You are here

Article Text

Article menu

Download PDFPDF

Psychosocial research
Impaired glucose regulation, depressive symptoms, and health-related quality of life
  1. Jannica S Selenius1,2,
  2. Niko S Wasenius1,2,
  3. Hannu Kautiainen1,
  4. Minna Salonen2,3,
  5. Mikaela von Bonsdorff2,4,
  6. Johan G Eriksson1,2,5,6
  1. 1Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
  2. 2Public Health Research Porgramme, Folkhälsan Research Center, Helsinki, Finland
  3. 3Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki, Finland
  4. 4Gerontology Research Center and Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
  5. 5Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
  6. 6Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  1. Correspondence to Dr Jannica S Selenius; jannica.selenius{at}helsinki.fi

Abstract

Introduction This study aims to investigate whether the associations between impaired glucose regulation and health-related quality of life are modified by severity or type of depressive symptoms.

Research design and methods For this cross-sectional study, we included 1939 individuals (mean age 61.5 years) from the Helsinki Birth Cohort Study. Between 2001 and 2004, a standard 2-hour 75 g oral glucose tolerance test was applied to define normoglycemia, pre-diabetes, and newly diagnosed diabetes. Information on previously diagnosed diabetes was collected from national registers and questionnaires. Pre-diabetes was defined as having either impaired fasting glucose or impaired glucose tolerance. The Mental and Physical Component Scores of health-related quality of life were assessed with Short Form-36. Beck’s Depression Inventory was employed to investigate the severity of depressive symptoms and to define minimal (depression score <10), non-melancholic, and melancholic types of depression. We analyzed data with general linear models adjusted for sex, age, lifestyle factors, comorbidities, and body mass index.

Results Glucose regulation subgroups, especially previously known diabetes, were associated with lower Physical Component Score (p=0.001) and higher depression score (p=0.015), but not with the Mental Component Score (p=0.189). Non-melancholic depression was associated with lower Physical and Mental Component Scores compared with those with depression score <10 and melancholic depression (p<0.001), independently of glucose regulation status (p for glucose regulation status by depression type interaction >0.54).

Conclusions Non-melancholic type of depression and previously known diabetes are independently associated with lower health-related quality of life. This should be appraised in long-term treatment of diabetes and when treating non-melancholic depressive symptoms to maintain a higher health-related quality of life.

  • diabetes mellitus
  • type 2
  • insulin resistance
  • quality of life
  • depression
https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/bmjdrc-2020-001568

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Significance of this study

    What is already known about this subject?

    • Diabetes and depression are widespread diseases on the rise with a considerable negative influence on health-related quality of life.

    What are the new findings?

    • In addition to contributing to the perception that previously known diabetes is associated with poorer health-related quality of life, we found that decreased health-related quality of life is seen in subjects with non-melancholic depressive symptoms regardless of glucose regulation status.

    How might these results change the focus of research or clinical practice?

    • These results highlight the importance of maintaining mental health in individuals suffering from diabetes to prevent health-related quality of life from deteriorating, further our findings promote more research aiming at characterizing type of depressive symptoms associated with impairment in glucose regulation.

    Introduction

    Globally, both type 2 diabetes and depression show a high and increasing prevalence. Diabetes is estimated to affect 463 million people globally,1 the corresponding number for depression being over 300 million.2 Diabetes attributed to 5 million deaths in 2017 while depression was the leading cause for suicide globally.2 3 In addition, being global health burdens, diabetes and depression affect the individuals suffering from the disorders in several ways, with impact on physical and mental health and overall well-being.4–6

    Diabetes is strongly associated with both depression and poorer quality of life.7 Poorer quality of life and non-optimal mental health can influence diabetes care negatively, predisposing to worse glycemic control and an increased risk for diabetes-associated complications.4 According to a recent meta-analysis, individuals with diabetes were around 30% more likely to develop depression than individuals with normoglycemia (NGT).8 Some studies have reported no association between pre-diabetic and depressive symptoms, suggesting that depression stems more from the burden of diabetes than from insulin resistance, which is a characteristic feature of the pre-diabetic states.9 10 Furthermore, compared with newly diagnosed diabetes, previously diagnosed diabetes has been more closely linked to both depression and poorer quality of life.8

    Metabolic syndrome is characterized by disturbances in glucose and lipid metabolism as well as adiposity and is a major risk factor for the development of type 2 diabetes. Studies have reported a linkage between metabolic syndrome and subgroups of depression, that is, mainly for non-melancholic and melancholic depression, and metabolic syndrome.11–13 Non-melancholic depression is characterized by hypersomnia and weight gain, whereas melancholic depression is linked with loss of appetite and disturbances in affect and psychomotor functions.14 Non-melancholic depression has been proposed to show a stronger association with inflammatory diseases, while melancholic depression mainly is a disease of the central nervous system not associated with systemic processes.14 15

    Although the link between type 2 diabetes and depression has been studied, less is known about the type of depression associated with different degrees of impairment in glucose regulation. Furthermore, only few studies have focused on the relationship between pre-diabetes and health-related quality of life (HRQoL). Previous studies have reported that previously known diabetes is associated with poorer physical and mental components of HRQoL.4 5 16 Only a few studies have investigated the relationship between pre-diabetes and HRQoL, and the findings have been inconsistent.17–19

    The aim of this study was to investigate whether the association between the different degrees of impairment in glucose regulation and HRQoL is modified by the severity and type of depressive symptoms.

    Research design and methods

    Participants

    The Helsinki Birth Cohort Study (HBCS) includes 13 345 individuals born between 1934 and 1944 at the Helsinki University Central Hospital or the Helsinki City Maternity Hospital.20 They attended child welfare clinics in Helsinki and were living in Finland in 1971 when a unique personal identification number was allocated to all individuals of the Finnish population.21 A baseline clinical examination was conducted between 2001 and 2004, involving 2003 cohort members. Of those, 1999 had sufficient data on glucose regulation status and were included in this study. After excluding individuals with missing information, 1939 had sufficient information on depressive symptoms and 1930 on HRQoL (figure 1).

    Figure 1
    Figure 1

    Flow chart of the data screening process.

    Glucose regulation

    The WHO 2006 criteria22 were used for diagnosing diabetes, impaired glucose tolerance (IGT), and impaired fasting glucose (IFG). Fasting plasma glucose was measured in all individuals participating in the clinical examination and a standard 2-hour 75 g oral glucose tolerance test (OGTT) was conducted, except for those with previously known diabetes. Previously known diabetes was defined by self-report, register linkage, or use of medication for diabetes. Individuals who received a diagnosis of diabetes for the first time based on the OGTT were classified as having newly diagnosed diabetes. Individuals who met the criteria for both IFG and IGT were classified as having IGT. IFG or IGT were grouped together and called pre-diabetes.

    Health-related quality of life

    HRQoL was assessed using the validated Finnish 36-Item Short Form Health Survey (SF-36) version 1.0 questionnaire.23 The average age at the time of filling the questionnaire was 61.5 years (SD 2.9). The SF-36 consists of eight domains: physical functioning (10 items), role limitations caused by physical health problems (four items), role limitations caused by emotional health problems (three items), bodily pain (two items), general health (five items), vitality (four items), mental health (five items), and social functioning (two items). Possible scores for each item ranged from 0 (lowest perceived functioning) to 100 (highest perceived functioning). Physical and mental health component scores were aggregated using the US reference population (1990) to standardize the eight domains and for factor score coefficients. The summary scores were standardized using a mean of 50 and a SD of 10. The Finnish SF-36 has been reported as reliable and well validated.24

    Depression

    Depression was screened for using the validated 21-item Beck’s Depression Inventory (BDI) questionnaire. The psychometric properties of the Finnish BDI have been well validated.25 26 Patients who scored 10 or higher were considered to suffer from depression, as previously suggested.27 Participants with a BDI score ≥10 were divided into melancholic and non-melancholic types of depression28 based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition criteria. Melancholic symptoms in the BDI (sadness, past failure, loss of pleasure, feelings of guilt, punishment feelings, loss of interest, irritability, change of sleeping, and appetite) were applied to divide the participants into melancholic and non-melancholic types of depression.12 29–31

    Covariates

    At the clinical examination, subjects were asked about their current health situation, use of medication, and lifestyle characteristics. Socioeconomic status was obtained from Statistics Finland and was coded as high official, low official, self-employed, and laborer based on the original classification system.32 Smoking was coded as never, former, and current, and alcohol use was coded as never or having quit, less than once a week, or weekly. Anthropometrics including weight and height were measured and body mass index (BMI) was calculated as weight in kilograms divided with height in meters squared. At the clinical examination, performed by three trained study nurses, the cohort members were asked about their past 12-month leisure-time physical activity using a validated exercise questionnaire: the Kuopio Ischemic Heart Disease Risk Factor Study.33 Leisure-time physical activity was measured in metabolic equivalents of task (MET),34 which were multiplied with time (hours) and frequency to calculate MET-hours as previously suggested.35 Cohort members were also asked about chronic diseases and conditions, including cardiovascular conditions (congestive heart failure, arrhythmias, claudication, angina pectoris, previous heart attack, and stroke), lung diseases (asthma, emphysema, and chronic bronchitis), musculoskeletal disorders (rheumatoid arthritis, osteoporosis) and presence of cancer using validated questionnaires. The presence of comorbidities was coded as none, one, two, or more.

    Statistical analysis

    Characteristics of the cohort members were compared across glucose regulation groups using general linear models. In the case of violation (eg, non-normal distribution), a bootstrap-type analysis with 2500 replications was used. General linear models were also applied to investigate the associations between glucose regulation status and physical functioning, mental functioning, and depressive symptoms. Adjustments were first made for sex and age, then for lifestyle factors (smoking status, alcohol consumption, socioeconomic status, leisure-time physical activity, and comorbidities), and lastly for BMI as our early models showed a relatively strong effect by BMI when controlling for it. All tests were performed two-tailed and the level of significance was set at p<0.05. Statistical analyses were carried out using Stata Version/MP V.15.1 (Stata, College Station, Texas, USA) and IBM SPSS Statistics V.25 (IBM, Armonk, New York, USA).

    Results

    Participants’ characteristics

    This study included 897 men and 1042 women. The mean age at the time of the clinical examination was 61.5 years for both sexes (SD 2.8 for men and 3.0 for women). The characteristics of the cohort members across the glucose regulation groups—that is, NGT, pre-diabetes, newly diagnosed diabetes, and previously known diabetes—are shown in table 1.

    View this table:
    Table 1

    Characteristics of the study population according to glucose regulation

    Subgroups of glucose regulation and HRQoL and BDI

    As described in table 2, in the HRQoL subscales, previously known diabetes was associated with lower physical functioning, general health, vitality, social functioning, and PCS after the model was fully adjusted for covariates. In addition, we detected a significant (p<0.02) association between the physical and emotional role limitations and bodily pain after adjusting for sex, age, smoking status, alcohol consumption, socioeconomic status, leisure-time physical activity, and comorbidities in patients with previously known diabetes. However, these associations attenuated after further adjusting for BMI (p>0.073). Previously known diabetes was also associated with higher BDI score (table 2).

    View this table:
    Table 2

    Association between the subgroups of glucose regulation and HRQoL and depression scores

    HRQoL according to types of depressive symptoms across glucose regulation subgroups

    Both non-melancholic and melancholic depressive symptoms were associated with lower Physical Component Score (PCS) and Mental Component Score (MCS) compared with those with a BDI score <10 regardless of glucose regulation status (table 3 and figure 2). No significant interaction was detected between the different groups of impairment in glucose regulation and type of depressive symptoms on the HRQoL components (figure 2).

    View this table:
    Table 3

    PCS and MCS of health-related quality of life among subjects with depressive symptoms according to glucose regulation subgroups

    Figure 2
    Figure 2

    Adjusted differences of non-melancholic and melancholic depression types from the Beck’s Depression Inventory (BDI) <10 group in the Physical Component Score (PCS) and Mental Component Score (MCS) of health-related quality of life (HRQoL) among subgroups of glucose regulation. Analyses are adjusted for sex, age, smoking status, alcohol consumption, socioeconomic status, leisure-time physical activity, comorbidities, and body mass index. Error bars indicate 95% CIs. ; ALL, all groups; NDD, newly diagnosed diabetes; NGT, normal glucose tolerance; PD, pre-diabetes; PKD, previously known diabetes.

    Conclusions

    Our findings showed no interaction between impairment in glucose regulation and type of depressive symptoms on HRQoL. However, both types of depressive symptoms had a stronger influence on the mental component of HRQoL than on the physical component. This association was detected regardless of impairment in glucose regulation. We also found that non-melancholic depressive symptoms were associated with lower physical quality of life in all subjects regardless of glucose regulation status. Based on these findings, it seems that type of depressive symptoms does not modify the association between glucose regulation and HRQoL.

    Previously known diabetes was associated with higher BDI scores, which is consistent with previous findings.7 8 36 No association was detected between pre-diabetes and newly diagnosed diabetes and BDI score. Other studies have also been unable to find an association between depression and pre-diabetes and newly diagnosed diabetes.37 38 It has previously been suggested that the burden of diabetes as a disease is responsible for the decline in mental health among people with diabetes, which could explain our findings as they indicate that pre-diabetes is not linked with depressive symptoms nor poorer mental functioning.39

    We found that HRQoL was lower among subjects with previously known diabetes, but not among those with pre-diabetes and newly diagnosed diabetes. In line with our findings, it has been suggested that longer duration of diabetes, but not pre-diabetes, is related to decreased HRQoL.4 5 16 Decreased HRQoL related to years of living with diabetes may be attributed to higher prevalence of comorbidities and complications, such as neuropathy and retinopathy that have been associated with lower HRQoL.40 41 Unfortunately, we were unable to adjust for specific diabetes complications, which might affect our findings. The PCS was lower among subjects with previously known diabetes compared with the other subgroups. Despite this, previously known diabetes was not associated with a decrease in the physical role limitations domain nor with the bodily pain domain of the SF-36 score. Physical inactivity and obesity affect physical performance, and both are known risk factors for developing diabetes42; however, even after adjusting for them, there was a significant association between impaired glucose regulation and poorer physical functioning. According to our adjusted models, BMI strongly affects the associations between diabetes and poorer HRQoL.

    As expected, both types of depression lowered MCS more than the PCS in all subgroups of glucose regulation. Subjects with symptoms of non-melancholic depression were associated with lower physical HRQoL than subjects with BDI score <10 or symptoms of melancholic depression. This association was independent of diabetes status, although most clearly seen in subjects with NGT and previously known diabetes. This is to the best of our knowledge the first study with the aim to investigate whether type of depressive symptoms affects HRQoL among the diabetic population. According to previous studies, non-melancholic depressive symptoms have been found to associate with elevated fasting glucose and IGT13 as well as with metabolic and inflammatory conditions,12 14 and has been suggested to originate from a systemic inflammation in the organism, whereas melancholic depression is thought to be purely a product of imbalances in the function of the central nervous system.14 15 43 This theory suggests that non-melancholic depression shares a common underlying denominator with other systemic diseases, such as diabetes, which our findings support.

    Our study has several strengths. The cohort is well characterized, and our results were produced using both clinical and registered-based data. We were able to control for several factors known to influence both prevalence of depression and HRQoL. We used an OGTT to assess glucose regulation status, the validated SF-36 for assessing HRQoL, and the standardized BDI questionnaire for assessing depression. We used the summary scores of the SF-36 as well as the component scores. The study also has some limitations. Our study sample is from a homogeneous, restricted area in Finland and the findings from this study might therefore have to be cautiously implemented in other populations. As a result, the sample selected may not be representative of the entire Finnish population and the findings may not be generalized to represent the Finnish population. Although our sample is large when it comes to glucose regulation status, we may have limited power to investigate association related to the non-melancholic depressive symptoms. Due to the cross-sectional study design, we were unable to assess causal relationships. Lastly, although we did take in consideration comorbidities, we were unable to adjust for complications of diabetes.

    In conclusion, both the non-melancholic and melancholic type of depressive symptoms affect mental HRQoL more severely than physical HRQoL regardless of impairment in glucose regulation. Symptoms of non-melancholic depression are associated with lower physical HRQoL in all subjects, especially in normoglycemic subjects and in subjects with previously known diabetes. Thus, our findings support the necessity of preventing pre-diabetes from turning into diabetes and identifying the type of depression to maintain high HRQoL. Resources should also be focused on preventing a decline in HRQoL and mental health in all stages of impaired glucose regulation. Further research is needed to investigate the association between diabetes and non-melancholic depressive symptoms.

    References

    1. ↵
      1. Saeedi P,
      2. Petersohn I,
      3. Salpea P, et al
      . Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:107843. doi:10.1016/j.diabres.2019.107843pmid:http://www.ncbi.nlm.nih.gov/pubmed/31518657
      OpenUrlPubMed
    2. ↵
      1. World Health Organization
      . Depression and other common mental disorders -Global health estimates. Geneva: World Health Organization, 2017.
    3. ↵
      1. Cho NH,
      2. Shaw JE,
      3. Karuranga S, et al
      . IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 2018;138:271–81.doi:10.1016/j.diabres.2018.02.023pmid:http://www.ncbi.nlm.nih.gov/pubmed/29496507
      OpenUrlCrossRefPubMed
    4. ↵
      1. Rubin RR,
      2. Peyrot M
      . Quality of life and diabetes. Diabetes Metab Res Rev 1999;15:205–18.doi:10.1002/(SICI)1520-7560(199905/06)15:3<205::AID-DMRR29>3.0.CO;2-Opmid:http://www.ncbi.nlm.nih.gov/pubmed/10441043
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      1. Schanner C,
      2. Falck A,
      3. Keskitalo A, et al
      . Health-Related quality of life of Finnish patients with diabetes. Scand J Public Health 2016;44:765–71.doi:10.1177/1403494816666732pmid:http://www.ncbi.nlm.nih.gov/pubmed/27655783
      OpenUrlCrossRefPubMed
    6. ↵
      1. Ali S,
      2. Stone MA,
      3. Peters JL, et al
      . The prevalence of co-morbid depression in adults with type 2 diabetes: a systematic review and meta-analysis. Diabet Med 2006;23:1165–73.doi:10.1111/j.1464-5491.2006.01943.xpmid:http://www.ncbi.nlm.nih.gov/pubmed/17054590
      OpenUrlCrossRefPubMedWeb of Science
    7. ↵
      1. Eaton WW,
      2. Armenian H,
      3. Gallo J, et al
      . Depression and risk for onset of type II diabetes. A prospective population-based study. Diabetes Care 1996;19:1097–102.doi:10.2337/diacare.19.10.1097pmid:http://www.ncbi.nlm.nih.gov/pubmed/8886555
      OpenUrlAbstract/FREE Full Text
    8. ↵
      1. Chireh B,
      2. Li M,
      3. D'Arcy C
      . Diabetes increases the risk of depression: a systematic review, meta-analysis and estimates of population attributable fractions based on prospective studies. Prev Med Rep 2019;14:100822. doi:10.1016/j.pmedr.2019.100822pmid:http://www.ncbi.nlm.nih.gov/pubmed/30815337
      OpenUrlPubMed
    9. ↵
      1. Aujla N,
      2. Abrams KR,
      3. Davies MJ, et al
      . The prevalence of depression in white-European and South-Asian people with impaired glucose regulation and screen-detected type 2 diabetes mellitus. PLoS One 2009;4:e7755. doi:10.1371/journal.pone.0007755pmid:http://www.ncbi.nlm.nih.gov/pubmed/19898618
      OpenUrlCrossRefPubMed
    10. ↵
      1. Icks A,
      2. Kruse J,
      3. Dragano N, et al
      . Are symptoms of depression more common in diabetes? results from the Heinz Nixdorf recall study. Diabet Med 2008;25:1330–6.doi:10.1111/j.1464-5491.2008.02585.xpmid:http://www.ncbi.nlm.nih.gov/pubmed/19046224
      OpenUrlPubMed
    11. ↵
      1. Takeuchi T,
      2. Nakao M,
      3. Kachi Y, et al
      . Association of metabolic syndrome with atypical features of depression in Japanese people. Psychiatry Clin Neurosci 2013;67:532–9.doi:10.1111/pcn.12104pmid:http://www.ncbi.nlm.nih.gov/pubmed/24152284
      OpenUrlPubMed
    12. ↵
      1. Seppälä J,
      2. Vanhala M,
      3. Kautiainen H, et al
      . Prevalence of metabolic syndrome in subjects with melancholic and non-melancholic depressive symptoms. A Finnish population-based study. J Affect Disord 2012;136:543–9.doi:10.1016/j.jad.2011.10.032pmid:http://www.ncbi.nlm.nih.gov/pubmed/22119085
      OpenUrlCrossRefPubMed
    13. ↵
      1. Korniloff K,
      2. Kotiaho S,
      3. Vanhala M, et al
      . Musculoskeletal pain in melancholic and atypical depression. Pain Med 2017;18:341–7.doi:10.1093/pm/pnw202pmid:http://www.ncbi.nlm.nih.gov/pubmed/28204727
      OpenUrlPubMed
    14. ↵
      1. Penninx BWJH,
      2. Milaneschi Y,
      3. Lamers F, et al
      . Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile. BMC Med 2013;11:129. doi:10.1186/1741-7015-11-129pmid:http://www.ncbi.nlm.nih.gov/pubmed/23672628
      OpenUrlCrossRefPubMed
    15. ↵
      1. Lamers F,
      2. Vogelzangs N,
      3. Merikangas KR, et al
      . Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry 2013;18:692–9.doi:10.1038/mp.2012.144pmid:http://www.ncbi.nlm.nih.gov/pubmed/23089630
      OpenUrlCrossRefPubMed
    16. ↵
      1. Wändell PE
      . Quality of life of patients with diabetes mellitus. An overview of research in primary health care in the Nordic countries. Scand J Prim Health Care 2005;23:68–74.doi:10.1080/02813430510015296pmid:http://www.ncbi.nlm.nih.gov/pubmed/16036544
      OpenUrlCrossRefPubMedWeb of Science
    17. ↵
      1. Hunger M,
      2. Holle R,
      3. Meisinger C, et al
      . Longitudinal changes in health-related quality of life in normal glucose tolerance, prediabetes and type 2 diabetes: results from the KorA S4/F4 cohort study. Qual Life Res 2014;23:2515–20.doi:10.1007/s11136-014-0689-5pmid:http://www.ncbi.nlm.nih.gov/pubmed/24729056
      OpenUrlPubMed
    18. ↵
      1. Neumann A,
      2. Schoffer O,
      3. Norström F, et al
      . Health-Related quality of life for pre-diabetic states and type 2 diabetes mellitus: a cross-sectional study in Västerbotten Sweden. Health Qual Life Outcomes 2014;12:150. doi:10.1186/s12955-014-0150-zpmid:http://www.ncbi.nlm.nih.gov/pubmed/25342083
      OpenUrlCrossRefPubMed
    19. ↵
      1. Ghorbani A,
      2. Ziaee A,
      3. Esmailzadehha N, et al
      . Association between health-related quality of life and impaired glucose metabolism in Iran: the Qazvin metabolic diseases study. Diabet Med 2014;31:754–8.doi:10.1111/dme.12415pmid:http://www.ncbi.nlm.nih.gov/pubmed/24708393
      OpenUrlPubMed
    20. ↵
      1. von Bondorff MB,
      2. Törmäkangas T,
      3. Salonen M, et al
      . Early life origins of all-cause and cause-specific disability pension: findings from the Helsinki Birth Cohort Study. PLoS One 2015;10:e0122134. doi:10.1371/journal.pone.0122134pmid:http://www.ncbi.nlm.nih.gov/pubmed/25849578
      OpenUrlPubMed
    21. ↵
      1. Eriksson JG,
      2. Kajantie E,
      3. Lampl M, et al
      . Trajectories of body mass index amongst children who develop type 2 diabetes as adults. J Intern Med 2015;278:219–26.doi:10.1111/joim.12354pmid:http://www.ncbi.nlm.nih.gov/pubmed/25683182
      OpenUrlPubMed
    22. ↵
      1. World Health Organization
      . Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. Geneva: World Health Organization, 2006.
    23. ↵
      1. Ware JE,
      2. Sherbourne CD
      . The mos 36-item short-form health survey (SF-36). I. conceptual framework and item selection. Med Care 1992;30:473–83.pmid:http://www.ncbi.nlm.nih.gov/pubmed/1593914
      OpenUrlCrossRefPubMedWeb of Science
    24. ↵
      1. Aalto A-M,
      2. Aro AR,
      3. Teperi J
      . RAND-36 as a measure of health-related quality of life. reliability, construct validity and reference values in the Finnish General population.Stakes, research reports. 101, 1999.
    25. ↵
      1. Viinamäki H,
      2. Tanskanen A,
      3. Honkalampi K, et al
      . Is the Beck depression inventory suitable for screening major depression in different phases of the disease? Nord J Psychiatry 2004;58:49–53.doi:10.1080/08039480310000798pmid:http://www.ncbi.nlm.nih.gov/pubmed/14985154
      OpenUrlCrossRefPubMedWeb of Science
    26. ↵
      1. Wang Y-P,
      2. Gorenstein C
      . Psychometric properties of the Beck depression Inventory-II: a comprehensive review. Braz J Psychiatry 2013;35:416–31.doi:10.1590/1516-4446-2012-1048pmid:http://www.ncbi.nlm.nih.gov/pubmed/24402217
      OpenUrlCrossRefPubMed
    27. ↵
      1. Beck AT,
      2. Steer RA,
      3. Carbin MG
      . Psychometric properties of the Beck depression inventory: twenty-five years of evaluation. Clin Psychol Rev 1988;8:77–100.doi:10.1016/0272-7358(88)90050-5
      OpenUrlCrossRefWeb of Science
    28. ↵
      1. Sheehan DV,
      2. Lecrubier Y,
      3. Sheehan KH, et al
      . The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998;59 Suppl 20:22–33.pmid:http://www.ncbi.nlm.nih.gov/pubmed/9881538
      OpenUrlPubMed
    29. ↵
      1. Vanhala M,
      2. Jokelainen J,
      3. Keinänen-Kiukaanniemi S, et al
      . Depressive symptoms predispose females to metabolic syndrome: a 7-year follow-up study. Acta Psychiatr Scand 2009;119:137–42.doi:10.1111/j.1600-0447.2008.01283.xpmid:http://www.ncbi.nlm.nih.gov/pubmed/19016666
      OpenUrlCrossRefPubMedWeb of Science
    30. ↵
      1. Sheehan DV LY
      . The Mini‐International neuropsychiatric interview (mini). Tampa, fl: University of South Florida, Institute for research in psychiatry. Paris: INSERM‐Hopital de la Salpetriere, 1994.
    31. ↵
      1. Steer RA,
      2. Ball R,
      3. Ranieri WF, et al
      . Dimensions of the Beck depression Inventory-II in clinically depressed outpatients. J Clin Psychol 1999;55:117–28.doi:10.1002/(SICI)1097-4679(199901)55:1<117::AID-JCLP12>3.0.CO;2-Apmid:http://www.ncbi.nlm.nih.gov/pubmed/10100838
      OpenUrlCrossRefPubMedWeb of Science
    32. ↵
      1. Central Statstical Office of Finland Classification of Socio-economic Group
      . Handbooks 17. Helsinki, 1989.
    33. ↵
      1. Lakka TA,
      2. Venäläinen JM,
      3. Rauramaa R, et al
      . Relation of leisure-time physical activity and cardiorespiratory fitness to the risk of acute myocardial infarction. N Engl J Med 1994;330:1549–54.doi:10.1056/NEJM199406023302201pmid:http://www.ncbi.nlm.nih.gov/pubmed/8177243
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      1. Alberti KG,
      2. 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 1998;15:539–53.doi:10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-Spmid:http://www.ncbi.nlm.nih.gov/pubmed/9686693
      OpenUrlCrossRefPubMedWeb of Science
    35. ↵
      1. Wasenius N,
      2. Venojärvi M,
      3. Manderoos S, et al
      . Unfavorable influence of structured exercise program on total leisure-time physical activity. Scand J Med Sci Sports 2014;24:404–13.doi:10.1111/sms.12015pmid:http://www.ncbi.nlm.nih.gov/pubmed/23157542
      OpenUrlCrossRefPubMed
    36. ↵
      1. Lyra E Silva NdeM,
      2. Lam MP,
      3. Soares CN, et al
      . Insulin resistance as a shared pathogenic mechanism between depression and type 2 diabetes. Front Psychiatry 2019;10:57.doi:10.3389/fpsyt.2019.00057pmid:http://www.ncbi.nlm.nih.gov/pubmed/30837902
      OpenUrlPubMed
    37. ↵
      1. Sun JC,
      2. Xu M,
      3. Lu JL, et al
      . Associations of depression with impaired glucose regulation, newly diagnosed diabetes and previously diagnosed diabetes in Chinese adults. Diabet Med 2015;32:935–43.doi:10.1111/dme.12649pmid:http://www.ncbi.nlm.nih.gov/pubmed/25439630
      OpenUrlPubMed
    38. ↵
      1. Aujla N,
      2. Skinner TC,
      3. Khunti K, et al
      . The prevalence of depressive symptoms in a white European and South Asian population with impaired glucose regulation and screen-detected type 2 diabetes mellitus: a comparison of two screening tools. Diabet Med 2010;27:896–905.doi:10.1111/j.1464-5491.2010.03042.xpmid:http://www.ncbi.nlm.nih.gov/pubmed/20653747
      OpenUrlPubMed
    39. ↵
      1. Engum A,
      2. Mykletun A,
      3. Midthjell K, et al
      . Depression and diabetes: a large population-based study of sociodemographic, lifestyle, and clinical factors associated with depression in type 1 and type 2 diabetes. Diabetes Care 2005;28:1904–9.doi:10.2337/diacare.28.8.1904pmid:http://www.ncbi.nlm.nih.gov/pubmed/16043730
      OpenUrlAbstract/FREE Full Text
    40. ↵
      1. Shao H,
      2. Yang S,
      3. Fonseca V, et al
      . Estimating quality of life decrements due to diabetes complications in the United States: the health utility index (HUI) diabetes complication equation. Pharmacoeconomics 2019;37:921–9.doi:10.1007/s40273-019-00775-8pmid:http://www.ncbi.nlm.nih.gov/pubmed/30778865
      OpenUrlPubMed
    41. ↵
      1. Stojanović M,
      2. Cvetanović G,
      3. Anđelković Apostolović M, et al
      . Impact of socio-demographic characteristics and long-term complications on quality of life in patients with diabetes mellitus. Cent Eur J Public Health 2018;26:104–10.doi:10.21101/cejph.a5022pmid:http://www.ncbi.nlm.nih.gov/pubmed/30102498
      OpenUrlPubMed
    42. ↵
      1. Weisman A,
      2. Fazli GS,
      3. Johns A, et al
      . Evolving trends in the epidemiology, risk factors, and prevention of type 2 diabetes: a review. Can J Cardiol 2018;34:552–64.doi:10.1016/j.cjca.2018.03.002pmid:http://www.ncbi.nlm.nih.gov/pubmed/29731019
      OpenUrlPubMed
    43. ↵
      1. Lamers F,
      2. de Jonge P,
      3. Nolen WA, et al
      . Identifying depressive subtypes in a large cohort study: results from the Netherlands study of depression and anxiety (NESDA). J Clin Psychiatry 2010;71:1582–9.doi:10.4088/JCP.09m05398blupmid:http://www.ncbi.nlm.nih.gov/pubmed/20673552
      OpenUrlCrossRefPubMed

    Footnotes

    • Contributors JSS researched data, contributed to discussion, and wrote the manuscript. NSW researched data, contributed to discussion, and wrote the manuscript. HK contributed to discussion and reviewed/ edited the manuscript. MS, MvB, and JGE contributed to discussion and reviewed/edited the manuscript. All authors contributed substantially to the conception and design or analysis and interpretation of data and the drafting of the article or critical revision.

    • Funding HBCS was supported by Finnish Foundation for Cardiovascular Research, Finnish Foundation for Diabetes Research, Juho Vainio Foundation, Academy of Finland, Novo Nordisk Foundation, Signe and Ane Gyllenberg Foundation, Samfundet Folkhälsan, Finska Läkaresällskapet, Liv och Hälsa, European Commission FP7 (DORIAN) Grant Agreement No 278 603 and EU H2020-PHC-2014-DynaHealth Grant No 633 595 and EU Horizon 2020 Award 733 206 LIFECYCLE.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval The study was approved by the Ethics Committee of Epidemiology and Public Health of the Hospital District of Helsinki and Uusimaa and that of the National Public Health Institute, Helsinki, and follows the guidelines of the Declaration of Helsinki. All participants gave a written, informed consent.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article.