Research Article How Much Do We Know about the Biopsychosocial Predictors of Glycaemic Control? Age and Clinical Factors Predict Glycaemic Control, but Psychological Factors Do Not Mohammad Farris Iman Leong Bin Abdullah , 1 Hatta Sidi, 2 Arun Ravindran, 3 Paula Junggar Gosse, 4 Emily Samantha Kaunismaa, 4 Roslyn Laurie Mainland, 4 Norlaila Mustafa, 5 Nurul Hazwani Hatta, 6 Puteri Arnawati, 6 Amelia Yasmin Zulkifli, 6 and Luke Sy-Cherng Woon 2 1 Advanced Medical and Dental Institute, Universiti Sains Malaysia, Malaysia 2 Department of Psychiatry, Universiti Kebangsaan Malaysia Medical Centre, Malaysia 3 Centre for Addiction and Mental Health, University of Toronto, Canada 4 Faculty of Medicine, University of Toronto, Canada 5 Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Malaysia 6 University of Galway, Ireland Correspondence should be addressed to Luke Sy-Cherng Woon; lukewoon@ukm.edu.my Received 15 November 2019; Accepted 22 April 2020; Published 5 May 2020 Academic Editor: Ed Randell Copyright © 2020 Mohammad Farris Iman Leong Bin Abdullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. Diabetes mellitus is one of the most common noncommunicable diseases in Malaysia. It is associated with significant complications and a high cost of treatment, especially when glycaemic control is poor. Despite its negative impact on health, data is still lacking on the possible biopsychosocial predictors of poor glycaemic control among the diabetic population. This study is aimed at determining the prevalence of poor glycaemic control as well as its association with biopsychosocial factors such as personality traits, psychiatric factors, and quality of life (QOL) among Malaysian patients with diabetes. Methods.A cross-sectional study was conducted at the Universiti Kebangsaan Malaysia Medical Centre (UKMMC) using outpatient population diabetic patients. Demographic data on social and clinical characteristics were collected from participants. Several questionnaires were administered, including the Beck Depression Inventory-II (BDI-II) to measure depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) to assess anxiety symptoms, the Big Five Inventory (BFI) to evaluate personality traits, and the WHO Quality of Life-BREF (WHOQOL-BREF) to assess QOL. Multivariate binary logistic regression was performed to determine the predictors of poor glycaemic control. Results. 300 patients with diabetes mellitus were recruited, with the majority (90%) having type 2 diabetes. In this population, the prevalence of poor glycaemic control (HbA 1C ≥ 7:0%) was 69%, with a median HbA 1C of 7.6% (IQR = 2:7). Longer duration of diabetes mellitus and a greater number of days of missed medications predicted poor glycaemic control, while older age and overall self-perception of QOL protected against poor glycaemic control. No psychological factors were associated with poor glycaemic control. Conclusion. This study emphasizes the importance of considering the various factors that contribute to poor glycaemic control, such as duration of diabetes, medication adherence, age, and QOL. These findings should be used by clinicians, particularly when planning a multidisciplinary approach to the management of diabetes. Hindawi Journal of Diabetes Research Volume 2020, Article ID 2654208, 11 pages https://doi.org/10.1155/2020/2654208