Prevalence of Common Mental Disorders among Adults with Non Communicable Diseases in a Rural South Indian Population

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Ramakrisha Goud, Johnson Pradeep, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8961010/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background and Objectives: Common Mental Disorders are increasingly recognized as comorbid conditions in patients with Non-Communicable Diseases, exacerbated by social and systemic factors. This study aimed to estimate the prevalence of Common Mental Disorders among adults with DM and/or HTN and identify sociodemographic and clinical factors associated with mental health status. Methodology: A cross-sectional study was conducted between 2023 and 2024 among 303 adults diagnosed with diabetes mellitus and/or hypertension residing in the Sarjapur Primary Health Centre area of Bengaluru Urban District. The study tool consisted of sociodemographic characteristics, lifestyle factors, clinical parameters, out-of-pocket expenditure, and the Patient Health Questionnaire-9 and Generalised Anxiety Disorder Scale-7, which were administered. Data analysis included descriptive statistics and bivariate and multivariable analyses. Results: The mean age of participants was 61.9 years, with the majority having no education; most men were retired, while most women were homemakers. Catastrophic Health Expenditure was reported in 77% of those with both Diabetes and Hypertension. Participants with uncontrolled diabetes and hypertension were found to be 46.9% and 50.2% respectively. Depression and anxiety were prevalent in 40.9% and 44.6% of participants. Common Mental Disorders were significantly associated with female gender, low socioeconomic status, consumption of fried or junk food, alcohol use with dependence, and CHE. Conclusion: Depression and anxiety are highly prevalent among adults with Non-Communicable Diseases in this rural setting, with significant associations to gender, lifestyle, and economic burden. Routine screening for common mental disorders is recommended in primary care settings. Depression Anxiety Hypertension Diabetes Mellitus Common Mental Disorders Non-Communicable Disorders Catastrophic Health Expenditure Suicidality INTRODUCTION Non-communicable diseases (NCDs) such as Diabetes Mellitus (DM) and Hypertension (HTN) are major public health concerns worldwide. ( 1 ) Approximately 88 million people throughout Southeast Asia have diabetes, of which 77 million reside in India in 2022. ( 2 , 3 ) Around 207 million adults suffer from hypertension in India. ( 4 ) Both diabetes and hypertension are significant risk factors for cardiovascular disease (CVD) ( 5 , 6 , 7 ), which is the leading cause of global mortality. It is estimated that in India, the prevalence of diabetes is around 11%. ( 8 ) The prevalence of hypertension in India is slightly lower than the global average of 31%. ( 9 ) According to a study that used data from the Jaipur Heart Watch Studies and forecasting, hypertension prevalence in India is projected to increase to 44% by 2030. ( 10 ) Burden of diabetes has increased by 80% in the last two decades, probably because of a combination of genetic, physiological, environmental, and behavioural factors.( 11 )( 12 ) Complications of diabetes and hypertension result in catastrophic medical expenditure for families. ( 4 ) Depression and anxiety, collectively known as common mental disorders (CMDs), are highly prevalent among individuals with chronic illnesses, with the prevalence of anxiety disorder and depression in those with diabetes and hypertension ranging from 3.9–44% and 8–44%. ( 13 ) CMDs are responsible for 7.4% of global disability-adjusted life years (DALYs) and 22.9% of years lived with disabilities (YLDs), making them the fifth leading cause of DALYs and YLDs. ( 14 ) Approximately 280 million people in the world have depression according to the WHO. ( 15 ) CMDs are one of the known major contributors to the NCD burden. There have been debates to include CMDs such as depression and anxiety under the umbrella of NCDs. The prevention and control of both diseases remain separate and independent. However, anxiety and depression, just like NCDs, cause limitations in daily activities. ( 16 ) CMDs and NCDs are interlinked and highly comorbid. The pathway of comorbidity is bidirectional in nature. ( 17 , 18 ) For example, diabetes elevates the risk of depression and vice versa. ( 19 ) Concomitant hypertension with depression and or anxiety increases the chance of cardiovascular mortality and morbidity. ( 20 ) Poor organizational and system-level integration difficulties alongside manpower deficits, financial, and infrastructure issues further compound the problem of mental healthcare delivery in NCD clinics in our country. ( 21 ) There is a dearth of studies on the prevalence of Depression and Anxiety among people living with Hypertension and Diabetes Mellitus in the rural population of South India, specifically in rural Karnataka. The study aimed to estimate the prevalence of depression and anxiety and identify associated sociodemographic, lifestyle, clinical and economic factors among adults with diabetes mellitus and hypertension in a rural South Indian population, thereby addressing a key evidence gap and generating contextually relevant evidence for integrated NCD–mental health care. METHODOLOGY Study settings: Our study area was 7 villages (Kuthaganahalli, Bovipalya, Kammanahalli, MC Halli, Doddathimmasandra, Bilapura and Burugunte) served by the Kuthaganahalli sub-centre of Sarjapur Primary Health Centre, Anekal taluk, Bengaluru Urban district. These villages are located around 30 kilometers from Bangalore City with adequate transport facilities and connections. These villages are served by the Government Health System and Private health care facilities for primary care. Our institution provides primary and community health care services through our Community Health Training Centre, located near these villages. Ethics approval: Approval was obtained from the Institutional Ethics Committee (TH-89/2023, 9 April 2023). All participants received an information sheet and gave written informed consent. All participants screened positive for CMDs were referred to our Weekly Manasi Clinic (Mental Health Clinic), which offers community mental health services by a community medicine physician and psychiatrist at the Community Health Training Centre. Our community health workers were instructed to follow up on these patients. Study design: A cross-sectional study was conducted in the villages from 2023 to 2024. Eligible residents were adults (≥ 18 y) recorded in the Health Management Information System (HMIS) of our institution as having diabetes mellitus (DM) and/or hypertension (HTN). Moribund individuals and those unable to comprehend the questionnaire were excluded. The sample size for the study was calculated based on an estimated prevalence of depression/anxiety of 27% as reported by Raval et al. ( 22 ). Using a 95% confidence level and a 5% margin of error, the sample size was determined to be 303. From 521 eligible HMIS records, proportionate stratified random sampling allocated quotas to each village, then selected individuals by random-number generation. Study tools: The study was done by visiting households, administering a structured interview schedule in Kannada, performing anthropometry, and measuring random blood glucose using a portable digital glucometer (Accu Chek Active 4 New-Gen kit) via a finger-prick capillary blood sample (regardless of the time of the last meal). Blood pressure was measured twice in a seated position using a calibrated digital sphygmomanometer (Omron HEM 7124). The average of the two readings was taken as the final value. Two repeat visits were made on different occasions (times and days) to meet the selected study participant. The interview schedule was developed specifically for this study after an extensive review of literature and expert consultation, and incorporated components adapted from previously validated instruments where appropriate. The schedule captured socio-demographics, socioeconomic status (modified BG Prasad 2023 and Standard-of-Living Index), ( 23 , 24 ) medical history, clinical examination and anthropometry, complications, health expenditure, diet, physical activity, tobacco use (Fagerström FTND/FTND-ST) and alcohol use (CAGE). ( 25 – 27 ) Body mass index and waist–hip ratio was measured using standardised anthropometric methods. Physical activity was assessed based on self-reported activity patterns and categorised as adequate or inadequate. Depression and anxiety were screened with validated Kannada versions of PHQ-9 and GAD-7 ( 28 ). Scores were categorised according to standard cut-offs to indicate severity. A score of ≥ 10 on either scale was considered indicative of clinically significant depression or anxiety. Participants who scored ≥ 10 were counselled and referred for further evaluation and management in accordance with standard clinical guidelines. The prepared data collection tool was used after pilot testing to understand the flow and time taken to complete the interview. The English version of the interview schedule is provided as Supplementary File 1. Data analysis : Data were entered in EpiCollect5 Software, exported to Excel and analysed with Jamovi 2.6.44. Categorical variables were summarised as frequencies and percentages, while continuous variables were described using means with standard deviation or medians with inter-quartile range, as appropriate. Exposure variables included disease status (diabetes mellitus only, hypertension only, and coexisting diabetes mellitus and hypertension) and selected sociodemographic, socioeconomic, lifestyle, and clinical factors. Outcome variables were depression and anxiety, assessed using PHQ-9 and GAD-7, respectively. Associations between exposure and outcome variables were examined using χ² or Fisher’s exact tests. Variables with p < 0.05 and those considered clinically relevant were entered into multivariable logistic regression models. Adjusted odds ratios with 95% confidence intervals were reported. A two-tailed p-value < 0.05 was considered statistically significant. RESULTS A total of 303 adults with diabetes mellitus and or hypertension were included in the study. The sociodemographic and socioeconomic characteristics of the participants are presented in Table 1. Clinical, behavioural and disease profile As shown in Table 2, 152 participants (50.2%) had both DM and HTN. Prevalence of depression and anxiety The prevalence of depression and anxiety by disease group is shown in Table 3. Overall, 124 participants (40.9%) screened positive for depression and 135 (44.6%) screened positive for anxiety. Both depression and anxiety were present in 71 participants (23.4%). Factors associated with depression and anxiety The results of the multivariable logistic regression analysis are presented in Table 4. After adjustment for relevant covariates, female sex was independently associated with depression (AOR 2.3, 95% CI 1.2–4.3). Low Standard of Living Index was associated with both depression (AOR 1.2, 95% CI 1.0–1.9) and anxiety (AOR 1.2, 95% CI 1.0–2.3). Frequent consumption of fried or oily food was associated with higher odds of depression (AOR 1.6, 95% CI 1.2–2.5). Alcohol dependence was significantly associated with both depression (AOR 1.8, 95% CI 1.1–3.6) and anxiety (AOR 2.1, 95% CI 1.3–3.5). Participants with both diabetes mellitus and hypertension had higher odds of depression compared to those with a single condition (AOR 1.7, 95% CI 1.3–2.8), while hypertension alone was strongly associated with anxiety (AOR 5.4, 95% CI 2.2–13.5). Peripheral neuropathy was inversely associated with depression (AOR 0.4, 95% CI 0.3–0.9). Uncontrolled blood pressure was associated with increased odds of both depression (AOR 1.4, 95% CI 1.1–2.2) and anxiety (AOR 1.5, 95% CI 1.2–2.3). DISCUSSION Our study shows a substantial and often unrecognised mental health burden within routine NCD care in rural primary care settings. Depression was present in 124 participants (40.9%) and anxiety in 135 participants. (44.6%) These prevalences are comparable to those reported in earlier Indian studies among individuals with cardiometabolic illnesses, where depression and anxiety have been reported in the ranges of 8–46% and 3.9–44%, respectively. ( 29 ) The relatively higher prevalence observed in this rural cohort compared to several prior Indian studies may be attributable to older age distribution, high multimorbidity burden and substantial financial strain related to chronic disease care. The mean age of the study participants was 61.9 years, with a long duration of illness, averaging 8.3 years for diabetes mellitus and 9.6 years for hypertension. Prolonged disease duration may contribute to a higher prevalence of depression and anxiety through chronic inflammation, hypothalamic–pituitary–adrenal axis dysregulation, and autonomic imbalance associated with long-standing non-communicable diseases. ( 18 ) The observed burden of common mental disorders may reflect the cumulative psychosocial stress, functional limitations and long-term treatment demands associated with ageing and multimorbidity in chronic non-communicable diseases. Suicidal ideation was reported by 51 participants (16.8%), which is higher than estimates from the National Mental Health Survey general population sample. ( 30 ) This finding highlights the importance of routine mental health screening in non-communicable disease clinics to facilitate early identification, counselling, and referral. The PHQ-9 and GAD-7 were found to be feasible screening tools in this primary care setting, consistent with previous validation studies. ( 31 , 32 ) Their use by trained frontline health workers at the time of diagnosis and during follow-up visits may enable early psychosocial support and appropriate referral. Several determinants identified in this study are consistent with global and Indian evidence. Diabetes and hypertension were both associated with depression and anxiety, reflecting the shared biological and behavioural pathways between non-communicable diseases and common mental disorders. Female participants had significantly higher odds of depression compared to males. This gender difference likely reflects social and structural determinants, including caregiving responsibilities, reduced financial autonomy, and barriers to accessing health care, rather than biological vulnerability alone. ( 33 ) Occupation and education also showed important associations. Homemakers and daily wage workers had higher odds of depression and anxiety, while higher educational attainment appeared protective, consistent with earlier findings linking socioeconomic disadvantage to poor mental health outcomes. ( 34 ). Dietary and lifestyle factors were significantly associated with mental health outcomes. Frequent consumption of fried or oily food was associated with depression, while frequent intake of sweets or junk food was associated with anxiety. These findings support emerging evidence linking unhealthy dietary patterns to neuroinflammation and altered reward pathways. ( 35 ) Alcohol dependence and high nicotine dependence were strongly associated with both depression and anxiety, underscoring the need for integrated substance-use interventions within non-communicable disease programmes. ( 36 ) Lower Standard of Living Index and higher out-of-pocket health expenditure were associated with anxiety and depression. These findings are consistent with studies showing that financial strain and catastrophic health expenditure adversely affect mental well-being and contribute to psychological distress. ( 37 ) Infrequent healthcare utilisation was also associated with poorer mental health, suggesting barriers to access or low perceived need for care. Hypertension alone was independently associated with higher odds of anxiety. ( 38 ) Depression and anxiety also showed a bidirectional relationship, sharing neurobiological pathways and reinforcing each other over time. ( 39 ) An inverse association was observed between peripheral neuropathy and depression. Participants with peripheral neuropathy were more likely to visit health centres frequently for symptom management, resulting in greater interaction with health-care providers and increased opportunities for counselling and reassurance. This increased health system contact may have contributed to the lower observed prevalence of depression in this subgroup. ( 40 ) This hypothesis requires confirmation through longitudinal studies examining patterns of healthcare utilisation and psychological outcomes. Finally, uncontrolled blood pressure and blood glucose were associated with both depression and anxiety, highlighting the bidirectional relationship between poor biomedical control and psychological distress. These findings emphasise the need for integrated care models that address both physical and mental health needs simultaneously. CONCLUSION This study identified a high prevalence of depression and anxiety among adults with diabetes mellitus and or hypertension in a rural South Indian population. Depression affected 124 participants (40.9%) and anxiety affected 135 participants (44.6%), indicating a substantial mental health burden among individuals living with chronic non-communicable diseases. Female gender, low socioeconomic status, unhealthy dietary patterns, alcohol dependence, uncontrolled blood pressure, and coexisting diabetes and hypertension were significantly associated with these outcomes. These findings underscore the importance of routine mental health screening within non-communicable disease care, particularly for high-risk groups such as women, individuals with low socioeconomic status, and those with poor disease control. Validated tools such as the PHQ-9 and GAD-7 can be effectively administered by trained primary care health workers to facilitate early detection and referral. In keeping with the conclusions of the larger thesis, this study highlights the need for convergence and integration of mental health services with existing diabetes and hypertension programmes at the primary care level. Strengthening coordination between non-communicable disease and mental health programmes can improve holistic care delivery, reduce unmet mental health needs, and enhance long-term outcomes in rural and underserved populations. Strengths and limitations : Our study has several limitations. Although proportionate stratified random sampling was used, selection was limited to individuals recorded in the institutional HMIS, which may affect generalisability. Our cross-sectional study design offered little insight into the causality and temporality of the observed association. The study villages might not be representative of typical Indian villages, due to their proximity to Bangalore City. Villages proximal to our Community Health Training Centre, with its Manasi (Mental Health clinic and outreach services), could have affected our CMD prevalence rates. Our community health workers have excellent rapport with the community, and our participatory services could have made our study findings not applicable to other areas. However, our study offers clear insights into the bidirectional relationship between NCDs and CMDs. The results indicate that the burden of NCDs will increase due to the co-occurrence of CMDs, underscoring the urgent need to integrate CMD care with NCD care. It also offers directions for policymakers on the feasibility of screening for CMDs in primary care settings to ensure secondary prevention of CMDs in NCD patients. There is no conflict of interest. Declarations Ethics approval and consent to participate: The study was approved by the Institutional Ethics Committee of St. John’s Medical College, Bangalore, India (TH-89/2023, 9 April 2023). Written informed consent was obtained from all participants prior to enrolment. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 2013 revision of the Declaration of Helsinki. Consent for publication: Not applicable, since no identifiable data is published Competing interests: The authors declare that they have no competing interests. Funding: The work was supported by an extramural grant from the Research Society for the Study of Diabetes in India (RSSDI). Author Contribution K.M.D. conceptualised the study, developed the protocol, conducted data collection, performed the analysis and wrote the main manuscript text. B.R.G. contributed to conceptualisation, protocol development and critically reviewed the manuscript. J.P. contributed to conceptualisation and critically reviewed the manuscript. N.A. contributed to conceptualisation, protocol development and critically reviewed the manuscript. All authors reviewed and approved the final manuscript. Acknowledgement: None Data Availability The datasets generated and/or analysed during the current study are not publicly available due to ethical and confidentiality considerations but are available from the corresponding author on reasonable request. References Habib SH, Saha S. Burden of non-communicable disease: Global overview, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Volume 4, Issue 1, 2010, Pages 41–47. Sathyanath S, Kundapur R, Deepthi R, Poojary SN, Rai S, Modi B, et al. An economic evaluation of diabetes mellitus in India: A systematic review. Diabetes Metab Syndr. 2022;16(11):102641. International Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019. Gupta R, Gaur K, Ram S. Emerging trends in hypertension epidemiology in India. J Hum Hypertens. 2019;33(8):575–87. Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. 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Sociodemographic and socioeconomic characteristics of study participants (n =303) Variable Category n (%) Age (years) Mean ± SD 61.9 ± 9.8 Sex Male 119 (39.3) Female 184 (60.7) Marital status Married 253 (83.5) Widowed or single 50 (16.5) Education status Illiterate 202 (66.7) Primary education 49 (16.2) Secondary education and above 52 (17.1) Occupation Retired or homemaker 177 (58.4) Daily wage worker 70 (23.1) Salaried 33 (10.9) Self-employed 23 (7.6) Religion Hindu 275 (90.8) Muslim 28 (9.2) Type of family Nuclear 182 (60.1) Joint 82 (27.1) Three-generation 39 (12.8) Socioeconomic status (modified BG Prasad classification 2023) Class I or II 74 (24.4) Class III 80 (26.4) Class IV or V 149 (49.2) Standard of Living Index High 33 (10.9) Medium 82 (27.1) Low 188 (62.0) Table 2. Clinical, behavioural and disease profile of study participants (n = 303) Variable Category n (%) NCD diagnosis Diabetes mellitus only 106 (35.0) Hypertension only 45 (14.9) Diabetes mellitus and hypertension 152 (50.2) Duration of illness (years) Diabetes mellitus, mean 8.3 Hypertension, mean 9.6 Body mass index category Normal 121 (39.9) Overweight 129 (42.6) Obese 53 (17.5) Central obesity (waist–hip ratio) Present 222 (73.3) Physical activity Inadequate 126 (41.6) Dietary habits Fried or oily food ≥5 times per week 60 (19.8) Sweets or junk food ≥3 times per week 131 (43.2) Alcohol use Current 35 (11.5) Past 29 (9.6) Alcohol dependence (CAGE ≥2) Present 17 (5.6) Tobacco use Current 38 (12.5) Past 31 (10.2) High nicotine dependence Present 25 (8.3) Macrovascular or microvascular complications Present 132 (43.5) Out-of-pocket expenditure on NCD care >10% of total income 61 (20.1) Healthcare utilisation ≥1 visit per year 278 (91.8) Body mass index (BMI) was categorised using standard cut-offs: normal (18.5–22.9 kg/m²), overweight (23.0–24.9 kg/m²), and obese (≥25.0 kg/m²). Central obesity defined using waist–hip ratio cut-offs as per WHO guidelines, * cut off-<0.9 for males, and <0.85 for females Physical activity was categorised as adequate or inadequate based on self-reported duration and intensity of activity in accordance with recommended guidelines. Alcohol dependence was defined as a CAGE score of ≥2. Nicotine dependence was assessed using the Fagerström Test for Nicotine Dependence, with high dependence defined as per standard scoring criteria. Macrovascular and microvascular complications included documented complications related to diabetes mellitus and or hypertension. Out-of-pocket expenditure refers to the proportion of total household income spent on non-communicable disease care. Table 3. Prevalence of depression and anxiety by disease group among study participants (n = 303) Disease group Total in group, n Depression n (%) Anxiety n (%) Both depression and anxiety n (%) Diabetes mellitus only 106 39 (36.8) 40 (37.7) 27 (25.5) Hypertension only 45 20 (44.4) 29 (64.4) 15 (33.3) Diabetes mellitus and hypertension 152 65 (42.7) 66 (43.4) 37 (24.3) Total 303 124 (40.9) 135 (44.6) 71 (23.4) Depression assessed using PHQ-9 and anxiety assessed using GAD-7. Cut-off score ≥10 used to define clinically significant symptoms. Table 4. Multivariable logistic regression analysis showing predictors of depression and anxiety among study participants (n = 303) Predictor variable Depression AOR (95% CI) p value Anxiety AOR (95% CI) p value Female sex 2.3 (1.2–4.3) 0.01 1.6 (0.9–2.9) 0.08 Low Standard of Living Index 1.2 (1.0–1.9) 0.03 1.2 (1.0–2.3) 0.04 Fried or oily food ≥5 times per week 1.6 (1.2–2.5) 0.01 — — Alcohol dependence (CAGE ≥2) 1.8 (1.1–3.6) 0.02 2.1 (1.3–3.5) 0.01 Diabetes mellitus and hypertension 1.7 (1.3–2.8) 0.01 — — Hypertension only — — 5.4 (2.2–13.5) 0.001 Peripheral neuropathy 0.4 (0.3–0.9) 0.02 — — Uncontrolled blood pressure 1.4 (1.1–2.2) 0.03 1.5 (1.2–2.3) 0.02 AOR adjusted odds ratio, CI confidence interval Variables entered into the model were selected based on clinical relevance and statistical significance in univariable analysis. A two-tailed p value <0.05 was considered statistically significant. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor invited by journal 03 Apr, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 02 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8961010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627300775,"identity":"1e12d19d-7bff-4b61-938a-19a2c20af61a","order_by":0,"name":"Kimberley Maria D’Souza","email":"data:image/png;base64,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","orcid":"","institution":"St. John’s Medical College","correspondingAuthor":true,"prefix":"","firstName":"Kimberley","middleName":"Maria","lastName":"D’Souza","suffix":""},{"id":627300776,"identity":"960c1899-8910-4269-a618-f7b6d8157329","order_by":1,"name":"B. Ramakrisha Goud","email":"","orcid":"","institution":"St. John’s Medical College","correspondingAuthor":false,"prefix":"","firstName":"B.","middleName":"Ramakrisha","lastName":"Goud","suffix":""},{"id":627300777,"identity":"6418a4aa-90fb-4dd8-b405-2561a1e5ebb2","order_by":2,"name":"Johnson Pradeep","email":"","orcid":"","institution":"St. John’s Medical College","correspondingAuthor":false,"prefix":"","firstName":"Johnson","middleName":"","lastName":"Pradeep","suffix":""},{"id":627300778,"identity":"69c00dc7-50d4-4326-bd0c-f804e0e614aa","order_by":3,"name":"Nancy Angeline","email":"","orcid":"","institution":"St. John’s Medical College","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"","lastName":"Angeline","suffix":""}],"badges":[],"createdAt":"2026-02-24 20:54:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8961010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8961010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108181246,"identity":"ff884c2d-720d-4b38-8b9a-eb4864089391","added_by":"auto","created_at":"2026-04-30 08:58:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":325084,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8961010/v1/55d57bbe-0051-4dea-ad3f-05c15febab0c.pdf"},{"id":107854967,"identity":"f8b75369-4e97-417a-8143-8b7d9ffcd633","added_by":"auto","created_at":"2026-04-27 03:33:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":285744,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8961010/v1/271045aee2b43e39823d07af.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of Common Mental Disorders among Adults with Non Communicable Diseases in a Rural South Indian Population","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNon-communicable diseases (NCDs) such as Diabetes Mellitus (DM) and Hypertension (HTN) are major public health concerns worldwide. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Approximately 88\u0026nbsp;million people throughout Southeast Asia have diabetes, of which 77\u0026nbsp;million reside in India in 2022. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Around 207\u0026nbsp;million adults suffer from hypertension in India. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Both diabetes and hypertension are significant risk factors for cardiovascular disease (CVD) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), which is the leading cause of global mortality. It is estimated that in India, the prevalence of diabetes is around 11%. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) The prevalence of hypertension in India is slightly lower than the global average of 31%. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) According to a study that used data from the Jaipur Heart Watch Studies and forecasting, hypertension prevalence in India is projected to increase to 44% by 2030. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Burden of diabetes has increased by 80% in the last two decades, probably because of a combination of genetic, physiological, environmental, and behavioural factors.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Complications of diabetes and hypertension result in catastrophic medical expenditure for families. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Depression and anxiety, collectively known as common mental disorders (CMDs), are highly prevalent among individuals with chronic illnesses, with the prevalence of anxiety disorder and depression in those with diabetes and hypertension ranging from 3.9\u0026ndash;44% and 8\u0026ndash;44%. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) CMDs are responsible for 7.4% of global disability-adjusted life years (DALYs) and 22.9% of years lived with disabilities (YLDs), making them the fifth leading cause of DALYs and YLDs. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Approximately 280\u0026nbsp;million people in the world have depression according to the WHO. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) CMDs are one of the known major contributors to the NCD burden. There have been debates to include CMDs such as depression and anxiety under the umbrella of NCDs. The prevention and control of both diseases remain separate and independent. However, anxiety and depression, just like NCDs, cause limitations in daily activities. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) CMDs and NCDs are interlinked and highly comorbid. The pathway of comorbidity is bidirectional in nature. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) For example, diabetes elevates the risk of depression and vice versa. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Concomitant hypertension with depression and or anxiety increases the chance of cardiovascular mortality and morbidity. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Poor organizational and system-level integration difficulties alongside manpower deficits, financial, and infrastructure issues further compound the problem of mental healthcare delivery in NCD clinics in our country. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) There is a dearth of studies on the prevalence of Depression and Anxiety among people living with Hypertension and Diabetes Mellitus in the rural population of South India, specifically in rural Karnataka. The study aimed to estimate the prevalence of depression and anxiety and identify associated sociodemographic, lifestyle, clinical and economic factors among adults with diabetes mellitus and hypertension in a rural South Indian population, thereby addressing a key evidence gap and generating contextually relevant evidence for integrated NCD\u0026ndash;mental health care.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy settings:\u003c/h2\u003e \u003cp\u003eOur study area was 7 villages (Kuthaganahalli, Bovipalya, Kammanahalli, MC Halli, Doddathimmasandra, Bilapura and Burugunte) served by the Kuthaganahalli sub-centre of Sarjapur Primary Health Centre, Anekal taluk, Bengaluru Urban district. These villages are located around 30 kilometers from Bangalore City with adequate transport facilities and connections. These villages are served by the Government Health System and Private health care facilities for primary care. Our institution provides primary and community health care services through our Community Health Training Centre, located near these villages.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics approval:\u003c/h3\u003e\n \u003cp\u003eApproval was obtained from the Institutional Ethics Committee (TH-89/2023, 9 April 2023). All participants received an information sheet and gave written informed consent. All participants screened positive for CMDs were referred to our Weekly Manasi Clinic (Mental Health Clinic), which offers community mental health services by a community medicine physician and psychiatrist at the Community Health Training Centre. Our community health workers were instructed to follow up on these patients.\u003c/p\u003e\n\u003ch3\u003eStudy design:\u003c/h3\u003e\n\u003cp\u003eA cross-sectional study was conducted in the villages from 2023 to 2024. Eligible residents were adults (\u0026ge;\u0026thinsp;18 y) recorded in the Health Management Information System (HMIS) of our institution as having diabetes mellitus (DM) and/or hypertension (HTN). Moribund individuals and those unable to comprehend the questionnaire were excluded. The sample size for the study was calculated based on an estimated prevalence of depression/anxiety of 27% as reported by Raval et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Using a 95% confidence level and a 5% margin of error, the sample size was determined to be 303. From 521 eligible HMIS records, proportionate stratified random sampling allocated quotas to each village, then selected individuals by random-number generation.\u003c/p\u003e\n\u003ch3\u003eStudy tools:\u003c/h3\u003e\n\u003cp\u003eThe study was done by visiting households, administering a structured interview schedule in Kannada, performing anthropometry, and measuring random blood glucose using a portable digital glucometer (Accu Chek Active 4 New-Gen kit) via a finger-prick capillary blood sample (regardless of the time of the last meal). Blood pressure was measured twice in a seated position using a calibrated digital sphygmomanometer (Omron HEM 7124). The average of the two readings was taken as the final value. Two repeat visits were made on different occasions (times and days) to meet the selected study participant.\u003c/p\u003e \u003cp\u003eThe interview schedule was developed specifically for this study after an extensive review of literature and expert consultation, and incorporated components adapted from previously validated instruments where appropriate. The schedule captured socio-demographics, socioeconomic status (modified BG Prasad 2023 and Standard-of-Living Index), (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) medical history, clinical examination and anthropometry, complications, health expenditure, diet, physical activity, tobacco use (Fagerstr\u0026ouml;m FTND/FTND-ST) and alcohol use (CAGE). (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) Body mass index and waist\u0026ndash;hip ratio was measured using standardised anthropometric methods. Physical activity was assessed based on self-reported activity patterns and categorised as adequate or inadequate. Depression and anxiety were screened with validated Kannada versions of PHQ-9 and GAD-7 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Scores were categorised according to standard cut-offs to indicate severity. A score of \u0026ge;\u0026thinsp;10 on either scale was considered indicative of clinically significant depression or anxiety. Participants who scored\u0026thinsp;\u0026ge;\u0026thinsp;10 were counselled and referred for further evaluation and management in accordance with standard clinical guidelines. The prepared data collection tool was used after pilot testing to understand the flow and time taken to complete the interview. The English version of the interview schedule is provided as Supplementary File 1.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eData analysis\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eData were entered in EpiCollect5 Software, exported to Excel and analysed with Jamovi 2.6.44. Categorical variables were summarised as frequencies and percentages, while continuous variables were described using means with standard deviation or medians with inter-quartile range, as appropriate. Exposure variables included disease status (diabetes mellitus only, hypertension only, and coexisting diabetes mellitus and hypertension) and selected sociodemographic, socioeconomic, lifestyle, and clinical factors. Outcome variables were depression and anxiety, assessed using PHQ-9 and GAD-7, respectively. Associations between exposure and outcome variables were examined using χ\u0026sup2; or Fisher\u0026rsquo;s exact tests. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and those considered clinically relevant were entered into multivariable logistic regression models. Adjusted odds ratios with 95% confidence intervals were reported. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 303 adults with diabetes mellitus and or hypertension were included in the study. The sociodemographic and socioeconomic characteristics of the participants are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical, behavioural and disease profile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, 152 participants (50.2%) had both DM and HTN.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of depression and anxiety\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of depression and anxiety by disease group is shown in Table 3. Overall, 124 participants (40.9%) screened positive for depression and 135 (44.6%) screened positive for anxiety. Both depression and anxiety were present in 71 participants (23.4%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with depression and anxiety\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the multivariable logistic regression analysis are presented in Table 4. After adjustment for relevant covariates, female sex was independently associated with depression (AOR 2.3, 95% CI 1.2\u0026ndash;4.3). Low Standard of Living Index was associated with both depression (AOR 1.2, 95% CI 1.0\u0026ndash;1.9) and anxiety (AOR 1.2, 95% CI 1.0\u0026ndash;2.3). Frequent consumption of fried or oily food was associated with higher odds of depression (AOR 1.6, 95% CI 1.2\u0026ndash;2.5). Alcohol dependence was significantly associated with both depression (AOR 1.8, 95% CI 1.1\u0026ndash;3.6) and anxiety (AOR 2.1, 95% CI 1.3\u0026ndash;3.5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants with both diabetes mellitus and hypertension had higher odds of depression compared to those with a single condition (AOR 1.7, 95% CI 1.3\u0026ndash;2.8), while hypertension alone was strongly associated with anxiety (AOR 5.4, 95% CI 2.2\u0026ndash;13.5). Peripheral neuropathy was inversely associated with depression (AOR 0.4, 95% CI 0.3\u0026ndash;0.9). Uncontrolled blood pressure was associated with increased odds of both depression (AOR 1.4, 95% CI 1.1\u0026ndash;2.2) and anxiety (AOR 1.5, 95% CI 1.2\u0026ndash;2.3).\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study shows a substantial and often unrecognised mental health burden within routine NCD care in rural primary care settings. Depression was present in 124 participants (40.9%) and anxiety in 135 participants. (44.6%) These prevalences are comparable to those reported in earlier Indian studies among individuals with cardiometabolic illnesses, where depression and anxiety have been reported in the ranges of 8\u0026ndash;46% and 3.9\u0026ndash;44%, respectively. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) The relatively higher prevalence observed in this rural cohort compared to several prior Indian studies may be attributable to older age distribution, high multimorbidity burden and substantial financial strain related to chronic disease care.\u003c/p\u003e \u003cp\u003eThe mean age of the study participants was 61.9 years, with a long duration of illness, averaging 8.3 years for diabetes mellitus and 9.6 years for hypertension. Prolonged disease duration may contribute to a higher prevalence of depression and anxiety through chronic inflammation, hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis dysregulation, and autonomic imbalance associated with long-standing non-communicable diseases. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) The observed burden of common mental disorders may reflect the cumulative psychosocial stress, functional limitations and long-term treatment demands associated with ageing and multimorbidity in chronic non-communicable diseases.\u003c/p\u003e \u003cp\u003eSuicidal ideation was reported by 51 participants (16.8%), which is higher than estimates from the National Mental Health Survey general population sample. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) This finding highlights the importance of routine mental health screening in non-communicable disease clinics to facilitate early identification, counselling, and referral.\u003c/p\u003e \u003cp\u003eThe PHQ-9 and GAD-7 were found to be feasible screening tools in this primary care setting, consistent with previous validation studies. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) Their use by trained frontline health workers at the time of diagnosis and during follow-up visits may enable early psychosocial support and appropriate referral.\u003c/p\u003e \u003cp\u003eSeveral determinants identified in this study are consistent with global and Indian evidence. Diabetes and hypertension were both associated with depression and anxiety, reflecting the shared biological and behavioural pathways between non-communicable diseases and common mental disorders. Female participants had significantly higher odds of depression compared to males. This gender difference likely reflects social and structural determinants, including caregiving responsibilities, reduced financial autonomy, and barriers to accessing health care, rather than biological vulnerability alone. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOccupation and education also showed important associations. Homemakers and daily wage workers had higher odds of depression and anxiety, while higher educational attainment appeared protective, consistent with earlier findings linking socioeconomic disadvantage to poor mental health outcomes. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDietary and lifestyle factors were significantly associated with mental health outcomes. Frequent consumption of fried or oily food was associated with depression, while frequent intake of sweets or junk food was associated with anxiety. These findings support emerging evidence linking unhealthy dietary patterns to neuroinflammation and altered reward pathways. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) Alcohol dependence and high nicotine dependence were strongly associated with both depression and anxiety, underscoring the need for integrated substance-use interventions within non-communicable disease programmes. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eLower Standard of Living Index and higher out-of-pocket health expenditure were associated with anxiety and depression. These findings are consistent with studies showing that financial strain and catastrophic health expenditure adversely affect mental well-being and contribute to psychological distress. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) Infrequent healthcare utilisation was also associated with poorer mental health, suggesting barriers to access or low perceived need for care.\u003c/p\u003e \u003cp\u003eHypertension alone was independently associated with higher odds of anxiety. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) Depression and anxiety also showed a bidirectional relationship, sharing neurobiological pathways and reinforcing each other over time. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAn inverse association was observed between peripheral neuropathy and depression. Participants with peripheral neuropathy were more likely to visit health centres frequently for symptom management, resulting in greater interaction with health-care providers and increased opportunities for counselling and reassurance. This increased health system contact may have contributed to the lower observed prevalence of depression in this subgroup. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) This hypothesis requires confirmation through longitudinal studies examining patterns of healthcare utilisation and psychological outcomes.\u003c/p\u003e \u003cp\u003eFinally, uncontrolled blood pressure and blood glucose were associated with both depression and anxiety, highlighting the bidirectional relationship between poor biomedical control and psychological distress. These findings emphasise the need for integrated care models that address both physical and mental health needs simultaneously.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study identified a high prevalence of depression and anxiety among adults with diabetes mellitus and or hypertension in a rural South Indian population. Depression affected 124 participants (40.9%) and anxiety affected 135 participants (44.6%), indicating a substantial mental health burden among individuals living with chronic non-communicable diseases. Female gender, low socioeconomic status, unhealthy dietary patterns, alcohol dependence, uncontrolled blood pressure, and coexisting diabetes and hypertension were significantly associated with these outcomes.\u003c/p\u003e \u003cp\u003eThese findings underscore the importance of routine mental health screening within non-communicable disease care, particularly for high-risk groups such as women, individuals with low socioeconomic status, and those with poor disease control. Validated tools such as the PHQ-9 and GAD-7 can be effectively administered by trained primary care health workers to facilitate early detection and referral.\u003c/p\u003e \u003cp\u003e In keeping with the conclusions of the larger thesis, this study highlights the need for convergence and integration of mental health services with existing diabetes and hypertension programmes at the primary care level. Strengthening coordination between non-communicable disease and mental health programmes can improve holistic care delivery, reduce unmet mental health needs, and enhance long-term outcomes in rural and underserved populations.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eOur study has several limitations. Although proportionate stratified random sampling was used, selection was limited to individuals recorded in the institutional HMIS, which may affect generalisability. Our cross-sectional study design offered little insight into the causality and temporality of the observed association. The study villages might not be representative of typical Indian villages, due to their proximity to Bangalore City. Villages proximal to our Community Health Training Centre, with its Manasi (Mental Health clinic and outreach services), could have affected our CMD prevalence rates. Our community health workers have excellent rapport with the community, and our participatory services could have made our study findings not applicable to other areas. However, our study offers clear insights into the bidirectional relationship between NCDs and CMDs. The results indicate that the burden of NCDs will increase due to the co-occurrence of CMDs, underscoring the urgent need to integrate CMD care with NCD care. It also offers directions for policymakers on the feasibility of screening for CMDs in primary care settings to ensure secondary prevention of CMDs in NCD patients. There is no conflict of interest.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e The study was approved by the Institutional Ethics Committee of St. John\u0026rsquo;s Medical College, Bangalore, India (TH-89/2023, 9 April 2023). Written informed consent was obtained from all participants prior to enrolment. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 2013 revision of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable, since no identifiable data is published\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe work was supported by an extramural grant from the Research Society for the Study of Diabetes in India (RSSDI).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.M.D. conceptualised the study, developed the protocol, conducted data collection, performed the analysis and wrote the main manuscript text. B.R.G. contributed to conceptualisation, protocol development and critically reviewed the manuscript. J.P. contributed to conceptualisation and critically reviewed the manuscript. N.A. contributed to conceptualisation, protocol development and critically reviewed the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ethical and confidentiality considerations but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHabib SH, Saha S. Burden of non-communicable disease: Global overview, Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews, Volume 4, Issue 1, 2010, Pages 41\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSathyanath S, Kundapur R, Deepthi R, Poojary SN, Rai S, Modi B, et al. An economic evaluation of diabetes mellitus in India: A systematic review. Diabetes Metab Syndr. 2022;16(11):102641.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta R, Gaur K, Ram S. Emerging trends in hypertension epidemiology in India. J Hum Hypertens. 2019;33(8):575\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol. 2018;34(5):575\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson AR, D\u0026rsquo;Souza KM, Fathima FN, Angeline N, Shetty. Anupama\u003csup\u003e1\u003c/sup\u003e. Assessment of Diabetes, Hypertension, and Cardiovascular Disease Risk Factors among Adults in an Urban Underprivileged Community in Bangalore, India. Indian J Community Med 51(1):p 104\u0026ndash;10, Jan\u0026ndash;Feb 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P, Lang CC, Rumboldt Z, Onen CL, Lisheng L, Tanomsup S, Wangai P Jr, Razak F, Sharma AM, Anand SS. INTERHEART Study Investigators. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet. 2005;366(9497):1640\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradeepa R, Mohan V. Epidemiology of type 2 diabetes in India. 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Risk Factors for Noncommunicable Diseases; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTandon N, Anjana RM, Mohan V, Kaur T, Afshin A, Ong K, et al. The increasing burden of diabetes and variations among the states of India: The Global Burden of Disease Study 1990\u0026ndash;2016. Lancet Global Health. 2018;6(12):e1352\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajan S, Krishna A, Muliyala KP, Chaturvedi SK. Comorbidity of anxiety and depression with hypertension, diabetes, and cardiovascular disease: a selective systematic review from India. EMJ Diabetes.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382:1575\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Indian J Psychol Med. 2010;32(2):119\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma A, Anand T, Kishore J, Tripathi S. Comorbidity of anxiety and depression with hypertension, diabetes, and cardiovascular disease: a selective systematic review from India. EMJ Diabet. 2021;9(1):84\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGururaj G, Varghese M, Benegal V, Rao GN, Pathak K, Singh LK, Mehta RY, Ram D, Shibukumar TM, Kokane A, et al. National Mental Health Survey of India, 2015\u0026ndash;16: Summary. Bengaluru: National Institute of Mental Health and Neuro Sciences (NIMHANS); 2016. NIMHANS Publication No. 128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. 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High fried food consumption impacts anxiety and depression due to lipid metabolism disturbance and neuroinflammation. Proc Natl Acad Sci U S A. 2023;120(18):e2221097120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParry CD, Patra J, Rehm J. Alcohol consumption and non-communicable diseases: epidemiology and policy implications. Addiction. 2011;106(10):1718\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav J, Allarakha S, John D, Menon GR, Venkateswaran C, Singh R. Catastrophic Health Expenditure and Poverty Impact Due to Mental Illness in India. J Health Manage. 2023;25(1):8\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgede LE. Effect of comorbid chronic diseases on prevalence and odds of depression in adults with diabetes. Psychosom Med. 2005 Jan-Feb;67(1):46\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirschfeld RM. The Comorbidity of Major Depression and Anxiety Disorders: Recognition and Management in Primary Care. Prim Care Companion J Clin Psychiatry. 2001;3(6):244\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGustin SM, Wrigley PJ, Henderson LA, et al. The relationship between pain and depression: a review of the literature. J Pain. 2015;16(10):1077\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Sociodemographic and socioeconomic characteristics of study participants (n =303)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.9 \u0026plusmn; 9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e184 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e253 (83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWidowed or single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e202 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSecondary education and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRetired or homemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e177 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDaily wage worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSalaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHindu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e275 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eType of family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNuclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182 (60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThree-generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocioeconomic status (modified BG Prasad classification 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClass I or II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClass III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClass IV or V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e149 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard of Living Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Clinical, behavioural and disease profile of study participants (n = 303)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNCD diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus and hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration of illness (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus, mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension, mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBody mass index category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral obesity (waist\u0026ndash;hip ratio)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInadequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDietary habits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFried or oily food \u0026ge;5 times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSweets or junk food \u0026ge;3 times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol dependence (CAGE \u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTobacco use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh nicotine dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMacrovascular or microvascular complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOut-of-pocket expenditure on NCD care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;10% of total income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthcare utilisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1 visit per year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e278 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eBody mass index (BMI) was categorised using standard cut-offs: normal (18.5\u0026ndash;22.9 kg/m\u0026sup2;), overweight (23.0\u0026ndash;24.9 kg/m\u0026sup2;), and obese (\u0026ge;25.0 kg/m\u0026sup2;).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentral obesity defined using waist\u0026ndash;hip ratio cut-offs as per WHO guidelines, * cut off-\u0026lt;0.9 for males, and \u0026lt;0.85 for females\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhysical activity was categorised as adequate or inadequate based on self-reported duration and intensity of activity in accordance with recommended guidelines.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAlcohol dependence was defined as a CAGE score of \u0026ge;2.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNicotine dependence was assessed using the Fagerstr\u0026ouml;m Test for Nicotine Dependence, with high dependence defined as per standard scoring criteria.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMacrovascular and microvascular complications included documented complications related to diabetes mellitus and or hypertension.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOut-of-pocket expenditure refers to the proportion of total household income spent on non-communicable disease care.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Prevalence of depression and anxiety by disease group among study participants (n = 303)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDisease group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal in group, n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDepression n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBoth depression and anxiety n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus and hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e303\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e124 (40.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e135 (44.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e71 (23.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eDepression assessed using PHQ-9 and anxiety assessed using GAD-7.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCut-off score \u0026ge;10 used to define clinically significant symptoms.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariable logistic regression analysis showing predictors of depression and anxiety among study participants (n = 303)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDepression AOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety AOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3 (1.2\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 (0.9\u0026ndash;2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow Standard of Living Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (1.0\u0026ndash;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (1.0\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFried or oily food \u0026ge;5 times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6 (1.2\u0026ndash;2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol dependence (CAGE \u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8 (1.1\u0026ndash;3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1 (1.3\u0026ndash;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus and hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7 (1.3\u0026ndash;2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.4 (2.2\u0026ndash;13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePeripheral neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4 (0.3\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUncontrolled blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4 (1.1\u0026ndash;2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5 (1.2\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAOR adjusted odds ratio, CI confidence interval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVariables entered into the model were selected based on clinical relevance and statistical significance in univariable analysis.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA two-tailed p value \u0026lt;0.05 was considered statistically significant.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression, Anxiety, Hypertension, Diabetes Mellitus, Common Mental Disorders, Non-Communicable Disorders, Catastrophic Health Expenditure, Suicidality","lastPublishedDoi":"10.21203/rs.3.rs-8961010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8961010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Objectives:\u003c/h2\u003e \u003cp\u003eCommon Mental Disorders are increasingly recognized as comorbid conditions in patients with Non-Communicable Diseases, exacerbated by social and systemic factors. This study aimed to estimate the prevalence of Common Mental Disorders among adults with DM and/or HTN and identify sociodemographic and clinical factors associated with mental health status.\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted between 2023 and 2024 among 303 adults diagnosed with diabetes mellitus and/or hypertension residing in the Sarjapur Primary Health Centre area of Bengaluru Urban District. The study tool consisted of sociodemographic characteristics, lifestyle factors, clinical parameters, out-of-pocket expenditure, and the Patient Health Questionnaire-9 and Generalised Anxiety Disorder Scale-7, which were administered. Data analysis included descriptive statistics and bivariate and multivariable analyses.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe mean age of participants was 61.9 years, with the majority having no education; most men were retired, while most women were homemakers. Catastrophic Health Expenditure was reported in 77% of those with both Diabetes and Hypertension. Participants with uncontrolled diabetes and hypertension were found to be 46.9% and 50.2% respectively. Depression and anxiety were prevalent in 40.9% and 44.6% of participants. Common Mental Disorders were significantly associated with female gender, low socioeconomic status, consumption of fried or junk food, alcohol use with dependence, and CHE.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eDepression and anxiety are highly prevalent among adults with Non-Communicable Diseases in this rural setting, with significant associations to gender, lifestyle, and economic burden. Routine screening for common mental disorders is recommended in primary care settings.\u003c/p\u003e","manuscriptTitle":"Prevalence of Common Mental Disorders among Adults with Non Communicable Diseases in a Rural South Indian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 03:33:30","doi":"10.21203/rs.3.rs-8961010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T08:03:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110538938749475719761419052322846003908","date":"2026-04-21T21:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-19T19:44:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T14:41:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T09:27:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T04:47:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-02T16:54:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a2b085a-4d81-47d9-8995-cbfd7dcd5017","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T08:03:18+00:00","index":42,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T03:33:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 03:33:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8961010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8961010","identity":"rs-8961010","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}