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Sleep Disorders in Individuals With High Risk for Diabetes in Indian Population

Sleep Disorders in Individuals With High Risk for Diabetes in Indian Population Background: Sleep restores physiology and neurochemical components of our body and is essential for physical and mental health. Sleep disorders (SDs) are associated with insulin resistance and metabolic disorders. The association between SDs and diabetes needs to be understood in the Indian population. Purpose: The purpose was to investigate the association between SD and diabetes in the Indian population. Methods: As a part of nationwide Niyantrita Madhumeha Bharata Abhiyaan-2017 (NMB-2017), a cross-sectional study was conducted and data was collected from seven zones of India, after screening through the Indian Diabetes Risk Score (IDRS). The sleep quality was assessed on a scale of 1 to 4 (very good = 1, very bad = 4). The time taken to fall asleep (sleep latency) was assessed on a scale of 0 to 5 (“0” = nil and “5” = >1.5 h). Stress was assessed by the perceived stress scale. Results: Bad sleep quality was positively (odds ratio 1.055, CI [1.001, 1.113], and P < .01) associated with self-reported known diabetes. Increased sleep time (sleep latency) was associated significantly with IDRS high risk (odds ratio 1.085, CI [1.008, 1.168], and P = .01), with an average sleep latency (maximum range 5 [>1.5 h], mode 2 [10 to 30 min]) minutes. Moderate stress was significantly associated with bad sleep quality (odds ratio 1.659). Conclusion: A positive association of bad sleep quality and stress with diabetes, and an increased sleep latency in the IDRS high-risk population point to the role of modifiable risk factors. Behavioral modification and stress reduction by using yoga may be beneficial in the better management of diabetes. Keywords Sleep quality, diabetes, Indian diabetes risk score, stress Received 03 October 2020; revised 07 October 2020; accepted 07 October 2020 impaired memory, metabolic disorders like diabetes, Introduction 9 10 obesity, and hypertension. It is estimated that approximately Sleep is a biological process involving both physiological and Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bengaluru, 1,2 neurochemical aspects of life. Circadian rhythm and sleep Karnataka, India homeostasis influence each other and regulate the sleep–wake Department of Neurology, Neuroscience Research Lab, Postgraduate 3 4 cycle. Sleep can be considered as a health indicator. The Institute of Medical Education and Research (PGIMER), Chandigarh, India quality of sleep decides the physical and mental wellbeing of an Vivekananda Yoga Anusandhana Samsthana (VYASA), Bengaluru, 3 Karnataka, India individual, especially slow-wave sleep or deep sleep has been Centre for Mind Body Medicine, PGIMER, Chandigarh, India demonstrated to be practically identified with optimal recovery Centre for Mind Body Medicine, PGIMER, Chandigarh, India 5–6 and neuroplasticity. Seven to eight hours of night sleep is Department of General Surgery, Postgraduate Institute of Medical medically considered to be good enough to refresh the body. Education and Research (PGIMER), Chandigarh, India Sleep promotes and performs important restorative Corresponding author: functions for body homeostasis. Sleep deprivation or Raghuram Nagarathna, Vivekananda Yoga Anusandhana Samsthana inadequate sleep is directly associated with sleep disorders (VYASA), Bengaluru, Karnataka 560019, India. (SDs) and other health issues, such as mood disturbance, E-mail: rnagaratna@gmail.com Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution- NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https:// us.sagepub.com/en-us/nam/open-access-at-sage). 184 Annals of Neurosciences 27(3-4) 20% to 30% of the general population has one or the other India, the second-most populous country, reports around type of SD. seven million new diabetes patients every year, and SDs are It has been seen that common health conditions such as common amongst these patients. Previous studies show that diabetes, cardiovascular, neurological, urinary, or respiratory type 2 diabetes mellitus (DM) patients have a higher disorders are some of the major SD-associated prevalence of insomnia, increased daytime sleep, and comorbidities. Low quality of sleep has been reported in increased rapid eye movement sleep timing. OSA and DM are 38.4% of diabetes patients. Homeostasis model assessment common amongst the aged and obese individuals. Both OSA of insulin resistance showed a strong association of and DM are associated with a higher risk of developing obstructive sleep apnea (OSA) with high insulin resistance, cardiovascular complications, resulting in increased 13 22 excessive body weight or obesity, and hypertension. morbidity and mortality. Earlier studies on short sleep or Another study showed a lack of sleep or fragmented sleep disturbed sleep suggest impaired glucose tolerance in healthy leads to insulin resistance, gain in body weight, type 2 individuals. However, an association between SDs and 14,15 16,17 diabetes, and hypertension. The duration of sleep also diabetes is poorly understood in the Indian population. Hence, plays a major role as a diabetes risk factor. Tan et al. in their our study explored the prevalence of SDs in individuals with review on the effect of sleep duration on diabetes individuals a high risk for diabetes, based on Indian Diabetes Risk Score showed that both short-duration sleep (≤5 and 6 h/night) and (IDRS) which is a validated simple screening tool used to long-duration sleep (>8 h/night) are detrimental to diabetes detect undiagnosed individuals with DM or those at a higher 19,20 23 individuals. risk of developing DM. Figure 1. Trial Profile Phase 1 3 months Level 1 Screening forms with IDRS and Known DM 1,62,330 IDRS high risk IDRS moderate risk IDRS low risk Rural-77,870 (48%) Urban- 84,360 (52%) Rural Level 2 assessment (blood test) Urban 24,096 (48%) 50,199 26,103 (52%) Self-reported DM Invited for High Risk 7,461 interven�on 42,738 Phase 2 Reasons for no response a. Time constraints: out of town for business b. Weather: Snowfall J&K, Hot(>45 C) -Odisha c. Heavy rains: Karnataka, Maharashtra d. Poli�cal: State elec�on-UP e. Agita�on: Road blockages Manipur f. Not interested in yoga Rural-6,571 (40.1%) Detailed data Urban- 9,812 (59.8%) in high risk group 16,383 Analyzed for sleep factor 16,383 Maity et al. 185 The data set was analyzed using the SPSS software for Methods biostatistical analyses. To calculate the odds ratio, ordinal regression was Study Design implemented; reference was set to sequential contrast for all As a part of nationwide trial, the data was collected from ordinal variables. For known diabetes and known seven zones of India (east, west, central, north, northeast, hypertension, unknown diabetes and unknown hypertension northwest, and south) in 2017 (the details have been published were the references, respectively. For IDRS high risk, IDRS in methodology of our part 1 paper). In short, the sampling low risk was the reference category. For obesity, normal was process was divided into seven levels. With the help of 1,200 the reference. Regression was done to analyze the association volunteers for diabetes movements, 35 senior research of overall stress with overall sleep quality and that of overall fellows, 2 research associates, and 7 zonal coordinators, this depression with sleep quality. cross-sectional survey was completed. IDRS was used to Ethical clearance was obtained from the ethical committee identify high-risk individuals. of Indian Yoga Association. Participants Results A total of 1,62,330 subjects [rural, 77,870 (48%); urban, Table 1 represents the demographic details of 16,383 84,360 (52%)] were screened. Of these, 50,199 subjects participants; the mean age of the participants was 48 years. [rural, 24,096 (48%); urban, 26,103 (52%); IDRS high risk, The distribution of participants with respect to their location 42,738; self-reported DM, 7,461] with IDRS high risk and was as follows: urban cluster constituted 60%, and rural 40%, self-reported DM were selected for detailed investigations. females constituted 53% of the study population, while males Data for a sleep factor analysis was acquired from 16,383 constituted 47%. The average body mass index (BMI) of the subjects [rural, 6,571 (40.1%); urban, 9,812 (59.8%)]. participants was 25.90, which suggest that the participants fall under the overweight category based on BMI estimates. Outcome Measures Overall fasting blood glucose (FBG) was 117.1 mg/dL and glycated hemoglobin (HbA1c) was 6.33%. An initial survey was done to screen the participants by the Table 2 represents the sleep quality and sleep latency in IDRS to identify high-risk individuals. IDRS consists of four different groups; 1.055 (1.001–1.113) is significantly parameters: age, family history, physical activity, and waist associated with the status of known diabetes. Increased sleep circumference. A score of 0 to 30 is considered as low risk, 30 time/sleep latency is associated significantly with IDRS high to 50 moderate risk, and ≥60 as high risk. risk, with an odds ratio of 1.085 (CI [1.008, 1.168]). Bad Sleep Assessments: To measure sleep habits, there was a sleep quality and increased sleep time are associated sleep questionnaire comprising six questions. nonsignificantly with obesity and the status of known Of these, the two most important questions were taken for hypertension. the present sleep analysis. In Table 3, we can see that the moderate stress was (a) Sleep quality: During the past month, how would you significantly associated with bad sleep quality, but was not rate your sleep quality overall? (Score 1, very good; 2, fairly significantly associated with sleep latency/sleep time. good; 3, fairly bad; and 4, very bad.) Although moderate depression and medium perceived stress (b) How long has it taken you to fall asleep each night? were associated positively with sleep quality, they were not (None 0; at least 10 min, 1; 10 to 30 min, 2; 30 min to 1 h, 3; significant. 1 to 1.5 h, 4; and >1.5 h, 5.) Stress: Stress was measured by using an analog scale Table 1. Demographic Characteristics of Participants questionnaire which contained six questions related to work, family, health, financial, social, and other stress. It measured Total N 16,383 levels of stress from 0 (none) to 10 (severe). Age Mean (SD) 47.7 (12.5) Perceived Stress Scale: Perceived stress was measured Area Rural 6,571 (40.1%) by using the perceived stress scale which contains 10 Urban 9,812 (59.9%) questions related to feelings and thoughts in the last month. It Gender Male 7,700 (47.0%) was associated with internal consistency (Cronbach’s α) of 0.82 and convergent validity of 0.64–0.71. Female 8,683 (53.0%) Total 16,383 BMI Overall 25.90 (13.80) Statistical Analysis FBG 117.1 (54.50) Data was uploaded via Mobile Apps by trained field HbA1c 6.33 (1.74) personnel under the supervision of senior research fellows. 186 Annals of Neurosciences 27(3-4) Table 2. Sleep Quality and Sleep Latency in Different Groups Variables Sleep Quality Significance Sleep Time/Sleep Latency Significance Known diabetes 1.055(1.001–1.113)* 0.04 .962(.919–1.006) 0.09 Known hypertension 1.019(.968–1.072) 0.46 1.015(.972–1.060) 0.49 IDRS high risk 1.072(.982–1.170) 0.12 1.085(1.008–1.168)* 0.03 Obesity 1.005(.939–1.076) 0.88 1.009(.953–1.068) 0.76 *How long has it taken you to fall asleep each night? *During the past month, how would you rate your sleep quality overall? Note: 1 = very good; 2 = fairly good; 3 = fairly bad; 4 = very bad. Comment: There was significant predictive association between diabetes, high diabetes. Table 3. Association Between Sleep Quality, Stress, and Depression 95% Wald Confidence Interval for Exp(B) Sleep Factor Parameter Significance Exp(B) Lower Upper Sleep time Minimum stress .368 1.342 .708 2.544 Moderate stress .120 1.659 .877 3.142 Minimal depression .909 .953 .417 2.180 Mild depression .913 .955 .422 2.161 Moderate depression .710 1.171 .509 2.693 Moderately severe depression .875 .934 .400 2.185 Perceived stress low .895 .920 .265 3.193 Perceived stress medium .858 .893 .260 3.072 Sleep quality Minimum stress .296 1.417 .737 2.727 Moderate stress .043 1.965 1.023 3.776 Minimal depression .935 1.036 .449 2.386 Mild depression .716 1.165 .511 2.659 Moderate depression .644 1.219 .526 2.826 Moderately severe depression .944 1.031 .437 2.431 Perceived stress low .834 1.134 .349 3.688 Perceived stress medium .681 1.278 .397 4.110 diabetes population. In our study, we checked the association Discussion of sleep quality with depression and perceived stress. Even though it shows a positive association, it was not significant. This pan-India data that used two general questions to assess A cross-sectional study on 332 Gujarati subjects (between the previous month’s sleep quality and sleep latency of the 13 and 20 years of age) showed that inadequate sleep does not participants revealed a significant positive association of affect the blood glucose levels in adolescents. That study sleep quality and sleep latency with known diabetes and was on adolescents, but our study was on adults; this seems to IDRS high diabetes risk, respectively. It was a point out that although insufficient sleep does not affect the noninterventional study. So, there is no control group. We blood glucose level of adolescents, it may affect the blood have taken nondiabetics to compare diabetic subjects. We glucose level of adults. Another cross-sectional study on 1,258 have done the regression analysis, and the results showed that subjects (Indian = 855, Malay = 403) between 40 and 80 years moderate stress was significantly associated with poor sleep of age showed that the abnormal sleep duration is associated quality, but not associated with sleep latency. with the diabetic kidney disease. Another review showed Earlier studies showed that stress is significantly associated 26,27 that poor sleep quality and short sleep duration were associated with the bad quality of sleep. Our study showed a with cardiometabolic risk and adverse effects on diabetes, significant association between moderate stress and poor 32–34 hypertension, obesity, and in turn some epigenetic changes. sleep quality. Our study showed similar results with a significant association A previous study showed complications and durations of between poor sleep quality and known diabetes. Our study did diabetes can influence the quality of sleep and depression in a Maity et al. 187 not show any significant association between sleep quality accomplishing this project by monitoring the project and preparing the necessary DVDs (by Mr. Advait, Mrs. Akanksha, and the team) and obesity or sleep quality and hypertension. and books for the project. We are appreciative to all individuals of This is the first study from the Indian population consisting the scientific advisory committee of Niyantrita Madhumeha Bharata of a large sample size where an association between sleep (NMB). We would also like to thank the members of advisory factors and diabetes risk has been demonstrated. committee, members of executive board, each and every individual In our earlier studies, we have examined various markers involved in developing the common yoga protocol for NMBA, and for different neurodegenerative disorders such as age-related Dr Anand Balayogi Bhavanani, Director, Standing Research 35–40 41,42 macular degeneration, amyotrophic lateral sclerosis, Committee of the Indian Yoga Association (IYA), for their and Parkinson’s disease, and described various treatment simultaneousness and dynamic investment at various periods of the strategies for the brain and nervous system, and retinal venture. We would like to thank all directors and masters of all the degeneration. However, in the recent study, we did not major yoga institutions around the nation for providing trained and include any biomarker to examine the sleep factor. As sleep is responsible yoga volunteers. We thank the software development team for their extraordinary support throughout the project. We are associated with our brain function, therefore further studies appreciative to all yoga volunteer for diabetes movements, junior can be undertaken to examine the correlation between the research fellows, senior research fellows, and research associates neurodegenerative diseases and sleep factor in this diabetic who worked with incredible energy to finish the venture inside the population. Alzheimer’s disease and Brahmi (Bacopa booked timetables under troublesome climate conditions and monniera) as one of the treatment modalities for it have also political and other issues. We express gratitude toward Mr. Jain and 46,47 been discussed in our previous review paper. An the office staff of NMBA. We would like to thank all the staff and association between memory loss, sleep deficits, and the students of S-VYASA for their support and service. corresponding interventions such as yoga and Brahmi can add a new dimension to the research in the diabetic population. Author Contribution Our earlier literature has also provided evidence for the 1 2 3 Kalyan Maity , Raghuram Nagarathna , Akshay Anand , Suchitra S. correlation between oxidative stress and neurodegenerative 4 5 6 7 8 Patil , Amit Singh , Rajesh SK , Latha Ramesh , Sridhar P , Uttam 48 49 disorders and stem cell transplantation for neural disorders. 9 10 Kumar Thakur , and Hongasandra R Nagendra Stress biomarkers and a sleep factor analysis for a larger diabetic population are thus warranted in future. We have developed different animal models for various diseases such 2. 3. 4. 5. 6. LR 8. 9. 10. 50 51,52 as Alzheimer’s disease, and amnesia, and also discussed KM RN AA SSP AS RSK SP UKT HRN various animal models of neural metabolism for developing Concept of ✓ ✓ ✓ ✓ ✓ the treatment modalities. For mechanistic studies, the manuscript animal model resources can be used. Design ✓ ✓ ✓ ✓ ✓ ✓ ✓ Defini- ✓ ✓ ✓ ✓ ✓ Limitation of the Study tion of intel- The limitation of the study is that a standardized validated lectual questionnaire was not used to assess the sleep factor. So, we content were unable to do the domain analysis of different variables Lit- ✓ ✓ ✓ ✓ ✓ of sleep. erature search Data Conclusion ✓ ✓ ✓ ✓ ✓ acquisi- tion Poor sleep quality is associated with known diabetes, and increased sleep latency is associated with a high diabetes risk. Data ✓ ✓ ✓ analysis Bad sleep quality or SD can be an indicator of diabetes, and increased sleep latency can be an indicator of IDRS high risk. Statis- ✓ ✓ ✓ However, further study is required by using a comprehensive tical analysis sleep questionnaire to confirm the results in the Indian diabetes population. Manu- ✓ ✓ ✓ script prepara- Acknowledgment tion We would like to thankfully acknowledge the Ministry of Health Manu- ✓ ✓ ✓ ✓ ✓ ✓ and Family Welfare, and the Ministry of AYUSH, Government of script India, New Delhi, India, for funding this project. We would like to editing thank Dr Ishwar Acharya for his great contribution. He helped in 188 Annals of Neurosciences 27(3-4) 11. Panda S, Taly AB, Sinha S, et al. Sleep-related disorders among a healthy population in South India. Neurol India 2012; 60(1): 68. 2. 3. 4. 5. 6. LR 8. 9. 10. 12. Birhanu TE, Getachew B, Gerbi A, et al. Prevalence of poor sleep qual- KM RN AA SSP AS RSK SP UKT HRN ity and its associated factors among hypertensive patients on follow up Manu- ✓ ✓ ✓ ✓ ✓ ✓ ✓ at Jimma University Medical Center. J Hum Hypertens 2020; 1–7. script 13. 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Sleep Disorders in Individuals With High Risk for Diabetes in Indian Population

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Abstract

Background: Sleep restores physiology and neurochemical components of our body and is essential for physical and mental health. Sleep disorders (SDs) are associated with insulin resistance and metabolic disorders. The association between SDs and diabetes needs to be understood in the Indian population. Purpose: The purpose was to investigate the association between SD and diabetes in the Indian population. Methods: As a part of nationwide Niyantrita Madhumeha Bharata Abhiyaan-2017 (NMB-2017), a cross-sectional study was conducted and data was collected from seven zones of India, after screening through the Indian Diabetes Risk Score (IDRS). The sleep quality was assessed on a scale of 1 to 4 (very good = 1, very bad = 4). The time taken to fall asleep (sleep latency) was assessed on a scale of 0 to 5 (“0” = nil and “5” = >1.5 h). Stress was assessed by the perceived stress scale. Results: Bad sleep quality was positively (odds ratio 1.055, CI [1.001, 1.113], and P < .01) associated with self-reported known diabetes. Increased sleep time (sleep latency) was associated significantly with IDRS high risk (odds ratio 1.085, CI [1.008, 1.168], and P = .01), with an average sleep latency (maximum range 5 [>1.5 h], mode 2 [10 to 30 min]) minutes. Moderate stress was significantly associated with bad sleep quality (odds ratio 1.659). Conclusion: A positive association of bad sleep quality and stress with diabetes, and an increased sleep latency in the IDRS high-risk population point to the role of modifiable risk factors. Behavioral modification and stress reduction by using yoga may be beneficial in the better management of diabetes. Keywords Sleep quality, diabetes, Indian diabetes risk score, stress Received 03 October 2020; revised 07 October 2020; accepted 07 October 2020 impaired memory, metabolic disorders like diabetes, Introduction 9 10 obesity, and hypertension. It is estimated that approximately Sleep is a biological process involving both physiological and Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bengaluru, 1,2 neurochemical aspects of life. Circadian rhythm and sleep Karnataka, India homeostasis influence each other and regulate the sleep–wake Department of Neurology, Neuroscience Research Lab, Postgraduate 3 4 cycle. Sleep can be considered as a health indicator. The Institute of Medical Education and Research (PGIMER), Chandigarh, India quality of sleep decides the physical and mental wellbeing of an Vivekananda Yoga Anusandhana Samsthana (VYASA), Bengaluru, 3 Karnataka, India individual, especially slow-wave sleep or deep sleep has been Centre for Mind Body Medicine, PGIMER, Chandigarh, India demonstrated to be practically identified with optimal recovery Centre for Mind Body Medicine, PGIMER, Chandigarh, India 5–6 and neuroplasticity. Seven to eight hours of night sleep is Department of General Surgery, Postgraduate Institute of Medical medically considered to be good enough to refresh the body. Education and Research (PGIMER), Chandigarh, India Sleep promotes and performs important restorative Corresponding author: functions for body homeostasis. Sleep deprivation or Raghuram Nagarathna, Vivekananda Yoga Anusandhana Samsthana inadequate sleep is directly associated with sleep disorders (VYASA), Bengaluru, Karnataka 560019, India. (SDs) and other health issues, such as mood disturbance, E-mail: rnagaratna@gmail.com Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution- NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https:// us.sagepub.com/en-us/nam/open-access-at-sage). 184 Annals of Neurosciences 27(3-4) 20% to 30% of the general population has one or the other India, the second-most populous country, reports around type of SD. seven million new diabetes patients every year, and SDs are It has been seen that common health conditions such as common amongst these patients. Previous studies show that diabetes, cardiovascular, neurological, urinary, or respiratory type 2 diabetes mellitus (DM) patients have a higher disorders are some of the major SD-associated prevalence of insomnia, increased daytime sleep, and comorbidities. Low quality of sleep has been reported in increased rapid eye movement sleep timing. OSA and DM are 38.4% of diabetes patients. Homeostasis model assessment common amongst the aged and obese individuals. Both OSA of insulin resistance showed a strong association of and DM are associated with a higher risk of developing obstructive sleep apnea (OSA) with high insulin resistance, cardiovascular complications, resulting in increased 13 22 excessive body weight or obesity, and hypertension. morbidity and mortality. Earlier studies on short sleep or Another study showed a lack of sleep or fragmented sleep disturbed sleep suggest impaired glucose tolerance in healthy leads to insulin resistance, gain in body weight, type 2 individuals. However, an association between SDs and 14,15 16,17 diabetes, and hypertension. The duration of sleep also diabetes is poorly understood in the Indian population. Hence, plays a major role as a diabetes risk factor. Tan et al. in their our study explored the prevalence of SDs in individuals with review on the effect of sleep duration on diabetes individuals a high risk for diabetes, based on Indian Diabetes Risk Score showed that both short-duration sleep (≤5 and 6 h/night) and (IDRS) which is a validated simple screening tool used to long-duration sleep (>8 h/night) are detrimental to diabetes detect undiagnosed individuals with DM or those at a higher 19,20 23 individuals. risk of developing DM. Figure 1. Trial Profile Phase 1 3 months Level 1 Screening forms with IDRS and Known DM 1,62,330 IDRS high risk IDRS moderate risk IDRS low risk Rural-77,870 (48%) Urban- 84,360 (52%) Rural Level 2 assessment (blood test) Urban 24,096 (48%) 50,199 26,103 (52%) Self-reported DM Invited for High Risk 7,461 interven�on 42,738 Phase 2 Reasons for no response a. Time constraints: out of town for business b. Weather: Snowfall J&K, Hot(>45 C) -Odisha c. Heavy rains: Karnataka, Maharashtra d. Poli�cal: State elec�on-UP e. Agita�on: Road blockages Manipur f. Not interested in yoga Rural-6,571 (40.1%) Detailed data Urban- 9,812 (59.8%) in high risk group 16,383 Analyzed for sleep factor 16,383 Maity et al. 185 The data set was analyzed using the SPSS software for Methods biostatistical analyses. To calculate the odds ratio, ordinal regression was Study Design implemented; reference was set to sequential contrast for all As a part of nationwide trial, the data was collected from ordinal variables. For known diabetes and known seven zones of India (east, west, central, north, northeast, hypertension, unknown diabetes and unknown hypertension northwest, and south) in 2017 (the details have been published were the references, respectively. For IDRS high risk, IDRS in methodology of our part 1 paper). In short, the sampling low risk was the reference category. For obesity, normal was process was divided into seven levels. With the help of 1,200 the reference. Regression was done to analyze the association volunteers for diabetes movements, 35 senior research of overall stress with overall sleep quality and that of overall fellows, 2 research associates, and 7 zonal coordinators, this depression with sleep quality. cross-sectional survey was completed. IDRS was used to Ethical clearance was obtained from the ethical committee identify high-risk individuals. of Indian Yoga Association. Participants Results A total of 1,62,330 subjects [rural, 77,870 (48%); urban, Table 1 represents the demographic details of 16,383 84,360 (52%)] were screened. Of these, 50,199 subjects participants; the mean age of the participants was 48 years. [rural, 24,096 (48%); urban, 26,103 (52%); IDRS high risk, The distribution of participants with respect to their location 42,738; self-reported DM, 7,461] with IDRS high risk and was as follows: urban cluster constituted 60%, and rural 40%, self-reported DM were selected for detailed investigations. females constituted 53% of the study population, while males Data for a sleep factor analysis was acquired from 16,383 constituted 47%. The average body mass index (BMI) of the subjects [rural, 6,571 (40.1%); urban, 9,812 (59.8%)]. participants was 25.90, which suggest that the participants fall under the overweight category based on BMI estimates. Outcome Measures Overall fasting blood glucose (FBG) was 117.1 mg/dL and glycated hemoglobin (HbA1c) was 6.33%. An initial survey was done to screen the participants by the Table 2 represents the sleep quality and sleep latency in IDRS to identify high-risk individuals. IDRS consists of four different groups; 1.055 (1.001–1.113) is significantly parameters: age, family history, physical activity, and waist associated with the status of known diabetes. Increased sleep circumference. A score of 0 to 30 is considered as low risk, 30 time/sleep latency is associated significantly with IDRS high to 50 moderate risk, and ≥60 as high risk. risk, with an odds ratio of 1.085 (CI [1.008, 1.168]). Bad Sleep Assessments: To measure sleep habits, there was a sleep quality and increased sleep time are associated sleep questionnaire comprising six questions. nonsignificantly with obesity and the status of known Of these, the two most important questions were taken for hypertension. the present sleep analysis. In Table 3, we can see that the moderate stress was (a) Sleep quality: During the past month, how would you significantly associated with bad sleep quality, but was not rate your sleep quality overall? (Score 1, very good; 2, fairly significantly associated with sleep latency/sleep time. good; 3, fairly bad; and 4, very bad.) Although moderate depression and medium perceived stress (b) How long has it taken you to fall asleep each night? were associated positively with sleep quality, they were not (None 0; at least 10 min, 1; 10 to 30 min, 2; 30 min to 1 h, 3; significant. 1 to 1.5 h, 4; and >1.5 h, 5.) Stress: Stress was measured by using an analog scale Table 1. Demographic Characteristics of Participants questionnaire which contained six questions related to work, family, health, financial, social, and other stress. It measured Total N 16,383 levels of stress from 0 (none) to 10 (severe). Age Mean (SD) 47.7 (12.5) Perceived Stress Scale: Perceived stress was measured Area Rural 6,571 (40.1%) by using the perceived stress scale which contains 10 Urban 9,812 (59.9%) questions related to feelings and thoughts in the last month. It Gender Male 7,700 (47.0%) was associated with internal consistency (Cronbach’s α) of 0.82 and convergent validity of 0.64–0.71. Female 8,683 (53.0%) Total 16,383 BMI Overall 25.90 (13.80) Statistical Analysis FBG 117.1 (54.50) Data was uploaded via Mobile Apps by trained field HbA1c 6.33 (1.74) personnel under the supervision of senior research fellows. 186 Annals of Neurosciences 27(3-4) Table 2. Sleep Quality and Sleep Latency in Different Groups Variables Sleep Quality Significance Sleep Time/Sleep Latency Significance Known diabetes 1.055(1.001–1.113)* 0.04 .962(.919–1.006) 0.09 Known hypertension 1.019(.968–1.072) 0.46 1.015(.972–1.060) 0.49 IDRS high risk 1.072(.982–1.170) 0.12 1.085(1.008–1.168)* 0.03 Obesity 1.005(.939–1.076) 0.88 1.009(.953–1.068) 0.76 *How long has it taken you to fall asleep each night? *During the past month, how would you rate your sleep quality overall? Note: 1 = very good; 2 = fairly good; 3 = fairly bad; 4 = very bad. Comment: There was significant predictive association between diabetes, high diabetes. Table 3. Association Between Sleep Quality, Stress, and Depression 95% Wald Confidence Interval for Exp(B) Sleep Factor Parameter Significance Exp(B) Lower Upper Sleep time Minimum stress .368 1.342 .708 2.544 Moderate stress .120 1.659 .877 3.142 Minimal depression .909 .953 .417 2.180 Mild depression .913 .955 .422 2.161 Moderate depression .710 1.171 .509 2.693 Moderately severe depression .875 .934 .400 2.185 Perceived stress low .895 .920 .265 3.193 Perceived stress medium .858 .893 .260 3.072 Sleep quality Minimum stress .296 1.417 .737 2.727 Moderate stress .043 1.965 1.023 3.776 Minimal depression .935 1.036 .449 2.386 Mild depression .716 1.165 .511 2.659 Moderate depression .644 1.219 .526 2.826 Moderately severe depression .944 1.031 .437 2.431 Perceived stress low .834 1.134 .349 3.688 Perceived stress medium .681 1.278 .397 4.110 diabetes population. In our study, we checked the association Discussion of sleep quality with depression and perceived stress. Even though it shows a positive association, it was not significant. This pan-India data that used two general questions to assess A cross-sectional study on 332 Gujarati subjects (between the previous month’s sleep quality and sleep latency of the 13 and 20 years of age) showed that inadequate sleep does not participants revealed a significant positive association of affect the blood glucose levels in adolescents. That study sleep quality and sleep latency with known diabetes and was on adolescents, but our study was on adults; this seems to IDRS high diabetes risk, respectively. It was a point out that although insufficient sleep does not affect the noninterventional study. So, there is no control group. We blood glucose level of adolescents, it may affect the blood have taken nondiabetics to compare diabetic subjects. We glucose level of adults. Another cross-sectional study on 1,258 have done the regression analysis, and the results showed that subjects (Indian = 855, Malay = 403) between 40 and 80 years moderate stress was significantly associated with poor sleep of age showed that the abnormal sleep duration is associated quality, but not associated with sleep latency. with the diabetic kidney disease. Another review showed Earlier studies showed that stress is significantly associated 26,27 that poor sleep quality and short sleep duration were associated with the bad quality of sleep. Our study showed a with cardiometabolic risk and adverse effects on diabetes, significant association between moderate stress and poor 32–34 hypertension, obesity, and in turn some epigenetic changes. sleep quality. Our study showed similar results with a significant association A previous study showed complications and durations of between poor sleep quality and known diabetes. Our study did diabetes can influence the quality of sleep and depression in a Maity et al. 187 not show any significant association between sleep quality accomplishing this project by monitoring the project and preparing the necessary DVDs (by Mr. Advait, Mrs. Akanksha, and the team) and obesity or sleep quality and hypertension. and books for the project. We are appreciative to all individuals of This is the first study from the Indian population consisting the scientific advisory committee of Niyantrita Madhumeha Bharata of a large sample size where an association between sleep (NMB). We would also like to thank the members of advisory factors and diabetes risk has been demonstrated. committee, members of executive board, each and every individual In our earlier studies, we have examined various markers involved in developing the common yoga protocol for NMBA, and for different neurodegenerative disorders such as age-related Dr Anand Balayogi Bhavanani, Director, Standing Research 35–40 41,42 macular degeneration, amyotrophic lateral sclerosis, Committee of the Indian Yoga Association (IYA), for their and Parkinson’s disease, and described various treatment simultaneousness and dynamic investment at various periods of the strategies for the brain and nervous system, and retinal venture. We would like to thank all directors and masters of all the degeneration. However, in the recent study, we did not major yoga institutions around the nation for providing trained and include any biomarker to examine the sleep factor. As sleep is responsible yoga volunteers. We thank the software development team for their extraordinary support throughout the project. We are associated with our brain function, therefore further studies appreciative to all yoga volunteer for diabetes movements, junior can be undertaken to examine the correlation between the research fellows, senior research fellows, and research associates neurodegenerative diseases and sleep factor in this diabetic who worked with incredible energy to finish the venture inside the population. Alzheimer’s disease and Brahmi (Bacopa booked timetables under troublesome climate conditions and monniera) as one of the treatment modalities for it have also political and other issues. We express gratitude toward Mr. Jain and 46,47 been discussed in our previous review paper. An the office staff of NMBA. We would like to thank all the staff and association between memory loss, sleep deficits, and the students of S-VYASA for their support and service. corresponding interventions such as yoga and Brahmi can add a new dimension to the research in the diabetic population. Author Contribution Our earlier literature has also provided evidence for the 1 2 3 Kalyan Maity , Raghuram Nagarathna , Akshay Anand , Suchitra S. correlation between oxidative stress and neurodegenerative 4 5 6 7 8 Patil , Amit Singh , Rajesh SK , Latha Ramesh , Sridhar P , Uttam 48 49 disorders and stem cell transplantation for neural disorders. 9 10 Kumar Thakur , and Hongasandra R Nagendra Stress biomarkers and a sleep factor analysis for a larger diabetic population are thus warranted in future. We have developed different animal models for various diseases such 2. 3. 4. 5. 6. LR 8. 9. 10. 50 51,52 as Alzheimer’s disease, and amnesia, and also discussed KM RN AA SSP AS RSK SP UKT HRN various animal models of neural metabolism for developing Concept of ✓ ✓ ✓ ✓ ✓ the treatment modalities. For mechanistic studies, the manuscript animal model resources can be used. Design ✓ ✓ ✓ ✓ ✓ ✓ ✓ Defini- ✓ ✓ ✓ ✓ ✓ Limitation of the Study tion of intel- The limitation of the study is that a standardized validated lectual questionnaire was not used to assess the sleep factor. So, we content were unable to do the domain analysis of different variables Lit- ✓ ✓ ✓ ✓ ✓ of sleep. erature search Data Conclusion ✓ ✓ ✓ ✓ ✓ acquisi- tion Poor sleep quality is associated with known diabetes, and increased sleep latency is associated with a high diabetes risk. Data ✓ ✓ ✓ analysis Bad sleep quality or SD can be an indicator of diabetes, and increased sleep latency can be an indicator of IDRS high risk. Statis- ✓ ✓ ✓ However, further study is required by using a comprehensive tical analysis sleep questionnaire to confirm the results in the Indian diabetes population. Manu- ✓ ✓ ✓ script prepara- Acknowledgment tion We would like to thankfully acknowledge the Ministry of Health Manu- ✓ ✓ ✓ ✓ ✓ ✓ and Family Welfare, and the Ministry of AYUSH, Government of script India, New Delhi, India, for funding this project. We would like to editing thank Dr Ishwar Acharya for his great contribution. He helped in 188 Annals of Neurosciences 27(3-4) 11. Panda S, Taly AB, Sinha S, et al. Sleep-related disorders among a healthy population in South India. Neurol India 2012; 60(1): 68. 2. 3. 4. 5. 6. LR 8. 9. 10. 12. Birhanu TE, Getachew B, Gerbi A, et al. Prevalence of poor sleep qual- KM RN AA SSP AS RSK SP UKT HRN ity and its associated factors among hypertensive patients on follow up Manu- ✓ ✓ ✓ ✓ ✓ ✓ ✓ at Jimma University Medical Center. J Hum Hypertens 2020; 1–7. script 13. 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