Abstracting and Indexing

  • Google Scholar
  • CrossRef
  • WorldCat
  • ResearchGate
  • Academic Keys
  • DRJI
  • Microsoft Academic
  • Academia.edu
  • OpenAIRE

Hyperglycemia and FEP: Does Migration Status Matter?

Article Information

Gianmarco Iuzzolino2, Giuseppe D’Andrea2*, Tiziano De Matteis2, Lorenzo Guidi1, Domenico Berardi2, Ilaria Tarricone1,3

1Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy

2Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy

3Department of Mental Health, AUSL Bologna, Bologna, Italy

*Corresponding Author: Dr. Giuseppe D’Andrea, Giuseppe D’Andrea, Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy, University of Bologna, Via Ugo Foscolo 7, 40123, Bologna, Italy

Received: 18 December 2019; Accepted: 02 January 2020; Published: 15 January 2020

Citation:

Gianmarco Iuzzolino, Giuseppe D’Andrea, Tiziano De Matteis, Lorenzo Guidi, Domenico Berardi, Ilaria Tarricone. Hyperglycemia and FEP: Does Migration Status Matter?. Journal of Psychiatry and Psychiatric Disorders 4 (2020): 1-9.

View / Download Pdf Share at Facebook

Abstract

Background: Several evidence have shown hyperglycemia and diabetes are frequent in patients with psychoses. There are evidences that some ethnic minorities are at higher risk of psychosis. It is less clear if migration history is a risk factor for diabetes and hyperglycemia during first-episode psychosis (FEP). The present study aims to evaluate if migration history might influence fasting plasma glucose change during antipsychotic treatment (APs) in (FEP).

Materials and Methods: We carried out a retrospective follow-up of all FEP drug na?ve patients at their first contact with Bologna West Community Mental Health Centres from January 2010 to December 2015. Blood tests carried out during the follow-up period were collated from clinical charts to evaluate the baseline fasting plasma glucose level upon starting APs treatment and at the time of follow-up. Out of 50 patients who had FEP during the recruitment period and consented to take part in the study, 25 cases had blood test results available at follow-up. We performed linear multivariate regression analysis to adjust the association between migrant status and fasting plasma glucose level of follow-up by gender, age, education, employment, APs treatment and dose.

Results: At baseline, the mean fasting plasma glucose level was within the normal range and at follow-up we observed a significant increase in the mean fasting plasma glucose in migrants. Upon multivariate linear regression analysis, migration history remained significantly associated with the follow-up fasting plasma glucose level.

Conclusions: In conclusion, we found that migrants with FEP are particularly at risk of developing hyperglycemia and type II diabetes during APs treatment.

Keywords

Antipsychotic agents; Side effects; Diabetes; Hyperglycemia; Migration; Follow up; First-episode psychosis

Antipsychotic agents articles, Side effects articles, Diabetes articles, Hyperglycemia articles, Migration articles, Follow up articles, First-episode psychosis articles

Hyperglycemia articles Hyperglycemia Research articles Hyperglycemia review articles Hyperglycemia PubMed articles Hyperglycemia PubMed Central articles Hyperglycemia 2023 articles Hyperglycemia 2024 articles Hyperglycemia Scopus articles Hyperglycemia impact factor journals Hyperglycemia Scopus journals Hyperglycemia PubMed journals Hyperglycemia medical journals Hyperglycemia free journals Hyperglycemia best journals Hyperglycemia top journals Hyperglycemia free medical journals Hyperglycemia famous journals Hyperglycemia Google Scholar indexed journals " first-episode psychosis  articles first-episode psychosis  Research articles first-episode psychosis  review articles first-episode psychosis  PubMed articles first-episode psychosis  PubMed Central articles first-episode psychosis  2023 articles first-episode psychosis  2024 articles first-episode psychosis  Scopus articles first-episode psychosis  impact factor journals first-episode psychosis  Scopus journals first-episode psychosis  PubMed journals first-episode psychosis  medical journals first-episode psychosis  free journals first-episode psychosis  best journals first-episode psychosis  top journals first-episode psychosis  free medical journals first-episode psychosis  famous journals first-episode psychosis  Google Scholar indexed journals antipsychotic treatment  articles antipsychotic treatment  Research articles antipsychotic treatment  review articles antipsychotic treatment  PubMed articles antipsychotic treatment  PubMed Central articles antipsychotic treatment  2023 articles antipsychotic treatment  2024 articles antipsychotic treatment  Scopus articles antipsychotic treatment  impact factor journals antipsychotic treatment  Scopus journals antipsychotic treatment  PubMed journals antipsychotic treatment  medical journals antipsychotic treatment  free journals antipsychotic treatment  best journals antipsychotic treatment  top journals antipsychotic treatment  free medical journals antipsychotic treatment  famous journals antipsychotic treatment  Google Scholar indexed journals Community Mental Health Centres articles Community Mental Health Centres Research articles Community Mental Health Centres review articles Community Mental Health Centres PubMed articles Community Mental Health Centres PubMed Central articles Community Mental Health Centres 2023 articles Community Mental Health Centres 2024 articles Community Mental Health Centres Scopus articles Community Mental Health Centres impact factor journals Community Mental Health Centres Scopus journals Community Mental Health Centres PubMed journals Community Mental Health Centres medical journals Community Mental Health Centres free journals Community Mental Health Centres best journals Community Mental Health Centres top journals Community Mental Health Centres free medical journals Community Mental Health Centres famous journals Community Mental Health Centres Google Scholar indexed journals duration of untreated psychosis articles duration of untreated psychosis Research articles duration of untreated psychosis review articles duration of untreated psychosis PubMed articles duration of untreated psychosis PubMed Central articles duration of untreated psychosis 2023 articles duration of untreated psychosis 2024 articles duration of untreated psychosis Scopus articles duration of untreated psychosis impact factor journals duration of untreated psychosis Scopus journals duration of untreated psychosis PubMed journals duration of untreated psychosis medical journals duration of untreated psychosis free journals duration of untreated psychosis best journals duration of untreated psychosis top journals duration of untreated psychosis free medical journals duration of untreated psychosis famous journals duration of untreated psychosis Google Scholar indexed journals metabolic glycaemia  articles metabolic glycaemia  Research articles metabolic glycaemia  review articles metabolic glycaemia  PubMed articles metabolic glycaemia  PubMed Central articles metabolic glycaemia  2023 articles metabolic glycaemia  2024 articles metabolic glycaemia  Scopus articles metabolic glycaemia  impact factor journals metabolic glycaemia  Scopus journals metabolic glycaemia  PubMed journals metabolic glycaemia  medical journals metabolic glycaemia  free journals metabolic glycaemia  best journals metabolic glycaemia  top journals metabolic glycaemia  free medical journals metabolic glycaemia  famous journals metabolic glycaemia  Google Scholar indexed journals mood stabilizers articles mood stabilizers Research articles mood stabilizers review articles mood stabilizers PubMed articles mood stabilizers PubMed Central articles mood stabilizers 2023 articles mood stabilizers 2024 articles mood stabilizers Scopus articles mood stabilizers impact factor journals mood stabilizers Scopus journals mood stabilizers PubMed journals mood stabilizers medical journals mood stabilizers free journals mood stabilizers best journals mood stabilizers top journals mood stabilizers free medical journals mood stabilizers famous journals mood stabilizers Google Scholar indexed journals antidepressants articles antidepressants Research articles antidepressants review articles antidepressants PubMed articles antidepressants PubMed Central articles antidepressants 2023 articles antidepressants 2024 articles antidepressants Scopus articles antidepressants impact factor journals antidepressants Scopus journals antidepressants PubMed journals antidepressants medical journals antidepressants free journals antidepressants best journals antidepressants top journals antidepressants free medical journals antidepressants famous journals antidepressants Google Scholar indexed journals Diabetes articles Diabetes Research articles Diabetes review articles Diabetes PubMed articles Diabetes PubMed Central articles Diabetes 2023 articles Diabetes 2024 articles Diabetes Scopus articles Diabetes impact factor journals Diabetes Scopus journals Diabetes PubMed journals Diabetes medical journals Diabetes free journals Diabetes best journals Diabetes top journals Diabetes free medical journals Diabetes famous journals Diabetes Google Scholar indexed journals stress articles stress Research articles stress review articles stress PubMed articles stress PubMed Central articles stress 2023 articles stress 2024 articles stress Scopus articles stress impact factor journals stress Scopus journals stress PubMed journals stress medical journals stress free journals stress best journals stress top journals stress free medical journals stress famous journals stress Google Scholar indexed journals

Article Details

1. Introduction

Migrant patients are at higher risk of developing hyperglycemia and diabetes than natives (Marchesini et al. 2014; Oldroyd et al. 2005). Several risk factors have been hypothesized to be involved, including changes in life style. Moving from a frugal nutrition to a hypercaloric one (so- called pattern four transition) and reducing the amount of physical activity (Misra and Ganda 2007) are considered prime reasons. Moreover, urbanization (Katchunga et al. 2012), psychophysical stress, low socio-economic status and poor access to the national health system (Lanting et al. 2005) contribute to increase the risk of developing hyperglycemia and diabetes. Among the general population, the prevalence of diabetes is higher in men than women (Menke et al. 2015). However, the female gender has a higher risk of developing type II diabetes in certain conditions, such as low socio-economic status (Rivera et al. 2015), psycho-social stress factors or work-related ones (Heraclides et al. 2012; Krajnak 2014), and an unhealthy life style (hypercaloric diet and low physical activity) (Hare-Bruun et al. 2006).

The risk of developing diabetes is also higher (2 to 5 times) among people suffering from psychotic disorders (Ward and Druss, 2015). One possible explanation is linked to risk factors similar to those previously reported for migrants, such as unhealthy life style and low socio-economic status (Mamakou et al. 2018). In addition, patients suffering from psychotic disorders have specific risk factors that might have additive or even synergistic effects: many evidences point to both psychotic disorders and diabetes sharing a similar genetic susceptibility (Mamakou et al. 2018). Antipsychotic medications (APs) have been associated with an increased risk of developing metabolic side effects, including hyperglycemia and Type II diabetes. Such medications seem to contribute both indirectly, through weight gain, or directly, by promoting insulin resistance (Correll et al. 2015, Scheen and De Hert 2007). There are specific subpopulations of individuals at higher risk of developing diabetes during treatment with APs medications. African-American and Hispanic ethnicity seems to be associated with an increased prevalence of diabetes in both patients with psychosis and the general population (Dixon et al. 2000). Other studies support these results (Voruganti et al. 2007), and also report that European ethnicity is associated with a decreased risk of developing diabetes. What is less clear is the relation between APs treatment and Hyperglycemia in migrants.

Our study aimed to evaluate the glycaemia changes of an incidence sample of first episode psychosis patients in Bologna West (Northern Italy). We especially aimed to evaluate the impact of migration history on the risk of developing hyperglycemia during Aps treatment in FEP patients.

2. Materials and Methods

2.1 Participants

This study is part of the Bologna West First Episode Psychosis project (Bo-FEP). As described in our previous work (Tarricone et al. 2012), Bo-FEP is a naturalistic incidence study that included all patients between 18 and 64 years, drug na?ve upon FEP, who had a contact with one of the three Community Mental Health Centres (CMHCs) of the West Bologna area (CMHC “Nani”, “Tiarini” and “Scalo”). The inclusion period for the current study was from January 2010 to December 2015. The first episode psychosis was defined, according to APA 2004 criteria, as presence of delusions and/or hallucinations and/or disorganized speech and/or excited and/or catatonic behaviour. The diagnosis was defined according to International Statistical Classification of Disease and Related Health Problems, Tenth Revision (ICD-10) criteria. The following exclusion criteria were applied: I) patients with previous contacts with mental health services or a history of previous antipsychotic treatment; II) evidence of psychotic disorders due to organic causes or acute intoxications.

2.2 Study design

We carried out a retrospective follow-up in November 2016 of all FEP drug na?ve patients at their first contact with BoWest CMHC from January 2010 to December 2015. The sociodemographic and clinical characteristics (psychiatric diagnoses, duration of untreated psychosis [DUP], kind of APs and other pharmacological treatment) were evaluated from the clinical charts and from the local computerized information system (SIT) and discussed with the clinicians responsible for the patients. The diagnoses were coded with ICD-10 system and were divided in non-affective (ICD-10 codes F20-F29) and affective psychoses (ICD-10 codes F30-F33). Blood tests carried out during the follow-up period were collated from clinical charts to evaluate the baseline fasting plasma glucose level upon starting APs treatment and at the time of follow-up (November 2016). The blood tests were done as part of clinical practice, to monitor metabolic changes during treatment. Fasting plasma glucose, triglycerides and cholesterol levels were determined by enzymatic procedures applying the Roche/Hitachi Modular D-P automated chemistry analyser and using the standard analytical system packs Glucose/God-pap, Cholesterol/CHOD cod-pap and Triglycerides/GPOpap. Abnormal glycaemia levels were defined based on the National Cholesterol Education Program (McIntyre et al. 2003) and World Health Organisation (Alberti and Zimmet 1998) criteria as follows: blood glucose ≥110 mg/dl for hyperglycemia and ≥126 mg/dl for diabetes. SPSS for Windows (version 23.0) was employed for statistical analyses.

The study design did not affect the clinical routine: the choice of antipsychotic and the dosage were entirely left to the treating psychiatrists. APs prescribed were divided into typical (e.g. Haloperidol, Fluphenazine, etc) and atypical (Risperidone, Aripiprazole, Olanzapine, Quetiapine and Clozapine). Chlorpromazine-equivalent doses were calculated based on standardized methods for comparing exposure to different APs drugs (Andreasen et al. 2010). The patients did not follow a standard exercise or diet regimen and were looked after by their clinicians as part of the usual care program. This study was performed with the approval of the Local Health Ethical Committee and informed consent was obtained from eligible patients.

2.3 Statistical analyses

We used parametric test procedures, the distribution of dependent variables being normal. The baseline mean fasting plasma glucose variance according to socio-demographic and clinical characteristics was investigated by an independent sample t-test. We used a dependent t-test to examine the time course of mean metabolic glycaemia values between baseline and follow-up. Finally, we performed linear multivariate regression analysis to adjust the association between migrant status and glycaemia level at follow up by gender, age, education, employment, APs treatment and dose.

3. Results

3.1 Sample description

Out of 105 patients who had contacts with CMHCs for FEP between 2010 and 2015, 50 agreed to take part in the study and 25 had blood test results available at follow-up (2016) and were therefore included. The follow-up period varied from patient to patient with a mean of 44.04 ± 17.75 months (range 16-82). All participants were treated with APs. Eleven patients also received antidepressants (AD) or mood stabilizers (MS). The sample’s socio-demographic and clinical characteristics at baseline are described in Table 1. Fifty-two percent were men, had a mean age at onset of 28.56 years and were on average two years older at first contact with the CMHC, 32% were migrants (12% Asian, 12% African, 4% Hispanic and 4% European). Forty-eight percent were single and 52% had a high school diploma or higher qualification, 52% were employed and 48% were still living with the family of origin. Sixty-four percent had a DUP < 1 year. Sixty percent were diagnosed with non-affective psychosis (ICD-10 codes F20-F29), 40% with affective psychosis (ICD-10 codes F30-F33).

Three subjects started on haloperidol (mean dose: 4.33 ± 1.53 mg, chlorpromazine-equivalent dose: 276.84 ± 97.18 mg), 2 on fluphenazine (mean dose: 1.00 ± 0.00 mg, chlorpromazine-equivalent dose: 55.65 ± 0.00 mg), 5 on risperidone (mean dose: 3.40 ± 1.52 mg, chlorpromazine-equivalent dose: 42.65 ± 19.07 mg), 3 on aripiprazole (mean dose: 15.00 ± 5.00 mg, chlorpromazine- equivalent dose: 188.18 ± 62.73 mg), 6 on olanzapine (mean dose: 13.33 ± 7.53 mg, chlorpromazine- equivalent dose: 13.33 ± 141.29 mg) and 6 on quetiapine (mean dose: 425.00 ± 223.05 mg, chlorpromazine-equivalent dose: 242.17 ± 127.07 mg). Five started antidepressant or mood stabilizer co-therapy (three were treated with valproic acid and two were treated with paroxetine).

Cases

n %

Total

25 (100)

Male

13 (52)

Mean age (± SD)

30.56 ± 7.5

Mean age at onset (± SD)

28.56 ± 7.7

Marital status

 

Single

12 (48)

Married

8 (32)

Divorced

3 (12)

Choabiting

2 (8)

Birth Origin

 

Italy

17 (68)

Banglade

1 (4)

Brazil

1 (4)

India

1 (4)

Marocco

1 (4)

Iran

1 (4)

Tunisia

1 (4)

Moldavia

1 (4)

Camerun

1 (4)

Years of full time education

12 ± 3.8

Education

 

Elementary school

1 (4)

Middle school

8 (32)

High school

5 (20)

Professional vocational school

5 (20)

University degree and above

6 (32)

Housing

 

Private rental

3 (12)

Home owner

6 (24)

Living with a family

12 (48)

Other

4 (16)

Occupational Status

 

Unemployed

12 (48)

Full time

9 (36)

Part time

3 (12)

Economically inactive

1 (4)

DUP

 

<1 year

16 (64)

>= 1 Year

9 (36)

Diagnosis

 

Non-affective psychoses

15 (60)

Affective psychoses

10 (40)

APs treatment

 

Haloperidol

3 (12)

Fluphenazine

2 (8)

Risperidone

5 (20)

Aripiprazole

3 (12)

Olanzapine

6 (24)

Quetiapine

6 (24)

Co-therapy

 

Citalopram

2 (8)

Carbolithium

1 (4)

Valproic acid

6 (24)

Paroxetine

2 (8)

FEP, First Episode Psychosis; BO, Bologna; SD, Standard deviation; DUP, Duration of Untreated Psychosis. See text for details

Table 1: FEP Bo West Sample socio-demographic and clinical characteristics.

3.2 Glycaemia course

At baseline the mean metabolic parameters were within the normal range. Two patients had hyperglycemia. We observed a significant increase in the mean fasting plasma glucose level (from 87.7 ± 16.1 to 97.3 ± 28.9, p=0.048). Two patients developed hyperglycemia and two diabetes (Table 2). The impact of socio-demographic and clinical characteristics on fasting plasma glucose is reported in Table 2. At baseline we observed a trend for mean fasting plasma glucose difference between men and women (81.92 ± 11.03 in males and 94.00 ± 18.68 in females; p=0.068 from independent sample t-test). Other groups (native vs migrant, white vs non-white, employed vs unemployed, compulsory education alone vs higher education, typical vs atypical APs therapy, no co-therapy vs co-therapy with AD and/or MS drugs) showed a slight non-significant mean variance.

We found that migrants showed a significantly higher increase in mean fasting plasma glucose. We observed a trend for significant increase in mean fasting plasma glucose among women. Moreover, we found that unemployed patients had a significantly higher increase in mean fasting plasma glucose levels. Finally, lower educated patients showed a significant increase in the mean fasting plasma glucose level. Other groups (ethnicity, APs treatment, AD and/or MS co-therapy) showed slight non-significant mean fasting plasma glucose changes during the follow-up period.

Characteristics

Baseline Fasting Plasma Glucose

Last Fasting Plasma Glucose

% Patients Diagnosedc

 

Mean ± Std.dev.

P-valuea

Mean ± Std.dev.

P-valueb

Hypergly.

Diabetes

Total

87.72 ± 16.08

 

97.32 ± 28.94

0.048**

24%

12%

Migration status

           

Native

86.29 ± 12.35

0.618

87.53 ± 13.87

0.682

12%

0%

Migrant

90.75 ± 22.86

118.13 ± 41.28

0.040**

50%

38%

Ethnicity

           

White

84.74 ± 12.56

0.254

89.37 ± 14.94

0.229

16%

0%

Non-white

97.17 ± 32.09

122.50 ± 47.06

0.134

50%

50%

Gender

           

Male

81.92 ± 11.03

0.068*

87.46 ± 15.20

0.336

8%

8%

Female

94.00 ± 18.68

108.00 ± 36.55

0.090*

42%

17%

Occupational status

           

Employed

85.29 ± 11.78

0.438

88.07 ± 15.90

0.553

14%

7%

Unemployed

90.82 ± 20.53

109.09 ± 37.54

0.050**

36%

18%

Education

           

Over CE

85.25 ± 11.69

0.405

87.81 ± 16.03

0.525

13%

6%

Compulsory edu.

92.11 ± 22.05

114.22 ± 39.092

0.052**

44%

22%

APs treatment

           

Typical

86.20 ± 20.05

0.851

109.20 ± 46.61

0.222

40%

20%

Atypical

88.10 ± 15.54

94.35 ± 23.54

0.147

20%

10%

AD or MS co- therapy

           

No

83.13 ± 11.76

0.271

98.00 ± 22.19

0.126

38%

13%

Yes

89.89 ± 17.67

97.00 ± 32.25

0.213

18%

12%

FEP, First Episode Psychosis; BO, Bologna; APs, antipsychotic medications; CE, compulsory education; AD, antidepressive medications; MS, mood stabilizer medications. aP-values are calculated from independent sample t-test; bP-values are calculated from dependent sample t-test; c% patients diagnosed hyperglycemia (fasting plasma glucose≥110mg/dL) and diabetes (fasting plasma glucose≥126mg/dL) on follow-up; **p≤0.05; *p≤0.1

Table 2: FEP Bo West sample fasting plasma glucose changes according to the socio-demographic and clinical characteristics.

Multiple linear regression analysis including gender, age, occupation status, level of education, and history of migration revealed that only migrant status was a significant determinant of fasting plasma glucose level at follow up (β=0.404; p=0.028). When including in the model the kind of AP treatment (typical vs atypical) and the chlorpromazine-equivalent APs dosage, the association between migrant status and mean fasting plasma glucose level at follow-up still showed a trend for statistical significance (β=0.312; p=0.111). No other factors added in the linear regression model showed any significant association with the mean fasting plasma glucose level at follow-up.

4. Discussion

We found a significant mean fasting plasma glucose increase during APs treatment; this result is consistent with those of previous studies (Tsygankov et al. 2014; Whicher et al. 2018). Our study adds to the already available evidence that the strongest factor predicting an elevated mean fasting plasma glucose level at follow-up is the patient’s history of migration. The present study on APs-related hyperglycemia and other metabolic disorders is one of the few follow-up research projects carried out among an incidence cohort of FEP patients in an everyday clinical setting designed independently of drug companies. These results are, in our opinion, valuable because the patients enrolled were completely drug naïve and representative of the FEP population in our catchment area.

This study to our knowledge is the first one to suggest that FEP migrants are at greater risk of developing metabolic disorders such as hyperglycemia and type II diabetes during APs treatment. Previous studies focusing on ethnicity found that African-American and Hispanic ethnicity are associated with an increased prevalence of diabetes, while European ethnicity is associated with a decreased risk of developing diabetes during APs treatment (Ward and Druss 2015). Thus, patients with a history of migration should be even more carefully monitored than other drug- naïve patients from the very first few weeks of treatment, to limit these adverse effects.

The limitations of our study are the naturalistic study design (lack of standardized diet and treatment programmes). Moreover, the small sample size limited statistical power, which was sufficient only to observe large effect sizes. Clearly, further research with larger samples and a control group is needed to characterize the specific role of different APs in the development of metabolic side effects.

Acknowledgements

We acknowledge the contribution of the entire Bologna - FEP Study team. We wish to thank the patients and clinical staff of the Bologna-CMHCs.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Alberti KGMM, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation. Diabet Med 15 (1998): 539-553.
  2. Andreasen NC, Pressler M, Nopoulos P, et al. Antipsychotic Dose Equivalents and Dose-Years: A Standardized Method for Comparing Exposure to Different Drugs. Biol. Psychiatry 67 (2010): 255-262.
  3. Correll CU, Detraux J, De Lepeleire J, et al. Effects of antipsychotics, antidepressants and mood stabilizers on risk for physical diseases in people with schizophrenia, depression and bipolar disorder. World Psychiatry 14 (2015): 119-136.
  4. Dixon L, Weiden P, Delahanty J, et al. Prevalence and correlates of diabetes in national schizophrenia samples. Schizophr. Bull 26 (2000): 903-912.
  5. Hare-Bruun H, Flint A, Heitmann BL. Glycemic index and glycemic load in relation to changes in body weight, body fat distribution, and body composition in adult Danes. Am. J. Clin. Nutr 84 (2006): 871-879.
  6. Heraclides AM, Chandola T, Witte DR, et al. Work Stress, Obesity and the Risk of Type 2 Diabetes: Gender-Specific Bidirectional Effect in the Whitehall II Study. Obesity 20 (2012): 428-433.
  7. Katchunga P, Masumbuko B, Belma M, et al. Age and living in an urban environment are major determinants of diabetes among South Kivu Congolese adults. Diabetes Metab 38 (2012): 324-331.
  8. Krajnak KM. Potential Contribution of Work-Related Psychosocial Stress to the Development of Cardiovascular Disease and Type II Diabetes: A Brief Review. Environ. Health Insights 8 (2014): 41-45
  9. Lanting LC, Joung IMA, Mackenbach JP, et al. Ethnic differences in mortality, end-stage complications, and quality of care among diabetic patients: a review. Diabetes Care 28 (2005): 2280-2288.
  10. Mamakou V, Hackinger S, Zengini E, et al. Combination therapy as a potential risk factor for the development of type 2 diabetes in patients with schizophrenia: the GOMAP study. BMC Psychiatry (2018): 249.
  11. Mamakou V, Thanopoulou A, Gonidakis F, et al. Schizophrenia and type 2 diabetes mellitus. Psychiatriki 29 (2018): 64-73.

Journal Statistics

Impact Factor: * 2.6

CiteScore: 2.9

Acceptance Rate: 11.01%

Time to first decision: 10.4 days

Time from article received to acceptance: 2-3 weeks

Discover More: Recent Articles

Grant Support Articles

© 2016-2024, Copyrights Fortune Journals. All Rights Reserved!