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Cited by 40Artificial Intelligence in HealthcareCardiac Health and Mental HealthDiabetes, Cardiovascular Risks, and Lipoproteins

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach

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Chu, Haiyun, Chen, Lu, Yang, Xiuxian, Qiu, Xiaohui, Qiao, Zhengxue, Song, Xuejia, Zhao, Erying, Zhou, Jiawei +4 more·2021·Frontiers in Psychology
DOI 10.3389/fpsyg.2021.645418PMC PMC8113686
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Abstract

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.

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  • Chu et al. incorporated psychosocial determinants into a DNN framework, achieving AUC = 0.91, suggesting that psychosocial and behavioral variables provide additional predictive value beyond traditional biomarkers.

    “Chu et al. [97] further incorporated psychosocial determinants into a DNN framework, achieving AUC = 0.91, suggesting that psychosocial and behavioral variables provide additional predictive value beyond traditional biomarkers.”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [97]

    The Results (CVD Risk Prediction Model in Patients With T2DM; page unavailable) report a DNN AUC of 0.91 and rank anxiety and depression among its top five predictors. The paper does not report a comparison with a biomarkers-only model, so it does not establish additional predictive value beyond traditional biomarkers; the cited results also do not establish that for behavioral variables. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Chu et al. applied XGBoost framework with psychosocial variables, significantly improving CVD risk prediction.

    “Chu et al. [97] applied XGBoost framework with psychosocial variables, significantly improving CVD risk prediction.”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [97]

    The Methods and Results describe a CVD prediction model for patients with type 2 diabetes that includes anxiety and depression, but identify it as a deep neural network, not XGBoost. The Results report its performance (AUC 0.91), but do not establish a statistically significant improvement in prediction from adding psychosocial variables. The indexed passages provide section labels but no page numbers. (LLM, conf=0.91)

    Experimental — accuracy not yet measured

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References 45 · Cited by 40

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  1. Allabadi H. Alkaiyat A. Alkhayyat A. Hammoudi A. Odeh H. Shtayeh J.. (2019). Depression and anxiety symptoms in cardiac patients: a cross-sectional hospital-based study in a Palestinian population. BMC Public Health 19: 232. 10.1186/s12889-019-6561-3 30808333 PMC6390372 · DOI
  2. American Diabetes Association (2014). Diagnosis and classification of diabetes mellitus. Diabetes Care 37, S81– 90. 10.2337/dc14-S081 24357215 · DOI · Paper page
  3. Baum A. Scarpa J. Bruzelius E. Tamler R. Basu S. Faghmous J. (2017). Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. Lancet Diabetes Endocrinol. 5, 808– 815. 10.1016/S2213-8587(17)30176-6 28711469 PMC5815373 · DOI
  4. Bommer C. Sagalova V. Heesemann E. Manne-Goehler J. Atun R. Bärnighausen T.. (2018). Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care 41, 963– 970. 10.2337/dc17-1962 29475843 · DOI
  5. Caruso G. Fresta C. G. Grasso M. Santangelo R. Lazzarino G. Lunte S. M.. (2020). Inflammation as the common biological link between depression and cardiovascular diseases: can carnosine exert a protective role? Curr. Med. Chem. 27, 1782– 1800. 10.2174/0929867326666190712091515 31296155 · DOI
  6. Deschênes S. S. Burns R. J. Schmitz N. (2018). Trajectories of anxiety symptoms and associations with incident cardiovascular disease in adults with type 2 diabetes. J. Psychosom. Res. 104, 95– 100. 10.1016/j.jpsychores.2017.11.015 29275792 · DOI
  7. Einarson T. R. Acs A. Ludwig C. Panton U. H. (2018). Economic burden of cardiovascular disease in type 2 diabetes: a systematic review. Value Health 21, 881– 890. 10.1016/j.jval.2017.12.019 30005761 · DOI
  8. Emdin C. A. Odutayo A. Wong C. X. Tran J. Hsiao A. J. Hunn B. H. (2016). Meta-analysis of anxiety as a risk factor for cardiovascular disease. Am. J. Cardiol. 118, 511– 519. 10.1016/j.amjcard.2016.05.041 27324160 · DOI · Paper page
  9. Gray N. Picone G. Sloan F. Yashkin A. (2015). Relation between BMI and diabetes mellitus and its complications among US older adults. South. Med. J. 108, 29– 36. 10.14423/SMJ.0000000000000214 25580754 PMC4457375 · DOI
  10. Hazuda H. P. Gaussoin S. A. Wing R. R. Yanovski S. Z. Johnson K. C. Coday M.. (2019). Long-term association of depression symptoms and antidepressant medication use with incident cardiovascular events in the look ahead (action for health in diabetes) clinical trial of weight loss in type 2 diabetes. Diabetes Care 42, 910– 918. 10.2337/dc18-0575 30833373 PMC6489104 · DOI
  11. Hiriscau E. I. Bodolea C. (2019). The role of depression and anxiety in frail patients with heart failure. Diseases 7: 45. 10.3390/diseases7020045 31248108 PMC6631213 · DOI
  12. Jiang J. L. Su X. Zhang H. Zhang X. H. Yuan Y. J. (2013). A novel approach to active compounds identification based on support vector regression model and mean impact value. Chem. Biol. Drug Des. 81, 650– 657. 10.1111/cbdd.12111 23350785 · DOI
  13. Jiang W. (2015). Emotional triggering of cardiac dysfunction: the present and future. Curr. Cardiol. Rep. 17: 91. 10.1007/s11886-015-0635-3 26298307 · DOI
  14. Johnson S. T. Al Sayah F. Mathe N. Johnson J. A. (2016). The relationship of diabetes-related distress and depressive symptoms with physical activity and dietary behaviors in adults with type 2 diabetes: a cross-sectional study. J. Diabetes Complicat. 30, 967– 970. 10.1016/j.jdiacomp.2016.02.019 27083444 · DOI
  15. Kibel A. Selthofer-Relatic K. Drenjancevic I. Bacun T. Bosnjak I. Kibel D.. (2017). Coronary microvascular dysfunction in diabetes mellitus. J. Int. Med. Res. 45, 1901– 1929. 10.1177/0300060516675504 28643578 PMC5805190 · DOI
  16. Kothari V. Stevens R. J. Adler A. I. Stratton I. M. Manley S. E. Neil H. A.. (2002). UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke 33, 1776– 1781. 10.1161/01.STR.0000020091.07144.C7 12105351 · DOI
  17. Larsson S. C. Wallin A. Håkansson N. Stackelberg O. Bäck M. Wolk A. (2018). Type 1 and type 2 diabetes mellitus and incidence of seven cardiovascular diseases. Int. J. Cardiol. 262, 66– 70. 10.1016/j.ijcard.2018.03.099 29605469 · DOI
  18. Lee S. Park H. Kim S. Lee E. K. Lee J. Hong Y. S.. (2019). Fine particulate matter and incidence of metabolic syndrome in non-CVD patients: a nationwide population-based cohort study. Int. J. Hyg. Environ. Health 222, 533– 540. 10.1016/j.ijheh.2019.01.010 30797734 · DOI
  19. Ley S. H. Hamdy O. Mohan V. Hu F. B. (2014). Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet 383, 1999– 2007. 10.1016/S0140-6736(14)60613-9 24910231 PMC4751088 · DOI · Paper page
  20. Libianto R. Batu D. MacIsaac R. J. Cooper M. E. Ekinci E. I. (2018). Pathophysiological links between diabetes and blood pressure. Can. J. Cardiol. 34, 585– 594. 10.1016/j.cjca.2018.01.010 29731021 · DOI
  21. Martino G. Caputo A. Vicario C. M. Catalano A. Schwarz P. Quattropani M. C. (2020). The relationship between alexithymia and type 2 diabetes: a systematic review. Front. Psychol. 11: 2026. 10.3389/fpsyg.2020.02026 32982843 PMC7484475 · DOI
  22. Meyer M. L. Lin F. C. Jaensch A. Mons U. Hahmann H. Koenig W.. (2019). Multi-state models of transitions in depression and anxiety symptom severity and cardiovascular events in patients with coronary heart disease. PLoS ONE 14: e0213334. 10.1371/journal.pone.0213334 30845176 PMC6405099 · DOI
  23. Miras A. D. Ravindra S. Humphreys A. Lascaratos G. Quartey K. Ahmed A. R.. (2019). Metabolic changes and diabetes microvascular complications 5 years after obesity surgery. Obes. Surg. 29, 3907– 3911. 10.1007/s11695-019-04095-w 31372874 · DOI
  24. Nowak C. Carlsson A. C. Östgren C. J. Nyström F. H. Alam M. Feldreich T.. (2018). Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes. Diabetologia 61, 1748– 1757. 10.1007/s00125-018-4641-z 29796748 PMC6061158 · DOI
  25. Perego C. Da Dalt L. Pirillo A. Galli A. Catapano A. L. Norata G. D. (2019). Cholesterol metabolism, pancreatic β-cell function and diabetes. Biochim. Biophys. Acta Mol. Basis Dis. 1865, 2149– 2156. 10.1016/j.bbadis.2019.04.012 31029825 · DOI
  26. Petrie J. R. Guzik T. J. Touyz R. M. (2018). Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Can. J. Cardiol. 34, 575– 584. 10.1016/j.cjca.2017.12.005 29459239 PMC5953551 · DOI · Paper page
  27. Ricci C. Wood A. Muller D. Gunter M. J. Agudo A. Boeing H.. (2018). Alcohol intake in relation to non-fatal and fatal coronary heart disease and stroke: EPIC-CVD case-cohort study. BMJ 361: k934. 10.1136/bmj.k934 29844013 PMC5972779 · DOI
  28. Rutledge T. Linke S. E. Krantz D. S. Johnson B. D. Bittner V. Eastwood J. A.. (2009). Comorbid depression and anxiety symptoms as predictors of cardiovascular events: results from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE) study. Psychosom. Med. 71, 958– 964. 10.1097/PSY.0b013e3181bd6062 19834049 PMC2783707 · DOI
  29. Saeedi P. Petersohn I. Salpea P. Malanda B. Karuranga S. Unwin N.. (2019). 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. 157: 107843. 10.1016/j.diabres.2019.107843 31518657 · DOI · Paper page
  30. Stewart J. Manmathan G. Wilkinson P. (2017). Primary prevention of cardiovascular disease: a review of contemporary guidance and literature. JRSM Cardiovasc. Dis. 6: 2048004016687211. 10.1177/2048004016687211 28286646 PMC5331469 · DOI
  31. Tonhajzerova I. Sekaninova N. Bona Olexova L. Visnovcova Z. (2020). Novel insight into neuroimmune regulatory mechanisms and biomarkers linking major depression and vascular diseases: the dilemma continues. Int. J. Mol. Sci. 21: e2317. 10.3390/ijms21072317 32230840 PMC7177743 · DOI
  32. van der Ende M. Y. Hartman M. H. Hagemeijer Y. Meems L. M. de Vries H. S. Stolk R. P.. (2017). The LifeLines Cohort Study: prevalence and treatment of cardiovascular disease and risk factors. Int. J. Cardiol. 228, 495– 500. 10.1016/j.ijcard.2016.11.061 27875724 · DOI
  33. Wang H. H. Garruti G. Liu M. Portincasa P. Wang D. Q. (2017). Cholesterol and lipoprotein metabolism and atherosclerosis: recent advances in reverse cholesterol transport. Ann. Hepatol. 16, s27– s42. 10.5604/01.3001.0010.5495 29080338 · DOI
  34. Zarkogianni K. Athanasiou M. Thanopoulou A. C. (2018). Comparison of machine learning approaches toward assessing the risk of developing cardiovascular disease as a long-term diabetes complication. IEEE J. Biomed. Health Inform. 22, 1637– 1647. 10.1109/JBHI.2017.2765639 29990007 · DOI
  35. Zhang Y. Vittinghoff E. Pletcher M. J. Allen N. B. Zeki Al Hazzouri A. Yaffe K.. (2019). Associations of blood pressure and cholesterol levels during young adulthood with later cardiovascular events. J. Am. Coll. Cardiol. 74, 330– 341. 10.1016/j.jacc.2019.03.529 31319915 PMC6764095 · DOI
  36. Zung W. W. (1965). A self-rating depression scale. Arch. Gen. Psychiatry 12, 63– 70. 10.1001/archpsyc.1965.01720310065008 14221692 · DOI · Paper page
  37. Zung W. W. (1971). A rating instrument for anxiety disorders. Psychosomatics 12, 371– 379. 10.1016/S0033-3182(71)71479-0 5172928 · DOI · Paper page

Authors
Chu, H., Chen, L., Yang, X., Qiu, X., Qiao, Z., Song, X., Zhao, E., Zhou, J., Zhang, W., Mehmood, A., Pan, H., & Yang, Y.
Affiliations
  • Harbin Medical University (CN)
  • Peking Union Medical College Hospital (CN)
Corresponding author
  • Pan, Hui
  • Yang, Yanjie
ORCID
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2021-04-28
Volume
12
Pages
645418-645418
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2021.645418
PMC
PMC8113686
OpenAlex
W3159591988
Cited by
40
References
45
Citations by year
2026: 10 · 2025: 7 · 2024: 6 · 2023: 11 · 2022: 5 · 2021: 1
FWCI
5.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Health and Mental Health
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords
type 2 diabetes, cardiovascular disease, cardiovascular risk prediction, anxiety, depression, psychological risk factors, deep neural network, body mass index, total cholesterol, systolic blood pressure, biopsychosocial model

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