Depression, obesity, and smoking were independently associated with inadequate glycemic control in patients with type 1 diabetes

in European Journal of Endocrinology

Objective

The aim of this study was to explore the associations between inadequate glycemic control of diabetes and psychological, anthropometric, and lifestyle variables in a population-based cohort of type 1 diabetes patients.

Design

Cross-sectional study.

Methods

In this study, 292 patients with type 1 diabetes, aged 18–59 years, participated. Psychological data were assessed by self-report instruments: Hospital Anxiety and Depression Scale and Toronto Alexithymia Scale-20. Anthropometrics, blood analyses, data from medical records, and data from the Swedish National Diabetes Registry were collected.

Results

Self-reported depression (adjusted odds ratio (AOR) 4.8), obesity (AOR 4.3), and smoking (AOR 3.0) were independently associated with inadequate glycemic control of diabetes (HbA1c>8.6%). Gender-stratified analyses showed that self-reported depression (AOR 19.8) and obesity (AOR 7.0) in women and smoking in men (AOR 4.2) were associated with HbA1c>8.6%. Alexithymia, antidepressant medication, and physical inactivity were associated with HbA1c>8.6% only in bivariate analyses. Alexithymia, self-rated anxiety, physical inactivity, and absence of abdominal obesity were associated with self-reported depression.

Conclusions

Depression was the only psychological factor independently associated with HbA1c>8.6%. The association was of comparable importance as obesity and smoking, well-known risk factors for inadequate glycemic control and diabetes complications. The association between depression and HbA1c>8.6% was particularly strong for women. Alexithymia, which is a relatively stable personality trait, was associated with depression. In the future care of patients with diabetes, psychological aspects should be considered alongside anthropometrics and lifestyle factors in order to achieve the goals for HbA1c.

Abstract

Objective

The aim of this study was to explore the associations between inadequate glycemic control of diabetes and psychological, anthropometric, and lifestyle variables in a population-based cohort of type 1 diabetes patients.

Design

Cross-sectional study.

Methods

In this study, 292 patients with type 1 diabetes, aged 18–59 years, participated. Psychological data were assessed by self-report instruments: Hospital Anxiety and Depression Scale and Toronto Alexithymia Scale-20. Anthropometrics, blood analyses, data from medical records, and data from the Swedish National Diabetes Registry were collected.

Results

Self-reported depression (adjusted odds ratio (AOR) 4.8), obesity (AOR 4.3), and smoking (AOR 3.0) were independently associated with inadequate glycemic control of diabetes (HbA1c>8.6%). Gender-stratified analyses showed that self-reported depression (AOR 19.8) and obesity (AOR 7.0) in women and smoking in men (AOR 4.2) were associated with HbA1c>8.6%. Alexithymia, antidepressant medication, and physical inactivity were associated with HbA1c>8.6% only in bivariate analyses. Alexithymia, self-rated anxiety, physical inactivity, and absence of abdominal obesity were associated with self-reported depression.

Conclusions

Depression was the only psychological factor independently associated with HbA1c>8.6%. The association was of comparable importance as obesity and smoking, well-known risk factors for inadequate glycemic control and diabetes complications. The association between depression and HbA1c>8.6% was particularly strong for women. Alexithymia, which is a relatively stable personality trait, was associated with depression. In the future care of patients with diabetes, psychological aspects should be considered alongside anthropometrics and lifestyle factors in order to achieve the goals for HbA1c.

Introduction

Patients with diabetes have an increased prevalence of depression (1, 2, 3). Depression in diabetes is associated with low quality of life (3), impaired glycemic control (3), increased frequency of complications (4, 5, 6), and all-cause mortality (6). Depressive symptoms are associated with poor diet (3, 7, 8), smoking habits (9), less exercise (7), low adherence to medication (3, 7, 8), and higher health care costs (3, 8). There are suggested overlapping biological links between depression and autoimmune diabetes indicated by high levels of circulating cytokines associated with both conditions, insulin deficiency impairing neurogenesis and neurotransmitter metabolism, a chronic hyperglycemic state and hypoglycemia episodes, and hyperactivity of the hypothalamic–pituitary–adrenal axis. All these factors may induce or worsen depression (1).

Affects are signals to the individual of one's inner state. Affect dysregulation and absence of conscious awareness of affects have health implications (10, 11, 12). Alexithymia is defined by four factors: difficulty identifying feelings, difficulty describing feelings, externally oriented thinking (low degree of introspection and reflection), and constricted imaginative processes (10). A deficit in the awareness of affects results in abnormal physiological reactions, a propensity for impulsive behavior, and an impaired capacity for self-care and self-regulation (10). Inadequate metabolic control of diabetes (13), altered immune function (14), altered cortisol responses (15), and changes of C-reactive protein and serum lipid levels (16) are associated with alexithymia, which in turn has been linked with depression, anxiety, stress-related disorders, diabetes, and obesity (11, 17, 18, 19, 20). Alexithymia has also been associated with reduced social support, which negatively affects the prognosis of disease and health problems (11, 21).

Poor glycemic control is associated with smoking in patients with diabetes (22, 23). Abdominal obesity is associated with dyslipidemia and increased risk of cardiovascular disease (24, 25). Physical inactivity is associated with insulin resistance, dyslipidemia, increased blood pressure, and microvascular dysfunction (26, 27).

The hypotheses of this study are that psychological factors, particularly depression, are important variables associated with inadequate glycemic control and that the associations are underestimated compared with smoking, physical inactivity, and obesity. The aim of this population-based study of adult patients with type 1 diabetes was to explore whether glycemic control measured as HbA1c was associated with psychological and anthropometric variables and lifestyle factors.

Materials and methods

Participants and procedures

Between March and December 2009, 292 patients with type 1 diabetes were recruited consecutively at a specialist diabetes outpatient clinic at a central hospital with a catchment population of 125 000 in Southern Sweden. They were enrolled by a specialist nurse or physician. Inclusion criteria were age 18–59 years and diabetes duration for at least 1 year. Exclusion criteria were severe somatic comorbidities (cancer, hepatic failure, end-stage renal disease, and social blindness), severe mental disorder (psychotic disorder, bipolar disorder, severe personality disorder, severe substance abuse, mental retardation, or other severe cognitive deficiency), or inadequate knowledge of Swedish.

Psychological data were assessed by self-report instruments. Blood analyses and anthropometric measurements were performed by nurses. Information regarding general diabetes-related data, clinical psychiatric diagnoses, and antidepressant medication was collected from the Swedish National Diabetes Registry (S-NDR) and computerized medical records. All patients provided written informed consent, and the study was approved by the Regional Ethical Review Board of Linköping University: Dnr M120-07, T89 - 08. Clinical Trials Registry: NCT01498614.

Self-report psychological instruments

Hospital Anxiety and Depression Scale

Anxiety and depression were assessed by the Hospital Anxiety and Depression Scale (HADS) (11, 28, 29, 30). HADS consists of two subscales with seven items reflecting depression (HADS-D) and seven items reflecting anxiety (HADS-A). Each statement has four response alternatives with scores from 0 to 3. The recommended cutoff level was used for both subscales: ≥8 points. A major characteristic of HADS is that potential symptoms from somatic diseases are not included, and it allows identification of cases of anxiety disorders and depression in patients from non-psychiatric hospital clinics (28). In a Swedish population sample, mean (s.d.) for HADS-D was 4.0 (±3.5) and for HADS-A 4.6 (±3.7) (29). The validity of the HADS-D was controlled by analyzing the associations between HADS-D and clinical psychiatric diagnosis and use of antidepressant medication.

Toronto Alexithymia Scale-20

Alexithymia was assessed by the 20-item Toronto Alexithymia Scale-20 (TAS-20) (11, 14, 15, 16, 18, 19, 20, 21, 31, 32, 33) based on three subscales: ‘difficulty identifying feelings’, ‘difficulty describing feelings’, and ‘externally oriented thinking’. TAS-20 consists of 20 statements rated from 1 to 5. The recommended cutoff point was used: ≥61 points, which in a Swedish normative sample of 137 persons yielded a prevalence of alexithymia of 2% (20).

Blood analyses and anthropometric measurements

HbA1c

Venous samples for analyses of HbA1c were analyzed with high-pressure liquid chromatography, HPLC – variant II, Turbo analyzer (Bio-Rad) (34). The results were converted from Mono-S HbA1c % to HbA1c % according to the National Glycohemoglobin Standardization Program (NGSP) (35). HbA1c (NGSP) was dichotomized at the third quartile (HbA1c>8.6%), which was defined as inadequate glycemic control of diabetes. HbA1c was analyzed at the Department of Clinical Chemistry, Växjö Central Hospital.

Anthropometrics

WC, weight and length were measured by a nurse according to standard procedures. Abdominal obesity was defined for men as WC ≥1.02 m and for women as WC ≥0.88 m (36). General obesity was defined as BMI ≥30 kg/m2 for both genders.

Data collected from the S-NDR and medical records

Data were collected from both the S-NDR and the medical records regarding diabetes type, diabetes duration, age, psychiatric and somatic diagnoses, medication, number of severe hypoglycemic episodes, smoking habits, and physical activity. Computerized medical record data collected from the Departments of Internal Medicine and Ophthalmology, and Primary Care, including primary care psychological counseling, also served as a validation of the S-NDR data. Comprehensive computerized drug prescription data (including prescription data from the Department of Psychiatry) were studied as well.

Hypoglycemia

A severe hypoglycemic episode was defined as a patient needing help from another person.

Smoking

Patients were defined as smokers if they had smoked any amount of tobacco during the last year.

Physical inactivity

In the S-NDR, physical activity is categorized into four groups, <1/week, 1–2 times/week, 3–5 times/week, or daily activities. Patients were defined as physically active if they performed moderate activities such as 30 min of walking at least once a week.

Foot complications

These were defined as neuropathy, angiopathy, earlier or present diabetes foot ulcer, foot infection, foot deformity, arthropathy, or amputation of the lower limb.

Retinopathy

Diabetes retinopathy was defined as non-proliferative or proliferative retinopathy with microangiopathy changes as viewed by fundus photography through a dilated pupil.

Macrovascular complications

Macrovascular complications were defined as ischemic heart disease, stroke, or transient ischemic attack (TIA). Ischemic heart disease was defined as angina pectoris, previous myocardial infarction, percutaneous transluminal coronary angioplasty (PTCA), or coronary artery bypass graft (CABG) surgery.

AITD

Due to the association of type 1 diabetes with an increased risk of additional autoimmune diseases, special care was given to identify patients with an autoimmune thyroid disease (37). Patients diagnosed with hypothyroidism or hyperthyroidism were considered suffering from autoimmune thyroid diseases (AITD), though all were not tested for specific antibodies.

Statistical analysis

Frequencies of psychological factors, metabolic and lifestyle variables, and diabetes complications were calculated using ordinary descriptive measures. Fisher's exact test was used to analyze differences of prevalence. After checking for normality, all continuous variables were analyzed using Student's t-test. Crude odds ratios (CORs) were calculated for the associations between self-reported depression (HADS-D ≥8) and the four variables: antidepressant medication, clinical psychiatric diagnosis, macrovascular complications, and AITD. CORs were calculated for the associations between HbA1c>8.6% and each variable in the study. CORs were calculated for the associations between alexithymia and the two variables: smoking and abdominal obesity. In a backward elimination multiple logistic regression analysis, abdominal obesity showed a higher association with HbA1c>8.6% than general obesity and was therefore chosen in the subsequent multiple regression models.

Multiple logistic regression analyses (Backward: Wald) were performed with HbA1c>8.6% and self-reported depression as dependent variables and were controlled for gender, age, and diabetes duration. Patients with macrovascular complications were excluded from the multiple logistic regression analyses as they all had multiple complications. When HbA1c>8.6% was used as dependent variable, the following factors were used as independent variables: the psychological factors, antidepressant medication, abdominal obesity, and lifestyle variables. When self-reported depression was used as dependent variable, the two variables, diabetes retinopathy and foot complications, were added to the variables previously mentioned, and antidepressant medication was excluded. The Hosmer and Lemeshow test for goodness-of-fit and Nagelkerke R2 were used to evaluate each regression analyses model. P≤0.05 was considered statistically significant. CIs of 95% were used.

In the self-report instruments, missing values were imputed using regression on the other variables in the same subgroup. Multinomial regression was used as there were a limited number of possible answers. Imputation for patients with type 1 diabetes was done in 1/292 cases of HADS-D and in 2/292 cases of TAS-20. SPSS version 18 (IBM), was used for statistical analyses.

Results

Characteristics of the participants

In this study, 292 patients with type 1 diabetes participated. Data regarding age, anthropometrics, complications, diabetes duration, lifestyle, metabolic factors, and self-reported psychological variables are presented for all and gender stratified in Table 1. Women had higher prevalence rates of self-reported anxiety and obesity, and men had a higher prevalence of foot complications. There were no gender differences in mean HbA1c, diabetes duration, or age.

Table 1

Characteristics of participants. Data are n (%), and Fisher's exact test was used unless otherwise indicated.

AllMenWomenP
Number (%)292162 (55)130 (45)
Age range (years)18–5919–5918–59
Age mean (s.d.) (years) 41.0 (±11.6)42.0 (±11.8)39.7 (±11.4)0.10a
Diabetes duration range (years) 1–552–531–55
Diabetes duration mean (s.d.) (years) 21.1 (±12.1)22.0 (±12.7)19.9 (±11.2)0.16a
Psychological variables
 Depressionb30 (10)16 (10)14 (11)0.84
 Alexithymiab44 (15)20 (12)24 (18)0.18
 Anxietyb101 (35)40 (25)61 (47)<0.001
Metabolic factors
 HbA1c (%) mean (s.d.)8.0 (±1.2)7.9 (±1.1)8.0 (±1.3)0.33a
 HbA1c>8.6%80 (27)41 (24)39 (32)0.19
 Hypoglycemiac13 (4)7 (4)6 (5)1.0
Anthropometric variables
 General obesityd36 (12)12 (7)24 (19)0.004
 Abdominal obesitye49 (17)13 (8)36 (29)<0.001
Lifestyle variables
 Smokingf28 (10)18 (12)10 (8)0.42
 Physical inactivityg36 (13)19 (12)17 (14)0.85
Complications
 Diabetes retinopathy209 (72)118 (73)91 (70)0.69
 Foot complications47 (17)34 (22)13 (11)0.016
 Macrovascular complicationsh10 (3)6 (4)4 (3)1.0

Student's t-test.

Self-reported.

Definition: ≥1 severe episode last year.

Definition: BMI ≥30 kg/m2.

Definition: WC for men ≥1.02 m, WC for women ≥0.88 m.

Any amount of tobacco during the last year.

Less than 30 min of physical activity/week.

Stroke/TIA and/or ischemic heart disease.

According to medical records, 41 (14%) patients had a history of clinical psychiatric diagnoses; 19 had clinical depression, eight had clinical anxiety disorders, and 14 had stress-related disorders, substance abuse, anorexia nervosa, or attention deficit hyperactivity disorder. Twenty-three patients (8%) used antidepressant medication. Thirty-three patients (11%) had AITD.

Macrovascular complications were present in ten patients (3%) and their HbA1c mean (s.d.) was 8.3% (±1.7). Three patients had stroke or TIA, five had ischemic heart disease, and two had both types of complications. These ten patients had retinopathy, seven had foot complications, four had self-reported depression, four had self-reported anxiety, and three had alexithymia. Macrovascular complications were associated with depression (COR=6.6 (1.7–24.8), P=0.005) but not with HbA1c>8.6% (COR=1.1 (0.3–4.5), P=0.85).

Associations between clinical psychiatric diagnoses, antidepressant medication, and HADS

Of the 41 patients with a clinical psychiatric diagnosis, 26 (63%) scored positive on at least one of the two HAD subscales compared with 30% for patients without known clinical psychiatric diagnoses. For patients with a clinical psychiatric diagnosis, the COR (CI) for scoring positive on HADS-D was 10.8 (4.7–24.8), P<0.001. For the 23 patients using antidepressant medication, the COR (CI) for scoring positive on HADS-D was 9.6 (3.7–24.6), P<0.001.

Glycemic control

Mean HbA1c levels for patients with and without depression, alexithymia, anxiety, severe hypoglycemia episodes, obesity, physical inactivity, smoking, retinopathy, foot, and macrovascular complications are presented gender stratified in Table 2. Mean HbA1c levels were higher for men who were smoking or physically inactive than for non-smoking or physically active. Mean HbA1c levels were higher for women with self-rated depression, alexithymia, obesity, smoking, retinopathy, foot complications, and macrovascular complications than for women without these factors (Table 2).

Table 2

Gender-stratified mean levels of HbA1c for positive and negative findings for the variables in the study. Data are n (%) and mean (s.d.).

MenWomen
n (%)HbA1c %Pan (%)HbA1c %Pa
Gender162 (55%)7.9 (±1.1)130 (45%)8.0 (±1.3)
Psychological factors
 Depressionb
    Yes16 (10)7.9 (±1.0)0.9014 (11)9.0 (±1.3)0.004
    No146 (90)7.9 (±1.1)116 (89)7.9 (±1.3)
 Alexithymia
    Yes20 (12)8.1 (±1.4)0.3424 (18)8.8 (±1.5)0.002
    No142 (88)7.9 (±1.1)106 (82)7.9 (±1.3)
 Anxiety
    Yes40 (25)7.8 (±1.0)0.4161 (47)8.3 (±1.5)0.088
    No122 (75)8.0 (±1.2)69 (53)7.9 (±1.2)
Metabolic factor
 Hypoglycemia
    Yes7 (4)8.4 (±1.0)0.266 (5)7.7 (±1.0)0.50
    No155 (96)7.9 (±1.1)124 (95)8.1 (±1.4)
Anthropometric variables
 General obesity
    Yes12 (7)8.0 (±1.4)0.6924 (19)8.7 (±1.1)0.010
    No150 (93)7.9 (±1.1)104 (81)7.9 (±1.3)
 Abdominal obesity
    Yes13 (8)8.5 (±0.9)0.0736 (29)8.7 (±1.2)0.001
    No146 (92)7.9 (±1.1)89 (71)7.8 (±1.3)
Lifestyle variables
 Smokingc
    Yes18 (12)8.4 (±0.9)0.04610 (8)9.2 (±1.9)0.004
    No136 (88)7.8 (±1.1)111 (92)7.9 (±1.2)
 Physical inactivity
    Yes18 (12)8.5 (±1.3)0.00713 (11)8.5 (±0.8)0.20
    No136 (88)7.8 (±1.0)108 (89)8.0 (±1.4)
Complications
 Diabetes retinopathy
    Yes118 (73)8.0 (±1.1)0.1791 (70)8.4 (±1.3)<0.001
    No43 (27)7.7 (±1.2)38 (30)7.3 (±1.0)
 Foot complications
    Yes34 (22)7.9 (±1.1)0.913 (11)8.9 (±1.4)0.006
    No123 (78)7.9 (±1.1)110 (89)7.9 (±1.3)
 Macrovascular complications
    Yes6 (4)7.4 (±1.1)0.294 (3)9.5 (±1.9)0.028
    No156 (96)7.9 (±1.1)126 (97)8.0 (±1.3)

Student's t-test.

Self-reported.

Definitions of the variables are the same as presented earlier.

There were 80 (27%) patients with HbA1c>8.6%: 41 men (24%) and 39 women (32%). Age (P=0.27) and diabetes duration (P=0.80) were not associated with HbA1c>8.6%.

In bivariate analyses, the following variables were associated with HbA1c>8.6%: abdominal obesity (COR 3.3 (1.7–6.2), P<0.001), general obesity (COR 3.6 (1.8–7.4), P<0.001), alexithymia (COR 2.6 (1.3–5.1), P=0.004), self-reported depression (COR 2.6 (1.2–5.6), P=0.015), smoking (COR 2.8 (1.2–6.1), P=0.013), antidepressant medication (COR 2.7 (1.1–6.3), P=0.026), physical inactivity (COR 2.2 (1.0–4.7), P=0.049), and retinopathy (COR 2.7 (1.4–5.5), P=0.003).

The following variables were not associated with HbA1c>8.6%: self-reported anxiety (COR 1.2 (0.70–2.0), P=0.52), macrovascular complications (COR 1.1 (0.3–4.5), P=0.85), AITD (COR 1.8 (0.9–3.9), P=0.11), and foot complications (COR 1.1 (0.6–2.3), P=0.74). In multiple logistic regression analysis, abdominal obesity (adjusted odds ratio (AOR) 4.3), self-reported depression (AOR 4.8), and smoking (AOR 3.0) were associated with HbA1c>8.6%. Gender sub-analyses showed that the associations between self-reported depression (AOR 19.8), abdominal obesity (AOR 7.0), and HbA1c>8.6% remained significant for women and smoking (AOR 4.2) for men (Table 3).

Table 3

Factors associated with inadequate glycemic control. Patients with macrovascular complications were not included. Data are controlled for age, diabetes duration, and gender. The values from the last step in the model are presented for the nonsignificant results. Nagelkerke R2: a0.162; b0.115; and c0.309. Hosmer and Lemeshow test: a0.153; b0.218; and c0.902.

HbA1c>8.6%
AllaMenbWomenc
AOR (95% CI)PdAOR (95% CI)PdAOR (95% CI)Pd
Abdominal obesity4.3 (2.0–9.3)<0.0013.3 (0.9–12.2)0.077.0 (2.5–19.7)<0.001
Depressione4.8 (1.9–11.9)0.0012.0 (0.5–7.5)0.2919.8 (3.9–99.7)<0.001
Smoking3.0 (1.2–7.2)0.0174.2 (1.5–11.9)0.0081.5 (0.2–9.5)0.66
Antidepressants2.0 (0.7–6.2)0.222.0 (0.4–11.7)0.431.7 (0.3–9.8)0.59
Anxietyd0.6 (0.3–1.3)0.240.4 (0.1–1.2)0.100.9 (0.3–2.5)0.82
Alexithymia1.3 (0.5–3.2)0.621.1 (0.2–4.9)0.952.3 (0.6–8.0)0.23
Physical inactivity1.0 (0.4–2.5)0.962.1 (0.7–6.6)0.210.2 (0.04–1.3)0.09

n=257.

n=145.

n=112.

Multiple logistic regression (Backward: Wald).

Self-reported.

Associations with self-reported depression

In patients with self-reported depression, the alexithymia prevalence was 50% and the self-reported anxiety prevalence was 83%. In patients without self-reported depression, the alexithymia prevalence was 11% and the self-reported anxiety prevalence was 29%. In multiple logistic regression analysis, alexithymia (AOR 14.8), self-reported anxiety (AOR 31.1), HbA1c>8.6% (AOR 11.0), physical inactivity (AOR 8.2), and absence of abdominal obesity (AOR 0.04) were associated with self-reported depression (Table 4); smoking, retinopathy, and foot complications were not associated with self-reported depression (Table 4). AITD was not associated with depression (COR 0.8 (0.2–2.9), P=0.76).

Table 4

Factors associated with self-reported depression. n=246. Logistic regression (Backward: Wald). Patients with macrovascular complications were not included. Data are controlled for age, diabetes duration, and gender. For the nonsignificant results, the values from the last step in the model are presented. Nagelkerke R2: 0.533. Hosmer and Lemeshow test: 0.142.

Self-reported depressiona
AOR (95% CI)P
Alexithymia14.8 (3.5–62.4)<0.001
Anxietya31.1 (5.9–164)<0.001
HbA1c>8.6%11.0 (2.8–44.1)0.001
Abdominal obesity0.04 (0.004–0.3)0.003
Physical inactivity8.2 (1.3–51.1)0.025
Foot complications3.3 (0.6–17.6)0.16
Retinopathy3.1 (0.6–17.6)0.24
Smoking0.8 (0.1–5.9)0.80

Self-reported.

Associations between smoking, obesity, and alexithymia

Alexithymia was associated with abdominal obesity (COR 2.2 (1.1–4.8), P=0.037) but not with smoking (COR 1.7 (0.6–4.5), P=0.28).

Patients not included in the study

The 141 patients who chose not to participate had a mean HbA1c of 8.0%. Thirty-eight patients were non-eligible: seven patients had severe somatic comorbidity (mean HbA1c 7.8%); 21 patients had severe mental disorder, mental retardation, or other cognitive deficiency (mean HbA1c 8.9%); and ten had inadequate knowledge of Swedish (mean HbA1c 7.9%).

Discussion

In this population-based study of self-reported depression, alexithymia, and self-reported anxiety in 292 patients with type 1 diabetes, we found that self-reported depression was the only psychological factor that was independently associated with inadequate glycemic control of diabetes (HbA1c>8.6%). With an AOR of 4.8, the association was of comparable importance as abdominal obesity (AOR 4.3) and smoking (AOR 3.0). Gender analyses showed that self-reported depression was particularly associated with inadequate glycemic control in women (AOR 19.8). Abdominal obesity (25) and smoking (22) are well-known risk factors for poor glycemic control and have been associated with dysfunction of the cortisol metabolism (38, 39), which is also the case for depression (1). This shared underlying mechanism should be further explored. Physical inactivity was associated with self-reported depression, but in contrast to earlier research, smoking and abdominal obesity were not (7, 8, 9, 40). Previous research (4, 5), as opposed to data in this study, has shown associations between depression and diabetes retinopathy and foot complications. Associations between the severity of these complications and depression were not investigated in this study. This is an appeal for future research. Macrovascular complications were associated with self-reported depression in bivariate analyses, but we did not enter them in the regression analyses as these patients had many other complications with spurious interactions. Treated AITD was not associated with HbA1c>8.6% or with self-reported depression. TSH is controlled every other year in these patients, so the risk for undiagnosed AITD is low.

Alexithymia, a relatively stable personality trait and a risk factor for depression, but often enhanced during depressive periods (17), was strongly associated with self-reported depression in this study. The prevalence of alexithymia among patients with self-reported depression was 25 times higher, and among patients without self-reported depression, five times higher than in a Swedish normative sample (20). Alexithymia was associated with abdominal obesity but not with smoking. Both findings are consistent with previous research (20, 41). Alexithymia showed stronger association with depression than retinopathy and foot complications. It is remarkable that retinopathy and foot complications were not associated with depression in logistic regression analyses as it could be assumed that these conditions would eventually cause secondary depression. There was a high frequency of comorbidity between self-reported anxiety and self-reported depression. Anxiety affects quality of life but was not associated with high HbA1c in this study.

There are limitations to this cross-sectional study. The diagnosis of depression was not confirmed by a diagnostic interview. On the other hand, the use of antidepressant medication (COR 9.6) and a clinical psychiatric diagnosis (COR 10.8) were significantly associated with HADS-D ≥8, which supports that the test is adequate for testing depression. HADS-A does not differentiate between different anxiety syndromes, so for further analyses of the role of anxiety in diabetes, more specific diagnostic tools must be used. HbA1c was only measured once. As this was a cross-sectional study, it was necessary to measure HbA1c at the same time as the patients answered the self-report instruments. Yet, had we asked for venous samples 2 days in a row, fewer patients would eventually have accepted to participate.

Whether depression is primary or secondary to poor metabolic control of diabetes, or bidirectional, cannot be clarified by this study. The same can be concluded for the association between alexithymia and depression. Alexithymia can be a risk factor for depression but can also be a symptom of reduced overall cognition associated with diabetes or depression (1). The probable directions of the associations discussed here are shown in Fig. 1.

Figure 1
Figure 1

Probable directions of the most important associations discussed in the article.

Citation: European Journal of Endocrinology 168, 6; 10.1530/EJE-13-0137

To summarize, in this study, it is clearly shown that self-reported depression was of similar importance for glycemic control as obesity and smoking. It is important to pay attention to the personality trait alexithymia – a risk factor for both depression and obesity. Much attention in the care of patients with diabetes is focused on physical factors; however, in this study, it is clearly demonstrated that psychological factors are related to glycemic control as well. An AOR of 4.8 for the association between self-reported depression and poor metabolic control of diabetes should not be neglected. For the future care of patients with type 1 diabetes, it may be valuable to consider both depression and alexithymia to achieve an adequate HbA1c and optimize the patients' self-care abilities. Despite the efforts we spend on reducing risk factors such as obesity and smoking, we still do not reach the goals for HbA1c. This could be due to an undiagnosed or inadequately treated depressive disorder. Future suggestions for managing patients with unacceptable high levels of HbA1c are to test them for depression (30) and alexithymia (31, 32). If test results are positive, clinical exploration and decisions regarding treatment should be made. Antidepressant medication (3) and/or psychotherapy can be used, either as cognitive behavioral therapy (42), and in case of alexithymia emotion focused therapy (43), or psycho-educational methods (11, 44).

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This study is supported by the Research and Development Fund of the County of Kronoberg and by the Research and Development Fund of the Regions of South Sweden.

Author contribution statement

E O Melin, M Thunander, H O Thulesius, R Svensson, and M Landin-Olsson participated as investigators and reviewed and edited the manuscript. E O Melin, M Thunander, and R Svensson contributed to the study design and implementation. E O Melin, H O Thulesius, and M Landin-Olsson contributed to the analysis and wrote the statistical methods. E O Melin is the guarantor of this work and, as such, 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.

Acknowledgements

The authors are indebted to Anna Lindgren, PhD, and Per Nyberg, PhD, for their statistical skills.

References

  • 1

    KorczakDJPereiraSKoulajianKMatejcekAGiaccaA. Type 1 diabetes mellitus and major depressive disorder: evidence for a biological link. Diabetologia20115424832493. (doi:10.1007/s00125-011-2240-3).

    • Search Google Scholar
    • Export Citation
  • 2

    AndersonRFreedlandKClouseRLustmanP. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care20012410691078. (doi:10.2337/diacare.24.6.1069).

    • Search Google Scholar
    • Export Citation
  • 3

    LustmanPClouseR. Depression in diabetic patients: the relationship between mood and glycemic control. Journal of Diabetes and its Complications200519113122. (doi:10.1016/j.jdiacomp.2004.01.002).

    • Search Google Scholar
    • Export Citation
  • 4

    IsmailKWinkleyKStahlDChalderTEdmondsM. A cohort study of people with diabetes and their first foot ulcer: the role of depression on mortality. Diabetes Care20073014731479. (doi:10.2337/dc06-2313).

    • Search Google Scholar
    • Export Citation
  • 5

    de GrootMAndersonRFreedlandKEClouseRELustmanPJ. Association of depression and diabetes complications: a meta-analysis. Psychosomatic Medicine200163619630.

    • Search Google Scholar
    • Export Citation
  • 6

    EgedeLENietertPJZhengD. Depression and all-cause and coronary heart disease mortality among adults with and without diabetes. Diabetes Care20052813391345. (doi:10.2337/diacare.28.6.1339).

    • Search Google Scholar
    • Export Citation
  • 7

    LinEHKatonWVon KorffMRutterCSimonGEOliverMCiechanowskiPLudmanEJBushTYoungB. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care20042721542160. (doi:10.2337/diacare.27.9.2154).

    • Search Google Scholar
    • Export Citation
  • 8

    CiechanowskiPSKatonWJRussoJE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Archives of Internal Medicine200016032783285. (doi:10.1001/archinte.160.21.3278).

    • Search Google Scholar
    • Export Citation
  • 9

    Haire-JoshuDHeadySThomasLSchechtmanKFisherEBJr. Depressive symptomatology and smoking among persons with diabetes. Research in Nursing & Health199417273282. (doi:10.1002/nur.4770170406).

    • Search Google Scholar
    • Export Citation
  • 10

    Nemiah J & Sifnoes P. Affect and fantasy in patients with psychosomatic disorder. In Modern trends in Psychosomatic Medicine Ed. OW Hill. pp 26–34. London UK: Butterworths 1970

  • 11

    MelinEOThulesiusHOPerssonBA. Affect school for chronic benign pain patients showed improved alexithymia assessments with TAS-20. BioPsychoSocial Medicine20104110. (doi:10.1186/1751-0759-4-5).

    • Search Google Scholar
    • Export Citation
  • 12

    Taylor GJ Bagby R & Parker J. Disorders of Affect Regulation: Alexithymia in Medical and Psychiatric Illness. Cambridge: Cambridge University Press 1999

  • 13

    HousiauxMLuminetOVan BroeckNDorchyH. Alexithymia is associated with glycaemic control of children with type 1 diabetes. Diabetes and Metabolism201036455462. (doi:10.1016/j.diabet.2010.06.004).

    • Search Google Scholar
    • Export Citation
  • 14

    HonkalampiKLehtoSMKoivumaa-HonkanenHHintikkaJNiskanenLValkonen-KorhonenMViinamakiH. Alexithymia and tissue inflammation. Psychotherapy and Psychosomatics201180359364. (doi:10.1159/000327583).

    • Search Google Scholar
    • Export Citation
  • 15

    de TimaryPRoyELuminetOFilleeCMikolajczakM. Relationship between alexithymia, alexithymia factors and salivary cortisol in men exposed to a social stress test. Psychoneuroendocrinology2008331160. (doi:10.1016/j.psyneuen.2008.06.005).

    • Search Google Scholar
    • Export Citation
  • 16

    De BerardisDSerroniNCampanellaDCaranoAGambiFValcheraAContiCSepedeGScaliMFulcheriM. Alexithymia and its relationships with C-reactive protein and serum lipid levels among drug naive adult outpatients with major depression. Progress in Neuro-Psychopharmacology & Biological Psychiatry20083219821986. (doi:10.1016/j.pnpbp.2008.09.022).

    • Search Google Scholar
    • Export Citation
  • 17

    TolmunenTHelisteMLehtoSMHintikkaJHonkalampiKKauhanenJ. Stability of alexithymia in the general population: an 11-year follow-up. Comprehensive Psychiatry201152536541. (doi:10.1016/j.comppsych.2010.09.007).

    • Search Google Scholar
    • Export Citation
  • 18

    FriedmanSVilaGEvenCTimsitJBoitardCDardennesRGuelfiJDMouren-SimeoniMC. Alexithymia in insulin-dependent diabetes mellitus is related to depression and not to somatic variables or compliance. Journal of Psychosomatic Research200355285287. (doi:10.1016/S0022-3999(02)00636-0).

    • Search Google Scholar
    • Export Citation
  • 19

    ChatziLBitsiosPSolidakiEChristouIKyrlakiESfakianakiMKogevinasMKefalogiannisNPappasA. Type 1 diabetes is associated with alexithymia in nondepressed, non-mentally ill diabetic patients: a case–control study. Journal of Psychosomatic Research200967307313. (doi:10.1016/j.jpsychores.2009.04.011).

    • Search Google Scholar
    • Export Citation
  • 20

    ElfhagKLundhLG. TAS-20 alexithymia in obesity, and its links to personality. Scandinavian Journal of Psychology200748391398. (doi:10.1111/j.1467-9450.2007.00583.x).

    • Search Google Scholar
    • Export Citation
  • 21

    KojimaMSendaYNagayaTTokudomeSFurukawaTA. Alexithymia, depression and social support among Japanese workers. Psychotherapy and Psychosomatics200372307314. (doi:10.1159/000073027).

    • Search Google Scholar
    • Export Citation
  • 22

    GuntonJEDaviesLWilmshurstEFulcherGMcElduffA. Cigarette smoking affects glycemic control in diabetes. Diabetes Care200225796797. (doi:10.2337/diacare.25.4.796-a).

    • Search Google Scholar
    • Export Citation
  • 23

    KapoorDJonesTH. Smoking and hormones in health and endocrine disorders. European Journal of Endocrinology2005152491499. (doi:10.1530/eje.1.01867).

    • Search Google Scholar
    • Export Citation
  • 24

    BloomgardenZT. Cardiovascular disease in diabetes. Diabetes Care201033e49e54. (doi:10.2337/dc10-zb04).

  • 25

    SelvinECoreshJGoldenSHBolandLL. Glycemic control, atherosclerosis, and risk factors for cardiovascular disease in individuals with diabetes: the atherosclerosis risk in communities study. Diabetes Care20052819651973. (doi:10.2337/diacare.28.8.1965).

    • Search Google Scholar
    • Export Citation
  • 26

    HamburgNMMcMackinCJHuangALShenoudaSMWidlanskyMESchulzEGokceNRudermanNBKeaneyJFJrVitaJA. Physical inactivity rapidly induces insulin resistance and microvascular dysfunction in healthy volunteers. Arteriosclerosis Thrombosis and Vascular Biology20072726502656. (doi:10.1161/ATVBAHA.107.153288).

    • Search Google Scholar
    • Export Citation
  • 27

    EkelundUBrageSGriffinSJWarehamNJ. Objectively measured moderate- and vigorous-intensity physical activity but not sedentary time predicts insulin resistance in high-risk individuals. Diabetes Care20093210811086. (doi:10.2337/dc08-1895).

    • Search Google Scholar
    • Export Citation
  • 28

    ZigmondASSnaithRP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica198367361370. (doi:10.1111/j.1600-0447.1983.tb09716.x).

    • Search Google Scholar
    • Export Citation
  • 29

    LisspersJNygrenASodermanE. Hospital anxiety and depression scale (HAD): some psychometric data for a Swedish sample. Acta Psychiatrica Scandinavica199796281286. (doi:10.1111/j.1600-0447.1997.tb10164.x).

    • Search Google Scholar
    • Export Citation
  • 30

    RoyTLloydCEPouwerFHoltRIGSartoriusN. Screening tools used for measuring depression among people with type 1 and type 2 diabetes: a systematic review. Diabetic Medicine201229164175. (doi:10.1111/j.1464-5491.2011.03401.x).

    • Search Google Scholar
    • Export Citation
  • 31

    BagbyRMParkerJDTaylorGJ. The twenty-item Toronto Alexithymia Scale – I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research1994382332. (doi:10.1016/0022-3999(94)90005-1).

    • Search Google Scholar
    • Export Citation
  • 32

    BagbyRMTaylorGJParkerJD. The Twenty-item Toronto Alexithymia Scale – II. Convergent, discriminant, and concurrent validity. Journal of Psychosomatic Research1994383340. (doi:10.1016/0022-3999(94)90006-X).

    • Search Google Scholar
    • Export Citation
  • 33

    Simonsson-SarneckiMLundhLGTorestadBBagbyRMTaylorGJParkerJD. A Swedish translation of the 20-item Toronto Alexithymia Scale: cross-validation of the factor structure. Scandinavian Journal of Psychology2000412530. (doi:10.1111/1467-9450.00167).

    • Search Google Scholar
    • Export Citation
  • 34

    LavalardESzymezakJLeroyNGilleryP. Evaluation of variant II analyzer equipped with the new 270-2101 NU kit (Bio-Rad) for HbA1c assay. Annales de Biologie Clinique2009675565. (doi:10.1684/abc.2008.0289).

    • Search Google Scholar
    • Export Citation
  • 35

    HoelzelWWeykampCJeppssonJOMiedemaKBarrJRGoodallIHoshinoTJohnWGKoboldULittleR. IFCC reference system for measurement of hemoglobin A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden: a method-comparison study. Clinical Chemistry200450166174. (doi:10.1373/clinchem.2003.024802).

    • Search Google Scholar
    • Export Citation
  • 36

    ReisJPHankinsonALLoriaCMLewisCEPowell-WileyTWeiGSLiuK. Duration of abdominal obesity beginning in young adulthood and incident diabetes through middle age: the CARDIA Study. Diabetes Care2013In pressdoi:10.2337/dc12-1714).

    • Search Google Scholar
    • Export Citation
  • 37

    BarkerJM. Type 1 diabetes-associated autoimmunity: natural history, genetic associations, and screening. Journal of Clinical Endocrinology and Metabolism20069112101217. (doi:10.1210/jc.2005-1679).

    • Search Google Scholar
    • Export Citation
  • 38

    MüssigKRemerTMaser-GluthC. Brief review: glucocorticoid excretion in obesity. Journal of Steroid Biochemistry and Molecular Biology2010121589593. (doi:10.1016/j.jsbmb.2010.01.008).

    • Search Google Scholar
    • Export Citation
  • 39

    RohlederNKirschbaumC. The hypothalamic–pituitary–adrenal (HPA) axis in habitual smokers. International Journal of Psychophysiology200659236243. (doi:10.1016/j.ijpsycho.2005.10.012).

    • Search Google Scholar
    • Export Citation
  • 40

    LuppinoFWitLDBouvyPStijnenTCuijpersPPenninxBZitmanF. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry201067220229. (doi:10.1001/archgenpsychiatry.2010.2).

    • Search Google Scholar
    • Export Citation
  • 41

    LumleyMADowneyKStettnerLWehmerFPomerleauOF. Alexithymia and negative affect: relationship to cigarette smoking, nicotine dependence, and smoking cessation. Psychotherapy and Psychosomatics199461156162. (doi:10.1159/000288884).

    • Search Google Scholar
    • Export Citation
  • 42

    LustmanPJGriffithLSFreedlandKEKisselSSClouseRE. Cognitive behavior therapy for depression in type 2 diabetes mellitus. A randomized, controlled trial. Annals of Internal Medicine1998129613621.

    • Search Google Scholar
    • Export Citation
  • 43

    GreenbergLSPascual-LeoneA. Emotion in psychotherapy: a practice-friendly research review. Journal of Clinical Psychology200662611630. (doi:10.1002/jclp.20252).

    • Search Google Scholar
    • Export Citation
  • 44

    BergdahlJLarssonANilssonLGAhlstromKRNybergL. Treatment of chronic stress in employees: subjective, cognitive and neural correlates. Scandinavian Journal of Psychology200546395402. (doi:10.1111/j.1467-9450.2005.00470.x).

    • Search Google Scholar
    • Export Citation

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References

  • 1

    KorczakDJPereiraSKoulajianKMatejcekAGiaccaA. Type 1 diabetes mellitus and major depressive disorder: evidence for a biological link. Diabetologia20115424832493. (doi:10.1007/s00125-011-2240-3).

    • Search Google Scholar
    • Export Citation
  • 2

    AndersonRFreedlandKClouseRLustmanP. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care20012410691078. (doi:10.2337/diacare.24.6.1069).

    • Search Google Scholar
    • Export Citation
  • 3

    LustmanPClouseR. Depression in diabetic patients: the relationship between mood and glycemic control. Journal of Diabetes and its Complications200519113122. (doi:10.1016/j.jdiacomp.2004.01.002).

    • Search Google Scholar
    • Export Citation
  • 4

    IsmailKWinkleyKStahlDChalderTEdmondsM. A cohort study of people with diabetes and their first foot ulcer: the role of depression on mortality. Diabetes Care20073014731479. (doi:10.2337/dc06-2313).

    • Search Google Scholar
    • Export Citation
  • 5

    de GrootMAndersonRFreedlandKEClouseRELustmanPJ. Association of depression and diabetes complications: a meta-analysis. Psychosomatic Medicine200163619630.

    • Search Google Scholar
    • Export Citation
  • 6

    EgedeLENietertPJZhengD. Depression and all-cause and coronary heart disease mortality among adults with and without diabetes. Diabetes Care20052813391345. (doi:10.2337/diacare.28.6.1339).

    • Search Google Scholar
    • Export Citation
  • 7

    LinEHKatonWVon KorffMRutterCSimonGEOliverMCiechanowskiPLudmanEJBushTYoungB. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care20042721542160. (doi:10.2337/diacare.27.9.2154).

    • Search Google Scholar
    • Export Citation
  • 8

    CiechanowskiPSKatonWJRussoJE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Archives of Internal Medicine200016032783285. (doi:10.1001/archinte.160.21.3278).

    • Search Google Scholar
    • Export Citation
  • 9

    Haire-JoshuDHeadySThomasLSchechtmanKFisherEBJr. Depressive symptomatology and smoking among persons with diabetes. Research in Nursing & Health199417273282. (doi:10.1002/nur.4770170406).

    • Search Google Scholar
    • Export Citation
  • 10

    Nemiah J & Sifnoes P. Affect and fantasy in patients with psychosomatic disorder. In Modern trends in Psychosomatic Medicine Ed. OW Hill. pp 26–34. London UK: Butterworths 1970

  • 11

    MelinEOThulesiusHOPerssonBA. Affect school for chronic benign pain patients showed improved alexithymia assessments with TAS-20. BioPsychoSocial Medicine20104110. (doi:10.1186/1751-0759-4-5).

    • Search Google Scholar
    • Export Citation
  • 12

    Taylor GJ Bagby R & Parker J. Disorders of Affect Regulation: Alexithymia in Medical and Psychiatric Illness. Cambridge: Cambridge University Press 1999

  • 13

    HousiauxMLuminetOVan BroeckNDorchyH. Alexithymia is associated with glycaemic control of children with type 1 diabetes. Diabetes and Metabolism201036455462. (doi:10.1016/j.diabet.2010.06.004).

    • Search Google Scholar
    • Export Citation
  • 14

    HonkalampiKLehtoSMKoivumaa-HonkanenHHintikkaJNiskanenLValkonen-KorhonenMViinamakiH. Alexithymia and tissue inflammation. Psychotherapy and Psychosomatics201180359364. (doi:10.1159/000327583).

    • Search Google Scholar
    • Export Citation
  • 15

    de TimaryPRoyELuminetOFilleeCMikolajczakM. Relationship between alexithymia, alexithymia factors and salivary cortisol in men exposed to a social stress test. Psychoneuroendocrinology2008331160. (doi:10.1016/j.psyneuen.2008.06.005).

    • Search Google Scholar
    • Export Citation
  • 16

    De BerardisDSerroniNCampanellaDCaranoAGambiFValcheraAContiCSepedeGScaliMFulcheriM. Alexithymia and its relationships with C-reactive protein and serum lipid levels among drug naive adult outpatients with major depression. Progress in Neuro-Psychopharmacology & Biological Psychiatry20083219821986. (doi:10.1016/j.pnpbp.2008.09.022).

    • Search Google Scholar
    • Export Citation
  • 17

    TolmunenTHelisteMLehtoSMHintikkaJHonkalampiKKauhanenJ. Stability of alexithymia in the general population: an 11-year follow-up. Comprehensive Psychiatry201152536541. (doi:10.1016/j.comppsych.2010.09.007).

    • Search Google Scholar
    • Export Citation
  • 18

    FriedmanSVilaGEvenCTimsitJBoitardCDardennesRGuelfiJDMouren-SimeoniMC. Alexithymia in insulin-dependent diabetes mellitus is related to depression and not to somatic variables or compliance. Journal of Psychosomatic Research200355285287. (doi:10.1016/S0022-3999(02)00636-0).

    • Search Google Scholar
    • Export Citation
  • 19

    ChatziLBitsiosPSolidakiEChristouIKyrlakiESfakianakiMKogevinasMKefalogiannisNPappasA. Type 1 diabetes is associated with alexithymia in nondepressed, non-mentally ill diabetic patients: a case–control study. Journal of Psychosomatic Research200967307313. (doi:10.1016/j.jpsychores.2009.04.011).

    • Search Google Scholar
    • Export Citation
  • 20

    ElfhagKLundhLG. TAS-20 alexithymia in obesity, and its links to personality. Scandinavian Journal of Psychology200748391398. (doi:10.1111/j.1467-9450.2007.00583.x).

    • Search Google Scholar
    • Export Citation
  • 21

    KojimaMSendaYNagayaTTokudomeSFurukawaTA. Alexithymia, depression and social support among Japanese workers. Psychotherapy and Psychosomatics200372307314. (doi:10.1159/000073027).

    • Search Google Scholar
    • Export Citation
  • 22

    GuntonJEDaviesLWilmshurstEFulcherGMcElduffA. Cigarette smoking affects glycemic control in diabetes. Diabetes Care200225796797. (doi:10.2337/diacare.25.4.796-a).

    • Search Google Scholar
    • Export Citation
  • 23

    KapoorDJonesTH. Smoking and hormones in health and endocrine disorders. European Journal of Endocrinology2005152491499. (doi:10.1530/eje.1.01867).

    • Search Google Scholar
    • Export Citation
  • 24

    BloomgardenZT. Cardiovascular disease in diabetes. Diabetes Care201033e49e54. (doi:10.2337/dc10-zb04).

  • 25

    SelvinECoreshJGoldenSHBolandLL. Glycemic control, atherosclerosis, and risk factors for cardiovascular disease in individuals with diabetes: the atherosclerosis risk in communities study. Diabetes Care20052819651973. (doi:10.2337/diacare.28.8.1965).

    • Search Google Scholar
    • Export Citation
  • 26

    HamburgNMMcMackinCJHuangALShenoudaSMWidlanskyMESchulzEGokceNRudermanNBKeaneyJFJrVitaJA. Physical inactivity rapidly induces insulin resistance and microvascular dysfunction in healthy volunteers. Arteriosclerosis Thrombosis and Vascular Biology20072726502656. (doi:10.1161/ATVBAHA.107.153288).

    • Search Google Scholar
    • Export Citation
  • 27

    EkelundUBrageSGriffinSJWarehamNJ. Objectively measured moderate- and vigorous-intensity physical activity but not sedentary time predicts insulin resistance in high-risk individuals. Diabetes Care20093210811086. (doi:10.2337/dc08-1895).

    • Search Google Scholar
    • Export Citation
  • 28

    ZigmondASSnaithRP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica198367361370. (doi:10.1111/j.1600-0447.1983.tb09716.x).

    • Search Google Scholar
    • Export Citation
  • 29

    LisspersJNygrenASodermanE. Hospital anxiety and depression scale (HAD): some psychometric data for a Swedish sample. Acta Psychiatrica Scandinavica199796281286. (doi:10.1111/j.1600-0447.1997.tb10164.x).

    • Search Google Scholar
    • Export Citation
  • 30

    RoyTLloydCEPouwerFHoltRIGSartoriusN. Screening tools used for measuring depression among people with type 1 and type 2 diabetes: a systematic review. Diabetic Medicine201229164175. (doi:10.1111/j.1464-5491.2011.03401.x).

    • Search Google Scholar
    • Export Citation
  • 31

    BagbyRMParkerJDTaylorGJ. The twenty-item Toronto Alexithymia Scale – I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research1994382332. (doi:10.1016/0022-3999(94)90005-1).

    • Search Google Scholar
    • Export Citation
  • 32

    BagbyRMTaylorGJParkerJD. The Twenty-item Toronto Alexithymia Scale – II. Convergent, discriminant, and concurrent validity. Journal of Psychosomatic Research1994383340. (doi:10.1016/0022-3999(94)90006-X).

    • Search Google Scholar
    • Export Citation
  • 33

    Simonsson-SarneckiMLundhLGTorestadBBagbyRMTaylorGJParkerJD. A Swedish translation of the 20-item Toronto Alexithymia Scale: cross-validation of the factor structure. Scandinavian Journal of Psychology2000412530. (doi:10.1111/1467-9450.00167).

    • Search Google Scholar
    • Export Citation
  • 34

    LavalardESzymezakJLeroyNGilleryP. Evaluation of variant II analyzer equipped with the new 270-2101 NU kit (Bio-Rad) for HbA1c assay. Annales de Biologie Clinique2009675565. (doi:10.1684/abc.2008.0289).

    • Search Google Scholar
    • Export Citation
  • 35

    HoelzelWWeykampCJeppssonJOMiedemaKBarrJRGoodallIHoshinoTJohnWGKoboldULittleR. IFCC reference system for measurement of hemoglobin A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden: a method-comparison study. Clinical Chemistry200450166174. (doi:10.1373/clinchem.2003.024802).

    • Search Google Scholar
    • Export Citation
  • 36

    ReisJPHankinsonALLoriaCMLewisCEPowell-WileyTWeiGSLiuK. Duration of abdominal obesity beginning in young adulthood and incident diabetes through middle age: the CARDIA Study. Diabetes Care2013In pressdoi:10.2337/dc12-1714).

    • Search Google Scholar
    • Export Citation
  • 37

    BarkerJM. Type 1 diabetes-associated autoimmunity: natural history, genetic associations, and screening. Journal of Clinical Endocrinology and Metabolism20069112101217. (doi:10.1210/jc.2005-1679).

    • Search Google Scholar
    • Export Citation
  • 38

    MüssigKRemerTMaser-GluthC. Brief review: glucocorticoid excretion in obesity. Journal of Steroid Biochemistry and Molecular Biology2010121589593. (doi:10.1016/j.jsbmb.2010.01.008).

    • Search Google Scholar
    • Export Citation
  • 39

    RohlederNKirschbaumC. The hypothalamic–pituitary–adrenal (HPA) axis in habitual smokers. International Journal of Psychophysiology200659236243. (doi:10.1016/j.ijpsycho.2005.10.012).

    • Search Google Scholar
    • Export Citation
  • 40

    LuppinoFWitLDBouvyPStijnenTCuijpersPPenninxBZitmanF. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry201067220229. (doi:10.1001/archgenpsychiatry.2010.2).

    • Search Google Scholar
    • Export Citation
  • 41

    LumleyMADowneyKStettnerLWehmerFPomerleauOF. Alexithymia and negative affect: relationship to cigarette smoking, nicotine dependence, and smoking cessation. Psychotherapy and Psychosomatics199461156162. (doi:10.1159/000288884).

    • Search Google Scholar
    • Export Citation
  • 42

    LustmanPJGriffithLSFreedlandKEKisselSSClouseRE. Cognitive behavior therapy for depression in type 2 diabetes mellitus. A randomized, controlled trial. Annals of Internal Medicine1998129613621.

    • Search Google Scholar
    • Export Citation
  • 43

    GreenbergLSPascual-LeoneA. Emotion in psychotherapy: a practice-friendly research review. Journal of Clinical Psychology200662611630. (doi:10.1002/jclp.20252).

    • Search Google Scholar
    • Export Citation
  • 44

    BergdahlJLarssonANilssonLGAhlstromKRNybergL. Treatment of chronic stress in employees: subjective, cognitive and neural correlates. Scandinavian Journal of Psychology200546395402. (doi:10.1111/j.1467-9450.2005.00470.x).

    • Search Google Scholar
    • Export Citation

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