Comorbidity of depression and diabetes: an application of biopsychosocial model

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, 14‐22]. Even though most studies was on Western samples, there have been emerging studies in developing countries including Ethiopia [16, 23‐25]. 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 [30‐33] 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).

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.

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, 41‐43], age [16, 17, 20, 44, 45], diabetic treatment [21, 46, 47], body mass index [21, 48], fasting plasma glucose [17‐19, 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, 67‐70], 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 [39‐42, 47‐49, 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 [71‐73]. 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.

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].