Abstract
Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L1-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.
Highlights
Diabetes mellitus is a common metabolic disorder characterized by hyperglycemia (McCrimmon, et al, 2012)
Through Amplitude of low frequency fluctuations (ALFF) analysis and subsequent matching, 15 impaired subregions have been identified for Type 2 diabetes mellitus (T2DM)-cognitive impairment (CI) versus healthy controls (HC), T2DMNC versus HC, and T2DM-induced cognitive impairment (T2DM-CI) versus T2DM with normal cognition (T2DM-NC) (Figure 2 and Table 2)
Combining the functions of these networks, previous research, and the findings found in this study, we speculated that the cognitive impairment caused by T2DM may be mainly related to the abnormal connectivity patterns between Default Model Network (DMN) and executive control network (ECN), frontoparietal network (FPN), or other resting state networks
Summary
Diabetes mellitus is a common metabolic disorder characterized by hyperglycemia (McCrimmon, et al, 2012). The chronic hyperglycemia of T2DM patients may cause systemic damage to nerves, eyes, kidneys, and blood vessels, which may bring many complications, such as cognitive impairment (CI), microvascular complications (Valencia and Florez, 2017),and olfactory dysfunction (Yazla et al, 2018). T2DM-induced cognitive impairment (T2DM-CI), known as diabetic encephalopathy, mainly manifests through learning, judgment, and memory deficits, a decline in executive function, and decreased information processing speed (Mijnhout et al, 2006; McCrimmon, et al, 2012; Biessels and Despa, 2018). Due to the diversity of clinical manifestations of T2DM-CI and its relatively slow onset, there is no gold standard for diagnosis, which is likely to cause misdiagnosis or missed diagnosis and delay the treatment of patients (Srikanth et al, 2020)
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