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
prolekare.web.journal.doi_sk: 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:

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

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