| Article Access Statistics|
| Viewed||1831 |
| Printed||58 |
| Emailed||0 |
| PDF Downloaded||229 |
| Comments ||[Add] |
| Cited by others ||8 |
Click on image for details.
BRIEF RESEARCH COMMUNICATION
|Year : 2014
: 56 | Issue : 4 | Page
|Cardiac risk factors and metabolic syndrome in patients with schizophrenia admitted to a general hospital psychiatric unit
Sandeep Grover1, Naresh Nebhinani2, Subho Chakrabarti1, Ajit Avasthi1, Debasish Basu1, Parmanand Kulhara1, Surendra Kumar Mattoo1, Savita Malhotra1
1 Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, Punjab, India
2 Department of Psychiatry, AIIMS, Jodhpur, Rajasthan, India
Click here for correspondence address and
|Date of Web Publication||8-Dec-2014|
| Abstract|| |
Objective: The study aimed to evaluate the prevalence of cardiovascular risk (CVR) factors and metabolic syndrome (MS) in patients with schizophrenia.
Materials and Methods: By consecutive sampling, 143 patients (of age ≥ 20 years), out of total 159 patients with schizophrenia admitted to the inpatient unit were evaluated for the coronary heart disease (CHD) risk as per Framingham (10-year all CHD events) function/risk equation and systematic coronary risk evaluation (SCORE) - 10-year cardiovascular mortality risk (CMR). Prevalence of MS was estimated by using the consensus definition.
Results: Fifty-two (36.4%) patients fulfilled the criteria for MS. 10-year CHD risk was 1.65%, and 10-year CMR was 1.39%. Compared to females, males had higher Framingham score (1.96 ± 2.74 vs. 1.09 ± 0.41, U value 1987.5*, P < 0.05).
Conclusion: Patients of schizophrenia have a high prevalence of MS and CVR factors. Hence, there is a need to screen the patient of schizophrenia for the same and manage the same as early as possible during the course of illness.
Keywords: Cardiovascular risk, morbidity, schizophrenias
|How to cite this article:|
Grover S, Nebhinani N, Chakrabarti S, Avasthi A, Basu D, Kulhara P, Mattoo SK, Malhotra S. Cardiac risk factors and metabolic syndrome in patients with schizophrenia admitted to a general hospital psychiatric unit. Indian J Psychiatry 2014;56:371-6
|How to cite this URL:|
Grover S, Nebhinani N, Chakrabarti S, Avasthi A, Basu D, Kulhara P, Mattoo SK, Malhotra S. Cardiac risk factors and metabolic syndrome in patients with schizophrenia admitted to a general hospital psychiatric unit. Indian J Psychiatry [serial online] 2014 [cited 2022 Nov 28];56:371-6. Available from: https://www.indianjpsychiatry.org/text.asp?2014/56/4/371/146520
| Introduction|| |
Several studies suggest that patients with serious mental illness die about 20 years earlier than the general population, ,, with cardiovascular disease being the leading cause of mortality in patients with schizophrenia. ,,,, A population-based study in a nationally representative sample from United States reported two-fold increase in the risk of mortality in persons with mental disorders, with an average life span of 8.2 years less than the rest of the population, with the majority of deaths (95.4%) attributed to medical causes rather than unnatural causes.  Relative risk of all-cause mortality for patients with schizophrenia has been reported to be 2.6. 
Poor health status, socioeconomic deprivation, adverse health behaviors, and poor quality of medical care are important factors associated with high mortality in subjects with severe mental illness.  Despite this these patients usually receive inconsistent and insufficient physical monitoring and management. 
Recently, some studies have assessed the prevalence of cardiovascular risk (CVR) factors in patients with schizophrenia and have calculated the 10-year risk of having a cardiovascular event using various parameters such as Framingham risk equation score , and systematic coronary risk evaluation (SCORE) function indicating 10-year cardiovascular mortality risk (CMR).  The Framingham risk equation score, provides a validated calculation of 10-year risk of development of coronary heart disease (CHD) , and the SCORE function indicates the 10-year CMR.  Recognition of these factors is emphasized as these are modifiable risk factors, which if identified and managed properly in time can contribute to a reduction in cardiovascular mortality.
Few studies have assessed the future risk of cardiovascular events in the patient of schizophrenia. Correll et al.  reported a 10-year CHD risk of 6.5% for inpatients with schizophrenia (n = 111). They also reported that 23.4% of patients with schizophrenia had a CHD risk more than equal to 10%. In the Clinical Antipsychotic Trials of Intervention Effectiveness study, it was observed that patients with schizophrenia had significantly higher 10-year risk of CHD than the general population (9.4% vs. 6.3% in males and 7% vs. 4.2% in females).  These findings were replicated in a later study too.  Other studies reported 10-year CHD risk of 6.5-7.2% and CMR to be 0.9% in patient with schizophrenia , with high/very high risk of CHD (≥10%) in 22-23% patients with schizophrenia , and high CMR risk (≥5%) in 6.5-8% patients with schizophrenia. ,
Although several studies have evaluated the prevalence of MS in patients with schizophrenia in India,  no study from India has evaluated the prevalence of cardiovascular factors in patients with schizophrenia as Framingham function for development of CHD and the SCORE function - 10-year CMR. The present study aimed at estimating the prevalence of CVR factors as the Framingham function for development of CHD and the SCORE function - 10-year CMR, in patients with schizophrenia.
| Materials and methods|| |
The study was approved by the Ethics Review Committee of the Institute. Informed consent was sought from all patients who were part of this study. If the patient was incompetent on account of the severity of illness to provide informed consent, the primary caregiver staying with the patient was approached for the informed consent. The study was carried out at the inpatient unit of a multi-specialty tertiary-care hospital in North India. All consecutive patients (aged 20 or above), admitted to the inpatient unit from January 2009 to December 2011 and diagnosed with schizophrenia according to the International Classification of Diseases - Classification of Mental and Behavioral Disorders - Clinical Descriptions and Diagnostic Guidelines 10 th revision  were invited to participate in the study. Sociodemographic and clinical details of all subjects were recorded in structured formats. Patients were enrolled in the study during their first admission in the study period.
Data pertaining to the prevalence of metabolic syndrome in a subgroup of this cohort was published earlier.  Patients found to have metabolic abnormalities, and high CVR factors were informed about the same and they were provided information about the need for proper diet and regular exercise, and were referred to specialist care whenever required.
Calibrated scales were used to measure body weight in kilogram (kg) and height in centimeters (cm). Waist circumference was measured in centimeters (cm), at a point midway between the inferior costal margin and the superior iliac crest, at the end of the normal expiration while standing. By using standard mercury manometer at least two readings at 5-min intervals, were taken to measure the blood pressure (BP) in the supine position. If BP was found to be high (≥140/90) then a third reading after 30 min was obtained; the lowest of these readings was taken. Fasting venous blood sample was collected under aseptic condition to estimate fasting blood sugar, triglycerides (TGA) and high-density lipoprotein (HDL).
Metabolic syndrome was defined by the consensus definition, , according to which presence of abdominal obesity is no more a mandatory criterion for defining MS and presence of any of three of five risk factors indicates the diagnosis of MS. For waist circumference, country-specific cut offs have been suggested. 
Ten years coronary heart disease risk calculation
Ten years CVR was evaluated on the basis of Framingham risk equation  and SCORE function.  Both of these methods are mathematical probability models which were developed by using multivariate analysis on the basis of findings of follow-up studies of individuals from the general populations who had fatal or nonfatal CHD event in relation to the individual risk factors. Framingham risk equation  evaluates the chances of developing cardiovascular disease. Framingham risk score provides an estimate of the chances that a person will develop cardiovascular disease over a specified period, usually 10-year. It includes fatal and nonfatal events such as angina, myocardial infarction, stroke, other type of coronary ischemia, congestive heart failure, intermittent claudication or peripheral arterial ischemia. The score is estimated on the basis of age, gender, total cholesterol, HDL cholesterol, systolic arterial pressure, diabetes mellitus and smoking habit. On the basis of the Framingham risk score, CHD risk is estimated in terms of percentage. Higher percentage score indicates higher risk and the percentage scores are categorized arbitrarily into as low risk (<10%), and high risk (>10%). The SCORE function  estimates the risk for CVM risk (including coronary death, sudden death, stroke, aortic aneurism, and heart failure) within 10-year. It takes into account the age, gender, total cholesterol, HDL cholesterol, systolic arterial pressure, and smoking habit. Both Framingham risk score and SCORE function can be calculated by using various websites (Framingham risk equation - [http://www.framingham heartstudy.org/risk/coronary.html] and SCORE function at website [https://escol.escardio.org/heartscore/calc.aspx?model = europehigh]). In this study, patients were classified according to the probability of presenting "very high/high" CHD risk if they had Framingham function of ≥10%  and "high" CMR risk if they had SCORE ≥5%. , It has been suggested that for patients with diabetes mellitus, the SCORE function generated by the equations be multiplied by two for men and four for women. ,
The SPSS version 14.0 for Windows (Chicago, Illinois, USA) was used for analysis. Frequencies with percentages were calculated for nominal and ordinal variables and mean, and standard deviation were calculated for continuous variables. Chi-Square test and t-test were used for comparisons. For variables with a skewed distribution, nonparametric tests like Mann-Whitney U-test and Fisher exact test were used for comparison.
| Results|| |
During the study period of 3 years (January 2009-December 2011), 560 patients were admitted to the inpatient unit. A total of 159 (28.4%) patients were diagnosed with schizophrenia and of these 143 patients were aged 20 years or above. All of them/their caregivers provided informed consent to participate in the study and formed the study cohort.
Sociodemographic and clinical profile of the sample
As shown in [Table 1], the mean age of the sample was 31 years. Males formed about two-third of the sample. About three-fourth of the patient's belonged to urban locality and more than half belonged to nuclear families. Nearly one-third patients were married and were on employment prior to admission to the inpatient unit.
|Table 1: Sociodemographic and clinical profile of the study sample (n=143)|
Click here to view
The mean duration of illness was 69 months. Paranoid schizophrenia (54.5%) formed the most-common diagnostic subtype, and this was followed by undifferentiated (35.7%) subtype. Olanzapine was the most commonly prescribed antipsychotic [Table 1]. Very few patients were receiving antidepressants and mood stabilizers along with the antipsychotic medication. Most commonly prescribed benzodiazepines were clonazepam and lorazepam.
Cardiovascular risk factors
Prevalence of various CVR factors is shown in [Table 2]. The most commonly present CVR factor was low HDL level, followed by hyperglycemia, high diastolic pressure, being a smoker and high cholesterol level.
Framingham score for the study sample was 1.65, and only 2% of the sample was found to have very high/high CHD risk (≥10). SCORE value was 1.39, and only 2% of the sample had high CMR risk (≥5).
Compared to females, significantly higher proportion of males were smoker (23.8% vs. 7.8%, χ2: 5.69*, P < 0.05), and had higher Framingham score (1.96 ± 2.74 vs. 1.09 ± 0.41, U value 1987.5*, P < 0.05).
Details of the anthropometric findings and physical examination findings and biochemical profile are shown in [Table 3]. One-third of the study sample (36.4%) was found to have MS. Increased waist circumference was the most common abnormality, followed by low HDL levels, high TGA levels, increased fasting glucose level and abnormal BP was the least common abnormality noted. Compared to males, significantly greater proportion of females had low HDL levels (P < 0.001).
| Discussion|| |
Findings with respect to CHD risk (Framingham score) for patients with schizophrenia in the present study is less than that reported from the West (6.5-11.3%). ,, The major reason for this difference could be a lower age and lower prevalence rate of smoking in our cohort compared to those evaluated in studies from the West, , in which the mean age of the study groups was 40.7 years , and the percentage of smokers varied from 53.7  to 58.  This findings also suggest that age is an important variable which influences the Framingham risk score. However, the Framingham score obtained in the present study was higher than that reported in patients with first episode psychosis (1.65 vs. 1).  This suggests that possibly with age and treatment the CHD risk increases in patients with schizophrenia.
Studies from different ethnic groups and that from India have reported prevalence rate of MS to be 9-68% in patients with schizophrenia.  In the present study, 36.4% of patients had MS, which is in the above reported range. However, the prevalence of MS in the patients with schizophrenia in the present study is significantly higher in comparison to findings reported in drug-naïve schizophrenia patients from India ,,, this possibly suggests that treatment with antipsychotic contribute to the development of MS in patients with schizophrenia. Greater proportion of females had low HDL levels which is similar to the finding of an earlier study in drug-naïve subjects with schizophrenia. 
When we compare the rate of MS as reported in most of the studies from the West, which have assessed CHD risk and CMR risk in patients of schizophrenia, ,,,, the rate of MS in our study are much higher. This indicates a disparity between the prevalence of MS and CHD rates compared to findings from the West. One possible reason for this discordance could be due to use of ethnic specific cut-offs for estimation of MS, compared to the methods used for assessment of CHD risk and CMR risk, which are based on the Western population. Therefore, the CHD risk and CMR risk estimated in this study serve as crude composite indices of the various factors affecting CVR factors rather than as accurate estimates of absolute risk, similar to an earlier study in the diabetic population of the capital of India.  The disparity in the MS and CHD risk also suggests that MS is independent of age, whereas Framingham and SCORE are age dependent, which may be a limitation of these indices.
In a previous study from our center, we evaluated the CHD risk and CMR risk in patients with bipolar disorder. In this study, 10-year CHD risk was found to be 3.36% and 10-year CMR was 1.73%. In terms of severity of risk, one-tenth (10.7%) of the sample was found to have very high/high CHD risk (≥10) and 6.45% of the sample had high CMR risk (≥5).  When we compare the findings of the present study with patients of bipolar disorder, it is apparent that the CHD risk and CMR risk are lower in patients with schizophrenia. However, this finding must be interpreted in the light of the fact that the mean age of patients with bipolar disorder was significantly higher than the schizophrenia cohort. To overcome this limitation, when we took an age and gender-matched group of patients with bipolar disorder and compared with patients of schizophrenia, no significant difference was noted between the two groups in terms of CMR risk, very high/high CHD risk (≥10) and high CMR risk (≥5). However, the CHD risk was still higher for patients with bipolar disorder. Accordingly our findings suggest that the risk of CMR is similar in patients with schizophrenia and bipolar disorder. However, the risk of CHD is higher in patients with bipolar disorder.
The limitations of the present study are cross-sectional design, inclusion of inpatients only, which are usually more severely ill and lack of healthy control group. The relationship between the disease severity and impact of medication on the prevalence of MS was not studied. We also did not assess the effect of lifestyle factors, physical activity, dietary factors and family history of diabetes, all of which can confound the prevalence of MS. The study was also constrained by the lack of validation of the CVR scores used for 10-year risk projection in the Indian population (the Framingham risk score is based on data from Whites, whereas SCORE was developed for European populations).  In future studies with the longitudinal design should try to overcome the limitations of this study.
| Conclusion|| |
To conclude, the present study suggests that about one-third of patients diagnosed with schizophrenia have MS. Among the various subcomponents of MS, increased waist circumference is the most common abnormality. The CVR in terms of CHD and CMR in patients with schizophrenia in patients from India may be less than that reported from the West. However, although the CVR factors in Indian patients is less than that reported from the west, this risk cannot be ignored. These findings suggest that patients of schizophrenia should be closely monitored for the CVR factors, especially the waist circumference and BP, which can be done easily without any extra cost. Further, the CVR factors must be taken into account, while choosing various antipsychotics medications which are known to be associated with higher weight gain and metabolic abnormalities. There is a need to increase the awareness among mental health professional about the various cardio-metabolic risk factors in patients with schizophrenia and how to manage the same. Further, there is a need for active collaboration between the treating psychiatrist and the cardiologists to address the various CVR factors to reduce the future risk of cardiovascular morbidity and CVM.
| References|| |
Tiihonen J, Lönnqvist J, Wahlbeck K, Klaukka T, Niskanen L, Tanskanen A, et al
. 11-year follow-up of mortality in patients with schizophrenia: A population-based cohort study (FIN11 study). Lancet 2009;374:620-7.
Hennekens CH, Hennekens AR, Hollar D, Casey DE. Schizophrenia and increased risks of cardiovascular disease. Am Heart J 2005;150:1115-21.
Osby U, Correia N, Brandt L, Ekbom A, Sparén P. Mortality and causes of death in schizophrenia in Stockholm county, Sweden. Schizophr Res 2000;45:21-8.
Ryan MC, Thakore JH. Physical consequences of schizophrenia and its treatment: The metabolic syndrome. Life Sci 2002;71:239-57.
Lawrence DM, Holman CD, Jablensky AV, Hobbs MS. Death rate from ischaemic heart disease in Western Australian psychiatric patients 1980-1998. Br J Psychiatry 2003;182:31-6.
Allison DB, Newcomer JW, Dunn AL, Blumenthal JA, Fabricatore AN, Daumit GL, et al.
Obesity among those with mental disorders: A National Institute of Mental Health meeting report. Am J Prev Med 2009;36:341-50.
Druss BG, Zhao L, Von Esenwein S, Morrato EH, Marcus SC. Understanding excess mortality in persons with mental illness: 17-year follow up of a nationally representative US survey. Med Care 2011;49:599-604.
Millar HL. Development of a health screening clinic. Eur Psychiatry 2010;25 Suppl 2:S29-33.
Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837-47.
D'Agostino RB, Russell MW, Huse DM, Ellison RC, Silbershatz H, Wilson PW, et al.
Primary and subsequent coronary risk appraisal: New results from the Framingham study. Am Heart J 2000;139:272-81.
Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al.
Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur Heart J 2003;24:987-1003.
Correll CU, Frederickson AM, Kane JM, Manu P. Equally increased risk for metabolic syndrome in patients with bipolar disorder and schizophrenia treated with second-generation antipsychotics. Bipolar Disord 2008;10:788-97.
Goff DC, Sullivan LM, McEvoy JP, Meyer JM, Nasrallah HA, Daumit GL, et al.
A comparison of ten-year cardiac risk estimates in schizophrenia patients from the CATIE study and matched controls. Schizophr Res 2005;80:45-53.
Mackin P, Bishop D, Watkinson H, Gallagher P, Ferrier IN. Metabolic disease and cardiovascular risk in people treated with antipsychotics in the community. Br J Psychiatry 2007;191:23-9.
Bobes J, Arango C, Aranda P, Carmena R, Garcia-Garcia M, Rejas J, et al.
Cardiovascular and metabolic risk in outpatients with schizophrenia treated with antipsychotics: Results of the CLAMORS Study. Schizophr Res 2007;90:162-73.
Bernardo M, Cañas F, Banegas JR, Casademont J, Riesgo Y, Varela C; RICAVA Study Group. Prevalence and awareness of cardiovascular risk factors in patients with schizophrenia: A cross-sectional study in a low cardiovascular disease risk geographical area. Eur Psychiatry 2009;24:431-41.
Malhotra N, Grover S, Chakrabarti S, Kulhara P. Metabolic syndrome in schizophrenia. Indian J Psychol Med 2013;35:227-40.
World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders-Clinical Descriptions and Diagnostic Guidelines. Geneva: WHO; 1992.
Grover S, Nebhinani N, Chakrabarti S, Avasthi A, Kulhara P, Basu D, et al.
Comparative study of prevalence of metabolic syndrome in bipolar disorder and schizophrenia from North India. Nord J Psychiatry 2014;68:72-7.
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001;285:2486-97.
Alberti KG, Zimmet P, Shaw J. Metabolic syndrome - a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet Med 2006;23:469-80.
Hartz I, Njølstad I, Eggen AE. Does implementation of the European guidelines based on the SCORE model double the number of Norwegian adults who need cardiovascular drugs for primary prevention? The Tromsø study 2001. Eur Heart J 2005;26:2673-80.
Nagpal J, Bhartia A. Cardiovascular risk profile of subjects with known diabetes from the middle-and high-income group population of Delhi: The DEDICOM survey. Diabet Med 2008;25:27-36.
Daumit GL, Goff DC, Meyer JM, Davis VG, Nasrallah HA, McEvoy JP, et al.
Antipsychotic effects on estimated 10-year coronary heart disease risk in the CATIE schizophrenia study. Schizophr Res 2008;105:175-87.
Phutane VH, Tek C, Chwastiak L, Ratliff JC, Ozyuksel B, Woods SW, et al.
Cardiovascular risk in a first-episode psychosis sample: A 'critical period' for prevention? Schizophr Res 2011;127:257-61.
Grover S, Nebhinani N, Chakrabarti S, Parakh P, Ghormode D. Metabolic syndrome in antipsychotic naïve patients diagnosed with schizophrenia. Early Interv Psychiatry 2012;6:326-31.
Padmavati R, McCreadie RG, Tirupati S. Low prevalence of obesity and metabolic syndrome in never-treated chronic schizophrenia. Schizophr Res 2010;121:199-202.
Saddichha S, Ameen S, Akhtar S. Incidence of new onset metabolic syndrome with atypical antipsychotics in first episode schizophrenia: A six-week prospective study in Indian female patients. Schizophr Res 2007;95:247.
Saddichha S, Manjunatha N, Ameen S, Akhtar S. Metabolic syndrome in first episode schizophrenia - A randomized double-blind controlled, short-term prospective study. Schizophr Res 2008;101:266-72.
Grover S, Nebhinani N, Chakrabarti S, Avasthi A, Basu D, Kulhara P, et al.
Cardiovascular risk factors among bipolar disorder patients admitted to an inpatient unit of a tertiary care hospital in India. Asian J Psychiatr 2014;10:51-5.
Dr. Sandeep Grover
Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh - 160 012, Punjab
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Physical fitness in patients with bipolar disorder compared with a population-based sample
| ||Ali Kheradmand, Amir Hossein AbediYekta, Hannaneh Safarzadeh, Maryam Ganjalikhani |
| ||Health Science Reports. 2022; 5(2) |
|[Pubmed] | [DOI]|
||Correlates of Metabolic Syndrome in Patients with Schizophrenia: An Exploratory Study
| ||Naresh Nebhinani, Swapnil Tripathi, Navratan Suthar, Vrinda Pareek, Priyanka Purohit, Praveen Sharma |
| ||Indian Journal of Clinical Biochemistry. 2022; 37(2): 232 |
|[Pubmed] | [DOI]|
||Prevalence and predictors of metabolic syndrome in patients with schizophrenia and healthy controls: A study in rural South Indian population
| ||Vikram Singh Rawat, Suhas Ganesh, Somashekar Bijjal, K. Shanivaram Reddy, Vikas Agarwal, Renuka Devi, Chennaveerachari Naveen Kumar, Rita Christopher, Jagadisha Thirthalli |
| ||Schizophrenia Research. 2018; 192: 102 |
|[Pubmed] | [DOI]|
||Cause, consequence or coincidence: The relationship between psychiatric disease and metabolic syndrome
| ||Gordon Ferns |
| ||Translational Metabolic Syndrome Research. 2018; 1: 23 |
|[Pubmed] | [DOI]|
||No Effect of Adjunctive Minocycline Treatment on Body Metabolism in Patients With Schizophrenia
| ||Fang Liu, Liqin Xie, Bingkui Zhang, Ye Ruan, Yong Zeng, XiuFeng Xu, Jingping Zhao, Xiaoduo Fan |
| ||Journal of Clinical Psychopharmacology. 2018; 38(2): 125 |
|[Pubmed] | [DOI]|
||Prevalence and predictors of metabolic syndrome in schizophrenia patients from Assam
| ||Dulmoni Das, Kaustubh Bora, Banti Baruah, Gitumoni Konwar |
| ||Indian Journal of Psychiatry. 2017; 59(2): 228 |
|[Pubmed] | [DOI]|
||Prevalence and determinants of metabolic syndrome in patients with schizophrenia: A systematic review and meta-analysis of Indian studies
| ||Suhas Ganesh, Abhishekh Hulegar Ashok, Chennaveerachari Naveen Kumar, Jagadish Thirthalli |
| ||Asian Journal of Psychiatry. 2016; 22: 86 |
|[Pubmed] | [DOI]|
||Metabolic syndrome and central obesity in depression: A cross-sectional study
| ||Anju Agarwal, Manu Agarwal, Kabir Garg, PronobKumar Dalal, JitendraKumar Trivedi, JS Srivastava |
| ||Indian Journal of Psychiatry. 2016; 58(3): 281 |
|[Pubmed] | [DOI]|