Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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Short-term heart rate variability in older patients with newly diagnosed depression Jee Hyun Ha a, Soyeon Park b, Daehyun Yoon c, Byungsu Kim b,d,n a
Department of Psychiatry, School of Medicine, Konkuk University, Seoul, Republic of Korea Department of Psychiatry, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea c Department of Psychiatry, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea d Stress Clinic, Health Promotion Center, Asan Medical Center, Seoul, Republic of Korea b
art ic l e i nf o
a b s t r a c t
Article history: Received 6 January 2014 Received in revised form 10 February 2015 Accepted 11 February 2015
Dysfunction of the autonomic nervous system has been considered to be a risk factor for major depressive disorder (MDD) and cardiovascular disease (CVD). The aim of this study was to evaluate short-term heart rate variability (HRV) in elderly patients with newly diagnosed MDD. Thirty MDD patients over 60 years old newly diagnosed by a structured interview were enrolled, free from antidepressants. Socio-demographic data, blood tests, and heart rate variability (HRV) obtained from 5-min ECG were gathered. The MDD group showed significantly lower very low frequency power, low frequency power, high frequency power, and total power in frequency domain. In time domain analysis, the MDD group showed a significantly smaller standard deviation of the NN, root mean square of the differences of the successive NN, and NN50/total number of all NNs. These findings demonstrated a lower HRV in older patients who were newly diagnosed with depression without a history of CVD and antidepressants effect, compared with the control subjects. Low HRV may be an important predictor of both MDD and CVD in elderly. The use of HRV in elderly depressive patients could be a meaningful screening method for risk of CVD. & 2015 Published by Elsevier Ireland Ltd.
Keywords: Depression Heart rate variability HRV Autonomic nervous system Old age
1. Introduction Depression is a common and disabling health problem worldwide. The estimated lifetime prevalence of major depressive disorder (MDD) is 16.2%, and the disease causes remarkable psychosocial impairments (Kupfer et al., 2012). Depression is associated with various medical conditions and is a leading cause of disability, accounting for 40.5% of the years lived with disability worldwide (Whiteford et al., 2013). Medical illness is a risk factor for depression, and the prevalence of depression increases with increasing illness severity (Li and Rodin, 2011). Co-morbid depression has been associated with 20– 40% of cardiovascular disease (CVD) patients (Gonzalez et al., 1996). A meta-analysis of prospective studies reported a dose–response relationship between the severity of depression and the risk of CVD (Ferketich et al., 2000; Rugulies, 2002; Williams et al., 2002). Depression is associated with many unhealthy behaviors, such as smoking and drinking to excess, which increase the risk of future
n Correspondence to: Department of Psychiatry and Health Promotion Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 136-736, Republic of Korea. Tel.: þ82 2 3010 3424; fax: þ82 2 485 8381. E-mail address: [email protected] (B. Kim).
CVD. CVD patients who are depressed might have a greater sympathetic response to stress, which may lower the threshold for hypertension and facilitate the progression of the atherosclerotic process (Li and Rodin, 2011). CVD may impair social functioning and has a significant burden of illness, which may increase the risk of developing depression (Alexopoulos et al., 2002). Although the exact mechanism underlying the association between depression and CVD is not clear, one possible hypothesis is that autonomic dysfunction might be a co-risk factor for both illnesses (Carney et al., 2002; Grippo and Johnson, 2002). Measurement of heart rate variability (HRV) is regarded as a non-invasive and highly sensitive method to evaluate autonomic nervous system (ANS). Low HRV has been associated with the development of CVD and MDD (Carney et al., 2005); furthermore, lower HRV could predict cardiac events in healthy adults and mortality in CVD patients (Vaishnav et al., 1994; Dekker et al., 2000). Many previous studies have revealed that lower HRV was associated with unfavorable health outcomes in depressed patients (Veith et al., 1994; Lehofer et al., 1997; Moser et al., 1998). There is a significant negative correlation between the severity of depression and HRV (Kemp et al., 2010). A systematic metaanalysis revealed that individuals with more severe depression are likely to have a lower HRV compared with individuals with less severe depression (Kemp et al., 2010). Elderly depressed patients
http://dx.doi.org/10.1016/j.psychres.2015.02.005 0165-1781/& 2015 Published by Elsevier Ireland Ltd.
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
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have a significant coherent deficit in autonomic tone indicated by lower LF-HRV and total power spectral density compared with non-depressed individuals (Vasudev et al., 2011), which indicate a remarkable deficit in autonomic tone in patients with late-life depression. However, these findings are needed to confirm by other studies because of methodological limitations, such as lack of structured interview for diagnosis and including just frequency domain analysis of HRV test (Vasudev et al., 2011). Considering the fact that the ANS changes increase with aging, it is difficult to demonstrate that elderly depressed patients display decreased HRV against a background of decreased HRV with aging. It is quite possible that depression in elderly patients further reduces HRV, which potentially results in an increased risk of cardiovascular morbidity and mortality. It is notable, however, that previous reports on these phenomena had several limitations. First, most studies were performed in previously diagnosed depressed patients who were taking anti-depressants. The use of antidepressants may have an effect on the ANS status evaluated by HRV (Licht et al., 2008). Some psychotropic drugs are known to alter cardiac autonomic activity in diverse ways (Glassman et al., 2003). A recent study showed antidepressant effect was significant in lowering HRV in depressive elderly, while depression per se did not affect in lowering HRV (O'Regan et al., 2014). Second, studies on the association between HRV and depression have generally been conducted with patients with known CVD. The research results from those subjects can hardly discern the effect of depression on the lowering of HRV from CVD. Last but not least, there were inconsistent results about the association of the lowering of HRV and late-life depression. Vasudev et al. reported that the patients with previous and current late-life depression showed significant changes in HRV compared to controls (Vasudev et al., 2011). However, the other study did not show the differences in the time domain or frequency domain measures of HRV between depressed elderly and normal control (Jindal et al., 2008; O'Regan et al., 2014). The inconsistency among the previous studies could draw from the methodological limitations, such as the use of psychotropics and CVD history of subjects. Therefore, we have performed a study on newly diagnosed and treatment-naive elderly MDD patients without co-morbid CVD to identify whether elderly depression is associated with the lowering of HRV.
2. Methods 2.1. Subjects We enrolled subjects over 60 years of age who presented at the Health Promotion Center of Asan Medical Center, Seoul, Korea, for a health examination from January to August 2011. The patients visited the Health Promotion Center for regular medical check-ups. All study participants were treatment-naïve and never received treatment for psychiatric illness. All subjects without a past history of a depressive episode received a psychiatric interview by an experienced psychiatrist. Thirty patients who were newly diagnosed with depression using the Structured Clinical Interview for Axis I DSM-IV Disorders (SCID-I/P 2.0) were enrolled through the Health Promotion Center (depression group). All subjects met the criteria of MDD without psychotic features, were free of antidepressants or benzodiazepines during their lifetime, and were in a clinically and medically euthyroid state confirmed by a thyroid function test. We only included patients who had MDD to focus our analyses on a certain degree of depressive symptom severity in patients who sought clinical attention. The severity of depressive symptoms in the MDD subjects was evaluated by the Montgomery-Asberg Depression Rating Scale (MADRS) and had a mean score of 24.67 5.0. In addition, 30 age- and sexmatched healthy individuals were recruited from a Health Promotion Center as the control group. Subjects who had a previous history of psychiatric illness or CVD were excluded. The controls were examined for depression using the Center for Epidemiological Studies for Depression Scale (CES-D) in the identical time period; an experienced psychiatrist (BSK) confirmed the absence of psychiatric illness in the control group. The institutional review board of the Asan Medical Center Ethics Committee approved this study and waived the requirement for informed consent because the measurements of clinical features and the assessments of the variables included in this study were part of a routine health examination.
2.2. Exclusion criteria The subjects with known CVD, such as myocardial infarct and arrhythmia, possible dementia assessed by a Mini Mental State Examination score below 24, comorbid or previous drug or alcohol abuse, head injury, history of epilepsy, and psychiatric illness except MDD in the previous 6 months were excluded from the study. An abnormal electrocardiogram (ECG) (determined by a heart rate o 50 beats per minute (bpm) or by the presence of conduction disturbance) was also used as an exclusion criterion for the study.
2.3. Basic demographical data and biochemical tests Basic socio-demographic data were obtained and included occupation, level of education, estimated income, marital status, alcohol use, smoking, and regularity of exercise. Blood pressure, body mass index, and blood biochemical test results (e.g., high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG), total cholesterol, and glycated hemoglobin (HbA1c)) were obtained for all patients in both groups. Additionally, existing medical illnesses, including diabetes mellitus, hypertension, and metabolic syndrome, and the current use of medications, were investigated. Lifestyle factors including alcohol, exercise, and smoking were obtained from medical data that were recorded on the basis of the frequency of alcohol consumption ( r 1 day/week, 2–3 days/week, Z 4 days/week) and exercise (no regular exercise, 1–2 day/week, 3–5 days/week, 6–7 days/week) with categorical variables; smoking was categorized as never smoking, past smoking, and current smoking. The diagnosis and treatment history of hypertension and diabetes among the subjects were collected from medical records based on laboratory tests and clinical evaluations. Metabolic parameters including waist circumference, blood pressure, and triglycerides, high-density-lipoprotein cholesterol, and fasting glucose levels were measured before starting treatment. We used the modified the NCEP in Adult Treatment Panel III (Alberti et al., 2009) to define MeS as 3 or more of 5 components in which the cut-off point of waist circumference was modified for Asian according to the recommendation by the International Diabetes Federation (Alberti et al., 2006). According to the modified NCEP in Adult Treatment Panel III definition, MeS is diagnosed by the presence of 3 or more of the following criteria: (1) abdominal obesity—waist circumference Z 90 cm in men and Z 80 cm in women; (2) high blood pressure Z 130/ 85 mm Hg or use of antihypertensive medication; (3) hypertriglyceridemia Z 150 mg/dL or use of antihyperlipidemic medication (4) low high-density-lipoprotein cholesterol levels, o 40 mg/dL in men and o 50 mg in women; and (5) high fasting glucose, Z 100 mg/dL or use of diabetic medication.
2.4. Measuring HRV All examinations occurred in the morning, between 8 and 10 AM. The subjects were asked not to drink caffeine or smoke for at least 8 h prior to the examination. The participants were asked to breathe normally with their eyes open and to lie quietly without moving. After approximately 10 min of supine rest, an ECG was recorded for 5 min in the supine position using limb leads according to the QECG-3 model (LXC3203; LAXTHA, Daegu, Korea), and the data were analyzed to obtain the time and frequency domain parameters of HRV using TeleScan software (version 2.0, LAXTHA, Daegu, Korea). The following ECG variables were calculated as indicators of ANS function. In the time domain, the following statistical parameters were calculated: normal-to normal R–R interval (NN), HRV index (total no. of all NNs/height of the histogram of all NNs), standard deviation of the NN (SDNN), root mean square of the differences of successive NNs (RMSSD), the number of pairs of adjacent NN intervals that differed by 450 ms in the entire recording (NN50), and the NN50/total number of all NNs (pNN50). In the frequency domain, the power spectrum of the HRV signals was calculated using Fourier transformation, and the power (the area under the curve related to each component) was calculated for the following four components: the total power (TP; variance of NN intervals over the temporal segment), very low frequency power (VLF; o0.04 Hz), low frequency power (LF; 0.04–0.15 Hz), and high frequency power (HF; 0.15–0.4 Hz). Most HRV measures are predominantly under vagal control, in particular, the short-term measures obtained under laboratory conditions in which participants are typically supine (Martinmaki et al., 2006). Experimental studies have shown that short-term recordings are predictive of CVD (Dekker et al., 2000), are stable (Sinnreich et al., 1998), and provide a more accurate picture of physiological changes compared with longer-term recordings (Serrador et al., 1999).
2.5. Statistical analysis The socio-demographic and clinical variables are reported as the means and standard deviations for the continuous variables and the frequency for the categorical variables. The Fisher's exact test for sex, occupation, and marital status and the Mann–Whitney test for age, education and monthly earnings were used to assess the differences between the depression and control groups. Lifestyle factors,
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
J.H. Ha et al. / Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎ including alcohol use, smoking, and exercise, were analyzed by Fisher's exact test. Based on the education level, the subjects were classified as 4 18 years, 16–17 years, 12–15 years, and o 12 years of education. Based on the level of monthly earnings, the subjects were classified as having 47 million won, 5–7 million won, 3–5 million won, and o 3 million won, as shown in Table 1. The Mann–Whitney test was used for the HRV variables. Cohen's d effect size was calculated for the differences of HRV between the depression and the control using means and standard deviations for each group. All statistical analyses were performed using the Statistical Package for Social Science (SPSS) version 18 for Windows, and significance was defined as p o 0.05.
3. Results There were no significant differences in the socio-demographic data or lifestyle characteristics, such as the exercise and alcohol Table 1 Socio-demographic characteristics of subjects. Depression (N ¼ 30)
Control (N ¼ 30)
p
Sex Female Male
21 (70.0%) 9 (30.0%)
21 (70.0%) 9 (30.0%)
1.000
Age Years (mean7 S.D.)
65.2 7 4.8
65.2 7 4.8
1.000
Occupation Employed Unemployed
10 (33.3%) 20 (66.7%)
12 (40.0%) 18 (60.0%)
0.793
Education Z 18 years Z 16 years Z 12 years o 12 years
2 (6.7%) 7 (23.3%) 8 (26.7%) 13 (43.3%)
5 9 8 8
(16.7%) (30.0%) (26.7%) (26.7%)
0.159
Monthly earnings Z 7 million won Z 5 million won Z 3 million won o 3 million won
6 (20.0%) 2 (6.7%) 3 (10.0%) 19 (63.3%)
5 (16.7%) 4 (13.3%) 11 (36.7%) 10 (33.3%)
0.122
Marital status Married/living together Divorced/Separated/widowed
26 (86.7%) 4 (14.3%)
28 (93.3%) 2 (6.7%)
0.671
Comparison using Fisher's exact test and Mann-Whitney test.
Table 2 Comparisons of lifestyle factors and medication between depression and control group. Depression (N ¼ 30)
Control (N ¼ 30)
p
Alcohol r 1day/week 2–3 days/week Z 4 days/week
27 (90.0%) 2 (6.7%) 1 (3.3%)
25 (83.3%) 3 (10.0%) 2 (6.7%)
0.748
Smoking Never smoking Past smoking Current smoking
23 (76.7%) 5 (16.7%) 2 (6.7%)
24 (80.0%) 4 (13.3%) 2 (6.7%)
1.000
Exercise 6–7 days/week 3–5 days/week 1–2 days/week No regular exercise
4 (13.3%) 8 (26.7%) 8 (26.7%) 10 (33.3%)
8 9 7 6
(26.7%) (30.0%) (23.3%) (20.0%)
0.507
16 (53.3%)
12 (40.0%)
0.438
History of anti-diabetic medication 10 (33.3%)
4 (13.3%)
0.125
Metabolic syndrome
11 (36.7%)
0.004n
History of anti-hypertensive medication
n
Fisher's exact test, p o0.05.
22 (73.3%)
3
drinking/smoking habits, between the two groups (Tables 1 and 2). Blood pressure (depression group vs. control group: systolic 124.37 12.4 vs. 119.3714.5, respectively; diastolic 75.779.7 vs. 71.778.7, respectively), the mean body mass index (25.773.0 vs. 24.572.6, respectively), HbA1c level (6.070.6 vs. 6.071.0, respectively), total cholesterol level (192.3735.0 mg/dl vs. 196.3733.8 mg/dl, respectively), LDL level (121.2732.5 vs. 120.5727.7, respectively), HDL level (49.0711.0 vs. 59.5715.1, respectively) and TG level (130.0771.6 vs. 100.0745.9, respectively) were not significantly different between the two groups. The prevalence of metabolic syndrome (22 (73.3%) vs. 11 (36.7%), respectively, p¼ 0.004) was significantly higher in the depression group compared with the control group. Within the diagnostic criteria of metabolic syndrome, only the number of subjects with a high waist circumference was higher in the depression group compared with the control group (26 (86.7%) vs. 18 (60.0%), respectively, po0.05). There were no differences between the two groups in the frequency of diabetes mellitus or hypertension or the existence of thyroid nodule screening by ultrasound. The history of medication, such as antihypertensive drugs, hypoglycemic agents, and cholesterol-lowering drugs, was not different between the groups. In the HRV results, the depression group showed significantly lower VLF, LF, HF, and total power in the frequency domain compared with the control subjects except LF/HF. Significantly lower SDNN, RMSSD, and pNN50 in the time domains were identified in the depressive patients (Table 3). A comparison of HRV variables of the depression with the control showed large effect sizes for the differences in LF (Cohen's d¼1.43, po0.001), SDNN (Cohen's d¼1.29, po0.001), and pNN50 (Cohen's d¼1.32, po0.001). Medium effect size of differences was observed between the two groups in terms of HF (Cohen's d¼1.07, po0.001), VLF (Cohen's d¼1.05, po0.001), and RMSSD (Cohen's d¼ 0.76, p¼0.001). After obtaining the results, we made a further analysis of differences in HRV using the subjects who did not have metabolic syndrome because there was a significant difference in the frequency of metabolic syndrome between the two groups. We performed a non-parametric Mann–Whitney test using eight subjects in the depressed patient group and 19 subjects in the control group. In this analysis, the significant lowering of HRV was observed in VLF (p¼0.016), LF (p¼0.003), SDNN (p¼0.009), and pNN50 (p¼0.009); HF (p¼ 0.132) and RMSSD (p¼0.132) did not show the significant difference.
4. Discussion Patients with depression have a 2–4-fold higher risk of cardiac mortality compared with the general population (Glassman et al., 2003). The association between depression and CVD has been explained by several potential mechanisms. First, common lifestyle factors, such as lack of exercise and poor nutritional habits, as well as smoking and alcohol abuse, affect both illnesses (Glassman et al., 2003; Kemp and Quintana, 2013). Second, endocrine disturbances, such as hyperproduction of cortisol and an abnormality of the hypothalamus–pituitary–adrenal axis, may lead to cardiovascular problems in depressed patients (Glassman et al., 2003; Nemeroff and Goldschmidt-Clermont, 2012). Sustained somatic symptoms and physiological disturbances could increase the risk for CVD in depressed patients. Third, autonomic dysfunction may accelerate the progression of CVD and promote procoagulant and proinflammatory processes, thereby lowering the threshold of mental stressinduced ischemia and prolonging the QT interval (Nemeroff and Goldschmidt-Clermont, 2012). In this study, the MDD subjects showed significantly lower HRV indexes in the time and power/ spectrum domains compared with the control subjects. There were few differences in the health and cardiovascular-related factors
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Table 3 Comparisons of heart rate variability between depression and control group. Depression (N ¼ 30)
Control (N ¼ 30)
p value
Time domain HR (beat/min) SDNN (ms) RMSSD (ms) pNN50 (%)
68.27 12.4 25.2 7 10.0 16.2 7 10.0 84.0 7 13.8
62.17 7.8 39.3 7 11.7 23.3 7 8.3 66.0 713.5
0.096 p o 0.001n p ¼ 0.001n p o 0.001n
Frequency domain Ln Total Power Ln VLF Ln LF Ln HF LF nu (%) HF nu (%) LF/HF
6.17 0.8 5.5 7 0.8 4.2 7 0.8 4.17 1.2 50.8 7 6.5 49.27 6.5 1.2 7 0.7
7.0 70.6 6.5 7 0.8 5.2 7 0.7 5.17 0.7 50.5 7 4.1 49.5 74.1 1.0 70.2
p o 0.001n p o 0.001n p o 0.001n p o 0.001n 0.773 0.773 0.819
HRV, heart rate variability; HR, heart rate; SDNN, standard deviation of the normalto-normal interval; RMSSD, root mean square of differences of successive normalto-normal intervals; pNN50, no. pairs of adjacent normal-to-normal intervals differing by 4 50 ms in the entire recording/total no. normal-to-normal intervals; VLF, power in very low frequency range ( o 0.04 Hz); LF, power in low frequency range (0.04–0.15 Hz); power in high frequency range (0.15–0.4 Hz). n
Mann-Whitney test, p o0.001.
between the two groups. These findings suggest that low HRV indexes may be associated with MDD pathophysiology. Previous studies have provided evidence that depressive disorders are associated with a dysregulation of the ANS. A meta-analytical study showed that cardiac vagal control was lower in depressed individuals compared with non-depressed subjects in cardiovascularly compromised and healthy samples (Rottenberg, 2007). ANS dysfunction may be mediated by deficits in the central autonomic network, including the orbitofrontal and medial prefrontal cortices and the central nucleus of the amygdala that project to the hypothalamic and brainstem autonomic nuclei where sympathetic and parasympathetic efferents to the heart originate (Thayer and Siegle, 2002). Furthermore, comparative hypofunction of the right hemisphere and predominant activity of the left hemisphere have been associated with autonomic dysfunction in depressed subjects (Friedman, 1996), which suggests that cerebral dysfunction of the ANS network, rather than possible concomitant cardiovascular abnormalities, affects HRV dysfunction in depressed patients. This finding may explain why the two groups in this study showed limited differences in cardiac factors despite differences in HRV. Low HRV is a marker of CVD risk. The HRV index is associated with cardiovascular morbidity and mortality because it reflects the presence of systematic problems, including inflammatory-mediated atherosclerosis and ventricular fibrillation, compared with specific vascular occlusion or perfusion defects (Taylor, 2010). A previous study analyzed subjects with acute myocardial infarction who underwent HRV 1 year after a cardiovascular incident. Low HRV has been known to indicate a hypoactive parasympathetic (vagal) system and is associated with cognitive and affective dysregulation and psychological inflexibility (Kemp and Quintana, 2013; Reyes Del Paso et al., 2013). Vagal withdrawal (low HRV) is associated with negative social engagement according to the polyvagal theory (Porges, 2009). Based on the data that autonomic dysfunctions are commonly affected by depression and CVD, HRV findings may be a sensitive indicator of both illnesses. Depression could contribute to the development of brain-derived ANS dysfunction, thereby increasing the risk of major CVD in some patients. This study supports this hypothesis. We found no significant differences in cardiovascularrelated risk factors, such as blood pressure, body mass index, HbA1c levels, or lipid profiles, between the two groups. Only the prevalence of metabolic syndrome was higher in the depression
group compared with the control group. In healthy young adults, metabolic syndrome components have been associated with less favorable HRV profiles (Soares-Miranda et al., 2012). The HRV index changes with increasing age, with a trend toward a decrease in cardiac autonomic function (Kuo et al., 1999; Fukusaki et al., 2000). Consequently, the HRV index may be more valuable in older patients. Depression is one of the most prevalent mental disorders in old age (Luppa et al., 2012), and the symptoms and etiology of depression, including degeneration of the brain and atherosclerotic changes in vessels, in elderly patients are different compared with other age groups (Nobler et al., 1999). The vascular changes and ANS dysfunction are common to depression and CVD, which suggests that measuring HRV in elderly depressed patients to screen for the risk of CVD is important. It is known that HRV could be affected by the use of antidepressant treatments (Kemp et al., 2010). Degree of anticholinergic activity or serotonin effect on raphe nuclei in various antidepressants would partly affect in decreasing HRV (O'Regan et al., 2014). A study by O'Regan et al. even insisted that antidepressant effect might be more significant effect in lowering HRV. It was done with large number of subjects, but measuring of depression was only conducted by self-reporting questionnaire. Therefore interpretation of association with depression might have some limitation. Whereas we diagnosed the subjects by the structured interview, we think the association between HRV and depression should be more reliable in this study. Furthermore we have conducted this study on subjects who had not taken such medications, thus excluding any medication-induced effects. After excluding potential medication effects, it is evident that newly diagnosed elderly MDD patients have lower HRV indexes compared with control subjects, which suggest that HRV could be a significant predictive factor for future CVD morbidity. In this study, there was a higher frequency of metabolic syndrome in the MDD group compared with the control group. This finding may be because of a less healthy lifestyle, which is reflected by the degree of exercise and the eating habits in the MDD patients (Vancampfort et al., 2013). Metabolic syndrome is a significant risk factor of CVD. Low HRV may be an important predictor of MDD and CVD in elderly patients. It is possible that MDD in older patients further reduces HRV, which potentially contributes to an increased risk of cardiovascular morbidity and mortality. The use of the HRV test in old age MDD patients to identify the risk of potential CVD could be merited. Strengths of this study were as in the followings. First, the study contained detailed medical information including blood test that could influence the HRV results. There was no difference between two groups. Second, enrolled elderly depressive subjects were diagnosed for the first time using the structured interview, and free from any psychotropics such as antidepressants and benzodiazepines throughout their lifetime. It also has the following limitations. First, the number of enrolled subjects was relatively small; therefore, it might be difficult to generalize the results. Second, patients taking anti-diabetic or anti-hypertensive medications were included; however, there were no significant differences between the two groups. In addition, metabolic syndrome was different in the two groups, which might affect the results. However, further analysis using the subjects without metabolic syndrome showed that the statistically significant changes of HRV between the two groups still remained among most of the variables except HF and RMSSD. This indicates that the metabolic syndrome did not affect the overall findings of this study. In conclusion, this study showed lower HRV in older patients who were newly diagnosed with depression without a history of CVD compared with the control subjects. HRV is an easily accessible and meaningful measure of the risk of CVD. Future longitudinal studies should determine whether patients with MDD develop CVD.
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
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Short-term heart rate variability in older patients with newly diagnosed depression Jee Hyun Ha a, Soyeon Park b, Daehyun Yoon c, Byungsu Kim b,d,n a
Department of Psychiatry, School of Medicine, Konkuk University, Seoul, Republic of Korea Department of Psychiatry, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea c Department of Psychiatry, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea d Stress Clinic, Health Promotion Center, Asan Medical Center, Seoul, Republic of Korea b
art ic l e i nf o
a b s t r a c t
Article history: Received 6 January 2014 Received in revised form 10 February 2015 Accepted 11 February 2015
Dysfunction of the autonomic nervous system has been considered to be a risk factor for major depressive disorder (MDD) and cardiovascular disease (CVD). The aim of this study was to evaluate short-term heart rate variability (HRV) in elderly patients with newly diagnosed MDD. Thirty MDD patients over 60 years old newly diagnosed by a structured interview were enrolled, free from antidepressants. Socio-demographic data, blood tests, and heart rate variability (HRV) obtained from 5-min ECG were gathered. The MDD group showed significantly lower very low frequency power, low frequency power, high frequency power, and total power in frequency domain. In time domain analysis, the MDD group showed a significantly smaller standard deviation of the NN, root mean square of the differences of the successive NN, and NN50/total number of all NNs. These findings demonstrated a lower HRV in older patients who were newly diagnosed with depression without a history of CVD and antidepressants effect, compared with the control subjects. Low HRV may be an important predictor of both MDD and CVD in elderly. The use of HRV in elderly depressive patients could be a meaningful screening method for risk of CVD. & 2015 Published by Elsevier Ireland Ltd.
Keywords: Depression Heart rate variability HRV Autonomic nervous system Old age
1. Introduction Depression is a common and disabling health problem worldwide. The estimated lifetime prevalence of major depressive disorder (MDD) is 16.2%, and the disease causes remarkable psychosocial impairments (Kupfer et al., 2012). Depression is associated with various medical conditions and is a leading cause of disability, accounting for 40.5% of the years lived with disability worldwide (Whiteford et al., 2013). Medical illness is a risk factor for depression, and the prevalence of depression increases with increasing illness severity (Li and Rodin, 2011). Co-morbid depression has been associated with 20– 40% of cardiovascular disease (CVD) patients (Gonzalez et al., 1996). A meta-analysis of prospective studies reported a dose–response relationship between the severity of depression and the risk of CVD (Ferketich et al., 2000; Rugulies, 2002; Williams et al., 2002). Depression is associated with many unhealthy behaviors, such as smoking and drinking to excess, which increase the risk of future
n Correspondence to: Department of Psychiatry and Health Promotion Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 136-736, Republic of Korea. Tel.: þ82 2 3010 3424; fax: þ82 2 485 8381. E-mail address: [email protected] (B. Kim).
CVD. CVD patients who are depressed might have a greater sympathetic response to stress, which may lower the threshold for hypertension and facilitate the progression of the atherosclerotic process (Li and Rodin, 2011). CVD may impair social functioning and has a significant burden of illness, which may increase the risk of developing depression (Alexopoulos et al., 2002). Although the exact mechanism underlying the association between depression and CVD is not clear, one possible hypothesis is that autonomic dysfunction might be a co-risk factor for both illnesses (Carney et al., 2002; Grippo and Johnson, 2002). Measurement of heart rate variability (HRV) is regarded as a non-invasive and highly sensitive method to evaluate autonomic nervous system (ANS). Low HRV has been associated with the development of CVD and MDD (Carney et al., 2005); furthermore, lower HRV could predict cardiac events in healthy adults and mortality in CVD patients (Vaishnav et al., 1994; Dekker et al., 2000). Many previous studies have revealed that lower HRV was associated with unfavorable health outcomes in depressed patients (Veith et al., 1994; Lehofer et al., 1997; Moser et al., 1998). There is a significant negative correlation between the severity of depression and HRV (Kemp et al., 2010). A systematic metaanalysis revealed that individuals with more severe depression are likely to have a lower HRV compared with individuals with less severe depression (Kemp et al., 2010). Elderly depressed patients
http://dx.doi.org/10.1016/j.psychres.2015.02.005 0165-1781/& 2015 Published by Elsevier Ireland Ltd.
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
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have a significant coherent deficit in autonomic tone indicated by lower LF-HRV and total power spectral density compared with non-depressed individuals (Vasudev et al., 2011), which indicate a remarkable deficit in autonomic tone in patients with late-life depression. However, these findings are needed to confirm by other studies because of methodological limitations, such as lack of structured interview for diagnosis and including just frequency domain analysis of HRV test (Vasudev et al., 2011). Considering the fact that the ANS changes increase with aging, it is difficult to demonstrate that elderly depressed patients display decreased HRV against a background of decreased HRV with aging. It is quite possible that depression in elderly patients further reduces HRV, which potentially results in an increased risk of cardiovascular morbidity and mortality. It is notable, however, that previous reports on these phenomena had several limitations. First, most studies were performed in previously diagnosed depressed patients who were taking anti-depressants. The use of antidepressants may have an effect on the ANS status evaluated by HRV (Licht et al., 2008). Some psychotropic drugs are known to alter cardiac autonomic activity in diverse ways (Glassman et al., 2003). A recent study showed antidepressant effect was significant in lowering HRV in depressive elderly, while depression per se did not affect in lowering HRV (O'Regan et al., 2014). Second, studies on the association between HRV and depression have generally been conducted with patients with known CVD. The research results from those subjects can hardly discern the effect of depression on the lowering of HRV from CVD. Last but not least, there were inconsistent results about the association of the lowering of HRV and late-life depression. Vasudev et al. reported that the patients with previous and current late-life depression showed significant changes in HRV compared to controls (Vasudev et al., 2011). However, the other study did not show the differences in the time domain or frequency domain measures of HRV between depressed elderly and normal control (Jindal et al., 2008; O'Regan et al., 2014). The inconsistency among the previous studies could draw from the methodological limitations, such as the use of psychotropics and CVD history of subjects. Therefore, we have performed a study on newly diagnosed and treatment-naive elderly MDD patients without co-morbid CVD to identify whether elderly depression is associated with the lowering of HRV.
2. Methods 2.1. Subjects We enrolled subjects over 60 years of age who presented at the Health Promotion Center of Asan Medical Center, Seoul, Korea, for a health examination from January to August 2011. The patients visited the Health Promotion Center for regular medical check-ups. All study participants were treatment-naïve and never received treatment for psychiatric illness. All subjects without a past history of a depressive episode received a psychiatric interview by an experienced psychiatrist. Thirty patients who were newly diagnosed with depression using the Structured Clinical Interview for Axis I DSM-IV Disorders (SCID-I/P 2.0) were enrolled through the Health Promotion Center (depression group). All subjects met the criteria of MDD without psychotic features, were free of antidepressants or benzodiazepines during their lifetime, and were in a clinically and medically euthyroid state confirmed by a thyroid function test. We only included patients who had MDD to focus our analyses on a certain degree of depressive symptom severity in patients who sought clinical attention. The severity of depressive symptoms in the MDD subjects was evaluated by the Montgomery-Asberg Depression Rating Scale (MADRS) and had a mean score of 24.67 5.0. In addition, 30 age- and sexmatched healthy individuals were recruited from a Health Promotion Center as the control group. Subjects who had a previous history of psychiatric illness or CVD were excluded. The controls were examined for depression using the Center for Epidemiological Studies for Depression Scale (CES-D) in the identical time period; an experienced psychiatrist (BSK) confirmed the absence of psychiatric illness in the control group. The institutional review board of the Asan Medical Center Ethics Committee approved this study and waived the requirement for informed consent because the measurements of clinical features and the assessments of the variables included in this study were part of a routine health examination.
2.2. Exclusion criteria The subjects with known CVD, such as myocardial infarct and arrhythmia, possible dementia assessed by a Mini Mental State Examination score below 24, comorbid or previous drug or alcohol abuse, head injury, history of epilepsy, and psychiatric illness except MDD in the previous 6 months were excluded from the study. An abnormal electrocardiogram (ECG) (determined by a heart rate o 50 beats per minute (bpm) or by the presence of conduction disturbance) was also used as an exclusion criterion for the study.
2.3. Basic demographical data and biochemical tests Basic socio-demographic data were obtained and included occupation, level of education, estimated income, marital status, alcohol use, smoking, and regularity of exercise. Blood pressure, body mass index, and blood biochemical test results (e.g., high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG), total cholesterol, and glycated hemoglobin (HbA1c)) were obtained for all patients in both groups. Additionally, existing medical illnesses, including diabetes mellitus, hypertension, and metabolic syndrome, and the current use of medications, were investigated. Lifestyle factors including alcohol, exercise, and smoking were obtained from medical data that were recorded on the basis of the frequency of alcohol consumption ( r 1 day/week, 2–3 days/week, Z 4 days/week) and exercise (no regular exercise, 1–2 day/week, 3–5 days/week, 6–7 days/week) with categorical variables; smoking was categorized as never smoking, past smoking, and current smoking. The diagnosis and treatment history of hypertension and diabetes among the subjects were collected from medical records based on laboratory tests and clinical evaluations. Metabolic parameters including waist circumference, blood pressure, and triglycerides, high-density-lipoprotein cholesterol, and fasting glucose levels were measured before starting treatment. We used the modified the NCEP in Adult Treatment Panel III (Alberti et al., 2009) to define MeS as 3 or more of 5 components in which the cut-off point of waist circumference was modified for Asian according to the recommendation by the International Diabetes Federation (Alberti et al., 2006). According to the modified NCEP in Adult Treatment Panel III definition, MeS is diagnosed by the presence of 3 or more of the following criteria: (1) abdominal obesity—waist circumference Z 90 cm in men and Z 80 cm in women; (2) high blood pressure Z 130/ 85 mm Hg or use of antihypertensive medication; (3) hypertriglyceridemia Z 150 mg/dL or use of antihyperlipidemic medication (4) low high-density-lipoprotein cholesterol levels, o 40 mg/dL in men and o 50 mg in women; and (5) high fasting glucose, Z 100 mg/dL or use of diabetic medication.
2.4. Measuring HRV All examinations occurred in the morning, between 8 and 10 AM. The subjects were asked not to drink caffeine or smoke for at least 8 h prior to the examination. The participants were asked to breathe normally with their eyes open and to lie quietly without moving. After approximately 10 min of supine rest, an ECG was recorded for 5 min in the supine position using limb leads according to the QECG-3 model (LXC3203; LAXTHA, Daegu, Korea), and the data were analyzed to obtain the time and frequency domain parameters of HRV using TeleScan software (version 2.0, LAXTHA, Daegu, Korea). The following ECG variables were calculated as indicators of ANS function. In the time domain, the following statistical parameters were calculated: normal-to normal R–R interval (NN), HRV index (total no. of all NNs/height of the histogram of all NNs), standard deviation of the NN (SDNN), root mean square of the differences of successive NNs (RMSSD), the number of pairs of adjacent NN intervals that differed by 450 ms in the entire recording (NN50), and the NN50/total number of all NNs (pNN50). In the frequency domain, the power spectrum of the HRV signals was calculated using Fourier transformation, and the power (the area under the curve related to each component) was calculated for the following four components: the total power (TP; variance of NN intervals over the temporal segment), very low frequency power (VLF; o0.04 Hz), low frequency power (LF; 0.04–0.15 Hz), and high frequency power (HF; 0.15–0.4 Hz). Most HRV measures are predominantly under vagal control, in particular, the short-term measures obtained under laboratory conditions in which participants are typically supine (Martinmaki et al., 2006). Experimental studies have shown that short-term recordings are predictive of CVD (Dekker et al., 2000), are stable (Sinnreich et al., 1998), and provide a more accurate picture of physiological changes compared with longer-term recordings (Serrador et al., 1999).
2.5. Statistical analysis The socio-demographic and clinical variables are reported as the means and standard deviations for the continuous variables and the frequency for the categorical variables. The Fisher's exact test for sex, occupation, and marital status and the Mann–Whitney test for age, education and monthly earnings were used to assess the differences between the depression and control groups. Lifestyle factors,
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
J.H. Ha et al. / Psychiatry Research ∎ (∎∎∎∎) ∎∎∎–∎∎∎ including alcohol use, smoking, and exercise, were analyzed by Fisher's exact test. Based on the education level, the subjects were classified as 4 18 years, 16–17 years, 12–15 years, and o 12 years of education. Based on the level of monthly earnings, the subjects were classified as having 47 million won, 5–7 million won, 3–5 million won, and o 3 million won, as shown in Table 1. The Mann–Whitney test was used for the HRV variables. Cohen's d effect size was calculated for the differences of HRV between the depression and the control using means and standard deviations for each group. All statistical analyses were performed using the Statistical Package for Social Science (SPSS) version 18 for Windows, and significance was defined as p o 0.05.
3. Results There were no significant differences in the socio-demographic data or lifestyle characteristics, such as the exercise and alcohol Table 1 Socio-demographic characteristics of subjects. Depression (N ¼ 30)
Control (N ¼ 30)
p
Sex Female Male
21 (70.0%) 9 (30.0%)
21 (70.0%) 9 (30.0%)
1.000
Age Years (mean7 S.D.)
65.2 7 4.8
65.2 7 4.8
1.000
Occupation Employed Unemployed
10 (33.3%) 20 (66.7%)
12 (40.0%) 18 (60.0%)
0.793
Education Z 18 years Z 16 years Z 12 years o 12 years
2 (6.7%) 7 (23.3%) 8 (26.7%) 13 (43.3%)
5 9 8 8
(16.7%) (30.0%) (26.7%) (26.7%)
0.159
Monthly earnings Z 7 million won Z 5 million won Z 3 million won o 3 million won
6 (20.0%) 2 (6.7%) 3 (10.0%) 19 (63.3%)
5 (16.7%) 4 (13.3%) 11 (36.7%) 10 (33.3%)
0.122
Marital status Married/living together Divorced/Separated/widowed
26 (86.7%) 4 (14.3%)
28 (93.3%) 2 (6.7%)
0.671
Comparison using Fisher's exact test and Mann-Whitney test.
Table 2 Comparisons of lifestyle factors and medication between depression and control group. Depression (N ¼ 30)
Control (N ¼ 30)
p
Alcohol r 1day/week 2–3 days/week Z 4 days/week
27 (90.0%) 2 (6.7%) 1 (3.3%)
25 (83.3%) 3 (10.0%) 2 (6.7%)
0.748
Smoking Never smoking Past smoking Current smoking
23 (76.7%) 5 (16.7%) 2 (6.7%)
24 (80.0%) 4 (13.3%) 2 (6.7%)
1.000
Exercise 6–7 days/week 3–5 days/week 1–2 days/week No regular exercise
4 (13.3%) 8 (26.7%) 8 (26.7%) 10 (33.3%)
8 9 7 6
(26.7%) (30.0%) (23.3%) (20.0%)
0.507
16 (53.3%)
12 (40.0%)
0.438
History of anti-diabetic medication 10 (33.3%)
4 (13.3%)
0.125
Metabolic syndrome
11 (36.7%)
0.004n
History of anti-hypertensive medication
n
Fisher's exact test, p o0.05.
22 (73.3%)
3
drinking/smoking habits, between the two groups (Tables 1 and 2). Blood pressure (depression group vs. control group: systolic 124.37 12.4 vs. 119.3714.5, respectively; diastolic 75.779.7 vs. 71.778.7, respectively), the mean body mass index (25.773.0 vs. 24.572.6, respectively), HbA1c level (6.070.6 vs. 6.071.0, respectively), total cholesterol level (192.3735.0 mg/dl vs. 196.3733.8 mg/dl, respectively), LDL level (121.2732.5 vs. 120.5727.7, respectively), HDL level (49.0711.0 vs. 59.5715.1, respectively) and TG level (130.0771.6 vs. 100.0745.9, respectively) were not significantly different between the two groups. The prevalence of metabolic syndrome (22 (73.3%) vs. 11 (36.7%), respectively, p¼ 0.004) was significantly higher in the depression group compared with the control group. Within the diagnostic criteria of metabolic syndrome, only the number of subjects with a high waist circumference was higher in the depression group compared with the control group (26 (86.7%) vs. 18 (60.0%), respectively, po0.05). There were no differences between the two groups in the frequency of diabetes mellitus or hypertension or the existence of thyroid nodule screening by ultrasound. The history of medication, such as antihypertensive drugs, hypoglycemic agents, and cholesterol-lowering drugs, was not different between the groups. In the HRV results, the depression group showed significantly lower VLF, LF, HF, and total power in the frequency domain compared with the control subjects except LF/HF. Significantly lower SDNN, RMSSD, and pNN50 in the time domains were identified in the depressive patients (Table 3). A comparison of HRV variables of the depression with the control showed large effect sizes for the differences in LF (Cohen's d¼1.43, po0.001), SDNN (Cohen's d¼1.29, po0.001), and pNN50 (Cohen's d¼1.32, po0.001). Medium effect size of differences was observed between the two groups in terms of HF (Cohen's d¼1.07, po0.001), VLF (Cohen's d¼1.05, po0.001), and RMSSD (Cohen's d¼ 0.76, p¼0.001). After obtaining the results, we made a further analysis of differences in HRV using the subjects who did not have metabolic syndrome because there was a significant difference in the frequency of metabolic syndrome between the two groups. We performed a non-parametric Mann–Whitney test using eight subjects in the depressed patient group and 19 subjects in the control group. In this analysis, the significant lowering of HRV was observed in VLF (p¼0.016), LF (p¼0.003), SDNN (p¼0.009), and pNN50 (p¼0.009); HF (p¼ 0.132) and RMSSD (p¼0.132) did not show the significant difference.
4. Discussion Patients with depression have a 2–4-fold higher risk of cardiac mortality compared with the general population (Glassman et al., 2003). The association between depression and CVD has been explained by several potential mechanisms. First, common lifestyle factors, such as lack of exercise and poor nutritional habits, as well as smoking and alcohol abuse, affect both illnesses (Glassman et al., 2003; Kemp and Quintana, 2013). Second, endocrine disturbances, such as hyperproduction of cortisol and an abnormality of the hypothalamus–pituitary–adrenal axis, may lead to cardiovascular problems in depressed patients (Glassman et al., 2003; Nemeroff and Goldschmidt-Clermont, 2012). Sustained somatic symptoms and physiological disturbances could increase the risk for CVD in depressed patients. Third, autonomic dysfunction may accelerate the progression of CVD and promote procoagulant and proinflammatory processes, thereby lowering the threshold of mental stressinduced ischemia and prolonging the QT interval (Nemeroff and Goldschmidt-Clermont, 2012). In this study, the MDD subjects showed significantly lower HRV indexes in the time and power/ spectrum domains compared with the control subjects. There were few differences in the health and cardiovascular-related factors
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
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Table 3 Comparisons of heart rate variability between depression and control group. Depression (N ¼ 30)
Control (N ¼ 30)
p value
Time domain HR (beat/min) SDNN (ms) RMSSD (ms) pNN50 (%)
68.27 12.4 25.2 7 10.0 16.2 7 10.0 84.0 7 13.8
62.17 7.8 39.3 7 11.7 23.3 7 8.3 66.0 713.5
0.096 p o 0.001n p ¼ 0.001n p o 0.001n
Frequency domain Ln Total Power Ln VLF Ln LF Ln HF LF nu (%) HF nu (%) LF/HF
6.17 0.8 5.5 7 0.8 4.2 7 0.8 4.17 1.2 50.8 7 6.5 49.27 6.5 1.2 7 0.7
7.0 70.6 6.5 7 0.8 5.2 7 0.7 5.17 0.7 50.5 7 4.1 49.5 74.1 1.0 70.2
p o 0.001n p o 0.001n p o 0.001n p o 0.001n 0.773 0.773 0.819
HRV, heart rate variability; HR, heart rate; SDNN, standard deviation of the normalto-normal interval; RMSSD, root mean square of differences of successive normalto-normal intervals; pNN50, no. pairs of adjacent normal-to-normal intervals differing by 4 50 ms in the entire recording/total no. normal-to-normal intervals; VLF, power in very low frequency range ( o 0.04 Hz); LF, power in low frequency range (0.04–0.15 Hz); power in high frequency range (0.15–0.4 Hz). n
Mann-Whitney test, p o0.001.
between the two groups. These findings suggest that low HRV indexes may be associated with MDD pathophysiology. Previous studies have provided evidence that depressive disorders are associated with a dysregulation of the ANS. A meta-analytical study showed that cardiac vagal control was lower in depressed individuals compared with non-depressed subjects in cardiovascularly compromised and healthy samples (Rottenberg, 2007). ANS dysfunction may be mediated by deficits in the central autonomic network, including the orbitofrontal and medial prefrontal cortices and the central nucleus of the amygdala that project to the hypothalamic and brainstem autonomic nuclei where sympathetic and parasympathetic efferents to the heart originate (Thayer and Siegle, 2002). Furthermore, comparative hypofunction of the right hemisphere and predominant activity of the left hemisphere have been associated with autonomic dysfunction in depressed subjects (Friedman, 1996), which suggests that cerebral dysfunction of the ANS network, rather than possible concomitant cardiovascular abnormalities, affects HRV dysfunction in depressed patients. This finding may explain why the two groups in this study showed limited differences in cardiac factors despite differences in HRV. Low HRV is a marker of CVD risk. The HRV index is associated with cardiovascular morbidity and mortality because it reflects the presence of systematic problems, including inflammatory-mediated atherosclerosis and ventricular fibrillation, compared with specific vascular occlusion or perfusion defects (Taylor, 2010). A previous study analyzed subjects with acute myocardial infarction who underwent HRV 1 year after a cardiovascular incident. Low HRV has been known to indicate a hypoactive parasympathetic (vagal) system and is associated with cognitive and affective dysregulation and psychological inflexibility (Kemp and Quintana, 2013; Reyes Del Paso et al., 2013). Vagal withdrawal (low HRV) is associated with negative social engagement according to the polyvagal theory (Porges, 2009). Based on the data that autonomic dysfunctions are commonly affected by depression and CVD, HRV findings may be a sensitive indicator of both illnesses. Depression could contribute to the development of brain-derived ANS dysfunction, thereby increasing the risk of major CVD in some patients. This study supports this hypothesis. We found no significant differences in cardiovascularrelated risk factors, such as blood pressure, body mass index, HbA1c levels, or lipid profiles, between the two groups. Only the prevalence of metabolic syndrome was higher in the depression
group compared with the control group. In healthy young adults, metabolic syndrome components have been associated with less favorable HRV profiles (Soares-Miranda et al., 2012). The HRV index changes with increasing age, with a trend toward a decrease in cardiac autonomic function (Kuo et al., 1999; Fukusaki et al., 2000). Consequently, the HRV index may be more valuable in older patients. Depression is one of the most prevalent mental disorders in old age (Luppa et al., 2012), and the symptoms and etiology of depression, including degeneration of the brain and atherosclerotic changes in vessels, in elderly patients are different compared with other age groups (Nobler et al., 1999). The vascular changes and ANS dysfunction are common to depression and CVD, which suggests that measuring HRV in elderly depressed patients to screen for the risk of CVD is important. It is known that HRV could be affected by the use of antidepressant treatments (Kemp et al., 2010). Degree of anticholinergic activity or serotonin effect on raphe nuclei in various antidepressants would partly affect in decreasing HRV (O'Regan et al., 2014). A study by O'Regan et al. even insisted that antidepressant effect might be more significant effect in lowering HRV. It was done with large number of subjects, but measuring of depression was only conducted by self-reporting questionnaire. Therefore interpretation of association with depression might have some limitation. Whereas we diagnosed the subjects by the structured interview, we think the association between HRV and depression should be more reliable in this study. Furthermore we have conducted this study on subjects who had not taken such medications, thus excluding any medication-induced effects. After excluding potential medication effects, it is evident that newly diagnosed elderly MDD patients have lower HRV indexes compared with control subjects, which suggest that HRV could be a significant predictive factor for future CVD morbidity. In this study, there was a higher frequency of metabolic syndrome in the MDD group compared with the control group. This finding may be because of a less healthy lifestyle, which is reflected by the degree of exercise and the eating habits in the MDD patients (Vancampfort et al., 2013). Metabolic syndrome is a significant risk factor of CVD. Low HRV may be an important predictor of MDD and CVD in elderly patients. It is possible that MDD in older patients further reduces HRV, which potentially contributes to an increased risk of cardiovascular morbidity and mortality. The use of the HRV test in old age MDD patients to identify the risk of potential CVD could be merited. Strengths of this study were as in the followings. First, the study contained detailed medical information including blood test that could influence the HRV results. There was no difference between two groups. Second, enrolled elderly depressive subjects were diagnosed for the first time using the structured interview, and free from any psychotropics such as antidepressants and benzodiazepines throughout their lifetime. It also has the following limitations. First, the number of enrolled subjects was relatively small; therefore, it might be difficult to generalize the results. Second, patients taking anti-diabetic or anti-hypertensive medications were included; however, there were no significant differences between the two groups. In addition, metabolic syndrome was different in the two groups, which might affect the results. However, further analysis using the subjects without metabolic syndrome showed that the statistically significant changes of HRV between the two groups still remained among most of the variables except HF and RMSSD. This indicates that the metabolic syndrome did not affect the overall findings of this study. In conclusion, this study showed lower HRV in older patients who were newly diagnosed with depression without a history of CVD compared with the control subjects. HRV is an easily accessible and meaningful measure of the risk of CVD. Future longitudinal studies should determine whether patients with MDD develop CVD.
Please cite this article as: Ha, J.H., et al., Short-term heart rate variability in older patients with newly diagnosed depression. Psychiatry Research (2015), http://dx.doi.org/10.1016/j.psychres.2015.02.005i
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