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Table 1.  
Sociodemographic and Clinical Characteristics
Sociodemographic and Clinical Characteristics
Table 2.  
Association Between Psychological Symptoms and Sociodemographic and Clinical Variablesa
Association Between Psychological Symptoms and Sociodemographic and Clinical Variablesa
Table 3.  
Multiple Linear Regression Analysis of Association Between Depressive Symptoms and Sociodemographic and Clinical Variables
Multiple Linear Regression Analysis of Association Between Depressive Symptoms and Sociodemographic and Clinical Variables
Table 4.  
Multiple Linear Regression Analysis of Association Between Anxiety Symptoms and Sociodemographic and Clinical Variables
Multiple Linear Regression Analysis of Association Between Anxiety Symptoms and Sociodemographic and Clinical Variables
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Original Investigation
September 2016

Association Between Diabetes-Related Eye Complications and Symptoms of Anxiety and Depression

Author Affiliations
  • 1Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia
  • 2Singapore Eye Research Institute, National University of Singapore, Singapore
  • 3Duke–National University of Singapore Graduate Medical School, Singapore
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Ophthalmol. 2016;134(9):1007-1014. doi:10.1001/jamaophthalmol.2016.2213
Abstract

Importance  This study is needed to clarify inconsistent findings regarding the association between diabetes-related eye complications and psychological well-being.

Objective  To examine the association between severity of diabetic retinopathy (DR) and diabetic macular edema (DME) with symptoms of depression and anxiety in adults with diabetes.

Design, Setting, and Participants  A cross-sectional study was conducted in a tertiary eye hospital in Melbourne, Australia. The study comprised 519 participants with diabetes. The median duration of diabetes was 13.0 (interquartile range, 14.0) years. The study was conducted from March 1, 2009, to December 24, 2010.

Exposures  Patients underwent a comprehensive eye examination in which dilated fundus photographs (disc and macula centered) were obtained and graded for the presence and severity of DR and DME. Presenting distance uniocular and binocular visual acuity were assessed using a 3-m logMAR chart.

Main Outcomes and Measures  Symptoms of depression and anxiety were measured using the Hospital Anxiety and Depression Scale (HADS), which comprises 7 questions specific to anxiety and 7 specific to depression with scores ranging from 0 to 21; scores higher than 8 signify possible anxiety or depression. The ordinal raw scores of the HADS questionnaire were transformed to estimates of interval measure using Rasch analysis and evaluated as continuous variables. Participants also completed standardized interview-administered questionnaires. Blood samples were assessed for hemoglobin A1c, fasting blood glucose, and serum lipids. Multiple linear regression models were used to determine the associations between the severity of DR and DME with symptoms of anxiety and depression and commonality analysis was used to quantify the unique variance explained.

Results  Of the 519 participants in the study, 170 individuals (32.8%) were female; mean (SD) age was 64.9 (11.6) years. Raw scores indicated that 80 individuals (15.4%) screened positive for depressive symptoms and 118 persons (22.7%) screened positive for symptoms of anxiety. In multivariate analysis using Rasch scores, severe nonproliferative DR (NPDR)/PDR was independently associated with greater depressive symptoms (regression coefficient [β] = 0.69; 95% CI, 0.03-1.34) after controlling for sociodemographic factors and clinical characteristics, including visual acuity. A history of depression or anxiety accounted for 60.6% (95% CI, 23.9%-83.2%) of the unique variance in depressive symptoms, and severe NPDR or PDR contributed to 19.1% (95% CI, 1.7%-44.4%) of the total explained variance of depressive symptoms. Diabetic macular edema was not associated with depressive symptoms. No association between DR and symptoms of anxiety was identified.

Conclusions and Relevance  Severe NPDR or PDR, but not DME, was independently associated with depressive symptoms. The severity of DR could be an indicator to prompt monitoring of depression in at-risk individuals with diabetes. Further work is required to replicate these findings and determine the clinical significance of the association.

Introduction

Diabetic retinopathy (DR) is a common microvascular complication of diabetes. It is a progressive eye disease that is characterized by an asymptomatic nonproliferative stage (NPDR) and symptomatic proliferative stage (PDR). The PDR stage, together with diabetic macular edema (DME), which can develop at any stage, are the primary causes of vision loss in people with diabetes.1 Diabetic retinopathy has an enormous effect on quality of life, particularly in the vision-threatening stages,2 and qualitative work3 has highlighted the profound emotional impact of DR, including feelings of distress, anger, anxiety, and low mood. Systematic reviews4,5 have shown elevated symptoms of anxiety and depression in people with diabetes. Moreover, rates of depression and anxiety are higher for those with complications from diabetes, such as DR.6-8

Studies investigating the association between clinical characteristics and psychological outcomes in DR are scarce; those that have been conducted3 have small sample sizes and have produced contradictory results. Even large cohort studies contain relatively few participants with DR and do not include details on DR severity. Hence, our understanding of the psychosocial impact of DR or DME across the spectrum of the disease is limited and has been described as “almost entirely lacking from the literature.”9(p1595)

Therefore, the purpose of our study was to examine the association between DR and DME with psychological outcomes in a sample of patients with diabetes recruited from a specialized eye hospital in which the rates of diabetic eye diseases are high. In particular, we examined the effect of clinical characteristics, including DR/DME severity and visual acuity loss, on symptoms of anxiety and depression.

Box Section Ref ID

Key Points

  • Question What is the association between clinical characteristics of diabetic retinopathy complications and symptoms of anxiety and depression?

  • Findings In this cross-sectional study, a personal history of depression or anxiety was associated with symptoms of anxiety or depression. Vision-threatening diabetic retinopathy and moderate or severe vision impairment were identified as independent risk factors for increased depressive symptoms in people with diabetes; an association of symptoms of anxiety with diabetic eye disease was not identified.

  • Meaning The association of diabetic retinopathy with depressive symptoms may warrant further studies to understand its implications.

Methods
Study Design and Participants

Our cross-sectional study was part of the Diabetes Management Project (DMP), a large study of adults (aged >18 years) with type 1 or 2 diabetes. Details of the methods of DMP have been outlined previously.10 Briefly, participants with diabetes were recruited from specialized eye clinics at the Royal Victorian Eye and Ear Hospital from March 1, 2009, to December 24, 2010. Participants were eligible for the study if they were English-speaking, free of significant hearing and cognitive impairment, and living independently. They underwent a comprehensive assessment that included clinical, biochemical, and anthropometric measures as well as interviewer-administered questionnaires. Each participant provided written informed consent, and ethical approval for the DMP was provided by the Royal Victorian Eye and Ear Hospital Human Research and Ethics Committee. Participants received financial compensation. The DMP protocol adhered to the tenets of the Declaration of Helsinki.11

Assessment of Psychological Well-being

Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS).12 The HADS comprises 14 questions: 7 specific to anxiety and 7 to depression. A 4-point category scale (0-3) is used to measure participants’ responses, with a total score of 21 possible for both the anxiety and depression scales. Scores between 0 and 7 are considered normal, and scores higher than 8 signify a possible clinical level of anxiety or depression requiring further assessment.

Assessment of DR, DME, and Vision Impairment

Diabetic retinopathy was graded from 2-color, 45° nonstereoscopic fundus photographs, with 1 image centered on the optic disc and 1 centered on the macula (Canon CR6-45NM; Canon Inc) following the modified Airlie House classification system13 by independent retinal graders. The severity of DR was categorized as no DR (Early Treatment of Diabetic Retinopathy Study level 10-15), mild NPDR (level 20), moderate NPDR (level 31-43), severe NPDR (level 53-60), and PDR (level 61-80) using the eye with the most severe grading. The DR categories were then collapsed to no DR, mild or moderate NPDR, and severe NPDR or PDR for analysis (frequency data on clinical characteristics across all categories provided in eTable 1 in the Supplement). The presence of DME was confirmed using optical coherence tomography scans (Stratus; Ziess), with the severity quantified by the measurement of the central macular thickness. The severity of DME was classified using the American Academy of Ophthalmology1 scale as no, mild, moderate, and severe DME. For analysis, the mild and moderate DME categories were combined.

Presenting distance uniocular and binocular visual acuity were assessed using a 3-m logMAR chart. We collapsed presenting visual acuity (better eye) into 5 categories of vision impairment: none (≤0.18 logMAR, ≤20/32 Snellen equivalent), minimal (<0.18 to 0.3 logMAR, <20/32 to 20/40 Snellen equivalent), mild (<0.3 to 0.48 logMAR, <20/40 to 20/63 Snellen equivalent), moderate (<0.48 to 0.78 logMAR, <20/63 to 20/125 Snellen equivalent), and severe (>0.78 logMAR, >20/125 Snellen equivalent) based on the distribution of the visual acuity data. For analysis, visual acuity categories minimal and mild as well as moderate and severe were combined.

Assessment of Other Clinical Variables

A blood sample was collected via venipuncture, and fasting plasma glucose, hemoglobin A1c, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride levels were obtained. Body mass index (calculated as weight in kilograms divided by height in meters squared) was measured using a wall-mounted scale, and weight was determined using a digital scientific weight scale. Systolic and diastolic blood pressure was assessed using an automated blood pressure machine.

Assessment of Other Risk Factors

Participants’ sociodemographic details, comorbidities, and diabetic complications other than DR were obtained by self-report. Key covariates included age (years); sex; educational level; annual household income; marital status; smoking status (nonsmoker, current, or past smoker); history of depression or anxiety (yes or no); presence of 1 or more comorbidities, including hypertension, myocardial infarction/angina, arrhythmia, stroke, high cholesterol level, asthma, anemia, migraine, arthritis, and osteoporosis (yes or no), duration of diabetes (years); diabetes type; insulin use (yes or no); and presence of diabetic complications other than DR, including nephropathy, peripheral vascular disease, and neuropathy (yes or no).

Rasch Analysis of the HADS

Rasch analysis14 was used to assess the psychometric properties of the anxiety and depression scales of the HADS using the Andrich rating scale model with Winsteps software, version 3.75 (http://www.winsteps.com/winsteps.htm). Rasch analysis is a form of item response theory in which ordinal raw questionnaire scores are transformed to estimates of interval measures (expressed in log of the odds units or logits) for use in subsequent parametric analyses.15 A high person measure indicates that an individual possesses a high level of the assessed latent trait (eg, depressive symptoms).16 Rasch analysis also provides significant insight into the psychometric properties of the scale, including (1) appropriate use of response categories; (2) measurement precision; (3) how well items fit the underlying trait; (4) unidimensionality; (5) targeting of item difficulty to patients’ ability; (6) differential item functioning, which occurs when subgroups of participants systematically respond differently to a certain item despite having similar underlying ability levels; and (7) person fit.16

Overall, fit to most Rasch model variables was adequate for both the anxiety and depression HADS scales. Both scales were unidimensional with no misfitting items and eigenvalues for the first contrast lower than 2.0. There was also no differential item functioning for age, sex, diabetes type, or presence of DR vision impairment in either scale. However, precision and targeting were suboptimal for both the depression and anxiety scales (person separation index <2.0 and difference between person and item means >1.0 logits), which was a direct result of extreme and misfitting person responses. When people obtain the maximum or minimum scores on an instrument, the error of their person measure is infinite (ie, they may have a much smaller or much greater ability than the most difficult item in the scale; it cannot be determined). Someone who is at the top limit of the scale and therefore has infinite measurement error does not contribute to the computation of measures as well as someone who does not have infinite error.17 Highly misfitting persons, especially if there are many, are also problematic since they may degrade measurement and warp the person measures that are subsequently computed.17 Therefore, it is standard procedure to remove extreme and misfitting responses to optimize a scale’s psychometric properties if necessary.18,19 Several studies20,21 have demonstrated that, when questionnaires are optimized to fit the Rasch measurement model, the increase in the relative precision of measurement, and thus the sensitivity to detect associations, is substantial. Therefore, 61 persons with extreme minimum scores and 25 misfitting persons (infit mean square >2.0) were deleted from the depression scale, and 49 persons with extreme minimum scores and 66 misfitting persons were deleted from the anxiety scale. This adjustment substantially improved precision and targeting for both HADS scales.

Once fit to the Rasch model was achieved, we exported the person measures for anxiety and depression that ranged across a negative to positive logit scale. Because interpretation of negative and positive logit scores is challenging, person measures were subsequently converted to a scale with scores from 0 to 10 logits to ease interpretation of the results. High scores represent greater symptoms of anxiety and depression. However, these scores are still interval-scaled Rasch scores and are not equivalent to 0 to 10 on a raw score scale.

Statistical Analysis

Descriptive statistics were computed and normality of the variables was examined using boxplots, Kolmogorov-Smirnov, and Shapiro-Wilks tests. The linearity of nominal and ordinal data was assessed using χ2-based measures. Symptoms of depression and anxiety, as measured by Rasch person measures of the 2 HADS scales, were the main outcomes and were analyzed as continuous variables. Factors associated with symptoms of depression or anxiety (P < .10) in univariate analysis were examined using a multivariable linear regression model controlling for several demographic, clinical, and psychosocial variables. A plot of the residuals compared with estimates was examined to determine whether the assumptions of linearity and homoscedasticity were met. We used 4 relevant criteria for evaluating linear regression models: adjusted R2, Akaike information criterion (AIC), Akaike corrected information criterion (AICc), and Bayesian information criterion. Generally, higher variance explained by the model (adjusted R2) and lower AIC, AICc, and Bayesian information criterion values indicate the best-fitting model. We used the Stata program vselect to perform variable selection after performing linear regression.22 All statistical analyses were conducted with Stata, version 12.1.0 (StataCorp).

The relative importance of the risk factors was determined using the percentages of unique variance explained. Commonality analysis quantifies the percentage of variance that is unique to each risk factor, and the percentage is common to all of the possible combinations of risk factors. Unique effects reflect how much variance a risk factor independently contributes to the outcome that is not shared with the other risk factors. The variables that were significant in the final regression models were used in commonality analysis, which was conducted using the software package relaimpo, version 2.2-2.23

Results

Of the 609 participants in the DMP study, we excluded those who were currently taking medication for depression and anxiety (n = 55) and who did not complete the HADS (n = 35). The full data set of participants (519, with 170 [32.8% women]) had a mean (SD) age of 64.9 (11.6) years and most had type 2 diabetes (433 [83.4%]) (Table 1). A total of 310 patients (59.7%) had DR, 149 individuals (28.7%) had DME, and 262 participants (50.5%) had some loss of visual acuity. A total of 445 participants (85.7%) reported comorbidities (eg, hypertension or stroke) and 165 individuals (31.8%) reported diabetes complication in addition to DR (ie, nephropathy, peripheral vascular disease, or neuropathy). According to their raw HADS scores, 80 patients (15.4%) screened positive for depressive symptoms and 118 persons (22.7%) screened positive for symptoms of anxiety (eTable 2 in the Supplement). Removal of extreme minimum and misfitting cases resulted in a total sample of 433 and 404 participants for the depression and anxiety scales, respectively. Compared with patients who were retained in the analysis for depressive symptoms, those who were excluded were more likely to be male (88 of 115 [76.5%]; P = .02). There were comparable DR severity levels between those excluded and included in the analysis of Rasch depression scores (eTable 3 in the Supplement). For the anxiety scale, those who were excluded were less likely to be using insulin (25 of 86 [29.1%]) than those who remained in the analysis (193 of 430 [44.9%]; P = .02). The mean (SD) diastolic blood pressure was also lower in participants excluded from (74.2 [9.4]) compared with those who remained (76.5 [8.8]) (P = .03) in the sample for analysis.

Univariate analysis using Rasch transformed scores revealed that a self-reported history of anxiety or depression, longer duration of diabetes, more severe DR, poorer blood glucose control, lower educational level, and severe vision impairment were associated with greater depressive symptoms (Table 2). Greater symptoms of anxiety were associated with self-reported history of anxiety or depression, presence of comorbidity, younger age, lower educational level, and female sex (Table 2). Diabetic macular edema was not associated with symptoms of anxiety or depression in univariate analysis. eTable 2 in the Supplement outlines univariate analysis of raw scores from the full sample. Similar results were obtained, although body mass index was identified as a significant indicator of depression and anxiety and the association between DR and depression was not significant. The eFigure in the Supplement provides a graph of raw depression scores across DR severity groups.

Multivariable analysis using the adjusted R2 criterion to select variables in the univariate analysis (P < .10) identified that history of depression or anxiety (regression coefficient [β] = 1.40; 95% CI, 0.68-2.12), moderate or severe vision impairment (β = 1.28; 95% CI, 0.26-2.31), and severe NPDR or PDR (β = 0.69; 95% CI, 0.03-1.34) were independently associated with depressive symptoms (Table 3, models 1 and 2). Commonality analysis showed that a history of depression or anxiety accounted for 60.6% (95% CI, 23.9%-83.2%) of the unique variance in depressive symptoms; having severe NPDR or PDR, 19.1% (95% CI, 1.7%-44.4%); moderate or severe vision impairment, 15.8% (95% CI, 1.0%-44.2%); minimal or mild vision impairment, 2.7% (95% CI, 0.4%-20.0%); and mild or moderate DR, 1.8% (95% CI, 0.7%-14.5%). With the use of raw cutoff points from the full sample, history of depression was the only independent factor significantly associated with depressive symptoms (eTable 4 in the Supplement).

Personal history of depression or anxiety was independently associated with symptoms of anxiety (β = 1.45; 95% CI, 0.82 to 2.08; P < .001) as was younger age (β = −0.02; 95% CI, −0.04 to −0.002; P < .05) (Table 4, models 1 and 2). In model 1, presence of comorbidity (β = 0.73; 95% CI, 0.12 to 1.33; P = .02) was also associated with heightened anxiety.

Discussion

The findings of our study demonstrate that severe NPDR or PDR and moderate or severe vision impairment, but not DME, were independent risk factors for depressive symptoms in people with diabetes. Having a self-reported history of depression or anxiety was consistently associated with heightened psychological symptoms in this sample of individuals with diabetes and the most important risk factor for depressive symptoms.

Our findings support previous studies that have identified a link between diabetic complications6,24 and vision impairment25,26 with depressive symptoms. The findings presented here highlight that the severity of DR heightens the risk of depressive symptoms independent from the presence or degree of vision impairment and duration of diabetes. This association likely reflects the long-term burden of managing and coping with DR in its advanced stages. Advanced DR has been shown27 to negatively affect social life, family and community roles, work, finance, and personal relationships as well as generate treatment concerns. To our knowledge, this is the first study that has examined the association between DME and psychological well-being. Our findings are in line with those of a recent study28 that found that, in individuals with type 2 diabetes, the presence and severity of DR, but not DME, was related to a lower diabetes-specific quality of life. Diabetic macular edema was associated with lower treatment satisfaction.28

Consistent with mental health research in diabetes and other conditions, previous depression was the strongest indicator of depressive symptoms.29,30 The association between depressive symptoms, diabetes, and DR is likely to be bidirectional; the impairment and burden of diabetes and its complications can precipitate depression and vice versa, and depression can impair diabetes control through various biological and behavioral pathways.31,32 Qualitative research3 has found that patients with DR report fear, anxiety, and stress; however, an association between DR or DME and anxiety was not observed in our study. Rather, anxiety was associated with factors known to contribute to anxiety in the general population, including a personal history of anxiety, medical comorbidity, and younger age.29 The fact that older age may be protective against anxiety supports previous research in diabetes8,33 and the aging literature34 more widely, which suggests that older age may infer greater coping ability.

Strengths of our study include the large sample of individuals with different stages of DR and our detailed clinical assessment of DR status and visual acuity. Our use of Rasch analysis to convert ordinal HADS scores to interval-level data ensured our findings were robust. However, Rasch scores are more difficult to interpret than raw scores and statistical significance may not reflect clinical relevance since we do not know how the Rasch transformation applies to the definitions of anxiety and depression from the raw HADS scoring. Moreover, a fairly large proportion of participants were removed to optimize the HADS’ scale precision. Although important from a psychometric perspective,35 this deletion may have introduced bias into the analysis. Although no systematic differences in DR severity were found in participants excluded from the analysis, those removed to optimize scale precision were critical in determining the association between DR and depression. Furthermore, given that our main finding on the association between severe DR and depressive symptoms was not identified in analysis of the raw scores and cutoff points, further replication of these results with Rasch-transformed scores is required. A further limitation of our study is that current psychological symptoms and history were based on self-report data; a clinical assessment or diagnostic interview was not conducted. Despite these shortcomings, our findings highlight that a simple self-report question about mental health history, which is feasible to implement in clinical practice, with records of vision and DR status can be used to identify individuals with diabetes who are at risk for poor mental health.

Conclusions

Vision-threatening stages of DR, but not DME, are associated with higher levels of depressive symptoms. This finding is independent from known risk factors of a history of anxiety or depression and vision impairment. The severity and progression of DR may be a useful indicator to prompt assessment of psychological well-being, particularly in individuals with other risk factors.

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Article Information

Submitted for Publication: December 7, 2015; final revision received May 10, 2016; accepted May 20, 2016.

Corresponding Author: Gwyneth Rees, PhD, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia (grees@unimelb.edu.au).

Published Online: July 7, 2016. doi:10.1001/jamaophthalmol.2016.2213.

Author Contributions: Dr Xie had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Rees, Lamoureux.

Acquisition, analysis, or interpretation of data: Rees, Xie, Fenwick, Sturrock, Finger, Rogers, Lim.

Drafting of the manuscript: Rees, Fenwick, Rogers.

Critical revision of the manuscript for important intellectual content: Rees, Xie, Fenwick, Sturrock, Finger, Lim, Lamoureux.

Statistical analysis: Rees, Xie, Fenwick, Sturrock, Finger, Rogers, Lamoureux.

Obtained funding: Lamoureux.

Administrative, technical, or material support: Rees, Sturrock, Lamoureux.

Study supervision: Rees, Lim, Lamoureux.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: This study was supported by grant LP0884108 from the Australian Research Council Linkage, with Diabetes Australia-Victoria as a partner organization. Dr Rees is funded by National Health and Medical Research Council Career Development Award 1061801. The Centre for Eye Research Australia receives operational infrastructure support from the Victorian government.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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