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Diabetes-diabetes comorbidity

Diabetes-diabetes comorbidity


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Is it possible to have both type-1 (insulin-dependent) and type-2 (non-insulin-dependent) diabetes mellitus? That is, to have both insulin resistance and zero (or negligible) insulin production? If not, why not? And if so, then how common is this (among humans with each type of diabetes)? Any information on why the comorbidity may be high or low is also most welcome.


Yes you could think of an individual as having both(1). A good place to start looking at "newer" diabetes other than the classic 1 and 2 is latent autoimmune diabetes (LADA). Though, I don't know if one would say you had no insulin production. It would seem as though patients exhibit some characteristics of both types(2), and indeed there maybe a continuum possible(3) between ether "pure" type on each extreme.

(1) The type 2 diabetes-associated variant in TCF7L2 is associated with latent autoimmune diabetes in adult Europeans and the gene effect is modified by obesity: a meta-analysis and an individual study. Diabetologia. 2012 Mar;55(3):689-93. doi: 10.1007/s00125-011-2378-z. Epub 2011 Nov 23.

(2) Latent autoimmune diabetes (LADA) is perched between type 1 and type 2: evidence from adults in one region of Spain. Diabetes/Metabolism Research and Reviews 2013; 1520-7560.

(3) Latent autoimmune diabetes in adults: evidences for diabetes spectrum?.Chin Med J 2013;126:783-788


Quantification of Diabetes Comorbidity Risks across Life Using Nation-Wide Big Claims Data

Despite substantial progress in the study of diabetes, important questions remain about its comorbidities and clinical heterogeneity. To explore these issues, we develop a framework allowing for the first time to quantify nation-wide risks and their age- and sex-dependence for each diabetic comorbidity, and whether the association may be consequential or causal, in a sample of almost two million patients. This study is equivalent to nearly 40,000 single clinical measurements. We confirm the highly controversial relation of increased risk for Parkinson’s disease in diabetics, using a 10 times larger cohort than previous studies on this relation. Detection of type 1 diabetes leads detection of depressions, whereas there is a strong comorbidity relation between type 2 diabetes and schizophrenia, suggesting similar pathogenic or medication-related mechanisms. We find significant sex differences in the progression of, for instance, sleep disorders and congestive heart failure in diabetic patients. Hypertension is a highly sex-sensitive comorbidity with females being at lower risk during fertile age, but at higher risk otherwise. These results may be useful to improve screening practices in the general population. Clinical management of diabetes must address age- and sex-dependence of multiple comorbid conditions.


Abstract

To explore the longitudinal effect of chronic comorbid diseases on glycemic control (HbA1C) and systolic blood pressure (SBP) in type 2 diabetes patients.

Methods

In a representative primary care cohort of patients with newly diagnosed type 2 diabetes in The Netherlands (n = 610), we tested differences in the five year trend of HbA1C and SBP according to comorbidity profiles. In a mixed model analysis technique we corrected for relevant covariates. Influence of comorbidity (a chronic disease already present when diabetes was diagnosed) was tested as total number of comorbid diseases, and as presence of specific disease groups, i.e. cardiovascular, mental, and musculoskeletal disease, malignancies, and COPD. In subgroup effect analyses we tested if potential differences were modified by age, sex, socioeconomic status, and BMI.

Results

The number of comorbid diseases significantly influenced the SBP trend, with highest values after five years for diabetes patients without comorbidity (p = 0.005). The number of diseases did not influence the HbA1C trend (p = 0.075). Comorbid musculoskeletal disease resulted in lower HbA1C at the time of diabetes diagnosis, but in higher values after five years (p = 0.044). Patients with cardiovascular diseases had sustained elevated levels of SBP (p = 0.014). Effect modification by socioeconomic status was observed in some comorbidity subgroups.

Conclusions

Presence of comorbidity in type 2 diabetes patients affected the long-term course of HbA1C and SBP in this primary care cohort. Numbers and types of comorbidity showed differential effects: not the simple sum of diseases, but specific types of comorbid disease had a negative influence on long-term diabetes control parameters. The complex interactions between comorbidity, diabetes control and effect modifiers require further investigation and may help to personalize treatment goals.

Citation: Luijks H, Biermans M, Bor H, van Weel C, Lagro-Janssen T, de Grauw W, et al. (2015) The Effect of Comorbidity on Glycemic Control and Systolic Blood Pressure in Type 2 Diabetes: A Cohort Study with 5 Year Follow-Up in Primary Care. PLoS ONE 10(10): e0138662. https://doi.org/10.1371/journal.pone.0138662

Editor: Andrea Icks, Heinrich-Heine University, Faculty of Medicine, GERMANY

Received: January 12, 2015 Accepted: September 2, 2015 Published: October 1, 2015

Copyright: © 2015 Luijks et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: HL has received a research grant from SBOH, Foundation for General Practice Residency Training in the Netherlands. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors received no specific funding for the presented work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: BMI, body mass index DM, diabetes mellitus FP, family physician SBP, systolic blood pressure SES, socioeconomic status


Results

The EpiChron Cohort follows 1,070,762 adult users of the public health system of the Spanish region of Aragón. A total of 63,365 adults (46% women, mean age of 69.9 years) in the cohort had a diagnosis of T2D, resulting in a prevalence of 6%. Most of the patients with T2D had at least one more simultaneous chronic disease (Table 1), and approximately one in five individuals (19%) had concurrent mental health comorbidity. The proportion of women was significantly higher in the population with at least one mental health problem than in the group with no mental health comorbidity registered in the health records (62.5% vs. 42.1%, p < 0.001). The mean number of chronic comorbidities (excluding mental health ones) was significantly higher in patients with concurrent mental health comorbidities compared with those T2D patients free of mental health problems (4.91 ± 3.02 vs. 3.74 ± 2.55 chronic conditions, p < 0.001). More than 90% of patients with T2D and mental health comorbidity had at least two additional comorbidities, and only 2% of them had no other concurrent chronic disease.

The most common mental health comorbidities among T2D patients were depression (13.6%) and anxiety (3.17%), both of them more frequent in women (Table 2). Substance use disorder was more frequent in men, mainly in adults up to 64 years old. The prevalence of depression increased with age, while anxiety, substance use disorder and schizophrenia were more frequent in the younger population.

The presence of mental health comorbidity was associated with an increased risk of all the T2D outcomes considered in this study. The risk of 4-year all-cause mortality was 1.24 times higher (odds ratio, OR 1.24 95% confidence interval, CI 1.16–1.31) in patients with at least one concurrent mental health comorbidity, after controlling for sex, age and number of non-mental comorbidities and the presence of the other types of mental health comorbidities (Table 3). The magnitude of this effect was different for each mental health problem. Thus, mortality risk was 2.18 (CI 1.84–2.57) times higher in patients with a diagnosis of substance use disorder, 1.82 (CI 1.50–2.21) times higher in patients with schizophrenia, and 1.14 (CI 1.07–1.22) times higher in those with depression. On the contrary, the likelihood of mortality was not influenced by the presence of anxiety (OR 0.98 CI 0.85–1.13).

The simultaneous presence of mental health comorbidity in patients with T2D was associated with a 1.16 (CI 1.10–1.23) times higher risk of 1-year all-cause hospitalization (Table 4). The magnitude of this effect was again different depending on the specific type of mental health comorbidity. The likelihood of all-cause hospitalization was 1.12 (CI 1.05–1.19), 1.40 (CI 1.18–1.66) and 1.58 (CI 1.38–1.81) times higher in patients with depression, schizophrenia and substance use disorder, respectively, whereas it was not associated with the presence of anxiety (OR 1.04 CI 0.92–1.18). We observed similar results for the risk of hospitalization related to T2D, which increased on average 1.51 (CI 1.18–1.93) times when mental health comorbidity was present. Patients with a diagnosis of substance use disorder had the highest risk of T2D-related hospitalization, which was 1.79 (CI 1.05–3.06) times higher, followed by those with depression (OR 1.49 CI 1.14–1.96) whereas anxiety and schizophrenia were not associated with higher risk of T2D-hospitalization. The likelihood of visiting the emergency room was 1.26 (CI 1.21–1.32) times higher when mental health comorbidity was present. The size of this effect was significant for all the specific mental health problems studied, which increased this risk by 22% (OR 1.22 CI 1.16–1.29), 28% (OR 1.28 CI 1.17–1.42), 43% (OR 1.43 CI 1.27–1.61) and 28% (OR 1.28 CI 1.11–1.47) for depression, anxiety, substance use disorder, and schizophrenia, respectively.


Outcomes: Weight Reduction and Comorbidity Remission

A review of the literature reveals primarily single institution data for demonstration of ethnic difference in weight reduction and comorbidity remission after metabolic surgery. In general, the themes of equivalency of comorbidity remission and normalization of weight variation over time among ethnic groups emerge. In one study of 1,903 patients who underwent gastric bypass or banding, African American patients had a higher postoperative BMI and less postoperative percent excess weight loss (% EWL) than either Caucasian or Hispanic patients. However, African American and Hispanic patients no longer differed by year 3 in RYGB and by year 2 in laparoscopic adjustable gastric banding. Of note, by year 1, there were no significant ethnic differences in remission of diabetes, hyperlipidemia, hypertension, and sleep apnea (16). In another study of 3,268 patients, there were significant differences in 1-year % EWL (66.0 Hispanics, 64.0 non-Hispanic whites, and 54.1 ± 21.3 non-Hispanic blacks P < 0.001) that remained at 2 years (68.6 ± 24.1 Hispanics, 69.5 ± 21.2 non-Hispanic whites, and 57.6 ± 25.4 non-Hispanic blacks P < 0.001) (17). In a study of 597 patients from Detroit, MI, 86 patients (72.3%) had resolution of diabetes 1 year after surgery with no effect from ethnicity (18).

There are several large data registry studies that do demonstrate ethnic differences. In one publication from an integrated health care system in southern California, a prospective registry of 20,296 patients had the following proportion of procedures: 58% RYGB, 40% vertical sleeve gastrectomy, and 2% rare banding.

This study showed that the type of procedure may have impact on weight loss and ethnic differences (19). At 3 years, non-Hispanic white RYGB patients had a higher % EWL than non-Hispanic black (P < 0.001) and Hispanic (P < 0.001) RYGB patients however, there were no differences between sleeve gastrectomy racial/ethnic groups in % EWL.

Another large database (Bariatric Outcomes Longitudinal Database [BOLD]) was used to examine ethnic differences for metabolic surgery outcomes. In this study of 108,333 patients, the ethnic composition was 79% white, 12% black, and 9% Hispanic (20). Fewer black males underwent surgery (15%) compared with white or Hispanic males (22%). Compared with white patients, black patients were heavier (mean BMI, 50 vs. 47.4 kg/m 2 ), younger (42.7 vs. 46.4 years), and more hypertensive (57 vs. 52%). Other comorbidities were higher in whites. Thirty-day mortality rate was equivalent among all groups (0.23–0.26%), but serious adverse events were higher for blacks (3.65%) versus whites (3.19%) and Hispanics (2.01%). At 1 year, all ethnic groups showed significant improvement in weight and comorbidity burden from baseline but black patients had less improvement in comparison despite adjustment for baseline characteristics.

This same database also examined ethnic differences in metabolic surgery outcomes among adolescents (21). In this study of 827 adolescents, mean estimated weight loss for all ethnic groups differed by a maximum of only 1.5 kg, being 34.3 kg for Hispanics, 33.8 kg for non-Hispanic blacks, and 32.8 kg for non-Hispanic whites.

Whereas most of these publications have focused on 1-year outcomes, this study demonstrated equivalent 3-year outcomes in diabetes remission and weight loss across an ethnically diverse group of 1,603 patients (22). Significant improvements occurred for patients with undiagnosed diabetes who achieved a 43% fasting plasma glucose decrease followed by diagnosed patients with diabetes with a 33% decrease in fasting plasma glucose. As demonstrated in Table 1, there are several large databases that have examined ethnic variations in metabolic surgery outcomes.

Metabolic surgery outcomes by ethnicity in large databases


Comorbidity in Adult Patients Hospitalized with Type 2 Diabetes in Northeast China: An Analysis of Hospital Discharge Data from 2002 to 2013

This study aims to evaluate the comorbidity burden and patterns among adult patients hospitalized with a diagnosis of type 2 diabetes mellitus (T2DM) in Northeast China using hospital discharge data derived from the electronic medical record database between 2002 and 2013. 12.8% of 4,400,892 inpatients aged ≥18 had a diagnosis of T2DM. Sex differences in prevalence varied among those aged <50, 50–59, and ≥60. Twenty-seven diseases were determined as major comorbidities of T2DM. Essential hypertension was the most common comorbidity of T2DM (absolute cooccurrence risk, 58.4%), while T2DM was also the most popular comorbidity of essential hypertension. Peripheral and visceral atherosclerosis showed the strongest association (relative cooccurrence risk, RCoR 4.206). For five leading comorbidities among patients aged ≥40, male patients had a stronger association with disorders of lipid metabolism than female patients (RCoR 2.779 versus 2.099), and female patients had a stronger association with chronic renal failure than male patients (RCoR 2.461 versus 2.155). Leading comorbidities, except chronic renal failure, had declining associations with T2DM with increased age. Collectively, hospital discharge data can be used to estimate disease prevalence and identify comorbidities. The findings provided comprehensive information on comorbidity patterns, helping policy makers and programs in public health domains to estimate and evaluate the epidemic of chronic diseases.

1. Introduction

The prevalence of diabetes is increasing worldwide [1]. Clinical cross-sectional study and cohort study revealed that patients with type 2 diabetes mellitus (T2DM) are at increased risk of cardiovascular and cerebrovascular diseases and associated clinical complications, leading to diabetes being a major cause of premature illness and death. It is predicted that, by 2030, T2DM will be the seventh leading cause of death in the world [2]. Therefore, precise and clear understanding of the epidemiology of diseases that coexist with diabetes, especially chronic illnesses, is important for setting treatment goals.

While patients with T2DM are at increased risk of comorbidity, few data sources are available for evaluating the comorbidity burden and patterns among patients with T2DM. Many population-based surveys and clinical studies have attempted to determine how T2DM affects the risk of cardiovascular and cerebrovascular diseases and associated complications [3–5], focusing on specific disorders related to T2DM, such as cardiovascular autonomic neuropathy [6, 7], pulmonary tuberculosis [8], and chronic kidney disease [9], and/or on specific populations with T2DM, such as patients with dementia [5], the elderly [10], and people with depression [11]. Clinical studies may have inconsistent findings because of relatively small sample sizes and variations in sample characteristics and settings [12], whereas survey data usually focus on specific disorders and sometimes include inadequate information on diagnoses and treatment. Therefore, there is a need for comprehensive information from large long-term datasets to improve understanding of the prevalence of T2DM-related comorbidities, along with subgroup analysis.

With the emergence of the big data era, national or regional adoption of electronic medical records (EMR) systems has improved the efficiency and quality of healthcare delivery and allowed the opportunity to use real-world patient information for clinical data mining. EMR data have become a priority for research on disease relationships, such as assessing comorbidities of substance use [13, 14], studying temporal relationships between T2DM and cancer [15], analyzing disease networks [16], and modeling to predict disease severity [17] and to identify patients [18, 19]. Hospital discharge data, as a kind of administrative data derived from EMR, allow investigators access to a broad range of illness, whose discharge diagnosis codes are assigned by trained doctors following standard guideline. Therefore, hospital discharge data are becoming one of the available data sources for assessing hospital prevalence and comorbidity for a specific disease [20–22]. However, to our best knowledge, none of these studies has focused on analyzing the trend in both the prevalence and comorbidity patterns with respect to T2DM.

China has the largest number of individuals with diabetes in the world. In 2014, the prevalence of T2DM was estimated at 9.32% among the adult Chinese population aged 18–79 years, representing an estimated 96.3 million people [23]. China is estimated to have approximately 143 million T2DM patients by 2035 [23]. However, most current epidemic information about T2DM in China was collected through surveys [24–27]. Few studies [28, 29] have utilized real-world data from a single hospital to assess T2DM prevalence and/or comorbidity in China. On the contrary, the Chinese government has invested huge amounts of funding to deploy EMR systems at hospitals across the nation in the past decade. EMRs are expected to be deployed and implemented nationwide in all public hospitals at county level and above by 2017 [30]. The rapid implementation of EMRs in China has accumulated huge amounts of clinical data, which are suitable for answering questions such as T2DM prevalence and comorbidity.

In this study, we used a large administrative database (involving 4,123,405 patients), which includes hospital discharge information derived from EMRs of all hospitals in a large city in Northeast China during 2002 through 2013, to estimate the risk of T2DM-related comorbidities, as well as their trends along the timeline. We believe this is the first study that utilizes large EMR-derived data to assess T2DM status in China, especially in Northeast China. We hope this study also serves as a new model for better understanding diseases using real-world data.

2. Materials and Methods

2.1. Data Source and Study Population

Hospital discharge data were derived from EMR databases of all hospitals in Dalian, China, from January 2002 to December 2013. Dalian is the second largest city in Northeast China, with 6.9 million permanent residents in 2013. The dataset contained more than 6 million records, including demographic information (sex and date of birth), date of admission, date of discharge, one primary discharge diagnosis, and up to 5 secondary discharge diagnoses. Data for patients aged ≥18 years were deidentified and included in this study. The use of these data in an anonymous manner was authorized by the Information Center, Health and Family Planning Commission of Dalian Municipality.

All diagnoses were identified with International Classification of Diseases, Tenth Revision (ICD-10) codes [31]. These diagnostic codes were then recoded into one of 259 categorization codes defined by Clinical Classifications Software (CCS) for ICD-10-CM [32], which is a diagnosis categorization scheme based on ICD-10 codes. CCS codes are diagnosis categories with more clinical meanings, which can sometimes be more useful for presenting descriptive statistics than individual ICD-10 codes for relatively specific conditions. They are now widely used in many study scenarios for identifying comorbidities and outcomes [33–35], predicting mortality and risk [36, 37], and estimating hospital utilization and costs [38].

2.2. Statistical Analysis

All samples were stratified by age, sex, and calendar year. Age in years was categorized into following groups: 18–29, 30–39, 40–49, 50–59, 60–69, 70–79, and ≥80.

In each individual medical record with two and above diagnoses, all possible disease pairs among these diagnoses were extracted. For specific diseases

, a two-by-two table was constructed as seen in Table 1 where

are numbers of records having disease with and without , respectively, and

are numbers of records not having disease with and without , respectively. The absolute cooccurrence risk (ACoR) of disease in condition of was calculated as

, and the relative cooccurrence risk (RCoR) of disease was calculated as the ACoR of disease with divided by the ACoR of disease without that is,


Prevention

Physicians should recognize signs of diabetes in all age groups, and should educate patients and caregivers on how to recognize them as well (eTable A) . In one study, persons with DKA had symptoms of diabetes for 24.5 days before developing DKA.17 Persons with diabetes and their caregivers should be familiar with adjusting insulin during times of illness. This includes more frequent glucose monitoring continuing insulin, but at lower doses, during times of decreased food intake and checking urine ketone levels with a dipstick test if the glucose level is greater than 240 mg per dL (13.32 mmol per L).47 More accessible home measurement of serum ketones with a commercial glucometer may allow for earlier detection of DKA and decreased hospital visits.48 Persons with an insulin pump need to know their pump settings, and should maintain a prescription for basal insulin in case of pump failure.

ETable A. Preventive Strategies for Diabetic Ketoacidosis

Education for physicians on early recognition of diabetes mellitus symptoms for prompt diagnosis A1

Education for patients and caregivers on diabetes care

24-hour hotline for urgent questions

Referral for diabetes education with certified educator or pharmacist A3, A4

Early contact with clinician

Insulin reduction rather than elimination

Measurement of urine or serum ketone level

Backup insulin protocol in case of insulin pump failure

Psychological counseling for those who eliminate insulin for body image concerns, and those who have major depression or other psychological illnesses that interfere with proper management

Assess reasons for discontinuation of insulin (e.g., access to health care social, cultural, economic barriers)

Referral to community resources

Copay reduction for medication A8

A1 . Vanelli M, Chiari G, Ghizzoni L, Costi G, Giacalone T, Chiarelli F. Effectiveness of a prevention program for diabetic ketoacidosis in children. An 8-year study in schools and private practices . Diabetes Care. 199922(1):7𠄹 .

A2 . Riley SB, Marshall ES. Group visits in diabetes care: a systematic review . Diabetes Educ. 201036(6):936� .

A3. Funnell MM, Brown TL, Childs BP, et al. National standards for diabetes self-management education . Diabetes Care. 201033(suppl 1):S89–S96 .

A4. Taveira TH, Friedmann PD, Cohen LB, et al. Pharmacist-led group medical appointment model in type 2 diabetes . Diabetes Educ. 201036(1):109� .

A5 . Mayes PA, Silvers A, Prendergast JJ. New direction for enhancing quality in diabetes care: utilizing telecommunications and paraprofessional outreach workers backed by an expert medical team . Telemed J E Health. 201016(3):358� .

A6. Hall DL, Drab SR, Campbell RK, Meyer SM, Smith RB. A Web-based interprofessional diabetes education course . Am J Pharm Educ. 200771(5):93 .

A7. Brink S, Laffel L, Likitmaskul S, et al. Sick day management in children and adolescents with diabetes . Pediatr Diabetes. 200910(suppl 12):146� .

A8. Nair KV, Miller K, Park J, Allen RR, Saseen JJ, Biddle V. Prescription co-pay reduction program for diabetic employees . Popul Health Manag. 201013(5):235� .

ETable A. Preventive Strategies for Diabetic Ketoacidosis

Education for physicians on early recognition of diabetes mellitus symptoms for prompt diagnosis A1

Education for patients and caregivers on diabetes care

24-hour hotline for urgent questions

Referral for diabetes education with certified educator or pharmacist A3, A4

Early contact with clinician

Insulin reduction rather than elimination

Measurement of urine or serum ketone level

Backup insulin protocol in case of insulin pump failure

Psychological counseling for those who eliminate insulin for body image concerns, and those who have major depression or other psychological illnesses that interfere with proper management

Assess reasons for discontinuation of insulin (e.g., access to health care social, cultural, economic barriers)

Referral to community resources

Copay reduction for medication A8

A1 . Vanelli M, Chiari G, Ghizzoni L, Costi G, Giacalone T, Chiarelli F. Effectiveness of a prevention program for diabetic ketoacidosis in children. An 8-year study in schools and private practices . Diabetes Care. 199922(1):7𠄹 .

A2 . Riley SB, Marshall ES. Group visits in diabetes care: a systematic review . Diabetes Educ. 201036(6):936� .

A3. Funnell MM, Brown TL, Childs BP, et al. National standards for diabetes self-management education . Diabetes Care. 201033(suppl 1):S89–S96 .

A4. Taveira TH, Friedmann PD, Cohen LB, et al. Pharmacist-led group medical appointment model in type 2 diabetes . Diabetes Educ. 201036(1):109� .

A5 . Mayes PA, Silvers A, Prendergast JJ. New direction for enhancing quality in diabetes care: utilizing telecommunications and paraprofessional outreach workers backed by an expert medical team . Telemed J E Health. 201016(3):358� .

A6. Hall DL, Drab SR, Campbell RK, Meyer SM, Smith RB. A Web-based interprofessional diabetes education course . Am J Pharm Educ. 200771(5):93 .

A7. Brink S, Laffel L, Likitmaskul S, et al. Sick day management in children and adolescents with diabetes . Pediatr Diabetes. 200910(suppl 12):146� .

A8. Nair KV, Miller K, Park J, Allen RR, Saseen JJ, Biddle V. Prescription co-pay reduction program for diabetic employees . Popul Health Manag. 201013(5):235� .

Nonadherence to medical regimens is often the cause of recurrent DKA. Physicians need to recognize patient barriers to getting care, such as financial, social, psychological, and cultural reasons. Diabetes education with certified educators and pharmacists enhances patient care.49 , 50 Other prevention techniques include group visits, telecommunication, web-based learning, and copay reduction for diabetes medications however, evidence for their effectiveness is mixed.51 – 55

Data Sources: In July 2010, an initially broad search of PubMed, Essential Evidence Plus, and sources such as the Cochrane database and Clinical Evidence was conducted using the key term diabetic ketoacidosis. In the fall of 2010, another search was conducted using additional key terms, such as incidence and prevalence. As information was collected, individual questions were then searched to add finer points to the documentation. The searches were repeated with each draft of the manuscript.


Probiotics, prebiotics and brain functions

A beneficial microorganism defends the host organisms against the penetration of harmful microorganisms, and has many other functions in the gut wall integrity, innate immunity, insulin sensitivity, metabolism, and it is in cross talk with the brain functions as well. It is a recent recognition, that intestinal microbiota has a direct effect on the brain, and the brain influences the microbiota [105]. The effects of manipulating enteric flora by probiotics (live bacteria given in oral quantities that allow for colonization of the colon) or pre-biotics (nondigestible oligosaccharides like insulin and oligofructose that are fermented by colonic microbiota and enhance the growth of beneficial commensally organisms like Bifidobacterium and Lactobacillus spp.) have been evaluated in several controlled trials [117]. In a recent report it has been demonstrated that probiotics treatment improves diabetes-induced impairment of synaptic activity and cognitive function [123]. This two-way gut-brain axis consists of microbiota, immune and neuroendocrine system, as well as of the autonomic and central nervous system [108, 124]. Compared with the carbohydrate-alone diet, the pre-biotic with carbohydrate diet increased the intestinal proportion of Lactobacilli and Bifidobacteria, preserved tight junction integrity and intestinal barrier function, and lowered endotoxinemia and systemic and hepatic cytokines and oxidative stress [116]. Now, the evidence of the gut-microbiota influence on behavior and brain chemistry is well-documented [125]. It is also known that normal healthy microbiota influences the development and function of CNS, via behavioral and molecular changes [102]. The oral treatment of rats with Lactobacillus reuteri, which activated calcium dependent potassium channels in enteric neurons in the colonic myenteric plexus, proves that that gut microbiota may affect brain via autonomic nervous system [126].


ACE Inhibitors and ARBs

Angiotensin II is a potent vasoconstrictor that also causes cardiac myocytic hypertrophy and production of collagen by these cells, leading to myocardial fibrosis.27 Not only are these effects ameliorated by ACE inhibition and blocking of the AT1 receptor, but also with ACE inhibitors bradykinin and prostacyclin levels are increased, which mediates the release of nitric oxide and improves both the hypertrophy of the myocyte and cardiac fibrosis.28 Previously, based on retrospective subgroup analysis of the Valsartan in Heart Failure Study,29 it was believed that in patients already on ACE inhibitors and β-blockers the addition of an ARB increased mortality. However, the recent Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) study showed a benefit of adding candesartan to a HF regimen of β-blockers and ACE inhibitors.30 Conversely, studies of high-risk type 2 diabetic patients with macroalbuminuria utilizing either losartin or ibersartin, while showing improvement in progression of renal disease, did not show a benefit in cardiac or overall mortality.31

On the other hand, ACE inhibitors have repeatedly shown a reduction in mortality in diabetic HF patients with or without systolic dysfunction. The mechanism for this improvement is through myocardial remodeling. ACE inhibitors prevent rather than reverse remodeling so that their effect depends on how soon after an acute event they are initiated.32

ACE inhibitors are at least as effective in reducing mortality in diabetic patients as in nondiabetic patients and are clearly of value in the treatment of diabetic patients with HF. Evidence of this is available from the Survival and Ventricular Enlargement Study, in which captopril reduced the mortality rate in both diabetic and nondiabetic subjects following MI, though the mortality rate of treated diabetic patients was higher than that of untreated nondiabetic patients.33 Use of enalapril in the SOLVD trials34 was associated with greater efficacy in asymptomatic, compared with symptomatic, diabetic patients, which reemphasizes the prophylactic rather than curative effects of ACE inhibitors on myocardial remodeling. In the Gruppo Italiano per lo Studio della Sopravvivenza nell Infarto Miocardico (GISSI-3) trial,35 use of lisinopril post-MI resulted in a lower 6-week mortality in diabetic subjects. In the Assessment of Treatment with Lisinopril and Survival study36 of class II-IV HF patients, the risk of death was reduced by more than half in diabetic subjects utilizing lisinopril.


A comparison of diabetic complications and health care utilization in diabetic patients with and without comorbid depression

Background: Depression has been found to interfere with patient self-management of diabetes and adherence to a medication regimen. Data on health care utilization indicate that depression is at least as prevalent as diabetes, and that both of these conditions represent substantial costs to the health care system. In this analysis, we used Canadian data from outpatient visits, hospital discharges, extended health care claims, and long-term disability claims to compare the rate of diabetic complications for subjects with diabetes alone and those with diabetes and depression. We then determined whether there is a higher rate of utilization of health care services by diabetic patients with depression.

Methods: In this cross-sectional study, 1427 diabetic patients were identified in a group of acute care hospital workers employed in British Columbia in 1998. Diabetic complications and depression were identified based on ICD-9 diagnostic codes. Rates of diabetes-related complications and use of health care services were then considered for diabetic patients with and without comorbid depression.

Results: Overall, the rate of utilization of health care services was found to be greater in diabetic patients with comorbid depression than those with diabetes alone. The rates of ischemic heart disease, peripheral vascular disease, and altered consciousness experienced by the group of diabetic individuals with depression were found to be significantly higher than those with diabetes alone.

Conclusions: The interaction between diabetes and depression results in an increased risk of diabetic complications, as well as increased utilization of health care services.

A Canadian cross-sectional study of patients with diabetes found that those patients with comorbid depression experienced more diabetes-related complications.

Recent studies have estimated that patients with diabetes are twice as likely as members of the general population to be diagnosed with depression, and that depression in turn interferes with patient self-management of diabetes and adherence to a medication regimen.[1-3] Longitudinal data have also suggested that the interaction between diabetes and depression predicts greater mortality, greater incidence of both macrovascular and microvascular complications, as well as accelerated onset of these complications.[4-6] Lustman and colleagues noted a significant association between depression and hyperglycemia, a well-established predictor of diabetic complications in both type 1 and type 2 diabetes.[7-10] Futhermore, a randomized controlled trial suggested that antidepressant therapy in patients with type 1 and type 2 diabetes improved glycemic control.[10]

US data on health care utilization and expenditures indicate that depression is at least as prevalent as diabetes, and that both of these conditions represent substantial costs to the health care system.3 Several studies have attempted to quantify the increase in health care costs in diabetic patients with comorbid depression. Egede and colleagues reported 4.5 times greater annual health care expenditures in diabetic patients with major depression, while Ciechanowski and colleagues reported that diabetic patients with depressive symptoms have between 51% and 86% higher health care costs.[2,3] Many of these previous studies, however, relied on small samples or self-reported data, and only evaluated total health care costs. As a result, it is difficult to determine whether the increased costs are mainly due to treatment of mental health or due to the comorbid diagnosis of diabetes. Using US data, Finkelstein and colleagues conducted a retrospective analysis of Medicare claims and found increased health care utilization unrelated to mental illness in elderly claimants with both diabetes and major depression, thus indicating that diabetic patients with comorbid depression incurred higher medical costs than claimants with diabetes alone.[11]

Similar studies comparing Canadian health care expenditures in such patients are currently lacking. Furthermore, there are no studies indicating whether there is a difference between the rates of utilization of specific health care services by diabetic patients with and without depression.

In this analysis, we used Canadian data from a combination of records for outpatient visits, hospital discharges, extended health care claims, and long-term disability claims to compare the rate of diabetic complications for patients with diabetes and depression and those with diabetes alone. We then compared the use of specific health care services by the two groups in order to determine whether there is a higher rate of utilization of health care services by patients with diabetes and depression.

This cross-sectional study examined data from health care workers at acute care hospitals in British Columbia employed in 1998. Access to all data was granted through application to the data steward responsible for each data set, and the unique identifiers were removed to protect patient confidentiality. The study protocol was approved by the Clinical Research Ethics Board at the University of British Columbia. The study included data from several sources that were merged to create a person-specific longitudinal database of health care utilization. The data included outpatient physician visits and hospitalization discharges (provided by the British Columbia Linked Health Database) and long-term disability (LTD) and extended health benefits data (provided by a universal health benefits provider to the BC health care workforce). From this group a study cohort of 1427 diabetic patients was identified based on methods described previously.[12] Specifically, we used ICD-9 diagnostic codes 250 (diabetes mellitus), 357.2 (neuropathy in diabetes), 362.01 (diabetic retinopathy NOS), 362.02 (proliferative diabetic retinopathy), and 366.41 (diabetic cataract) as a primary or secondary diagnosis to identify diabetic patients within the database. Subjects with depression were identified using ICD-9 diagnostic codes 263.2 or 263.3 (either single or multiple episodes of major depressive disorders). In addition, subjects submitting claims for antidepressant medication or receiving LTD benefits because of depression were assumed to be suffering from depression.
The utilization of health care services was examined by evaluating the following variables: visits to a primary care physician, total physician visits (primary care and specialist), hospitalizations, long-term disability claims, total extended health care claims, medication claims to extended health, diabetes-specific claims to extended health (i.e., all services coded under diabetes mellitus therapy by the health benefits provider), and claims for visits to allied health care workers.

A number of common diabetes-related complications were identified in the database for analysis. These included ischemic heart disease, peripheral vascular disease, diabetic retinopthy, diabetic neuropathy, diabetic nephropathy, and gangrene. These complications were identified based on ICD-9 codes in the database provided by the MSP and hospitalization data.

Rates of diabetes-related complications and utilization of health care services were then considered for diabetic patients with and without comorbid depression. For continuous variables, t test analysis was used, while chi-square and fisher-exact testing were used for analysis of proportions.

Table 1 presents the demographic characteristics of the two patient groups (depressed and not depressed). In total, the study cohort consisted of 1427 patients. Of these patients, 180 were identified as depressed. The two groups were comparable with one exception: there were significantly more females in the group with diabetes and depression (88.9%) than in the group with diabetes alone (82.3%).

Table 2 presents the rates of diabetic complications in the two groups, as well as the number of visits per year to a primary care physician or specialist for each complication in those who experienced the complication. The rates of ischemic heart disease, peripheral vascular disease, and altered consciousness experienced by the group of diabetic individuals with depression were found to be significantly higher than those with diabetes alone. However, the number of visits to primary care physicians or specialists per year for these complications remained comparable between the two groups with the exception of nephropathy complications. The rate of all other diabetic complications analyzed remained comparable between the two groups.

Table 3 compares the rates of utilization of specific health care services for the two groups. Overall, the rate of utilization of health care services was found to be greater in diabetic patients with comorbid depression than those with diabetes alone. Specifically, the rate of hospitalizations, primary care physician visits, total physician services (primary care and specialist visits), total extended health care claims, drug claims, and long-term disability claims were found to be significantly higher in the group with diabetes and depression. However, diabetic-specific claims were comparable between the two groups.

Because it might be expected that diabetic patients with comorbid depression would seek mental health services at a rate greater than their nondepressed counterparts, we also analyzed the data without reference to these services. Even with mental health services removed, most relationships in Table 3 continued to hold true. Specifically, the rate of total physician services (primary care and specialist visits) used by those individuals with comorbid depression was 28.2 visits per year versus 22.4 visits per year for those without depression (P<.01). Visits to primary care physicians for the depressed group equaled 11.3 visits per year versus 9.3 visits for the nondepressed group (P<.01). Similarly, drug claims (again, excluding medication for mental health) in the diabetic subjects with depression were much higher, equaling 21.6 claims per year versus 11.7 claims per year for the nondepressed group (P<.01). With the removal of hospitalizations for mental health reasons, the difference between the groups in the rate of hospitalizations for other reasons was still apparent, although to a lesser degree, with 1.7 visits per year for the depressed group versus 1.5 visits per year for the nondepressed group (P=.13).

Our findings suggest that the interaction between diabetes and depression does result in an increased risk of diabetic complications, as well as increased utilization of health care services. The results of our study are similar to those of other retrospective analyses, longitudinal studies, and meta-analyses using US data.[2-4,11,12] In addition, our findings support those of Finkelstein, who found that diabetic patients with depression will seek services for health problems unrelated to mental health at a greater rate than those with diabetes alone.11

Our study is unique in that we have accessed an extensive Canadian database that includes extended health care claims and long-term disability information. Earlier studies have also reported increased health care use and expenditures in patients with diagnoses of diabetes and comorbid depression, but did not focus on the pattern of use and were mostly US-based.[2,3,13-20] Analysis of the pattern of use is crucial in deriving hypotheses for the driving force behind the increase in health care costs found in depressed individuals with diabetes. For example, in our study we noted that there was increased utilization of health care services in all areas, with the exception of diabetes-specific claims and allied health care. The increase in health care costs in such a wide range of services reflects the dramatic effect of depression on diabetes. Furthermore, when comparing the two groups, those who experienced increased complications in the depressed group did not seek increased medical care from primary care physicians or specialists, perhaps reflecting a change in patient motivation to seek treatment in the depressed group.

The general increase in the rate of diabetic complications found in this study, particularly those of ischemic heart disease, is consistent with results of several other studies.[4,6,21] However, there exists some controversy in the literature regarding which diabetic complications are increased in individuals with comorbid depression. For example, Cohen and colleagues[22] and Miyaoka and colleagues[23] have correlated depression with diabetic retinopathy and nephropathy, but others have failed to find such an association.[24]
There are limitations to this study. Additional baseline information on our study cohort would have been useful, and the lack of such information may have been a source of confounding factors. In addition, our database did not subdivide the different types of depression and diabetes. Finally, because of the cross-sectional nature of this study, we cannot show causality and cannot conclude that the increase in health care utilization and expenditure is due solely to the effect of depression in individuals with diabetes.

Conclusions
Health care utilization was greater in diabetic individuals with comorbid depression than those without such a diagnosis. The increased utilization of resources may be due to management of diabetic complications, changes in patient compliance to diabetic management, a combination of the two factors, or unknown factors. Our findings further suggest that screening diabetic patients for depression may decrease the rate of diabetic complications and potentially decrease health care expenditure however, further cost-benefit analyses are required to define the economic benefit of such a protocol.

Acknowledgments
This research was funded by the Student Summer Research Program, UBC Faculty of Medicine.

Competing interests
None declared.

References

1. Rubin RR, Ciechanowski P, Egede LE, et al. Recognizing and treating depression in patients with diabetes. Curr Diab Rep 20044:119-125.
2. Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: Impact of depressive symptoms on adherence, function, and costs. Arch Intern Med 2000160:3278-3285.
3. Egede LE, Zheng D, Simpson K. Comorbid depression is associated with increased health care use and expenditures in individuals with diabetes. Diabetes Care 200225:464-470.
4. Black SA, Markides KS, Ray LA. Depression predicts increased incidence of adverse health outcomes in older Mexican Americans with type 2 diabetes. Diabetes Care 200326:2822-2828.
5. Musselman DL, Betan E, Larsen H, et al. Relationship of depression to diabetes types 1 and 2: Epidemiology, biology, and treatment. Biol Psychiatry 200354:317-329.
6. De Groot M, Anderson R, Freedland KE, et al. Association of depression and diabetes complications: A meta-analysis. Psychosomatic Med 200163:619-630.
7. Lustman PJ, Anderson RJ, Freedland KE, et al. Depression and poor glycemic control: A meta-analytic review of the literature. Diabetes Care 200023:434-442.
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10. Lustman PJ, Griffith LS, Freedland KE, et al. Cognitive behavior therapy for depression in type 2 diabetes. A randomized, controlled trial. Ann Intern Med 1998129:613-621.
11. Finkelstein EA, Bray JW, Chen H, et al. Prevalence and costs of major depression among elderly claimants with diabetes. Diabetes Care 200326:415-420.
12. Anderson RJ, Freedland KE, Clouse RE, et al. The prevalence of comorbid depression in adults with diabetes: A meta-analysis. Diabetes Care 200124:1069-1078.
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14. Simon G, Ormel J, VonKorff M, et al. Health care costs associated with depressive and anxiety disorders in primary care. Am J Psychiatry 1995152:352-357.
15. Henk HJ, Katzelnick DJ, Kobak KA, et al. Medical costs attributed to depression among patients with a history of high medical expenses in a health maintenance organization. Arch Gen Psychiatry 199653:899-904.
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23. Miyaoka Y, Miyaoka H, Motomiya T, et al. Impact of sociodemographic and diabetes-related characteristics on depressive state among non-insulin-dependent diabetic patients. Psychiatry Clin Neurosci 199751:203-206.
24. Karlson B, Agardh CD. Burden of illness, metabolic control, and complications in relation to depressive symptoms in IDDM patients. Diabet Med 199714:1066-1072.

Dr Dufton is a medical resident in the Department of Radiology at Queen’s University. Dr Li is a medical resident in the Department of Internal Medicine at the University of British Columbia. Dr Koehoorn is an assistant professor in the Department of Health Care and Epidemiology with an associate appointment at the School of Occupational and Environmental Hygiene at the University of British Columbia.



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