Evaluation of a collaborative care model for integrated primary care of common mental disorders comorbid with chronic conditions in South Africa

Inge Petersen
Arvin Bhana
Lara R Fairall
Tasneem Kathree
Emily C Baron
Sujit D Rathod
Crick Lund
Evaluation of a collaborative care model for integrated primary care of common mental disorders comorbid with chronic conditions in South Africa
10.1186/s12888-019-2081-zR E S E A R C H A R T I C L E Open Access
Background: The rise in multimorbid chronic conditions in South Africa, large treatment gap for common mental disorders (CMDs) and shortage of mental health specialists demands a task sharing approach to chronic disease management that includes treatment for co-existing CMDs to improve health outcomes. The aim of this study was thus to evaluate a task shared integrated collaborative care package of care for chronic patients with co-existing depressive and alcohol use disorder (AUD) symptoms. Methods: The complex intervention strengthened capacity of primary care nurse practitioners to identify, diagnose and review symptoms of CMDs among chronic care patients; and implemented a stepped up referral system, that included clinic-based psychosocial lay counsellors, doctors and mental health specialists. Under real world conditions, in four PHC facilities, a repeat cross-sectional Facility Detection Survey (FDS) assessed changes in capacity of nurses to correctly detect CMDs in 1310 patients before implementation and 1246 patients following implementation of the intervention at 12 months; and a non-randomly assigned comparison group cohort study comprising 373 screen positive patients with depressive symptoms using the Patient Health Questionnaire-9 (PHQ9) at baseline, evaluated responses of patients correctly identified and referred for treatment (intervention arm) or not identified and referred (control arm) at three and 12 months. Results: The FDS showed a significant increase in the identification of depression and AUD from pre-implementation to 12-month post-implementation. Depression: (5.8 to 16.4%) 95% CI [2.9, 19.1]); AUD: (0 to 13.8%) 95% CI [0.6-24.9]. In the comparison group cohort study, patients with depressive symptoms having more than a 50% reduction in PHQ-9 scores were greater in the treatment group (n = 69, 55.2%) compared to the comparison group (n = 49, 23.4%) at 3 months (RR = 2.10, p < 0.001); and 12 months follow-up (intervention: n = 57, 47.9%; comparison: n = 60, 30.8%; RR = 1.52, p = 0.006). Remission (PHQ-9 ≤ 5) was greater in the intervention group (n = 32, 26.9%) than comparison group (n = 33, 16.9%) at 12 months (RR = 1.72, p = 0.016). Conclusion: A task shared collaborative stepped care model can improve detection of CMDs and reduce depressive symptoms among patients with chronic conditions under real world conditions.

Background

Mental disorders are on the rise globally, and are often comorbid with other chronic conditions, being two to five times more prevalent in people with chronic physical health conditions than the rest of the population [1][2][3][4]. Mental-physical comorbidities are associated with greater decrements in health outcomes [1] and increased health care utilization costs [5]. The need for chronic disease management to include treatment for co-existing common mental disorders (CMDs) is thus increasingly viewed as a priority in the global challenge of care for multi-morbidity [6].

South Africa is one of a growing number of low-and middle-income countries (LMICs) experiencing a rising burden of multi-morbid chronic conditions [7]. This is a consequence of the transition of HIV to a chronic condition with the scale-up of antiretroviral treatment; as well as the intensifying non-communicable disease (NCD) burden. In response the South African Department of Health has pioneered an Integrated Clinical Services Management (ICSM) approach that strives to service the majority of patients in primary health care (PHC) at a single delivery point using integrated clinical chronic care guidelines [8]. Integration of mental health care is part of ICSM in South Africa, but has been shown to be inadequate [9]; with a treatment gap of 75% for CMDs in South Africa [10]. While there is evidence of the effectiveness of collaborative care models for the treatment of common mental disorders (CMDs) comorbid with chronic physical conditions from high-income countries [11], there is little evidence of the effectiveness of task-shared collaborative care models for physical and mental multi-morbidity from LMICs.

The aim of this study was to evaluate an integrated collaborative care package of care for chronic patients with co-existing depressive and alcohol use disorder (AUD) symptoms that strengthened identification and management of these CMDs under real world conditions through strengthened referral pathways in one case study district in South Africa [12]. The study forms part of the PRogramme for Improving Mental Health CarE (PRIME) research consortium concerned with the development, implementation and evaluation of integrated packages of care for priority mental disorders at PHC level in five LMIC countries [13]. The specific objectives of this study were to assess primary outcomes of whether the collaborative care package: i) improved provider identification of depressive and AUD symptoms in chronic care patients and ii) reduced depressive symptoms and improved functioning in screen positive chronic patients identified and referred for care within the task shared stepped up collaborative care model. Secondary outcomes assessed effects in relation to health equity criteria.

Methods

Setting

The study site was in the Matlosana municipality in Dr. Kenneth Kaunda District (DKK) in the North West Province. The district population was estimated to be approximately 796,823 at the time of the study, while the catchment areas where this study was located comprised over 90,000 people serviced by four primary health care facilities varying in size, serving between 2353 and 6058 chronic care patients per month. Further details of the DKK district can be found elsewhere [12].

Description of the collaborative mental health care package Details of the collaborative care package that was developed through the formative phase and evaluated by this study are described in greater detail in Petersen et al. [12]. In brief, it comprised the following five components: i) PHC nurses functioned as case managers and were oriented to the ICSM, trained in clinical communication skills to facilitate person-centered care, and provided with supplementary mental health training in basic adult care guidelines (known as Adult Primary Care in South Africa) [14]; ii) Doctors were oriented to the importance of mental health and upskilled to prescribe antidepressant medications; iii) Referral pathways for psychosocial counselling for patients with mild to moderate depressive symptoms were strengthened with the introduction of clinic-based lay counsellors trained and supervised to deliver individual and group-based counselling drawing on cognitive behavioural therapy techniques which have international evidence of effectiveness [15]; and v) A referral form to monitor nurse referrals to the counsellor was introduced.

Research design

The research design was pragmatic with the intervention delivered independently of the evaluation. Given the complex nature of the collaborative care package, the evaluation comprised two main components: i) a Facility Survey to assess effects of the intervention on provider detection of depression and AUD; and ii) a non-randomly assigned comparison group cohort study to assess changes in symptom severity and functioning among screen positive patients identified and referred for further care by the PHC nurses compared to those not identified.

Facility detection survey (FDS) Study procedure, sample and measures

The primary objective of the Facility Detection Study was to estimate the change in detection of depression and of AUD by clinicians serving the adult chronic care population in intervention clinics. The design and power calculations for the sample sizes are described in greater detail elsewhere [16]. The FDS was conducted in three of the four facilities where the mental health APC module had not yet been implemented. The study procedures used at baseline and follow-up FDS were the same, with independent samples recruited in each study round. The baseline FDS was conducted from February-April 2014, before the implementation of the intervention began in April 2014. Training and embedding continued to September 2014. The follow-up FDS was 12 months after completion of the embedding period (October-December 2015) (see Fig. 1).

Adult patients were recruited from the chronic care waiting areas of the three PHC facilities by trained recruiters who gave short oral presentations on the study. No reference to mental health was made to minimize sampling bias and limit stigma. Eligibility of patients who volunteered was confirmed in a private space and study objectives discussed. Eligibility criteria were: 18 years or older; attending the clinic for treatment for a chronic illness (e.g., HIV, tuberculosis, hypertension, etc) and capacity to understand the questions posed in either seTswana (the dominant local language) or English. Written informed consent was obtained from literate participants and illiterate participants consented by marking the form with a cross; with a witness countersigning.

Trained fieldworkers administered a structured questionnaire, programmed into mobile devices. Questions included items on demographic characteristics, treated chronic condition(s), and screening instruments for probable AUD and depression. The Alcohol Use Disorder Identification Test (AUDIT), validated for use in South Africa [17], was used to screen for probable AUD, with participants scoring ≥16 considered positive for probable AUD, given nurse guidelines to provide advice for harmful drinking and refer patients with dependent drinking. Cronbach's alphas were 0.78 (baseline) and 0.74 (follow-up). For probable depression, we used the Patient Health Questionnaire-9 (PHQ-9), with a cut-off of ≥10 being previously validated on a primary care population in South Africa [18]. Cronbach's alphas were 0.88 (baseline) and 0.86 (follow up).

All screen positive participants on either screening tool, as well as 15% of randomly selected screen-negative participants (both AUDIT and PHQ-9) were asked to return for an exit interview immediately following their clinical consultation.

Participants' exit interview data were used to assess clinical detection on the day of the interview. Broad criteria were used to classify participants as detected for AUD given our experience of nurse reticence to make a diagnosis of AUD (patients who reported a diagnosis of harmful or dependent drinking, and/or a referral to specialist alcohol services, and/or who received advice about managing problems with drinking alcohol). Two classifications (narrow and broad) were used for detection of depression: narrow -a diagnosis of depression was reported; broaddiagnosis of depression and/or referral for psychosocial counselling reported. The latter was included given our experience that nurses did not always inform patients of a diagnosis of depression when referring patients. Anti-depressant medication prescribed was also assessed. Participants who reported being on current treatment for either condition were excluded from the analysis.

Statistical analyses

The socio-demographic and clinical characteristics of the participants recruited over the two study rounds were analyzed using means and standard deviations for continuous measures and counts and proportions for categorical measures. For each study round we report the number of participants who screened positive on AUDIT and PHQ-9, the number of screen positive participants who completed the exit interview, and the proportion who were classified as having been detected for AUD (among AUDIT positive), or for depression (among PHQ-9 positive) using both narrow and broad criteria. For the equity analysis, where sufficient data were available, we assessed whether change in detection over time was equitable by sex and by household food security. Both these demographic variables were shown as having the highest odds ratios for depression/AUD comorbidity in the same population in a previous study [19]. For the inequity analysis, we used binomial regression models to estimate change in detection, and included the relevant interaction term into the models, with an interaction term p-value< 0.20 suggestive of inequity. Second, for the screen negative PHQ-9 participants who were randomly selected to complete the exit interview, we tabulated the number of depression diagnoses and anti-depressant medication prescription. When it was not possible to estimate the change in detection because of zero counts in the baseline round, we reported the proportion detected and 95% confidence interval for the follow up round only. Procedures to estimate proportions, 95% confidence intervals and p-values incorporated weights to adjust for the imbalance in clinic-level sample sizes between rounds [20].

Depression cohort study Study procedure, sample and measures

Details of the sample size calculation, recruitment, questionnaire design, data collection procedures, and analysis plan for the PRIME cohort studies have been described in detail by Baron et al. [21] A cohort study for AUD patients was not conducted in South Africa because of the very low levels of AUD identification by the providers in the baseline round of the FDS.

Eligible participants were at least 18 years old; receiving care for a chronic physical condition; screened positive for probable depression and/or had been identified and referred for depression care by the providers; had the capacity to provide informed consent and comprehend the interview; and did not have a diagnosis of AUD or psychosis (see Fig. 2).

Chronic care patients were informed about the study in the waiting areas of the clinics prior to their clinic consultations and informed written consent was obtained from volunteers. The same informed consent procedure was followed for low-literacy patients as described for the FDS. Immediately after their clinical consultations, individuals who had given their consent were screened using the PHQ-9, and asked whether, during the consultation, they had been identified as having depression and/ or had been referred for care to a provider within the collaborative care model. Individuals who provided affirmative responses to these questions were enrolled into the depression treatment group, regardless of their PHQ-9 score. Individuals who did not provide affirmative responses to these post consultation questions, but who scored 10 or more on the PHQ-9 were enrolled into a comparison group. Comparison group participants who later received a depression diagnosis were re-enrolled in the treatment group, and only the treatment group data of these participants were analyzed (see Fig. 2).

Cohort study participants completed three assessments: at enrolment (baseline); 3 months and 12-months post-enrolment. Cohort recruitment and enrolment occurred from August 2014 to July 2015, and follow-up was conducted from November 2014 to September 2016 (see Fig. 1).

Mobile devices were used by trained seTswane/English speaking fieldworkers to administer the questionnaire in private spaces at the clinic or participants' homes. Each assessment comprised a range of demographic, clinical, health care use, social, economic, food security, and stigma-related measures. These are described in greater detail by Baron et al. [21]. Only measures pertaining to the socio-demographic and clinical characteristics of the sample are reported here.

Primary outcomes of the cohort study were response on the PHQ-9, defined as at least 50% reduction in score from baseline to 3 months and 12 months follow-up; and remission on the PHQ-9 at both follow-up visits, defined as a score of 5 or less -used as measures of clinically significant improvement in treatment trials using the PHQ-9, e.g. Huijbregts et al. (2013) [22]. Functional impairment, was assessed at the three time points using the 12-item WHO Disability Assessment Schedule (WHODAS 2.0) -previously used in South Africa amongst older and HIV populations [23]. Item response theory (IRT)-based scoring was used, with scores ranging from 0 to 100, with a higher score indicating greater functional impairment.

Analysis

Given the non-normal distribution of the sample's demographic and clinical characteristics, baseline characteristics of participants in the treatment and comparison groups were compared using non-parametric teststhe Mann-Whitney U test for continuous measures, and Exact Fisher's test for categorical variables. The mean symptom severity and functioning were compared between groups at each follow-visit. Given that neither measure was normally distributed at follow-ups, a multilevel mixed effect negative binomial regression was used, controlling for HIV status and recruitment clinic, as these were imbalanced at baseline [21]. Risk ratios for the primary outcomes (i.e. 50% reduction in scores at 3 months and 12 months follow-up, as well as remission) were assessed using a modified Poisson regression, with robust variance estimator, as binomial models failed to converge [24]. Again, the models were adjusted for demographic or other clinical differences between the comparison and treatment groups at baseline. To assess equity in primary outcomes across gender and household food security, the same negative binomial regressions were conducted, this time including either gender or household food security at baseline as an interaction term.

Results

Facility detection survey

In the first round, 1322 participants were eligible and consented to participate in the study. Twelve of these were on current treatment for depression and excluded from the study, resulting in a total of 1310 participants at baseline. During the second round, 1257 were eligible and consented to participate in the study; of these 11 were found to be on current treatment for depression and excluded, resulting in a total of 1246 participants at follow-up. The demographic and clinical characteristics of the Facility Detection Survey participants are presented in Table 1.

Across both survey rounds, the mean age of the sample was 46 years; approximately three-quarters were women; the majority were receiving care for HIV or hypertension. There were significant between-round differences in participants' employment status, food security, clinic site, and AUDIT screening.

For depression, as seen in Table 2(a), the pre-implementation diagnosis of depressive symptoms was 5.2% using the narrow definition, and 14.2% using the broader definition. Post-implementation, using the narrow definition, detection of depressive symptoms reached 16.2%, an increase of 11.0% (95% CI 3.3, 18.6); using the broad definition, detection increased to 26.7%, an increase of 12.5% (95% CI 1.9, 23.0). In the inequity analysis, the change in detection for food secure participants, using the broad definition, increased to 33.8%, compared to 23.0% for food insecure participants (P = 0.080). There was no evidence of inequity (P = 0.656) for men versus women participants.

For AUD, no AUDIT-positive participants were detected in the pre-implementation round. In the post-implementation round, 11.7% (95% CI 0.6, 22.8) were detected. There were insufficient data for conducting inequity analyses for AUD detection and treatment.

As seen in Table 2(b), of the 1310 participants enrolled in the pre-implementation round, 1084 screened negative on both PHQ-9 and AUDIT. Of the 1084, 118 were selected randomly to complete the exit interview, and 110 were successfully interviewed after their consultations. Of the 110, 0.9% had been detected and treatment initiated for depression using the narrow definition; and 6.7% using the broad definition. For AUD, 1.6% were prescribed anti-depressant medication and 1.6% were diagnosed with AUD. These proportions did not change significantly (all P > 0.05) between the pre-and post-implementation surveys.

Depression cohort study

Of 2602 patients screened, a total of 453 participants were enrolled in the cohort study. An initial 205 patients were diagnosed with depression and recruited into the treatment group. Another 248 patients were not diagnosed but screened positive on the PHQ-9, and were recruited into the comparison group; of these, 12 participants were subsequently diagnosed with depression at a follow-up visit at the clinic, and re-enrolled into the treatment group. The final sample was 236 for the comparison group and 217 for the treatment group (see Fig. 2). There were 82 participants (18.1%) lost to follow-up, mostly because of relocation and refusals. In the intervention group, 88.9% (n = 193) participants were followed at 3 months and 81.5% (n = 177) completed the 12-month assessment. In the comparison group, 88.6% (n = 209) completed the midline assessment and 82.6% (n = 195) the end-line assessment (see Fig. 1).

Of the 217 participants in the treatment group, 80 participants screened below the clinical cut-off of 10 on the PHQ-9 at baseline. These participants were excluded from the analysis, to ensure that both treatment and control groups were comparable. These participants did not differ from those included in the analyses in terms  Reported diagnosis of depression f Reported diagnosis or referral for psychosocial counselling *Reported diagnosis of depression **Reported diagnosis or referral for psychosocial counselling of demographic characteristics at recruitment, besides food insecurity ( Table 3). The final sample included in the analysis comprised 373 participants: 137 and 236 in the treatment and comparison groups, respectively.

Participants in the comparison and treatment groups who were included in the analyses differed in terms of clinic of recruitment and in HIV status (Table 3). Also, participants recruited into the treatment group had significantly higher PHQ-9 scores (mean = 14.5, SD = 3.47), compared to participants in the comparison group (mean = 12.8, SD = 3.01).

Results of the modified Poisson regressions are presented in Table 4. The proportion of participants showing at least a 50% reduction in PHQ-9 scores from baseline to the 3-month follow-up was greater in the treatment group (N = 69, 55.2%) than in the comparison group (N = 49, 23.4%; RR = 2.10, p < 0.001). The rate of participants who showed remission on the PHQ-9 (score < 5) was also greater in the treatment group (N = 40, 32.0%) compared to the comparison group (N = 25, 12.0%; RR = 2.78, p < 0.001). The same significant trends were found at the 12-month follow-up, with 57 (47.9%) participants in the treatment group reporting at least 50% reduction in PHQ-9 score compared to baseline, and 32 (26.9%) participants scoring below 5, compared to 60 participants (30.8%; RR = 1.52, p = 0.006) and 33 participants (16.9%; RR = 1.72, p = 0.016) in the comparison group, respectively.    Change in mean scores on the PHQ-9 and WHODAS over time are also presented in Table 4. After controlling for HIV status and clinic of recruitment, the mixed effects analyses reveal a significant difference between the two groups at the 3-month follow-up; a greater decrease in PHQ-9 scores in the treatment group (M = -5.05, 95%CI: -6.02 to The change in WHODAS scores at the 12-month follow-up was not significantly different between the two groups.

Inequity analyses of the impact of the intervention on outcomes by gender and household food security are presented in Table 5. There is no evidence of inequity by gender at either follow-up time points. At 3 months follow-up, however, the intervention was found to have a significantly more positive effect on reducing depressive scores among participants who reported being food secure at baseline (β = − 3.73, 95%CI -5.30 to − 2.15) (M = − 6.62, 95% CI: -7.95-5.28) compared to those reporting being food insecure (β = − 1.23, 95%CI -2.65 to 0.20; β = − 2.50, p < 0.05). This trend persisted at 12 months follow-up.

Discussion

The results of the FDS suggest an improvement in nurse-detection and treatment initiation of depressive and AUD symptoms following implementation of the integrated collaborative care package in the district. Notably, identification remained essentially absent for individuals who were probable non-cases. In other words, the positive predictive value of nurse identification and treatment initiation was high for both depression and AUD.

The results of the cohort study indicate that patients correctly identified and referred for further care for their depressive symptoms had a greater chance of having a clinically significant reduction in depressive symptoms to the point of being in remission at both 3 and 12 month follow-up than those not referred. They also showed a significant reduction in functional disability at 3-month follow-up compared to the non-intervention group, although this effect was not sustained at 12 months. The intervention was significantly more successful with food secure participants, suggesting that food insecurity, may serve as a barrier to improvement in depressive symptoms; being associated with poverty in more urban areas in South Africa [25]. This adds to the growing body of evidence suggesting the need for accompanying income generating initiatives for people with depressive symptoms from poor socio-economic contexts [26].

While an improvement in the correct identification and treatment initiation of patients with depressive and AUD symptoms was noted, the treatment gap post training still remained large, with 75% of probable cases of depression and 84% of probable cases of AUD still not detected at 12 months follow-up. Previous studies suggest that training alone may be insufficient to improve identification of CMDs in PHC [27]. Other factors contributing to this gap resonate with our understanding of possible reasons and include individual provider level factors -with psychiatric stigma as well as providers' own personal unresolved problems previously shown to act as barriers to the identification of emotional problems in patients [28,29]. The inclusion of anti-stigma interventions [28], stress management and debriefing sessions to assist PHC personnel to engage in emotional labour has been previously suggested to assist integration efforts [29]. Further, the need for change management processes to accompany organizational changes associated with integrated care has also previously been highlighted [8].

Limitations

There are a number of limitations of this evaluation. With regard to the FDS: i) Non-random sampling compromised the representativeness of the sample. An imbalance in the demographics between the two rounds was notedwith an upward trend in food insecurity, unemployment and alcohol screen positives in the second round -partially explained by retrenchments on the mines between the two survey rounds, with mining being a major industry and source of employment in the study site; ii) There were no control clinics involved in the FDSwith the possibility that the some of the improvements in detection were due to other factors in the health system; iii) An upwards bias in detection may have been introduced given patients' heightened awareness of their potential symptoms through exposure to the survey interview prior to their consultation -although offset by being the case for both rounds as well as having screen-negative cases; and iv) The FDS only assessed clinical detection on the day of the interviewwith diagnosis of a CMD generally taking several visits [27] thus potentially under-representing detection rates. In relation to the cohort study, recruitment into the intervention and control arm was not randomized, thus increasing potential for bias from unknown confounders, although partially mitigated by multi-variable analyses.

Conclusion

This evaluation shows that the PRIME-SA intervention package assisted PHC teams to identify and manage comorbid CMDs in chronic patients under real world conditions using a collaborative stepped care model. There was an improvement in accurate detection of CMDs by PHC nurses, and a reduction in depressive symptoms in patients identified with comorbid depression and referred for care. Further research is required to assess patient outcomes in patients with comorbid AUD. In the face of the negative impact that comorbid CMDs have on treatment adherence and overall health outcomes in chronic patients, this collaborative task shared model provides a potential model for mental physical multi-morbid disease management in South Africa and other LMICs in the context of specialist resource shortages. Additional findings will be provided by two parallel pragmatic cluster randomized control trials that are currently underway, testing effectiveness of this model on depression and physical outcomes (viral load suppression in HIV patients and reductions in blood pressure in hypertensive patients) in chronic care patients [30,31].

Counts are reported as observed, while proportions, differences, 95% confidence intervals and P-values are estimated with clinicon PHQ-9 and AUDIT, and no depression symptoms over the past 12 months e

a
Comparison group is the reference group; b defined as at least 50% reduction in score at follow-up compared to baseline; c defined as PHQ-9 < 5 at follow-up; d RR = Risk ratio

− 4.08) compared to the comparison group (M = -2.63, 95%CI: -3.34 to − 1.92; β = − 2.42, p < 0.001) and a greater decrease in WHODAS scores from baseline in the treatment group (M = -11.11, 95%CI: -15.18 to − 7.05) compared to the comparison group (M = -4.65, 95%CI: -8.02 to − 1.28; β = − 6.46, p = 0.010). A similar trend was seen for the PHQ-9 scores at the 12-month follow-up compared to baseline (treatment: M = -5.07, 95%CI: -6.05 to − 4.09; comparison: M = -3.10, 95%CI: -3.83 to − 2.37, β = − 1.97, p < 0.001).

Abbreviations AUD: Alcohol Use Disorder; AUDIT: Alcohol Use Disorder; CMD: Common Mental Disorder/s; DKK: Dr. Kenneth Kaunda district; FDS: Facility Detection Survey; ICSM: Integrated Clinical Services Management; IRT: Item response theory; LMICs: Low -middle-income countries; NCD: Non communicable disorder; PHC: Primary Health Care; PRIME: PRogramme for Improving Mental health carE; WHODAS 2.0: World Health Organization Disability Assessment Schedule

Table 1
1Demographic and clinical characteristics of participants in PRIME implementation clinics, Matlosana sub-district, Northwest Province, South Africa, 2014 & 2015Pre-implementation (n = 1310)
Post-implementation (n = 1246)
p

Age, years (SD)
46.9 (13.3)
46.2 (13.5)
0.18

Female (%)
983 (75.0)
967 (77.6)
0.14

Marital status (%)
0.20

Single
405 (30.9)
358 (28.7)

Partnered
619 (47.2)
581 (46.6)

Divorced/Separated/Widowed
286 (21.8)
307 (24.6)

Education (%)
0.07

Never attended school
89 (6.8)
76 (6.1)

Some primary
320 (24.4)
261 (20.9)

Completed primary
720 (55.0)
705 (56.6)

Completed secondary
181 (13.8)
204 (16.4)

Employment Status (%)
0.01

Unemployed
519 (39.6)
513 (41.2)

Paid
505 (38.5)
521 (41.8)

Other a
286 (21.8)
212 (17.0)

Household food insecurity (%)
272 (20.8)
538 (43.2)
0.01

Clinic (%)
0.01

Grace Mokhomo
583 (44.5)
495 (39.7)

Kanana
274 (20.9)
342 (27.4)

Orkney
453 (34.6)
409 (32.8)

Chronic condition (%)

HIV
783 (59.8)
781 (62.8)
0.12

Hypertension
671 (51.2)
664 (53.4)
0.28

Diabetes
123 (9.4)
113 (9.1)
0.84

Arthritis
53 (4.1)
44 (3.5)
0.54

Chronic respiratory disease
56 (4.3)
31 (2.5)
0.02

Tuberculosis
73 (5.6)
13 (1.1)
0.01

Epilepsy
47 (3.6)
29 (2.3)
0.06

Screening (%)

PHQ-9 Screen Positive
107 (8.1)
130 (10.3)
0.056

AUDIT Screen Positive
43 (3.2)
63 (5.0)
0.03

P-value calculated with Fisher's exact

a

Voluntary, student, retired/pensioner, other

Table 2
2Detection of Depression and Alcohol Use Disorder among chronic care patients in PRIME implementation clinics, Matlosana sub-district, Northwest Province, South Africa, 2014 & 2015Disorder
Pre-implementation
Post-implementation
Detection difference (95% CI) a

a

Depression

Screened
1310
1246

Screen positive
106/1310
124/1246

Detection data available c
102/106
116/124

Detection-narrow*
6/102 (5.2)
19/116 (16.2)
+ 11.0 (3.3, 18.6) a

Detection-broad**
14/102 (14.2)
31/116 (26.7)
+ 12.5 (1.9, 23.0) a

Alcohol use disorder

Screened
1310
1246

Screen positive
42/1310
62/1246

Detection data available c
37/42
58/62

Detection
0/37 (0.0)
8/58 (11.7)
11.7 (0.6, 22.8) b

b. Screen negative patients

Screened
1310
1246

Probable non-case d
1084/1310
996/1246

Randomly Selected
118/1084
113/996

Detection data available
110/118
109/113

Depression

Detection-narrow e
1/110 (0.9)
2/109 (1.9)

Detection-broad f
8/110 (6.7)
8/109 (7.5)

Antidepressant prescribed
2/110 (1.6)
0/109 (0.0)

Alcohol use disorder

Table 3
3Baseline demographic characteristics of the depression control and treatment cohorts included in the analysisExcluded from analysis a
Included in the analysis

Treatment (n = 80)
Treatment (n = 137)
Comparison (n = 236)
p

Age, years (mean, SD)
44.8 (14.77)
42.6 (13.22)
44.0 (12.64)
0.469

Female (N, %)
61 (76.3%)
114 (83.2%)
190 (80.5%)
0.581

Marital status (N, %)
0.909

Single
22 (27.5%)
55 (40.2%)
90 (38.1)

Partnered
46 (57.5%)
59 (43.1%)
103 (43.6%)

Widowed/divorced
12 (15.0%)
23 (16.8%)
43 (18.2%)

Education (N, %)
0.405

No or some primary
24 (30.0%)
38 (27.7%)
60 (25.4%)

Completed primary
38 (47.5%)
80 (58.4%)
152 (64.4%)

Completed secondary
18 (22.5%)
19 (13.9%)
24 (10.2%)

Employment Status (%)
0.305

Unemployed
56 (70.0%)
102 (74.5%)
187 (78.2%)

Paid
24 (30.0%)
35 (25.5%)
49 (20.8%)

Food insecurity

No
60 (75.0%)
62 (45.3%)
102 (43.2%)
0.746

Yes
20 (25.0%)
75 (54.7%)
134 (56.8%)

Clinic (%)
0.005*

OR
10 (12.5%)
11 (8.0%)
21 (8.9%)

KA
16 (20.0%)
32 (23.4%)
93 (39.4%)

GM
38 (47.5%)
67 (48.9%)
77 (32.6%)

MJ
16 (20.0%)
27 (19.7%)
45 (19.1%)

Chronic condition (%)

HIV
26 (32.9%)
57 (41.6%)
173 (73.3%)
< 0.001**

Hypertension
31 (39.2%)
53 (38.7%)
107 (45.3%)
0.233

Diabetes
3 (3.80%)
6 (4.4%)
15 (6.4%)
0.492

Other
9 (11.4%)
17 (12.4%)
24 (10.2%)
0.498

Main outcomes

PHQ-9 (mean, SD)
6.3 (2.15)
14.5 (3.47)
12.8 (3.01)
< 0.001**

WHODAS (mean, SD)
24.1 (17.97)
37.6 (17.19)
40.0 (19.48)
0.251

a Diagnosed but scored < 10 on PHQ-9
*p = < 0.01; **p = < 0.001

Table 4
4PHQ-9 and WHODAS outcomes over time and across cohort groupsControl group
Treatment group
adjusted β or RR d
(95%CI)

Table 5
5Equity analysis of Cohort StudyControl group
Treatment group
Difference of
difference

adjusted
β (95%CI)
N
Mean PHQ-9
score (SD)

Adjusted mean change in
PHQ-9 (95%CI) from baseline

N
Mean PHQ-9
score (SD) or N (%)

Adjusted mean change in
PHQ-9 (95%CI) from baseline

Midline (3 months)

Sex

Male
43 8.3 (4.19)
−3.56 (− 5.04 to − 2.08)
21 6.8 (4.98)
−5.39 (− 7.45 to − 3.33)
− 1.83 (− 4.28 to
0.62)

0.76
(− 1.94
to 3.45)
Female
166 9.9 (4.73)
−2.41 (− 3.18 to − 1.64)
104 8.2 (6.36)
−5.00 (− 6.03 to − 3.97)
−2.59 (− 3.77 to
− 1.41)

Household food insecurity

Food
secure

89 8.7 (4.20)
−2.89 (− 3.92 to − 1.86)
58 6.2 (4.84)
− 6.62 (− 7.95 to − 5.28)
−3.73 (− 5.30 to
− 2.15)

− 2.50 a
(− 4.60 to
− 0.41)
Food
insecure

120 10.3 (4.88)
−2.56 (− 3.47 to − 1.65)
67 9.5 (6.79)
−3.79 (− 4.98 to − 2.59)
−1.23 (− 2.65 to
0.20)

Endline (12 months)

Sex

Male
38 7.3 (4.29)
−4.44 (− 5.94 to − 2.94)
21 7.7 (4.34)
− 4.64 (− 6.72 to − 2.56)
−0.20 (− 2.67
to 2.28)

2.18
(− 0.55
to 4.91)
Female
157 9.4 (4.95)
−2.80 (− 3.59 to − 2.01)
98 8.0 (5.35)
−5.17 (− 6.22 to − 4.13)
−2.38 (− 3.55
to − 1.20)

Household food insecurity

Food
secure

86 8.3 (4.81)
−3.27 (− 4.31 to − 2.24)
55 6.5 (4.97)
−6.42 (− 7.76 to − 5.08)
−3.15 (− 4.72
to − 1.58)

− 2.24 a
(− 4.34 to
− 0.14)
Food
insecure

109 9.6 (4.91)
− 3.10 (− 4.04 to − 2.17)
64 9.2 (5.06)
−4.01 (− 5.22 to − 2.81)
−0.91 (− 2.33
to 0.52)

a Significant at < 0.05 level

AcknowledgementsNot applicableFunding This study is an output of the PRogramme for Improving Mental health carE (PRIME). This work was supported by the UK Department for International Development [201446]. The views expressed do not necessarily reflect the UK Government's official policies. The funder did not have any involvement in the study design, collection, analysis or interpretation of data or writing of the manuscript.Availability of data and materialsThe anonymised data will be made publically available, in accordance with PRIME publication and data management policies available at https://bit.ly/ 2tXQQsVCompeting interestsThe authors declare they have no Competing interest.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Authors' contributions IP led the conceptualization of the study, design and coordination, data analysis including data acquisition and wrote the first draft of the manuscript. AB contributed to the conceptualization of the study, data analysis and drafting of the manuscript. LF contributed to the conceptualization of the study and drafting the manuscript. OS participated in planning and managing the fieldwork, data collection and monitoring and led coordination of the study. TK participated in the fieldwork, planning data collection and data monitoring. ECB and SDR contributed to data management, data analysis and drafting the manuscript. CL contributed to the conceptualization of the study and drafting of the manuscript. All authors critically reviewed and revised the manuscript and approved the final version for publishing.Ethics approval and consent to participateEthical approvals for the FDS and Cohort studies were obtained from the University of KwaZulu-Natal (South Africa) Biomedical Research Ethics Committee (BE400/13; BE258/14; BE372/13), the University of Cape Town (South Africa) Faculty of Health Sciences Human Research Ethics Committee (HREC REF 412/2011), and the World Health Organization (Geneva, Switzerland) Research Ethics Review Committee (RPC497). In relation to participant safety concerns, patients who responded positively to the PHQ-9 suicide ideation question were referred by the fieldworkers to clinic nurses for further assessment and onward referral, if necessary. Written informed consent was obtained from literate participants and illiterate participants consented by marking the form with a cross; with a witness countersigning.Consent for publicationNot applicable.
Depression, chronic diseases, and decrements in health: results from the world health surveys. S Moussavi, S Chatterji, E Verdes, A Tandon, V Patel, B Ustun, Lancet. 3709590Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the world health surveys. Lancet. 2007;370(9590):851-8.

Meta-analysis of the relationship between HIV infection and risk for depressive disorders. J A Ciesla, J E Roberts, Am J Psychiatry. 1585Ciesla JA, Roberts JE. Meta-analysis of the relationship between HIV infection and risk for depressive disorders. Am J Psychiatry. 2001;158(5):725- 30.

Alcohol consumption, progression of disease and other comorbidities, and responses to antiretroviral medication in people living with HIV. M G Neuman, M Schneider, R M Nanau, C Parry, AIDS research and treatment. 751827Neuman MG, Schneider M, Nanau RM, Parry C. Alcohol consumption, progression of disease and other comorbidities, and responses to antiretroviral medication in people living with HIV. AIDS research and treatment. 2012;2012:751827.

Alcohol use as a risk factor for tuberculosis -a systematic review. K Lonnroth, B G Williams, S Stadlin, E Jaramillo, C Dye, BMC Public Health. 8289Lonnroth K, Williams BG, Stadlin S, Jaramillo E, Dye C. Alcohol use as a risk factor for tuberculosis -a systematic review. BMC Public Health. 2008;8:289.

Long-term conditions and mental health. C Naylor, M Parsonage, D Mcdaid, M Knapp, M Fossey, A Galea, The cost of co-morbidities. LondonThe Kings FundNaylor C, Parsonage M, McDaid D, Knapp M, Fossey M, Galea A. Long-term conditions and mental health. In: The cost of co-morbidities. London: The Kings Fund; 2012.

Grand challenges: integrating mental health services into priority health care platforms. V Patel, G S Belkin, A Chockalingam, J Cooper, S Saxena, J Unutzer, PLoS Med. 1051001448Patel V, Belkin GS, Chockalingam A, Cooper J, Saxena S, Unutzer J. Grand challenges: integrating mental health services into priority health care platforms. PLoS Med. 2013;10(5):e1001448.

Patterns of HIV, TB, and non-communicable disease multi-morbidity in peri-urban South Africa-a cross sectional study. T Oni, E Youngblood, A Boulle, N Mcgrath, R J Wilkinson, N S Levitt, BMC Infect Dis. 1520Oni T, Youngblood E, Boulle A, McGrath N, Wilkinson RJ, Levitt NS. Patterns of HIV, TB, and non-communicable disease multi-morbidity in peri-urban South Africa-a cross sectional study. BMC Infect Dis. 2015;15:20.

Development and implementation of an integrated chronic disease model in South Africa: lessons in the management of change through improving the quality of clinical practice. O H Mahomed, S Asmall, Int J Integr Care. 1538Mahomed OH, Asmall S. Development and implementation of an integrated chronic disease model in South Africa: lessons in the management of change through improving the quality of clinical practice. Int J Integr Care. 2015;15:e038.

Educational outreach with an integrated clinical tool for nurse-led noncommunicable chronic disease Management in Primary Care in South Africa: a pragmatic cluster randomised controlled trial. L R Fairall, N Folb, V Timmerman, C Lombard, K Steyn, M O Bachmann, PLoS Med. 13111002178Fairall LR, Folb N, Timmerman V, Lombard C, Steyn K, Bachmann MO, et al. Educational outreach with an integrated clinical tool for nurse-led non- communicable chronic disease Management in Primary Care in South Africa: a pragmatic cluster randomised controlled trial. PLoS Med. 2016; 13(11):e1002178.

Twelve-month treatment of psychiatric disorders in the south African stress and health study (world mental health survey initiative). S Seedat, D J Stein, A Herman, R Kessler, J Sonnega, S Heeringa, Soc Psychiatry Psychiatr Epidemiol. 4311Seedat S, Stein DJ, Herman A, Kessler R, Sonnega J, Heeringa S, et al. Twelve-month treatment of psychiatric disorders in the south African stress and health study (world mental health survey initiative). Soc Psychiatry Psychiatr Epidemiol. 2008;43(11):889-97.

Collaborative care for depression and anxiety problems. J Archer, P Bower, S Gilbody, K Lovell, D Richards, L Gask, Cochrane Database Syst Rev. 106525Archer J, Bower P, Gilbody S, Lovell K, Richards D, Gask L, et al. Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev. 2012;10:CD006525.

Integrating mental health into chronic care in South Africa: the development of a district mental healthcare plan. I Petersen, L Fairall, A Bhana, T Kathree, O Selohilwe, C Brooke-Sumner, Br J Psychiatry. 208Suppl 56Petersen I, Fairall L, Bhana A, Kathree T, Selohilwe O, Brooke-Sumner C, et al. Integrating mental health into chronic care in South Africa: the development of a district mental healthcare plan. Br J Psychiatry. 2016; 208(Suppl 56):s29-39.

PRIME: a programme to reduce the treatment gap for mental disorders in five low-and middle-income countries. C Lund, M Tomlinson, De Silva, M Fekadu, A Shidhaye, R Jordans, M , PLoS Med. 9121001359Lund C, Tomlinson M, De Silva M, Fekadu A, Shidhaye R, Jordans M, et al. PRIME: a programme to reduce the treatment gap for mental disorders in five low-and middle-income countries. PLoS Med. 2012;9(12):e1001359.

Innovating to improve primary care in less developed countries: towards a global model. L Fairall, E Bateman, R Cornick, G Faris, V Timmerman, N Folb, BMJ Innov. 14Fairall L, Bateman E, Cornick R, Faris G, Timmerman V, Folb N, et al. Innovating to improve primary care in less developed countries: towards a global model. BMJ Innov. 2015;1(4):196-203.

Psychological treatments for the world: lessons from low-and middle-income countries. D R Singla, B A Kohrt, L K Murray, A Anand, B F Chorpita, V Patel, Annu Rev Clin Psychol. 13Singla DR, Kohrt BA, Murray LK, Anand A, Chorpita BF, Patel V. Psychological treatments for the world: lessons from low-and middle-income countries. Annu Rev Clin Psychol. 2017;13:149-81.

Evaluation of district mental healthcare plans: the PRIME consortium methodology. De Silva, M J Rathod, S D Hanlon, C Breuer, E Chisholm, D Fekadu, A , Br J Psychiatry. 208Suppl 56De Silva MJ, Rathod SD, Hanlon C, Breuer E, Chisholm D, Fekadu A, et al. Evaluation of district mental healthcare plans: the PRIME consortium methodology. Br J Psychiatry. 2016;208(Suppl 56):s63-70.

Common mental disorders among HIV-infected individuals in South Africa: prevalence, predictors, and validation of brief psychiatric rating scales. L Myer, J Smit, L L Roux, S Parker, D J Stein, S Seedat, AIDS Patient Care STDs. 222Myer L, Smit J, Roux LL, Parker S, Stein DJ, Seedat S. Common mental disorders among HIV-infected individuals in South Africa: prevalence, predictors, and validation of brief psychiatric rating scales. AIDS Patient Care STDs. 2008;22(2):147-58.

Validity of the patient health Questionnaire-9 to screen for depression in a high-HIV burden primary healthcare clinic in. R Cholera, B N Gaynes, B W Pence, J Bassett, N Qangule, C Macphail, J Affect Disord. 167Cholera R, Gaynes BN, Pence BW, Bassett J, Qangule N, Macphail C, et al. Validity of the patient health Questionnaire-9 to screen for depression in a high-HIV burden primary healthcare clinic in Johannesburg, South Africa. J Affect Disord. 2014;167:160-6.

Risk correlates for physical-mental multimorbidities in South Africa: a cross-sectional study. Epidemiology and psychiatric sciences. I Petersen, S Rathod, T Kathree, O Selohilwe, A Bhana, 10.1017/S2045796017000737Petersen I, Rathod S, Kathree T, Selohilwe O, Bhana A. Risk correlates for physical-mental multimorbidities in South Africa: a cross-sectional study. Epidemiology and psychiatric sciences. 2017. p. 1-9. https://doi.org/10.1017/ S2045796017000737.

Epidemiology in medicine. Philadelphia: Lippinicott, Williams and Wilkins. C H Hennekens, J F Buring, Hennekens CH, Buring JF. Epidemiology in medicine. Philadelphia: Lippinicott, Williams and Wilkins; 1987.

Impact of district mental health care plans on symptom severity and functioning of patients with priority mental health conditions: the Programme for improving mental health care (PRIME) cohort protocol. E C Baron, S D Rathod, C Hanlon, M Prince, A Fedaku, F Kigozi, BMC Psychiatry. 18161Baron EC, Rathod SD, Hanlon C, Prince M, Fedaku A, Kigozi F, et al. Impact of district mental health care plans on symptom severity and functioning of patients with priority mental health conditions: the Programme for improving mental health care (PRIME) cohort protocol. BMC Psychiatry. 2018;18(1):61.

A target-driven collaborative care model for major depressive disorder is effective in primary care in the Netherlands. A randomized clinical trial from the depression initiative. K M Huijbregts, F J De Jong, H W Van Marwijk, A T Beekman, H J Ader, L Hakkaart-Van Roijen, J Affect Disord. 1463Huijbregts KM, de Jong FJ, van Marwijk HW, Beekman AT, Ader HJ, Hakkaart-van Roijen L, et al. A target-driven collaborative care model for major depressive disorder is effective in primary care in the Netherlands. A randomized clinical trial from the depression initiative. J Affect Disord. 2013; 146(3):328-37.

Disability and living with HIV: baseline from a cohort of people on long term ART in South Africa. J Hanass-Hancock, H Myezwa, B Carpenter, PLoS One. 1012143936Hanass-Hancock J, Myezwa H, Carpenter B. Disability and living with HIV: baseline from a cohort of people on long term ART in South Africa. PLoS One. 2015;10(12):e0143936.

A modified poisson regression approach to prospective studies with binary data. G Zou, Am J Epidemiol. 1597Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702-6.

Perceptions of poverty: a study of food secure and food insecure households in an urban area in South Africa. Wcj Grobler, Procedia Economics and Finance. 35Grobler WCJ. Perceptions of poverty: a study of food secure and food insecure households in an urban area in South Africa. Procedia Economics and Finance. 2016;35:224-31.

Community-based mental health intervention for underprivileged women in rural India: an experiential report. K Rao, P Vanguri, S Premchander, Int J Family Med. 621426Rao K, Vanguri P, Premchander S. Community-based mental health intervention for underprivileged women in rural India: an experiential report. Int J Family Med. 2011;2011:621426.

Integrating mental health and primary care. S Thielke, S Vannoy, J Unutzer, Primary care. 343Thielke S, Vannoy S, Unutzer J. Integrating mental health and primary care. Primary care. 2007;34(3):571-92 vii.

Psychiatric stigma and discrimination in South Africa: perspectives from key stakeholders. C O Egbe, C Brooke-Sumner, T Kathree, O Selohilwe, G Thornicroft, I Petersen, BMC Psychiatry. 14191Egbe CO, Brooke-Sumner C, Kathree T, Selohilwe O, Thornicroft G, Petersen I. Psychiatric stigma and discrimination in South Africa: perspectives from key stakeholders. BMC Psychiatry. 2014;14:191.

Comprehensive integrated primary mental health care for South Africa. Pipedream or possibility?. I Petersen, Soc Sci Med. 513Petersen I. Comprehensive integrated primary mental health care for South Africa. Pipedream or possibility? Soc Sci Med. 2000;51(3):321-34.

Collaborative care for the detection and management of depression among adults receiving antiretroviral therapy in South Africa: study protocol for the CobALT randomised controlled trial. L Fairall, I Petersen, B Zani, N Folb, D Georgeu-Pepper, O Selohilwe, Trials. 191193Fairall L, Petersen I, Zani B, Folb N, Georgeu-Pepper D, Selohilwe O, et al. Collaborative care for the detection and management of depression among adults receiving antiretroviral therapy in South Africa: study protocol for the CobALT randomised controlled trial. Trials. 2018;19(1):193.

Collaborative care for the detection and management of depression among adults with hypertension in South Africa: study protocol for the PRIME-SA randomised controlled trial. I Petersen, A Bhana, N Folb, G Thornicroft, B Zani, O Selohilwe, Trials. 191192Petersen I, Bhana A, Folb N, Thornicroft G, Zani B, Selohilwe O, et al. Collaborative care for the detection and management of depression among adults with hypertension in South Africa: study protocol for the PRIME-SA randomised controlled trial. Trials. 2018;19(1):192.


Decreased Thioredoxin-1 and Increased HSP90 Expression in Skeletal Muscle in Subjects with Type 2 Diabetes or Impaired Glucose Tolerance
Published 4 February 2014

M Venojärvi email@example.com
Institute of Biomedicine, Exercise Medicine
University of Eastern Finland
P.O. Box 162770211KuopioFinland

A Korkmaz
Institute of Biomedicine, Physiology
University of Eastern Finland
P.O. Box 162770211KuopioFinland

S Aunola
Department of Health, Functional Capacity and Welfare
Functional Capacity Unit
National Institute for Health and Welfare
20720TurkuFinland

K Hällsten
PET Centre
University of Turku
20520TurkuFinland

K Virtanen
PET Centre
University of Turku
20520TurkuFinland

J Marniemi
Department of Chronic Disease Prevention
Population Studies Unit
National Institute for Health and Welfare
20520TurkuFinland

J.-P Halonen
Research Department
Social Insurance Institution
20720TurkuFinland

O Hänninen
Institute of Biomedicine, Physiology
University of Eastern Finland
P.O. Box 162770211KuopioFinland

P Nuutila
PET Centre
University of Turku
20520TurkuFinland

M Atalay
Institute of Biomedicine, Physiology
University of Eastern Finland
P.O. Box 162770211KuopioFinland

Decreased Thioredoxin-1 and Increased HSP90 Expression in Skeletal Muscle in Subjects with Type 2 Diabetes or Impaired Glucose Tolerance
Published 4 February 201410.1155/2014/386351Received 26 April 2013; Revised 29 August 2013; Accepted 21 November 2013;Research Article Correspondence should be addressed to M. Venojärvi; Academic Editor: Mark M. Kushnir
In diabetes, the endogenous defence systems are overwhelmed, causing various types of stress in tissues. In this study, newly diagnosed or diet-treated type 2 diabetics (T2D) ( = 10) were compared with subjects with impaired glucose tolerance (IGT) ( = 8). In both groups, at resting conditions, blood samples were drawn for assessing metabolic indices and skeletal muscle samples (m. vastus lateralis) were taken for the measurements of cellular defence markers: thioredoxin-1 (TRX-1) and stress proteins HSP72, HSP90. The protein level of TRX-1 was 36.1% lower ( = 0.031) and HSP90 was 380% higher ( < 0.001) in the T2D than in the IGT subjects, with no significant changes in HSP72. However, after the adjustment of both analyses with HOMA-IR only HSP90 difference remained significant. In conclusion, level of TRX-1 in skeletal muscle tissue was lower while that of HSP90 was higher in T2D than in IGT subjects. This may impair antioxidant defence and lead to disruptions of protein homoeostasis and redox regulation of cellular defences. Because HSP90 may be involved in sustaining functional insulin signalling pathway in type 2 diabetic muscles and higher HSP90 levels can be a consequence of type 2 diabetes, our results are potentially important for the diabetes research.

Introduction

Increased oxidative stress is defined as excessive production of reactive oxygen species (ROS) overwhelming the endogenous antioxidant production in tissues and possibly impairing cellular functions. Hyperglycaemia stimulates the ROS production; both lipid peroxidation and protein oxidation have been shown to be increased in type 1 and type 2 diabetes [1]. At lower concentrations, ROS also serve as secondary messengers regulating cellular functions and adaptations in skeletal muscle tissue [2]. The redox states of thiol systems are controlled by thioredoxin (TRX), glutathione (GSH) and cysteine (Cys); an imbalance between ROS and antioxidant defence appears as aberrant cell signalling and dysfunctional redox control [3].

ROS also induce heat shock protein (HSP) expression and upregulate the HSP defence mechanism, which principally restores protein homeostasis, promotes cell survival, and also provides an additional protection system against overwhelming ROS [4]. HSP90 regulates the heat-shock response and, if this has been inhibited, HSP70 will be upregulated [5]. TRX, a major cellular protein disulfide reductase, modulates protein structure and aggregation by cross-linking proteins with disulfides and reduction of protein cysteine residues.  Therefore, in addition to the crucial functions of TRX in tissues in supporting the large network of antioxidant defence, TRX has also crucial role in regulating various protein functions, including enzyme activity, cell growth, proliferation, and ultimately redox-sensitive signal transduction [6,7]. TRX protects cells from apoptosis [8] and controls many inflammatory genes through redox regulation of transcription factors [9]. Impaired HSP and TRX-1 responses exert a negative impact on antioxidant defence and tissue protection in diabetic patients and experimental diabetes [4,10]. An early study showed that serum TRX levels were higher in type 2 diabetics (T2D) compared to controls [11]. It has been shown that hyperglycaemia, which was induced by oral glucose loading impaired both serum TRX levels and insulinogenic index, is an indicator of pancreatic -cell function [12]. In contrast, in a pooled group of IGT and T2D patients glucose intolerance was independently associated with the high plasma levels of TRX [13]. Therefore, limited literature information on the association of TRX with glucose homeostasis showed controversy and was restricted with plasma and serum levels, while, to our knowledge, TRX responses to impaired glucose homeostasis in skeletal muscle, the main side for insulin action, have not been studied yet.

Previous data on the effect of type 2 diabetes or IGT on HSP levels were partly limited on mRNA results and conflicting with protein levels: decreased at mRNA levels [14,15], but increased at protein levels in type 2 diabetics [16]. Furthermore, HSP protein levels were lower in skeletal muscle of insulin resistant, obese humans compared to healthy controls [17]. As far as we are aware, there is no study comparing the levels of HSP and TRX-1 protein levels in skeletal muscle between impaired glucose tolerance (IGT) and T2D subjects. For this purpose in the current study, we evaluated the skeletal muscle protein expressions of HSP72, HSP90, and TRX-1 between subjects with a newly diagnosed or diet-treated type 2 diabetes (T2D) and prediabetic subjects with IGT.

Material and Methods

Study Subjects.

Newly diagnosed or diet-treated T2D subjects ( = 10) from a study of Hällsten et al. [18] were compared with a group of IGT subjects ( = 8) from a substudy of Finnish Diabetes Prevention Study (DPS) [19][20][21]. Both studies have been described in detail elsewhere [18,21]. The study protocol [18] was briefly as follows. A total of 45 patients having T2D, as defined by the criteria of the World Health Organization [22], but no diabetic complications were assigned to the protocol. Patients with a cardiovascular disease, blood pressure > 160/100 mmHg, previous or present abnormal hepatic or renal function, antidiabetic medication, anaemia, or oral corticosteroid treatment were excluded. Age, BMI, and gender matched IGT subjects ( = 8) from a study of Venojärvi et al. [21] were included in the present study. There was no difference in maximal oxygen uptake (VO 2 max , mL/kg −1 /min −1 ) between the groups. The test was performed as previously described [21]. The characteristics of the subjects are shown in Table 1. The Ethical Committee of the Hospital District of South-West Finland, Turku, Finland, and the Ethical Committee of the Rehabilitation Research Centre of the Social Insurance Institution of Finland approved the protocol of this substudy. All subjects gave their written informed consent to participate.

Blood and Muscle Tissue

Samples. Blood samples for assessing the metabolic indices were drawn and skeletal muscle samples (m. vastus lateralis) were taken for determining the profile of myosin heavy chains (MHC), TRX and HSPs. Plasma glucose was assayed enzymatically with hexokinase (Olympus System Reagent, Hamburg, Germany) and serum insulin was analyzed with a radioimmunoassay (Pharmacia, Uppsala, Sweden). Insulin resistance was determined by using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and -cell function (HOMA-B), as described by Matthews et al. [23]. Haemoglobin A1c (HbA 1c ) concentration was assayed by using latex immunoagglutination inhibition methodology (DCA, 2000 Reagent Kit, Bayer Corporation, Elkhart, IN, USA). Muscle biopsies were taken in resting conditions (no heavy exercising during the preceding 2 days) from the vastus lateralis muscle under local anaesthesia (lidocaine 10 mg/mL). From each subject a percutaneous needle biopsy was performed using the conchotome technique [24] and biopsy materials were further divided into three equal portions. Muscle samples were immediately frozen in liquid nitrogen and stored at −70 ∘ C until analyzed. Samples for biochemical analyses were melted in ice-bath, weighed, and homogenized in 1 : 10 (w/v) of 1 M Tris buffer pH adjusted to 7.5, containing 5 mol/L dithiothreitol and 5 mol/L phenylmethylsulphoxide, using a manually operated all-glass small Potter-Elvehjem homogenizer. The muscle homogenates were centrifuged at 3000 g for 15 min at 4 ∘ C to obtain clear supernatants and stored in multiple aliquots to avoid repetitive thawing and freezing. We, however, did not observe any significant effect of repeated freezing and thawing on any of the Western blot signals.

Determination of Myosin Heavy Chain

Profile. The MHC profile consisting of the isoform composition of MHC I, MHC IIa, and MHC IIx was determined from muscle homogenate by using the SDS-PAGE gel electrophoresis and Bio-Rad Protean II Xi vertical slab gel system [25].

Western Blot

Assays. Skeletal muscle sample homogenates were stored at -70 ∘ C until analyzed. Western blot assays were performed as described previously [4,21]. Briefly, one-dimensional sodium dodecyl sulfate polyacrylamide (10%) gel electrophoresis was done to separate proteins according to their molecular weight. The blots were incubated overnight at +4 ∘ C with the following antibodies: anti-HSP72, anti-HSP-90 (Stressgen, Victoria, BC, Canada), and anti-Thioredoxin (IMCO Corp. AB, Stockholm, Sweden). Horseradish peroxidase-conjugated immunoglobulins were used as secondary antibodies. Antibody binding was viewed by using an enhanced chemiluminescence method (NEN Life Science Products, Boston, MA, USA) and quantified by using the image analysis software (NIHImage, MD, USA). The results were normalized according to beta-actin (Lab-Vision/NeoMarkers, Fremont, CA, USA) values. Coefficient of variation for intra-assay replicate samples was less than 7.3% and for interassay samples less than 11.0% for beta-actin signal in western blot analyses. A control sample was used in all the Westernblot runs. The protein concentration was measured in duplicate from the homogenates using BCA method (Pierce, Rockford, IL, USA), and equal amounts of the protein extracts (20 g) were loaded in the gels. For BCA assay coefficient of intra-and interassay variations was less than 2.10% and 7.94%, respectively (with mean CV% values of 1.36% and 4.34%, resp.). Equal loading and transfer of the western blot samples were further verified and compared with beta-actin by reversible total protein staining of the nitrocellulose membrane with Ponceau-S reversible membrane staining. Unless otherwise stated, all chemicals

2.5.

Statistics. Data are reported as means ± standard errors (SE). The Kruskal-Wallis test was used to assess differences between the groups. In addition, we applied analysis of covariance (ANCOVA) that was adjusted for HOMA-IR values to compare the difference in TRX-1 and HSP90 expression between IGT and T2D subjects. Pearson's correlation coefficients were used to express the associations between the variables.

Results

The T2D subjects had significantly higher fasting glucose ( = 0.004) and HbA 1c ( = 0.006) concentrations than the IGT subjects (Table 1). In contrast, the IGT subjects showed significantly higher fasting insulin ( = 0.009) and HOMA-IR ( = 0.020) values than the T2D subjects (Table 1). In the vastus lateralis muscle, the proportion of MHC I isoforms was higher ( = 0.001) and the proportion of MHC IIx isoforms was lower ( = 0.009) in the T2D than in the IGT subjects ( Table 1). The expression of TRX was 36.1% lower ( = 0.031) and the expression of HSP90 was 380% higher ( < 0.001) in the T2D than in the IGT subjects ( Figure 1). After adjusting the TRX and HSP90 expressions with HOMA-IR values, statistically significant differences were found between T2D and IGT subjects in HSP90 ( = 0.001). The expression of HSP72 was 10.9% higher in the IGT than in the T2D subjects, but the difference was not statistically significant ( = 0.170; Figure 1). The expression of TRX and HSP90 correlated positively in the IGT ( = 0.788; = 0.020) but not in the T2D subjects ( = −0.193; = 0.593). After adjustment with HOMA-IR, a positive correlation remained between the expressions of TRX and HSP90 in the IGT group ( = 0.777, = 0.45). No such correlation was found in the T2D subjects with or without adjustments ( = −0.221, = 0.568).

Discussion

The protein expression of thioredoxin-1 (TRX-1) in the vastus lateralis muscle was significantly lower in the type 2 diabetes (T2D) patients than in the IGT subjects. However, after adjustment with insulin resistance index (HOMA-IR), fasting glucose, or insulin levels (data not shown), the significant difference in TRX-1 values between groups disappeared. These observations may be interpreted as that both insulin resistance and hyperglycaemia increase oxidative stress and may be one reason behind the lower TRX-1 expression in the T2D subjects. Previous studies have shown that obese or type 2 diabetic subjects have a higher proportion of type IIb fibers (cf. MHC IIx) in skeletal muscle tissue [26,27], and, for the insulin sensitivity, the muscle fibers follow the order type I > type IIa > type IIb [28,29]. However, in our study IGT subjects had a similar MHC profile as in previous reports on obese or T2D subjects. Nevertheless HOMA-IR could be altered and significantly be higher in IGT than T2D subjects. Furthermore, while making a direct comparison of HOMA-IR between IGT and T2D subjects, it has to be kept in mind that T2D subjects are less insulin resistant but their insulin secretion is decreased compared to IGT subjects. In addition, HOMA-IR reflects better liver and whole body insulin resistance than skeletal muscle insulin resistance. A recent study demonstrated that compared to the age-matched healthy controls plasma and lymphocyte levels of TRX-1 were lower in T2D patients with nephropathy complication [16]. In previous studies serum, plasma, and lymphocyte levels of TRX-1 were significantly higher in T2D patients than in healthy controls [11,16]. However, TRX did not correlate with fasting blood sugar or haemoglobin A1c, although a significant correlation was found between TRX and fasting immunoreactive insulin in the diabetic patients treated with diet/exercise or oral hypoglycaemic agents [11]. In another study a significant correlation has been shown between decreasing serum TRX levels and impaired insulinogenic index, an indicator of pancreatic -cell function in response to hyperglycaemia which was induced by oral glucose loading, while such relationship between TRX levels and insulinogenic index has not been observed at baseline [12]. In contrast to these reports Miyamoto et al. [13] showed that in IGT and T2D patients high plasma concentration of TRX was an independent determinant of glucose intolerance and positively correlated with glycosylated haemoglobin, while no significant difference between IGT and T2D patients in plasma TRX-1 levels was observed. Most of these reports except that of Miyamoto et al. [13] support our results that T2D may lead to an imbalance of antioxidant defence and redox regulation through impaired TRX regulation. In addition, we found a positive correlation between TRX-1 and HSP90 levels in the IGT subject. In the compensated phase of increased oxidative stress (as rather in IGT than T2D), high ROS levels may stimulate synthesis of endogenous antioxidants including TRX and, simultaneously, as a metabolic stress factor, oxidative stress may also induce HSP expression. In T2D further downregulation of antioxidant protection and uncontrolled oxidative stress may cause a more oxidized thiol redox ratio; oxidation of TRX and GSH eventually results in a net loss of intracellular thiol pools which has also been described in other studies [3].

On the other hand, controversy in the literature on TRX responses to impaired glucose homeostasis and lack of difference between IGT and T2D patients could be explained by the fact that in all these studies peripheral levels of TRX-1 have been measured, which may not reflect precisely TRX response in skeletal muscle; the main side for insulin action. Therefore our results may suggest that impaired skeletal muscle TRX-1 response can be a better parameter or even a determinant of the insulin resistance through its function in the network of cytoprotection.

In the present study, the basal expression of HSP72 protein did not differ significantly between the T2D and the IGT subjects. The only study comparing HSP expression between IGT and T2D showed that identical twins with IGT did not have any statistically significant difference in skeletal muscle HSP72 mRNA levels compared with their diabetic cotwins, where HSP72 protein levels were not studied [14]. Nevertheless there is a wide variation in HSP72 responses to T2D and IGT in the literature, where tissue specific response could also be one of the reasons. HSP72 protein levels were reported to be increased in T2D patients in serum [30] and in lymphocytes [16] but HSP72 mRNA was lower in skeletal muscle [15]. Furthermore, HSP72 protein expression was lower in the skeletal muscle of insulin resistant obese humans compared to healthy controls [17]. On the other hand, although HSP70 levels were significantly higher in the patients with T2D for more than 5 years than in newly diagnosed patients, HSP70 was inversely correlated with fasting blood sugar in the patients with diabetes for more than 5 years [30]. These results suggest that although higher HSP levels can be a consequent and a marker of sustained metabolic stress, lower HSP levels may contribute to disrupted glucose homeostasis.

Regarding the association of TRX with HSPs, Calabrese et al. [16] have recently shown that plasma and lymphocyte levels of TRX-1 were lower and lymphocyte inducible HSP70 levels were higher in T2D patients compared to the age-matched healthy controls. In contrast, in our study, we observed a significant decrease of TRX-1 parallel with a nonsignificant (10.9%) decrease in HSP72 levels in skeletal muscle of T2D patients than IGT subjects. Besides the characteristics of different tissues and methodological aspects, the variations in the results can also be explained by the rather large difference in the muscle-fibre composition: myosin heavy chain (MHC) profile between the T2D and the IGT subjects. Thus, it can be assumed that the expression of HSP72 is dependent on the type of muscle MHC profile [31] thereby reflecting the state of insulin resistance. This assumption is supported by the findings of Locke et al. [32], showing that the expression of HSP72 in the basal state is higher in type I fibres than type II fibres in rat skeletal muscle. Similarly heat shock protein (HSP) 72 expression was higher in soleus muscle, composed of type I fibres, and compared with epitrochlearis muscle of type II fibres in Fiscer 344 rats [33]. In addition, training status, physical activity levels, thermal exposure history, energy availability, sex, and age are possible factors that affect the baseline levels of HSP [34]. The aerobic performance capacity of IGT and T2D subjects was similar as their age and BMI. It would strengthen our results significantly if we could have additional information on the training status, thermal exposure history, and energy availability of both study groups. In addition, lack of comparison to healthy normoglycemic subjects is an important weakness of this study, and also a more direct comparison could be possible if the subjects would have similar MCH profile in IGT and T2D groups. In order to confirm the role of the TRX system in the pathogenesis of diabetes, future studies should be designed with larger populations, comparing both thioredoxin related proteins and HSP proteins in different states and phases of impaired glucose regulation in animals as well as in humans.

The HSP90 expression was markedly higher in the vastus lateralis muscle in the T2D than in the IGT subjects. HSP90 is expressed in the cytosol, nucleus, and endoplasmic reticulum [35] and plays several physiological roles, such as mediating the stability of protein kinase B (PKB/AKT) and maturation of tyrosine kinase receptors [35][36][37]. Højlund et al. [38] reported increased HSP90 levels in the skeletal muscle of diabetic subjects, but they did not find any correlation between fasting plasma glucose and HSP90 levels. This may be due to the fact that HSP90 contributes to maintain the activity of protein kinase B, which participates in the regulation of both insulin-stimulated glucose transport and glycogen synthesis [39]. It seems that the expression of HSP90 is markedly upregulated in human skeletal muscle in T2D or corresponding conditions. In future studies, it would be interesting to investigate the causes and consequences of increased HSP levels in diabetes and, more specifically, its relationship to impaired glucose regulation and protein kinase B signalling in animals and humans.

To summarize, in the basal state of skeletal muscle tissue, the protein expression of TRX-1 was significantly lower while that of HSP90 was significantly higher in the T2D patients than in the IGT subjects. Decreased skeletal muscle TRX levels in T2D may lead to an imbalance in the antioxidant defence mechanisms and weaken the HSP protection, which may be associated with the progression of IGT to T2D. Because HSP90 may be involved in the insulin signalling pathway in T2D muscles or can be a consequence of type 2 diabetes and may be sign of oxidative stress which is induced by chronic hyperglycaemia, our results may potentially be important for diabetes research.

Figure 1 :
1Protein expressions of HSP72, HSP90, and TRX in the vastus lateralis muscle of the impaired glucose tolerance (IGT) and type 2 diabetic (T2D) subjects. Data are given as means ± SE. * < 0.05, * * * < 0.001 between groups. Representative Western blots, using anti-HSP72, anti-HSP90, anti-TRX, and beta-actin antibodies, of whole tissue homogenates from skeletal muscle biopsies of two IGT and T2D subjects. (a.u.) arbitrary units. and reagents were obtained from Sigma-Aldrich (St. Louis, MO) and were of analytic grade or the highest grade available.

Table 1 :
1Characteristics of the subjects, myosin heavy chain profiles, and variables of glucose metabolism.Data are given as means ± SE. * HbA 1c values (in mmol) are 39 and 46 mmol/mol in the IGT and T2D groups respectively (IFCC-HbA 1c ).Characteristics
IGT
T2D
P value
n = (female/male)
8 (4/4)
10 (4/6)
Age, year
61.4 ± 2.8
62.5 ± 1.6
0.240
BMI
29.7 ± 0.9
29.0 ± 0.5
0.366
VO 2 max , mL/kg −1 /min −1
26.1 ± 2.8
26.5 ± 1.9
0.907
Myosin heavy chain profile
MHC I, %
37.3 ± 2.0
58.1 ± 5.0
0.001
MHC IIa, %
42.9 ± 2.8
34.8 ± 4.7
0.081
MHC IIx, %
19.8 ± 2.6
7.0 ± 2.1
0.009
Blood chemistry
Fp-glucose, mmol/L
5.6 ± 0.2
7.1 ± 0.3
0.004
S-insulin, U/mL
15.4 ± 3.3
8.4 ± 0.5
0.009
HbA 1c , %  *
5.7 ± 0.2
6.4 ± 0.1
0.006
HOMA-IR
5.0 ± 0.9
2.7 ± 0.2
0.020
HOMA-beta %
50.2 ± 5.4
20.3 ± 1.4
0.001

AcknowledgmentsThe authors wish to express their gratitude to Mrs. Minna Glad for the excellent technical assistance. The study was supported by grants from the Research Council for Physical Education and Sports, the Finnish Ministry of Education, the Juho Vainio Foundation, the National Doctoral Programme of Musculoskeletal Disorders and Biomaterials, the Turku University of Applied Sciences R&D program, and the COST Actions CM1001 and TD1304.Conflict of InterestsThe authors declare that there is no conflict of interests regarding the publication of this paper.
Diabetes, oxidative stress and physical exercise. M Atalay, D E Laaksonen, Journal of Sports Science and Medicine. 11M. Atalay and D. E. Laaksonen, "Diabetes, oxidative stress and physical exercise, " Journal of Sports Science and Medicine, vol. 1, no. 1, pp. 11-14, 2002.

Radical-free biology of oxidative stress. D P Jones, American Journal of Physiology: Cell Physiology. 2954D. P. Jones, "Radical-free biology of oxidative stress, " American Journal of Physiology: Cell Physiology, vol. 295, no. 4, pp. C849- C868, 2008.

Oxygen consumption and usage during physical exercise: the balance between oxidative stress and ROS-dependent adaptive signaling. Z Radak, Z Zhao, E Koltai, H Ohno, M Atalay, Antioxidants & Redox Signaling. 1810Z. Radak, Z. Zhao, E. Koltai, H. Ohno, and M. Atalay, "Oxygen consumption and usage during physical exercise: the balance between oxidative stress and ROS-dependent adaptive signal- ing, " Antioxidants & Redox Signaling, vol. 18, no. 10, pp. 1208- 1246, 2013.

Exercise training modulates heat shock protein response in diabetic rats. M Atalay, N K J Oksala, D E Laaksonen, Journal of Applied Physiology. 972M. Atalay, N. K. J. Oksala, D. E. Laaksonen et al., "Exercise training modulates heat shock protein response in diabetic rats, " Journal of Applied Physiology, vol. 97, no. 2, pp. 605-611, 2004.

Inhibiting heat-shock protein 90 reverses sensory hypoalgesia in diabetic mice. M J Urban, C Li, C Yu, ID e00040American Society for Neurochemistry. 24M. J. Urban, C. Li, C. Yu et al., "Inhibiting heat-shock protein 90 reverses sensory hypoalgesia in diabetic mice, " American Society for Neurochemistry, vol. 2, no. 4, Article ID e00040, 2010.

Peroxiredoxins. B Hofmann, H.-J Hecht, L Flohé, Biological Chemistry. 3833-4B. Hofmann, H.-J. Hecht, and L. Flohé, "Peroxiredoxins, " Biological Chemistry, vol. 383, no. 3-4, pp. 347-364, 2002.

Thioredoxin and related molecules-from biology to health and disease. C H Lillig, A Holmgren, Antioxidants and Redox Signaling. 91C. H. Lillig and A. Holmgren, "Thioredoxin and related molecules-from biology to health and disease, " Antioxidants and Redox Signaling, vol. 9, no. 1, pp. 25-47, 2007.

Thioredoxin system in cell death progression. J Lu, A Holmgren, Antioxidants & Redox Signaling. 1712J. Lu and A. Holmgren, "Thioredoxin system in cell death progression, " Antioxidants & Redox Signaling, vol. 17, no. 12, pp. 1738-1747, 2012.

Oxidoreductive regulation of nuclear factor B. Involvement of a cellular reducing catalyst thioredoxin. T Hayashi, Y Ueno, T Okamoto, The Journal of Biological Chemistry. 26815T. Hayashi, Y. Ueno, and T. Okamoto, "Oxidoreductive regula- tion of nuclear factor B. Involvement of a cellular reducing catalyst thioredoxin, " The Journal of Biological Chemistry, vol. 268, no. 15, pp. 11380-11388, 1993.

Treatments with sodium selenate or doxycycline offset diabetes-induced perturbations of thioredoxin-1 levels and antioxidant capacity. M Atalay, A Bilginoglu, T Kokkola, N Oksala, B Turan, Molecular and Cellular Biochemistry. 3511-2M. Atalay, A. Bilginoglu, T. Kokkola, N. Oksala, and B. Turan, "Treatments with sodium selenate or doxycycline offset diabetes-induced perturbations of thioredoxin-1 levels and antioxidant capacity, " Molecular and Cellular Biochemistry, vol. 351, no. 1-2, pp. 125-131, 2011.

Elevation of serum thioredoxin levels in patients with type 2 diabetes. Y Kakisaka, T Nakashima, Y Sumida, Hormone and Metabolic Research. 343Y. Kakisaka, T. Nakashima, Y. Sumida et al., "Elevation of serum thioredoxin levels in patients with type 2 diabetes, " Hormone and Metabolic Research, vol. 34, no. 3, pp. 160-164, 2002.

Pancreatic Bcell function is altered by oxidative stress induced by acute hyperglycaemia. Y Miyazaki, H Kawano, T Yoshida, Diabetic Medicine. 242Y. Miyazaki, H. Kawano, T. Yoshida et al., "Pancreatic B- cell function is altered by oxidative stress induced by acute hyperglycaemia, " Diabetic Medicine, vol. 24, no. 2, pp. 154-160, 2007.

Increased plasma levels of thioredoxin in patients with glucose intolerance. S Miyamoto, H Kawano, J Hokamaki, Internal Medicine. 4411S. Miyamoto, H. Kawano, J. Hokamaki et al., "Increased plasma levels of thioredoxin in patients with glucose intolerance, " Internal Medicine, vol. 44, no. 11, pp. 1127-1132, 2005.

Decreased expression of heat shock protein 72 in skeletal muscle of patients with type 2 diabetes correlates with insulin resistance. I Kurucz, Á Morva, A Vaag, Diabetes. 514I. Kurucz,Á. Morva, A. Vaag et al., "Decreased expression of heat shock protein 72 in skeletal muscle of patients with type 2 diabetes correlates with insulin resistance, " Diabetes, vol. 51, no. 4, pp. 1102-1109, 2002.

Intramuscular heat shock protein 72 and heme oxygenase-1 mRNA are reduced in patients with type 2 diabetes: evidence that insulin resistance is associated with a disturbed antioxidant defense mechanism. C R Bruce, A L Carey, J A Hawley, M A Febbraio, Diabetes. 529C. R. Bruce, A. L. Carey, J. A. Hawley, and M. A. Febbraio, "Intramuscular heat shock protein 72 and heme oxygenase-1 mRNA are reduced in patients with type 2 diabetes: evidence that insulin resistance is associated with a disturbed antioxidant defense mechanism, " Diabetes, vol. 52, no. 9, pp. 2338-2345, 2003.

Oxidative stress, glutathione status, sirtuin and cellular stress response in type 2 diabetes. V Calabrese, C Cornelius, V Leso, Biochimica et Biophysica Acta. 18225V. Calabrese, C. Cornelius, V. Leso et al., "Oxidative stress, glutathione status, sirtuin and cellular stress response in type 2 diabetes, " Biochimica et Biophysica Acta, vol. 1822, no. 5, pp. 729-736, 2012.

HSP72 protects against obesity-induced insulin resistance. J Chung, A.-K Nguyen, D C Henstridge, Proceedings of the National Academy of Sciences of the United States of America. the National Academy of Sciences of the United States of America105J. Chung, A.-K. Nguyen, D. C. Henstridge et al., "HSP72 protects against obesity-induced insulin resistance, " Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 5, pp. 1739-1744, 2008.

Rosiglitazone but not metformin enhances insulin-and exercise-stimulated skeletal muscle glucose uptake in patients with newly diagnosed type 2 diabetes. K Hällsten, K A Virtanen, F Lönnqvist, Diabetes. 5112K. Hällsten, K. A. Virtanen, F. Lönnqvist et al., "Rosiglitazone but not metformin enhances insulin-and exercise-stimulated skeletal muscle glucose uptake in patients with newly diagnosed type 2 diabetes, " Diabetes, vol. 51, no. 12, pp. 3479-3485, 2002.

Prevention of Type II diabetes in subjects with impaired glucose tolerance: the Diabetes Prevention Study (DPS) in Finland. Study design and 1-year interim report on the feasibility of the lifestyle intervention programme. J Eriksson, J Lindström, T Valle, Diabetologia. 427J. Eriksson, J. Lindström, T. Valle et al., "Prevention of Type II diabetes in subjects with impaired glucose tolerance: the Diabetes Prevention Study (DPS) in Finland. Study design and 1-year interim report on the feasibility of the lifestyle intervention programme, " Diabetologia, vol. 42, no. 7, pp. 793- 801, 1999.

Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. J Tuomilehto, J Lindström, J G Eriksson, The New England Journal of Medicine. 34418J. Tuomilehto, J. Lindström, J. G. Eriksson et al., "Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance, " The New England Journal of Medicine, vol. 344, no. 18, pp. 1343-1350, 2001.

Exercise training with dietary counselling increases mitochondrial chaperone expression in middle-aged subjects with impaired glucose tolerance. M Venojärvi, S Aunola, R Puhke, BMC Endocrine Disorders. 8M. Venojärvi, S. Aunola, R. Puhke et al., "Exercise training with dietary counselling increases mitochondrial chaperone expression in middle-aged subjects with impaired glucose tolerance, " BMC Endocrine Disorders, vol. 8, article 3, 2008.

Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. K G Alberti, P Z Zimmet, Diabetic Medicine. 157K. G. Alberti and P. Z. Zimmet, "Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation, " Diabetic Medicine, vol. 15, no. 7, pp. 539-553, 1998.

Homeostasis model assessment: insulin resistance and -cell function from fasting plasma glucose and insulin concentrations in man. D R Matthews, J P Hosker, A S Rudenski, Diabetologia. 287D. R. Matthews, J. P. Hosker, and A. S. Rudenski, "Homeostasis model assessment: insulin resistance and -cell function from fasting plasma glucose and insulin concentrations in man, " Diabetologia, vol. 28, no. 7, pp. 412-419, 1985.

Semi-open' muscle biopsy technique. A simple outpatient procedure. K G Henriksson, Acta Neurologica Scandinavica. 596K. G. Henriksson, "'Semi-open' muscle biopsy technique. A simple outpatient procedure, " Acta Neurologica Scandinavica, vol. 59, no. 6, pp. 317-323, 1979.

Role of skeletal muscle-fibre type in regulation of glucose metabolism in middle-aged subjects with impaired glucose tolerance during a long-term exercise and dietary intevention. M Venojärvi, R Puhke, H Hämäläinen, Diabetes, Obesity and Metabolism. 76M. Venojärvi, R. Puhke, H. Hämäläinen et al., "Role of skele- tal muscle-fibre type in regulation of glucose metabolism in middle-aged subjects with impaired glucose tolerance during a long-term exercise and dietary intevention, " Diabetes, Obesity and Metabolism, vol. 7, no. 6, pp. 745-754, 2005.

The insulin action-fiber type relationship in humans is muscle group specific. M S Hickey, M D Weidner, K E Gavigan, D Zheng, G L Tyndall, J A Houmard, American Journal of Physiology: Endocrinology and Metabolism. 2691M. S. Hickey, M. D. Weidner, K. E. Gavigan, D. Zheng, G. L. Tyndall, and J. A. Houmard, "The insulin action-fiber type relationship in humans is muscle group specific, " American Journal of Physiology: Endocrinology and Metabolism, vol. 269, no. 1, pp. E150-E154, 1995.

Muscle fiber type is associated with obesity and weight loss. C J Tanner, H A Barakat, G Lynis Dohm, American Journal of Physiology: Endocrinology and Metabolism. 2826C. J. Tanner, H. A. Barakat, G. Lynis Dohm et al., "Muscle fiber type is associated with obesity and weight loss, " American Journal of Physiology: Endocrinology and Metabolism, vol. 282, no. 6, pp. E1191-E1196, 2002.

Heterogeneity of insulin action in individual muscles in vivo: euglycemic clamp studies in rats. D E James, A B Jenkins, E W Kraegen, The American Journal of Physiology. 2485D. E. James, A. B. Jenkins, and E. W. Kraegen, "Heterogeneity of insulin action in individual muscles in vivo: euglycemic clamp studies in rats, " The American Journal of Physiology, vol. 248, no. 5, pp. E567-E574, 1985.

Skeletal muscle lipid content and oxidative enzyme activity in relation to muscle fiber type in type 2 diabetes and obesity. J He, S Watkins, D E Kelley, Diabetes. 504J. He, S. Watkins, and D. E. Kelley, "Skeletal muscle lipid content and oxidative enzyme activity in relation to muscle fiber type in type 2 diabetes and obesity, " Diabetes, vol. 50, no. 4, pp. 817-823, 2001.

Increased serum HSP70 levels are associated with the duration of diabetes. M Nakhjavani, A Morteza, L Khajeali, Cell Stress and Chaperones. 156M. Nakhjavani, A. Morteza, L. Khajeali et al., "Increased serum HSP70 levels are associated with the duration of diabetes, " Cell Stress and Chaperones, vol. 15, no. 6, pp. 959-964, 2010.

Muscle fiber type-specific response of Hsp70 expression in human quadriceps following acute isometric exercise. A R Tupling, E Bombardier, R D Stewart, C Vigna, A E Aqui, Journal of Applied Physiology. 1036A. R. Tupling, E. Bombardier, R. D. Stewart, C. Vigna, and A. E. Aqui, "Muscle fiber type-specific response of Hsp70 expression in human quadriceps following acute isometric exercise, " Journal of Applied Physiology, vol. 103, no. 6, pp. 2105- 2111, 2007.

Inducible isoform of HSP70 is constitutively expressed in a muscle fiber type specific pattern. M Locke, E G Noble, B G Atkinson, American Journal of Physiology: Cell Physiology. 2615M. Locke, E. G. Noble, and B. G. Atkinson, "Inducible isoform of HSP70 is constitutively expressed in a muscle fiber type specific pattern, " American Journal of Physiology: Cell Physiology, vol. 261, no. 5, pp. C774-C779, 1991.

Age-related differences in skeletal muscle insulin signaling: the role of stress kinases and heat shock proteins. A A Gupte, G L Bomhoff, P C Geiger, Journal of Applied Physiology. 1053A. A. Gupte, G. L. Bomhoff, and P. C. Geiger, "Age-related differences in skeletal muscle insulin signaling: the role of stress kinases and heat shock proteins, " Journal of Applied Physiology, vol. 105, no. 3, pp. 839-848, 2008.

The Exercise-Induced stress response of skeletal muscle, with specific emphasis on humans. J P Morton, A C Kayani, A Mcardle, B Drust, Sports Medicine. 398J. P. Morton, A. C. Kayani, A. McArdle, and B. Drust, "The Exercise-Induced stress response of skeletal muscle, with spe- cific emphasis on humans, " Sports Medicine, vol. 39, no. 8, pp. 643-662, 2009.

Akt forms an intracellular complex with heat shock protein 90 (Hsp90) and Cdc37 and is destabilized by inhibitors of Hsp90 function. A D Basso, D B Solit, G Chiosis, B Giri, P Tsichlis, N Rosen, Journal of Biological Chemistry. 27742A. D. Basso, D. B. Solit, G. Chiosis, B. Giri, P. Tsichlis, and N. Rosen, "Akt forms an intracellular complex with heat shock protein 90 (Hsp90) and Cdc37 and is destabilized by inhibitors of Hsp90 function, " Journal of Biological Chemistry, vol. 277, no. 42, pp. 39858-39866, 2002.

The 90-kDa molecular chaperone family: structure, function, and clinical applications. A comprehensive review. P Csermely, T Schnaider, C Soti, Z Prohászka, G Nardai, Pharmacology and Therapeutics. 792P. Csermely, T. Schnaider, C. Soti, Z. Prohászka, and G. Nardai, "The 90-kDa molecular chaperone family: structure, function, and clinical applications. A comprehensive review, " Pharmacology and Therapeutics, vol. 79, no. 2, pp. 129-168, 1998.

Physiological regulation of Akt activity and stability. Y Liao, M.-C Hung, American Journal of Translational Research. 21Y. Liao and M.-C. Hung, "Physiological regulation of Akt activ- ity and stability, " American Journal of Translational Research, vol. 2, no. 1, pp. 19-42, 2010.

Proteome analysis reveals phosphorylation of ATP synthase -subunit in human skeletal muscle and proteins with potential roles in type 2 diabetes. K Højlund, K Wrzesinski, P M Larsen, Journal of Biological Chemistry. 27812K. Højlund, K. Wrzesinski, P. M. Larsen et al., "Proteome analysis reveals phosphorylation of ATP synthase -subunit in human skeletal muscle and proteins with potential roles in type 2 diabetes, " Journal of Biological Chemistry, vol. 278, no. 12, pp. 10436-10442, 2003.

Heat shock proteins in diabetes and wound healing. M Atalay, N Oksala, J Lappalainen, D E Laaksonen, C K Sen, S Roy, Current Protein and Peptide Science. 101M. Atalay, N. Oksala, J. Lappalainen, D. E. Laaksonen, C. K. Sen, and S. Roy, "Heat shock proteins in diabetes and wound healing, " Current Protein and Peptide Science, vol. 10, no. 1, pp. 85-95, 2009.