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Comorbidity of depression and diabetes: an application of biopsychosocial model



Type 2 diabetes (T2D) is one of the most psychologically demanding chronic medical illness in adult. Comorbidity between diabetes and depression is quite common, but most studies were based on developed country sample. Limited data exists to document biopsychosocial predictors of depressive symptoms in Ethiopian patients. Therefore, the aim of the study was to describe the association of depressive symptoms and T2D and explore the potential underlying associated biopsychosocial risk factors.


Institution based cross-sectional study was conducted on 276 patient with T2D at diabetic clinic, Black Lion General Specialized Hospital in Ethiopia. Patients were selected using systematic random sampling technique. Depressive symptoms score, which constructed from a validated nine-item Patient Health Questionnaire (PHQ-9), was an outcome variable. Finally, significant associated factors were identified using multiple linear regression analysis with backward elimination procedure. Statistical Package for Social Science (SPSS) version 22.0 (IBM SPSS Corp.) was used to perform all analysis.


Total of 264 patient data was analyzed with 95.7% response rate. Patients mean (SD) current age and age at diagnosis was 55.9 (10.9) and 43.9 (10.9) years, respectively. Patients waist circumference (mean ± SD) was 98.9 ± 11.1 cm. The average PHQ-9 score was 4.9 (SD 4.1) and fasting blood glucose was 166.4 (SD 73.2). Marital status (divorced), occupation (housewife), diabetic complication (nephropathy), negative life event in the last six months, and poor social support significantly associated with increased mean PHQ-9 score after adjustment for covariates. Whereas not fearing diabetic-related complication and death significantly lower mean PHQ-9 score.


Biopsychosocial variables including marital status, negative life event in the last 6 months, occupation, diabetic complication, and poor social support significantly increase average depressive symptoms score. Evidence-based intervention focusing on these identified biopsychosocial factors are necessary to prevent the development of depressive symptoms.


Diabetes mellitus (DM) has been affecting millions of people from all over the world. In 2013, 382 million people had diabetes; this estimate is expected to rise to 592 million by 2035 [1, 2]. More than 77% of morbidity and 88% of mortality due to DM occur in low and middle-income countries. In Ethiopia, the prevalence of diabetes was 0.34–5.0% [3, 4]. During the last decades, the comorbidity of mental disorders with chronic health conditions have emerged as a topic of considerable clinical and policy interest. Due to complex nature of disease pathophysiology, cause, and treatment, type 2 diabetes (T2D) is considered one of the most psychologically demanding chronic medical illness in an adult patient [5, 6]. In spite of this, up to 45% of cases of comorbid mental disorder and severe psychological distress were poorly identified and inadequately treated among patients with diabetes in sub-Saharan Africa [7, 8]. The prevalence of psychiatric disorders in diabetic patients may reach 84% for mood disorders and 80% for anxiety disorders [9, 10]. Based on a study report by Ana Claudia and colleagues the most prevalent comorbid disorders were generalized anxiety disorder (21%), dysthymia (15%), social phobia (7%), lifelong depression (3.5%), panic disorder (2.5%), and risk of suicide (2%) [10]. Depression was among the most common neuropsychiatric disorders in patients with T2D [8].

Thomas Willis, British physician, recognized the association between depression and diabetes since 17th century [11]. Epidemiologically, one in every four patient with T2D develops clinically significant depression [12]. The estimated lifetime prevalence of depression was higher in women (21%) [13]. The prevalence of depression in T2D patient was 5.5–49.6% [10, 1422]. Even though most studies was on Western samples, there have been emerging studies in developing countries including Ethiopia [16, 2325]. A cross-sectional study by Erkie et al. described depression was diagnosed in 64.8% of T2D outpatient [23]. The exact mechanisms of relationship are elusive, and models for the associated factors are multidimensional.

Engel’s [26] biopsychosocial model of health and illness is a model for clinical practice and research for psychologists, nurses, physicians, and social workers [27]. American Psychiatric Association and American Board of Psychiatry and Neurology have officially approved Engel’s model [28, 29]. According to Engel’s model any disease such as depression [3033] caused by biological (physiological or genetic predispositions), psychological (health beliefs and lifestyle) and social factors (family relationships, socioeconomic status, and social support). The model reveals the interaction of this factor to create patient’s state of mind and body [34, 35] (Fig. 1).

Fig. 1

Engle’s biopsychosocial model of health and disease adapted for our study

T2D patients were poorly diagnosed and inadequately treated in sub-Saharan Africa [8]. In general, the data is limited, and the conclusion seems inadequate to identify biopsychosocial risk factors of depressive symptoms in Ethiopian diabetic patients. In the present study, we aimed to describe the association of depressive symptoms and T2D, and explore the potential underlying associated risk factors.


Study design and population

We conducted an institution based cross-sectional study among T2D outpatients on regular follow-up at Black Lion General Specialized Hospital from February to April 2013.

Determinants and covariables

The outcome variable was depressive symptoms score. The explanatory variables were biological factors: age, sex, comorbid disease, diabetic complication, diabetic treatment, fasting blood glucose, body mass index; psychological factors: unemployment, financial stress, negative life event, polypharmacy, smoking, lack of regular physical activity, perceived fear of complication and death, perceived high healthcare cost; and social factors: socioeconomic status, educational status, marital status, major family conflict, poor social support. We defined polypharmacy as taking greater than or equal to four prescribed medications per day. Poor social support was defined as lack of support or care from the family, friends, and neighbors. Another studied factor was, which referred to an event such as accident and death in the last six months that leads to a feeling of stress or anxiety, negative life event. Perceived fear of complication and death was defined as individual feeling or opinion about his/her illness and related complication. Perceived high healthcare cost was defined as personal feeling or idea about the expense of diabetes treatment. Physical activity was defined as doing any aerobic exercise 3–5 times per week at least for 30 min.

Sampling and data collection

Patients were chosen based on three criteria: T2D diagnosis at least for one year, age ≥20 years old, capable of independent communication, and signed written informed consent. Patients treated for depression, or other psychological illnesses (e.g. anxiety or personality disorders) were excluded. Systematic random sampling technique was used to reach individual patients. The data was collected by two trained Nurses from every three patients (sampling interval/k/ = 3). All biological (physiological) data was collected from patients’ medical chart. Face-to-face interview was conducted in the treating private clinics to collect psychological and social data. Twelve patients refused to take part because of lack of interest to participate and a shortage of time. In total 264 cases were included in the final analysis.

Measuring depressive symptoms

Depression, which refers to symptoms experienced during the last two weeks, was measured by Patient Health Questionnaire (PHQ-9) [36, 37]. The PHQ-9 includes nine items with individual score ranges from 0 (not at all) to 3 (nearly every day). The total sum score ranging from 0 to 27. PHQ-9 scores with cut-off point 5, 10, 15 and 20 represent mild, moderate, moderately severe, and severe depression, respectively [36]. In our study, T2D patients’ depression status was measured by administering a validated Ethiopian version PHQ-9 questionnaire. Gelaye and colleagues showed PHQ-9 internal reliability of 0.81, test re-test reliability of 0.92, sensitivity of 86%, and specificity of 67% [38].

Statistical analysis

First of all, four cases were not included in our analysis because of outlying PHQ-9 score (≥20). Descriptive statistics including mean, standard deviation, percentage, and cross-tabulation was performed for all patient parameters. Univariate linear regression analysis was performed per each biopsychosocial variable. The full model of multiple linear regression included all significant variables. Finally, significantly associated factors were identified by backward elimination procedure. QQ-plot, histogram and scatter plot of ‘Standardized residuals’ against ‘Standardized predicted values’ were used to check the assumptions of linearity of relationships, normal distribution and homoscedasticity of residuals for the final model. Two-tailed test at 5% level of significance was used for all association test. Statistical Package for Social Science (SPSS) version 22.0 (IBM SPSS Corp.) was used to perform all analysis. The study was adherent to the STROBE criteria.


Biopsychosocial characteristics of patients

Total of 264 patient data was analyzed with 95.7% response rate. Patients mean (SD) current age and age at diagnosis was 55.9 (10.9) and 43.9 (10.9) years, respectively. Also, patients waist circumference (mean ± SD) was 98.9 ± 11.1 cm while patients family median monthly income was 750 Ethiopian Birr (651–1400). The average PHQ-9 score was 4.9 (SD 4.1) and fasting blood glucose was 166.4 (SD 73.2). The mean ± SD of PHQ-9 score was 6 ± 4.7 in female, 7.3 ± 5.7 in divorced, 6.6 ± 4.5 in educational level of grade 1–8, and 6.77 ± 5.3 in housewife patient. The mean ± SD of number of comorbid diseases and body mass index was 1.1 ± 0.9 and 25.4 ± 3.7, respectively (Table 1).

Table 1 Distribution of patients PHQ-9 score and fasting blood glucose by biopsychosocial characteristics of patients

Univariate linear regression test of association

Patient mean PHQ-9 score was significantly increased by 1.4 (95% CI 0.4–2.4) in female, 2.2 (95% CI 0.7–3.7) in divorced, and 1.7 (95% CI 0.4–3.0) in housewife. One unit increase in number of comorbidities was associated with a 0.6 unit (p = 0.04) increase in PHQ-9 score. One unit increase in number of diabetic complication was associated with a 0.5 unit (p = 0.02) increase in PHQ-9 score. Increased age at diagnosis (i.e. late-onset diabetes), increased monthly family income, high educational status (college or university), doing physical activity and not fearing diabetes-related complication and death significantly lower mean PHQ-9 score (Table 2).

Table 2 Univariate linear regression test examining the association between biopsychosocial variables and PHQ-9 score of patients

Multiple linear regression tests of association

All significant biopsychosocial variables, Table 3, in the final model together explained about 25.3% of the variability of patients PHQ-9 score. Divorce, housewife, diabetic nephropathy, negative life event, and poor social support were significant risk factors associated with increased PHQ-9 score after adjustment for covariates. However, not fearing diabetic-related complications and death significantly lower PHQ-9 score. Additional file 1: Table S1 presented all confounding factors.

Table 3 Multiple linear regression test examining the relation between different biopsychosocial associated factors and PHQ-9 score of patients with T2D mellitus

The final model reasonably fulfilled three assumptions: linearity of relationship (Additional file 1: Figure S1), homoscedasticity (Additional file 1: Figure S2), and normal distribution (Additional file 1: Figure S3) assumptions. For further information, residual statistics table (Additional file 1: Table S2) accompanied as well.


This study examined biopsychosocial factors associated with comorbid depression in patients with T2D.

In this study diabetic nephropathy, biologic factor consistent with other studies [39, 40], significantly increased the risk of depression. However, several other studies recognized gender [16, 17, 21, 4143], age [16, 17, 20, 44, 45], diabetic treatment [21, 46, 47], body mass index [21, 48], fasting plasma glucose [1719, 49, 50], poor diabetes mellitus control [15], number of comorbidities [21, 48, 51, 52], diabetic complications [16, 17, 53, 54], duration of diabetes [23, 45], age at diabetes diagnosis [55, 56], large waist circumference [39], diabetic retinopathy [40], diabetic neuropathy [39, 40, 57, 58], cardiovascular disease comorbidity [39, 40, 59, 60], sexual dysfunction [40] were physiologic (biologic) risk factors that significantly associated with depression.

In this study occupational status (housewife) and experiencing negative life events, psychological factors, significantly increased risk of depression in line with other earlier studies [39, 57, 61]. Interestingly, our final model uncovered not fearing diabetic related complications and death significantly lower risk of depression. On the other hand, depression was associated with diabetes treatment complexity [62], experienced loss of business or crop failure [16], unemployment [44, 47, 52], lack of regular physical activity [14, 21, 47], smoking [21, 48, 63], financial stress [39, 57, 61], poor quality of life [61, 64], and polypharmacy [39, 65].

Finally, we confirmed marital status (divorce) and poor social support, social factors similar to previous studies [19, 21, 57], significantly increased risk of depression. Contrariwise, urban residence [59], low socioeconomic status [19, 20, 42, 47, 66], lower educational status [23, 47, 49, 52], marital status [15, 17, 19, 21, 24, 44], major family conflicts and unavailability of food or medicines [16] were significant associated factors for depression.

Similar to previous studies [39, 58, 59, 6770], our final model proved risk of depression was not significantly associated with current age, sex, educational status, residence, ethnicity, socioeconomic status, poor body weight control, insulin treatment users, duration of diabetes, obesity, hypertensive disorder, and diabetic retinopathy. Recent studies [3942, 4749, 51, 66] found diabetic neuropathy, doing physical activity, diabetic retinopathy, educational status, perceived fear of diabetes-related death and complication, number of diabetic complication, being female, physical disability, increased body mass index, low monthly family income, age at diagnosis, and increased number of co-morbid disease significantly associated with depression. However, our study lacks to confirm this robust fact.

Most of these inconsistencies might be attributed to inadequacies in study design, implementation (i.e. data analysis and sample selection), interpretation (i.e. categorizing and using different cutoff point to diagnose depression), inadequately powered sample groups, and using different depression diagnostic tool. Using dichotomized PHQ-9 score as an outcome variable clearly causes loss of information, loss of power, bias, incomplete correction for confounding factors, and difficulty for robust replication of associated risk factors [7173]. Similarly, Olivier Naggara and colleagues argued dichotomization is unnecessary for statistical analysis, and continuous variable should be left alone in statistical model [74]. Researchers have used different cut-off point for dichotomizing PHQ-9 score [22, 59] that compromise replication for an unbiased view of the evidence from a particular study.

This study has important public health implication for health care practice in Black Lion General Specialized Hospital and another health facility, where clinician diagnosis of mental illness (depression) rate is low because of high patient load, lack of screening tool, role confusion, and lack of training. Another important barrier to the care of people with mental and physical health problem in lower and middle-income country is the lack of an integrated model for mental and medical health service [75]. We suggest physician or physiotherapist screen mental health and psychiatrist screen physical health of patient. Finally, clinicians should be aware of various factors and use biopsychosocial model to integrate their patient care.

Strengths and limitations

The strength of this study includes the use of PHQ-9 score as continuous outcome variable. Variables were defined based on Engel’s biopsychosocial model as well. However, this study has certain limitation. Most parameters estimated were biologic (physiologic) factors. This might underestimate the effect of psychological and social factors. Poor social support, which was identified as a highly significant associated factor, was assessed by a single item. Additionally, this study was conducted in one institution that might limit external validity of the finding. This study examined only the associations between the selected variables and PHQ-9 score. Lastly, the role of inflammation and genetic susceptibility for the emergence of depressive symptoms was not assessed.


In general biopsychosocial variables including marital status, negative life event in the last six months, occupational status, diabetic complication, and poor social support significantly increased risk of depression. Evidence-based intervention focusing on these identified biopsychosocial factors are necessary to prevent the development of depressive symptoms. Our study finding described the effect of various biopsychosocial factors on patient’s mood because depression-related factors frequently missed in people with diabetes [76]. This will improve evidence-based practice for comprehensive management physical and mental illness [77].


  1. 1.

    Harrison TA, Hindorff LA, Kim H, Wines RC, Bowen DJ, McGrath BB, et al. Family history of diabetes as a potential public health tool. Am J Prev Med. 2003;24(2):152–9.

    Article  PubMed  Google Scholar 

  2. 2.

    Guariguata L, Whiting D, Hambleton I, Beagley J, Linnenkamp U, Shaw J. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–49.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Kassahun T, Eshetie T, Gesesew H. Factors associated with glycemic control among adult patients with type 2 diabetes mellitus: a cross-sectional survey in Ethiopia. BMC Res Notes. 2016;9(1):78.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Habtewold TD, Tsega WD, Wale BY. Diabetes mellitus in outpatients in Debre Berhan referral hospital, Ethiopia. J Diabetes Res. 2016;2016:3571368.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kruse J, Schmitz N, Thefeld W, German National Health Interview and Examination Survey. On the association between diabetes and mental disorders in a community sample: results from the German National Health Interview and Examination Survey. Diabetes Care. 2003;26(6):1841–6.

    Article  PubMed  Google Scholar 

  6. 6.

    Chew BH, Vos R, Mohd-Sidik S, Rutten GE. Diabetes-related distress, depression and distress-depression among adults with type 2 diabetes mellitus in Malaysia. PLoS ONE. 2016;11(3):e0152095.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Balhara YP. Diabetes and psychiatric disorders. Indian J Endocrinol Metab. 2011;15(4):274–83.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Akena D, Kadama P, Ashaba S, Akello C, Kwesiga B, Rejani L, et al. The association between depression, quality of life, and the health care expenditure of patients with diabetes mellitus in Uganda. J Affect Disord. 2015;15(174):7–12.

    Article  Google Scholar 

  9. 9.

    Chaudhry R, Mishra P, Mishra J, Parminder S, Mishra BP. Psychiatric morbidity among diabetic patients: a hospital-based study. Ind Psychiatry J. 2010;19(1):47–9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    de Ornelas Maia AC, de Azevedo Braga A, Brouwers A, Nardi AE, Oliveira e Silva AC. Prevalence of psychiatric disorders in patients with diabetes types 1 and 2. Compr Psychiatry. 2012;53(8):1169–73.

    Article  Google Scholar 

  11. 11.

    Moulton CD, Pickup JC, Ismail K. The link between depression and diabetes: the search for shared mechanisms. Lancet Diabetes Endocrinol. 2015;3(6):461–71.

    Article  PubMed  Google Scholar 

  12. 12.

    Semenkovich K, Brown ME, Svrakic DM, Lustman PJ. Depression in type 2 diabetes mellitus: prevalence, impact, and treatment. Drugs. 2015;75(6):577–87.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Snoek FJ, Bremmer MA, Hermanns N. Constructs of depression and distress in diabetes: time for an appraisal. Lancet Diabetes Endocrinol. 2015;3(6):450–60.

    Article  PubMed  Google Scholar 

  14. 14.

    Lee CM, Chang CF, Pan MY, Hsu TH, Chen MY. Depression and its associated factors among rural diabetic residents. J Nurs Res. 2016. doi:10.1097/jnr.0000000000000143.

    Google Scholar 

  15. 15.

    El Mahalli AA. Prevalence and predictors of depression among type 2 diabetes mellitus outpatients in Eastern Province, Saudi Arabia. Int J Health Sci (Qassim). 2015;9(2):119–26.

    Google Scholar 

  16. 16.

    Islam SM, Rawal LB, Niessen LW. Prevalence of depression and its associated factors in patients with type 2 diabetes: a cross-sectional study in Dhaka, Bangladesh. Asian J Psychiatr. 2015;17:36–41.

    Article  PubMed  Google Scholar 

  17. 17.

    Rodriguez Calvin JL, Zapatero Gaviria A, Martin Rios MD. Prevalence of depression in type 2 diabetes mellitus. Rev Clin Esp. 2015;215(3):156–64.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Wang L, Song R, Chen Z, Wang J, Ling F. Prevalence of depressive symptoms and factors associated with it in type 2 diabetic patients: a cross-sectional study in China. BMC Public Health. 2015;15:188.

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Zhang W, Xu H, Zhao S, Yin S, Wang X, Guo J, et al. Prevalence and influencing factors of co-morbid depression in patients with type 2 diabetes mellitus: a General Hospital based study. Diabetol Metab Syndr. 2015;7:60 (eCollection 2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Ganasegeran K, Renganathan P, Manaf RA, Al-Dubai SA. Factors associated with anxiety and depression among type 2 diabetes outpatients in Malaysia: a descriptive cross-sectional single-centre study. BMJ Open. 2014;4(4):e004794.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Gorska-Ciebiada M, Saryusz-Wolska M, Ciebiada M, Loba J. Mild cognitive impairment and depressive symptoms in elderly patients with diabetes: prevalence, risk factors, and comorbidity. J Diabetes Res. 2014;2014:179648.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Dejenie Habtewold T, Radie YT, Sharew NT. Prevalence of depression among type 2 diabetic outpatients in black lion general specialized hospital, Addis Ababa, Ethiopia. Depress Res Treat. 2015;2015:184902.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Erkie M, Feleke Y, Desalegne F, Anbessie J, Shibre T. Magnitude, clinical and sociodemographic correlate of depression in diabetic patients, Addis Ababa, Ethiopia. Ethiop Med J. 2013;51(4):249–59.

    PubMed  Google Scholar 

  24. 24.

    Khan MA, Sultan SM, Nazli R, Akhtar T, Khan MA, Sher N, et al. Depression among patients with type-II diabetes mellitus. J Coll Physicians Surg Pak. 2014;24(10):770–1.

    PubMed  Google Scholar 

  25. 25.

    Birhanu AM, Alemu FM, Ashenafie TD, Balcha SA, Dachew BA. Depression in diabetic patients attending University of Gondar Hospital Diabetic clinic, Northwest Ethiopia. Diabetes Metab Syndr Obes. 2016;9:155.

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Engel, GL. The need for a new medical model: a challenge for biomedicine. Science. 1977;196(4286):129–36.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Hatala AR. The status of the “biopsychosocial” model in health psychology: towards an integrated approach and a critique of cultural conceptions. Open J Med Psychol. 2012;1(4):51–62.

    Article  Google Scholar 

  28. 28.

    Ghaemi SN. The rise and fall of the biopsychosocial model. Br J Psychiatry. 2009;195(1):3–4.

    Article  PubMed  Google Scholar 

  29. 29.

    Tavakoli HR. A closer evaluation of current methods in psychiatric assessments: a challenge for the biopsychosocial model. Psychiatry (Edgmont). 2009;6(2):25–30.

    Google Scholar 

  30. 30.

    Schotte CK, Van Den Bossche B, De Doncker D, Claes S, Cosyns P. A biopsychosocial model as a guide for psychoeducation and treatment of depression. Depress Anxiety. 2006;23(5):312–24.

    Article  PubMed  Google Scholar 

  31. 31.

    Garcia-Toro M, Aguirre I. Biopsychosocial model in depression revisited. Med Hypotheses. 2007;68(3):683–91.

    Article  PubMed  Google Scholar 

  32. 32.

    Frankel RM, Quill TE, McDaniel SH. The biopsychosocial approach: past, present, and future. Rochester: University Rochester Press; 2003.

    Google Scholar 

  33. 33.

    Campbell LC, Clauw DJ, Keefe FJ. Persistent pain and depression: a biopsychosocial perspective. Biol Psychiatry. 2003;54(3):399–409.

    Article  PubMed  Google Scholar 

  34. 34.

    Havelka M, Lučanin JD, Lučanin D. Biopsychosocial model–the integrated approach to health and disease. Coll Antropol. 2009;33(1):303–10.

    PubMed  Google Scholar 

  35. 35.

    Robbins A. Biopsychosocial aspects in understanding and treating depression in men: a clinical perspective. J Men’s Health Gend. 2006;3(1):10–8.

    Article  Google Scholar 

  36. 36.

    Kroenke K, Spitzer RL, Williams JB. The Phq-9. J Gen Intern Med. 2001;16(9):606–13.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Yu X, Tam WW, Wong PT, Lam TH, Stewart SM. The patient health questionnaire-9 for measuring depressive symptoms among the general population in Hong Kong. Compr Psychiatry. 2012;53(1):95–102.

    Article  PubMed  Google Scholar 

  38. 38.

    Gelaye B, Williams MA, Lemma S, Deyessa N, Bahretibeb Y, Shibre T, et al. Validity of the patient health questionnaire-9 for depression screening and diagnosis in East Africa. Psychiatry Res. 2013;210(2):653–61.

    Article  PubMed  Google Scholar 

  39. 39.

    Naranjo DM, Fisher L, Arean PA, Hessler D, Mullan J. Patients with type 2 diabetes at risk for major depressive disorder over time. Ann Fam Med. 2011;9(2):115–20.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    De Groot M, Anderson R, Freedland KE, Clouse RE, Lustman PJ. Association of depression and diabetes complications: a meta-analysis. Psychosom Med. 2001;63(4):619–30.

    Article  PubMed  Google Scholar 

  41. 41.

    Demmer RT, Gelb S, Suglia SF, Keyes KM, Aiello AE, Colombo PC, et al. Sex differences in the association between depression, anxiety, and type 2 diabetes mellitus. Psychosom Med. 2015;77(4):467–77.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Hapunda G, Abubakar A, Pouwer F, van de Vijver F. Diabetes mellitus and comorbid depression in Zambia. Diabet Med. 2015;32(6):814–8.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Lopez-de-Andres A, Jimenez-Trujillo MI, Hernandez-Barrera V, de Miguel-Yanes JM, Mendez-Bailon M, Perez-Farinos N, et al. Trends in the prevalence of depression in hospitalized patients with type 2 diabetes in Spain: analysis of hospital discharge data from 2001 to 2011. PLoS ONE. 2015;10(2):e0117346.

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Milanovic SM, Erjavec K, Poljicanin T, Vrabec B, Brecic P. Prevalence of depression symptoms and associated socio-demographic factors in primary health care patients. Psychiatr Danub. 2015;27(1):31–7.

    PubMed  Google Scholar 

  45. 45.

    Mir K, Mir K, Malik I, Shehzadi A. Prevalence of co-morbid depression in diabetic population. J Ayub Med Coll Abbottabad. 2015;27(1):99–101.

    PubMed  Google Scholar 

  46. 46.

    Berge LI, Riise T, Tell GS, Iversen MM, Ostbye T, Lund A, et al. Depression in persons with diabetes by age and antidiabetic treatment: a cross-sectional analysis with data from the Hordaland Health Study. PLoS ONE. 2015;10(5):e0127161.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Park CY, Kim SY, Gil JW, Park MH, Park JH, Kim Y. Depression among Korean adults with type 2 diabetes mellitus: Ansan-Community-Based Epidemiological Study. Osong Public Health Res Perspect. 2015;6(4):224–32.

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Kyung Lee H, Hee Lee S. Depression, diabetes, and healthcare utilization: results from the Korean Longitudinal Study of Aging (KLoSA). Iran J Public Health. 2014;43(1):6–15.

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Davis TM, Hunt K, Bruce DG, Starkstein S, Skinner T, McAullay D, et al. Prevalence of depression and its associations with cardio-metabolic control in Aboriginal and Anglo-Celt patients with type 2 diabetes: the Fremantle Diabetes Study Phase II. Diabetes Res Clin Pract. 2015;107(3):384–91.

    Article  PubMed  Google Scholar 

  50. 50.

    De la Roca-Chiapas JM, Hernandez-Gonzalez M, Candelario M, Villafana Mde L, Hernandez E, Solorio S, et al. Association between depression and higher glucose levels in middle-aged Mexican patients with diabetes. Rev Invest Clin. 2013;65(3):209–13.

    CAS  PubMed  Google Scholar 

  51. 51.

    Foran E, Hannigan A, Glynn L. Prevalence of depression in patients with type 2 diabetes mellitus in Irish primary care and the impact of depression on the control of diabetes. Ir J Med Sci. 2015;184(2):319–22.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Sweileh WM, Abu-Hadeed HM, Al-Jabi SW, Zyoud SH. Prevalence of depression among people with type 2 diabetes mellitus: a cross sectional study in Palestine. BMC Public Health. 2014;14:163.

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Siddiqui S. Depression in type 2 diabetes mellitus—a brief review. Diabetes Metab Syndr. 2014;8(1):62–5.

    Article  PubMed  Google Scholar 

  54. 54.

    Frederick FT, Maharajh HD. Prevalence of depression in type 2 diabetic patients in Trinidad and Tobago. West Indian Med J. 2013;62(7):628–31.

    CAS  PubMed  Google Scholar 

  55. 55.

    Ryerson B, Tierney EF, Thompson TJ, Engelgau MM, Wang J, Gregg EW, et al. Excess physical limitations among adults with diabetes in the U.S. population, 1997–1999. Diabetes Care. 2003;26(1):206–10.

    Article  PubMed  Google Scholar 

  56. 56.

    Zahid N, Asghar S, Claussen B, Hussain A. Depression and diabetes in a rural community in Pakistan. Diabetes Res Clin Pract. 2008;79(1):124–7.

    Article  PubMed  Google Scholar 

  57. 57.

    Musselman DL, Betan E, Larsen H, Phillips LS. Relationship of depression to diabetes types 1 and 2: epidemiology, biology, and treatment. Biol Psychiatry. 2003;54(3):317–29.

    Article  PubMed  Google Scholar 

  58. 58.

    Pouwer F, Geelhoed-Duijvestijn P, Tack C, Bazelmans E, Beekman A, Heine R, et al. Prevalence of comorbid depression is high in out-patients with Type 1 or Type 2 diabetes mellitus. Results from three out-patient clinics in the Netherlands. Diabet Med. 2010;27(2):217–24.

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Tapash R, Lloyd CE, Parvin M, Mohiuddin KGB, Rahman M. Prevalence of co-morbid depression in out-patients with type 2 diabetes in Bangladesh. BMC Psychiatry. 2012;12:123.

    Article  Google Scholar 

  60. 60.

    Guruprasad K, Niranjan M, Ashwin S. A study of association of depressive symptoms among the type 2 diabetic outpatients presenting to a tertiary care hospital. Indian J Psychol Med. 2012;34(1):30.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ell K, Katon W, Cabassa LJ, Xie B, Lee PJ, Kapetanovic S, et al. Depression and diabetes among low-income Hispanics: design elements of a socioculturally adapted collaborative care model randomized controlled trial. Int J Psychiatry Med. 2009;39(2):113–32.

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    de Groot M, Doyle T, Averyt J, Risaliti C, Shubroo J. Depressive symptoms and type 2 diabetes mellitus in rural appalachia: an 18-month follow-up study. Int J Psychiatry Med. 2015;48(4):263–77.

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Clyde M, Smith KJ, Gariepy G, Schmitz N. The association between smoking and depression in a Canadian community-based sample with type 2 diabetes. Can J Diabetes. 2013;37(3):150–5.

    Article  PubMed  Google Scholar 

  64. 64.

    Hermanns N, Kulzer B. Diabetes and depression-a burdensome co-morbidity. Eur Endocrinol. 2008;4(2):S19–22.

    Article  Google Scholar 

  65. 65.

    Gilmer TP, Walker C, Johnson ED, Philis-Tsimikas A, Unutzer J. Improving treatment of depression among Latinos with diabetes using project Dulce and IMPACT. Diabetes Care. 2008;31(7):1324–6.

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Camara A, Balde NM, Enoru S, Bangoura JS, Sobngwi E, Bonnet F. Prevalence of anxiety and depression among diabetic African patients in Guinea: association with HbA1c levels. Diabetes Metab. 2015;41(1):62–8.

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Kiani F, Hesabi N. The relationship between the religious beliefs of the diabetic patients and depression in a diabetes clinic in Iran. J Relig Health. 2016;55(227):1–6.

    Google Scholar 

  68. 68.

    Agbir T, Audu M, Adebowale T, Goar S. Depression among medical outpatients with diabetes: a cross-sectional study at Jos University Teaching Hospital, Jos, Nigeria. Ann Afr Med. 2010;9(1):5–10.

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    Egede LE, Ellis C. The effects of depression on diabetes knowledge, diabetes self-management, and perceived control in indigent patients with type 2 diabetes. Diabetes Technol Ther. 2008;10(3):213–9.

    Article  PubMed  Google Scholar 

  70. 70.

    Raval A, Dhanaraj E, Bhansali A, Grover S, Tiwari P. Prevalence and determinants of depression in type 2 diabetes patients in a tertiary care centre. Indian J Med Res. 2010;132(2):195.

    PubMed  Google Scholar 

  71. 71.

    Taylor JMG, Yu M. Bias and efficiency loss due to categorizing an explanatory variable. J Multivar Anal. 2002;83(1):248–63.

    Article  Google Scholar 

  72. 72.

    Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012;12:21.

    Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Salazar LF, Crosby RA, DiClemente RJ. Research methods in health promotion. Hoboken: Wiley; 2015.

    Google Scholar 

  74. 74.

    Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG. Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. AJNR Am J Neuroradiol. 2011;32(3):437–40.

    CAS  Article  PubMed  Google Scholar 

  75. 75.

    Weinmann S, Koesters M. Mental health service provision in low and middle-income countries: recent developments. Curr Opin Psychiatry. 2016;29(4):270–5.

    Article  PubMed  Google Scholar 

  76. 76.

    Holt RI, de Groot M, Golden SH. Diabetes and depression. Curr Diab Rep. 2014;14(6):491.

    Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Lloyd CE, Sartorius N, Cimino LC, Alvarez A, Guinzbourg de Braude M, Rabbani G, et al. The INTERPRET-DD study of diabetes and depression: a protocol. Diabet Med. 2015;32(7):925–34.

    CAS  Article  PubMed  Google Scholar 

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Authors’ contributions

TDH and YTR conceived and designed the study. TDH, MAI, and BST analyzed the data, interpreted the result and wrote the manuscript. All authors read and approved the final manuscript.


Our in-depth gratitude goes to Addis Ababa University for giving this chance and approval of the study too. Our sincerest thank goes to Dr. Behrooz Z. Alizadeh (Genetic Epidemiologist, Associate Professor, University Medical Center Groningen, the Netherlands) for his intellectual comment during manuscript writing. Data collectors and respondents were highly acknowledged for investing their precious time for collecting data and providing the necessary information. We would like to offer our great respect and appreciation to all our friends and senior instructors who gave us precious time for advice and comments during data entry and analysis.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All the relevant data was included in the article.

Ethics approval and consent to participate

To conform the Declaration of Helsinki (1964) and Population Screening Act (WBO), Addis Ababa University Institutional Review Board approved the study. Participation was voluntary, and information was collected anonymously after obtaining written consent from each respondent. Confidentiality of patient data was ensured throughout the study.


This study was conducted in collaboration with Addis Ababa University. Every step of the project was followed by Addis Ababa University, centralized school of nursing and midwifery. The university has no role in designing, analysis and writing of the study. The researchers received no specific funding for this work.

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Corresponding author

Correspondence to Tesfa Dejenie Habtewold.

Additional file

Additional file 1: Table S1.

List of confounding factors that affect the influence of explanatory variables on and PHQ-9 score of patients with type 2 diabetes mellitus. Figure S1. Normal P–P plot of regression standardized residual of the final model. Figure S2. Scatter plot of regression standardized residual of the final model. Figure S3. Histogram of regression standardized residual of the final model. Table S2. Residuals statistics for the final model.

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Habtewold, T.D., Islam, M.A., Radie, Y.T. et al. Comorbidity of depression and diabetes: an application of biopsychosocial model. Int J Ment Health Syst 10, 74 (2016).

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  • Biopsychosocial model
  • Comorbidity
  • Depression
  • Diabetes mellitus