Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Bidirectional association between depression and diabetic nephropathy by meta-analysis

  • Tingting Fang ,

    Roles Writing – original draft

    2020011012011@stu.hznu.edu.cn (TF); jun-ping.liu@hznu.edu.cn (JPL)

    Affiliations Institute of Ageing Research, Hangzhou Normal University, School of Basic Medicine, Hangzhou, Zhejiang Province, China, School of Public Health, Hangzhou Normal University, Hangzhou, Zhejiang Province, China

    ⨯
  • Qiuling Zhang,

    Roles Conceptualization

    Affiliation Department of Endocrinology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China

    ⨯
  • Zhiguo Wang,

    Roles Writing – review & editing

    Affiliation Institute of Ageing Research, Hangzhou Normal University, School of Basic Medicine, Hangzhou, Zhejiang Province, China

    ⨯
  • Jun-Ping Liu

    Roles Conceptualization, Writing – review & editing

    2020011012011@stu.hznu.edu.cn (TF); jun-ping.liu@hznu.edu.cn (JPL)

    Affiliations Institute of Ageing Research, Hangzhou Normal University, School of Basic Medicine, Hangzhou, Zhejiang Province, China, Monash University Department of Immunology and Pathology, Central Clinical School, Faculty of Medicine, Prahran, Victoria, Australia, Hudson Institute of Medical Research, Clayton, Victoria, Australia

    ⨯

Abstract

Background

Studies suggested that the association between depression and diabetic nephropathy may be bi-directional, but this hypothesis remains investigating. In this meta-analysis, the bi-directional relationship between depression and diabetic nephropathy was investigated.

Methods

A search for the publications on depression and diabetic nephropathy in the databases of PubMed, Web of science, and Embase from the earliest available to August 2022 was conducted. Two sets of pooled risk estimates were calculated using random effects models: diabetic nephropathy predicting depression and depression predicting diabetic nephropathy. Cross-sectional studies were assessed using Agency for Healthcare Research and Quality (AHRQ), cohort and case-control studies were assessed using Newcastle-Ottawa Scale (NOS).

Result

Of the 974,121 patients in 30 clinical studies, 24 studies met eligibility for diabetic nephropathy predicting onset of depression, representing 28,438 incident cases. The other 6 studies met criteria for depression predicting onset of diabetic nephropathy, representing 945,683 incident cases. The pooled odds ratio (OR) of diabetic nephropathy predicting depression was 1.46 (95% CI 1.27–1.67). The OR of depression predicting diabetic nephropathy was 1.22 (95% CI 1.13–1.31).

Conclusion

This meta-analysis shows that the relationship between depression and diabetic nephropathy may be bidirectional. Diabetic nephropathy may be a predictor of depression, and depression may also be an indicator of diabetic nephropathy. The mechanisms underlying the bidirectional relationship need to be further investigated and interventions of the comorbidity of depression and diabetic nephropathy need be studied in clinical practice.

Citation: Fang T, Zhang Q, Wang Z, Liu J-P (2022) Bidirectional association between depression and diabetic nephropathy by meta-analysis. PLoS ONE 17(12): e0278489. https://doi.org/10.1371/journal.pone.0278489

Editor: Donovan Anthony McGrowder, The University of the West Indies, JAMAICA

Received: June 6, 2022; Accepted: November 16, 2022; Published: December 20, 2022

Copyright: © 2022 Fang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: If the paper is accepted, we can provide data extracted from the included literature.

Funding: This work was supported by grants from the National Key Research and Development Program of China (2018YFC2000100, and 2021ZD0202402), the National Natural Science Foundation of China (91949207, 82130044, 92149302, 91849124, and 81871112), and the Victorian Government’s Operational Infrastructure Support Program of Australia.

Competing interests: Authors declare no conflict of interest.

Introduction

The global disease diabetes imposes threat to many aspects of public health. From 2005 to 2015, the cases of diabetes increased from 333 million to 435 million, predicting 439 million diabetics worldwide by 2030 [1, 2]. Of a variety of microvascular and macrovascular complications, including blindness, kidney disease, and lower limb amputation, diabetic nephropathy (DN) is the most common microvascular complication of diabetes. In the United States, the number of patients with end-stage renal failure (ESRD) due to DN increased from 40,000 to 50,000 between 2000 and 2014 [3]. Compared with the developed countries, greater burden of chronic kidney disease due to diabetes was in developing countries [4]. In the past decades, the occurrence of DN in China increased dramatically, with 24.3 million patients suffering from diabetic kidney disease in 2016 [5]. Studies showed that 95% of diabetics developed kidney injury after 10 years, 35% progressed to end-stage kidney disease after 5 years, and 18% died of kidney failure after 20 years [6]. In addition to the organic complications, diabetes underpins severe mental disorders such as depression and anxiety, the prevalence of depression was doubled in type 2 diabetics compared with subjects without diabetes [7].

Depression is a most common mental disorders, recently dramatically increased worldwide [1]. Depression is associated with unhealthy behaviors such as smoking, lack of exercise, and calorie intake, reducing life quality and diabetes self-care ability [8–10]. Depression-related pathophysiological mechanisms include dysregulation of hypothalamic-pituitary-adrenal-immune (HPAI) axis and activation of pro-inflammatory cytokines, potentially resulting in insulin resistance and increased risk of diabetes [9, 11].

The present study was undertaken to systematically review the reported data on DN and depression by performing meta-analysis. We found that DN and depression coexist as comorbidity developing shared risk of intimately influencing each other [12].

Research question

  • Whether diabetic nephropathy is a risk factor of depression?
  • Whether depression is a risk factor of diabetic nephropathy?
  • Is there a bidirectional relationship between diabetic nephropathy and depression?

Methods

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist (S1 Checklist) [13], and registered in the international prospective register of systematic reviews (PROSPERO) under the registration number CRD42022357342.

Inclusion and exclusion criteria

Included in the analysis were those that met the following criteria: (1) the study design was a cohort, case-control, cross-sectional study; (2) contained DN and depression (depressive disorders and symptoms, not only the syndrome of major depression); (3) reported odds ratios (ORs), relative risks (RRs) or hazard ratios (HRs) with corresponding 95% confidence intervals (CI); and (4) published in English. Exclusion criteria: (1) duplicate literature; (2) reviews, conferences abstracts, case reports, animal experiments; and (3) randomized controlled trial study (RCTs).

Search strategy

PubMed, Embase and Web of Science databases were searched for related studies the earliest available online to 31 August 2022. A text search with the following keywords singly or in combination was conducted: ‘Diabetic Nephropathy’, ‘Diabetic Kidney Disease’, ‘Chronic Kidney Disease’, ‘Nephropathy’, ‘Nephrosis’, ‘DN’, ‘DKD’, ‘Diabetes Mellitus’, ‘Diabetes Mellitus, Type 2’, ‘Diabetes Mellitus, Type 1’, ‘Depression’, ‘Emotional Depression’, ‘Tristimania’, ‘Depressive Symptom’. The detailed search strategy was shown in Fig 1. Additionally, we also searched and retrieved the references of all included studies [14].

thumbnail
Download:
Fig 1. Systematic review flow chart.

https://doi.org/10.1371/journal.pone.0278489.g001

Data extraction

Data extraction was conducted with the information including the following: (1) name of the first author, (2) publication year, (3) country, (4) study design, (5) sample size, (6) age, (7) method of depression assessment, (8) diagnostic methods of DN, (9) adjusted odds ratios (ORs), relative risks (RRs) or hazard ratio (HRs) with 95% CI. The DN assessment criteria was by (1) albuminuria, (2) estimate glomerular filtration rate (eGFR), (3) urinary albumin-to-creatinine ratio (UACR), and (4) urinary albumin excretion rate (UAER). The depression assessment criteria was by (1) diagnostic interview, (2) self-reported questionnaire, and (3) physician diagnosis [14]. In selected studies, when DN was divided into different grades according to the degree of renal impairment, the OR/RR/HR values of the most severe group were selected for summary analysis. In order to control the influence of confounding factors, when there were multiple adjusted OR/HR/RR values, the most adjusted value was selected for summary analysis [14].

Quality assessment

Quality control of cross‐sectional studies was evaluated using the assessment involving 11 items recommended by the Agency for Healthcare Research and Quality (AHRQ). The total score ranged from 0 to 11, with a score of 8 or higher considered high quality (S1 Table in S1 File) [15]. For cohort and case-control studies, we utilized the Newcastle-Ottawa Scale (NOS), where total scores range from 0 to 9 with a score of 7 or higher regarded as high quality (S2 Table in S1 File) [16]. All articles were independently assessed by two reviewers. Most studies were from moderate to high quality.

Statistical analysis

Since the estimated results of random effects model had larger confidence intervals than those of fixed-effects models, we used a random effects model to generate odds ratio [17]. Two separate analyses were conducted: DN predicting depression and depression predicting DN. The multivariable-adjusted effect estimated OR with 95% CI was used as a common measure. Statistical heterogeneity between the studies was tested with the Cochran Q and I2 indices. Subgroup analyses was used to explore potential variability, with subgroups including type of diabetes, study design, age, country, method of depression assessment, evaluation of DN, classification of depression. Begg’s test was used to assess the publication bias. All analyses were calculated by using STATA 13.0 software (Stata Corporation, College Station, TX, USA), and the statistical significance was set a priori at P ≤ 0.05.

Results

Literature search results

Our search strategy yielded 32,108 articles, of which 9,759 were found to be duplicate records and were therefore excluded. In addition, 22,304 articles were excluded on the account that they were reviews, meta-analyses, conferences abstracts, case reports, animal experiments or irrelevant studies. Consequently, 45 articles were eligible for full-text review and data assessment. Among them, 15 were excluded due to the following reasons: 7 lacked sufficient data to generate reliable risk estimates, and 8 described the relationship between depression and diabetes/diabetes complications (Fig 1). Ultimately, 30 studies were investigated, where 24 were about DN predicting depression [18–41] and 6 were about depression predicting DN [42–47] (Tables 1 and 2). It was noted that 4 of the final 30 studies reported HR and one reported RR in risk evaluation [27]. Since OR was a commonly used indicator in case-control studies in epidemiology [48], the reported RR/HR data was regarded as OR as an approximation [49].

thumbnail
Download:
Table 1. Studies of diabetic nephropathy (DN) and incident depression.

https://doi.org/10.1371/journal.pone.0278489.t001

thumbnail
Download:
Table 2. Studies of depression and incident diabetic nephropathy (DN).

https://doi.org/10.1371/journal.pone.0278489.t002

Study characteristics and quality assessment

The features of the screened studies were gathered in Tables 1 and 2. In the 30 studies published between 2009 and 2020 were involved 974,121 participants. Six studies were from Europe [21, 23, 28, 37, 40, 46], one from Africa [29], 16 from Asia [19, 20, 22, 24, 26, 30–33, 35, 36, 38, 39, 41, 45, 47], and 7 from America [18, 25, 27, 34, 42–44]. There were 10 prospective cohort studies [18, 22, 25, 35, 37, 40, 43, 44, 46, 47], 18 cross-sectional studies [19–21, 23, 26–30, 32, 33, 35, 36, 38, 39, 41], 2 case-control studies [23, 31]. The largest number of participants were 933,211, while the longest follow-up time was 25 years. Cross-sectional, case-control, cohort studies were evaluated by AHRQ and NOS assessment, respectively. Results of quality assessments in the included studies are shown in S1-S3 Tables in S1 File. All included studies exhibited moderate to high qualities. Therefore, 30 studies were included in the meta‐analysis (Tables 1 and 2).

There was 1 study analyzed the relationship between type 1 and type 2 DN and depression with two OR values and 95% confidence interval reported, this report was divided into two groups that were pooled with other studies [21]. Similarly, for the study investigating the relationship between albuminuria/eGFR and depression, the data were divided for two groups as well [42]. According to the conditions of patients, depression was marked as mild, moderate, and severe in 3 studies, we categorized the moderate and severe depression as one group [30, 43, 47]. For the literature that analyzed the relationship between albuminuria/eGFR and the mild, moderate/severe depression, the data were divided for four groups [32]. Thus, a total of 38 groups from 30 studies were included in our meta-analysis (Tables 1 and 2).

DN predicting depression

The result of meta-analysis showed a significant link between DN and depression (Fig 2). Analysis of the 28,438 participants in 24 studies showed DN predicting depression (Fig 2A). The pooled OR was 1.46 (95% CI: 1.27–1.67), showing that DN may be an apparent risk factor for depression. However, a significant heterogeneity was identified across the included studies (P = 0.000, I2 = 77.9%) (Fig 2A).

thumbnail
Download:
Fig 2.

Forest plot of depression and diabetic nephropathy A: diabetic nephropathy predicting incident depression. B: depression predicting incident diabetic nephropathy.

https://doi.org/10.1371/journal.pone.0278489.g002

Depression predicting DN

In 945,683 participants of 6 studies were shown that depression predicted DN (Fig 2B). Interestingly, a relatively low heterogeneity was noted across the studies (P = 0.324, I2 = 13.3%). As indicated by the pooled OR value of 1.22 (95% CI: 1.13–1.31), depression may be an indicator for DN (Fig 2B).

Subgroup analysis

Considering the significant heterogeneity across the studies on DN predicting depression, a series of random effects on subgroup analyses was explored in the identified association across groups. As shown in Table 3, the studies were categorized as subgroups based on the type of DN, study design, age, country, method of depression assessment, diagnostic methods of DN, and classification of depression. Subgroup analyses showed that T2DM-induced DN (OR 1.95, 95% CI: 1.43–2.66) exhibited a higher risk for depression than that induced by T1DM (OR 1.08, 95% CI: 0.99–1.19). Moreover, the case-control study (OR 1.49, 95% CI: 1.06–1.92) showed the highest risks of among different study designs. Furthermore, the DN participants from Africa were more prone to depression (OR 2.9, 95% CI: 1.23–6.85) than those from other areas, suggesting that the diagnostic and assessment methods for DN and depression showed high impacts on the risk estimates.

thumbnail
Download:
Table 3. Results of subgroup analysis about (DN) and incident depression.

https://doi.org/10.1371/journal.pone.0278489.t003

Publication bias

The publication bias of the included studies was evaluated using the funnel plot (Fig 3). Additionally, the Begg’s test was used to measure its potential symmetry (Fig 4). The funnel plot and statistical analysis showed an apparent publication bias in DN predicting depression (Begg’s test, P = 0.038, Figs 3A and 4A). However, for the analysis of depression predicting DN, the funnel plot together with the Begg’s test (P = 0.175) did not indicate any publication bias of the included studies (Figs 3B and 4B).

thumbnail
Download:
Fig 3.

Funnel plot: A: diabetic nephropathy predicting incident depression. B: depression predicting incident diabetic nephropathy.

https://doi.org/10.1371/journal.pone.0278489.g003

thumbnail
Download:
Fig 4. Begg’s funnel plot with 95% confidence limits.

A: diabetic nephropathy predicting incident depression. B: depression predicting incident diabetic nephropathy.

https://doi.org/10.1371/journal.pone.0278489.g004

Discussion

Previous studies show associations between depression and diabetes, with type 2 diabetes increasing a 24% risk of developing depression [14], and depression increasing a 60% risk of developing type 2 diabetes [12]. Through a systematic review and meta-analysis, the present study investigated a bidirectional association between depression and diabetes. Our meta-analysis shows that DN may be a significant risk factor of depression. When considering the different evaluation methods for DN, studies using the UAER/UACR/albuminuria as assessment criteria showed promoted increase in the risk estimate compared to those assessed by eGFR (Table 3). The result was in agreement with Yu’s cohort study, where a higher prevalence of microalbuminuria instead of eGFR was found in relation to depression [42]. Albuminuria was a risk factor of cardio-cerebrovascular disease [50], and cardio-cerebrovascular disease could aggravate the condition of patients with advanced kidney disease, leading to depression [51]. Hedayati et al. found a close relationship between depression and eGFR. Veterans with chronic kidney disease had a 21% increased risk of major depression in the United States [52]. This difference may be related to different study populations and study designs. The mechanisms underlying the comorbidity of DN and depression remain unclear. What specific biological or psychological factors that would mediate a specific, and bidirectional association between DN and depression is still intriguing. Similar to the established link between diabetes mellitus and depression, the mechanism between DN and depression may potentially involve factors of serious complication of diabetes and effect of patient therapies such as dialysis or kidney transplantation [53]. While patients with kidney failure experienced breathing difficulties, fatigue, or poor appetite, which rendered patients more prone to depression [27, 54], the risk of depression appeared reduced after a successful kidney transplant [55].

Depression impacts every aspect of human lives including social, psychological, behavioral, and biological activities. Our present study suggests an overall mild association between depression and the incidence of DN (Fig 2). Yu MK’s et al. reported that major depression led to an 85% increased risk of ESRD in patients with type 2 diabetes, while mild depression had no increased risk [43]. Hedayati et al. found that for the male veterans with CKD, major depression led to a 3.5-fold increased risk of chronic dialysis [52]. Tsai et al. showed that severe depressive symptoms in patients with CKD leaded to a faster decline in renal function, ESRD, or death [53]. All these reports support the model of depression coexisting as a comorbidity of DN. In addition, studies have revealed that depression-related behaviors and medications for depression lead to DN [56]. Similar to depression with diabetes mellitus, the mechanism of DN pathogenesis may be related to the biological and behavioral risk factors caused by depression [57]. Once depression occurs in diabetic patients, their self-care abilities such as diet, exercise, blood glucose monitoring, and so on will decline, leading to the development of DN [58–61]. Besides, depression relates to the disorder of immune system, and the expression of inflammation markers (IL-6, C-reactive protein, TNF-α) increases the risk of DN [62–66]. Furthermore, depression increases the activities of the hypothalamic-pituitary-adrenal axis, sympathetic nervous system, the stress hormone, cortisol excites glucose production, fat decomposition, free fatty acids circulation, and reduces insulin secretion and sensitivity, potentially resulting increased risk of DN [67–69].

Limitation of the study

The present study suggests for the first time a bidirectional risk development between DN and depression by meta-analysis of meaningful studies and participants. The findings are generally in agreement with the reported cases of illness and of value for consideration in the management of DN and depression. However, there are several limitations in this study for cautious interpretation. First, the quality of related studies is of limitation, with potential heterogeneity and publication bias. Although we chose adjusted estimates to analyze the DN-depression relationship, confounders may be adjusted differently by different investigators, leading to varied statistic result. Variability in adjustment for confounding factors, could be a possible source of heterogeneity. Second, DN and depression might be assessed differently in different studies that potentially influenced the result. For the diagnostic methods of DN, 18 studies did not describe any specific evaluation method [18–21, 23–26, 29–31, 33, 36–39, 41, 43], 5 studies used UAER/UACR/albuminuria to assess renal function [22, 33, 40, 45, 46], and 4 studies used eGFR [27, 28, 32, 44]. For the depression assessment, diagnostic interview was a gold standard for major depression, only 2 studies adopted this method [19, 21]. Additionally, 25 studies used different cutoff scores to assess depression [18, 20, 22–36, 38–43, 45, 47], some cutoff scores were not commonly used. Moreover, depression might change over time, and dynamic scores were unavailable. Three studies accessed depression by general practitioner’s diagnosis. However, as reported, approximately 50% of depressive symptoms were overlooked in general practice [37, 44, 45]. Furthermore, patients with depression tended to go to the hospital more frequently and diagnostic bias could not be excluded with impact on the results.

Conclusion

This meta-analysis shows that the relationship between DN and depression may be bidirectional. DN may be a sensitive risk factor of depression, and depression may be also a predictor of DN. The mechanisms underlying this relationship warrant further investigation, and so is clinical study in the comorbidity of depression and DN.

References

  1. 1. (2016) Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388: 1545–1602.
  2. 2. Shaw JE, Sicree RA, Zimmet PZ (2010) Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87: 4–14. pmid:19896746
  3. 3. Burrows NR, Hora I, Geiss LS, Gregg EW, Albright A (2017) Incidence of End-Stage Renal Disease Attributed to Diabetes Among Persons with Diagnosed Diabetes—United States and Puerto Rico, 2000–2014. MMWR Morb Mortal Wkly Rep 66: 1165–1170. pmid:29095800
  4. 4. Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, et al. (2018) Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int 94: 567–581.
  5. 5. Zhang L, Long J, Jiang W, Shi Y, He X, Zhou Z, et al. (2016) Trends in Chronic Kidney Disease in China. N Engl J Med 375: 905–906. pmid:27579659
  6. 6. Noubiap JJ, Naidoo J, Kengne AP (2015) Diabetic nephropathy in Africa: A systematic review. World J Diabetes 6: 759–773. pmid:26069725
  7. 7. Roy T, Lloyd CE (2012) Epidemiology of depression and diabetes: a systematic review. J Affect Disord 142 Suppl: S8–21. pmid:23062861
  8. 8. Schram MT, Baan CA, Pouwer F (2009) Depression and quality of life in patients with diabetes: a systematic review from the European depression in diabetes (EDID) research consortium. Curr Diabetes Rev 5: 112–119. pmid:19442096
  9. 9. Champaneri S, Wand GS, Malhotra SS, Casagrande SS, Golden SH (2010) Biological basis of depression in adults with diabetes. Curr Diab Rep 10: 396–405. pmid:20878274
  10. 10. Subba R, Sandhir R, Singh SP, Mallick BN, Mondal AC (2021) Pathophysiology linking depression and type 2 diabetes: Psychotherapy, physical exercise, and fecal microbiome transplantation as damage control. Eur J Neurosci 53: 2870–2900. pmid:33529409
  11. 11. Golden SH (2007) A review of the evidence for a neuroendocrine link between stress, depression and diabetes mellitus. Curr Diabetes Rev 3: 252–259. pmid:18220683
  12. 12. Mezuk B, Eaton WW, Albrecht S, Golden SH (2008) Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care 31: 2383–2390. pmid:19033418
  13. 13. Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6: e1000097. pmid:19621072
  14. 14. Nouwen A, Winkley K, Twisk J, Lloyd CE, Peyrot M, Ismail K, et al. (2010) Type 2 diabetes mellitus as a risk factor for the onset of depression: a systematic review and meta-analysis. Diabetologia 53: 2480–2486. pmid:20711716
  15. 15. Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, et al. (2008) Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 336: 1106–1110. pmid:18483053
  16. 16. Lo CK, Mertz D, Loeb M (2014) Newcastle-Ottawa Scale: comparing reviewers’ to authors’ assessments. BMC Med Res Methodol 14: 45. pmid:24690082
  17. 17. Berlin JA (1995) Invited commentary: benefits of heterogeneity in meta-analysis of data from epidemiologic studies. Am J Epidemiol 142: 383–387. pmid:7625402
  18. 18. Katon W, Russo J, Lin EH, Heckbert SR, Ciechanowski P, Ludman EJ, et al. (2009) Depression and diabetes: factors associated with major depression at five-year follow-up. Psychosomatics 50: 570–579. pmid:19996227
  19. 19. Yoshida S, Hirai M, Suzuki S, Awata S, Oka Y (2009) Neuropathy is associated with depression independently of health-related quality of life in Japanese patients with diabetes. Psychiatry Clin Neurosci 63: 65–72. pmid:19067994
  20. 20. Raval A, Dhanaraj E, Bhansali A, Grover S, Tiwari P (2010) Prevalence and determinants of depression in type 2 diabetes patients in a tertiary care centre. Indian J Med Res 132: 195–200. pmid:20716820
  21. 21. Pouwer F, Geelhoed-Duijvestijn PH, Tack CJ, Bazelmans E, Beekman AJ, Heine RJ, et al. (2010) Prevalence of comorbid depression is high in out-patients with Type 1 or Type 2 diabetes mellitus. Results from three out-patient clinics in the Netherlands. Diabet Med 27: 217–224. pmid:20546267
  22. 22. Poongothai S, Anjana RM, Pradeepa R, Ganesan A, Unnikrishnan R, Rema M, et al. (2011) Association of depression with complications of type 2 diabetes—the Chennai Urban Rural Epidemiology Study (CURES-102). J Assoc Physicians India 59: 644–648. pmid:22479744
  23. 23. van Steenbergen-Weijenburg KM, van Puffelen AL, Horn EK, Nuyen J, van Dam PS, van Benthem TB, et al. (2011) More co-morbid depression in patients with Type 2 diabetes with multiple complications. An observational study at a specialized outpatient clinic. Diabet Med 28: 86–89. pmid:21210541
  24. 24. Bajaj S, Agarwal SK, Varma A, Singh VK (2012) Association of depression and its relation with complications in newly diagnosed type 2 diabetes. Indian J Endocrinol Metab 16: 759–763. pmid:23087860
  25. 25. Hirai FE, Tielsch JM, Klein BE, Klein R (2012) Relationship between retinopathy severity, visual impairment and depression in persons with long-term type 1 diabetes. Ophthalmic Epidemiol 19: 196–203. pmid:22775274
  26. 26. Roy T, Lloyd CE, Parvin M, Mohiuddin KG, Rahman M (2012) Prevalence of co-morbid depression in out-patients with type 2 diabetes mellitus in Bangladesh. BMC Psychiatry 12: 123. pmid:22909306
  27. 27. Campbell KH, Huang ES, Dale W, Parker MM, John PM, Young BA, et al. (2013) Association between estimated GFR, health-related quality of life, and depression among older adults with diabetes: the Diabetes and Aging Study. Am J Kidney Dis 62: 541–548. pmid:23746376
  28. 28. D’Amato C, Morganti R, Greco C, Di Gennaro F, Cacciotti L, Longo S, et al. (2016) Diabetic peripheral neuropathic pain is a stronger predictor of depression than other diabetic complications and comorbidities. Diab Vasc Dis Res 13: 418–428. pmid:27334483
  29. 29. Habtewold TD, Alemu SM, Haile YG (2016) Sociodemographic, clinical, and psychosocial factors associated with depression among type 2 diabetic outpatients in Black Lion General Specialized Hospital, Addis Ababa, Ethiopia: a cross-sectional study. BMC Psychiatry 16: 103. pmid:27083154
  30. 30. Ishizawa K, Babazono T, Horiba Y, Nakajima J, Takasaki K, Miura J, et al. (2016) The relationship between depressive symptoms and diabetic complications in elderly patients with diabetes: Analysis using the Diabetes Study from the Center of Tokyo Women’s Medical University (DIACET). J Diabetes Complications 30: 597–602. pmid:26987919
  31. 31. Rajput R, Gehlawat P, Gehlan D, Gupta R, Rajput M (2016) Prevalence and predictors of depression and anxiety in patients of diabetes mellitus in a tertiary care center. Indian J Endocrinol Metab 20: 746–751. pmid:27867873
  32. 32. Takasaki K, Babazono T, Ishizawa K, Miura J, Uchigata Y (2016) Relationship between diabetic nephropathy and depression: a cross-sectional analysis using the Diabetes Study from the Center of Tokyo Women’s Medical University (DIACET). BMJ Open Diabetes Res Care 4: e000310. pmid:28074142
  33. 33. AlBekairy A, AbuRuz S, Alsabani B, Alshehri A, Aldebasi T, Alkatheri A, et al. (2017) Exploring Factors Associated with Depression and Anxiety among Hospitalized Patients with Type 2 Diabetes Mellitus. Med Princ Pract 26: 547–553. pmid:29131123
  34. 34. Bai JW, Lovblom LE, Cardinez M, Weisman A, Farooqi MA, Halpern EM, et al. (2017) Neuropathy and presence of emotional distress and depression in longstanding diabetes: Results from the Canadian study of longevity in type 1 diabetes. J Diabetes Complications 31: 1318–1324. pmid:28599823
  35. 35. Wang X, Shen B, Zhuang X, Wang X, Weng W (2017) Investigating Factors Associated with Depressive Symptoms of Chronic Kidney Diseases in China with Type 2 Diabetes. J Diabetes Res 2017: 1769897. pmid:28261621
  36. 36. Albasheer OB, Mahfouz MS, Solan Y, Khan DA, Muqri MA, Almutairi HA, et al. (2018) Depression and related risk factors among patients with type 2 diabetes mellitus, Jazan area, KSA: A cross-sectional study. Diabetes Metab Syndr 12: 117–121. pmid:29037887
  37. 37. Salinero-Fort MA, Gomez-Campelo P, San Andres-Rebollo FJ, Cardenas-Valladolid J, Abanades-Herranz JC, Carrillo de Santa Pau E, et al. (2018) Prevalence of depression in patients with type 2 diabetes mellitus in Spain (the DIADEMA Study): results from the MADIABETES cohort. BMJ Open 8: e020768. pmid:30249627
  38. 38. Khan P, Qayyum N, Malik F, Khan T, Khan M, Tahir A (2019) Incidence of Anxiety and Depression Among Patients with Type 2 Diabetes and the Predicting Factors. Cureus 11: e4254. pmid:31131177
  39. 39. Sharif S, Raza MT, Mushtaq S, Afreen B, Hashmi BA, Ali MH (2019) Frequency of Depression in Patients with Type 2 Diabetes Mellitus and its Relationship with Glycemic Control and Diabetic Microvascular Complications. Cureus 11: e5145. pmid:31523573
  40. 40. Ahola AJ, Radzeviciene L, Zaharenko L, Bulum T, Skrebinska S, Prakapiene E, et al. (2020) Association between symptoms of depression, diabetes complications and vascular risk factors in four European cohorts of individuals with type 1 diabetes—InterDiane Consortium. Diabetes Res Clin Pract 170: 108495. pmid:33058955
  41. 41. Aljohani W, Algohani L, Alzahrani A, Bazuhair M, Bawakid A, Aljuid L, et al. (2021) Prevalence of Depression Among Patients With Type 2 Diabetes at King Abdullah Medical City. Cureus 13: e18447. pmid:34745773
  42. 42. Yu MK, Katon W, Young BA (2013) Diabetes self-care, major depression, and chronic kidney disease in an outpatient diabetic population. Nephron Clin Pract 124: 106–112. pmid:24192760
  43. 43. Yu MK, Weiss NS, Ding X, Katon WJ, Zhou XH, Young BA (2014) Associations between depressive symptoms and incident ESRD in a diabetic cohort. Clin J Am Soc Nephrol 9: 920–928. pmid:24677559
  44. 44. Novak M, Mucsi I, Rhee CM, Streja E, Lu JL, Kalantar-Zadeh K, et al. (2016) Increased Risk of Incident Chronic Kidney Disease, Cardiovascular Disease, and Mortality in Patients With Diabetes With Comorbid Depression. Diabetes Care 39: 1940–1947. pmid:27311494
  45. 45. Pan S, Liu ZW, Shi S, Ma X, Song WQ, Guan GC, et al. (2017) Hamilton rating scale for depression-24 (HAM-D24) as a novel predictor for diabetic microvascular complications in type 2 diabetes mellitus patients. Psychiatry Res 258: 177–183. pmid:28774662
  46. 46. Ahola AJ, Harjutsalo V, Forsblom C, Pouwer F, Groop PH (2021) Depression Is Associated With Progression of Diabetic Nephropathy in Type 1 Diabetes. Diabetes Care 44: 174–180. pmid:33177173
  47. 47. Horiba Y, Ishizawa K, Takasaki K, Miura J, Babazono T (2022) Effect of depression on progression to end-stage renal disease or pre-end-stage renal disease death in advanced diabetic nephropathy: A prospective cohort study of the Diabetes Study from the Center of Tokyo Women’s Medical University. J Diabetes Investig 13: 94–101. pmid:34174034
  48. 48. Furcada JM, Patino CM, Ferreira JC (2022) Estimating risk in clinical studies: odds ratio and risk ratio. J Bras Pneumol 46: e20200137.
  49. 49. Wang D, Fang R, Han H, Zhang J, Chen K, Fu X, et al. (2022) Association between visceral adiposity index and risk of prediabetes: A meta-analysis of observational studies. J Diabetes Investig 13: 543–551. pmid:34592063
  50. 50. Anavekar NS, Gans DJ, Berl T, Rohde RD, Cooper W, Bhaumik A, et al. (2004) Predictors of cardiovascular events in patients with type 2 diabetic nephropathy and hypertension: a case for albuminuria. Kidney Int Suppl: S50–55. pmid:15485418
  51. 51. Loomba RS, Aggarwal S, Arora R (2015) Depressive Symptom Frequency and Prevalence of Cardiovascular Diseases-Analysis of Patients in the National Health and Nutrition Examination Survey. Am J Ther 22: 382–387. pmid:25658955
  52. 52. Hedayati SS, Minhajuddin AT, Afshar M, Toto RD, Trivedi MH, Rush AJ. (2010) Association between major depressive episodes in patients with chronic kidney disease and initiation of dialysis, hospitalization, or death. Jama 303: 1946–1953. pmid:20483971
  53. 53. Tsai YC, Chiu YW, Hung CC, Hwang SJ, Tsai JC, Wang SL, et al. (2012) Association of symptoms of depression with progression of CKD. Am J Kidney Dis 60: 54–61. pmid:22495469
  54. 54. Tovilla-Zárate C, Juárez-Rojop I, Peralta Jimenez Y, Jiménez MA, Vázquez S, Bermúdez-Ocaña D, et al. (2012) Prevalence of anxiety and depression among outpatients with type 2 diabetes in the Mexican population. PloS one 7: e36887. pmid:22629339
  55. 55. Ginieri-Coccossis M, Theofilou P, Synodinou C, Tomaras V, Soldatos C (2008) Quality of life, mental health and health beliefs in haemodialysis and peritoneal dialysis patients: investigating differences in early and later years of current treatment. BMC Nephrol 9: 14. pmid:19014597
  56. 56. Engum A (2007) The role of depression and anxiety in onset of diabetes in a large population-based study. J Psychosom Res 62: 31–38. pmid:17188118
  57. 57. Pouwer F, Schram MT, Iversen MM, Nouwen A, Holt RIG (2020) How 25 years of psychosocial research has contributed to a better understanding of the links between depression and diabetes. Diabet Med 37: 383–392. pmid:31909844
  58. 58. Hu FB, Stampfer MJ, Solomon C, Liu S, Colditz GA, Speizer FE, et al. (2001) Physical activity and risk for cardiovascular events in diabetic women. Ann Intern Med 134: 96–105. pmid:11177312
  59. 59. Gregg EW, Gerzoff RB, Caspersen CJ, Williamson DF, Narayan KM. (2003) Relationship of walking to mortality among US adults with diabetes. Arch Intern Med. 163: 1440–1447. pmid:12824093
  60. 60. Martin S, Schneider B, Heinemann L, Lodwig V, Kurth HJ, Kolb H, et al. (2006) Self-monitoring of blood glucose in type 2 diabetes and long-term outcome: an epidemiological cohort study. Diabetologia 49: 271–278. pmid:16362814
  61. 61. Wing RR (2010) Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial. Arch Intern Med 170: 1566–1575. pmid:20876408
  62. 62. Maes M, Bosmans E, De Jongh R, Kenis G, Vandoolaeghe E, Neels H (1997) Increased serum IL-6 and IL-1 receptor antagonist concentrations in major depression and treatment resistant depression. Cytokine 9: 853–858. pmid:9367546
  63. 63. Dentino AN, Pieper CF, Rao MK, Currie MS, Harris T, Blazer DG, et al. (1999) Association of interleukin-6 and other biologic variables with depression in older people living in the community. J Am Geriatr Soc 47: 6–11. pmid:9920223
  64. 64. Schmidt MI, Duncan BB, Sharrett AR, Lindberg G, Savage PJ, Offenbacher S, et al. (1999) Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study. Lancet 353: 1649–1652. pmid:10335783
  65. 65. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM (2001) C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. Jama 286: 327–334. pmid:11466099
  66. 66. Kiecolt-Glaser JK, Glaser R (2002) Depression and immune function: central pathways to morbidity and mortality. J Psychosom Res 53: 873–876. pmid:12377296
  67. 67. Björntorp P (2001) Do stress reactions cause abdominal obesity and comorbidities? Obes Rev 2: 73–86. pmid:12119665
  68. 68. Ramasubbu R (2002) Insulin resistance: a metabolic link between depressive disorder and atherosclerotic vascular diseases. Med Hypotheses 59: 537–551. pmid:12376076
  69. 69. Weber-Hamann B, Hentschel F, Kniest A, Deuschle M, Colla M, Lederbogen F, et al. (2002) Hypercortisolemic depression is associated with increased intra-abdominal fat. Psychosomatic Med 64: 274–277. pmid:11914443