Lacunes and type 2 diabetes mellitus have a joint effect on cognitive impairment: a retrospective study

Subjects

Participants enrolled in the program had to be able to independently complete brain health check tests and undergo MRI scanning for further evaluation. Participants also had to voluntarily accept the regulations provided on the informed consent form. Inclusion criteria: (1) diagnosis criteria for diabetes: Fasting plasma glucose (FPG) ≥126 mg/dl (7.0 mmol/l), or 2-h values in the oral glucose tolerance test (OGTT) ≥200 mg/dl (11.1 mmol/l), or glycated haemoglobin (HbA1c) ≥6.5%. Exclude other types of diabetes such as type 1 diabetes and gestational diabetes based on clinical diagnosis (Harreiter & Roden, 2019); (2) were over 18 years old and (3) could cooperate and complete the Mini-Mental State Examination, version 2 (MMSE-2) and Montreal Cognitive Assessment (MoCA) conventional tests. The exclusion criteria were as follows: (1) patients had severe brain organic diseases and other serious physical diseases; (2) women were lactating/pregnant; (3) patients had magnetic resonance contraindications; (4) patients had a family history of dementia; (5) patients refused to sign the informed consent form; or (6) MRI images did not validate the diagnostic criteria.

All 479 participants underwent a 3.0-T MRI scan between May 2017 and January 2019. Individual clinical characteristics, such as age, sex, hyperlipidemia, smoking status, presence of hypertension and diabetes, and years of schooling completed were collected, and the MMSE-2 and the MoCA were completed. Neuropsychological questionnaires were assessed by a professionally trained nurse in the cognitive function ward. MRI studies were evaluated by two radiologists (3 and 9 years of experience in neuroimaging), and controversial MRI images were subsequently evaluated by more experienced radiologists. The two radiologists were blinded to all the clinical information and independently evaluated the characteristics of each patient’s brain.

After excluding subjects who had missing or unsatisfactory MRI data for analysis (n = 50), missing basic clinical information (n = 17), or missing cognitive assessments (n = 185), 227 subjects were included in this study (Fig. 1). This study was approved by the human ethics review board of the University of South China. All participants provided both oral and written informed consent. The project was registered at the Chinese Clinical Trial Registry (ChiCTR1900027781).

Baseline assessment

According to current diabetes classifications (Harreiter & Roden, 2019), the definition of T2DM is a fasting serum glucose level ≥126 mg/dl or HbA1c ≥6.5%. Hypertension is defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. We systematically collected education information (years of schooling) and smoking history. The MMSE-2 scale defines cognitive impairment as a score ≤17 points for uneducated participants, ≤20 points for patients with an education period of less than or equal to 6 years, and ≤24 points for patients with an education period of more than 6 years (Cockrell & Folstein, 1988). Patients with a total score of <26 points on the MoCA scale are considered to have cognitive impairment (with an additional point added if the subject has been educated for less than or equal to 12 years) (Nasreddine et al., 2005).

Brain MRI acquisition

Brain MRI scans were obtained at The First Affiliated Hospital of University of South China using the Philips Achieva 3.0-T system with 8-channel SENSE receiver. The main sequences included the following. T1-weighted imaging: repetition time (TR), 600 ms; echo time (TE), 10 ms; flip angle (FA), 70°; section thickness, 5 mm; gap width, 1 mm; matrix, 256 × 163 mm²; and field of view (FOV), 230 × 230 mm². Fast spin-echo T2-weighted imaging: TR, 3,000 ms; TE, 80 ms; FA, 90°; section thickness, 5 mm; gap width, 1 mm; matrix, 400 × 255 mm²; and FOV, 230 × 230 mm². Fluid-attenuated inversion recovery (FLAIR) imaging: TR, 11,000 ms; inversion time, 2,800 ms; TE, 120 ms; FA, 120°; section thickness, 4 mm; gap width, 1 mm; matrix, 232 × 101 mm²; and FOV, 230 × 230 mm². Susceptibility-weighted imaging (SWI): TR, 18 ms; TE, 26 ms; FA, 10°; section thickness, 0.6 mm; gap width, 1 mm; matrix, 244 × 200 mm²; and FOV, 220 × 220 mm².

Lacunes, WMH, CMBs, and PVS analysis

All evaluators of the MRI examinations were blinded to the clinical data. The MR images were evaluated using the prestudy cerebral SVD study standard (Kim, MacFall & Payne, 2008; Gregoire et al., 2009; Wardlaw et al., 2013). Lacunes were defined as focal or ovoid, 3 to 15 mm in diameter, fluid-filled signal cavities located in the area of a perforating arteriole. On FLAIR images, lacunes generally have a central hypointensity, with similar signals as cerebrospinal fluid (CSF), surrounded by hyperintensity (Wardlaw et al., 2013). WMH were defined as hyperintense areas in the T2-weighted image and void-free hyperintense areas on FLAIR sequences. We graded the periventricular and deep lesions according to the modified Fazekas scale. Periventricular hyperintensities were graded as absent (Grade 0), caps or pencil-thin lining (Grade 1), bands (Grade 2), or irregular extension into the deep white matter (Grade 3), and the deep lesions were graded as absent (Grade 0), punctuate (Grade 1), beginning confluence (Grade 2), or confluent (Grade 3) (Fazekas et al., 1987). We counted the total number of CMBs in the deep, lobar, and infratentorial regions except for lesions in the subarachnoid space and low symmetry signal areas of the globus pallidus (as these possibly represent adjacent pial vessels and calcification, respectively) (Gregoire et al., 2009). The number of CMBs was graded as absent, mild (1–2), moderate (3–10), or severe (>10) (Lee et al., 2002). PVS was defined as small (<3 mm) structures with the same signal as CSF that followed the area of perforating arteries. The severity of PVS was assessed in the basal ganglia (BG), centrum semiovale, and hippocampus (Hip). In the BG, PVS was graded in the slice that had the largest number of PVS and was scored as follows: the number of PVS <5 (Grade 1), 5–10 (Grade 2), >10 but still countable (Grade 3), or uncountable (Grade 4). In the centrum semiovale, PVS was scored as follows: the sum of the number of PVS in all slices <10 (Grade 1), the sum of the number of PVS in all slices >10 but the number of PVS <10 in any slice (Grade 2), the number of PVS was 10–20 in the slice containing the largest number of PVS (Grade 3), or the number of PVS >20 in the slice containing the largest number of PVS (Grade 4) (Doubal et al., 2010). In the Hip, the number of PVS in the total slices of the Hip and parahippocampal gyrus was scored. PVS were scored as follows: number of PVS <5 (Grade 1), 5–10 (Grade 2), or >10 (Grade 3) (Zhang et al., 2014).

SVD score analysis

In accordance with previous studies (Klarenbeek et al., 2013; Staals et al., 2014), if the number of lacunes or CMBs was greater than or equal to one, they were each recorded as 1 point. The presence of white matter hyperintensities was defined as periventricular WMH Fazekas score 3 and/or deep WMH Fazekas score 3 and recorded as 1 point. The presence of moderate to severe PVS was recorded as 1 point. The total SVD score consists of the above four parts and could range from 0 to 4. The subjects were further divided based on the total SVD score: a none to mild group (SVD score: 0–1) and a moderate to severe group (SVD score: 2–4) (Yakushiji et al., 2014; Hara et al., 2019).

Statistical analysis

According to the above baseline assessment criteria, all subjects were divided into two groups (cognitive impairment group and noncognitive impairment group) using the MMSE-2 and MoCA scale. To apply the research to diabetic participants, we selected the appropriate assessment using chi-square or Fisher’s exact tests. The subjects were divided into two groups, and all baseline and imaging data were analyzed. Categorical and ordinal variables are presented as n (%) and were analyzed with chi-square or Fisher’s exact tests. Continuous variables are presented as the mean ± standard deviation (SD). The normality of data distributions and homogeneity of continuous variables were examined before analysis, and then the appropriate t-test was used. The joint effect of diabetes and lacunes was evaluated by calculating the synergy index using the method introduced by Andersson et al. (2005). An Excel sheet was built to calculate the synergy index. The Excel formula is Synergy index = [RR₁₁ – 1]/[(RR₁₀ – 1) + (RR₀₁ – 1)]. In this formula, RR₁₁ is the relative risk of both risk factors being present_, RR₁₀ is the relative risk of the first risk factor being absent and the second risk factor being present, and RR₀₁ is the relative risk of the second risk factor being absent and the first risk factor being present. If there was no biological interaction, the synergy index is equal to 1. If the synergy index is larger than 1, it indicates that there is a synergistic effect of the two factors. Otherwise, there is only a joint effect of the two factors. All statistical analyses were performed with SPSS software (Version 25; SPSS, Chicago, IL, USA), the Statistical Analysis System (Version 9.4; SAS Institute, Cary, NC, USA) and Microsoft Excel (Version 2019; Microsoft Corp, Redmond, WA, USA). Statistical significance was established at p < 0.05 for all tests.

Participant characteristics

Cohen’s kappa values were calculated as a measure of interrater reliability. The Cohen’s kappa values for lacunes, WMH, CMBs, and PVS were 0.76, 0.84, 0.89, and 0.66, respectively. The data were evaluated by two imaging specialists who were unaware of the results of each other’s assessment during the assessment period. Table 1 shows the demographic and clinical findings for the entire cohort (n = 227: 64.3% male; mean age: 62.5 years). Among the entire cohort, there were 115 (51.7%) people with diabetes, 74 (32.6%) people with hyperlipidemia, and 169 (74.4%) people with hypertension. There were 90 (39.6%) people with postsecondary education among the entire cohort and 64 (28.2%) people with a history of smoking.

Markers of SVD and cognitive assessment outcomes

The numbers of subjects with SVD scores of 0, 1, 2, 3, and 4 were 0 (0%), 55 (24.2%), 51 (22.4%), 57 (25.8%), and 64 (28.2%), respectively. Table 1 shows MMSE-2 scores for the entire cohort. There was a significant correlation between diabetes and cognitive impairment (p < 0.001, χ² test). The entire cohort was evaluated based on MMSE-2 scores, and Table 2 shows that total SVD scores were higher in the cognitive impairment group than in the noncognitive impairment group (p < 0.01, χ² test) and that the existence of lacunes was significantly associated with cognitive impairment (p ≤ 0.001, χ² test). Table 3 shows the statistics for diabetes patients in the entire cohort that show that increased age (p < 0.05, χ² test) was associated with cognitive impairment. Table 4 shows no statistically significant differences among the general characteristics in the non-diabetic group. Table 5 shows that in patients with diabetes, cognitive impairment was significantly associated with the presence of lacunes (p < 0.01, χ² test) and higher total SVD burden scores (p < 0.01, χ² test). Regarding CMBs, only the existence of lobar CMBs was related to cognitive impairment (p < 0.05, χ² test).

Joint effect of diabetes and lacunes on cognitive impairment

Table 6 shows that although there were no significant synergistic effects of diabetes and lacunes on cognitive impairment, the joint effects tended to be larger than independent effects after adjusting for sex, age, smoking status, education, hyperlipidemia and hypertension (adjusted OR: 7.084, 95% CI [2.836–17.698]; synergy index: 10.018, 95% CI [0.344–291.414]).