| Abstract|| |
Background: Metabolic syndrome in individuals taking second-generation antipsychotics is thought to be mediated by antipsychotic-induced weight gain. However, recent literature challenges this notion, and theoretically, it may also be mediated through obstructive sleep apnea (OSA). This study explores the contribution of OSA in antipsychotic-induced metabolic syndrome.
Materials and Methods: Forty-three participants suffering from schizophrenia spectrum disorder and major depressive disorder, taking second-generation antipsychotics were included in this study. Treatment history was taken in detail, and lifetime exposure to antipsychotics was converted to olanzapine-equivalent doses. Physical characteristics were noted. OSA was screened through the Hindi version of Berlin Questionnaire. Plasma glucose, serum total cholesterol, serum high-density lipoprotein, and serum triglyceride were measured after 12-h fasting. Adult treatment Panel-III criteria were used to diagnose metabolic syndrome.
Results: Gender distribution was comparable in the study sample. About 27% had continuous illness, 25.6% of participants had metabolic syndrome, and 20.9% were at high risk for sleep apnea. Participants with and without metabolic syndrome were comparable with regard to demographic variables, duration of illness, and lifetime exposure to antipsychotics. Logistic regression depicted that OSA (odds ratio [OR] = 15.09), waist circumference (OR = 1.15), and fasting plasma glucose (OR = 1.21) increased the risk of metabolic syndrome.
Conclusion: Results of the present study suggest that metabolic syndrome in participants taking second-generation antipsychotics is mediated through OSA.
Keywords: Antipsychotics, sleep apnea, metabolic syndrome
|How to cite this article:|
Rohatgi R, Gupta R, Ray R, Kalra V. Is obstructive sleep apnea the missing link between metabolic syndrome and second-generation antipsychotics: Preliminary study. Indian J Psychiatry 2018;60:478-84
|How to cite this URL:|
Rohatgi R, Gupta R, Ray R, Kalra V. Is obstructive sleep apnea the missing link between metabolic syndrome and second-generation antipsychotics: Preliminary study. Indian J Psychiatry [serial online] 2018 [cited 2019 Dec 10];60:478-84. Available from: http://www.indianjpsychiatry.org/text.asp?2018/60/4/478/246183
| Introduction|| |
Metabolic syndrome has been found to be associated with schizophrenia and mood disorders across a number of studies, both in drug naïve as well as patients who have received medications but with conflicting results.,,,,,, Available literature suggests higher prevalence of metabolic syndrome in drug-naïve participants with schizophrenia compared to control population with average prevalence of 10.8%. However, this prevalence increases largely in participants taking second-generation antipsychotics (SGAs) and ranges between 11% and 69%. This wide variation in the prevalence of metabolic syndrome could be attributed to various factors, namely diagnostic criteria used to ascertain metabolic syndrome, study design – longitudinal or cross-sectional, molecule included in the study, and duration of treatment. For example, Adapted Treatment Panel-IIIa criteria marginally overestimated metabolic syndrome compared to the International Diabetes Federation criteria, 43.9% versus 40.1%, respectively. Longitudinal studies have shown that nearly half of the participants taking SGAs to develop metabolic syndrome by the end of 3 months, and this risk is higher among patients taking clozapine and olanzapine. On the other hand, cross-sectional studies have reported lesser prevalence., Thus, the prevalence of metabolic syndrome in participants taking SGAs appears to vary across studies.
In addition, a higher risk for obstructive sleep apnea (OSA) among patients suffering from psychiatric disorders has been reported across a number of studies and reviews. Previous studies have suggested that 15%–48% of participants having schizophrenia, 21%–43% of participants with bipolar disorder, and 11%–18% of participants with recurrent depression have comorbid OSA. Many of these participants have been prescribed SGAs that cause weight gain. Weight gain is considered a risk factor for the development of OSA in these patients and could be one reason for the higher prevalence of OSA in patients suffering from schizophrenia and bipolar disorder compared to depression., Besides weight gain, aging, and male gender have been found to be associated with OSA in these groups.,, Thus, antipsychotic-induced weight gain has been the most consistent factor leading to OSA in these studies.
Finally, participants with OSA have higher odds to have metabolic syndrome (odds ratio [OR] 2.87 and 2.56 in cross-sectional and case–control studies, respectively) even when they have not taken SGAs in their life. The reverse association is also true, and 90% of participants having metabolic syndrome have been reported to have comorbid OSA. Thus, we have robust evidence regarding the coexistence of metabolic syndrome and OSA.
Despite having so much of literature, multiple gaps exist in our knowledge linking the second-generation antipsychotics with metabolic syndrome. For example, conflicting evidence and wide variation in the prevalence of metabolic syndrome in participants exposed to SGAs questions the causation between the two. Second, knowledge regarding the predictors of metabolic syndrome in patients exposed to SGAs is limited and conflicting. Third, as discussed for metabolic syndrome, literature is also not uniform regarding the prevalence of OSA among participants suffering from schizophrenia spectrum disorders and bipolar disorders. Finally, the association between SGAs and metabolic syndrome, between SGAs and OSA, and between OSA and metabolic syndrome has been shown across various studies. However, to the best of our knowledge, whether metabolic syndrome in participants taking the second-generation antipsychotics is mediated through OSA has never been examined. Thus, we hypothesized that in a naturalistic setting, metabolic syndrome in participants exposed to SGAs could be mediated through OSA after controlling for confounding variables. With this primary objective, the present study was planned. Secondary objectives were to find the prevalence of metabolic syndrome and OSA in participants taking SGAs.
| Materials and Methods|| |
This cross-sectional, observational, naturalistic study was done after seeking approval from the Institutional Ethics Committee using convenient sampling. Participants meeting criteria for schizophrenia spectrum disorder and major depressive disorder with psychotic features were screened for the prescription of SGAs. Participants of either gender who were taking second-generation antipsychotics for at least past 3 months and falling in age bracket of 18–60 years were requested to participate in the study after explaining the rationale. However, pregnant females; participants who were not able to provide information; poor adherence to medication (<75%) (see below); those who were using or have used any substance of abuse except nicotine; taking valproate or stimulant or dopaminergic medication; having any other neuropsychiatric disorder, for example, dementia, Parkinson's disease; and finally other medical disorders, namely hypothyroidism, Cushing's disease, polycystic ovarian disease, and congestive heart failure were not included in the study. The diagnosis of these disorders was based on history, examination, and medical records.
Written informed consent was taken from those who agreed to participate in this study. Details regarding their demographic data, details regarding the illness, for example, total duration of illness, number of episodes, and course of illness were ascertained through history and medical records. Records were screened for systemic hypertension and diabetes mellitus. Systemic hypertension was defined as blood pressure of >130/85 mmHg during examination or previous medical record showing these values on at least two different occasions or when the patient reported taking antihypertensive medications. Diabetes mellitus was defined as fasting blood sugar >126 mg% or when the participant's previous medical records depicted the diagnosis of diabetes mellitus or they were already taking hypoglycemic medications.
Lifetime exposure to second-generation antipsychotics
Past treatment records were analyzed. Details regarding the SGA molecules that have been prescribed, their doses (in milligrams) and duration of therapy (in days) were noted. Then, the cumulative dose for second-generation antipsychotic molecule was calculated (using formula dose per day multiplied by days taken). If a patient had taken more than one SDA, the cumulative dose for each molecule was calculated separately. After calculation of cumulative doses for each molecule, they were changed to olanzapine equivalents. In this manner, lifetime exposure to each antipsychotic (in milligrams olanzapine equivalents) was calculated. After this, olanzapine equivalents of all second-generation antipsychotics for each participant were added to find the lifetime total olanzapine equivalent exposure to SGA (e.g., SDA1 + SDA2 + SDA3….).
Adherence to medications
Adherence to medication was assessed through triangulation of information from refill of prescriptions, subjective information regarding adherence, and corroborative information from the caregiver as there is no gold standard method for the assessment of adherence.
Daily physical activity
It was assessed using the International Physical Activity Questionnaire. This questionnaire assesses activities in five major areas of life – occupation, transportation, household work, physical activity during leisure time, and finally, time spent sitting. Actions from each domain are further divided into vigorous and moderate activities. It contains total of 27 items that measure physical activity over a span of past 7 days. Scores from all items are added, and the result is used to categorize the participant in one of the three categories – low, moderate, or high activity using ordinal scale. This has been found to provide good measure of average daily physical activity across countries; this has also been validated against an accelerometer. This questionnaire was translated and validated to Hindi (unpublished data).
Obstructive sleep apnea
Participants were screened for OSA using a validated questionnaire – Berlin questionnaire. This questionnaire divides the participants according to the risk of having OSA – low and high. It's Hindi translation was used in the present study which has been validated against in-laboratory attended polysomnography. Hindi version was found to have 89% sensitivity; it had positive predictive value of 0.87 and negative predictive value of 0.63 for classifying participants in low risk and high risk for OSA.
Blood pressure was measured from the left arm in sitting position at least after 20 minutes of rest using a sphygmomanometer (Welch Allyn® Tycos 509). Height and weight were measured using a standard stadiometer and an electronic weighing scale. Sphygmomanometer and weighing scale underwent periodic calibrations as a policy of the institute. Waist circumference was measured using a nonelastic tape at the level of iliac crests after fasting of 12 h as per the Adult Treatment Panel III guidelines., Waist-hip ratio was measured following standard guidelines using a nonelastic measuring tape.
Blood sample was drawn from the antecubital vein using aseptic precautions after overnight fasting of around 12 h. Plasma glucose estimation was done using glucose oxidase-peroxidase method using DxC-800 autoanalyzer (Beckman Coulter Inc., USA). Total cholesterol and triglycerides in serum were estimated using enzymatic endpoint method, and serum high-density lipoprotein (HDL) using polymer polyanion method using DxC-800 autoanalyzer (Beckman Coulter Inc., USA).,
It was diagnosed following Adult Treatment Panel III criteria from National Cholesterol Education Program. These criteria include central obesity (waist circumference >100 cm in males and >88.5 cm in females); elevated blood sugar (fasting plasma glucose >100 mg% or patient taking treatment for diabetes); systemic hypertension (systolic blood pressure >130 mmHg or diastolic blood pressure >85 mmHg or patient undergoing treatment for hypertension); hypertriglyceridemia (serum triglycerides >150 mg% or patient taking treatment for the same), or low HDL (serum HDL <40 mg% in males and <50 mg% in females or taking treatment for the same). If a participant has fulfilled any three out of these five criteria, he was considered having metabolic syndrome.
Statistical analysis was done using the Statistical Package for the Social Sciences (SPSS v. 21.0; IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.). Descriptive statistics were calculated. Chi-square was used to compare proportions between two groups. For the normally distributed continuous variables, the mean was compared using independent sample t-test. The Mann–Whitney U test was used to compare continuous variable where distribution was skewed. Binary logistic regression analysis was done using forward stepwise method to develop a model for the factors contributing to metabolic syndrome in this sample. For this model, categories of moderate and high physical activity were combined in one category.
| Results|| |
A total of 63 participants were screened, out of which 20 were excluded from the study. Reasons for exclusion included pregnancy (1 participant), poor adherence to medications (5 participants), coprescription of valproate (10 participants), and falling out of age bracket (4 participants). Clinical characteristics were comparable between included as well excluded participants [Table 1].
|Table 1: Comparison of participants included and excluded from the study|
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Among subjects included in the study, about 20.9% of participants were at high risk for OSA, and 25.6% had metabolic syndrome. One participant had systemic hypertension while diabetes mellitus was found in two participants. [Table 2] depicts the comparison of participants with and without metabolic syndrome. Both groups were comparable with regard to most of the demographic, disease-related, and therapeutic parameters.
Binary logistic regression analysis was done to develop a model for the factors that contributed to the development of metabolic syndrome. A number of variables that were thought to influence metabolic derangement were entered including age, body mass index, waist-hip ratio, waist circumference, OSA risk (high or low), physical activity (low or high), total lifetime SGAs exposure (in milligram olanzapine equivalent), fasting plasma glucose, serum HDL concentration, and serum triglyceride concentration. Overall model was statistically significant (P = 0.002). It produced four different models, out of which, model 4 explained 61% variability of the included factors and classified 86% cases correctly [Table 3].
|Table 3: Logistic regression analysis of factors that may contribute to metabolic syndrome|
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| Discussion|| |
This study showed that nearly one-fifth of the participants taking second-generation antipsychotics are at high risk for OSA and approximately one-fourth of them had metabolic syndrome. Multivariate regression analysis has shown that after controlling for various factors that could contribute to metabolic syndrome, OSA appeared to have major contribution (OR = 15.09) [Table 3]. Rest of the variable reached significance in regression analysis, as they were part of criteria for metabolic syndrome.
A number of studies have reported a higher prevalence of OSA among participants taking antipsychotics., The prevalence was found to vary between 14.9% and 20.9% among participants with schizophrenia spectrum disorder across studies., Results of the present study also confirm that nearly one-fifth of the participants was at high risk for OSA, and the population was comparable to that in other studies. However, understanding regarding the pathogenesis of OSA per se, and also in this category of participants has changed over the years. Keeping with literature explaining the pathogenesis of OSA, studies involving participants taking SGAs emphasized on body weight and argued that antipsychotic-induced weight gain resulted in OSA., However, the present understanding suggests that OSA results from not only peripheral factors (e.g., obesity and large neck circumference to name a few) but also neural factors (e.g., chemosensitivity reflexes, behavioral state, and activation of respiratory centers) are equally important. A recent follow-up study showed that features OSA appear in as short as 8 weeks in participants taking SGAs without any change in weight supports this notion. Another cross-sectional study also reported that OSA in participants exposed to SGAs was seen independent of body weight and neck circumference. Apparently, SGAs induce some changes in respiratory centers of brain or at the level of pharyngeal muscles perturbing the fine balance between peripheral and central mechanisms that govern the patency of the upper airway.,,,, However, these evidences are weak, and well-designed studies are required to address this issue.
One-fourth of the participants in the present study fulfilled the criteria for metabolic syndrome. The prevalence of metabolic syndrome among participants taking SGAs was found to vary between 12.8% and 29.3% in the Indian population., While previous literature suggested a link between the SGAs and metabolic syndrome;, contrarily, the recent literature has questioned this association.,, Two 24 months long trials failed to report any change in physical and metabolic profile among patients taking SGAs., Going along with the recent findings, in the present study too, we did not find any association between SGAs and metabolic syndrome [Table 2] and [Table 3]. Moreover, there is a shift in understanding of the pathogenesis of metabolic syndrome in this group. Instead of weight gain and physical inactivity, it appears to be related to the activation of the hypothalamic-pituitary-adrenal axis, sympathetic stimulation, persistent proinflammatory state, and gene mutations. The present study also confirms that metabolic syndrome was independent of cumulative exposure to SGAs as well as physical characteristics except those that were included in diagnostic criteria for metabolic syndrome.
So far, it has been discussed that OSA and metabolic syndrome in participants receiving SGAs are independent of antipsychotic exposure and SGAs-induced changes in physical characteristics. However, the present and previous studies mentioned so far have shown that a significant proportion of participants exposed to SGAs have metabolic syndrome as well as OSA. Is it possible that OSA explains the relationship between the two? Is it possible that OSA produces certain physiological changes that then pave the way for metabolic syndrome? These questions become pertinent in light of literature which suggests that association between OSA and metabolic syndrome is independent of obesity. OSA is thought to induce metabolic syndrome through a variety of mechanisms that are independent of obesity and include sympathetic activation, oxidative stress, systemic inflammation, insulin resistance, and increased leptin level., Interestingly, as discussed earlier, similar mechanisms have been proposed for SGAs-induced metabolic syndrome as well. Thus, the current literature suggests two facts – first, mechanisms other than obesity work to develop metabolic syndrome, and second, OSA can induce these mechanisms. Finally, as has already been discussed, SGAs can induce OSA without changing physical characteristics.,,,,,, Together, these facts substantiate results of the present study which showed that metabolic syndrome in participants taking SGAs was mediated through OSA rather than body mass index and waist-hip ratio [Table 3]. We propose that SGAs precipitated OSA through neural mechanisms, which in turn, activated machinery to induce metabolic syndrome in these participants. This is a new understanding that deserves further research.
Similar to any other scientific investigation, the present study also had few methodological limitations. First, the sample size was small; second, the convenient sampling technique was used; third, the diagnosis of OSA was based on questionnaire; and fourth, for reasons already mentioned, compliance was measured through self-report, which was corroborated, by caregiver and refill of prescription. Finally, the study group appears heterogeneous with the inclusion of participants from diverse diagnostic categories, i.e., schizophrenia spectrum disorder and major depressive disorder. At present, we have conflicting data regarding the prevalence of metabolic syndrome in these groups. While one evidence suggests the highest prevalence in schizophrenia spectrum disorder, other in major depressive disorder., Moreover, although SGAs and antidepressant medications are prescribed in both groups, antidepressants have not been found to play a role in metabolic syndrome. We considered that these factors would have negated the effect of underlying illness. Despite these limitations, to the best of our knowledge, this is the first study to explore this area with robust exclusion criteria. Almost all the factors that could influence either metabolic syndrome or OSA were considered during the analysis, enhancing the reliability of data.
| Conclusion|| |
Preliminary evidence from the present study suggests that metabolic syndrome in participants exposed to SGAs is mediated through OSA.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Kawabe K, Ochi S, Yoshino Y, Mori Y, Onuma H, Osawa H, et al.
Metabolic status and resistin in chronic schizophrenia over a 2-year period with continuous atypical antipsychotics. Ther Adv Psychopharmacol 2015;5:271-7.
Das D, Bora K, Baruah B, Konwar G. Prevalence and predictors of metabolic syndrome in schizophrenia patients from Assam. Indian J Psychiatry 2017;59:228-32.
] [Full text]
Reddy SM, Goudie CT, Agius M. The metabolic syndrome in untreated schizophrenia patients: Prevalence and putative mechanisms. Psychiatr Danub 2013;25 Suppl 2:S94-8.
Gupta A, Dadheech G, Yadav D, Sharma P, Gautam S. Metabolic issues in schizophrenic patients receiving antipsychotic treatment. Indian J Clin Biochem 2014;29:196-201.
Franch Pato CM, Molina Rodríguez V, Franch Valverde JI. Metabolic syndrome and atypical antipsychotics: Possibility of prediction and control. Rev Psiquiatr Salud Ment 2017;10:38-44.
Kumar CN, Thirthalli J, Suresha KK, Arunachala U, Gangadhar BN. Metabolic syndrome among schizophrenia patients: Study from a rural community of South India. Asian J Psychiatr 2013;6:532-6.
Bai YM, Li CT, Tsai SJ, Tu PC, Chen MH, Su TP, et al.
Metabolic syndrome and adverse clinical outcomes in patients with bipolar disorder. BMC Psychiatry 2016;16:448.
Malhotra N, Grover S, Chakrabarti S, Kulhara P. Metabolic syndrome in schizophrenia. Indian J Psychol Med 2013;35:227-40.
] [Full text]
Ko YK, Soh MA, Kang SH, Lee JI. The prevalence of metabolic syndrome in schizophrenic patients using antipsychotics. Clin Psychopharmacol Neurosci 2013;11:80-8.
Szaulińska K, Pływaczewski R, Sikorska O, Holka-Pokorska J, Wierzbicka A, Wichniak A, et al.
Obstructive sleep apnea in severe mental disorders. Psychiatr Pol 2015;49:883-95.
Wirshing DA, Pierre JM, Wirshing WC. Sleep apnea associated with antipsychotic-induced obesity. J Clin Psychiatry 2002;63:369-70.
Stubbs B, Vancampfort D, Veronese N, Solmi M, Gaughran F, Manu P, et al.
The prevalence and predictors of obstructive sleep apnea in major depressive disorder, bipolar disorder and schizophrenia: A systematic review and meta-analysis. J Affect Disord 2016;197:259-67.
James BO, Inogbo CF, Archibong AO. Risk of obstructive sleep apnoea syndrome among in-patients at a neuropsychiatric hospital in Nigeria: A short report. Afr Health Sci 2015;15:967-71.
Winkelman JW. Schizophrenia, obesity, and obstructive sleep apnea. J Clin Psychiatry 2001;62:8-11.
Xu S, Wan Y, Xu M, Ming J, Xing Y, An F, et al.
The association between obstructive sleep apnea and metabolic syndrome: A systematic review and meta-analysis. BMC Pulm Med 2015;15:105.
Dubey AP, Rajput AK, Suhag V, Sharma D, Kandpal A, Keisham R. Prevalence of obstructive sleep apnoea in metabolic syndrome. Int J Adv Med 2017;4:722.
Gupta MA, Simpson FC. Obstructive sleep apnea and psychiatric disorders: A systematic review. J Clin Sleep Med 2015;11:165-75.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th
ed. Arlington: American Psychiatric Association; 2013.
Chalmers J, MacMahon S, Mancia G, Whitworth J, Beilin L, Hansson L, et al.
1999 World Health Organization-international society of hypertension guidelines for the management of hypertension. Guidelines sub-committee of the World Health Organization. Clin Exp Hypertens 1999;21:1009-60.
World Health Organization. Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia. Geneva: World Health Organization; 2006.
Leucht S, Samara M, Heres S, Davis JM. Dose equivalents for antipsychotic drugs: The DDD method. Schizophr Bull 2016;42 Suppl 1:S90-4.
Hansen RA, Kim MM, Song L, Tu W, Wu J, Murray MD, et al.
Comparison of methods to assess medication adherence and classify nonadherence. Ann Pharmacother 2009;43:413-22.
Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al.
International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35:1381-95.
Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP. Using the Berlin questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med 1999;131:485-91.
Gupta R, Ali R, Dhyani M, Das S, Pundir A. Hindi translation of Berlin questionnaire and its validation as a screening instrument for obstructive sleep apnea. J Neurosci Rural Pract 2016;7:244-9. [Full text]
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.
Ma WY, Yang CY, Shih SR, Hsieh HJ, Hung CS, Chiu FC, et al.
Measurement of waist circumference: Midabdominal or iliac crest? Diabetes Care 2013;36:1660-6.
World Health Organization. Waist Circumference and Waist-Hip Ratio Report of a WHO Expert Consultation. Geneva: World Health Organization; 2008.
Romano AT. Automated glucose methods: Evaluation of a glucose oxidase-peroxidase procedure. Clin Chem 1973;19:1152-7.
Shirai K, Nema T, Hiroh Y, Itoh Y, Miyashita Y, Watanabe H, et al.
Clinical efficacy of the direct assay method using polymers for serum high density lipoprotein cholesterol. J Clin Lab Anal 1997;11:82-6.
Wentz PW, Cross RE, Savory J. An integrated approach to lipid profiling: Enzymatic determination of cholesterol and triglycerides with a centrifugal analyzer. Clin Chem 1976;22:188-92.
Rishi MA, Shetty M, Wolff A, Amoateng-Adjepong Y, Manthous CA. Atypical antipsychotic medications are independently associated with severe obstructive sleep apnea. Clin Neuropharmacol 2010;33:109-13.
Shirani A, Paradiso S, Dyken ME. The impact of atypical antipsychotic use on obstructive sleep apnea: A pilot study and literature review. Sleep Med 2011;12:591-7.
Annamalai A, Palmese LB, Chwastiak LA, Srihari VH, Tek C. High rates of obstructive sleep apnea symptoms among patients with schizophrenia. Psychosomatics 2015;56:59-66.
Ramirez JM, Garcia AJ 3rd
, Anderson TM, Koschnitzky JE, Peng YJ, Kumar GK, et al.
Central and peripheral factors contributing to obstructive sleep apneas. Respir Physiol Neurobiol 2013;189:344-53.
Khazaie H, Sharafkhaneh A, Khazaie S, Ghadami MR. A weight-independent association between atypical antipsychotic medications and obstructive sleep apnea. Sleep Breath 2018;22:109-14.
Mutschler J, Obermann C, Grosshans M. Quetiapine-induced hyperventilation and dyspnea. Clin Neuropharmacol 2010;33:214.
Akyol A, Senel AC, Ulusoy H, Karip F, Erciyes N. Delayed respiratory depression after risperidone overdose. Anesth Analg 2005;101:1490-1.
Freudenmann RW, Süssmuth SD, Wolf RC, Stiller P, Schönfeldt-Lecuona C. Respiratory dysfunction in sleep apnea associated with quetiapine. Pharmacopsychiatry 2008;41:119-21.
Jabeen S, Polli SI, Gerber DR. Acute respiratory failure with a single dose of quetiapine fumarate. Ann Pharmacother 2006;40:559-62.
Krystal AD. Antidepressant and antipsychotic drugs. Sleep Med Clin 2010;5:571-89.
Coughlin SR, Mawdsley L, Mugarza JA, Calverley PM, Wilding JP. Obstructive sleep apnoea is independently associated with an increased prevalence of metabolic syndrome. Eur Heart J 2004;25:735-41.
Gami AS, Somers VK. Obstructive sleep apnoea, metabolic syndrome, and cardiovascular outcomes. Eur Heart J 2004;25:709-11.
Lin QC, Chen LD, Yu YH, Liu KX, Gao SY. Obstructive sleep apnea syndrome is associated with metabolic syndrome and inflammation. Eur Arch Otorhinolaryngol 2014;271:825-31.
Vancampfort D, Correll CU, Wampers M, Sienaert P, Mitchell AJ, De Herdt A, et al.
Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: A meta-analysis of prevalences and moderating variables. Psychol Med 2014;44:2017-28.
Dr. Ravi Gupta
Department of Psychiatry and Sleep Clinic, Himalayan Institute of Medical Sciences, Swami Ram Nagar, Jolly Grant, Dehradun - 248 016, Uttarakhand
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3]