Antidepressant medication use and glycaemic control in co-morbid type 2 diabetes and depression

Transcription

1 Family Practice, 2016, Vol. 33, No. 1, doi: /fampra/cmv100 Advance Access publication 7 January 2016 Epidemiology Antidepressant medication use and glycaemic control in co-morbid type 2 diabetes and depression Jay A Brieler a, *, Patrick J Lustman b,c, Jeffrey F Scherrer a, Joanne Salas a and F David Schneider a a Department of Family and Community Medicine, Saint Louis University School of Medicine, St. Louis, MO, b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO and c The Bell Street Clinic, VA St. Louis Health Care System John Cochran Division, St. Louis, MO, USA. *Correspondence to Jay A Brieler, Family and Community Medicine, Saint Louis University School of Medicine, 6420 Clayton Road, Room 2234, St. Louis, MO 63117, USA; brielerj@slu.edu Abstract Objective. Depression is prevalent in diabetes and is associated with increased risks of hyperglycaemia, morbidity and mortality. The effect of antidepressant medication (ADM) on glycaemic control is uncertain owing to a paucity of relevant data. We sought to determine whether the use of ADM is associated with glycaemic control in depressed patients with type 2 diabetes. Research design and methods. A retrospective cohort study (n = 1399) was conducted using electronic medical record registry data of ambulatory primary care visits from 2008 to Depression and type 2 diabetes were identified from ICD-9-CM codes; ADM use was determined from prescription orders; and glycaemic control was determined from measures of glycated haemoglobin (A1c). Good glycaemic control was defined as A1c < 7.0% (53 mmol/mol). Generalized estimating equations were used to determine the effect of depression and ADM use on glycaemic control. Results. Good glycaemic control was achieved by 50.9% of depressed subjects receiving ADM versus 34.6% of depressed subjects without ADM. After adjusting for covariates, depressed patients receiving ADM were twice as likely as those not receiving ADM to achieve good glycaemic control (odds ratio = 1.95; 95% confidence interval: ). Conclusions. In this retrospective cohort study of a large sample of primary care patients with type 2 diabetes, ADM use was associated with improved glycaemic control. Key words. Antidepressive agents, anxiety, depression, diabetes mellitus type 2, medical records, primary health care. Introduction Up to 20% of patients with diabetes mellitus have co-morbid major depressive disorder (MDD) (1). Epidemiological studies suggest that the relationship of type 2 diabetes and MDD is bidirectional; diabetes increases the risk of depression (2) and conversely, depression increases the risk of development of type 2 diabetes (3). Patients with co-morbid diabetes and depression have worse overall functioning and glycaemic control (4) and are at higher risk for myocardial infarction, vascular disease and early mortality (5 7). Hyperglycaemia is a primary target in the management of diabetes, as it is a significant predictor of cardiovascular disease, end-stage renal disease and all-cause mortality (8). Maintenance of glycaemic control sufficient to slow the progression of diabetes is difficult to achieve. In a general practice setting, two of every three patients are not able to maintain the level of glycaemic control recommended by the American Diabetes Association (A1c < 7.0%), even with intensive The Author Published by Oxford University Press. All rights reserved. For permissions, please journals.permissions@oup.com. 30

2 Depression treatment and glycaemic control 31 treatment and systematic follow-up (4). Accordingly, the American Diabetes Association in its clinical practice guidelines invites use of complementary treatments including depression treatment and stress reduction that supports good glycaemic control (9). Given the aforementioned adverse effects of depression on the course and outcome of type 2 diabetes, determining whether depression treatment would mitigate those effects is directly relevant to clinical practice. A Cochrane review published in 2012 looked at both psychological and pharmacologic interventions for depression in diabetic patients (10). The review identified only five randomized controlled trials (RCTs) involving 238 patients (11 15) that looked at the effect of antidepressant medication (ADM) on glycaemic control. The meta-analysis found a mean difference of 0.4% ( 4.4 mmol/mol) in the A1c of patients treated with the ADM class of selective serotonin reuptake inhibitors (SSRIs) compared to placebo (P = 0.002). Other reviews (16 18) have found non-significant statistical trends towards benefit of ADM on glycaemic control. Several investigators have underscored the limitations of available data and the need both for more trials and adequately powered trials (19). Semenkovich et al. indicated that without additional data, the claim of antidepressant efficacy in type 2 diabetes remains unproven, and furthermore, whether ADM has direct beneficial effects on A1c or indirect beneficial effects mediated via depression remains unclear. While randomized placebo-controlled trials (RCTs) are the cornerstone of evidence supporting the efficacy of pharmacotherapy, these studies are often costly, methodologically complex and logistically difficult. In response to these difficulties and the escalating funding challenges associated with RCTs, researchers have looked to clinical epidemiological methods for answers to clinical questions. With increasing use of electronic medical records (EMRs) in recent years, data on large numbers of patients in the naturalistic practice setting are more readily available and are being utilized more frequently in scientific research (20). Our literature search on the use of these data sets to investigate the effect of ADM on glycaemic control revealed only one study involving 568 patients that also did not show a significant association between antidepressant use and A1c (21). The purpose of the present study was to investigate whether use of ADM was associated with recommended A1c targets in a primary care population of type 2 diabetics. We hypothesized that depressed patients treated with ADM would be more likely than depressed patients not prescribed ADM to achieve good glycaemic control, defined as A1c below 7.0% (53 mmol/mol), after controlling for demographic characteristics and co-morbidities associated with glycaemic control. Research design and methods Subjects Clinical data were obtained from the Department of Family and Community Medicine s Primary Care Patient Data (PCPD) Registry. The PCPD Registry was developed by extracting EMR data from clinics staffed by family medicine (FM) and general internal medicine (GIM) providers at a large academic medical health system located in the St. Louis, MO, metropolitan area. The Registry contains ICD- 9-CM codes, prescription orders, CPT codes, social history, family history, demographics, laboratory orders, referrals and vital signs. The Registry consists of patients (FM = patients, GIM = patients) who had at least one encounter (e.g. office visit, procedure visit or clinical support) between 1 July 2008 and 31 July Encounters occurred in any of the three ambulatory care clinics staffed by faculty, residents and a small number (<5) of mid-level providers. Clinics are geographically dispersed throughout the St. Louis metropolitan area. For the present study, children (n = 1847), those with missing race (n = 549) or those with missing gender (n = 1) were excluded resulting in patients aged available for analysis. The study procedures were reviewed and approved by the university IRB. Measures Type 2 diabetes Patients were considered to have diabetes if the EHR contained either ICD-9-CM code 250.x0 or 250.x2. Depression Patients were considered to have a diagnosis of depression if the PCPD Registry contained at least two occurrences of ICD codes (296.2, 296.3, 311) indicating depression within a 12-month period. This method, requiring two visits in the same 12-month period, has excellent agreement with patient-reported depression (22) and written medical record (23). Depression treatment Patients were considered treated if they were prescribed ADM during the observation period. Patients could have received a prescription for any of the following antidepressants: tricyclic antidepressants (clomipramine, amitriptyline, desipramine, nortriptyline, doxepin and imipramine); SSRIs (citalopram, escitalopram, fluvoxamine, fluoxetine, paroxetine and sertraline); serotonin and norepinephrine reuptake inhibitors (venlafaxine, duloxetine and desvenlafaxine); and non-classified antidepressants (bupropion, nefazodone, trazodone and mirtazapine). We did not require a minimum dose, minimum duration of treatment, did not measure adherence to treatment and did not measure poly-antidepressant exposure. Outcome measure glycaemic control All A1c values available were included in analysis. Glycaemic control was defined by A1c < 7.0% (53 mmol/mol), consistent with current guidelines (9). Covariates Covariates were selected if they were potential confounders associated with depression, depression treatment and glycaemic control. Covariates included anxiety disorder, obesity, hyperlipidemia, hypertension, vascular disease, referral to dietary education, smoking history, age, gender, race, insulin (insulin, apidra, Humalog, Humulin, Lantus, Levemir, Novolin and Novolog) or other diabetic medication use (metformin, acarbose, alogliptin, canagliflozin, exenatide, glimepiride, glipizide, glyburide, linagliptin, liraglutide, nateglinide, pioglitazone, repaglinide, rosiglitazone, saxagliptin, sitagliptin, tolazamide and tolbutamide) and volume of health care utilization. A composite any anxiety disorder variable was defined by a diagnosis for any of the following anxiety disorders: anxiety disorder unspecified, generalized anxiety disorder, panic disorder, obsessive compulsive disorder, social phobia and post-traumatic stress disorders. For each anxiety disorder, two occurrences of the ICD- 9-CM codes must have occurred in a 12-month period. A history of smoking (current or past) was obtained either through ICD-9-CM code or social history file and was coded as never versus a past history of ever smoking. Obesity was defined by ICD-9-CM code or

3 32 Family Practice, 2016, Vol. 33, No. 1 body mass index > 30. Hypertension, hyperlipidemia and vascular disease were obtained from ICD-9-CM codes with the latter defined by a diagnosis of any of the following: hypertensive heart disease, ischemic heart disease, myocardial infarction, other heart disease, disease of pulmonary circulation and cerebrovascular disease. To adjust for protective effects of preventive health behaviour, we modelled referral to dietary education that was determined by the presence of EHR flags indicating a referral was made. Referral did not indicate utilization of education that was not available in our data. To control for detection bias, we adjusted for volume of health care utilization. Health care utilization was measured by computing the number of monthly outpatient visits. The distribution of the average number of visits per month was divided into quartiles. We modelled high utilization, defined as the highest quartile of use, versus all other levels of use. Demographic data included age, race and gender. Inclusion/exclusion criteria Of the patients aged available for analysis, 2855 had a diagnosis of type 2 diabetes. Of these, 1684 had a minimum of one A1c value. After excluding patients receiving an ADM who did not have a depression diagnosis, the final cohort available for analysis was Among the 1399 patients, 2721 A1c values were available. Statistical analysis Data analyses were conducted using SAS v9.4. Associations between depression treatment groups (no depression, untreated depression, treated depression) were assessed using chi-square tests for categorical covariates or an analysis of variance for continuous covariates. To control for repeated observations of A1c values and use every possible available value, marginal multilevel models using generalized estimating equations were used to assess the crude and adjusted odds of A1c control over time, by group, and any differential effect of course of A1c value over time based on depression treatment group. For average A1c over time, marginal linear multilevel mixed models were computed. All available A1c values from 2009 to 2012 for this cohort were used and fiscal quarter was used as the continuous time indicator from 0 to 15, representing the 16 quarters assessed in this analysis. If multiple A1c values were available for a patient in a quarter, an average for those values was used for that particular quarter. Temporal changes in A1c control by group were assessed by a quarter depression group interaction at alpha = If an interaction was present, stratified group analyses were presented. Multivariate, adjusted models were created by sequentially adding co-morbidities (anxiety, obesity, hyperlipidemia, hypertension, vascular disease), health behaviours (referral to dietary education, smoking history, insulin prescription, other diabetic drug prescription) and socio-demographics (age, race, gender, utilization) to the crude model. Results Patient characteristics On average, patients with type 2 diabetes were 61.6 (±12.8) years of age at the beginning of the observation period. Overall, patients were more often female (63.3%), predominately obese (74.1%) and co-morbidities were common (e.g. hyperlipidemia 65.8%, hypertension 83.3%). Among eligible patients, 265 patients met our criteria for depression, leaving 1134 without depression. Among the depressed patients, 225 received ADMs (84.9%) and 40 (15.1%) did not. Among the 1399 eligible patients, 654 (46.8%) had one quarterly A1c value available from 2009 to 2012, 382 (27.3%) had two, 206 had three (14.7%) and 157 (11.2%) had four or more. As shown in Table 1, among patients with type 2 diabetes, race and gender were significantly (P < ) associated with depression status. Among non-depressed, 66.7% were non-white, compared to 45.0% of untreated and 49.3% of treated depressed patients. Among patients without depression, 55.5% were female compared to 70% of untreated and 73.3% of treated depressed patients. Patients with depression were significantly more often high health care utilizers (70% of untreated and 74.7% of treated) compared to non-depressed (50.4%). A past history of smoking was positively associated with depression status (P < 0.001). Patients who were ever smokers accounted for 65.0% of untreated and 62.2% of treated depressed cases compared to 50.3% of non-depressed patients. The prevalence of anxiety disorder diagnosis (P < ) Table 1. Characteristics of primary care patients with diabetes, with and without depression and depression treatment, age 18 years and older, with at least one visit and HgA1c value, (n = 1399) Variable, % (n) Overall (n = 1399) No depression (n = 1134) Untreated depression (n = 40) Treated depression (n = 225) Mean age (SD) 61.6 (12.8) 61.6 (13.1) 62.0 (11.9) 61.1 (11.1) Non-white race 63.3 (886) 66.7 (757) 45.0 (18) 49.3 (111) < Female 58.8 (822) 55.5 (629) 70.0 (28) 73.3 (165) < High clinic utilization 54.8 (767) 50.4 (571) 70.0 (28) 74.7 (168) < Referral for dietary education 17.2 (241) 16.2 (184) 22.5 (9) 21.3 (48) Smoking history 52.6 (736) 50.3 (570) 65.0 (26) 62.2 (140) Insulin RX 490 (35.0) 383 (33.8) 16 (40.0) 91 (40.4) Other diabetic drug RX 1076 (76.9) 873 (77.0) 29 (72.5) 174 (77.3) Any anxiety disorder 4.9 (69) 1.4 (16) 15.0 (6) 20.9 (47) < Obese 74.1 (1036) 73.0 (828) 72.5 (29) 79.6 (179) Hyperlipidemia 65.8 (920) 65.2 (739) 57.5 (23) 70.2 (158) Hypertension 83.3 (1165) 83.0 (941) 80.0 (32) 85.3 (192) Vascular disease 33.9 (475) 32.9 (373) 32.5 (13) 39.6 (89) Total number HgA1c values Mean HgA1c (SD) 7.7 (1.9) 7.7 (1.9) 8.1 (2.0) 7.4 (1.7) HgA1c control (%) 43.9% 42.7% 34.6% 50.9% P value

4 Depression treatment and glycaemic control 33 increased from 1.4% among non-depressed patients to 15.0% of untreated and to 20.9% of treated depressed cases. Other co-morbid conditions were not significantly associated with depression status, and the proportion of patients treated with insulin, receiving other anti-diabetic drugs and referred to dietary education was not significantly different between groups. Glycaemic control Figure 1 shows overall percent A1c control (column) and Figure 2 mean A1c by group, where the unit of analysis is A1c value, not patient. Sample sizes represent number of A1c values overall and by group. As shown in Figure 2, the average A1c among all values for non-depressed patients with diabetes was 7.7(±1.9)% (61 ± 17 mmol/mol), compared to an average of 8.1(±2.0)% (65 ± 22 mmol/mol) among patients with untreated depression and an average of 7.4(±1.7)% (57 ± 19 mmol/mol) among patients with treated depression. When looking at percent of A1c values that are controlled in Figure 1, 42.7% of values for the non-depressed group compared to 34.6% of the untreated depression and 50.9% of the treated depression groups were controlled. The interaction of time by depression group was not significant (P = 0.398, model results not shown). Thus, non-stratified analyses are shown. As shown in Table 2, among all models, there was an indication that the odds of A1c control for this cohort was stable across quarterly time periods from 2009 to 2012 [odds ratio (OR) = 1.00; 95% confidence interval (CI): ]. Adjusting only for quarterly time periods, on average, there were no differences in the odds of A1c control across groups, although there was a trend for those with treated depression to be about twice as likely as those with untreated depression to have a controlled A1c at any point in the observation period (OR = 1.96; 95% CI: ). After adjusting for all covariates in Model 4, compared to patients with untreated depression, those with treated depression had a significantly greater odds of A1c control (OR = 1.95; 95% CI: ) at any point in the observation period. While not statistically significant, nondepressed patients were slightly more likely to achieve glycaemic control compared to untreated depressed patients (OR = 1.37; 95% CI: ). Covariates significantly and positively associated with A1c control at any point in the observation period included anxiety disorder (OR = 2.12; 95% CI: ) and age (OR = 1.01; 95% CI: ). Covariates negatively associated with A1c control Figure 1. Overall percent HgA1c control (A1c < 7.0) by group. These graphs show overall proportion of optimal A1c and mean A1c among groups the total ns are the total number of A1c values in that group. Figure 2. Overall mean A1c by group. These graphs show overall proportion of optimal A1c and mean A1c among groups the total ns are the total number of A1c values in that group.

5 34 Family Practice, 2016, Vol. 33, No. 1 included prescription of insulin (OR = 0.17; 95% CI: ) and prescription of any other diabetic medication (OR = 0.37; 95% CI: ). Table 3 shows the estimated mean difference in A1c based on treatment and various patient characteristics over time. The overall difference in A1c between treated and untreated depression was an improvement of 0.54 after control for co-morbidities, demographics and health behaviours. However, the difference did not reach statistical significance in this model. Treatment with insulin or oral hypoglycaemics was again associated with higher A1c, while anxiety was associated with a lower A1c. Non-white race was also associated with higher A1c values. Table 2. GEE models, associations of depression treatment status and patient characteristics with odds of HgA1c control, for primary care patients with diabetes, age 18 years and older, with at least one visit and HgA1c value, (n = 1399) a Variable, OR (95% CI) Model 1: crude Model 2: co-morbidities Model 3: health behaviours/ diabetes treatment Model 4: demographics Depression treatment No depression 1.41 (0.72 to 2.77) 1.59 (0.79 to 3.21) 1.30 (0.72 to 2.34) 1.37 (0.74 to 2.54) Treated depression 1.96 (0.97 to 3.99) 1.98 (0.95 to 4.12) 1.87 (1.01 to 3.49)* 1.95 (1.02 to 3.71)* Untreated depression Quarter (time period) b 1.00 (0.98 to 1.02) 1.00 (0.98 to 1.02) 1.00 (0.98 to 1.02) 1.00 (0.98 to 1.02) Any anxiety disorder 2.08 (1.30 to 3.34)** 2.14 (1.31 to 3.48)** 2.12 (1.31 to 3.45)** Obese 0.75 (0.59 to 0.96)* 0.81 (0.62 to 1.05) 0.86 (0.65 to 1.12) Hyperlipidemia 0.89 (0.71 to 1.12) 1.01 (0.79 to 1.28) 0.95 (0.75 to 1.21) Hypertension 1.31 (0.97 to 1.75) 1.41 (1.04 to 1.91)* 1.36 (0.99 to 1.88) Vascular disease 0.82(0.66 to 1.02) 1.08 (0.85 to 1.37) 1.00 (0.77 to 1.29) Referral dietary education 0.73 (0.55 to 0.96)* 0.76 (0.57 to 1.02) Smoking history 1.06 (0.85 to 1.33) 1.04 (0.83 to 1.31) Insulin RX 0.16 (0.13 to 0.21)*** 0.17 (0.13 to 0.22)*** Other diabetic drug RX 0.38 (0.29 to 0.50)*** 0.37 (0.28 to 0.49)*** Age 1.01 (1.00 to 1.02)* Non-white race 0.79 (0.62 to 1.00) Female 0.97 (0.76 to 1.24) High clinic utilization 0.99 (0.77 to 1.26) GEE, generalized estimating equation. a Overall, non-stratified models. Quarter depression treatment interaction not significant (P = 0.398). b Quarter was treated as a continuous variable ranging from 0 to 15 (2009, quarter 1 to 2012, quarter 4). *P < 0.05, **P < 0.01, ***P < Table 3. Marginal multilevel linear regression models, associations of depression treatment status and patient characteristics with HgA1c, for primary care patients with diabetes, age 18 years and older, with at least one visit and HgA1c value, (n = 1399) a Variable, OR (95% CI) Model 1: crude Model 2: co-morbidities Model 3: health behaviours/ diabetes treatment Model 4: demographics Intercept 7.99 (7.41 to 8.57)*** 8.18 (7.54 to 8.82)*** 7.22 (6.61 to 7.82)*** 8.43 (7.69 to 9.17)*** Depression treatment No depression 0.27 ( 0.85 to 0.31) 0.35 ( 0.93 to 0.23) 0.20 ( 0.72 to 0.32) 0.34 ( 0.85 to 0.17) Treated depression 0.56 ( 1.17 to 0.06) 0.51 ( 1.12 to 0.10) 0.47 ( 1.02 to 0.08) 0.54 ( 1.07 to 0.001) Untreated depression Quarter (time period) b ( to 0.02) ( to 0.02) ( to 0.02) ( to 0.02) Any anxiety disorder 0.66 ( 1.12 to 0.20)** 0.57 ( 0.98 to 0.15)** 0.52 ( 0.92 to 0.12)* Obese 0.30 (0.08 to 0.52)** 0.19 ( 0.01 to 0.39) 0.09 ( 0.11 to 0.29) Hyperlipidemia ( 0.25 to 0.16) 0.14 ( 0.32 to 0.05) 0.03 ( 0.21 to 0.15) Hypertension 0.34 ( 0.60 to 0.07)* 0.34 ( 0.58 to 0.11)** 0.26 ( 0.50 to 0.02)* Vascular disease ( 0.22 to 0.19) 0.31 ( 0.50 to 0.12)** 0.12 ( 0.31 to 0.07) Referral dietary education 0.22 ( 0.01 to 0.44) 0.13 ( 0.10 to 0.35) Smoking history 0.02 ( 0.20 to 0.15) 0.03 ( 0.14 to 0.20) Insulin RX 1.60 (1.41 to 1.78)*** 1.52 (1.34 to 1.70)*** Other diabetic drug RX 0.61 (0.40 to 0.82)*** 0.61 (0.40 to 0.82)*** Age 0.02 ( 0.03 to 0.01)*** Non-white race 0.46 (0.28 to 0.65)*** Female ( 0.17 to 0.19) High clinic utilization 0.14 ( 0.32 to 0.04) a Overall, non-stratified models. Quarter depression treatment interaction not significant (P = 0.179). b Quarter was treated as a continuous variable ranging from 0 to 15 (2009, quarter 1 to 2012, quarter 4). *P < 0.05, **P < 0.01, ***P <

6 Depression treatment and glycaemic control 35 Conclusions In this sample, depressed type 2 diabetes patients, those prescribed ADM, were approximately twice as likely as those not prescribed ADM (OR = 1.95; 95% CI: ) to achieve good glycaemic control (A1c < 7%, 53 mmol/mol) during the study interval. The effect size was similar before and after fully adjusting for covariates and was consistent through the study period. Our results provide further support for the correlation between ADM and glycaemic control suggested by the Cochrane meta-analysis (10) and the trends seen in some of the studies included in other systematic reviews (16 18). However, the number of patients in our study (n = 265 in the treated and untreated depression arms) is slightly higher than the total of the five studies (n = 238) used in the Baumeister Cochrane review. Further, our results also suggest that the effect seen in the highly specific environment of a RCT appears to generalize to measures captured in the process of diabetes management in a primary care setting. We observed some interesting and unexpected findings including the observation that anxiety disorder was associated with twice the odds of A1c control and that prescription of insulin or other diabetic medication was inversely associated with control (OR = 0.77). Anxiety is prevalent in type 2 diabetes patients and frequently comorbid with major depression. Anxiety has also been shown to increase the risk for metabolic syndrome, especially in women (24). In the present sample, however, anxiety diagnosis is strongly associated with treatment for depression. Thus, the association with A1c control may reflect patients with co-morbid anxiety disorder seeking and receiving treatment for their depression. The association between poor glycaemic control and use of diabetic medication may indicate that medication was a marker of diabetes severity in this study. Therefore, the equal distribution of medication prescription between groups may suggest uniformity in diabetes severity between groups as well. Our data do not suggest any specific mechanism for the effect seen on A1c. It is possible that the ADMs have a primary effect on glucose metabolism. Early studies suggested that the SSRI fluoxetine was associated with weight loss and improved glycaemic control (25). However, these effects seem to vary between ADMs and even among the SSRI class (26). Amitryptyline and paroxetine administered to depressed patients without type 2 diabetes is associated with improved glucose utilization (27) further suggesting a direct effect of some ADMs on glycaemic control. Other studies indicate SSRIs have a direct effect on reducing HPA hyperactivity that could then lead to better glucose regulation (28). There is some suggestion that serotonergic agents may have very different effects on glucose than those affecting norepinephrine (29), and this may represent an area for further study. We must also consider whether observed improvements in A1c occur through antidepressant effects on mood or via direct drug effects, or both. Antidepressant may impact glucose regulation in other ways as well. Remission of depression might then lead to improved adherence to antihyperglycaemic medication and increased health behaviours. Meta-analysis has not shown a significant effect for non-pharmacologic treatment of depression on A1c (10), though at least one individual study using Cognitive Behavioural Therapy was able to demonstrate a benefit (30). Silva and colleagues (28) review evidence that SSRIs have been shown to reduce HPA hyperactivity that may be evidence for a direct ADM contribution to glycaemic control interventions resulting in increased physical activity improve depression and glycaemic control, thus another mechanism for our findings would be treated patients increasing exercise. The PCPD Registry did not include corresponding measures of depression such as Beck Depression Inventory or PHQ-9 for the time period of our analysis, and we were therefore unable to account for severity of depression in our analysis. Further investigation controlling for differences in depression severity over time might better explain how treatment with ADMs correlates with better A1c. Limitations The number of depressed patients precluded analysis of the temporal ordering of depression treatment and glycaemic control. As a result, we are unable to conclude whether treatment leads to better A1c control by reducing depression severity or whether glucose control leads to reduced severity of depression. Currently, there is evidence pointing towards both possibilities (31,32). In addition, patients were included in the treatment group based on any duration and dose of ADM exposure and not receipt of acute phase treatment. This further limited our understanding of the mechanisms behind treatment and glycaemic control in this cohort. This study did not include measures of depression symptom severity that would have enabled determination of depression relief in relationship to glycaemic control and we do not know if treatment led to decreased depression. The relatively small number of patients from the large parent cohort that have type 2 diabetes and did not receive an ADM (n = 40) may be cause for concern regarding external validity. Last, variation in clinical practice for diagnosing patients likely increased variation in the range of depression severity that might increase error. Our data indicate that pharmacotherapy for depression in type 2 diabetes is associated with better glycaemic control. Specifically, effects seen previously in small RCTs also hold with larger cohorts in a primary care setting. These patient numbers are made possible by the analysis of EMR data and would be costly to replicate using RCT methods in the current funding environment. This study suggests that the investment of resources in research utilizing this clinical epidemiologic approach is warranted, perhaps for analyses of larger and independent data sets. Finally, our results support the emphasis on recognition and treatment of depression in diabetes by clinicians in the primary care setting. Declaration Funding: none. Ethical approval: none. Conflict of interest: none. Acknowledgements JAB initiated the investigation and wrote sections of the article; PJL contributed to the study formulation and reviewed/edited the manuscript; JFS wrote sections of the article, coordinated the research team and reviewed/edited the manuscript; JS completed the statistical analysis, prepared all tables, contributed to writing of the article and reviewed/edited the manuscript; FDS contributed to discussions and reviewed/edited the manuscript. References 1. Gavard JA, Lustman PJ, Clouse RE. Prevalence of depression in adults with diabetes. An epidemiological evaluation. Diabetes Care 1993; 16: Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care 2001; 24:

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