previously it was demonstrated that various clinical and laboratory parameters showed seasonal variations in (HD) patients. Cheung et al. demonstrated that various variables such as systolic and diastolic blood pressures (BPs), protein intake, blood urea nitrogen sodium, bicarbonate, albumin, and hematocrit showed seasonal variations in HD patients [11]. Kovacic et al. also demonstrated that blood urea nitrogen and ultrafiltration rates differed significantly [12]. However to the best of our knowledge no previous report analyzed seasonal variations of cognitive function, depressive behavior, sleep disorders and quality of life in HD patients. This is surprising since all these conditions are as important as laboratory values since they are related with morbidity and mortality. Thus, the current study was performed to analyze whether any seasonal variations exists regarding cognitive function, depressive behavior, sleep disorders, and quality of life in HD patients.
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2. Materials and methods The observational study was performed on regular HD patients with end-stage renal disease receiving HD therapy thrice weekly. The study was in accordance with the declaration of Helsinki and local ethical approval and informed consent was obtained before enrolment. The inclusion criteria for the patient enrollment was described as: patients N18 years of age with a minimum 1-year HD duration, patients without acute coronary syndrome in last 3 months, patients not taking antidepressants, patients without dementia and Alzheimer disease patients who wanted to participate the study. We recorded the sociodemographic and clinical characteristics of the patients including age, gender, living status (living alone or with partner), education status (illiterate, elementary school, secondary school, high school, and university graduate), marital status, economical status (monthly money income satisfactory or unsatisfactory), smoking status, previous renal transplantation (present or absent), etiologies of renal disease, presence of coronary artery disease, diabetes mellitus, and cerebrovascular disease. None of the patients was taking antidepressants and nutritional support and active liver disease during the study period. There were no patients in our study showing antibodies against human immunodeficiency virus, and there were no intravenous drug users. Body mass index was calculated as the ratio of dry weight in kilograms (end-dialysis weight) to height squared (in square meters). Interdialytic weight gain (IDWG) was calculated as: IDWG % = ½ðPredialysis Body Weight−Postdialysis Dry Body WeightÞ = Postdialysis Dry Body Weight × 100 Interdialytic weight gain was determined at the beginning of each HD session. Patients with an ischemic leg ulcer, patients who had peripheral revascularization procedures within last 6 months or amputation for critical limb ischemia, patients with Alzheimer disease and patients who were taking antidepressants were excluded. Patients who had suffered from acute coronary syndrome, myocardial infarction, angina pectoris, or coronary revascularization procedure (coronary stent replacement and coronary artery by-pass graft surgery) within last 3 months were not included. Coronary artery disease was defined as the presence of previous myocardial infarction, angina pectoris or coronary revascularization procedure. All included HD patients were undergone complete physical examination. The dialysis prescription in our study included 4–5 h of HD, thrice weekly for all patients with flow rates of 300–400 ml/min, using standard bicarbonate dialysis solution. All patients were clinically euvolemic. Urea kinetic modeling was performed in order to assess the delivered equilibrated dose of dialysis. Hemodialysis dose was evaluated using the following formula: spKt = V = −Ln ðR−0:008 × tÞ + ð4−½3:5 × RÞ × UF = W where spKt/V is a single-pool Kt/V, R is the ratio of post dialysis to pre-dialysis serum urea nitrogen, t is time on dialysis in hours, UF is the amount of ultrafiltration in liters and W is post-dialysis body weight in kilograms. The laboratory parameters including pre-dialysis blood urea nitrogen and creatinine, hemoglobin, albumin, high sensitive C-reactive protein (hs-CRP), calcium and phosphorus, serum iron, ferritin, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglyceride, and intact parathyroid hormone were measured twice (first in January and second in July) before the beginning of HD session. Postdialysis serum urea nitrogen levels, used to calculate urea reduction ratio, were also measured. After being given a brief explanation, measurements of depressive behavior using Beck Depression Inventory (BDI), health-related quality of life (HRQOL) using Short Form 36 (SF-36), SQ using Pittsburg Sleep Quality Index (PSQI) and cognitive function using standardized mini mental state examination (SMMSE) were performed twice (first in January and second in July) for each patient during regularly scheduled dialysis treatments. Assistance was available for patients who were illiterate. 2.1. Beck depression inventory The BDI, which was originally introduced by Beck et al., is a 21-item self-reported inventory that measures characteristic attitudes and symptoms of depression [13]. The 21 items are answered on a four-point Likert scale, in which 0 represents the absence of a problem and 3 represents the extreme severity of a problem. The total score ranges from 0 to 63. The BDI is documented as a valid index of depression and BDI scores correlate well with the diagnostic criteria for depression. It has been found to be a useful screening tool in HD patients [14]. 2.2. Quality of life assessment In order to evaluate the HRQOL of the patients, a short form of medical outcomes study (SF-36) was used [15]. The test consists of 36 items, which are assigned to eight subscales. Each subscale is scored with a range from 0 to 100. The higher the scale the better is the HRQOL. These 8 subscales can be summarized in a Physical Component Summary Score (PCS) and Mental Component Summary Score (MCS). SF-36 has been commonly used and validated in patients with end stage renal disease [16]. 2.3. Measurement of cognitive function The SMMSE was used for the analysis of cognitive function. The SMMSE scores range from 30 (unimpaired) to 0 (impaired) [17]. It provides a global score of cognitive ability that correlates with function in activities of daily living. The SMMSE measures various domains of cognitive function including orientation to time and place, registration, concentration, short-term recall, naming familiar items, repeating a common expression, and the ability to read and follow written instructions, write a sentence, construct a diagram, and follow a three-step verbal command. The SMMSE takes approximately 10 minutes to administer, provides a baseline score of cognitive function and pinpoints specific deficits that can aid in forming a diagnosis. The SMMSE is a reliable instrument that allows practitioners to accurately measure cognitive deficits and deterioration over time [18]. The Turkish version of the SMMSE has been validated and shown to be reliable in the Turkish population [19].
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2.4. Measurement of SQ The PSQI consists of 19 self-rated questions and five questions rated by the bed partner or roommate. The latter five questions are used for clinical information only and are not included in the scoring of the PSQI. The 19 self-rated questions assess a wide variety of factors and are grouped into seven component scores, each weighted equally on a 0–3 scale. According to the scoring guidelines provided by Buysse et al., the 19 items are analyzed to yield 7 sleep components: subjective SQ, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. The seven component scores are then summed to yield a global PSQI score, which ranges from 0 to 21. Higher scores indicate poorer SQ [20]. The validation of this index in Turkish patients has been performed by Agargun et al. [21]. 2.5. Statistics Statistical analysis was performed using SPSS 15.0 (SPSS Inc, Evanston, IL, USA). Results were considered statistically significant if two-tailed P value was less than .05. Data was checked for normality. For comparison of continuous variables between January and July, the paired t test or Wilcoxon signed rank test was used depending on whether the data were distributed normally or not. Linear regression analysis was performed to analyze the independent factors related with change in BDI scores and PCS scores of sf-36 between January and July. 3. Results Initially 82 patients were recruited. One patient with acute coronary syndrome, 1 patient with limp amputation, 1 patients with Alzheimer disease, 3 patients taking anti-depressants, 5 patients who did not want to participate, 2 patients with renal transplantation and 3 patients going to other centers were not included. The final population consisted of 66 patients. The etiologies of end stage renal disease were as follows: Diabetes mellitus (n:22), hypertension (n:13), glomerulonephritis (n:9), amyloidosis (n:5), vesicourethral reflux and pyelonephritis (n:4), nephrolithiaisis (n:2), polycystic kidney disease (n:3), ischemic nephropathy (n:1), and unknown (n:7). The baseline demographic characteristics of the patients were shown in Table 1. The comparative data analyzed in January and in July regarding clinical and laboratory variables are shown in Table 2. The comparative data analyzed in January and in July regarding quality of life, depression scores, SQ and cognitive function were shown in Table 3. Linear regression analysis revealed that among independent factors only Δalbumin and smoking status were related with seasonal change of BDI scores (ΔBDI score) as a dependent parameter. (ΔBDI score: difference of BDI scores between January and July) (Table 4). Additionally among independent factors (as mentioned above plus ΔBDI score) only ΔMCS score were related with ΔPCS scores (as an independent parameter) (B:1.525, Confidence Interval:0.360-2.691, P:0.011). 4. Discussion In the current study, to the best of our knowledge, we firstly demonstrated that depressive behavior and quality of life but not SQ Table 1 Baseline sociodemographic characteristics of 66 HD patients Parameter Male/female (n) Age (years) (mean±S.D.) Hemodialysis duration (months) Presence of coronary artery disease (n) Presence of hypertension (n) Smoker/nonsmoker (n) Presence of cerebrovascular accident (n) HD access (fistula-Greft-catheter) (n) Transplantation history (n) Educational status (Illiterate-primary school-secondary school-tertiary school-university) (n) Marital status (married/unmarried) (n) Living alone (yes/no) (n) Money income (satisfactory/unsatisfactory) (n)
42/24 49.7±12.9 66.6±58.3 26/40 45/21 27/39 9/57 54-4-8 3/66 13-25-9-13-6 61/5 10/56 40/26
and mental function showed seasonal variations between winter and summer months in HD patients. We observed that the more the decrease of BDI scores in July as compared to January, the more the serum albumin levels became higher in July compared to January. Also we found that the decrease of BDI scores is more prominent in July as compared to January in non-smoker patients. Additionally increase of PCS scores in July as compared to January was only related with increase of MCS score in July as compared to January. The depressive symptoms of HD patients were higher in January compared to July in the present study, although we don't know the exact mechanisms speculation can be made. It was demonstrated that exposure of the eyes to light of appropriate intensity and duration, at an appropriate time of day, can have marked effects on the affective and physical symptoms of depressive illness [22]. Thus lack of sunlight and short days on winter may negatively impact patients' mood. Indeed many studies have demonstrated seasonal increase of depressive symptoms in winter months called “winter depression” [23,24]. Accordingly light deficiency, especially shortened photoperiods, is the hypothesized etiological mechanism behind winter type depression and bright light treatment is the treatment of choice [25]. One would not be surprised by these findings, as certain neurotransmitters of thermoregulation, such as norepinephrine, dopamine, and
Table 2 The comparative clinical and laboratory data of 66 HD patients in January and July
Body mass indexa Interdialytic weight gain (%)a spKt/Va Pre-dialysis systolic BP (mmHg)a Pre dialysis diastolic BP (mmHg) a Predialysis creatinine (μmol/L) a Predialysis albumin (g/L) a Predialysis hemoglobin (g/L) a Predialysis serum iron (μg/dl) a Predialysis ferritin (ng/ml) a hs-CRP (mg/dl) a Intact parathyroid hormone (pg/ml) a Predialysis sodium (mmol/L) a Predialysis potassium (mmol/L) a Predialysis glucose (mmol/L) a Predialysis total cholesterol (mmol/L) a Triglyceride (mmol/L) a HDL cholesterol (mmol/L) a LDL cholesterol (mmol/L) a Predialysis calcium (mmol/L) a Pre dialysis phosphorus (mmol/L) a sp, Single pool. a Mean±S.D. ⁎ P: based on Wilcoxon. ⁎⁎ P: based on paired t test.
January
July
P
24.85±4.16 3.92±1.36 1.42±0.15 140.4±17.9 81.5±8.9 716.1±212.2 36.8±4.7 109.4±17.6 75.5±35.0 416.2±271.2 1.55±2.52 330.3±317.3 134.7±3.0 5.13±0.68 6.74±2.87 4.33±1.19 2.03±0.69 0.91±0.24 2.46±0.75 2.24±0.18 1.71±0.48
24.67±4.17 3.88±1.57 1.39±0.23 137.6±23.7 80.0±11.1 735.5±231.6 37.4±5.8 112.9±11.7 71.0±25.4 451.3±295.1 1.37±1.93 327.4±156,3 135.3±3.1 5.25±0.69 6.79±2.66 4.36±1.06 2.01±0.92 0.90±0.29 2.52±0.81 2.24±0.24 1.66±0.53
.302⁎⁎ .335⁎ .087⁎⁎ .004⁎⁎ .032⁎ .025⁎⁎ .366⁎⁎ .059⁎⁎ .392⁎ .293⁎ .091⁎ .054⁎ .041⁎⁎ .063⁎⁎ .507⁎ .762⁎ .064⁎ .571⁎ .429⁎ .914⁎⁎ .184⁎
B. Afsar, A. Kirkpantur / General Hospital Psychiatry 35 (2013) 28–32 Table 3 The comparative data of quality of life, depression scores, SQ and cognitive function in January and in July in 66 HD patients Parameter
January
July
P
Physical functioninga Role-physical limitationa Bodily paina General health perceptiona Vitalitya Social functioninga Role emotionala Mental healtha PCSa MCSa PSQI Score BDI Score SMMSE Score
59,7±14.3 61.1±15.5 47.0±11.8 60.1±13.8 60.2±12.9 63.1±17.4 62.9±16.9 64.9±15.0 42.3±4.0 46.2±8.1 4.62±3.1 15.9±10,9 24.4±3.43
61.0±15.4 61.8±16.3 47.4±12.7 61.4±14.5 61.1±12.8 63.0±17.7 64.0±17.2 65.0±15.6 44.2±7.3 46.4±8.3 4.59±3.1 14.4±10.0 24.2±3.36
b.0001⁎⁎ .032⁎⁎ .581⁎⁎ .017⁎⁎ .018⁎⁎ .602 .002⁎⁎ .551⁎⁎ .001⁎ .147⁎ .848⁎ b.0001⁎ .123⁎⁎
a Mean±S.D. ⁎ P: based on Wilcoxon. ⁎⁎ P: based on paired t test.
serotonin, are also key neurotransmitters of mood regulation [26]. Another factor may be the effect of ambient temperature. Shapira et al. examined 11 years of admissions data in a large, non-seasonal affective disorder population of both bipolar depressive and unipolar patients and discovered a positive correlation between admission rates and monthly temperatures in bipolar depressives, but not major depressive disorder patients [27]. We found that change in albumin levels were independently related with change in BDI score. As the difference of depressive symptoms between winter and summer increase, the change of albumin levels between winter and summer also increase. Thus, the lower levels of albumin during winter compared to summer were associated with higher levels of depression in winter compared to summer. To say it in other way, patients who did not experience significant increase of albumin levels in summer when compared to winter do not show significant optimization of depressive symptoms in summer compared to winter. We don't know the exact mechanisms regarding the relationships of ΔBDI score, Δalbumin. However the relationship between depression (as evaluated by BDI and albumin levels were shown before [28]. Additionally smoking status was related with less pronounced decrease of BDI scores in July when compared to January. To put it in another way, patients who smoke; feel depressive both in winter and in summer and do not show wide seasonal variation with respect to depression. The cause of this relationship in not known, and further research is necessary. Apart from PCS, Most of SF-36 domains related with physical function were higher in July compared to January in the present study. Again this finding may be related to the shortening hours of sunlight [29,30]. In one study it was shown that the best physical health was during the summer and the worst was during the winter [31]. Such findings were consistent with previous studies of the general population [32]. Thus, we extend previous findings and demonstrated that HD patients also exhibit seasonal variation with respect to physical performance which is better in summer then in winter. Previously seasonal changes in sleep have been described in both clinical and normal populations [30,33–35]. Numerous studies have found a higher incidence of depression with hypersomnia during the winter months than in other seasons [24,35]. Also a pattern of recurrent winter hypersomnia has also been described [36]. However, we found no difference of sleep disorders and cognitive function between January and July. Thus, unlike depression and quality of life, we suggest that sleep disorders and cognitive function did not show seasonal variation at least in HD patients. We believe that our findings have some potential implications for health care professionals. Firstly, since both depression and quality of
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life are closely related with morbidity and mortality in HD patients, this current study opens areas of new research whether deaths are higher in winter months in HD patients especially due to depression and bad life quality. If this is so, it should also be investigated whether timely intervention to these conditions will decreased morbidity and mortality given the fact that despite all major improvements and wide use of new therapeutic agents in HD patients morbidity and mortality was still high. Thus, the importance of timely intervention to improve depressive behavior and quality of life could not be overemphasized. Secondly, the albumin levels are lower in January as compared to July. This may be due to higher interdialytic weight gain in January so the dilutional hypoalbuminemia may occur. However the IDWG % was found to be higher in July compared to January (1.57±0.19 vs. 1.36± 0.17, Pb.0001). Thus, the lower levels of albumin in January may not be due to hypervolemia. Another hypothesis may be decreased appetite in January. Although we did not evaluate appetite in the current study, depression may inhibit appetite in winter months and lead to hypoalbuminemia. In support of our idea, we found that the changes in albumin levels were inversely associated with change in depressive behavior. Since hypoalbuminemia is strongly correlated with mortality in HD patients, whether improvement in depression will increase, the appetite and albumin levels and decrease in mortality should also be investigated. Regarding the finding that only ΔMCS score was related with ΔPCS score, we can only speculate that patients who feel well with mental dimension will have more urge to increase their daily activity. This study has limitations that deserve mention. Firstly, since our study is cross sectional, cause-and-effect relationship cannot be suggested. Secondly, our results are valid for only local patients and not a representative of the whole HD population. Thirdly, we rely on BDI to measure depressive symptoms, and patients with clinical depression may be missed without complete psychiatric evaluation. Lastly, our findings are only valid for winter and summer and not for spring and autumn. In conclusion we demonstrated for the first time that depressive symptoms and quality of life but not SQ and cognitive function showed seasonal variability in HD patients. Studies are needed to highlight the factors related with this seasonal variation in HD patients.
Table 4 The linear regression analyses of factors independently associated with seasonal change in BDI score
Age Gender (Δ% IDWG) ΔSBP ΔDBP HD vintage, Marital status, Presence coronary artery disease, Presence of diabetes mellitus, Presence of cerebrovascular disease, Smoking status, Living status, economical status ΔAlbumin ΔHemoglobin ΔPSQI score ΔSMMSE score ΔPCS ΔMCS
B
95% CI
beta
P
−0.022 0.201 −0.350 0.040 0.015 0.008 −0.408 −0.138 1.083 −0.022
−0.099 to 0.054 −1.359 to 1.762 −2.112 to 1.413 0.022–0.203 0.006–0.265 −0.005 to 0.020 −1.123 to (−3.096) −2.074 to 1.799 −0.653 to 2.819 −2.185 to 2.141
−0.101 0.034 −0.053 0.108 0.023 0.157 −0.038 −0.024 0.179 −0.003
.563 .796 .692 .621 .907 .235 .816 .887 .215 .984
−1.470 0.320 −0.177 2.207 −0.010 0.091 −0.076 −0.142 −0.209
−2.410 to (−0.470) −2.674 to 3.315 −1.797 to 1.443 0.696 to 3.718 −0.496 to 0.476 0.025 to 0.745 −0.599 to 0.447 −0.036 to (−0.319) −1.888 to 0.076
−0.311 0.041 −0.031 0.397 −0.005 0.037 −0.036 −0.220 −0.125
.042 .830 .827 .005 .967 .778 .771 .114 .098
R2: 0.478; DURBİN WATSON: 1.952. Δ denotes the differences between January and July; %IDWG, percent difference of interdialytic weight gain; SBP: systolic BP; DBP, diastolic BP.
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