Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study

English version

Autoři: Alex J. Mitchell ^aff001; Davy Vancampfort ^aff002; Peter Manu ^aff003; Christoph U. Correll ^aff005; Martien Wampers ^aff002; Ruud van Winkel ^aff002; Weiping Yu ^aff002; Marc De Hert ^aff002
Působiště autorů: University of Leicester, Leicester, England, United Kingdom ^aff001; University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium ^aff002; University Psychiatric Center, Kortenberg, Belgium ^aff003; School of Mental Health and Neuroscience (EURON), University Medical Center, Maastricht, The Netherlands ^aff004; Zucker Hillside Hospital, Glen Oaks, New York, United States ^aff005; Hofstra North Shore–LIJ School of Medicine, Hempstead, New York, United States ^aff006
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0210674

Souhrn

Objective

We aimed to investigate which clinical and metabolic tests offer optimal accuracy and acceptability to help diagnose diabetes among a large sample of people with serious mental illness in receipt of antipsychotic medication.

Methods

A prospective observational study design of biochemical and clinical factors was used. Biochemical measures were fasting glucose, insulin and lipids, oral glucose tolerance testing (OGTT), hemoglobin A1c, and insulin resistance assessed with the homeostatic model (HOMA-IR) were determined in a consecutive cohort of 798 adult psychiatric inpatients receiving antipsychotics. Clinical variables were gender, age, global assessment of functioning (GAF), mental health clinicians’ global impression (CGI), duration of severe mental illness, height, weight, BMI and waist/hip ratio. In addition, we calculated the risk using combined clinical predictors using the Leicester Practice Risk Score (LPRS) and the Topics Diabetes Risk Score (TDRS). Diabetes was defined by older criteria (impaired fasting glucose (IFG) or OGTT) as well as2010 criteria (IFG or OGTT or Glycated haemoglobin (HBA1c)) at conventional cut-offs.

Results

Using the older criteria, 7.8% had diabetes (men: 6.3%; women: 10.3%). Using the new criteria, 10.2% had diabetes (men: 8.2%, women: 13.2%), representing a 30.7% increase (p = 0.02) in the prevalence of diabetes. Regarding biochemical predictors, conventional OGTT, IFG, and HbA1c thresholds used to identify newly defined diabetes missed 25%, 50% and 75% of people with diabetes, respectively. The conventional HBA1c cut-point of ≥6.5% (48 mmol/mol) missed 7 of 10 newly defined cases of diabetes while a cut-point of ≥5.7% improved sensitivity from 44.4% to up to 85%. Specific algorithm approaches offered reasonable accuracy. Unfortunately no single clinical factor was able to accurately rule-in a diagnosis of diabetes. Three clinical factors were able to rule-out diabetes with good accuracy namely: BMI, waist/hip ratio and height. A BMI < 30 had a 92% negative predictive value in ruling-out diabetes. Of those not diabetic, 20% had a BMI ≥ 30. However, for complete diagnosis a specific biochemical protocol is still necessary.

Conclusions

Patients with SMI maintained on antipsychotic medication cannot be reliably screened for diabetes using clinical variables alone. Accurate assessment requires a two-step algorithm consisting of HBA1c ≥5.7% followed by both FG and OGTT which does not require all patients to have OGTT and FG.

Klíčová slova:

Medicine and health sciences – Endocrinology – Endocrine disorders – Metabolic disorders – Diagnostic medicine – Diabetes diagnosis and management – HbA1c – Pharmacology – Drugs – Antipsychotics – Vascular medicine – Blood pressure – Hypertension – Resistant hypertension – Mental health and psychiatry – Epidemiology – Medical risk factors – Biology and life sciences – Biochemistry – Proteins – Hemoglobin – Metabolism – Carbohydrate metabolism – Glucose metabolism

Zdroje

1. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al. Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis oaf health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet. 2011;378(9785):31–40. doi: 10.1016/S0140-6736(11)60679-X 21705069

2. International Diabetes Federation. IDF Diabetes Atlas, 8th edn. Brussels, Belgium: International Diabetes Federation, 2017. http://www.diabetesatlas.org

3. American Diabetes Association Standards of Medical Care in Diabetes—2010 Diabetes Care. 2010; 33(Supplement_1): S11–S61.

4. American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2016;39Suppl 1:S13–22.

5. Selvin E, Steffes MW, Zhu H et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults.N Engl J Med 2010;362:800–811 doi: 10.1056/NEJMoa0908359 20200384

6. Zhang X, Gregg EW, Williamson DF et al. A1C level and future risk of diabetes: a systematic review. Diabetes Care 2010;33:1665–167 doi: 10.2337/dc09-1939 20587727

7. van’t Riet E, Alssema M, Rijkelijkhuizen JM et al. Relationship between A1c and glucose levels in the general Dutch population. Diabetes Care 2010; 33: 61–66. doi: 10.2337/dc09-0677 19808928

8. Karnchanasorn R1, Huang J2, Ou HY3, Feng W2, Chuang LM4, Chiu KC2, et al. Comparison of the Current Diagnostic Criterion of HbA1c with Fasting and 2-Hour Plasma Glucose Concentration.J Diabetes Res. 2016:6195494. doi: 10.1155/2016/6195494 27597979

9. Mostafa SA, Davies MJ, Webb D, Gray LJ, Srinivasan BT, Jarvis J, et al. The potential impact of using glycated haemoglobin as the preferred diagnostic tool for detecting Type 2 diabetes mellitus.Diabet Med. 2010;27(7):762–9. doi: 10.1111/j.1464-5491.2010.03015.x 20636956

10. Cohen D., Stolk R.P., Grobbee D.E., Gispen-de Wied C.C. Hyperglycemia and diabetes in patients with schizophrenia or schizoaffective disorders.Diabetes care 2006; 29 (4), 786–791. 16567816

11. De Hert M, Dekker J.M., Wood D., Kahl K.G., Holt R.I.G., Möller H.-J. Cardiovascular disease and diabetes in people with severe mental illness position statement from the European Psychiatric Association (EPA), supported by the European Association for the Study of Diabetes (EASD) and the European Society of Cardiology (ESC) Eur Psychiatry. 2009;24(6):412–24. doi: 10.1016/j.eurpsy.2009.01.005 19682863

12. Ramaswamy K, Masand PS, Nasrallah HA. Do certain atypical antipsychotics increase the risk of diabetes? A critical review of 17 pharmacoepidemiologic studies. Ann Clin Psychiatry 2006;18:183–194. 16923657

13. Galling B1, Roldán A2, Nielsen RE3, Nielsen J4, Gerhard T5, Carbon M1, et al. Type 2 Diabetes Mellitus in Youth Exposed to Antipsychotics: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2016 Mar;73(3):247–59. doi: 10.1001/jamapsychiatry.2015.2923 26792761

14. De Hert M, Hanssens L, Wampers M, et al. Prevalence and incidence rates of metabolic abnormalities and diabetes in a prospective study of patients treated with second-generation antipsychotics. Schizophr Bull 2007;33:560.

15. Mitchell AJ, Vancampfort D, De Herdt A, Yu W, De Hert M. Is the prevalence of metabolic syndrome and metabolic abnormalities increased in early schizophrenia? A comparative meta-analysis of first episode, untreated and treated patients.Schizophr Bull. 2012. [Epub ahead of print]

16. van Winkel R, De Hert M, Wampers M, et al. Major changes in glucose metabolism, including new onset diabetes, within 3 months after initiation or switch to atypical antipsychotic medication in patients with schizophrenia and schizoaffective disorder. J Clin Psychiatry 2008; 69:472–479. 18348593

17. Roerig JL, Steffen KJ, Mitchell JE. Atypical antipsychotic-induced weight gain: insights into mechanisms of action. CNS Drugs. 2011 1;25(12):1035–59.

18. Correll CU, Kane JM, Manu P. Identification of high-risk coronary heart disease patients receiving atypical antipsychotics: Single low-density lipoprotein cholesterol threshold or complex national standard? J Clin Psychiatry 2008; 69:578–583. 18370572

19. De Hert M, Van Eyck D, Hanssens L, Peuskens H, Thys E, Wampers M, et al. Oral glucose tolerance tests in treated patients with schizophrenia. Data to support an adaptation of the proposed guidelines for monitoring of patients on second generation antipsychotics?Eur Psychiatry. 2006;21(4):224–6. 16139484

20. van Winkel R, De Hert M, Van Eyck D, Hanssens L, Wampers M, Scheen A, et al. Screening for diabetes and other metabolic abnormalities in patients with schizophrenia and schizoaffective disorder: evaluation of incidence and screening methods. J Clin Psychiatry. 2006; 67(10):1493–500. 17107239

21. Manu P, Correll CU, van Winkel R, Wampers M, DeHert M. Prediabetes in patients treated with antipsychotic drugs. J Clin Psychiatry. 2012; 73(4):460–6. doi: 10.4088/JCP.10m06822 22225552

22. Agarwal SK. Prediabetes in a schizophrenia population. European Psychiatry 2012, 27 (suppl 1)P-1198

23. Hanssens L, De Hert M, Van Eyck D, Wampers M, Peuskens J, Scheen A. Usefulness of Glycated haemoglobin (HbA1c) to screen for diabetes in patients with schizophrenia Schizophrenia Research 2006;. 85, iss. 1–3: 296–297. 16690257

24. Paulweber B, Valensi P, Lindstrom J, Lalic NM, Greaves CJ, McKee M, et al. A European evidence-based guideline for the prevention of type 2 diabetes.HormMetab Res 2010;42(suppl 1):S3–36.

25. Alberti KG, Zimmet P, Shaw J. International Diabetes Federation: a consensus on type 2 diabetes prevention. Diabet Med 2007;24:451–63 17470191

26. Schwarz PE, Li J, Lindstrom J, Tuomilehto J. Tools for predicting the risk of type 2 diabetes in daily practice. 2009. HormMetab Res 41: 86–97

27. Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9:103. doi: 10.1186/1741-7015-9-103 21902820

28. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 2011;33:46–62 doi: 10.1093/epirev/mxq019 21622851

29. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011;343:d7163 doi: 10.1136/bmj.d7163 22123912

30. Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study BMJ Open. 2012; 345: e5900.

31. Brown N, Critchley N, Bogowicz P, et al. Risk scores based on self-reported or available clinical data to detect undiagnosed Type 2 Diabetes: A systematic review Diabetes Research and Clinical Practice 2012 in press.

32. Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care. 2008;31(5):1040–1045 18070993

33. Gray LJ, Taub NA, Khunti K, Gardiner E, Hiles S, Webb DR, et al. The Leicester Risk Assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnicUK setting. Diabet Med. 2010;27(8):887–95. doi: 10.1111/j.1464-5491.2010.03037.x 20653746

34. Heianza Y, Arase Y, Saito K, Hsieh SD, Tsuji H, Kodama S, et al. Development of a Screening Score for Undiagnosed Diabetes and Its Application in Estimating Absolute Risk of Future Type 2 Diabetes in Japan: Toranomon Hospital Health Management Center Study 10 (TOPICS 10). J ClinEndocrinolMetab.2013

35. Lindström J, Absetz P, Hemio K et al. Reducing the risk of type 2 diabetes with nutrition and physical activity—efficacy and implementation of lifestyle interventions in Finland. Public Health Nutr 2010;13(6A):993–9 doi: 10.1017/S1368980010000960 20513271

36. Aleksandra Gilis-Januszewska A, Szybinski Z, Kissimova-Skarbek K et al. Prevention of type 2 diabetes by lifestyle intervention in primary health care setting in Poland: Diabetes in Europe Prevention using Lifestyle, physical Activity and Nutritional intervention (DE-PLAN) project. British Journal of Diabetes & Vascular Disease 2011;11:4 198–203

37. Costa B, Barrio F, Cabré JJ, Piñol JL, Cos X, Solé C, et al; DE-PLAN-CAT Research Group. Delaying progression to type 2 diabetes among high-risk Spanish individuals is feasible in real-life primary healthcare settings using intensive lifestyle intervention. Diabetologia. 2012;55(5):1319–28. doi: 10.1007/s00125-012-2492-6 22322921

38. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and and beta-cell function from fasting plsma glucose and insulin concentration in man. Diabetologia 1985;28:412–419. 3899825

39. Aas IH. Guidelines for rating Global Assessment of Functioning (GAF). Arch Gen Psychiatry 2011; 20;10:2.

40. Rabinowitz J, Mehnert A, Eerdekens M. To what extent do the PANSS and CGI-S overlap? J ClinPsychopharmacol 2006;26:303–307.

41. Mitchell AJ. The clinical significance of subjective memory complaints in the diagnosis of mild cognitive impairment and dementia: a meta-analysis. Int J Geriatr Psychiatry. 2008; 23(11): 1191–1202 doi: 10.1002/gps.2053 18500688

42. Mitchell AJ. A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. J Psychiatr Res. 2009;43(4):411–31. doi: 10.1016/j.jpsychires.2008.04.014 18579155

43. Mitchell AJ. Sensitivity × PPV is a recognized test called the clinical utility index (CUI+). Eur J Epidemiol. 2011;26(3):251–2 doi: 10.1007/s10654-011-9561-x 21442261

44. Gianfrancesco F, Pesa J, Wang RH, et al. Assessment of anti psychotic-related risk of diabetes mellitus in a Medicaid psychosis population: Sensitivity to study design. American Journal Of Health-System Pharmacy 2006; Volume: 63 Issue: 5 Pages: 431–441 16484517

45. Smith M; Hopkins D; Peveler RC, et al. First- V. second-generation antipsychotics and risk for diabetes in schizophrenia: systematic review and meta-analysis. British Journal Of Psychiatry 2008; 192 Issue: 6 Pages: 406–411 doi: 10.1192/bjp.bp.107.037184 18515889

46. Ramaswamy K, Masand PS, Nasrallah HA. Do certain atypical antipsychotics increase the risk of diabetes? A critical review of 17 pharmacoepidemiologic studies. Ann Clin Psychiatry 2006;18:183–194. 16923657

47. Rajkumar AP1, Horsdal HT1, Wimberley T1, Cohen D1, Mors O1, Børglum AD1, et al. Endogenous and Antipsychotic-Related Risks for Diabetes Mellitus in Young People with Schizophrenia: A Danish Population-Based Cohort Study. Am J Psychiatry. 2017 1;174(7):686–694 doi: 10.1176/appi.ajp.2016.16040442 28103712

48. Mitchell AJ, Vancampfort D, Sweers K, van Winkel R, Yu W, De Hert M. Prevalence of metabolic syndrome and metabolic abnormalities in schizophrenia and related disorders—a systematic review and meta-analysis. Schizophr Bull. 2013;39(2):306–18. doi: 10.1093/schbul/sbr148 22207632

49. Vancampfort D, Vansteelandt K, Correll CU, Mitchell AJ, De Herdt A, Sienaert P, et al. Metabolic syndrome and metabolic abnormalities in bipolar disorder: A meta-analysis of prevalence rates and moderators. Am J psychiatry 2013; doi: 10.1176/appi.ajp.2012.12050620

50. Vancampfort D, Correll CU, Wampers M, Sienaert P, Mitchell AJ, De Herdt A, et al. Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables. Psychol Med. 2014;44(10):2017–28. doi: 10.1017/S0033291713002778 24262678

51. De Hert M, Vancampfort D, Correll CU, Mercken V, Peuskens J, Sweers K, et al. Guidelines for screening and monitoring of cardiometabolic risk in schizophrenia: systematic evaluation. Br J Psychiatry. 2011;199(2):99–105. doi: 10.1192/bjp.bp.110.084665 21804146

52. Mitchell AJ, Delaffon V, Vancampfort D, Correll CU, De Hert M. Guideline concordant monitoring of metabolic risk in people treated with antipsychotic medication: systematic review and meta-analysis of screening practices. Psychol Med. 2012;42(1):125–47. doi: 10.1017/S003329171100105X 21846426

53. Hardy S, Hinks P, Gray R. Screening for cardiovascular risk in patients with severe mental illness in primary care: A comparison with patients with diabetes. J Ment Health. 2013;22(1):42–50. doi: 10.3109/09638237.2012.759194 23343046

54. Mitchell AJ, Hardy S. Surveillance for metabolic risk factors in patients with severe mental illness vs diabetes: National Comparison of Screening Practices. Psychiatric Services 2013 in press

55. Osborn DP, Baio G, Walters K, Petersen I, Limburg H, Raine R, et al. Inequalities in the provision of cardiovascular screening to people with severe mental illnesses in primary care: cohort study in the United Kingdom THIN Primary Care Database 2000–2007. Schizophr Res. 2011;129(2–3):104–10. doi: 10.1016/j.schres.2011.04.003 21550783

56. Owora AH1.Commentary: Diagnostic Validity and Clinical Utility of HbA1c Tests for Type 2 Diabetes Mellitus.Curr Diabetes Rev. 2018;14(2):196–199. doi: 10.2174/1573399812666161129154559 27897108

57. American Diabetes Association.Standards of medical care in diabetes—2012.Diabetes Care. 2012;35Suppl 1:S11–63.

58. American Diabetes A. Standards of medical care in diabetes 2011. Diabetes Care 2011; 34 (suppl 1): S11–61.

59. Krein SL, Bingham CR, McCarthy JF, Mitchinson A, Payes J, Valenstein M. Diabetes treatment among VA patients with comorbid serious mental illness. Psychiatr Serv. 2006;57:1016Y1021.

60. Brown CH, Medoff D, Dickerson FB, Kreyenbuhl JA, Goldberg RW, Fang L, et al. Long-term glucose control among type 2 diabetes patients with and without serious mental illness. J NervMent Dis. 2011;;199(11):899–902.

61. Castilla-Puentes R. Effects of psychotropics on Glycated hemoglobin (HbA1c) in a cohort of bipolar patients. Bipolar Disord. 2007;9(7):772–8. 17988369

62. Nasrallah HA, Meyer JM, Goff DC, et al. Low rates of treatment for hypertension, dyslipidemia and diabetes in schizophrenia: data from the CATIE schizophrenia trial sample at baseline. Schizophr Res 2006;86:15–22. 16884895

63. Mitchell AJ, Malone D, Doebbeling CC. Quality of medical care for people with and without comorbid mental illness and substance misuse: systematic review of comparative studies. Br J Psychiatry. 2009;194(6):491–9. doi: 10.1192/bjp.bp.107.045732 19478286

64. Mai Q, D'Arcy C, Holman J, Sanfilippo FM, Emery JD, Preen DB. Mental illness related disparities in diabetes prevalence, quality of care and outcomes: a population-based longitudinal study. BMC Medicine 2011;9:118 doi: 10.1186/1741-7015-9-118 22044777

65. Aekplakorn W, Bunnag P, Woodward M, Sritara P, Cheepudomwit S, Yamwong S, et al: A risk score for predicting incident diabetes in the Thai population. Diabetes Care 2006;29:1872–1877. 16873795

66. Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care 2008;31:2056–2061. doi: 10.2337/dc08-0368 18689695

67. Gao WG, Qiao Q, Pitkäniemi J, Wild S, Magliano D, Shaw J, et al. Risk prediction models for the development of diabetes in Mauritian Indians. Diabet Med 2009;16:996–1002.

68. Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Ann Intern Med 2009;150:741–751 19487709

69. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003;26:725–31. 12610029

70. Robinson CA, Agarwal G, Nerenberg K. Validating the CAN-RISK prognostic model for assessing diabetes risk in Canada’s multi-ethnic population. Chronic Dis Inj Can 2011;32:19–31 22153173

71. Schulze M, Hoffman K, Boeing H, Linseisen J, Rohrmann S, Mohlig M, et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 2007;30:510–5. 17327313

72. Al-Lawati JA, Tuomilehto J. Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East. Diabetes Res ClinPract 2007;77:438–44.

73. Baan CA, Ruige JB, Stolk RP, Witteman JCM, Dekker JM, Heine RJ, et al. Performance of a predictive model to identify undiagnosed diabetes in a health care setting. Diabetes Care 1999;22:213–219. 10333936

74. Bang H, Edwards A, Bomback A, Ballantyne C, Brillon D, Callahan M, et al. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med 2009;151:775–83. doi: 10.7326/0003-4819-151-11-200912010-00005 19949143

75. Gao WG, Dong YH, Pang ZC, Nan HR, Wang SJ, Ren J, et al: A simple Chinese risk score for undiagnosed diabetes. Diabet Med 2010;27:274–281. doi: 10.1111/j.1464-5491.2010.02943.x 20536489

76. Glümer C, Carstensen B, Sabdbaek A, Lauritzen T, Jørgensen T, Borch-Johnsen K: A Danish diabetes risk score for targeted screening. Diabetes Care 2004;27:727–733. 14988293

77. Pires de Sousa AG, Pereira AC, Marquezine GF, Marques do Nascimento-Neto R, Freitas SN, Nicolato RLdC, et al. Derivation and external validation of a simple prediction model for the diagnosis of type 2 diabetes mellitus in the Brazilian urban population. Eur J Epidemiol 2009;24:101–109. doi: 10.1007/s10654-009-9314-2 19190989

78. Ramachandran A, Snehalatha C, Vijay V, Wareham N, Colagiuri S. Derivation and validation of diabetes risk score for urban Asian Indians. Diabetes Res ClinPract 2005;70:63–70.

Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study

Souhrn

Objective

Methods

Results

Conclusions

Klíčová slova:

Zdroje

PLOS One

Svět praktické medicíny 1/2024 (znalostní test z časopisu)

Koncepce osteologické péče pro gynekology a praktické lékaře

Sekvenční léčba schizofrenie

Hypertenze a hypercholesterolémie – synergický efekt léčby

Význam metforminu pro „udržitelnou“ terapii diabetu